scholarly journals Reconstructing Early Erythroid Development In Vivo Using Single-Cell Transcriptomics

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 1195-1195
Author(s):  
Betsabeh Khoramian Tusi ◽  
Samuel Wolock ◽  
Caleb Weinreb ◽  
Yung Hwang ◽  
Daniel Hidalgo ◽  
...  

Abstract Erythroid differentiation may be divided into two broad stages: early development, and terminal differentiation. Early development was first explored using the colony-formation potential of hematopoietic tissue. This approach identified multi-potential progenitors (MPP) and unipotential erythroid progenitors that form 'bursts' (BFUe) and smaller colonies (CFUe). Early erythroid development is followed by erythroid terminal differentiation (ETD), which profoundly remodels erythroblasts into enucleated red cells. The molecular study of ETD was fundamentally transformed with the development of cell-surface marker strategies that identify sequential stage-specific erythroblasts in hematopoietic tissue. By contrast, there have been no strategies that systematically identify the entire cellular and molecular trajectory of the early erythroid lineage as it first arises from the MPP and progresses to the point where the ETD program is activated. To address this gap, we undertook single-cell transcriptomics using the InDrop-seq platform (Klein et al. Cell 161:1187 2015). We analyzed Kit+ cells in the bone marrow of mice in the basal state, mice stimulated with Erythropoietin (Epo) for 48 hours, or fetal liver cells. We used Next-Generation Sequencing data to construct k-nearest neighbor (kNN) graphs of cell states for each condition, and extracted the erythroid trajectory using Population Balance Analysis (PBA), a novel computational approach that predicts differentiation fates from single cell RNA profiles. We identified early erythroid developmental stages based on the modeled probability of erythroid commitment, gene expression dynamics, and the architecture of the kNN graph. By screening for appropriate cell surface markers, we developed a flow-cytometric strategy that isolates sub-populations corresponding to regions of the kNN graph, including sub-regions of the erythroid trajectory. This allowed validation of gene expression patterns and of cell fate predictions using in vitro colony formation assays. The earliest stage in the erythroid trajectory, the Erythroid/Basophil MPP stage (E/B-MPP) contains progenitors that emerge from MPPs, predicted to remain mutli-potential but be biased towards bipotential erythroid/basophil and eryrthroid/megakaryocytic fates. This stage is characterized by rapidly changing gene expression profiles. Genes whose expression correlates with the probability of erythroid commitment include both known transcriptional regulators of erythropoiesis such as GATA1, GATA2, Ldb1 and Klf1, as well as novel candidates. Downstream from the E/B-MPP, the two-dimensional projection of the kNN graph becomes a narrow bottleneck, indicating a transient stage. Here the erythroid trajectory contains cells with rapidly increasing probability of erythroid commitment (Emerging Erythroid Progenitors, EEP). The bottleneck connects to a bulge-like region in which progenitors have an extremely high probability of attaining the erythroid fate (committed erythroid progenitors, CEP). This region contains the majority of the marrow's committed erythroid progenitors, and functions as an amplification module, increasing in size in Epo-stimulated marrow and in the fetal liver. Functionally, cells in the bottleneck region give rise to multifocal erythroid colonies (early and late BFUe), whereas cells in the CEP amplification module give rise to unifocal erythroid colonies, including the CFUe. Therefore, the ability of a progenitor to give rise to either multifocal or unifocal colonies correlates closely with molecular stage. Cells in the CEP module express a unique set of genes, induced at the module entry, and repressed at its exit. These include growth-factor receptors mst1r, ryk and il17ra. Their ligands, MSP, Wnt5a and IL17a, are novel regulators of erythropoiesis, either stimulating or inhibiting the formation of erythroid colonies. Exit from the CEP module is marked by a rapid switch, in which the repression of CEP genes coincides with induction of the ETD program. Remarkably, this switch is synchronized with expression of G1/S and S phase genes, underlying a role for S phase progression in ETD activation (Pop et al., PLoS Biology 2010). Our work charts the erythroid trajectory of murine hematopoietic tissue, identifying developmental milestones, setting the stage for their molecular study and for discovery of novel erythroid regulators. Disclosures Klein: OneCell Bio: Equity Ownership, Membership on an entity's Board of Directors or advisory committees.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 1169-1169
Author(s):  
Julie A. Lambert ◽  
Nicolas Goardon ◽  
Patrick Rodriguez ◽  
Sabine Herblot ◽  
Pierre Thibault ◽  
...  

Abstract As highly proliferative erythroid progenitors commit to terminal differentiation, they also progressively undergo growth arrest. To determine the mechanisms underlying the appropriate timing of erythroid gene expression and those associated with growth cessation, we analyzed the dynamical composition of the multiprotein complex nucleated by the bHLH transcription factor SCL, a crucial regulator of erythropoiesis that absolutely requires interaction with other factors to activate transcription. In progenitor cells, the SCL complex marks a subset of erythroid specific genes (alpha-globin, P4.2, glycophorin A) that are transcribed later in differentiating cells, conducting cells toward terminal maturation. To unravel the regulation of transcription by SCL, we used tagging/proteomics approaches in two differentiation-inducible erythroid cell lines, coupled with binding assays to immobilized DNA templates and chromatin immunoprecipitation. Our analyses reveal that the core complex comprised of known proteins (SCL, GATA-1, LMO2, Ldb1 and E2A) and two additional E protein family members, HEB and E2-2, did not change with differentiation. Strikingly, this complex recruits HDAC1-2 in undifferentiated cells which were exchanged with TRRAP, a chromatin remodelling factor, upon differentiation, suggesting an epigenetic regulation of erythroid differentiation mediated by the core SCL complex. Finally, we identified the corepressor ETO2 targeted via this complex through direct interaction with E2A/HEB. In vivo, ETO2 represses the transcription of SCL target genes both in transient assays and in chromatin. During erythroid differentiation, ETO2 remains associated with the SCL complex bound to erythroid promoters. However, the stoichiometry of ETO2 and SCL/HEB changes as SCL and HEB levels increase with erythroid differentiation, both in nuclear extracts and on DNA. To determine the functional consequence of this imbalance in activator to co-repressor ratio, we delivered ETO2 siRNA in primary hematopoietic cells and found an accelerated onset of SCL target genes on induction of erythroid differentiation, and conversely, these genes were decreased following ectopic ETO2 expression. Strikingly, inhibition of ETO2 expression in erythroid progenitors arrests cell proliferation, indicating that ETO2 is required for their expansion. We therefore analyzed gene expression in purified erythroid progenitors and differentiating erythroid cells (E1-E5) and found an inverse correlation between the mRNA levels of ETO2 and cyclin-dependent kinase inhibitors. Moreover, ETO2 siRNA treatment of primary erythroid progenitors results in increased p21 CDKI and Gfi1b expression, as assessed by real-time PCR. Finally, we show by chromatin immunoprecipitation that Gfi-1b, p21 and p27, are direct targets of the SCL- ETO2 complex. We therefore conclude that ETO2 regulates the erythroid lineage fate by repressing SCL marked erythroid genes in undifferentiated cells, and by controlling the expansion of erythroid progenitors. Our study elucidates the dual function of ETO2 in the erythroid lineage and sheds light on epigenetic mechanisms coordinating red blood cell proliferation and differentiation.


Blood ◽  
2001 ◽  
Vol 98 (12) ◽  
pp. 3261-3273 ◽  
Author(s):  
Merav Socolovsky ◽  
Hyung-song Nam ◽  
Mark D. Fleming ◽  
Volker H. Haase ◽  
Carlo Brugnara ◽  
...  

Abstract Erythropoietin (Epo) controls red cell production in the basal state and during stress. Epo binding to its receptor, EpoR, on erythroid progenitors leads to rapid activation of the transcription factor Stat5. Previously, fetal anemia and increased apoptosis of fetal liver erythroid progenitors were found in Stat5a−/−5b−/− mice. However, the role of Stat5 in adult erythropoiesis was not clear. The present study shows that some adult Stat5a−/−5b−/− mice have a near-normal hematocrit but are deficient in generating high erythropoietic rates in response to stress. Further, many adult Stat5a−/−5b−/− mice have persistent anemia despite a marked compensatory expansion in their erythropoietic tissue. Analysis of erythroblast maturation in Stat5a−/−5b−/− hematopoietic tissue shows a dramatic increase in early erythroblast numbers, but these fail to progress in differentiation. Decreased expression of bcl-xLand increased apoptosis in Stat5a−/−5b−/−early erythroblasts correlate with the degree of anemia. Hence, Stat5 controls a rate-determining step regulating early erythroblast survival.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 1288-1288
Author(s):  
Devdeep Mukherjee ◽  
Gege Gui ◽  
Laura W. Dillon ◽  
Christopher S. Hourigan

Abstract BACKGROUND: The pathogenesis of acute myeloid leukemia (AML) is often attributed to the presence of somatic allelic variant(s) in hematopoietic stem/progenitor cells. However, malignant clones may have heterogenous cell-surface immunophenotypes including overlap with non-malignant cells. While leukemia-associated immunophenotypes and difference from normal approaches are used for flow cytometric assessment during and after treatment, such analysis may underrepresent true leukemia disease burden. Assessments of AML measurable residual disease (MRD) using flow cytometry and molecular methods have been reported as discrepant. Single-cell RNA sequencing experiments have recently attempted to distinguish malignant cells based on gene expression and/or immunophenotypic profiles alone. We hypothesized that single-cell genotyping of mutated transcript(s) coupled with broad surface proteome and transcriptome profiling could provide an integrated multimodal method for AML characterization. METHODS: We adapted the previously reported "genotyping of transcriptomes" (PMID: 31270458) to identify cells carrying the NPM1 type A mutation commonly seen, and typically stable throughout the disease course, in AML. Healthy human peripheral blood mononuclear cells (PBMC) were mixed with an AML cell line carrying NPM1 type A mutation (OCI-AML3) at 7:3 ratio and labelled with 163 oligo-tagged antibodies. Single cell 3'v3 gene expression- (GEX), antibody derived tag- (ADT) and genotyping of NPM1 (GNPM) -libraries (10X Genomics) were sequenced on the NovaSeq 6000 (Illumina). Results were processed using Seurat 4.0 toolkit. RESULTS: In total, 72% (n=1680) of barcoded cells could be genotyped for NPM1. Of the genotyped cells, 59% (n = 986) were not NPM1 mutated. Visualization using Uniform Manifold Approximation and Projection (UMAP) showed separation of healthy PBMCs and OCI-AML3 cells using protein data, confirmed by annotation using NPM1 genotyping (Figure 1). We found a significant positive correlation between mRNA and corresponding cell surface protein expressions in non-mutated (Pearson's coefficient, r = 0.502, p = 6.87e-11) and NPM1 mutated (r= 0.392, p= 7.5e-7) cells. Compared to non-mutated, NPM1 mutated cells showed nearly 14-fold higher NPM1 transcript levels. In addition, a total 63 proteins were highly expressed on the surface of NPM1 mutated cells (Figure 2). Among these, CD33 and CD36 showed maximum 8-fold increase in expression. Other highly expressed proteins with at least >2.5-fold change were cell adhesion molecules (including CD328, CD155, and CD56), extracellular matrix binding proteins (CD49a/b) and interleukin receptor (CD123). CONCLUSION: Overall, our results demonstrate proof of principle that high-throughput cell surface proteome, transcriptome and genotyping analysis can be simultaneously performed to comprehensively and confidently characterize individual AML cells. Patient-specific multiomics data with broad cell-surface proteomic screening may allow novel target identification for monitoring and/or therapeutic intervention. Ongoing work will now use this methodology to characterize a cohort of NPM1 mutated AML patient samples. Figure 1 Figure 1. Disclosures Hourigan: Sellas: Research Funding.


2017 ◽  
Author(s):  
Anissa Guillemin ◽  
Angelique Richard ◽  
Sandrine Gonin-Giraud ◽  
Olivier Gandrillon

AbstractRecent rise of single-cell studies revealed the importance of understanding the role of cell-to-cell variability, especially at the transcriptomic level. One of the numerous sources of cell-to-cell variation in gene expression is the heterogeneity in cell proliferation state. How cell cycle and cell size influences gene expression variability at single-cell level is not yet clearly understood. To deconvolute such influences, most of the single-cell studies used dedicated methods that could include some bias. Here, we provide a universal and automatic toxic-free label method, compatible with single-cell high-throughput RT-qPCR. This led to an unbiased gene expression analysis and could be also used for improving single-cell tracking and imaging when combined with cell isolation. As an application for this technique, we showed that cell-to-cell variability in chicken erythroid progenitors was negligibly influenced by cell size nor cell cycle.


Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1187-1187
Author(s):  
Kim Vanuytsel ◽  
Carlos Villacorta-Martin ◽  
Wilfredo Garcia Beltran ◽  
Taylor Matte ◽  
Alejandro Balazs ◽  
...  

Intro: In the mouse, hematopoietic stem cells (HSCs) can be isolated and characterized at single cell resolution using a well-defined panel of markers. While it is possible to enrich for human HSCs using a panel of associated markers, similar resolution has not been attained. By profiling HSCs residing in the human fetal liver (FL) using a novel technique called CITE-Seq that combines single cell RNA sequencing (scRNAseq) and cell surface marker interrogation using oligo-tagged antibodies, we aimed to establish an accurate molecular signature of engraftable human HSCs shortly after they arise in development. As HSCs are defined functionally, we have coupled this transcriptomic and protein-level characterization with transplantation assays in immunocompromised NOD scid gamma (NSG) mice to connect expression profiles of cell subsets with functional engraftment. Methods: CITE-Seq was performed on human FL cells (week 19) that showed robust engraftment capability in NSG mice. CD34+ and CD34- cells were magnetically separated and stained with a panel of 19 oligo-tagged antibodies that were deemed relevant to characterize HSCs, including classical HSC markers but also novel targets that were identified in a previous pilot scRNAseq experiment conducted on CD34+ FL cells. From the CD34+ fraction, we sorted live-gated cells (CD34+bulk) as well as a population of cells that was further enriched based on the expression of GPI-80, a marker tightly linked to engraftment potential (CD34+GPI-80+, ~3%). CD34-GlycophorinA(GYPA)- cells were also sorted to assay for the presence of CD34- HSCs. These fractions were then loaded onto the 10x Genomics platform for capture of single cells and subsequent reverse transcription and amplification of both mRNAs and antibody-derived tags (ADTs). Results: Both mRNA and ADT libraries were successfully sequenced, yielding 29-43,000 reads/cell for the mRNA portion and >1,500 reads/cell for the ADT fraction. After quality control and filtering, this effort resulted in 8,775 CD34+bulk cells, 7,279 CD34+GPI-80+ cells, and 6,937 CD34-GYPA- cells available for further analysis. Simultaneous transplantation experiments of the fractions assayed by CITE-seq revealed superior engraftment potential of the CD34+GPI-80+ fraction, confirming enrichment for bona fide HSCs at the functional level. This was also reflected in the scRNAseq data where we found enrichment for known HSC markers such as VNN2 (GPI-80), PROM1 (CD133), PROCR (EPCR), THY1 (CD90), ITGA6 (CD49f), HMGA2, CLEC9A and HLF in the CD34+GPI-80+ fraction compared to CD34+bulk cells. As our pilot studies revealed considerable differences in transcriptional expression (via scRNAseq) as compared to protein-level expression (via cell surface marker expression), integration of the transcriptomic and cell surface marker expression data will further refine the signature of engraftable HSCs. Both layers of information at single cell resolution will allow for the identification of novel markers or unique combinations of markers that are directly correlated with engraftment potential. Conclusion: By isolating the GPI-80+ population within the CD34+ fraction in human FL, we have achieved unprecedented resolution of the signature of engraftable HSCs as confirmed by transplantation experiments. The in-depth characterization of this compartment as well as the surrounding CD34+ and CD34- cells within the FL is expected to yield valuable insights with respect to several biological questions. This data can be directly harnessed in improving the purification and expansion of engraftable HSCs as well as in guiding the in vitro generation of HSCs from pluripotent stem cells. Disclosures No relevant conflicts of interest to declare.


Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. SCI-20-SCI-20
Author(s):  
Merav Socolovsky

The manner by which multipotent hematopoietic progenitors commit to the erythroid lineage, and the subsequent processes that govern early erythroid progenitor development, are not well understood. Part of the challenge for investigating these was the lack of a rigorous strategy for isolating directly from tissue the early erythroid progenitors, which are functionally defined as the cell 'units' that give rise to erythroid colonies (CFU-e) or bursts (BFU-e) in culture. Indeed, the early erythroid trajectory, that starts with multi-potential progenitors and gives rise to BFU-e, CFU-e and to erythroblasts undergoing terminal differentiation, was not fully elucidated. We addressed these gaps using single cell transcriptomics, combined with functional assays that validated computational predictions 1. These showed that early hematopoietic progenitors form a continuous, hierarchical branching structure, in which the erythroid and basophil/mast cell fates are unexpectedly coupled. We delineated a novel flow-cytometric strategy that prospectively isolates CFU-e and BFU-e progenitors with high purity, and in combination with computational predictions, identified novel growth factor receptors that regulate early erythropoiesis. We further discovered that early erythroid development entails profound remodeling of both G1 and S phases of the cycle, resulting in cell cycle specializations that orchestrate the developmental process, including a gradual shortening of G1 during the CFU-e phase, followed by a sharp increase in the speed of S phase during the S-phase dependent activation of the erythroid terminal differentiation program 1-3(Figure 2). 1. Tusi BK, Wolock SL, Weinreb C, et al. Population snapshots predict early haematopoietic and erythroid hierarchies. Nature. 2018;555(7694):54-60. 2. Hwang Y, Futran M, Hidalgo D, et al. Global increase in replication fork speed during a p57KIP2-regulated erythroid cell fate switch. Science Advances. 2017;3:e1700298. 3. Pop R, Shearstone JR, Shen Q, et al. A key commitment step in erythropoiesis is synchronized with the cell cycle clock through mutual inhibition between PU.1 and S-phase progression. PLoS Biol. 2010;8(9). Disclosures No relevant conflicts of interest to declare.


Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 1170-1170
Author(s):  
Orna Steinberg Shemer ◽  
Marta Byrska-Bishop ◽  
Jacob C Ulirsch ◽  
Osheiza Abdulmalik ◽  
Yu Yao ◽  
...  

Abstract Mammalian erythropoiesis during embryogenesis occurs in several distinct stages or "waves" that vary according to timing, site of production, gene expression and physiology. The ontogeny of mammalian erythropoiesis is most thoroughly studied in mice where the earliest circulating erythroblasts released from the yolk sac are termed primitive. Later, the first definitive erythroid lineage is established by erythro-myeloid progenitors (EMPs) that originate in the yolk sac and migrate to the fetal liver for terminal differentiation. A second wave of definitive erythropoiesis is established from hematopoietic stem/progenitor cells that originate in the dorsal aorta and migrate to later stage fetal liver for terminal differentiation. Finally around birth, definitive erythropoiesis shifts to the bone marrow. The ontogeny of erythropoiesis overlaps in mice and humans, although less is known about the latter, as hematopoietic tissues from precisely staged early human embryos are difficult to obtain. We hypothesized that the initial steps of human erythroid ontogeny could be recapitulated by induced pluripotent stem cells (iPSCs) induced to undergo hematopoietic differentiation. We used a serum- and feeder-free protocol to differentiate iPSCs into embryoid bodies (EBs) that produced two sequential waves of distinctly different erythroid precursors. At day 8 of differentiation, EBs began to release hematopoietic precursors. Thereafter, erythroid precursors were released from the EBs in the presence of stem cell factor (SCF), erythropoietin (EPO) and insulin-like growth factor 1 (IGF-1). Erythroid precursors produced during wave 1 (days 12-23 of differentiation) were relatively large and expressed embryonic-type globins (zeta and epsilon), resembling those produced during primitive erythropoiesis. In contrast, wave 2 erythroblasts (day 27 and later) were smaller and expressed mainly gamma and alpha globins with some beta globin, suggestive of fetal-type definitive erythropoiesis. To investigate further the similarity of wave 1 and wave 2 erythroblasts to cells at the primitive and definitive stages of ontogeny, respectively, we used Affymetrix Genechips to analyze the global transcriptomes of stage-matched (CD235+ CD71high) cells. As primary human primitive erythroblasts were not available for comparison, we compared the transcriptomes from the iPSC-derived erythroblasts with those of primary murine definitive and primitive erythroblasts that were flow cytometry-purified from embryonic day 15.5 (E15.5) fetal liver and E10.5 bloodstream, respectively. The comparisons showed that wave 1 erythroblasts from human pluripotent cells resembled more closely the erythroid primitive lineage from mice, while wave 2 erythroblasts from the human cells resembled the erythroid definitive lineage of mice (P-value < 0.05 by a modified Kolmogorov-Smirnov test). For example, SOX6 and BCL11A, preferentially expressed during definitive erythropoiesis, were expressed at relatively high levels in wave 2 erythroblasts. In addition, gene set enrichment analysis (GSEA) demonstrated that wave 2 human iPSC-derived erythroblasts and primary murine definitive erythroblasts expressed numerous genes related to immune/inflammatory pathways that were shown previously to be important for the formation of definitive hematopoietic stem and progenitor cells in zebrafish and mouse embryos. Our findings demonstrate that human iPSC-derived embryoid bodies recapitulate early stages of erythroid ontogeny with respect to the timing of emerging lineages and their gene expression. Additionally, gene expression studies of human iPSC-derived primitive and definitive erythroblasts indicate inflammatory signaling as a potential regulator of the later stage of erythroid development. Disclosures No relevant conflicts of interest to declare.


Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. SCI-28-SCI-28
Author(s):  
Mitchell J. Weiss

Abstract Long noncoding (Lnc) RNAs are RNA transcripts greater than 200 nucleotides (nt) that regulate gene expression independent of protein coding potential (1-3). It is estimated that thousands of lncRNAs play vital roles in diverse cellular processes. LncRNAs modulate many stages of gene expression by regulating transcription, epigenetics, splicing, translation, and protein localization. We hypothesize that multiple lncRNAs are expressed specifically during erythrocyte and megakaryocyte differentiation, and are likely to have important roles. To identify lncRNAs in erythro-megakaryopoiesis, we performed strand-specific, paired-end deep sequencing (RNA-Seq) to a depth of 200 million reads per sample on two replicates each of murine Ter119+erythroblasts, CD41+ megakaryocytes and bipotential megakaryocyte-erythroid progenitors (MEPs) [lin- Kit+ Sca1- CD16/32- CD34-], and used bioinformatic filtering tools to identify approximately 1,100 candidate lncRNAs. Over 60 percent of these lncRNAs are novel unannotated transcripts with exquisite lineage-specific expression. Using erythroid and megakaryocytic primary cell ChIP-Seq for key transcription factors (TFs) GATA1, TAL1, GATA2,and FLI1, we found that the loci of lncRNAs show similar degree of TF binding as coding genes. We used the erythroid line G1E-ER4 (which expresses estrogen-activated GATA1) to confirm that lncRNAs bound by GATA1 are also directly regulated by it. Furthermore, we used histone methylation ChIP-Seq to show that most lncRNAs arise from classical “promoters” with high H3K4me3 levels and low H3K4me1 levels. Thus, we find that lncRNAs show epigenetic features similar to the promoters of coding genes and are directly regulated by similar TF networks. Comparison of the transcriptomes of mouse fetal liver and human cord blood erythroblasts demonstrated that lncRNAs are expressed in a highly species-specific fashion, i.e., most lncRNAs identifiable in one species are not transcribed in the other, even though the corresponding genomic region is present in both species. Numerous non-conserved but functional lncRNAs are reported in the literature, and the significance of conservation in lncRNA biology is greatly debated. In order to identify functional lncRNAs, we are currently performing RNAi knockdown on numerous candidates to assess how loss of function affects erythroid maturation. We are also performing HITS-CLIP of key chromatin modifying complexes and erythroid transcription factors to identify lncRNAs bound to them. Our studies are beginning to define new layers of gene regulation in normal erythro-megakaryopoiesis, which may be relevant to the pathophysiology of related disorders including various anemias, myeloproliferative and myelodysplastic syndromes and leukemias. 1. Wang K.C., Chang H.Y. Molecular mechanisms of long noncoding RNAs. Molecular Cell. 2011;43(6):904-914. Prepublished on 2011/09/20 as DOI 10.1016/j.molcel.2011.08.018. 2. Hu W., Alvarez-Dominguez J.R., Lodish H.F. Regulation of mammalian cell differentiation by long non-coding RNAs. EMBO reports. 2012;13(11):971-983. Prepublished on 2012/10/17 as DOI 10.1038/embor.2012.145. 3. Paralkar V.R., Weiss M.J. Long noncoding RNAs in biology and hematopoiesis. Blood. 2013;121(24):4842-4846. Prepublished on 2013/05/07 as DOI 10.1182/blood-2013-03-456111. Disclosures: No relevant conflicts of interest to declare.


Blood ◽  
2014 ◽  
Vol 123 (5) ◽  
pp. 758-767 ◽  
Author(s):  
Zhenhua Sui ◽  
Roberta B. Nowak ◽  
Andrea Bacconi ◽  
Nancy E. Kim ◽  
Hui Liu ◽  
...  

Key Points Tmod3 deletion leads to reduced erythroid progenitors and impaired erythroblast survival, cell-cycle exit, and enucleation. Erythroblast-macrophage islands are reduced in the absence of Tmod3, which is required in both cell types for island formation.


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1274-1274
Author(s):  
Elisabeth F Heuston ◽  
Bethan Psaila ◽  
Stacie M Anderson ◽  
NISC Comparative Sequencing Program ◽  
David M. Bodine

Abstract The hierarchical model of hematopoiesis posits that hematopoietic stem cells (HSC) give rise to myeloid progenitors (CMP), that can become further restricted to bipotential granulocyte/monocyte progenitors (GMP) or megakaryocyte/erythroid progenitors (MEP). We and others have shown that this model may not accurately depict hematopoiesis. Recent studies have shown that shown that populations of mouse hematopoietic stem and progenitor cells (LSK) have a strong megakaryocyte (Mk) transcriptional profile (Heuston, 2018, Epig. & Chrom.), and single cell studies have identified lineage committed cells in progenitor populations thought to be multipotent. For example, we recently reported that human MEP contain 3 populations: erythroid (Ery) primed, Mk primed, and bipotential (Psaila, 2016; Gen. Bio.). To determine when Mk and Ery cells emerge during mouse hematopoiesis, we performed single cell RNASeq on 10000 LSK, 12000 CMP, 6000 MEP and 8000 GMP cells. Clustering analysis (Satija, 2018, Nat. Biotech.) of all 4 populations identified 33 transcriptionally distinct clusters. In 30 of 33 clusters, 85% of cells were from a single defined population (e.g. MEP). LSK and CMP clusters grouped closely together. We used gene set profiling (Gene Set Enrichment Analysis, GO and KEGG) to correlate transcriptional profiles of clusters with specific hematopoietic lineages and cellular activities. In LSK, the most common transcriptional profiles correlated with active cell cycling. Mk-associated genes (Meis1, Myct1, and Fli1), were co-expressed with lymphoid genes in 56% of all LSK. Consistent with previous studies, we conclude that cells with Mk transcriptional profiles are abundant in LSK. No cells with an Ery RNA signature were observed in LSK. 23% of all CMP cells expressed Mk genes (e.g., Pf4, Itga2b, and Fli1) and were enriched for processes involved in platelet biology (p < 3E-18). 12% of CMP had an Ery RNA signature (low expression of Gata1, Klf1, and Nfe2) and decreased Mk gene expression (e.g., Gata2 and Gfi1b, [p < 3E-18]) compared to other CMP clusters. The high ratio of Gata2/Gata1 expression (1.90) suggests that this cluster contained immature Ery cells. More than 94% of all mouse MEP had Ery RNA signatures. Clusters could be distinguished by gene expression (e.g., Gata1, Klf1, Tfrc) and biological processes (ribosome synthesis and heme-biology processes [p < 4 E-10]). Based on the transcriptional profiles, we determined the most mature erythroid cells in MEP were late BFU-E. To compare the differentiation of Mk and Ery cells, we pooled our LSK, CMP, and MEP data for analysis using the Monocle software package. GMP contained only clusters expressing granulocytic or monocytic genes and were excluded from the analysis. Monocle arranges cells into trajectories based on their transcriptional profiles, with more differentiated cells positioned further from a common node (Xiaojie, 2017, bioRxiv). We found that LSK cells near the node had overlapping lymphoid and Mk transcriptional profiles. Closest to the node, we found 38% of CMP expressed a profile similar to LSK. An additional 45% of CMP formed one trajectory with lymphoid and granulocyte RNA signatures. Another 17% of CMP formed a second trajectory, with cells expressing an Mk signature closest to the node, cells with a mixed Ery/Mk signature further along the trajectory, and MEP cells with Ery-only signatures furthest from the node. To clarify the Mk/Ery divergence, we focused our analysis on the CMP populations expressing Mk RNAs (Figure1). We observed cells in G1/S phase with an immature Mk signature to the left of the node where the trajectories diverge. On the right, cells with immature Mk signatures were nearest the node and cells with a mixed Ery/Mk signature were at the end of the trajectory (upper right; Mk/Ery). Along the second trajectory, rapidly cycling G2/M Mk cells with an early endomitosis-associated RNA signature (e.g., Pf4, Gp1bb, Gp9, and Vwf) were located at the end of the trajectory (lower right; Mk early endomitosis). Our data are consistent with a model in which two waves of Mk differentiation begin in LSK and progresses to CMP. The Mk lineage is divided in CMP, producing cells that begin endomitosis and cells that have an Mk-repressing/Ery-activating cell program that gives rise to the Ery lineage. We conclude that the erythroid lineage is derived from an Mk-like precursor and is the last lineage to be specified in mouse hematopoiesis. Disclosures No relevant conflicts of interest to declare.


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