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Altimmune Announces Submission of Investigational New Drug Application for AdCOVID™ a Single-dose Intranasal COVID-19 Vaccine; On Track to Commence…

By Dr. Matthew Watson

Simple, single-dose nasal spray offers greater ease and comfort of administration; positioning AdCOVID as a differentiated vaccine candidate for adults and children

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Agenus to Participate in Evercore ISI 3rd Annual HealthCONx Conference

By Dr. Matthew Watson

LEXINGTON, Mass., Nov. 25, 2020 (GLOBE NEWSWIRE) -- Agenus Inc. (NASDAQ: AGEN), an immuno-oncology company with an extensive pipeline of checkpoint antibodies, cell therapies, adjuvants, and vaccines designed to activate immune response to cancers and infections, today announced that Dr. Jennifer Buell, President and COO of Agenus, will participate in a fireside chat hosted by Umer Raffat at the Evercore ISI 3rd Annual HealthCONx Conference on Thursday, December 3, 2020 from 10:55 AM – 11:15 AM EST.

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Cronos Group Inc. to Speak at the Cowen 2020 Boston Cannabis Conference

By Dr. Matthew Watson

TORONTO, Nov. 25, 2020 (GLOBE NEWSWIRE) -- Cronos Group Inc. (NASDAQ: CRON) (TSX: CRON) (“Cronos Group” or the “Company”), an innovative global cannabinoid company, today announced that Mike Gorenstein, Executive Chairman, is scheduled to speak on a panel at the Cowen 2020 Boston Cannabis Conference on Wednesday, December 2, 2020 at 9:20 a.m. EST.

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Novavax to Participate in Upcoming Conferences

By Dr. Matthew Watson

GAITHERSBURG, Md., Nov. 25, 2020 (GLOBE NEWSWIRE) -- Novavax Inc. (Nasdaq: NVAX), a late-stage biotechnology company developing next-generation vaccines for serious infectious diseases, today announced that it will participate in two upcoming investor conferences. Novavax’ COVID-19 vaccine candidate, NVX-CoV2373, will be a topic of discussion in each conference.

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Shattuck Labs to Present Virtually at Evercore ISI 3rd Annual HealthCONx Conference

By Dr. Matthew Watson

AUSTIN, TX and DURHAM, NC, Nov. 25, 2020 (GLOBE NEWSWIRE) -- Shattuck Labs, Inc. (Shattuck) (NASDAQ: STTK), a clinical-stage biotechnology company pioneering the development of bi-functional fusion proteins as a new class of biologic medicine for the treatment of patients with cancer and autoimmune disease, today announced it will participate in the Evercore ISI 3rd Annual HealthCONx conference being held December 1-3, 2020.

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FDA Approves Y-mAbs’ DANYELZA® (naxitamab-gqgk) for the Treatment of Neuroblastoma

By Dr. Matthew Watson

NEW YORK, Nov. 25, 2020 (GLOBE NEWSWIRE) -- Y-mAbs Therapeutics, Inc. (the “Company” or “Y-mAbs”) (Nasdaq: YMAB) a commercial-stage biopharmaceutical company focused on the development and commercialization of novel, antibody-based therapeutic products for the treatment of cancer, today announced that the U.S. Food and Drug Administration (“FDA”) has approved DANYELZA (naxitamab-gqgk) 40mg/10ml. DANYELZA is indicated, in combination with granulocyte-macrophage colony-stimulating factor (“GM-CSF”), for the treatment of pediatric patients 1 year of age and older and adult patients with relapsed or refractory high-risk neuroblastoma in the bone or bone marrow who have demonstrated a partial response, minor response, or stable disease to prior therapy. This indication is approved under accelerated approval regulation based on overall response rate and duration of response. Continued approval for this indication may be contingent upon verification and description of clinical benefits in a confirmatory trial. DANYELZA is a humanized, monoclonal antibody that targets the ganglioside GD2, which is highly expressed in various neuroectoderm-derived tumors and sarcomas. DANYELZA is administered to patients three times in a week in an outpatient setting and the treatment is repeated every four weeks. The product has received Priority Review, Orphan Drug, Breakthrough Therapy, and Rare Pediatric Disease designations from the FDA.

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Half-year report

By Dr. Matthew Watson

DXS INTERNATIONAL PLC

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Change of management

By Dr. Matthew Watson

Please read the full announcement in PDF

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

By Dr. Matthew Watson

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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Notification of Managers’ transactions

By Dr. Matthew Watson

Company announcement no. 48 - 20 26 November 2020

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

By Dr. Matthew Watson

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

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

By Dr. Matthew Watson

PRESS RELEASE

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Kane Biotech Announces Third Quarter 2020 Financial Results

By Dr. Matthew Watson

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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Eve & Co Announces Financial Results for the Three and Nine Months Ended September 30, 2020

By Dr. Matthew Watson

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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Braingrid Limited Announces Changes to Board of Directors

By Dr. Matthew Watson

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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EXMceuticals Provides Bi-Weekly Default Status Report

By Dr. Matthew Watson

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

By daniellenierenberg

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

By daniellenierenberg

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

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

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

By daniellenierenberg

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...

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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…

By daniellenierenberg

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.

SOURCE Antengene Corporation Limited

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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...

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