MISSISSAUGA, Ontario, Nov. 26, 2020 (GLOBE NEWSWIRE) -- BioSyent Inc. (“BioSyent”, TSX Venture: RX) released today its financial results for the three and nine months ended September 30, 2020. Key highlights include:

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BioSyent Releases Financial Results for Third Quarter and First Nine Months of 2020

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Company announcement no. 48 - 20 26 November 2020

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Notification of Managers’ transactions

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On “Grindeks” results in the first nine months of 2020

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On “Grindeks” results in the first nine months of 2020

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PRESS RELEASE

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Saniona AB’s Nomination Committee for the Annual General Meeting 2021

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WINNIPEG, Manitoba, Nov. 26, 2020 (GLOBE NEWSWIRE) -- Kane Biotech Inc. (TSX- V:KNE; OTCQB:KNBIF) (the “Company” or “Kane Biotech”), a biotechnology company engaged in the research, development and commercialization of technologies and products that prevent and remove microbial biofilms, today announced their third quarter 2020 financial results.

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STRATHROY, Ontario, Nov. 26, 2020 (GLOBE NEWSWIRE) -- Eve & Co Incorporated (the “Company” or “Eve & Co”) (TSX-V: EVE; OTCQX: EEVVF) is pleased to announce its financial results for the three and nine months ended September 30, 2020. The financial statements and management’s discussion and analysis for such period are available on the System for Electronic Document Analysis and Retrieval ("SEDAR") at www.sedar.com and on Eve & Co's website at www.evecannabis.ca.

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TORONTO, Nov. 26, 2020 (GLOBE NEWSWIRE) -- Braingrid Limited (CSE: BGRD) (“Braingrid” or the “Company”) is pleased to announce that Gregory Pepin has been appointed to the Company’s Board of Directors.

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VANCOUVER, British Columbia, Nov. 26, 2020 (GLOBE NEWSWIRE) -- EXMceuticals Inc. (CSE: EXM) (FSE: A2PAW2) (the “Company” or “EXM”) is providing this bi-weekly default status report in accordance with National Policy 12-203 Management Cease Trade Orders (“NP 12-203”). On October 23, 2020, the Company announced that, for reasons set out in its news release of October 23, 2020, the filing of its annual audited financial statements for the year ended June 30, 2020, the accompanying management’s discussion and analysis and the related CEO and CFO certifications (collectively, the “Annual Filings”) would not be filed by the prescribed deadline of October 28, 2020.

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INTRODUCTION

The myeloproliferative neoplasms are a family of clonal blood disorders characterized by overproduction of platelets [essential thrombocythemia (ET)], overproduction of red blood cells [polycythemia vera (PV)], or bone marrow fibrosis [myelofibrosis (MF)]. The genetic bases for these diseases have largely been described: Mutations in JAK2 are found in 99% of PV and 50 to 60% of ET and MF cases, while frameshift mutations in CALR are responsible for 25 to 40% of cases of ET and MF (13). Frameshift mutants of calreticulin (CALR) have a novel C terminus that acts as a rogue ligand for the thrombopoietin receptor, MPL, and activates Janus kinasesignal transducer and activator of transcription (JAK-STAT) signaling (4, 5). We recently described the generation of a mouse model of mutant CALR-driven ET that faithfully recapitulates the key phenotypes of the human disease, namely, increased numbers of cells throughout the megakaryocytic (MK) lineage, particularly platelets (6).

Hematopoiesis is classically modeled as a stepwise process beginning with a multipotent hematopoietic stem cell (HSC), which is functionally defined by its capability to reconstitute multilineage hematopoiesis when transplanted into a myeloablated recipient (7). This HSC then transits through a series of intermediate stages with increasing lineage restriction to terminally differentiated blood cells (8, 9). However, newly popularized single-cell technologies such as single-cell RNA sequencing (scRNAseq) have reshaped our understanding of hematopoiesis and suggest that cells travel through a continuum of differentiation rather than a series of rigidly defined stages (10, 11). In a recent demonstration of the power of scRNAseq to untangle complex differentiation processes, it was used to interrogate the transcriptomes of hematopoietic stem and progenitor cells (HSPCs) to identify novel intermediate populations within erythropoiesis, which could then be isolated and characterized via fluorescence-activated cell sorting (FACS) strategies (12).

While HSCs are traditionally defined to be capable of reconstituting all blood lineages in transplantation experiments, there is an increasing body of evidence that some cells within the immunophenotypic HSC compartment already exhibit some lineage bias or restriction (1315). Studies in mice have shown that MK and erythroid lineages may branch off before other myeloid and lymphoid lineages (1618), and lineage tracing studies have shown the MK lineage to be the earliest generated from HSCs (1923). A transposon-based lineage tracing strategy showed some tags to be shared between long-term HSCs (LT-HSCs) and megakaryocyte progenitors (MkPs) but not multipotent progenitors (MPPs), indicative of a direct pathway linking HSCs and MK bypassing MPP (19). We therefore asked whether our mouse model of mutant CALR-driven ET could allow us to interrogate the differences in the hematopoietic landscapes between wild-type (WT) and disease model mice, with a particular focus on MK trajectories.

We generated scRNAseq data from FACS-sorted HSPCs [Lin Sca1+ cKit+ (LSK) and Lin Sca1 cKit+ (LK) populations] from a pair of WT and CALR DEL (knock-in of del52 allele) homozygous (HOM) littermate mice. After quality control, we retained 11,098 WT (5959 LSK and 5139 LK) and 15,547 HOM (7732 LSK and 7815 LK) cells for downstream analysis. We began by defining highly variable genes, which we used to perform principal component analysis (PCA) and generate a k = 7 nearest-neighbor graph. Cells were then assigned to clusters by mapping onto a previously published dataset of 44,082 LK cells (24), with manual annotation of clusters (fig. S1A). Cells from all major blood lineages can be seen and separate into distinct trajectories. To determine which cells were over- or underrepresented in the CALR DEL HOM mouse, we compared relative numbers of cells from each genotype. The most notable changes in relative cell abundance were increased numbers of cells in the HSC and MK clusters (fig. S1B), consistent with the increased platelet phenotype of our ET mouse model (6). We repeated the analysis on a second pair of WT and CALR DEL HOM littermate mice, in this case retaining 3451 WT (972 LSK and 2479 LK) and 12,372 HOM (4548 LSK and 7824 LK) cells for downstream analysis after quality control, and again observed an increase in cells in the HSC and MK clusters (fig. S1C).

To better understand the subgroups of cells within stem/progenitor cells, we chose to use partition-based graph abstraction (PAGA) (25) to visualize our data. This method generates a graph in which each node represents a group of closely related cells and edge weights correspond to the strength of connection between two nodes. We again compared relative abundances between WT and CALR DEL HOM mice and colored the nodes so red nodes are enriched in CALR mice, while blue nodes are underrepresented. We observed that the fine cluster that was most overrepresented in CALR DEL HOM mice (marked with an arrow) fell between the HSC and MK clusters in both repeats (Fig. 1A and fig. S1D). We plotted the expression of the MK markers Cd9, Itga2b (CD41), Mpl, Pf4, and VWF in our PAGA and hypothesized two MK trajectories, as indicated by the green and blue arrows (fig. S1E). As the fine cluster most overrepresented in CALR DEL HOM mice would be an intermediate on one of these trajectories (green arrow), we further hypothesized that these cells would be of particular relevance in the disease setting of mutant CALR-driven ET and thus aimed to further study them.

(A) PAGA of scRNAseq data from WT and CALR DEL HOM mice. Red nodes represent those present at increased abundance in CALR DEL HOM mice, while blue nodes represent those at reduced abundance. The most highly enriched node is noted with an arrow. (B) RNA expression of the flow cytometry markers CD48, EPCR (Procr), and CD150 (Slamf1) plotted on PAGA graphs from (A). Cells within our node of interest (marked with an arrow) are CD48, EPCR, and CD150+. (C) Representative plots of SLAM cells from WT and CALR DEL HOM mice. CALR DEL HOM mice show higher numbers of both ESLAMs (Lin CD48 CD150+ CD45+ EPCR+) and pMKPs (Lin CD48 CD150+ CD45+ EPCR). FITC, fluorescein isothiocyanate; PE, phycoerythrin. (D) Quantification of bone marrow frequency of pMKPs in WT and CALR DEL HOM mice. The frequency of pMKPs within live bone marrow mononuclear cells (BMMNCs) is significantly increased in CALR DEL HOM mice (WT, n = 3, 0.00029 0.00008; HOM, n = 3, 0.0025 0.0008; *P = 0.042).

We examined the expression of a series of genes typically used to FACS isolate different hematopoietic populations and found this fine cluster to be CD48, EPCR (Procr), and CD150+ (Slamf1) (Fig. 1B). We designed an immunophenotypic scheme to identify and isolate cells from this fine cluster, defining them to be Lin, CD150+, CD48, EPCR, and CD45+. On the basis of our subsequent characterization of these cells, we eventually termed them proliferative MkPs or pMKPs. Consistent with our transcriptomic data, when comparing WT mice to CALR mutant mice, we found an increase in the frequency of pMKPs in CALR DEL HOM mice as assayed by flow cytometry (Fig. 1, C and D). We also found that pMKPs were expanded in CALR DEL HET mice, albeit to a lesser extent than observed in CALR DEL HOM mice (fig. S1F).

To characterize pMKPs, we FACS-sorted single ESLAM (EPCR+ SLAM) HSCs (Lin CD45+ CD48 CD150+ EPCR+) (26), pMKPs (Lin CD45+ CD48 CD150+ EPCR), and MkPs (Lin Sca1 cKit+ CD41+ CD150+) (27) (fig. S2A) from WT mice into individual wells of a 96-well plate and observed them every day for 4 days. We analyzed our sort data and observed that in pMKPs, markers traditionally used to define MkPs were Sca1/lo/mid, cKit+, and CD41mid/+ (fig. S2B). pMKPs were additionally CD9+ and MPL+ (fig. S2C). On each day, we classified each well with surviving cell(s) into one of four categories, using cell size as a proxy for megakaryopoiesis (2830): (i) exactly one large cell, presumed to be a megakaryocyte; (ii) multiple large cells; (iii) mixed expansion, with both large and small cells; and (iv) expansion with only small cells (Fig. 2A). To verify that larger cells represented MK cells, using cells from day 4 ESLAM, pMKP, and MkP colonies, we quantified average CD41 intensity via immunofluorescence and classified cells as small or large via bright-field microscopy, using a small/large dichotomy assessed via bright-field microscopy to match the classification scheme used in Fig. 2A. Here, we confirmed that large cells have significantly higher CD41 staining, supporting their identification as MK (fig. S2D). In some cases, particularly large cells within mixed colonies showed very high CD41 staining and membrane extensions that resembled proplatelets (representative picture is shown in fig. S2E). Furthermore, we sorted pMKPs from VWF (von Willebrand factor)green fluorescent proteinpositive (GFP+) mice and found that large cells had a very bright VWF-GFP signal, supporting their identification as MK. Smaller cells in these clones had a much dimmer VWF-GFP signal, suggesting that they likely represent more immature cells that have not progressed as far through megakaryopoiesis (fig. S2F).

(A) Representative pictures of in vitro culture output of single ESLAMs, pMKPs, and MkPs into four categories: 1 MK, >1 MK, mixed, or proliferation only. (B) Classification of in vitro culture output of single ESLAMs, pMKPs, and MkPs at day 4 after FACS isolation. ESLAMs almost exclusively proliferated without producing megakaryocytes, while MkPs almost exclusively produced MKs, usually producing only a single MK. pMKPs showed a strong MK bias but were more likely to proliferate than were MkPs. ESLAMs, n = 306 wells from five experiments; pMKPs, n = 291 wells from six experiments; MkPs, n = 235 wells from five experiments. Chi-square test, ****P < 0.0001. (C) Timing of megakaryopoiesis in ESLAMs, pMKPs, and MkPs. Individual cells were observed for 4 days after sort, and the first date on which cell(s) showed signs of megakaryopoiesis was noted. MkPs were faster to begin megakaryopoiesis than were pMKPs (at day 2, MkPs: 89.5 0.7%; pMKPs: 50 6%; *P = 0.02). ESLAMs, n = 5; pMKPs, n = 6; MkPs, n = 5. (D) Log2-transformed cell counts of megakaryocytes from pMKPs and MkPs after 4 days of culture. Each point represents the average value from one of four separate experiments. Average of four experiments: pMKP, 1.12; MkP, 0.412, *P = 0.0295. (E) Histogram of the minimum number of cell divisions for 103 pMKPs and 158 MkPs that produced only megakaryocytes after 4 days of culture across four experiments. Chi-square test, ***P = 0.0001.

The vast majority of ESLAMs showed expansion with only small cells at day 4, consistent with being highly primitive HSCs with considerable proliferative potential, but not yet producing megakaryocytes. Similarly, as predicted for MkPs, more than 95% of wells showed exclusively production of MKs at day 4, with the majority producing only one MK. This lack of in vitro proliferation for single MkPs is consistent with previously published results, where 75% of MkPs did not divide and none produced more than 10 MKs (31). pMKPs exhibited an intermediate phenotype: While approximately 90% of wells showed production of some MKs, they were much more likely to produce multiple MK than were MkPs. In particular, pMKPs frequently proliferated into mixed colonies with both large and small cells, a behavior that was rarely seen for either ESLAMs or MkPs (Fig. 2B). Kinetic analysis showed that MkPs were faster to begin megakaryopoiesis than were pMKPs (Fig. 2C), and when considering only wells that produced only MKs, pMKPs produced more MKs than did MkPs (Fig. 2, D and E). pMKPs maintained their MK bias even when incubated under pro-erythroid or pro-myeloid conditions (fig. S3A). Culturing cells with thrombopoietin (THPO) increased the proportion of pMKPs that formed colonies with multiple MKs while reducing the number of mixed colonies (fig. S3B). To verify that our observed MK bias is not simply due to culture conditions supporting only megakaryopoiesis, we cultured ESLAMs under the same conditions for 10 days followed by flow cytometric analysis and observed multilineage differentiation (fig. S3C).

To examine the extent of overlap between our pMKPs and traditionally defined MkPs, we stained bone marrow with a panel incorporating all necessary markers and index sorted single pMKPs and MkPs. On the basis of index sort values, 97% of MkPs were CD45+, 50% were EPCR, and only 2% were CD48; when taken together, fewer than 1% of immunophenotypic MkPs also fell within the pMKP gate (fig. S3D); thus, pMKPs and MkPs can be FACS-separated on the basis of CD48 and EPCR. In contrast, we found that an average of 51% of pMKPs were also immunophenotypically MkPs (CD41+ Sca1 cKit+) (fig. S3E). As we observed a partial overlap between pMKPs and MkPs, we used our index sort data to assign each pMKP an overlap score based on the levels of CD41, Sca1, and cKit: 1/3 if only one marker overlapped, 2/3 if two overlapped, and 3/3 for pMKPs that also fall within the MkP immunophenotypic gate. No pMKPs had an overlap score of 0/3. We used the same classification scheme as in Fig. 2B and found that lower overlap scores correlated to a more proliferative, less MK-restricted phenotype: The pMKPs that are least similar to MkPs are the most proliferative and the least restricted to the MK lineage, although they still display a strong preference for MK production (fig. S3F). pMKPs with the lowest overlap score took the longest to enter megakaryopoiesis (fig. S3G). Together, our data indicate that pMKPs represent a group of cells with an MK bias and an increased proliferative potential as compared to traditionally defined MkPs.

We next determined whether pMKPs were capable of producing platelets in vivo. We made use of CD45.2 VWF-GFP donor mice and cKit W41/W41 CD45.1 recipient mice, which allowed us to track platelets (via VWF-GFP) and nucleated cells (by CD45.1/CD45.2 staining) (Fig. 3A). We FACS-sorted ESLAMs, pMKPs, and MkPs from VWF-GFP donor mice and transplanted 30, 60, or 120 cells per recipient into sublethally irradiated W41 mice along with 250,000 spleen MNCs (mononuclear cells) (SPMNCs) as helper cells and assayed peripheral blood chimerism every week for 4 weeks and at 16 weeks. We did not sort on VWF-GFP+ at this stage, but flow cytometry analysis showed that ESLAMs, pMKPs, and MkPs were all highly enriched for VWF-GFP expression when compared to total bone marrow (fig. S4A). We also transplanted one mouse per cohort with 250,000 SPMNCs alone to serve as a negative control to help with gating to avoid false positives. Representative gating strategies are shown in fig. S4 (B and C). As expected, ESLAMs were able to generate relatively high levels of platelets at all three cell doses, starting with a very low level at week 1 and increasing over the course of 4 weeks and continuing up to 16 weeks (although one recipient of 30 ESLAMs was lost to follow-up before the 16-week time point). pMKPs and MkPs were only able to reconstitute platelets at a very low level (1/105 to 1/104), even at the highest cell dose (Fig. 3, B to D and summarized in E). Low levels of donor-derived platelets were detected in 10 of 12 pMKP recipients and 8 of 13 MkP recipients within the first 4 weeks; extended observation up to 16 weeks showed that few recipients continued to produce VWF-GFP+ platelets, although all 3 pMKP recipients at the highest dose still showed VWF-GFP+ platelets. ESLAMs successfully produced CD11b+ myeloid cells in 10 of 10 recipients across varying cell doses, while pMKPs and MkPs only produced CD11b+ cells at a low level in 3 of 12 and 2 of 10 recipients, respectively (fig. S4, D to F and summarized in G). Therefore, we concluded that while pMKPs and MkPs have limited capabilities in a transplantation experiment, they both show an MK bias, in agreement with their in vitro behaviors. These low levels of reconstitution suggest that pMKPs and MkPs do not divide considerably in vivo, again similar to in vitro data.

(A) Schematic of VWF-GFP+ transplantation strategy. ESLAMs, pMKPs, and MkPs were sorted from VWF-GFP+, CD45.2 donor mice and transplanted into sublethally irradiated cKit W41/W41 CD45.1 recipients. PB, peripheral blood. (B) Platelet reconstitution from 30 donor cells. (C) Platelet reconstitution from 60 donor cells. (D) Platelet reconstitution from 120 donor cells. (E) Table summarizing numbers of mice with successful platelet production from ESLAMs, pMKPs, and MkPs. Here, transplanted cells were defined to have produced platelets if platelets were observed at a level of at least 1 in 105 at one or more time points within the first 4 weeks after transplantation.

Our single-cell transcriptomic analysis showed pMKPs to be an intermediate stage on an MK trajectory maintaining CD48 negativity (Fig. 1B and green arrow in fig. S1E), which suggests that they bypass the traditional MPP2 pathway (blue arrow in fig. S1E). We therefore asked whether we could show production of pMKPs from HSCs in an MPP2-independent manner by making use of a mouse model allowing inducible depletion of HSPCs. In this model, Tal1-Cre/ERT mice are crossed with R26DTA mice, wherein treatment with tamoxifen leads to specific expression of diphtheria toxin in HSCs and primitive progenitors and hence suicidal depletion of these early populations (Fig. 4A) (32). Within 6 weeks after HSC depletion, very few LT-HSCs remain, but levels of MPPs, committed progenitors, and mature blood cells are only slightly lower than in control animals (32). We reasoned that if pMKPs arise directly from HSCs, they should be depleted to a similar extent as HSCs, while if they arise from an MPP pathway, they should be depleted to a similar extent as MPPs (i.e., to a lesser extent than HSCs).

(A) Schematic of DTA (diphtheria toxin fragment A) HSC depletion model experiment. Tal1-CreERT/R26DTA mice were treated with four doses of tamoxifen at 0.1 mg/g to induce suicidal depletion of HSCs and then euthanized after 6 weeks for bone marrow (BM) analysis. (B) Frequencies of stem and progenitor cells with or without stem cell depletion. Cell populations that were significantly diminished by suicidal depletion of HSCs include ESLAMs (Cre, 17.1 10.8/105 BMMNC; Cre+, 4.3 2.0/105 BMMNC; *P = 0.012), LTHSCs (LSK CD48 CD150+) (Cre, 15 12/105 BMMNC; Cre+, 3.6 1.7/105 BMMNC; *P = 0.031), pMKPs (Cre, 13.0 7.6/105 BMMNC; Cre+, 4.1/105 BMMNC; *P = 0.013), and MkPs (Cre, 44.2 26.4/105 BMMNC; Cre+, 21.4 6.1/105 BMMNC; *P = 0.046); Cre, n = 8 and Cre+, n = 10. MPP2 (Cre, 25.1 29.1/105 BMMNC; Cre+, 13.3 3.6/105 BMMNC; P = 0.48) and preMegE (Cre, 90.0 62.9/105 BMMNC; Cre+, 73.9 29.6/105 BMMNC; P = 0.66) populations were depleted to lesser extents that did not reach statistical significance; Cre n = 4 and Cre+ n = 6. ns, not significant.

We compared mice carrying either no Cre or Tal1-Cre/ERT after treatment with tamoxifen to induce specific depletion of HSCs. We observed a depletion of approximately 75% in the numbers of HSCs [whether using ESLAM markers or LT-HSC (LSK CD48 CD150+) markers] and a 68% reduction in the numbers of pMKPs in HSC-depleted mice. By contrast, there was no significant reduction in MPP2 or preMegE populations, while MkPs were reduced by approximately 51% (Fig. 4B). Consistent with previously published results, we observed no statistically significant reduction in other multipotent populations, including MPP3 and MPP4 (33), and committed progenitor populations, including CFU-E (erythroid colony-forming units), pCFU-E, pGM (pre-granulocyte/macrophage), and GMP (granulocyte/monocyte progenitors) (fig. S5) (27). We noted that one Cre mouse was an outlier, with noticeably higher frequencies of almost all progenitor populations, and tested removing this outlier to ensure our conclusions were not unduly relying on this mouse. With the outlier removed, we calculated reductions of 68% in ESLAMs (P = 0.0001), 60% in pMKPs (P = 1.5 105), and an increase of 24% in MPP2 (P = 0.50). Our analysis is therefore robust to the removal of this outlier and demonstrates that the reduction in pMKP levels correlates more closely to that of ESLAMs than that of MPP2. Together, these data support a model in which pMKPs are produced from HSCs in an MPP2-independent manner and MkPs can be generated from pMKPs or via MPP2, accounting for their intermediate level of reduction.

After characterizing the pMKP population in WT mice, we next asked whether there were qualitative differences between WT and CALR DEL HOM cells along the MK trajectory and not solely a quantitative difference. To do so, we sorted single ESLAMs, pMKPs, and MkPs from WT and CALR DEL HOM mice and monitored their in vitro behavior over 4 days. While very few WT ESLAMs showed any MKs within the first 4 days after sort, a higher proportion of CALR DEL HOM ESLAMs showed MKs within mixed colonies (Fig. 5A). CALR DEL HOM pMKPs showed similar proportions of wells in each category (Fig. 5B), while CALR DEL HOM MkPs were more likely to form multiple MKs and less likely to form a single MK (Fig. 5C). To assess the statistical significance of these differences, using a Fishers exact or chi-square test required consolidation of our data into fewer categories, as some categories contained values that were too low (for example, for day 4 ESLAMs, the categories 1 MK and >1 MK were 0 in both WT and HOM). We thus consolidated ESLAM data into two categoriesno MK and MK (Fig. 5D)and pMKP and MkP data into three categories1 MK, >1 MK, and mixed + prolif only (Fig. 5, E and F). This showed that CALR DEL HOM ESLAMs were significantly more likely to form MKs (Fig. 5D). CALR DEL HOM pMKPs showed no statistically significant difference, suggesting no change in their MK bias or proliferative behavior compared to WT pMKPs (Fig. 5E). CALR DEL HOM MkPs were significantly more proliferative than were WT MkPs (Fig. 5F). We also extended our observation of ESLAM clones to day 7 and observed an even stronger increase in the production of megakaryocytes from CALR DEL HOM ESLAMs, an increase noted both in wells producing mixed clones and in those producing MK-only clones (Fig. 5, G and H).

(A) Classification of in vitro culture output of single ESLAMs from WT and CALR DEL HOM mice at day 4, using the classification scheme as in Fig. 2A. WT, n = 223; HOM, n = 225. (B) Classification of in vitro culture output of single pMKPs from WT and CALR DEL HOM mice at day 4; WT, n = 117; HOM, n = 161. Chi-square test P = 0.9201. (C) Classification of in vitro culture output of single MkPs from WT and CALR DEL HOM mice at day 4; WT, n = 136; HOM, n = 152. (D) Reclassification of data from (A) into two categories (MK or no MK) for a Fishers exact test, *P = 0.0191. (E) Reclassification of data from (B) into three categories (1 MK, >1 MK, and mixed + prolif only) for a chi-square test, P = 0.8183. (F) Reclassification of data from (C) into three categories (1 MK, >1 MK, and mixed + prolif only) for a chi-square test, **P = 0.0069. (G) Classification of in vitro culture output of single ESLAMs at day 7; WT, n = 136; HOM, n = 152. (H) Reclassification of data from (G) into two categories (MK or no MK) for a Fishers exact test, **P = 0.0014. (I) pMKPs as a proportion of live cells generated from in vitro culture of WT and CALR DEL HOM ESLAMs, assessed at day 3. WT, 0.062 0.015; HOM, 0.193 0.036, *P = 0.0135, n = 3 independent mice.

We also considered log2-transformed cell counts from those wells with exclusively megakaryocytes (i.e., 1 MK and >1 MK). In some cases, we observed the death of a cell or cells over our 4-day observation period; to account for cell death, we used the maximum number of cells observed over these 4 days. Mann-Whitney U tests showed no significant difference for pMKPs but a significant increase in MK production from CALR DEL HOM MkPs (fig. S6, A and B). Similarly, calculations of the minimum number of divisions required to produce the observed number of MKs found no difference for pMKPs but a significant shift to more divisions from CALR DEL HOM MkPs (fig. S6, C and D). We also cultured ESLAMs in vitro and assayed for the production of pMKPs, finding that CALR DEL HOM ESLAMs gave rise to significantly more pMKPs than did their WT counterparts (Fig. 5I). Together, we conclude that CALR DEL is acting at multiple stages of megakaryopoiesis, promoting an MK bias from the earliest HSC compartments and increased proliferation at both HSC and MkP levels. While pMKPs are increased in number in CALR DEL HOM mice, these cells do not show altered proliferation or MK bias in vitro.

Last, we made use of our scRNAseq data to compare gene expression between WT and CALR DEL HOM cells along the MK trajectory. We considered cells within 2 of the 13 clusters defined by our transcriptomic data (HSC and MK; fig. S1A) and 1 fine cluster (pMKP; arrow in Fig. 1A) (Fig. 6, A to C). As the pMKP fine cluster had fewer cells (24 in WT and 247 in CALR DEL HOM) than the larger HSC and MK clusters, we were only able to confidently call a small number of differentially expressed genes (DEGs) within this cluster. We performed Ingenuity Pathway Analysis (IPA) to determine which biological pathways and upstream regulators were most affected in the HSC and MK clusters; the small numbers of DEGs in pMKPs resulted in no statistically significant hits via IPA. The most affected canonical pathways fell into three broad groups: cell cycle (in blue), unfolded protein response (gold), and cholesterol biosynthesis (green) (Fig. 6, D and E). Full lists of canonical pathways, P values, and z scores are available in tables S1 (HSC) and S2 (MK). Genes contributing to these three pathways are highlighted in the same colors in Fig. 6, A to C; we note that pMKPs also show up-regulation of several UPR (unfolded protein response)associated genessuch as Hspa5, Pdia3, and Pdia6in addition to two known STAT targets (Ifitm2 and Socs2).

(A to C) Volcano plots showing DEGs between WT and CALR DEL HOM cluster 3 (HSC) (A), pMKP fine cluster (B), and cluster 11 (MK) (C). Genes within certain representative Gene Ontology (GO) terms are colored: regulation of cholesterol biosynthetic process (GO:0045540) (green), response to ER stress (GO:0034976) (gold), and regulation of mitotic cell cycle (GO:0007346) (blue). Other DEGs are colored in red. (D and E) Bar graphs showing z scores for up-regulated canonical pathways in cluster 3 (HSC) (C) and cluster 11 (MK) (D), filtered by P < 0.01 and z score of >1 or <1. Bars are highlighted in green for cholesterol biosynthesis, gold for ER stress/unfolded protein response, or blue for cell cycle. (F) Upstream regulator analysis. Hits were filtered by P < 0.01. Bar graph showing the 10 most up-regulated and 10 most down-regulated predicted upstream regulators, when comparing WT and CALR DEL HOM cluster 3 (HSC) (blue) and cluster 11 (MK) (red), as measured by combining the z scores from WT and MK analyses.

While cell cycle and UPR have previously been described as up-regulated in human CD34+ cells with CALR mutation (34), the discovery of cholesterol biosynthesis was somewhat unexpected. However, this aligned with the predicted significant activation of the lipid and cholesterol biosynthetic transcriptional machinery controlled by the sterol regulatory elementbinding proteins (SREBPs; SREBF1 and SREBF2) and the SREBF chaperone (SCAP) and their inhibitor insulin-induced gene 1 (INSIG1) (Fig. 6F). Moreover, as discussed further below, a role for cholesterol biosynthesis in a proliferative, platelet-biased blood disorder is biologically plausible. Upstream regulator analysis also pointed to activation of ERN1 (Ire1) and Xbp1, two constituents of UPR, as well as STAT5 (table S3), which is consistent with previous demonstrations that mutant CALR acts via STAT signaling (4, 3537). We additionally observed other previously undescribed signaling processes to be predicted to be activated, including drivers of proliferation such as CSF2 [granulocyte-macrophage colony-stimulating factor (GM-CSF)] and hepatocyte growth factor (HGF), or repressed, like the known tumor suppressors TP53 and let-7.

Single-cell transcriptomic approaches have allowed detailed examinations of differentiation landscapes in both normal and perturbed hematopoiesis without a requirement to initially define populations based on a set of cell surface markers. We therefore used single-cell transcriptomics to investigate our recently generated mutant CALR-driven mouse model of ET and found an expected increase in both HSCs and MK lineage cells. We also found an increase in a previously unknown group of cells, here termed pMKPs, linking HSCs with the MK lineage. In vitro, pMKPs displayed behaviors intermediate to those of HSCs and MkPs: Similarly to HSCs, they had some proliferative potential, but similarly to MkPs, they were almost exclusively restricted to the MK lineage. In transplantations, pMKPs and MkPs showed similar behavior: They both transiently produced platelets at a low level. We hypothesize that while pMKPs are more proliferative than MkPs in vitro, neither population is capable of sufficient proliferation to significantly contribute to platelet production in the transplant setting. While this manuscript was in preparation, another group described separating SLAM (Lin CD48 CD150+) cells based on EPCR and CD34, finding that EPCR SLAM cells performed poorly in transplants and showed gene expression profiles (high Gata1, Vwf, and Itga2b) indicative of MK bias (38), results that are broadly consistent with our own.

Our characterization of pMKPs accords well with an increasing understanding that at least a portion of megakaryopoiesis occurs via an early branch point directly from HSCs. While the standard model of hematopoiesis shows megakaryocytes subsequent to MPP2, lineage tracing experiments have shown that some MkPs are generated in an MPP2-independent way (19). Furthermore, in vivo labeling of the most primitive HSCs showed that within 1 week of label induction in LT-HSCs, label can be seen in MK lineages but no other, indicating that the HSC-to-MK pathway can be noticeably faster than pathways producing other lineages (22). Our results suggest that pMKPs are likely to arise independently of the MPP2 stage, as suicidal depletion of the earliest HSPCs reduces pMKPs to a much greater extent than MPP2s. It is therefore tempting to speculate that our pMKP sort scheme may isolate intermediate cells on this shorter, faster bypass trajectory. A recent study of JAK2 V617F-driven MF in humans attributed increased megakaryopoiesis to the expansion of both traditional MkPs and a novel MkP-like population, suggesting that cells that may be analogous to our pMKPs are relevant in human disease (30).

We also investigated an outstanding question about at which stages mutant CALR acts to drive a platelet phenotype. Mutant CALR has been demonstrated to increase the number of immunophenotypic HSCs and MkPs (6), and we also saw an expansion in the number of pMKPs. When considering the behavior of cells individually, it is clear that mutant CALR acts from the stem cell compartment: CALR DEL HOM HSCs were more proliferative and faster to produce megakaryocytes than were their WT counterparts. Mutant CALR did not show a strong effect on the proliferation or MK bias of pMKPs at the level of a single cell but drove an increase in proliferation of MkPs and thus the number of megakaryocytes produced. We therefore concluded that mutant CALR drives platelet bias and proliferation at multiple stages of megakaryopoiesis, although this effect is strongest within HSCs.

Last, we used our single-cell transcriptomic data to ask which biological pathways were most differentially regulated in our CALR DEL HOM mice. Mutant CALR was associated with an up-regulation of the unfolded protein response, as would be expected for cells with impaired chaperone activity and as has been seen in human patient cells (34). In addition, mutant CALR cells showed an increase in cell cycle genes, again consistent with observations from human patient cells (34) and in agreement with our in vitro data, which showed that mutant CALR HSCs and MkPs were more proliferative. We also found up-regulation of cholesterol biosynthesis pathway genes in mutant CALR hematopoietic cells. While cholesterol biosynthesis is broadly increased across numerous cancers (39), including hematological cancers (40), CALR has also been directly linked to cholesterol biosynthesis. CALR/ mouse embryonic fibroblasts show impaired endoplasmic reticulum (ER) Ca2+ levels, leading to overactivation of SREBPs, which then up-regulate cholesterol and triacylglycerol biosynthesis genes (41). As mutant CALR lacks its Ca2+-binding domain, it is possible that CALR DEL HOM cells phenocopy knockout cells with respect to ER Ca2+ stores, thus leading to the observed overactive transcription of cholesterol biosynthesis genes. While megakaryocytes derived from human patient samples have been shown to have increased store-operated Ca2+ entry due to the perturbation of a complex between STIM1, ERp57, and CALR (42), none of our differentially activated pathways from IPA pointed to altered cytoplasmic Ca2+ signaling in the stem and progenitor populations tested. This may reflect differences between progenitor and mature cells. Mice with impaired cholesterol efflux have more proliferative HSCs (43) and an increase in MkP proliferation and an ET-like phenotype (44), suggesting that there may be a previously unknown link between the CALR DEL mutation, cholesterol metabolism, proliferation of MkPs, and thus the overproduction of platelets. While cholesterol biosynthesis was the most prominent novel target found in our transcriptomic analysis, it was by no means alone. IPA upstream regulator analysis predicted an up-regulation of interleukin-5 (IL-5), GM-CSF, and HGFall with known roles in hematopoiesisin addition to several unexpected results, such as TBX2, a transcription factor that has not been studied in hematopoiesis. Upstream regulators predicted to be decreased include the tumor suppressor TP53; let-7, a microRNA with a role in the self-renewal of fetal HSCs (45); and KDM5B (Jarid1b), a histone methylase required for HSC self-renewal (46).

Overall, our study has characterized a previously undescribed MK trajectory implicated in the progression of ET. We find that pMKPs are an intermediate stage within one pathway of megakaryopoiesis and hypothesize that they may be situated within the MPP2-independent MK shortcut. Last, our analysis confirmed that JAK-STAT signaling, unfolded protein response, and cell cycle are all increased by the presence of mutant CALR and found up-regulation of cholesterol biosynthesis, in addition to numerous other potential upstream regulators. Functional validation of these biological pathways and upstream regulators may represent promising avenues of future research to better understand mutant CALR-driven disease and in the development of therapeutic strategies.

The objectives of the study were to generate transcriptomic data from our CALR mouse model of ET and to use these data to determine how the hematopoietic landscape is affected by the CALR DEL mutation. All mouse procedures were performed in strict accordance with the U.K. Home Office regulations for animal research under project license 70/8406.

Bone marrow cells were harvested from the femurs, tibia, and iliac crests of mice. Bones were crushed in a mortar and pestle in phosphate-buffered saline (PBS) and 2% fetal bovine serum (FBS) and 5 mM EDTA and then filtered through a 70-m filter to obtain a suspension of bone marrow cells. The suspension was incubated with an equal volume of ammonium chloride solution (STEMCELL Technologies, Vancouver, Canada) for 10 min on ice to lyse erythrocytes, followed by centrifugation for 5 min at 350g. The cell pellet was resuspended in PBS and 2% FBS and 5 mM EDTA, filtered again through a 70-m filter, and centrifuged again for 5 min at 350g. For cell sorting experiments, bone marrow mononuclear cell suspensions were immunomagnetically depleted of lineage (Lin)positive cells (EasySep Mouse Hematopoietic Progenitor Cell Isolation Kit, catalog no. 19856, STEMCELL Technologies). For staining, cells were incubated with the indicated antibodies for 40 min on ice; see attached tables for catalog information and concentrations used (table S4). Flow cytometry was performed on BD LSRFortessa analyzers, and flow cytometric sorting was performed on BD Influx 4 and 5 cell sorters (BD Biosciences, San Jose, USA). Flow data were analyzed using FlowJo software (Tree Star, Ashland, USA).

For 10x Chromium (10x Genomics, Pleasanton, CA) experiments, Lin c-Kit+ (LK) and Lin Sca1+ cKit+ (LSK) cells were sort purified as described above and processed according to the manufacturers protocol. Sample demultiplexing, barcodes processing, and gene counting were performed using the count commands from the Cell Ranger v1.3 pipeline (https://support.10xgenomics.com/single-cell-gene-expression/software/overview/welcome). After Cell Ranger processing, each sample (LK and LSK for WT and CALR HOM DEL) was filtered for potential doublets by simulating synthetic doublets from pairs of scRNAseq profiles and assigning scores based on a k nearest-neighbor classifier on PCA-transformed data. The 1 and 4.5% of cells with the highest doublets scores from each LSK or LK sample were removed from further analysis, respectively. Cells with >10% of unique molecular identifier (UMI) counts mapping to mitochondrial genes, expressing fewer than 500 genes, or with a total number of UMI counts further than 3 SDs from the mean were excluded. After quality control, 11,098 WT (5139 LK and 5959 LSK) and 15,547 HOM (7815 LK and 7732 LSK) cells were retained for downstream analysis from our first repeat. For our second repeat, 3451 WT (2479 LK and 972 LSK) and 12,372 HOM (7824 LK and 4548 LSK) cells were retained for downstream analysis. These cells were then normalized to the same total count. All scRNAseq data were analyzed using the Scanpy Python Module (47).

To assign cell type identities to WT and CALR samples, a previously published landscape of 45,000 WT LK and LSK hematopoietic progenitors (24) was used as a reference for cell type annotation. This reference was clustered using Louvain clustering, resulting in 13 clusters. LK + LSK samples were joined for each genotype (WT and CALR DEL HOM) and projected into the PCA space of this reference dataset. Nearest neighbors were calculated between the two datasets based on Euclidean distance in the top 50 PCA components. Cells were assigned to the same cluster to which the majority of their 15 nearest neighbors in the reference belonged.

A force-directed graph visualization of the 45,000 cell reference dataset was calculated by first constructing a k = 7 nearest-neighbor graph from the data, which was then used as input for the ForceAtlas2 algorithm as implemented in Gephi 0.9.1 (https://gephi.org). In the ForceAtlas2 algorithm, all cells are pushed away from each other, with the nearest-neighbor connections pulling them back together to segregate separate trajectories.

A fine-resolution clustering of the reference dataset was calculated using the Louvain algorithm, resulting in 63 clusters. These were used as input for a PAGA analysis of the reference dataset using the Scanpy Python Module with default parameters. The results of the PAGA analysis were visualized by using the nodes and their edge weights as input into the ForceAtlas2 algorithm for calculating force-directed graphs as implemented in Gephi 0.9.1. For visualization, only connections with edge weights of >0.3 were shown.

To visualize gene expression of the PAGA graph, the mean normalized expression of all cells belonging to each node was calculated and displayed on a per-node basis.

To calculate differential abundances, votes were given out from each WT LK and CALR LK cell to their k-nearest neighbors in the reference dataset, with k chosen such that the total number of votes given out by each sample was the same. For each cell in the reference dataset, the difference between the number of votes received from the WT and CALR HOM samples was calculated. This difference acts as a proxy for the differential abundance of WT and CALR HOM cells for the region of the LK landscape in which the reference cell is located. This differential abundance proxy could then be visualized either on the reference landscape itself or on the PAGA graph calculated using the reference landscape. In the latter case, each node of the PAGA graph was colored by the mean differential abundance of all cells belonging to that node.

After flow sorting, cells were cultured in StemSpan SFEM (serum-free expansion medium) (STEMCELL Technologies) supplemented with 10% FBS (STEMCELL Technologies), 1% penicillin/streptomycin (Sigma-Aldrich), 1% l-glutamine (Sigma-Aldrich), stem cell factor (SCF; 250 ng/ml), IL-3 (10 ng/ml), and IL-6 (10 ng/ml; STEMCELL Technologies), with or without thrombopoietin (100 ng/ml; STEMCELL Technologies), in round-bottom 96-well plates (Corning, Corning, USA). For pro-erythroid conditions, cells were cultured as above but with the following cytokines: SCF (250 ng/ml), THPO (thrombopoietin) (50 ng/ml), EPO (erythropoietin) (5 U/ml), IL-3 (20 ng/ml), and Flt3L (50 ng/ml). For pro-myeloid conditions, cells were cultured as above but with the following cytokines: SCF (250 ng/ml), THPO (50 ng/ml), granulocyte colony-stimulating factor (50 ng/ml), IL-3 (20 ng/ml), Flt3L (50 ng/ml), and GM-CSF (50 ng/ml).

At 1, 2, 3, 4, and, in some cases, 7 days after flow sorting, single cellderived clones were visually inspected. Wells with surviving cells were classified into one of four categories: (i) exactly one enlarged cell, presumed to be a megakaryocyte; (ii) multiple enlarged cells; (iii) mixed expansion, with both small and enlarged cells; and (iv) expansion with only small cells. In some cases, the experimenter was blinded to the identity of the cell population initially sorted into the well he/she was inspecting and the genotype of the mouse.

For immunofluorescence, cells were allowed to adhere to the surface of poly-l-lysinecoated slides for 30 min at 37C (Poly-Prep Slides, Sigma-Aldrich). Cells were then fixed with 4% paraformaldehyde (Sigma-Aldrich) in PBS overnight at 4C, permeabilized with 0.25% Triton X-100 (Sigma-Aldrich) in PBS for 10 min at room temperature, and blocked with 1% bovine serum albumin (Sigma-Aldrich) for 1 hour at room temperature. Cells were stained with CD41 Alexa Fluor 488 (BioLegend, catalog no. 133908) overnight and mounted with 4,6-diamidino-2-phenylindole (DAPI) (VECTASHIELD Mounting Medium with DAPI, Vector Laboratories Inc., Burlingame, USA; catalog no. H-1500). Pictures were acquired on LSM-710 and LSM-780 confocal microscopes (Zeiss) and analyzed using ZEN software (Zeiss). For quantification of immunofluorescence, cells were cultured on CD44-coated glass-bottom plates for immobilization (48), followed by fixation and staining as above. Pictures were acquired on a Leica DMI4000 microscope (Leica), and CD41 intensity and cell size were quantified using Fiji software.

FACS-sorted cells from VWF-GFP+ donors were injected into the tail veins of W41/W41 (CD45.1) recipient that had been sublethally irradiated with 1 400 centigrays with 250,000 spleen cells as helpers. Peripheral blood was analyzed 1, 2, 3, 4, and 16 weeks after transplant for all cohorts.

Differential expression analysis was performed between WT (LK + LSK) and CALR DEL HOM (LK + LSK) clusters using the Wilcoxon rank sum test on all genes that passed initial quality control (typically approximately 15,000). A Benjamini-Hochberg correction was applied to correct for multiple testing. Genes with an adjusted P value of <0.05 and a fold change of >1.5 between genotypes were marked as differentially expressed. The original normalized counts were used in all cases.

DEGs were studied using IPA (Qiagen). We imputed the whole transcriptome in IPA and then filtered for analysis only statistically significant (adjusted P < 0.01) items with a log2FC > 0.3785 or log2FC < 0.3785. Pathways and upstream regulator networks showing relationships and interactions experimentally confirmed between DEGs and others that functionally interact with them were generated and ranked in terms of significance of participating genes (P < 0.05) and activation status (z score).

Data were analyzed, and graphs were generated in Microsoft Excel (Microsoft) and GraphPad PRISM (GraphPad, La Jolla, USA). Data are presented as means SD. Unless otherwise stated, statistical tests were unpaired Students t tests. P values are as follows: *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

Acknowledgments: We would like to acknowledge J. Aungier, T. Hamilton, D. Pask, and R. Sneade for invaluable technical assistance; R. Schulte, C. Cossetti, and G. Grondys-Kotarba at the CIMR Flow Cytometry Core Facility for assistance with cell sorting; and S. Loughran, T. Klampfl, and E. Laurenti for valuable discussions. Funding: Work in the Gttgens laboratory is supported by the Medical Research Council (MR/M008975/1), Wellcome (206328/Z/17/Z), Blood Cancer UK (18002), and Cancer Research UK (RG83389, jointly with A.R.G.). Work in the Green laboratory is supported by Wellcome (RG74909), WBH Foundation (RG91681), and Cancer Research UK (RG83389, jointly with B.G.). Author contributions: D.P. and H.J.P. designed and conducted experiments with assistance from J.L. S.W. and H.P.B. performed bioinformatic analyses. M.V. performed IPA with supervision from A.V.-P. A.G. provided DTA mice. D.P. analyzed data and wrote the manuscript with input from H.J.P. and J.L. and supervision from B.G. and A.R.G. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. We have deposited scRNAseq data in the NCBI Gene Expression Omnibus (GEO) database with accession number GSE160466. Additional data related to this paper may be requested from the authors.

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The stem/progenitor landscape is reshaped in a mouse model of essential thrombocythemia and causes excess megakaryocyte production - Science Advances

Bone Marrow Stem Cells Comments Off on The stem/progenitor landscape is reshaped in a mouse model of essential thrombocythemia and causes excess megakaryocyte production – Science Advances | November 26th, 2020

Severe infections like malaria cause short and long-term damage to precursor blood cells in mice, but some damage could be reversed, find researchers.

A team led by researchers from Imperial College London and The Francis Crick Institute have discovered that severe infections caused by malaria disrupt the processes that form blood cells in mice. This potentially causes long-term damage that could mean people who have recovered from severe infections are vulnerable to new infections or to developing blood cancers.

The team also discovered that the damage could be reduced or partially reversed in mice with a hormone treatment that regulates bone calcium coupled with an antioxidant. The research could lead to new ways of preventing long-term damage from severe infections including malaria, TB and COVID-19.

First author Dr Myriam Haltalli, who completed the work while at the Department of Life Sciences at Imperial, said: "We discovered that malaria infection reprograms the process of blood cell production in mice and significantly affects the function of precursor blood cells. These changes could cause long-term alterations, but we also found a way to significantly reduce the amount of damage and potentially rescue the healthy production of blood cells."

Unexpectedly fast changes

Blood is made up of several different cell types, that all originate as haematopoietic stem cells (HSCs) in the bone marrow. During severe infection, the production of all blood cells ramps up to help the body fight the infection, depleting the HSCs.

Now, the team has shown how infections also damage the bone marrow environment that is crucial for healthy HSC production and function. They discovered this using advanced microscopy technologies at Imperial and the Crick, RNA analyses led by the Gottgens group at Cambridge University, and mathematical modelling led by Professor Ken Duffy at Maynooth University.

The mice developed malaria naturally, following bites from mosquitoes carrying Plasmodium parasites, provided by Dr Andrew Blagborough at Cambridge University. The researchers subsequently observed the changes in the bone marrow environment and the effect on HSC function.

Within days of infection, blood vessels became leaky and there was a dramatic loss in bone-forming cells called osteoblasts. These changes appear strongly linked to the decline in the pool of HSCs during infection.

Lead author Professor Cristina Lo Celso, from the Department of Life Sciences at Imperial, said: "We were surprised at the speed of the changes, which was completely unexpected. We may think of bone as an impenetrable fortress, but the bone marrow environment is incredibly dynamic and susceptible to damage."

Reducing the pool of HSCs can have several consequences. In the short-term, it appears to particularly affect the production of neutrophils - white blood cells that form an essential part of the immune system. This can leave patients vulnerable to further infections, with potentially long-term consequences for the functioning of the immune system.

In the long term, the pool of HSCs may remain below normal levels, which can increase the chances of the patient developing blood cancers like leukaemia.

Mitigating the impacts

After determining in detail how severe infection affects the bone marrow environment and HSC function, the team tested a way to prevent the damage. Before infecting the mice, they treated them with a hormone that regulates bone calcium and an antioxidant to counter cellular oxidative stress, and then again after infection.

This process led to a tenfold increase in HSC function following infection compared to mice that received no treatment (around 20-40 per cent function compared to two percent function, respectively). Although this is not a complete recovery, the vast increase in function is a positive sign.

The team note that the requirement to start the hormone treatment before infection, combined with its expense and need to be refrigerated, make it unviable as a solution, especially in many parts of the world where severe infections like malaria and TB are prevalent.

However, they hope that proof that the impact of severe infection on HSC function can be significantly lessened will lead to the development of new treatments that can be widely administered.

Professor Lo Celso said: "The long-term impacts of COVID-19 infection are just starting to be known. The impact on HSC function appears similar across multiple severe infections, suggesting our work on malaria could shed light on the possible long-term consequences of COVID-19, and how we might mitigate them."

Dr Haltalli concluded: "Protecting HSC function while still developing strong immune responses is key for healthy ageing."

Reference: Haltalli MLR, Watcham S, Wilson NK, et al. Manipulating niche composition limits damage to haematopoietic stem cells during Plasmodium infection. Nat. Cell Biol. 2020:1-12. doi:10.1038/s41556-020-00601-w

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.

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Severe Infections Wreak Havoc on Mouse Blood Cell Production - Technology Networks

Bone Marrow Stem Cells Comments Off on Severe Infections Wreak Havoc on Mouse Blood Cell Production – Technology Networks | November 26th, 2020

Hematopoietic stem cells are young or immature blood cells found to be living in bone marrow. These blood cells on mature in bone marrow and only a small number of these cells get to enter blood stream. These cells that enter blood stream are called as peripheral blood stems cells. Hematopoietic stem cells transplantation is replacement of absent, diseased or damaged hematopoietic stem cells due to chemotherapy or radiation, with healthy hematopoietic stem cells. Over last 30 years hematopoietic stem cells transplantation market seen rapid expansion and constant expansion with lifesaving technological advances. Hematopoietic stem cells transplantation is also known blood and marrow transplantation which brings about reestablishment of the patients immune and medullary function while treating varied range of about 70 hematological and non-hematological disorders. In general hematopoietic stem cells transplantation is used in treatment of hereditary, oncological, immunological and malignant and non-malignant hematological diseases.

There are two types of peripheral blood stem cell transplants mainly autologous and allogeneic transplantation. In autologous transplants patients own hematopoietic stem cells are harvested or removed before the high-dose treatment that might destroy the patients hematopoietic stem cells. While in allogeneic transplants stem cells are obtained from a tissue type of matched or mismatched donor. Hematopoietic stem cells are harvested from blood or bone marrow and is then frozen to use later. Depending upon the source of hematopoietic stem cells, worldwide there are three types of hematopoietic stem cells transplants namely bone marrow transplant (BMT), peripheral blood stem cell transplant and cord blood transplant. Major drivers in the hematopoietic stem cells transplantation market are establishment of strong and well developed network of hematopoietic stem cells transplantation organizations having global reach and presence has recognized NGO named Worldwide Network for Blood and Marrow Transplantation Group (WBMT) in official relation with World Health Organization (WHO) and rapid increase in number of transplants. Major restraints in hematopoietic stem cells transplantation market is high cost of transplantation and lack of funding for WBMT and other organizations such as regional, national and donor.

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The global market for Hematopoietic stem cells transplantation market is segmented on basis of transplant type, application, disease indication, end user and geography:

Based on transplantation type, hematopoietic stem cells transplantation market is segmented into allogeneic and autologous. Hematopoietic stem cells transplantation market is also segmented by application type into bone marrow transplant (BMT), peripheral blood stem cell transplant and cord blood transplant. The market for hematopoietic stem cells transplantation is majorly driven by bone marrow transplant (BMT) segment. Based on end user hematopoietic stem cells transplantation market is segmented into hospitals and specialty centers. Peripheral blood stem cell transplant type holds the largest market for hematopoietic stem cells transplantation. Hematopoietic stem cells transplantation market is further segmented by disease indication into three main categories i.e. lymphoproliferative disorders, leukemia, and non-malignant disorders. Segment lymphoproliferative disorder holds largest share amongst the three in Hematopoietic stem cells transplantation market. On the basis of regional presence, global hematopoietic stem cells transplantation market is segmented into five key regions viz. North America, Latin America, Europe, Asia Pacific, and Middle East & Africa. Europe leads the global hematopoietic stem cells transplantation market followed by U.S. due to easy technological applications, funding and high income populations. Other reasons for rise in hematopoietic stem cells transplantation market is high prevalence of lymphoproliferative disorders and leukemia; demand for better treatment options; and easy accessibility and acceptance of population to new technological advances. Transplantation rates in high income countries are increasing at a greater extent but continued rise is also seen in low income countries and expected to rise more. Hematopoietic stem cells transplantation market will have its potential in near future as being a perfect alternative to traditional system in many congenital and acquired hematopoietic disorders management. While India, China and Japan will be emerging as potential markets. An excellent and long term alternative to relief by side effects of chemotherapy, radiotherapy and immune-sensitive malignancies is another driver for hematopoietic stem cells transplantation market. The key players in global hematopoietic stem cells transplantation market are Lonza, Escape Therapeutics, Cesca Therapeutics Inc., Regen BioPharma, Inc., Invitrx Inc, StemGenex, Lion Biotechnologies, Inc., CellGenix GmbH, Actinium Pharmaceuticals, Inc., Pluristem, Kite Pharma, Novartis AG.

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Revenue from the Sales of Hematopoietic Stem Cells Transplantation Market to Witness Relatively Significant Growth During 2017 2025 - Canaan Mountain...

Bone Marrow Stem Cells Comments Off on Revenue from the Sales of Hematopoietic Stem Cells Transplantation Market to Witness Relatively Significant Growth During 2017 2025 – Canaan Mountain… | November 26th, 2020

SHANGHAI and HONG KONG, Nov. 25, 2020 /PRNewswire/ -- Antengene Corporation Limited ("Antengene", HKSE stock code: 6996.HK), a leading innovative biopharmaceutical company dedicated to discovering, developing and commercializing global first-in-class and/or best-inclass therapeutics in hematology and oncology, announced that the National Medical Products Administration (NMPA) has approved the clinical trial of ATG-016 (eltanexor) in patients with intermediate and higher risk myelodysplastic syndrome (MDS) according to the Revised International Prognostic Scoring System (IPSS-R) after the failure of hypomethylating agents (HMA) based therapy. The trial is a Phase I/II, single-arm, open-label clinical study, aiming to evaluate the pharmacokinetics, safety and efficacy of ATG-016 (eltanexor) monotherapy.

MDS is a heterogeneous group of clonal disorders of the bone marrow hematopoietic stem cells (HPSCs), characterized by ineffective hematopoiesis with peripheral blood cytopenia and a higher risk for developing acute myeloid leukemia (AML). Patients with high-risk MDS refractory to hypomethylating agents have a median overall survival (OS) of only 4 to 6 months with limited options for follow-up treatment. Pre-clinical studies have demonstrated that selective inhibitor of nuclear export (SINE) compounds are able to block the nuclear export of many tumor suppressor proteins (e.g. p53, IkB, p21) leading to their accumulation and activation in the nucleus thereby exerting anti-tumor effects. In addition, SINE compounds can also reduce the nuclear export and translation of many oncogenic mRNA (c-Myc, Bcl-2, Bcl-6, cyclin D) which are bound to elF4E and result in selective apoptosis of tumor cells. ATG-016 is a member of the latest-generation of SINE compounds. Compared to the first-generation nuclear export inhibitor, ATG-016 demonstrates minimal blood-brain barrier permeability and a broader therapeutic window. It has shown preliminary anti-cancer activity in high-risk MDS patients.

Dr. Jay Mei, the Founder, Chairman and CEO of Antengene expressed, "The approval of the ATG-016 clinical trial demonstrates the efficient execution of the Antengene R&D team and is also the first clinical trial approval obtained by Antengene in mainland China after its listing." He also mentioned, "Selinexor, the first-generation selective inhibitor of nuclear export, has shown extensive activity against hematological malignancies and solid tumors, and has been approved by the FDA for relapsed/refractory multiple myeloma and diffuse large B-cell lymphoma. As a second-generation orally available SINE compound, ATG-016 can reduce the blood-brain barrier penetration, thereby representing a broader therapeutic window with potentially less adverse events and better drug tolerability."

About ATG-016

ATG-016 (eltanexor) is a second-generation selective inhibitor of nuclear export compound. Compared to the first-generation SINE compound, ATG-016 has lower blood-brain barrier penetration and broader therapeutic window which allows more frequent dosing and a longer period of exposure at higher levels with better tolerability. Therefore, ATG-016 may be used to target a wider range of indications. We plan to conduct phase I/II clinical studies for MDS in China, and plan to further develop ATG-016 for cancers with high prevalence in the Asia-Pacific region (such as KRAS-mutant solid tumors) and virus infection related malignancies (such as nasopharyngeal carcinoma).

About Antengene

Antengene Corporation Limited ("Antengene", SEHK: 6996.HK) is a leading clinical-stage Asia-Pacific biopharmaceutical company focused on innovative oncology medicines. Antengene aims to provide the most advanced anti-cancer drugs to patients in China, the Asia Pacific Region and around the world. Since its establishment, Antengene has built a pipeline of 12 clinical and pre-clinical stage assets, obtained 10 investigational new drug (IND) approvals and has 9 ongoing cross-regional clinical trials in Asia Pacific. At Antengene, we focus on developing drug candidates with novel mechanisms of action (MoAs) and first-in-class/best-in-class potential to address significant unmet medical needs. The vision of Antengene is to "Treat Patients Beyond Borders" through research, development and commercialization of first-in-class/best-in-class therapeutics.

Forward-looking statements

The forward-looking statements made in this article relate only to the events or information as of the date on which the statements are made in this article. Except as required by law, we undertake no obligation to update or revise publicly any forward-looking statements, whether as a result of new information, future events or otherwise, after the date on which the statements are made or to reflect the occurrence of unanticipated events. You should read this article completely and with the understanding that our actual future results or performance may be materially different from what we expect. In this article, statements of, or references to, our intentions or those of any of our Directors or our Company are made as of the date of this article. Any of these intentions may alter in light of future development.

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Approval of Phase I/II Clinical Trial of ATG-016 (Eltanexor), a Second Generation Selective Inhibitor of Nuclear Export (SINE), in Mainland China for...

Bone Marrow Stem Cells Comments Off on Approval of Phase I/II Clinical Trial of ATG-016 (Eltanexor), a Second Generation Selective Inhibitor of Nuclear Export (SINE), in Mainland China for… | November 26th, 2020

Orthopedic Regenerative Medicine Market

Coherent Market Insights, 26-11-2020: The research report on the Orthopedic Regenerative Medicine Market is a deep analysis of the market. This is a latest report, covering the current COVID-19 impact on the market. The pandemic of Coronavirus (COVID-19) has affected every aspect of life globally. This has brought along several changes in market conditions. The rapidly changing market scenario and initial and future assessment of the impact is covered in the report. Experts have studied the historical data and compared it with the changing market situations. The report covers all the necessary information required by new entrants as well as the existing players to gain deeper insight.

Furthermore, the statistical survey in the report focuses on product specifications, costs, production capacities, marketing channels, and market players. Upstream raw materials, downstream demand analysis, and a list of end-user industries have been studied systematically, along with the suppliers in this market. The product flow and distribution channel have also been presented in this research report.

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The segments and sub-section of Orthopedic Regenerative Medicine market are shown below:

By Procedure Cell TherapyTissue EngineeringBy Cell TypeInduced Pluripotent Stem Cells (iPSCs)Adult Stem CellsTissue Specific Progenitor Stem Cells (TSPSCs),Mesenchymal Stem Cells (MSCs)Umbilical Cord Stem Cells (UCSCs)Bone Marrow Stem Cells (BMSCs)By SourceBone MarrowUmbilical Cord BloodAdipose TissueAllograftsAmniotic FluidBy ApplicationsTendons RepairCartilage RepairBone RepairLigament RepairSpine RepairOthers

Some of the key players/Manufacturers involved in the Orthopedic Regenerative Medicine Market are Curasan, Inc., Carmell Therapeutics Corporation, Anika Therapeutics, Inc., Conatus Pharmaceuticals Inc., Histogen Inc., Royal Biologics, Ortho Regenerative Technologies, Inc., Swiss Biomed Orthopaedics AG, Osiris Therapeutics, Inc., and Octane Medical Inc.

If opting for the Global version of Orthopedic Regenerative Medicine Market analysis is provided for major regions as follows:

North America (The US, Canada, and Mexico)

Europe (the UK, Germany, France, and Rest of Europe)

Asia Pacific (China, India, and Rest of Asia Pacific)

Latin America (Brazil and Rest of Latin America)

Middle East & Africa (Saudi Arabia, the UAE, South Africa, and Rest of Middle East & Africa)

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The Orthopedic Regenerative Medicine Market Report Consists of the Following Points:

The report consists of an overall prospect of the market that helps gain significant insights about the global market.

The market has been categorized based on types, applications, and regions. For an in-depth analysis and better understanding of the market, the key segments have been further categorized into sub-segments.

The factors responsible for the growth of the market have been mentioned. This data has been gathered from primary and secondary sources by industry professionals. This provides an in-depth understanding of key segments and their future prospects.

The report analyses the latest developments and the profiles of the leading competitors in the market.

The Orthopedic Regenerative Medicine Market research report offers an eight-year forecast.

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Impact of COVID 19 on Orthopedic Regenerative Medicine Market Detailed Research Study 2020-2027 | Curasan, Inc., Carmell Therapeutics Corporation,...

Bone Marrow Stem Cells Comments Off on Impact of COVID 19 on Orthopedic Regenerative Medicine Market Detailed Research Study 2020-2027 | Curasan, Inc., Carmell Therapeutics Corporation,… | November 26th, 2020

New York, Nov. 25, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Induced Pluripotent Stem Cell (iPSC) Industry" - https://www.reportlinker.com/p05798831/?utm_source=GNW 4 billion by the year 2027, trailing a post COVID-19 CAGR of 6.6%, over the analysis period 2020 through 2027. Stem cells are undifferentiated cells that hold the capability to divide, and differentiate into specialized cells in the body. Stem cells act as repair system and replenish adult tissues, maintaining the turnover of regenerative organs such as the blood and skin. In organs, such as the bone marrow, stem cells frequently form replacement cells to repair the worn out tissue. These cells can respond to signals from the body and transverse a particular developmental pathway to differentiate into one specific cell type. Due to their regenerative properties, stem cells are being researched for therapeutic applications in diabetes, cardiovascular disease, neurodegenerative disease, cancer, autoimmune diseases, spinal cord defects, among others. Stem Cell research is an exciting field where continuous discoveries are being made on new sources of stem cells and new methods of their acquisition and harvesting. Of late, adult stem cells have garnered a lions share of the stem cell space, purely based on the fact that they require less expensive clinical trials, need to comply with fewer regulatory norms and ethical issues compared to other stem cell variants such as embryonic stem cells.

Researchers around the world have been focusing research activities to develop adult stem cell therapies in order to combat a variety of diseases ranging from diabetes to heart disease. Factually, adult stem cells are the only stem cells that have been approved for use in transplants for the treatment of diseases such as cancer. Interestingly, with drug development based on embryonic stem cells being challenged amid growing debate over ethics and regulation of this research, iPSCS offers an alternate step forward in the commercialization of stem cell therapies and regenerative medicine. Embryonic stem cell research continues to remain embroiled in ethical, religious, and political controversies across various countries around the world. Induced Pluripotent Stem Cells (iPSs), which are reprogrammed to mimic embryonic stem cell-like state allowing expression of genes and human cells needed for therapeutic purposes, offers an attractive alternate way forwarding in furthering the goals of stem cell research. Pioneered in 2006 and developed in the following year, these cells are created by conversion of somatic cells into PSCs by introducing certain genes including Myc, Klf4, Oct3/4 and Sox2.

Pluripotent stem cells hold tremendous potential in the regenerative medicine arena. Based on their ability to proliferate indefinitely and develop into desirable cell type such as heart, liver, neuronal and pancreatic cells, iPSCs offer a source of new cells that can replace lost or damaged cells. For instance, iPSCs can be developed into beta islet cells, blood cells or neuronal cells for the treatment of diabetes, leukemia and neurological disorders, respectively. Parkinsons, Alzheimers & spinal cord injuries are key neurologic diseases expected to benefit from iPS research. Dramatic rise in cancer cases worldwide and the need for novel anti-cancer therapies will emerge as a key driver for the growth of iPSCs. Interest in cancer research soars high on new hopes of direct reprogramming of cancer cells with enforced expression of pluripotency factors and the resulting dedifferentiation of transformed cancer cells. The ongoing pandemic is also opening up new opportunities for Human induced pluripotent stem cells (hiPSCs) by offering a reliable model for researchers involved in studying how coronavirus indirectly or directly affects different cells in the human body. Made from a small sample of blood or skin cells, hiPSCs are robust stem cells that can be developed into any cell type and then infected with the coronavirus in order to analyse the disease prognosis and the resulting effects. By deploying hiPSCs, researchers have identified that stem cell-derived cardiomyocytes (heart muscle cells) and blood vessels remain directly exposed to COVID-19 infection. Scientists identified that a significant portion of stem cell-derived cardiomyocytes ceased beating and expired within 3 days after being infected by coronavirus. Researchers can leverage the infected cardiomyocytes to screen for potential drug candidates that can restore their function and improve their survival; and also for identifying new antiviral drugs that potentially curtail coronavirus replication in the heart, reduce cardiac injury and curb the disease prognosis. Researchers can also utilize the infected cardiomyocytes to analyze COVID-induced myocarditis through addition of immune cells to their lab experiments.

Competitors identified in this market include, among others,

Read the full report: https://www.reportlinker.com/p05798831/?utm_source=GNW

I. INTRODUCTION, METHODOLOGY & REPORT SCOPE I-1

II. EXECUTIVE SUMMARY II-1

1. MARKET OVERVIEW II-1 Impact of Covid-19 and a Looming Global Recession II-1 Induced Pluripotent Stem Cells (iPSCs) Market Gains from Increasing Use in Research for COVID-19 II-1 Studies Employing iPSCs in COVID-19 Research II-2 Stem Cells, Application Areas, and the Different Types: A Prelude II-3 Applications of Stem Cells II-4 Types of Stem Cells II-4 Induced Pluripotent Stem Cell (iPSC): An Introduction II-5 Production of iPSCs II-6 First & Second Generation Mouse iPSCs II-6 Human iPSCs II-7 Key Properties of iPSCs II-7 Transcription Factors Involved in Generation of iPSCs II-7 Noteworthy Research & Application Areas for iPSCs II-8 Induced Pluripotent Stem Cell ((iPSC) Market: Growth Prospects and Outlook II-9 Drug Development Application to Witness Considerable Growth II-11 Technical Breakthroughs, Advances & Clinical Trials to Spur Growth of iPSC Market II-11 North America Dominates Global iPSC Market II-12 Competition II-12 Recent Market Activity II-13 Select Innovation/Advancement II-16

2. FOCUS ON SELECT PLAYERS II-17 Axol Bioscience Ltd. (UK) II-17 Cynata Therapeutics Limited (Australia) II-17 Evotec SE (Germany) II-17 Fate Therapeutics, Inc. (USA) II-17 FUJIFILM Cellular Dynamics, Inc. (USA) II-18 Ncardia (Belgium) II-18 Pluricell Biotech (Brazil) II-18 REPROCELL USA, Inc. (USA) II-18 Sumitomo Dainippon Pharma Co., Ltd. (Japan) II-19 Takara Bio, Inc. (Japan) II-19 Thermo Fisher Scientific, Inc. (USA) II-20 ViaCyte, Inc. (USA) II-20

3. MARKET TRENDS & DRIVERS II-21 Effective Research Programs Hold Key in Roll Out of Advanced iPSC Treatments II-21 Induced Pluripotent Stem Cells: A Giant Leap in the Therapeutic Applications II-21 Research Trends in Induced Pluripotent Stem Cell Space II-22 Exhibit 1: Worldwide Publication of hESC and hiPSC Research Papers for the Period 2008-2010, 2011-2013 and 2014-2016 II-22 Exhibit 2: Number of Original Research Papers on hESC and iPSC Published Worldwide (2014-2016) II-23 Concerns Related to Embryonic Stem Cells Shift the Focus onto iPSCs II-23 Regenerative Medicine: A Promising Application of iPSCs II-24 Induced Pluripotent: A Potential Competitor to hESCs? II-25 Exhibit 3: Global Regenerative Medicine Market Size in US$ Billion for 2019, 2021, 2023 and 2025 II-27 Exhibit 4: Global Stem Cell & Regenerative Medicine Market by Product (in %) for the Year 2019 II-27 Exhibit 5: Global Regenerative Medicines Market by Category: Breakdown (in %) for Biomaterials, Stem Cell Therapies and Tissue Engineering for 2019 II-28 Pluripotent Stem Cells Hold Significance for Cardiovascular Regenerative Medicine II-28 Exhibit 6: Leading Causes of Mortality Worldwide: Number of Deaths in Millions & % Share of Deaths by Cause for 2017 II-30 Leading Causes of Mortality for Low-Income and High-Income Countries II-30 Growing Importance of iPSCs in Personalized Drug Discovery II-31 Persistent Advancements in Genetics Space and Subsequent Growth in Precision Medicine Augur Well for iPSCs Market II-33 Exhibit 7: Global Precision Medicine Market (In US$ Billion) for the Years 2018, 2021 & 2024 II-34 Increasing Prevalence of Chronic Disorders Supports Growth of iPSCs Market II-34 Exhibit 8: Worldwide Cancer Incidence: Number of New Cancer Cases Diagnosed for 2012, 2018 & 2040 II-35 Exhibit 9: Number of New Cancer Cases Reported (in Thousands) by Cancer Type: 2018 II-36 Exhibit 10: Fatalities by Heart Conditions: Estimated Percentage Breakdown for Cardiovascular Disease, Ischemic Heart Disease, Stroke, and Others II-37 Exhibit 11: Rising Diabetes Prevalence Presents Opportunity for iPSCs Market: Number of Adults (20-79) with Diabetes (in Millions) by Region for 2017 and 2045 II-38 Aging Demographics Add to the Global Burden of Chronic Diseases, Presenting Opportunities for iPSCs Market II-38 Exhibit 12: Expanding Elderly Population Worldwide: Breakdown of Number of People Aged 65+ Years in Million by Geographic Region for the Years 2019 and 2030 II-39 Growth in Number of Genomics Projects Propels Market Growth II-39 Genomic Initiatives in Select Countries II-40 Exhibit 13: New Gene-Editing Tools Spur Interest and Investments in Genetics, Driving Lucrative Growth Opportunities for iPSCs: Total VC Funding (In US$ Million) in Genetics for the Years 2014, 2015, 2016, 2017 and 2018 II-41 Launch of Numerous iPSCs-Related Clinical Trials Set to Benefit Market Growth II-41 Exhibit 14: Number of Induced Pluripotent Stem Cells based Studies by Select Condition: As on Oct 31, 2020 II-43 iPSCs-based Clinical Trial for Heart Diseases II-43 Induced Pluripotent Stem Cells for Stroke Treatment II-44 ?Off-the-shelf? Stem Cell Treatment for Cancer Enters Clinical Trial II-44 iPSCs for Hematological Disorders II-44 Market Benefits from Growing Funding for iPSCs-Related R&D Initiatives II-44 Exhibit 15: Stem Cell Research Funding in the US (in US$ Million) for the Years 2016 through 2021 II-46 Human iPSC Banks: A Review of Emerging Opportunities and Drawbacks II-46 Human iPSC Banks Worldwide: An Overview II-48 Cell Sources and Reprogramming Methods Used by Select iPSC Banks II-49 Innovations, Research Studies & Advancements in iPSCs II-50 Key iPSC Research Breakthroughs for Regenerative Medicine II-50 Researchers Develop Novel Oncogene-Free and Virus-Free iPSC Production Method II-51 Scientists Study Concerns of Genetic Mutations in iPSCs II-52 iPSCs Hold Tremendous Potential in Transforming Research Efforts II-52 Researchers Highlight Potential Use of iPSCs for Developing Novel Cancer Vaccines II-54 Scientists Use Machine Learning to Improve Reliability of iPSC Self-Organization II-54 STEMCELL Technologies Unveils mTeSR? Plus II-55 Challenges and Risks Related to Pluripotent Stem Cells II-56 A Glance at Issues Related to Reprogramming of Adult Cells to iPSCs II-57 A Note on Legal, Social and Ethical Considerations with iPSCs II-58

4. GLOBAL MARKET PERSPECTIVE II-59 Table 1: World Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 II-59

Table 2: World 7-Year Perspective for Induced Pluripotent Stem Cell (iPSC) by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets for Years 2020 & 2027 II-60

Table 3: World Current & Future Analysis for Vascular Cells by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 II-61

Table 4: World 7-Year Perspective for Vascular Cells by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2020 & 2027 II-62

Table 5: World Current & Future Analysis for Cardiac Cells by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 II-63

Table 6: World 7-Year Perspective for Cardiac Cells by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2020 & 2027 II-64

Table 7: World Current & Future Analysis for Neuronal Cells by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 II-65

Table 8: World 7-Year Perspective for Neuronal Cells by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2020 & 2027 II-66

Table 9: World Current & Future Analysis for Liver Cells by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 II-67

Table 10: World 7-Year Perspective for Liver Cells by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2020 & 2027 II-68

Table 11: World Current & Future Analysis for Immune Cells by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 II-69

Table 12: World 7-Year Perspective for Immune Cells by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2020 & 2027 II-70

Table 13: World Current & Future Analysis for Other Cell Types by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 II-71

Table 14: World 7-Year Perspective for Other Cell Types by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2020 & 2027 II-72

Table 15: World Current & Future Analysis for Cellular Reprogramming by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 II-73

Table 16: World 7-Year Perspective for Cellular Reprogramming by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2020 & 2027 II-74

Table 17: World Current & Future Analysis for Cell Culture by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 II-75

Table 18: World 7-Year Perspective for Cell Culture by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2020 & 2027 II-76

Table 19: World Current & Future Analysis for Cell Differentiation by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 II-77

Table 20: World 7-Year Perspective for Cell Differentiation by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2020 & 2027 II-78

Table 21: World Current & Future Analysis for Cell Analysis by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 II-79

Table 22: World 7-Year Perspective for Cell Analysis by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2020 & 2027 II-80

Table 23: World Current & Future Analysis for Cellular Engineering by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 II-81

Table 24: World 7-Year Perspective for Cellular Engineering by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2020 & 2027 II-82

Table 25: World Current & Future Analysis for Other Research Methods by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 II-83

Table 26: World 7-Year Perspective for Other Research Methods by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2020 & 2027 II-84

Table 27: World Current & Future Analysis for Drug Development & Toxicology Testing by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 II-85

Table 28: World 7-Year Perspective for Drug Development & Toxicology Testing by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2020 & 2027 II-86

Table 29: World Current & Future Analysis for Academic Research by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 II-87

Table 30: World 7-Year Perspective for Academic Research by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2020 & 2027 II-88

Table 31: World Current & Future Analysis for Regenerative Medicine by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 II-89

Table 32: World 7-Year Perspective for Regenerative Medicine by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2020 & 2027 II-90

Table 33: World Current & Future Analysis for Other Applications by Geographic Region - USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 II-91

Table 34: World 7-Year Perspective for Other Applications by Geographic Region - Percentage Breakdown of Value Sales for USA, Canada, Japan, China, Europe, Asia-Pacific and Rest of World for Years 2020 & 2027 II-92

III. MARKET ANALYSIS III-1

GEOGRAPHIC MARKET ANALYSIS III-1

UNITED STATES III-1 Table 35: USA Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Cell Type - Vascular Cells, Cardiac Cells, Neuronal Cells, Liver Cells, Immune Cells and Other Cell Types - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 III-1

Table 36: USA 7-Year Perspective for Induced Pluripotent Stem Cell (iPSC) by Cell Type - Percentage Breakdown of Value Sales for Vascular Cells, Cardiac Cells, Neuronal Cells, Liver Cells, Immune Cells and Other Cell Types for the Years 2020 & 2027 III-2

Table 37: USA Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Research Method - Cellular Reprogramming, Cell Culture, Cell Differentiation, Cell Analysis, Cellular Engineering and Other Research Methods - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 III-3

Table 38: USA 7-Year Perspective for Induced Pluripotent Stem Cell (iPSC) by Research Method - Percentage Breakdown of Value Sales for Cellular Reprogramming, Cell Culture, Cell Differentiation, Cell Analysis, Cellular Engineering and Other Research Methods for the Years 2020 & 2027 III-4

Table 39: USA Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Application - Drug Development & Toxicology Testing, Academic Research, Regenerative Medicine and Other Applications - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 III-5

Table 40: USA 7-Year Perspective for Induced Pluripotent Stem Cell (iPSC) by Application - Percentage Breakdown of Value Sales for Drug Development & Toxicology Testing, Academic Research, Regenerative Medicine and Other Applications for the Years 2020 & 2027 III-6

CANADA III-7 Table 41: Canada Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Cell Type - Vascular Cells, Cardiac Cells, Neuronal Cells, Liver Cells, Immune Cells and Other Cell Types - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 III-7

Table 42: Canada 7-Year Perspective for Induced Pluripotent Stem Cell (iPSC) by Cell Type - Percentage Breakdown of Value Sales for Vascular Cells, Cardiac Cells, Neuronal Cells, Liver Cells, Immune Cells and Other Cell Types for the Years 2020 & 2027 III-8

Table 43: Canada Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Research Method - Cellular Reprogramming, Cell Culture, Cell Differentiation, Cell Analysis, Cellular Engineering and Other Research Methods - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 III-9

Table 44: Canada 7-Year Perspective for Induced Pluripotent Stem Cell (iPSC) by Research Method - Percentage Breakdown of Value Sales for Cellular Reprogramming, Cell Culture, Cell Differentiation, Cell Analysis, Cellular Engineering and Other Research Methods for the Years 2020 & 2027 III-10

Table 45: Canada Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Application - Drug Development & Toxicology Testing, Academic Research, Regenerative Medicine and Other Applications - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 III-11

Table 46: Canada 7-Year Perspective for Induced Pluripotent Stem Cell (iPSC) by Application - Percentage Breakdown of Value Sales for Drug Development & Toxicology Testing, Academic Research, Regenerative Medicine and Other Applications for the Years 2020 & 2027 III-12

JAPAN III-13 Table 47: Japan Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Cell Type - Vascular Cells, Cardiac Cells, Neuronal Cells, Liver Cells, Immune Cells and Other Cell Types - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 III-13

Table 48: Japan 7-Year Perspective for Induced Pluripotent Stem Cell (iPSC) by Cell Type - Percentage Breakdown of Value Sales for Vascular Cells, Cardiac Cells, Neuronal Cells, Liver Cells, Immune Cells and Other Cell Types for the Years 2020 & 2027 III-14

Table 49: Japan Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Research Method - Cellular Reprogramming, Cell Culture, Cell Differentiation, Cell Analysis, Cellular Engineering and Other Research Methods - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 III-15

Table 50: Japan 7-Year Perspective for Induced Pluripotent Stem Cell (iPSC) by Research Method - Percentage Breakdown of Value Sales for Cellular Reprogramming, Cell Culture, Cell Differentiation, Cell Analysis, Cellular Engineering and Other Research Methods for the Years 2020 & 2027 III-16

Table 51: Japan Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Application - Drug Development & Toxicology Testing, Academic Research, Regenerative Medicine and Other Applications - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 III-17

Table 52: Japan 7-Year Perspective for Induced Pluripotent Stem Cell (iPSC) by Application - Percentage Breakdown of Value Sales for Drug Development & Toxicology Testing, Academic Research, Regenerative Medicine and Other Applications for the Years 2020 & 2027 III-18

CHINA III-19 Table 53: China Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Cell Type - Vascular Cells, Cardiac Cells, Neuronal Cells, Liver Cells, Immune Cells and Other Cell Types - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 III-19

Table 54: China 7-Year Perspective for Induced Pluripotent Stem Cell (iPSC) by Cell Type - Percentage Breakdown of Value Sales for Vascular Cells, Cardiac Cells, Neuronal Cells, Liver Cells, Immune Cells and Other Cell Types for the Years 2020 & 2027 III-20

Table 55: China Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Research Method - Cellular Reprogramming, Cell Culture, Cell Differentiation, Cell Analysis, Cellular Engineering and Other Research Methods - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 III-21

Table 56: China 7-Year Perspective for Induced Pluripotent Stem Cell (iPSC) by Research Method - Percentage Breakdown of Value Sales for Cellular Reprogramming, Cell Culture, Cell Differentiation, Cell Analysis, Cellular Engineering and Other Research Methods for the Years 2020 & 2027 III-22

Table 57: China Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Application - Drug Development & Toxicology Testing, Academic Research, Regenerative Medicine and Other Applications - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 III-23

Table 58: China 7-Year Perspective for Induced Pluripotent Stem Cell (iPSC) by Application - Percentage Breakdown of Value Sales for Drug Development & Toxicology Testing, Academic Research, Regenerative Medicine and Other Applications for the Years 2020 & 2027 III-24

EUROPE III-25 Table 59: Europe Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Geographic Region - France, Germany, Italy, UK and Rest of Europe Markets - Independent Analysis of Annual Sales in US$ Thousand for Years 2020 through 2027 III-25

Table 60: Europe 7-Year Perspective for Induced Pluripotent Stem Cell (iPSC) by Geographic Region - Percentage Breakdown of Value Sales for France, Germany, Italy, UK and Rest of Europe Markets for Years 2020 & 2027 III-26

Table 61: Europe Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Cell Type - Vascular Cells, Cardiac Cells, Neuronal Cells, Liver Cells, Immune Cells and Other Cell Types - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 III-27

Table 62: Europe 7-Year Perspective for Induced Pluripotent Stem Cell (iPSC) by Cell Type - Percentage Breakdown of Value Sales for Vascular Cells, Cardiac Cells, Neuronal Cells, Liver Cells, Immune Cells and Other Cell Types for the Years 2020 & 2027 III-28

Table 63: Europe Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Research Method - Cellular Reprogramming, Cell Culture, Cell Differentiation, Cell Analysis, Cellular Engineering and Other Research Methods - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 III-29

Table 64: Europe 7-Year Perspective for Induced Pluripotent Stem Cell (iPSC) by Research Method - Percentage Breakdown of Value Sales for Cellular Reprogramming, Cell Culture, Cell Differentiation, Cell Analysis, Cellular Engineering and Other Research Methods for the Years 2020 & 2027 III-30

Table 65: Europe Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Application - Drug Development & Toxicology Testing, Academic Research, Regenerative Medicine and Other Applications - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 III-31

Table 66: Europe 7-Year Perspective for Induced Pluripotent Stem Cell (iPSC) by Application - Percentage Breakdown of Value Sales for Drug Development & Toxicology Testing, Academic Research, Regenerative Medicine and Other Applications for the Years 2020 & 2027 III-32

FRANCE III-33 Table 67: France Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Cell Type - Vascular Cells, Cardiac Cells, Neuronal Cells, Liver Cells, Immune Cells and Other Cell Types - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 III-33

Table 68: France 7-Year Perspective for Induced Pluripotent Stem Cell (iPSC) by Cell Type - Percentage Breakdown of Value Sales for Vascular Cells, Cardiac Cells, Neuronal Cells, Liver Cells, Immune Cells and Other Cell Types for the Years 2020 & 2027 III-34

Table 69: France Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Research Method - Cellular Reprogramming, Cell Culture, Cell Differentiation, Cell Analysis, Cellular Engineering and Other Research Methods - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 III-35

Table 70: France 7-Year Perspective for Induced Pluripotent Stem Cell (iPSC) by Research Method - Percentage Breakdown of Value Sales for Cellular Reprogramming, Cell Culture, Cell Differentiation, Cell Analysis, Cellular Engineering and Other Research Methods for the Years 2020 & 2027 III-36

Table 71: France Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Application - Drug Development & Toxicology Testing, Academic Research, Regenerative Medicine and Other Applications - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 III-37

Table 72: France 7-Year Perspective for Induced Pluripotent Stem Cell (iPSC) by Application - Percentage Breakdown of Value Sales for Drug Development & Toxicology Testing, Academic Research, Regenerative Medicine and Other Applications for the Years 2020 & 2027 III-38

GERMANY III-39 Table 73: Germany Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Cell Type - Vascular Cells, Cardiac Cells, Neuronal Cells, Liver Cells, Immune Cells and Other Cell Types - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 III-39

Table 74: Germany 7-Year Perspective for Induced Pluripotent Stem Cell (iPSC) by Cell Type - Percentage Breakdown of Value Sales for Vascular Cells, Cardiac Cells, Neuronal Cells, Liver Cells, Immune Cells and Other Cell Types for the Years 2020 & 2027 III-40

Table 75: Germany Current & Future Analysis for Induced Pluripotent Stem Cell (iPSC) by Research Method - Cellular Reprogramming, Cell Culture, Cell Differentiation, Cell Analysis, Cellular Engineering and Other Research Methods - Independent Analysis of Annual Sales in US$ Thousand for the Years 2020 through 2027 III-41

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INTRODUCTION

Tissue-resident macrophages regulate immunity and are pivotal for development, homeostasis, and repair (1). Major research efforts have uncovered roles for tissue-resident macrophages during infection, insult, and repair. However, in many cases, these studies disproportionally focus on certain organs in animals while disregarding tissue macrophages in other locations (2). Because function in macrophages is shaped by their tissue of residence and the local environment, specific phenotypes may not be universally applicable to all tissues (3). Notably, the bladder has generally been overlooked in macrophage studies; consequently, the function, origin, and renewal of bladder-resident macrophages in health and disease are poorly characterized or even completely unknown (4, 5).

Tissue-resident macrophages in adult organisms originate from embryonic progenitors, adult bone marrow (BM), or a mixture of both (612). During development, hematopoiesis begins in the yolk sac, giving rise to erythrocytes and macrophages directly and to erythro-myeloid progenitors (EMPs) (6, 13, 14). As hematopoiesis declines in the yolk sac, an intraembryonic wave of definitive hematopoiesis begins in the aorta-gonad-mesonephro, generating hematopoietic stem cells (HSCs). EMPs and then HSCs colonize the fetal liver to give rise to fetal liver monocytes, macrophages, and other immune cells, whereas only HSCs migrate to the BM to establish hematopoiesis in postnatal animals (15). Embryo-derived macrophages can either self-maintain and persist into adulthood or undergo replacement by circulating monocytes at tissue-specific rates. For example, a majority of macrophages in the gut are continuously replenished by BM-derived cells, whereas brain macrophages, or microglia, are long-lived yolk sacderived cells that are not replaced in steady-state conditions (8, 14, 16, 17). In certain conditions, origin influences macrophage behavior; for example, following myocardial infarction, embryonic-derived cardiac macrophages promote tissue repair, whereas BM-derived macrophages induce inflammation (18). However, macrophage functions are also imprinted by their microenvironment (19, 20). In the small intestine, macrophages in the muscle express higher levels of tissue-protective genes, such as Retnla, Mrc1, and Cd163 compared to lamina propria macrophages, although both originate from adult BM (21).

While the origin and maintenance of bladder-resident macrophages are currently unknown, these macrophages do play a role in response to urinary tract infection (UTI), which affects up to 50% of all women at some point in their lifetimes (5, 22). The immune response to uropathogenic Escherichia coli (UPEC) infection in the bladder is characterized by robust cytokine expression leading to rapid infiltration of large numbers of neutrophils and classical Ly6C+ monocytes (2328). Although essential to bacterial clearance, neutrophil and monocyte infiltration likely also induce collateral tissue damage. Targeted depletion of one of these two cell types is associated with reduced bacterial burden after primary infection in mice, whereas elimination of both cell types together leads to unchecked bacteria growth (23, 25, 26). Tissue-resident macrophages also take up a large number of bacteria during UTI; however, depletion of resident macrophages just before infection does not change bacterial clearance in a first or primary UTI (23). The absence of macrophages in the early stages of a primary UTI significantly improves bacterial clearance during a second, or challenge, infection (23). Exactly how the elimination of resident macrophages improves the response to a challenge infection is unclear, particularly as tissue-associated macrophages return to homeostatic numbers in the time interval between the two infections. Of note, improved bacterial clearance is lost in macrophage-depleted mice that are also depleted of CD4+ and CD8+ T cells, suggesting that macrophages modulate T cell activation or limit differentiation of memory T cells, as observed in other tissues (2933). For example, ablation of embryonic-derived alveolar macrophages results in increased numbers of CD8+ resident memory T cells following influenza infection in mice (31). In the gut, monocyte-derived macrophages support the differentiation of CD8+ tissue-resident memory T cells by production of interferon- (IFN-) and interleukin-12 (IL-12) during Yersinia infection (32). The opposing roles of macrophages in modulating T cell responses in the lung and gut support the idea that tissue type and/or ontogeny determines how macrophages may influence adaptive immunity (13).

To understand the role of bladder-resident macrophages, we investigated the origin, localization, and function of these cells during infection. We identified two subpopulations of resident macrophages in nave mouse bladders with distinctive cell surface proteins, spatial distribution, and gene expression profiles. We found that bladder macrophage subsets were long-lived cells, slowly replaced by BM-derived monocytes over the lifetime of the mouse. During UTI, the macrophage subsets differed in their capacity to take up bacteria and survive infection; however, both subsets were replaced by BM-derived cells following resolution of infection. Thus, after a first infection, macrophage subsets had divergent transcriptional profiles compared to their nave counterparts, shaping the response to subsequent UTI.

We reported that macrophage depletion before a first UTI improves bacterial clearance during challenge infection (23). Thus, we initiated a follow-up study to investigate the role of bladder-resident macrophages during UTI. Using the macrophage-associated cell surface proteins CD64 and F4/80 (34, 35), we identified a clear CD64 and F4/80 double-positive resident macrophage population in nave bladders from 7- to 8-week-old female CX3CR1GFP/+ mice. This transgenic mouse is widely used to distinguish macrophage populations in other tissues as the chemokine receptor CX3CR1 is expressed by monocytes and macrophages at some point in their development (36). In most tissues, resident macrophages are either GFP+ as they express CX3CR1 or GFP because they no longer express CX3CR1 (10). Therefore, we were surprised to observe heterogeneity in green fluorescent protein (GFP) expression levels, revealing potentially two subpopulations (Maclo and Machi) of CD64+ F4/80+ macrophages in the bladder (Fig. 1A). Although the differences were small in magnitude, the Machi-expressing population was present in statistically significantly greater numbers and proportions compared to the Maclo population (Fig. 1A). As CX3CR1 deficiency results in decreased macrophage numbers and frequency in the intestine and brain, and the transgenic CX3CR1GFP/+ mouse we used is hemizygous for this receptor (3638), we investigated whether our putative bladder-resident macrophage subsets were similarly present in wild-type C57BL/6 mice. Using the same gating strategy and an anti-CX3CR1 antibody, we clearly identified that CX3CR1 expression levels distinguished two distinct macrophage populations in 7- to 8-week-old nave female wild-type mice (Fig. 1B). Notably, wild-type mice had similar numbers and proportions of each macrophage subset (Fig. 1B).

(A to C) Bladders from 7-week-old female CX3CR1GFP/+ and C57BL6/J mice were analyzed by flow cytometry. (A and B) Dot plots depict the gating strategy for macrophages subsets and graphs show the total cell number (log scale, left) and proportion (right) of bladder macrophage subset, derived from cytometric analysis in (A) CX3CR1GFP/+ and (B) C57BL6/J mice. (C) Histograms show the relative expression of CX3CR1, TIM4, and LYVE1 on macrophage subsets in C57BL6/J mice, Maclo is green and Machi is orange. (See fig. S1 for data on expression of additional proteins). (D) Representative confocal images of bladders from C57BL6/J mice at 20 and 40. Merged images and single channels with the target of interest are shown. DAPI, 4,6-diamidino-2-phenylindole. (E) Graphs show the proportion of each macrophage subset in the lamina propria and muscle of nave C57BL6/J mice. Data are pooled from three experiments, n = 3 to 6 mice per experiment. Each dot represents one mouse; lines are medians. Significance was determined using the nonparametric Mann-Whitney test to compare macrophage subset numbers (A and B) and the nonparametric Wilcoxon matched-pairs signed-rank test to compare the macrophage subset percentages (A, B, and E). All P values are shown; statistically significant P values (<0.05) are in red.

Next, we assessed the surface expression level of proteins known to define macrophage subsets in other tissues (39). We observed that the efferocytic receptor TIM4 and hyaluronan receptor LYVE1 were expressed by the Maclo population, whereas the Machi population was TIM4 and LYVE1 (Fig. 1C). Macrophage-associated proteins, such as CD64, F4/80, CD11b, CD11c, and MHC II, were differentially expressed between the subsets (fig. S1A), supporting the notion that these are distinct populations. A recent publication described several organs as having two distinct macrophage subsets, differentiated by their expression of LYVE1, CX3CR1, and, in particular, MHC II (39). To determine whether bladder macrophage subsets represented these two cell types, we used a similar gating strategy (fig. S1B); however, we observed that MHC II CD64+F4/80+ cells made up a very minor proportion (<2%) of bladder-resident macrophages (fig. SC). Last, to determine whether additional heterogeneity existed within the CD64+ F4/80+ bladder-resident macrophage population, we used the dimension reduction analyses tSNE and UMAP to visualize our data. In our analyses of the nave CD45+ cell population, a large CD64+ cluster contained two putative subsets that corresponded to traditionally gated Maclo and Machi populations and included the tiny proportion of MHC II macrophages (fig. S1D). tSNE (t-distributed stochastic neighbor embedding) and, more particularly, UMAP (uniform manifold approximation and projection) analysis of CD64+ F4/80+ macrophages revealed two groups, with differential expression of CX3CR1, F4/80, CD64, LYVE1, and TIM4, reflecting the data shown in the traditionally gated histograms (fig. S1, D and E). Thus, we concluded that two subsets of macrophages reside in nave mouse bladders with differential surface protein expression.

To determine the spatial orientation of the subsets, we stained nave female C57BL/6 bladders with antibodies to F4/80 and LYVE1 and phalloidin to demarcate the muscle layer from the lamina propria (Fig. 1D). We quantified the number of each subset in these two anatomical locations, observing a higher percentage of the LYVE1+ Maclo macrophage subset in the muscle compared to the LYVE1 Machi macrophage subset (Fig. 1E). Macrophages in the lamina propria were predominantly of the Machi phenotype (Fig. 1E). Thus, the phenotypic differences we observed in bladder-resident macrophage subsets extended to differential tissue localization. Given their spatial organization, we renamed the Maclo subset MacM for muscle and the Machi subset MacL for lamina propria. Together, these results reveal that two phenotypically distinct macrophage subsets reside in different regions of the nave bladder.

We next investigated whether macrophage heterogeneity in adult mouse bladders arose due to distinct developmental origins of the subsets. We analyzed bladders from newborn C57BL/6 pups by confocal imaging and by flow cytometry from CX3CR1-GFPexpressing E16.5 (embryonic day 16.5) embryos and newborn mice. We observed that, in E16.5 and newborn animals, a single CX3CR1hi macrophage population was present in the muscle and lamina propria of the bladder. By flow cytometry, these cells were uniformly positive for CD64 and negative for MHC II as expected for fetal macrophages (40) and stained positively for LYVE1 in confocal images of newborn mouse bladder, supporting that diversification of bladder macrophage subsets occurs after birth (Fig. 2A).

(A) Merged confocal and single channel images from a C57BL/6 newborn mouse bladder. Left image is enlarged at the right. Gating strategy in Cdh5-CreERT2Rosa26tdTomato CX3CR1GFP newborn mice and E16.5 embryos; histograms show CX3CR1 and MHC II expression. (B to E) Reporter recombination in microglia, monocytes, bladder macrophages, and MacM and MacL subsets in Cdh5-CreERT2Rosa26tdTomato mice: (B) E16.5 embryos, newborns 4-hydroxytamoxifen (4OHT)-treated at E7.5, (C) adults 4OHT-treated at E7.5, (D) E16.5 embryos, newborns 4OHT-treated at E10.5, (E) adults 4OHT-treated at E10.5. (F) Percentage of YFP+ cells in microglia, monocytes, MacM, and MacL macrophages in adult Flt3CreRosa26YFP mice. (G to I) Adult shield-irradiated C57BL/6 CD45.2 mice reconstituted with CCR2+/+ CD45.1 BM and C57BL/6 CD45.1 mice reconstituted with CCR2/ CD45.2 BM. Percentage of donor cells (G) in monocytes or (H) bladder-resident macrophages in mice transplanted with CCR2+/+ or CCR2/ BM at 3 and 6 months after transplantation. (See fig. S2 for data on blood leukocyte chimerism). (I) Bladder-resident macrophage replacement rate. Data pooled from two to three experiments, n = 2 to 6 mice per experiment. Each point represents one mouse; lines are medians. Significance determined using the Mann-Whitney test comparing (B to F) macrophages or subsets to monocytes or (G and H) CCR2+/+ to CCR2/ recipients, P values were corrected for multiple testing using the false discovery rate (FDR) method. All P values are shown; statistically significant P values (<0.05) are in red.

We hypothesized that, in adult mice, macrophage subsets arise following differentiation of cells seeded from embryonic progenitors or that one subset is derived from embryonic macrophages, whereas the second subset arises from BM-derived monocytes (41). To test these hypotheses, we used the Cdh5-CreERT2 Rosa26tdTomato transgenic mouse, in which the contribution of distinct hematopoietic progenitor waves to immune cell populations can be followed temporally, such that treatment of pregnant mice with 4-hydroxytamoxifen (4OHT) at E7.5 labels yolk sac progenitors and their progeny and treatment at E10.5 labels HSC that will settle in the BM (adult-type HSCs) and their cellular output (42). After treatment with 4OHT at E7.5, in which microglia were labeled as expected (8, 14), we found a significantly higher proportion of labeled bladder macrophages compared to monocytes in E16.5 embryos and newborn mice (Fig. 2B). Labeled bladder macrophage subsets were nearly absent, similar to monocytes, in adult (8- to 11-week-old) mice (Fig. 2C). These data support the fact that yolk sacderived bladder macrophages are diluted after birth in the adult and suggest that the subsets are composed of HSC-derived macrophages. Low levels of E10.5-labeled macrophages were detected in embryonic bladders (Fig. 2D), and their frequency increased in newborn and adult mice, although to a lesser degree than monocytes, supporting the idea that bladder macrophage subsets arise, at least in part, from adult-type HSCs (Fig. 2, D and E). Of note, both subsets found in the adult bladder showed similar frequencies of E10.5 labeling (Fig. 2E). Together, these results demonstrate that adult bladder macrophages are partially HSC-derived and the macrophage subsets cannot be distinguished from each other by their ontogeny.

To confirm that HSC-derived progenitors contribute to the bladder-resident macrophage pool, we analyzed bladders from adult Flt3Cre Rosa26YFP mice. In this transgenic mouse, expression of the tyrosine kinase receptor Flt3 in multipotent progenitors leads to expression of yellow fluorescent protein (YFP) in the progeny of these cells, such as monocytes, whereas microglia, arising from yolk sac progenitors, are essentially YFP (43). Recombination rates driven by Flt3 are very low during embryonic development, but blood monocyte labeling reaches 80 to 90% in adult mice (7). Therefore, if tissue-resident macrophages arise from postnatal BM-derived monocytes, labeling in adult mice should be similar to blood monocytes, whereas the presence of Flt3 tissue macrophages would indicate that they originated from either embryonic HSCs or adult Flt3-independent progenitors. We observed that, in 2- to 4-month-old and 22- to 24-month-old mice, ~50% of each macrophage subset was YFP+, which was significantly lower compared to circulating monocytes (Fig. 2F). This observation and those from the Cdh5-CreERT2 mice together support the fact that, in addition to adult HSCs, adult bladder macrophage subsets are derived from embryonic progenitors that may include fetal HSCs, and/or later yolk sac progenitors, but with no contribution from early yolk sac progenitors. In addition, the lack of equilibration of YFP labeling in the bladder with blood monocytes at 22 to 24 months suggests that tissue macrophages are not rapidly replaced over the lifetime of the mouse by BM-derived cells in the context of homeostasis.

To determine the replacement rate of bladder-resident macrophages by BM-derived cells in the adult mouse, we evaluated shielded irradiated mice, in which adult animals are irradiated with a lead cover over the bladder to protect this organ from radiation-induced immune cell death and nonhomeostatic immune cell infiltration. Animals were transplanted with congenic BM from wild-type or CCR2/ mice. Monocytes depend on CCR2 receptor signaling to exit the BM into circulation (44). At 3 and 6 months, we observed that a median of 27.7% (3 months) and 27.6% (6 months) of circulating Ly6C+ monocytes were of donor origin in mice reconstituted with wild-type BM, which is well in-line with published studies using this approach (45, 46), whereas only 6.1% (3 months) and 6.5% (6 months) of Ly6C+ monocytes were of donor origin in wild-type mice receiving CCR2/ BM (Fig. 2G). B and natural killer (NK) cells were replenished to a greater extent in mice reconstituted with CCR2/ BM compared to mice reconstituted with CCR2+/+ BM, which could be due to different engraftment efficiencies between CD45.1 and CD45.2 BM (fig. S2) (47, 48). In mice reconstituted with wild-type BM, 4.7% of MacM and 4.5% MacL were of donor origin at 3 months after engraftment. At 6 months after irradiation, 7% of MacM and 8.5% of MacL macrophages were of donor origin (Fig. 2H). Chimerism in bladder macrophage subsets was markedly reduced in CCR2/ BM recipients, suggesting that monocytes slowly replace bladder macrophage subsets in a CCR2-dependent manner (Fig. 2H). By dividing the median macrophage subset chimerism (7 or 8.5%) by the median circulating Ly6C+ monocyte chimerism at 6 months in mice receiving wild-type BM (27.6%), we determined that 25.3% of MacM and 30.8% MacL were replaced by BM-derived monocytes within 6 months (Fig. 2I).

Together, these results reveal that the establishment of distinct bladder-resident macrophage subsets occurs postnatally. Yolk sac macrophages initially seed the fetal bladder but are replaced by fetal HSC-derived macrophages. In adult mice, bladder macrophage subsets are partially maintained through a slow replacement by BM-derived monocytes, although a substantial number of fetally derived cells remain. The incomplete macrophage labeling we observed in our experiments supports the idea that a progenitor source, which cannot be labeled in either model, contributes to resident bladder macrophages. Currently, there is no fate-mapping model to discriminate or follow progeny specifically from late yolk sac EMPs or early fetally restricted HSC, as hematopoietic waves overlap in development. We can conclude that MacM and MacL macrophages do not differ in their developmental origin or rate of replacement by monocytes, supporting the view that one or more unique niches in adult tissue may be responsible for macrophage specialization into phenotypical and functionally distinct macrophage subsets.

Although bladder-resident macrophage subsets had similar ontogeny, their distinct spatial localization and surface protein expression suggested that they have different functions. To test this hypothesis, we first analyzed gene expression profiles of nave adult female MacM and MacL macrophages using bulk RNA sequencing (RNA-seq) (fig. S3A, gating strategy). To formally demonstrate that our cells of interest are macrophages, we aligned the transcriptomes of the bladder macrophage subsets with the macrophage core signature list published by the Immunological Genome Consortium and the bladder macrophage core list from the mouse cell atlas single-cell database (35, 49). The MacM and MacL subsets expressed 80% of the genes from the Immunological Genome Consortium macrophage core signature list and more than 95% of the genes in the bladder macrophage core list (fig. S3B), supporting the idea that our cells of interest are fully differentiated tissue-resident macrophages.

We observed that 1475 genes were differentially expressed between nave MacM and MacL macrophages, in which 899 genes were positively regulated and 576 genes were negatively regulated in the MacL subset relative to MacM macrophages (Fig. 3A). In the top 20 differentially expressed genes (DEG), MacM macrophages expressed higher levels of Tfrc, Ms4a8a, Serpinb6a, CCL24, Scl40a1, Clec10a, and Retnla, all of which are associated with an alternatively activated macrophage phenotype (5053); genes involved in iron metabolism, such as Tfrc, Steap4, and Slc40a1 (54); and genes from the complement cascade, including C4b and Cfp (Fig. 3B). In the same 20 most DEG, MacL macrophages expressed greater levels of Cx3cr1, Cd72, Itgb5, Axl, and Itgav, which are associated with phagocytosis, antigen presentation, and immune response activation (Fig. 3B) (5557). MacL macrophages also expressed inflammatory genes, such as Cxcl16, a chemoattractant for T and NKT cells (58, 59), and Lpcat2 and Pdgfb, which are involved in the metabolism of inflammatory lipid mediators (Fig. 3B) (60, 61). Using gene set enrichment analysis of the DEG to detect pathways up-regulated in the macrophage subsets, we observed that the MacM subset expressed genes linked to pathways such as endocytosis, mineral absorption, lysosome, and phagosome (Fig. 3C). Within the phagosome and endocytosis pathways, genes critical for bacterial sensing and alternative activation such as Tlr4, Mrc1 (encoding for CD206), Cd209, and Egfr (6264) were increased in the MacM subset. In the mineral absorption pathway, genes controlling iron metabolism that also enhance bacterial killing such as Hmox1 and Hmox2 were up-regulated in MacM macrophages (Fig. 3D) (65). In the MacL subset, genes linked to diverse inflammatory pathways, including Toll-like receptor signaling, apoptosis, antigen processing and presentation, and chemokine signaling, were present, as were many infectious and inflammatory diseaserelated pathways (Fig. 3E). Within these pathways, the MacL subset expressed genes related to bacterial sensing, such as Tlr1, Tlr2, and Cd14; initiation of inflammation, such as Il1b, Tnf, Ccl3, Ccl4, Cxcl10, Cxcl16, and Nfkb1; and apoptotic cell death, such as Mapk8, Pmaip1, Bcla1d, Cflar, Bcl2l11, and Birc2 (Fig. 3F).

MacM and MacL macrophages were sorted from 7- to 8-week-old female nave adult C57BL/6 mouse bladders and analyzed by RNA-seq (fig. S3, gating strategy). (A) Heatmaps show the gene expression profile of the 1475 differentially expressed genes and (B) the 20 most differentially expressed genes between the MacM and MacL subsets. (C to F) Using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of significantly up-regulated genes, the following are depicted: (C) pathways enriched in MacM macrophages, (D) up-regulated genes associated with selected pathways in MacM macrophages, (E) pathways enriched in MacL macrophages, and (F) up-regulated genes associated with selected pathways in MacL macrophages. In (C) and (E), the size of the nodes reflects the statistical significance of the term. (Q < 0.05; terms > 3 genes; % genes/term > 3; 0.4).

These findings suggest that MacM macrophages are more anti-inflammatory with increased endocytic activity, which is a common feature of highly phagocytic resident macrophages (66), and as such may play a prominent role in bacterial uptake or killing during infection. MacL, on the other hand, may play a greater role in antigen presentation and initiation or maintenance of inflammation.

As we observed enrichment of genes belonging to endocytosis, lysosome, and phagosome pathways in the MacM subset, we reasoned that the macrophage subsets differentially take up bacteria during infection. To test our hypothesis, we used a well-described mouse model of UTI, in which we transurethrally infect adult female mice via catheterization with 107 colony-forming units (CFU) of UPEC strain UTI89-RFP, which expresses a red fluorescent protein (RFP) (23). At 24 hours post-infection (PI), we investigated bacterial uptake by macrophage subsets (Fig. 4, A and B). Despite that MacM macrophages are farther from the infected urothelium than MacL macrophages, we observed that 20% of MacM and only 10% of MacL subsets contained bacteria at 24 hours PI, providing functional evidence to support the transcriptional data that MacM macrophages have a superior phagocytic capacity compared to MacL macrophages (Fig. 4B). Supporting this conclusion, we found that when we exposed sorted MacM and MacL macrophages to live UPEC in vitro, a greater proportion of MacM macrophages internalized bacteria after 2 hours compared to MacL macrophages (fig. S4A). In addition, despite very low levels of infection overall (~1% of macrophages), more UPEC could be found in MacM macrophages compared to MacL macrophages at 4 hours PI in vivo (fig. S4B). Taking the total population of UPEC-containing macrophages at 24 hours PI, we observed that ~80% of these cells were MacM macrophages, whereas the MacL subset comprised only 20% of this population, which was unusual given that MacM and MacL exist in the bladder in a 1:1 ratio (Fig. 4B and fig. S4C, gating strategy).

(A to H) Female C57BL6/J mice were infected with UTI89-RFP and bladders were analyzed by flow cytometry at (A to D) 24 hours or (E to H) 4 hours PI. (A) Gating strategy, resident macrophage subsets, and cells containing bacteria. (B) Percentage of infected macrophage subsets and UPEC distribution (fig. S4B, gating strategy). (C) IL-4R gMFI (geometric mean fluorescence intensity) in nave mice and 24 hours PI. (D) Total number and frequency of bladder macrophage subsets. (E) Gating strategy. (F) Total number and frequency of bladder macrophage subsets. Percentage of (G) macrophage subsets labeled with a live/dead marker (fig. S4D, gating strategy) and (H) dying macrophages containing UPEC. (I) MacM and MacL macrophage quantification in nave mice and 4 hours PI. (B to D and F to H) Data pooled from three experiments, n = 3 to 6 mice per experiment. (I) Data are pooled from two experiments, n = 2 to 3 mice per experiment. Each dot represents one mouse; lines are medians. In (D) and (F), Mann-Whitney test was used to compare the numbers and the nonparametric Wilcoxon matched-pairs signed-rank test was used to compare the percentages of each macrophage subset. (B and C and G to I) Mann-Whitney test. P values were corrected for multiple testing using the FDR method. All P values are shown; statistically significant P values (<0.05) are in red.

Given the predominance of genes associated with alternatively activated macrophages in the top 20 DEGs of MacM macrophages (Fig. 3B), we measured polarization of the macrophage subsets in nave and infected bladders by analyzing the expression of IL-4R by flow cytometry (Fig. 4C). IL-4R is the receptor of IL-4 and IL-13, two cytokines that drive alternative activation in macrophages (67). Both subsets had increased expression of IL-4R at 24 hours PI compared to their nave counterparts; however, MacM macrophages had consistently higher expression levels of IL-4R compared to MacL macrophages in nave and infected tissue (Fig. 4C).

In the course of our studies, we observed that the total number and proportion of MacL macrophages were significantly lower than those of MacM macrophages at 24 hours PI, whereas, in nave mice, both the number and proportion of the macrophage subsets were equivalent (Figs. 4D and 1B). To rule out the contribution of differentiated monocyte-derived cells to the macrophage pool, we assessed total macrophage cell numbers in the bladder at 4 hours PI, when there is minimal monocyte infiltration (Fig. 4E) (23). Macrophage subset numbers and proportions were significantly different at 4 hours PI (Fig. 4F). As the total numbers of each subset were not increased over nave levels (Fig. 1B), we hypothesized that macrophages die during infection, particularly as apoptosis pathways were more highly expressed in MacL macrophages (Fig. 3, C and D). Using a cell viability dye, which labels dying/dead cells, we found that a significantly higher proportion of MacL macrophages were dying compared to MacM macrophages at 4 hours PI (Fig. 4G and fig. S4D, gating strategy). As UPEC strains can induce macrophage death in vitro (68, 69), we asked whether macrophage cell death was induced by UPEC in vivo. We observed that only 20% of dying or dead cells in each subset were infected (Fig. 4H), suggesting that macrophage death was not primarily driven by UPEC uptake. To determine whether macrophage cell death was confined to a distinct location, we quantified macrophage subset numbers in the muscle and lamina propria. We observed that, at 4 hours PI, only MacL macrophages located in the lamina propria were reduced in numbers compared to nave mice (Fig. 4I). Given that, in the first hours after infection, the urothelium exfoliates massively (70), these results suggest that macrophage death, specifically in the lamina propria, may be due to the loss of a survival factor in this niche. To test whether alteration of the niche induced macrophage death, we chemically induced global urothelial exfoliation by intravesical instillation of protamine sulfate (71, 72). We observed that at 5 hours after treatment, the total numbers of both MacM and MacL subsets were reduced compared to macrophage subsets in nave mice (fig. S4E), suggesting that alterations in bladder urothelium are sufficient to reduce resident macrophage numbers in the bladder, although protamine sulfate may also directly induce macrophage death. Thus, we functionally validated the divergent gene expression observed between macrophage subsets, in which MacM macrophages are more phagocytic and MacL macrophages are more prone to die, supporting the idea that gene expression differences translate to divergent roles for the subsets in response to UTI.

As we observed macrophages dying during infection, we investigated the change in macrophage numbers over time as animals resolved their infection. Both populations significantly decreased at 24 hours PI, then subsequently increased nearly 10-fold at 7 days PI, and returned to numbers just above homeostatic levels at 4 weeks PI (Fig. 5A). With the dynamic increase of macrophage numbers over the course of UTI, we hypothesized that infiltrating monocytes replace resident macrophage subsets during infection, as we previously reported that infiltrating monocytes differentiate to cells resembling macrophages at 48 hours PI (23). To test this hypothesis, we used the CCR2CreERT2 Rosa26tdTomato mouse, in which administration of 4OHT induces recombination in CCR2-expressing cells, such as circulating Ly6C+ monocytes, leading to irreversible labeling of these cells in vivo (73). Blood monocytes and bladder-resident macrophages are not Tomato+ in untreated mice (fig. S5). We administered 4OHT to nave mice and, then, 24 hours later, infected half of the treated mice with 107 CFU of UTI89. At this time point, 24 hours after 4OHT treatment, we analyzed the labeling efficiency in circulating classical Ly6C+ monocytes, finding that approximately 80% of Ly6C+ monocytes were labeled in both nave and infected mice (Fig. 5B). After 6 weeks, when animals had resolved their infection, there were no labeled circulating Ly6C+ monocytes in nave or post-infected mice (Fig. 5B). When we analyzed the bladders of nave mice 6 weeks after the 4OHT pulse, only 2.9% of MacM and 2.1% of MacL macrophage subsets were labeled, supporting our earlier conclusion that monocytes contribute to bladder macrophage subsets at a very slow rate in the steady state (Fig. 5C). At 6 weeks PI, the total numbers of macrophage subsets finally returned to homeostatic levels (Fig. 5D), but PI MacM and MacL macrophages had two to three times more Tomato+ cells (median, MacM 8.4%, MacL 4.4%) than their nave counterparts. These data support the fact that, after monocytes infiltrate the bladder during infection, they remain in the tissue following resolution, integrating themselves into the resident macrophage pool, and thus contribute to the return of macrophage subsets to homeostatic levels.

(A) Total number of MacM (green) and MacL (orange) in nave and 1-, 7-, or 28-day PI mice. (B and C) CCR2CreERT2Rosa26tdTomato mice were pulsed with 4OHT. Twenty-four hours later, half were infected with UTI89-RFP. Percentage of Tomato+ (B) Ly6C+ monocytes 24 hours and 6 weeks after 4OHT-pulse or (C) bladder macrophage subsets 6 weeks after 4OHT-pulse. (D) Total number of macrophage subsets in nave and 6-week PI bladders. (E) Replicate-adjusted principal component analysis of all genes from nave and post-infected bladder macrophage subsets. Differentially expressed genes between nave and 6-week PI (F) MacM (513 genes) and (G) MacL (617 genes) macrophages. KEGG pathway analysis of significantly up-regulated genes, enriched in 6-week PI (H) MacM and (I) MacL macrophages. Up-regulated genes from selected pathways in 6-week PI (J) MacM and (K) MacL macrophages. (A, C, and D) Mann-Whitney test comparing infection to nave. P values were corrected for multiple testing using the FDR method. Higher left-shifted P values refer to MacM and lower right-shifted P values refer to MacL. (H and I) Node size reflects statistical significance of the term (Q < 0.05; terms > 3 genes; %genes/term > 3; 0.4). All P values are shown; statistically significant P values (<0.05) are in red.

As monocytes generally have different origins and developmental programs compared to tissue-resident macrophages, we used RNA-seq to determine whether the macrophage pool in post-infected bladders was different from nave tissue-resident cells. Using principal component analysis (PCA), we compared bladder macrophage subsets from 6-week post-infected mice to their nave counterparts. We found that macrophages clustered more closely together by subset, rather than by infection status, or, in other words, nave and post-infected MacL macrophages clustered more closely to each other than either sample clustered to nave or post-infected MacM macrophages (Fig. 5E). Five hundred thirteen genes (247 genes down-regulated and 266 genes up-regulated) were different between nave and post-infected MacM macrophages (Fig. 5F). Six hundred seventeen genes (401 genes down-regulated and 216 genes up-regulated) were differentially expressed between the nave and post-infected MacL subset (Fig. 5G). Applying gene set enrichment analysis to up-regulated genes in the post-infected macrophage subsets, we detected common pathways between the subsets including enrichment of genes linked to pathways such as antigen presentation; cell adhesion molecules; TH1, TH2, and TH17 cell differentiation; and chemokine signaling pathway (Fig. 5, H and I). Although the enriched genes were not identical within each subset for these pathways, some common up-regulated genes included those encoding for histocompatibility class 2 molecules, such as H2-Ab1, H2-Eb1, H2-DMb1, Ciita, and the Stat1 transcription factor (Fig. 5, I and J). As differentiation of monocytes into macrophages includes up-regulation of cell adhesion and antigen presentation molecules (74), including in the bladder (23), these data further support the idea that monocytes specifically contribute to the PI bladder-resident macrophage pool.

These results show that, in the context of UTI, dying macrophages are replaced by monocyte-derived cells. Tissue-resident macrophage subsets maintain their separate identities distinct from each other after infection, although each subset also takes on a different transcriptional profile compared to their nave counterparts, with up-regulated expression of genes related to adaptive immune responses.

We previously reported that macrophage depletion 24 hours before a primary UTI does not affect bacterial clearance (23). Given that post-infected macrophage subsets up-regulated pathways different from those associated with the transcriptomes of nave bladder macrophage subsets, and that these pathways were linked to inflammatory diseases and the adaptive immune response, we hypothesized that one or both macrophage subsets would mediate improved bacterial clearance to a challenge infection. To test this hypothesis, we infected mice with 107 CFU of kanamycin-resistant UTI89-RFP. Four weeks later, when the infection was resolved, mice were challenged with 107 CFU of the isogenic ampicillin-resistant UPEC strain, UTI89-GFP, and bacterial burden was measured at 24 hours PI. To test the contribution of the macrophage subsets to the response to challenge infection, we used different concentrations of anti-CSF1R depleting antibody to differently target the two macrophage subsets directly before challenge infection (Fig. 6A, experimental scheme). Using 500 g of anti-CSF1R antibody, we depleted 50% of MacM and 80% of MacL macrophages, whereas depletion following treatment with 800 g of anti-CSF1R antibody reduced MacM macrophages by 80% and the MacL subset by more than 90% (Fig. 6B and fig. S6A). Twenty-four hours after anti-CSF1R antibody treatment, the number of circulating neutrophils, eosinophils, NK, B, or T cells was not different from mock-treated mice at either concentration (fig. S6B). Classical Ly6C+ monocytes were modestly reduced in mice treated with 800 g of anti-CSF1R antibody but were unchanged in mice receiving 500 g of depleting antibody. Antibody treatment did not change circulating nonclassical monocyte numbers (fig. S6B). After challenge infection, the bacterial burden was not different in mice treated with 500 g of anti-CSF1R compared to mock-treated mice (Fig. 6C). By contrast, mice depleted with 800 g of anti-CSF1R had reduced bacterial burdens, indicative of a stronger response after challenge compared to nondepleted mice (Fig. 6D).

(A) Experimental scheme. (B) Efficacy of macrophage subset depletion in nave C57BL/6 mice treated with 500 or 800 g of anti-CSF1R antibody. (C and D) Bacterial burden per bladder 24 hours after challenge in female C57BL/6 mice infected with UTI89-RFP according to (A) and treated with phosphate-buffered saline (PBS) (mock) or (C) 500 g or (D) 800 g of anti-CSF1R antibody 72 hours before being challenged with the isogenic UTI89-GFP strain. (E to G) Mice were infected according to (A) and treated with 800 g of anti-CSF1R antibody 72 hours before challenge infection with 107 CFU of the isogenic UTI89-GFP strain. Graphs depict the (E) total number of the indicated cell type, (F) the percentage of the indicated cell type that was infected, and (G) the total number of the indicated cell type that contained UPEC at 24 hours after challenge in mice treated with PBS or 800 g of anti-CSF1R antibody. Data are pooled from three experiments, n = 3 to 6 mice per experiment. Each dot represents one mouse; lines are medians. (C to G) Mann-Whitney test, P values were corrected for multiple testing using the FDR method. All P values are shown; statistically significant P values (<0.05) are in red.

Neutrophils take up a majority of UPEC at early time points during UTI (23). Therefore, we hypothesized that the improved bacterial clearance in macrophage-depleted mice may be due to increased infiltration of inflammatory cells, such as neutrophils. At 24 hours after challenge infection, we observed that, while the numbers of resident macrophage subsets, MHCII+ monocytes, and MHCII monocytes in macrophage-depleted mice were reduced compared to mock-treated mice, as expected, the numbers of infiltrating neutrophils were unchanged by antibody treatment (Fig. 6E and fig. S6C, gating strategy). Fewer eosinophils infiltrated the tissue in macrophage-depleted mice, although the impact of this is unclear as their role in infection is unknown (Fig. 6E). Given that neutrophil infiltration was unchanged and that monocytes, which also take up a large number of bacteria during infection, were reduced in number, we considered that improved bacterial clearance in macrophage-depleted mice may be due to increased bacterial uptake on a per-cell basis during challenge infection. However, bacterial uptake was not different between depleted and mock-treated mice in neutrophils, MHCII+ and MHCII monocytes, or either macrophage subset (Fig. 6F). The lower numbers of the MacM subset in macrophage-depleted mice translated to lower numbers of infected MacM macrophages (Fig. 6, E and G, respectively). However, we observed no differences in the numbers of infected MacL macrophages, neutrophils, and MHCII+ or MHCII monocytes in macrophage-depleted mice compared to nondepleted animals (Fig. 6G). Together, these results support the notion that MacM macrophages negatively affect bacterial clearance in a challenge infection, but not at the level of direct bacterial uptake or myeloid cell infiltration.

As infiltration of inflammatory cells or the number of infected cells during challenge infection was not changed in macrophage-depleted mice, we questioned whether another host mechanism was involved in bacterial clearance. Exfoliation of infected urothelial cells is a host mechanism to eliminate bacteria (70, 75). We hypothesized that macrophage-depleted mice have increased urothelial exfoliation during challenge infection, leading to reduced bacterial numbers. We quantified the mean fluorescence intensity of uroplakins, proteins expressed by terminally differentiated urothelial cells (76), from bladders of post-challenged mice, depleted of macrophages or not (Fig. 7A). We did not detect a significant difference in urothelial exfoliation between mock-treated animals and mice depleted of macrophage before challenge infection, supporting that urothelial exfoliation is not the underlying mechanism behind improved bacterial clearance in macrophage-depleted mice (Fig. 7B). Infiltration of inflammatory cells is associated with bladder tissue damage and increased bacterial burden (26). As we observed fewer monocytes and eosinophils in macrophage-depleted mice during challenge infection, we investigated whether reduced cell infiltration was associated with less tissue damage. We assessed edema formation by quantifying the area of the lamina propria in post-challenged bladders, depleted of macrophages or not (Fig. 7A). We did not detect a difference in edema formation between nondepleted mice and mice depleted of macrophage before challenge infection (Fig. 7C).

Female C57BL/6 mice were infected according to the scheme shown in Fig. 6A and treated with 800 g of anti-CSF1R antibody 72 hours before challenge infection with 107 CFU of UTI89. (A) Representative confocal images of bladders from mice treated with PBS or 800 g of anti-CSF1R antibody 24 hours after challenge. Uroplakin, green; phalloidin, turquoise; DAPI, blue. (B) The graph shows the mean fluorescence intensity of uroplakin expression, quantified from imaging, at 24 hours after challenge. (C) The graph shows the area of the lamina propria, quantified from imaging, at 24 hours after challenge. (D to F) Graphs depict the (D and E) total number of the indicated cell type or (F) the total number of the indicated cell type expressing IFN- at 24 hours after challenge infection. Data are pooled from two experiments, n = 4 to 6 mice per experiment. Each dot represents one mouse; lines are medians. In (B) to (F), significance was determined using the nonparametric Mann-Whitney test and P values were corrected for multiple testing using the FDR method. All calculated/corrected P values are shown and P values meeting the criteria for statistical significance (P < 0.05) are depicted in red.

As we observed fewer eosinophils in macrophage-depleted mice during challenge infection, and our previous work demonstrated that type 2 immune responserelated cytokines are expressed early in UTI (24), we assessed the polarity of the T cell response to challenge infection (fig. S7, gating strategy). Macrophage depletion did not alter the infiltration of T regulatory cells or TH2 or TH17 T helper subsets (Fig. 7D). However, macrophage depletion did correlate with an increase in the numbers of TH1 T cells, NKT cells, NK cells, and type 1 innate lymphoid cells (ILC1s) (Fig. 7E). In macrophage-depleted mice, TH1 T cells, NKT cells, and NK cells had higher IFN- production compared to mock-treated mice (Fig. 7F), suggesting that, in the absence of post-infected macrophages, a more pro-inflammatory, bactericidal response to challenge infection arises in the bladder.

Despite numerous studies of macrophage ontogeny and function in many organs, the developmental origin and role of bladder macrophages are largely unknown. Here, we investigated this poorly understood compartment in homeostasis and a highly inflammatory infectious disease, UTI. A single macrophage population of yolk sac and HSC origin seeds the developing bladder; however, the yolk sac macrophage pool is ultimately replaced at some point after birth. After birth, two subsets, MacM and MacL, arise in the tissue, localizing to the muscle and the lamina propria, respectively. These subsets share similar developmental origin, in that they are primarily HSC-derived and, in adulthood, display a very slow turnover by Ly6C+ monocytes in the steady state. Their distinct transcriptomics support the idea that they play different roles in the bladder, at least in the context of infection. The MacM subset is poised to take up bacteria or potentially infected dying host cells, while polarizing toward a more alternatively activated profile during UTI. MacL macrophages express a profile with greater potential for the induction of inflammation and, whether due to direct consequences of this inflammation or potentially due to loss of the urothelium, undergo pronounced cell death during UTI.

In adult animals, steady-state tissue-resident macrophages are a mix of embryonic and adult monocyte-derived macrophages, with the exception of brain microglia (8, 14). The contributions from embryonic macrophages and circulating adult monocytes to the adult bladder macrophage compartment are similar to that of the lung and kidney (7, 11, 77). Although two macrophage subsets reside in the adult bladder, only a single LYVE1+CX3CR1+ macrophage population was identified in embryonic and newborn bladders. As the bladder is fully formed in newborn mice (78), it is unlikely that macrophage subsets arise to meet the needs of a new structure, as is the case for peritubular macrophages in the testis (41). Rather, although all structures are present, embryonic or prenatal bladder tissue demands are likely distinct from postnatal tissue remodeling in very young mice. For example, in the first weeks after birth, bladder macrophages may support urothelial cells undergoing increased proliferation to establish the three layers of urothelium in adult bladders (79). As these adult tissue niches become fully mature, they may provide different growth or survival factors, driving functional macrophage specialization in discrete locations in the tissue.

In the lung, spleen, BM, and liver, a subpopulation of pro-resolving macrophages are present that phagocytize blood-borne cellular material to maintain tissue homeostasis (66). These macrophages express Mrc1 (encoding for CD206), CD163, and Timd4 (encoding TIM4) (66). MacM macrophages likely represent this subpopulation in the bladder, as they expressed higher levels of genes associated with a pro-resolving phenotype, including the efferocytic receptor TIM4, CD206, and CD163. It is also possible that, similar to muscularis macrophages in the gut, MacM macrophages interact with neurons to control muscle contraction in the bladder and limit neuronal damage during infection (80, 81). By contrast, up-regulated pathways in the MacL subset, in combination with their localization under the urothelium, suggest that, similar to intestinal macrophages, they may regulate T cell responses to bladder microbiota or support urothelial cell integrity (82, 83).

Although it was somewhat unexpected, given that the MacM macrophage subset is located farther from the lumen and urothelium, where infection takes place, we favor the conclusion that MacM macrophages contain more bacteria because they are programmed to do so. This conclusion is supported by the higher expression of genes associated with complement, endocytosis, and phagosome pathways in the MacM subset. It is possible, although challenging to empirically demonstrate, that the MacM subset recognizes dying neutrophils, or even dying MacL macrophages, that have phagocytosed bacteria. We may also consider that, between the subsets, the rate at which bacteria are killed is different, UPEC may survive better in MacM macrophages, MacL macrophages may die after bacterial uptake, the near-luminal location of MacL macrophages may result in their disproportionate sloughing, or even that MacL macrophages break down phagocytosed content better. Additional genetic and knockout models would be needed to address these possibilities.

Significant numbers of MacL macrophages died in the first hours following infection, reflecting their enriched apoptosis pathway. The reduced numbers of both macrophage subsets in protamine sulfate-treated mice suggest that alterations in the urothelium may affect macrophage survival, although we cannot rule out the fact that protamine sulfate directly kills macrophages. Exfoliation induced by protamine sulfate is not comparable to infection, as protamine sulfate induces a rapid, large increase in trans-urothelial conductance (71), suggesting that it induces major disruptions in the urothelium. Protamine sulfate can also suppress cytokine activity and the inflammatory response in the bladder compared to UPEC infection (84). This severe disruption of the urothelium may lead to inadequate supplies of oxygen, nutrients, or survival factors, all of which would be detrimental to macrophage survival. It is less likely that bacteria induce macrophage death as only a small, and importantly equivalent, proportion of both subsets were infected. Instead, MacL macrophage death may be an important step to initiate immune responses to UTI. In the liver, Kupffer cell death by necroptosis during Listeria monocytogenes infection induces recruitment of monocytes, which, in turn, phagocytose bacteria (85). Here, macrophage depletion before challenge infection resulted in decreased infiltration of monocytes, likely due to diminished numbers of these cells in circulation, and fewer eosinophils; however, bacterial burden was also decreased. This suggests that macrophage-mediated immune cell recruitment is not their primary function in the bladder. Infiltration of inflammatory cells is not the only way macrophage cell death regulates infection, however. For instance, pyroptotic macrophages can entrap live bacteria and facilitate their elimination by neutrophils in vivo (86). As MacM macrophages express genes regulating iron metabolism, limiting iron to UPEC would also be a plausible mechanism to control bacterial growth (87).

In the steady state, tissue-resident macrophages can self-maintain locally by proliferation, with minimal input of circulating monocytes (9, 88). By contrast, under inflammatory conditions, resident macrophages are often replaced by monocyte-derived macrophages (85, 8890). Monocytes will differentiate into self-renewing functional macrophages if the endogenous tissue-resident macrophages are depleted or are absent (91, 92). Our results show that UPEC infection induces sufficient inflammation to foster infiltration and differentiation of newly recruited monocytes. It is likely, even, that greater macrophage replacement occurs than we actually measured, as we used a single 4OHT pulse in CCR2CreERT2 Rosa26tdTomato mice 24 hours before infection; however, these cells infiltrate infected bladders over several days. These experiments do not rule out a role for local proliferation in the bladder during UTI, but experiments to test this must be able to distinguish infiltrated monocytes that have already differentiated into tissue macrophages from bona fide tissue-resident macrophages when assessing proliferating cells. These data do support, however, the fact that infiltrating monocytes remain in the tissue, integrated into the resident macrophage pool, after tissue resolution.

Recruited monocyte-derived macrophages can behave differently than resident macrophages when activated, such as in the lung. Gamma herpes virus induces alveolar macrophage replacement by regulatory monocytes expressing higher levels of Sca-1 and MHC II (93). These post-infected mice have reduced perivascular and peribronchial inflammation and inflammatory cytokines, and fewer eosinophils compared to mock-infected mice when exposed to house dust mite to induce allergic asthma (93). Alveolar macrophages of mice infected with influenza virus are replaced by pro-inflammatory monocyte-derived macrophages. At 30 days PI, influenza-infected mice have more alveolar macrophages and increased production of IL-6 when challenged with S. pneumoniae compared to mock-infected mice, leading to fewer deaths (90). Although mechanisms regulating the phenotype of monocyte-derived macrophages are not known, the time of residency in the tissue and the nature of subsequent insults likely influence these cells. The longer that recruited macrophages reside in tissue, the more similar they become to tissue-resident macrophages and no longer provide enhanced protection to subsequent tissue injury (89, 90). In contrast to these studies in the lung, we found that elimination of macrophages, including those recruited during primary infection, led to improved bacterial clearance during secondary challenge, although it is not clear what the long-term consequences on bladder homeostasis might be when a more inflammatory type 1 immune response arises during infection.

Overall, our results demonstrate that two unique subsets of macrophages reside in the bladder. During UTI, these cells respond differently, and a proportion of the population dies. Thus, a first UPEC infection induces replacement of resident macrophage subsets by monocyte-derived cells. When sufficient numbers of MacM macrophages, composed of resident and replaced cells, are depleted, improved bacterial clearance follows, suggesting a major role of this subset in directing the immune response to challenge infection. While these findings greatly improve our understanding of this important immune cell type, much remains to be uncovered, such as the signals and niches that contribute to the establishment of two subsets of bladder-resident macrophages, their roles in the establishment and maintenance of homeostasis, and whether parallel populations and functions exist in human bladder tissue.

This study was conducted using a preclinical mouse model and transgenic mouse strains in controlled laboratory experiments to investigate the origin, maintenance, and function of bladder-resident macrophages in homeostasis and bacterial infection. At the onset of this study, our objective was to understand how bladder-resident macrophages negatively affect the development of adaptive immunity to UTI. Having found two resident macrophage subsets in the course of this work, our objectives were to determine whether these subsets have similar origins and homeostatic maintenance and whether they play divergent roles in response to primary or challenge infection. Mice were assigned to groups upon random partition into cages. In all experiments, a minimum of 2 and a maximum of 10 mice (and more typically 3 to 6 mice per experiment) made up an experimental group and all experiments were repeated two to three times. Sample size was based on our previous work and was not changed in the course of the study. In some cases, n was limited by the number of developing embryos available from timed pregnancies. Data collection is detailed below. Data from all repetitions were pooled before any statistical analysis. As determined a priori, all animals with abnormal kidneys (atrophied, enlarged, and white in color) at the time of sacrifice were excluded from all analyses, as we have observed that abnormal kidneys negatively affect resolution of infection. End points were determined before the start of experiments and researchers were not blinded to experimental groups.

All animals used in this study had free access to standard laboratory chow and water at all times. We used female C57BL/6J mice 7 to 8 weeks old from Charles River, France. Female CX3CR1GFP/+ mice 7 to 8 weeks old were bred in-house. CX3CR1GFP/GFP mice, used to maintain our hemizygous colony, were a gift from F. Chretien (Institut Pasteur). Cdh5-CreERT2 Rosa26tdTomato mice were crossed to CX3CR1GFP mice, producing Cdh5-CreERT2.Rosa26tdTomato.CX3CR1GFP mice at Centre dImmunologie de Marseille-Luminy. In Cdh5-CreERT2.Rosa26tdTomato.CX3CR1GFP mice, cells expressing the CX3CR1 receptor are constitutively GFP+, and treatment with 4OHT conditionally labels hemogenically active endothelial cells (42). We used female and male Cdh5-CreERT2.Rosa26tdTomato.CX3CR1GFP mice 8 to 11 weeks old, at E16.5, and newborns. Flt3Cre.Rosa26YFP mice were a gift from E.G.P. (Institut Pasteur). CCR2/ mice were a gift from M. Lecuit (Institut Pasteur). CCR2creERT2BB mice were a gift from B. Becher (University of Zurich) via S. Amigorena (Institut Curie). CCR2creERT2BB male mice were crossed to Rosa26tdTomato females to obtain CCR2creERT2BB-tdTomato mice at Institut Pasteur. We used female CCR2creERT2BB-tdTomato mice 7 to 8 weeks old. Additional details of the mouse strains used, including JAX and MGI numbers, are listed in table S1. Mice were anesthetized by injection of ketamine (100 mg/kg) and xylazine (5 mg/kg) and euthanized by carbon dioxide inhalation. Experiments were conducted at Institut Pasteur in accordance with approval of protocol number 2016-0010 and dha190501 by the Comit dthique en exprimentation animale Paris Centre et Sud (the ethics committee for animal experimentation), in application of the European Directive 2010/63 EU. Experiments with Cdh5-CreERT2 mice were performed in the laboratory of M. Bajenoff, Centre dImmunologie de Marseille-Luminy, in accordance with national and regional guidelines under protocol number 5-01022012 following review and approval by the local animal ethics committee in Marseille, France.

Antibodies, reagents, and software used in this study are listed in tables S2, S3, and S4, respectively.

Samples were acquired on a BD LSRFortessa using DIVA software (v8.0.1), and data were analyzed by FlowJo (Treestar) software, including the plugins for downsampling, tSNE, and UMAP (version 10.0). The analysis of bladder and blood was performed as described previously (23). Briefly, bladders were dissected and digested in buffer containing Liberase (0.34 U/ml) in phosphate-buffered saline (PBS) at 37C for 1 hour with manual agitation every 15 min. Digestion was stopped by adding PBS supplemented with 2% fetal bovine serum (FBS) and 0.2 M EDTA [fluorescence-activated cell sorting (FACS) buffer]. Fc receptors in single-cell suspensions were blocked with anti-mouse CD16/CD32 and stained with antibodies. Total cell counts were determined by addition of AccuCheck counting beads to a known volume of sample after staining, just before cytometer acquisition. To determine cell populations in the circulation, whole blood was incubated with BD PharmLyse and stained with antibodies (table S2). Total cell counts were determined by the addition of AccuCheck counting beads to 10 l of whole blood in 1-step Fix/Lyse Solution.

For intracellular staining, single-cell suspensions were resuspended in 1 ml of Golgi stop protein transport inhibitor, diluted (1:1500) in RPMI with 10% FBS, 1% sodium pyruvate, 1 Hepes, 1 nonessential amino acid, 1% penicillin-streptomycin, phorbol 12-myristate 13-acetate (50 ng/ml), and ionomycin (1 g/ml), and incubated for 4 hours at 37C. Samples were washed once with FACS buffer, and Fc receptors blocked with anti-mouse CD16/CD32. Samples were stained with antibodies listed in table S2 against surface markers and fixed and permeabilized with 1 fixation and permeabilization buffer and incubated at 4C for 40 to 50 min protected from light. After incubation, samples were washed two times with 1 permeabilization and wash buffer from the transcription factor buffer kit and stained with antibodies against IFN- and the transcriptional factors RORT, GATA3, T-bet, and FoxP3 (table S2), diluted in 1 permeabilization and wash buffer at 4C for 40 to 50 min protected from light. Last, samples were washed two times with 1 permeabilization and wash buffer and resuspended in FACS buffer. Total cell counts were determined by addition of counting beads to a known volume of sample after staining, just before cytometer acquisition.

Whole bladders were fixed with 4% paraformaldehyde (PFA) in PBS for 1 hour and subsequently washed with PBS. Samples were then dehydrated in 30% sucrose in PBS for 24 hours. Samples were cut transversally and embedded in optimal cutting temperature compound, frozen, and sectioned at 30 m. Sections were blocked for 1 hour with blocking buffer [3% bovine serum albumin (BSA) + 0.1% Triton X-100 + donkey serum (1:20) in PBS] and washed three times. Immunostaining was performed using F4/80, LYVE1 antibodies, or polyclonal asymmetrical unit membrane antibodies, recognizing uroplakins [gift from X.-R. Wu, NYU School of Medicine, (76)] (1:200) in staining buffer (0.5% BSA + 0.1% Triton X-100 in PBS) overnight. Sections were washed and stained with phalloidin (1:350) and secondary antibodies (1:2000) in staining buffer for 4 hours. Last, sections were washed and stained with 4,6-diamidino-2-phenylindole. Confocal images were acquired on a Leica SP8 confocal microscope. Final image processing was done using Fiji (version 2.0.0-rc-69/1.52p) and Icy software (v1.8.6.0).

Fate mapping of Cdh5-CreERT2 mice was performed as described previously (42). Briefly, for reporter recombination in offspring, a single dose of 4OHT supplemented with progesterone (1.2 mg of 4OHT and 0.6 mg of progesterone) was delivered by intraperitoneal injection to pregnant females at E7.5 or E10.5. Progesterone was used to counteract adverse effects of 4OHT on pregnancies. To fate map cells in CCR2creERT2BB-tdTomato mice, a single dose (37.5 g/g) of 4OHT injection was delivered intraperitoneally.

For shielded irradiation, 7- to 8-week-old wild-type female CD45.1 or CD45.2 C57BL6/J mice were anesthetized and dressed in a lab-made lead diaper, which selectively exposed their tail, legs, torso, and head to irradiation, but protected the lower abdomen, including the bladder. Mice were irradiated with 9 gray from an Xstrahl x-ray generator (250 kV, 12 mA) and reconstituted with ~3 107 to 4 107 BM cells isolated from congenic (CD45.1) wild-type mice or CD45.2 CCR2/ mice.

Samples were obtained from the whole bladders of nave and 6-week post-infected female C57BL/6J mice. Using FACS, four separate sorts were performed to generate biological replicates, and each sort was a pool of 10 mouse bladders. Macrophage subsets were FACS-purified into 350 l of RLT Plus buffer from the RNeasy Micro Kit plus (1:100) -mercaptoethanol. Total RNA was extracted using the RNeasy Micro Kit following the manufacturers instructions. Directional libraries were prepared using the Smarter Stranded Total RNA-Seq kit Pico Input Mammalian following the manufacturers instructions. The quality of libraries was assessed with the DNA-1000 kit on a 2100 Bioanalyzer, and quantification was performed with Quant-It assays on a Qubit 3.0 fluorometer. Clusters were generated for the resulting libraries with Illumina HiSeq SR Cluster Kit v4 reagents. Sequencing was performed with the Illumina HiSeq 2500 system and HiSeq SBS kit v4 reagents. Runs were carried out over 65 cycles, including seven indexing cycles, to obtain 65-bp single-end reads. Sequencing data were processed with Illumina Pipeline software (Casava version 1.9). Reads were cleaned of adapter sequences and low-quality sequences using cutadapt version 1.11. Only sequences of at least 25 nucleotides in length were considered for further analysis, and the five first bases were trimmed following the library manufacturers instructions. STAR version 2.5.0a (94), with default parameters, was used for alignment on the reference genome (Mus musculus GRCm38_87 from Ensembl version 87). Genes were counted using featureCounts version 1.4.6-p3 (95) from Subreads package (parameters: -t exon -g gene_id -s 1). Count data were analyzed using R version 3.4.3 and the Bioconductor package DESeq2 version 1.18.1 (96). The normalization and dispersion estimation were performed with DESeq2 using the default parameters, and statistical tests for differential expression were performed applying the independent filtering algorithm. A generalized linear model was set to test for the differential expression among the four biological conditions. For each pairwise comparison, raw P values were adjusted for multiple testing according to the Benjamini and Hochberg procedure and genes with an adjusted P value lower than 0.05 were considered differentially expressed. Count data were transformed using variance stabilizing transformation to perform samples clustering and PCA plot. The PCA was performed on the variance-stabilized transformed count matrix that was adjusted for the batch/replicate effect using the limma R package version 3.44.3.

To perform pathway analysis, gene lists of DEGs were imported in the Cytoscape software (version 3.7.2), and analyses were performed using the ClueGO application with the Kyoto Encyclopedia of Genes and Genomes as the database. Significant pathways were selected using the threshold criteria Q < 0.05; terms > 3 genes; % genes/term > 3; 0.4.

We used the human UPEC cystitis isolate UTI89 engineered to express the fluorescent proteins RFP or GFP and antibiotic-resistant cassettes to either kanamycin (UPEC-RFP) or ampicillin (UPEC-GFP) to infect animals for flow cytometric and bacterial burden analyses (23). We used the nonfluorescent parental strain UTI89 for confocal imaging experiments and flow cytometric experiments with CCR2CreERT2 Rosa26tdTomato mice (97). To allow expression of type 1 pili, necessary for infection (98), bacteria cultures were grown statically in Luria-Bertani broth medium for 18 hours at 37C in the presence of antibiotics [kanamycin (50 g/ml) or ampicillin (100 g/ml)]. Primary and challenge UTI were induced in mice as previously described (23, 99). For challenge infection, urine was collected twice a week, for 4 weeks, to follow the presence of bacteria in the urine. Once there were no UTI89-RFP bacteria in the urine, mice were challenged with UTI89-GFP bacteria and euthanized 24 hours after challenge infection. To calculate CFU, bladders were aseptically removed and homogenized in 1 ml of PBS. Serial dilutions were plated on LB agar plates with antibiotics, as required. For in vitro infections, macrophage subsets were sorted from a pool of 10 bladders of nave female C57BL/6J 7- to 8-week-old mice using FACS and 2 103 cells were incubated with 2 104 CFU of UPEC-RFP for 2 hours at 37C. Cells were acquired on a BD LSRFortessa using DIVA software (v8.0.1) and data were analyzed by FlowJo (Treestar) software (version 10.0).

Seven- to 8-week-old wild-type female C57BL6/J mice were anesthetized and instilled intravesically with 50 l of protamine sulfate (50 mg/ml) diluted in PBS and euthanized 5 hours after instillation for analysis.

To produce anti-CSF1R antibody, the hybridoma cell line AFS98 (gift from M. Merad at Icahn School of Medicine at Mount Sinai) (100) was cultured in disposable reactor cell culture flasks for 14 days, and antibodies were purified with disposable PD10 desalting columns. To deplete macrophages, wild-type C57BL/6 mice received intravenous injection of anti-CSF1R antibody (2 mg/ml) diluted in PBS. Animals received two or three intravenous injections, on consecutive days, of anti-CSF1R antibody or PBS. To deplete macrophages with a final concentration of 500 g of anti-CSF1R, we administered 250 g per mouse on day 1 and 250 g per mouse on day 2. To deplete macrophages with a final concentration of 800 g of anti-CSF1R, we administered 400 g per mouse on day 1, 200 g per mouse on day 2, and 200 g per mouse on day 3 to minimize the impact on circulating monocytes.

To quantify macrophage subsets in bladder tissue, six to seven images were randomly acquired of each of the areas of the muscle and lamina propria per mouse in wild-type C57BL/6 female mice with 40 magnification in an SP8 Leica microscope. Maximum intensity Z-projections were performed, and macrophage subsets were counted using Icy software (v1.8.6.0). To quantify urothelial exfoliation and tissue edema, images from whole bladder cross sections were acquired using 20 magnification in an SP8 Leica microscope. Maximum intensity Z-projections were performed, the urothelium was delimited, and mean fluorescence intensity of uroplakin staining was measured using Fiji (v1.51j) software. To quantify tissue edema, the lamina propria was delimited and the area was measured using Fiji software (v1.51j).

Statistical analysis was performed in GraphPad Prism 8 (GraphPad, USA) for Mac OS X applying the nonparametric Wilcoxon test for paired data or the nonparametric Mann-Whitney test for unpaired data in the case of two group comparisons. In the case that more than two groups were being compared or to correct for comparisons made within an entire analysis or experiment, calculated P values were corrected for multiple testing with the false discovery rate (FDR) method (https://jboussier.shinyapps.io/MultipleTesting/) to determine the FDR-adjusted P value. All calculated P values are shown in the figures, and those that met the criteria for statistical significance (P < 0.05) are denoted with red text.

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Functionally distinct resident macrophage subsets differentially shape responses to infection in the bladder - Science Advances

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CAMBRIDGE, Mass., Nov. 25, 2020 (GLOBE NEWSWIRE) -- Agios Pharmaceuticals, Inc. (NASDAQ:AGIO), a leader in the field of cellular metabolism to treat cancer and rare genetic diseases, today announced that the company is scheduled to present at the Piper Sandler 32nd Annual Virtual Healthcare Conference on Wednesday, December 2, 2020 at 2:00 p.m. ET.

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Agios to Present at the Piper Sandler 32nd Annual Virtual Healthcare Conference on Wednesday, December 2, 2020

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BOSTON, Nov. 25, 2020 (GLOBE NEWSWIRE) -- Atea Pharmaceuticals, Inc. (Nasdaq: AVIR) (“Atea”), a clinical-stage biopharmaceutical company focused on discovering, developing and commercializing antiviral therapeutics to improve the lives of patients suffering from life-threatening viral infections, today announced that management will participate in a fireside chat at the Evercore ISI 3rd Annual HealthCONx Conference on Thursday, December 3, 2020 at 12:35 p.m. ET.

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Atea Pharmaceuticals to Present at Evercore ISI 3rd Annual HealthCONx Conference

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NEW HAVEN, Conn., Nov. 25, 2020 (GLOBE NEWSWIRE) -- Arvinas, Inc. (Nasdaq: ARVN), a clinical-stage biotechnology company creating a new class of drugs based on targeted protein degradation, today announced that management will participate in two upcoming virtual investor conferences:

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COPENHAGEN, Denmark, Nov. 25, 2020 (GLOBE NEWSWIRE) -- Ascendis Pharma A/S (Nasdaq: ASND), a biopharmaceutical company that utilizes its innovative TransCon™ technologies to create product candidates that address unmet medical needs, today announced that the company will participate in the Evercore ISI 3rd Annual HealthCONx Virtual Conference on December 3, 2020. Company executives will provide a business overview and update on the company’s pipeline programs.

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Ascendis Pharma A/S Announces Participation at the Evercore ISI 3rd Annual HealthCONx Virtual Conference

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EDMONTON, Alberta, Nov. 25, 2020 (GLOBE NEWSWIRE) -- Radient Technologies Inc. (“Radient” or the “Company”) (TSX Venture: RTI; OTCQX: RDDTF), is pleased to announce a licensing agreement with Dreamy Co., (“Dreamy”) to bring in Dreamy CBD products under Radient’s platform to Canada and extend it to other international markets. Initially founded in the US, Dreamy has developed a lifestyle brand over the past decade focused on the Health and Wellness space before branching into the CBD vertical. Dreamy is focused on creating and inspiring people to use plant medicine and holistic healing in place of conventional pharmaceutical solutions. In the US, Dreamy CBD enjoys a strong presence in California, and the Dreamy brand has a strong association with a positive lifestyle in the rapidly growing Wellness space. The Dreamy CBD products comprise CBD tinctures, CBD distillates, CBD edibles, CBD cosmetics, and CBD Vape Cartridges geared towards the recreational and medical cannabis market.

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