scholarly journals Comparative Transcriptomic Analysis of the Hematopoietic System between Human and Mouse by Single Cell RNA Sequencing

Cells ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 973
Author(s):  
Shouguo Gao ◽  
Zhijie Wu ◽  
Jeerthi Kannan ◽  
Liza Mathews ◽  
Xingmin Feng ◽  
...  
Keyword(s):  
Single Cell ◽  
Gene Networks ◽  
Single Cells ◽  
Cell Types ◽  
Regulatory Elements ◽  
Regulatory Sequence ◽  
Sequence Motifs ◽  
Hematopoietic Stem ◽  
Stem And Progenitor Cells ◽  
Human And Mouse

(1) Background: mouse models are fundamental to the study of hematopoiesis, but comparisons between mouse and human in single cells have been limited in depth. (2) Methods: we constructed a single-cell resolution transcriptomic atlas of hematopoietic stem and progenitor cells (HSPCs) of human and mouse, from a total of 32,805 single cells. We used Monocle to examine the trajectories of hematopoietic differentiation, and SCENIC to analyze gene networks underlying hematopoiesis. (3) Results: After alignment with Seurat 2, the cells of mouse and human could be separated by same cell type categories. Cells were grouped into 17 subpopulations; cluster-specific genes were species-conserved and shared functional themes. The clustering dendrogram indicated that cell types were highly conserved between human and mouse. A visualization of the Monocle results provided an intuitive representation of HSPC differentiation to three dominant branches (Erythroid/megakaryocytic, Myeloid, and Lymphoid), derived directly from the hematopoietic stem cell and the long-term hematopoietic stem cells in both human and mouse. Gene regulation was similarly conserved, reflected by comparable transcriptional factors and regulatory sequence motifs in subpopulations of cells. (4) Conclusions: our analysis has confirmed evolutionary conservation in the hematopoietic systems of mouse and human, extending to cell types, gene expression and regulatory elements.

scholarly journals Simultaneous tracking of division and differentiation from individual hematopoietic stem and progenitor cells reveals within-family homogeneity despite population heterogeneity

2019 ◽  
Author(s):  
Tamar Tak ◽  
Giulio Prevedello ◽  
Gaël Simon ◽  
Noémie Paillon ◽  
Ken R. Duffy ◽  
...  
Keyword(s):  
Progenitor Cells ◽  
High Throughput ◽  
Common Ancestor ◽  
Single Cells ◽  
Population Level ◽  
Cell Types ◽  
Culture Conditions ◽  
Population Heterogeneity ◽  
Hematopoietic Stem ◽  
Stem And Progenitor Cells

AbstractThe advent of high throughput single cell methods such as scRNA-seq has uncovered substantial heterogeneity in the pool of hematopoietic stem and progenitor cells (HSPCs). A significant issue is how to reconcile those findings with the standard model of hematopoietic development, and a fundamental question is how much instruction is inherited by offspring from their ancestors. To address this, we further developed a high-throughput method that enables simultaneously determination of common ancestor, generation, and differentiation status of a large collection of single cells. Data from it revealed that while there is substantial population-level heterogeneity, cells that derived from a common ancestor were highly concordant in their division progression and share similar differentiation outcomes, revealing significant familial effects on both division and differentiation. Although each family diversifies to some extent, the overall collection of cell types observed in a population is largely composed of homogeneous families from heterogeneous ancestors. Heterogeneity between families could be explained, in part, by differences in ancestral expression of cell-surface markers that are used for phenotypic HSPC identification: CD48, SCA-1, c-kit and Flt3. These data call for a revision of the fundamental model of haematopoiesis from a single tree to an ensemble of trees from distinct ancestors where common ancestor effect must be considered. As HSPCs are cultured in the clinic before bone marrow transplantation, our results suggest that the broad range of engraftment and proliferation capacities of HSPCs could be consequences of the heterogeneity in their engrafted families, and altered culture conditions might reduce heterogeneity between families, possibly improving transplantation outcomes.

scholarly journals Single-cell regulatory landscape and disease vulnerability map of adult Macaque cortex

2020 ◽  
Author(s):  
Ying Lei ◽  
Mengnan Cheng ◽  
Zihao Li ◽  
Zhenkun Zhuang ◽  
Liang Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Primary Motor Cortex ◽  
Neurological Diseases ◽  
Single Cells ◽  
Cell Types ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Nucleotide Polymorphisms ◽  
Cell Type

Non-human primates (NHP) provide a unique opportunity to study human neurological diseases, yet detailed characterization of the cell types and transcriptional regulatory features in the NHP brain is lacking. We applied a combinatorial indexing assay, sci-ATAC-seq, as well as single-nuclei RNA-seq, to profile chromatin accessibility in 43,793 single cells and transcriptomics in 11,477 cells, respectively, from prefrontal cortex, primary motor cortex and the primary visual cortex of adult cynomolgus monkey Macaca fascularis. Integrative analysis of these two datasets, resolved regulatory elements and transcription factors that specify cell type distinctions, and discovered area-specific diversity in chromatin accessibility and gene expression within excitatory neurons. We also constructed the dynamic landscape of chromatin accessibility and gene expression of oligodendrocyte maturation to characterize adult remyelination. Furthermore, we identified cell type-specific enrichment of differentially spliced gene isoforms and disease-associated single nucleotide polymorphisms. Our datasets permit integrative exploration of complex regulatory dynamics in macaque brain tissue at single-cell resolution.

scholarly journals Comparative Single Cell Transcriptomic Analysis of the Hematopoietic System between Human and Mouse

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 3706-3706
Author(s):  
Shouguo Gao ◽  
Zhijie Wu ◽  
Xingmin Feng ◽  
Jichun Chen ◽  
Sachiko Kajigaya ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Single Cell ◽  
Species Conservation ◽  
Cell Types ◽  
Human Cells ◽  
Transcriptomic Analysis ◽  
Cell Type ◽  
Orthologous Genes ◽  
Hematopoietic Stem ◽  
Human And Mouse

Single cell RNA-seq has been extensively applied to the hematopoiesis of human and mouse, but the cross-species comparison still has not been thoroughly studied. Here, using the 10X single cell platform and a canonical correlation analysis (CCA, https://satijalab.org/seurat/) computational strategy, we conducted comparative transcriptomic analysis of the hematopoietic hierarchy between human and mouse. We found that the hematopoietic stem and progenitor cell (HSPC) compartments in the two species were composed of subpopulations characterized by the same set of homologous genes, and the hematopoietic lineages and transcriptional profiling in hematopoiesis were well conserved between human and mouse, indicating evolutionary similarity in the organization of hematopoietic systems. A total of 32,805 cells derived from human bone marrow CD3-CD14-CD19-CD34+ and lineage-CD117+ cell from bone marrow of C57BL/6 mice were used to construct a single-cell resolution transcriptomic atlas of HSPCs of human and mouse. We retained only orthologous genes of human and mouse collected in InParanoid (http://inparanoid.sbc.su.se). Based on known marker genes, we grouped human cells as hematopoietic stem cell (HSC), multilymphoid progenitor (MLP), granulocyte-monocyte progenitor (GMP), Pro-B cell (ProB), earliest thymic progenitor (ETP), and megakaryocytic-erythroid progenitor (MEP); and mouse cells as long-term HSCs (LTHSC), lymphoid multipotent progenitors (LMPP), multipotent progenitor (MPP), GMP, MEP, and common myeloid progenitors (CMP). tSNE plots showed that cells were preferentially clustered by species, rather than cell type, due to species specificity and batch effects (Fig A, mouse and human cells were profiled at different time). After alignment with CCA, the cells of mouse and human were well mixed and separated by same cell type categories (Fig B-C). The cells were grouped into 17 subpopulations by computational analysis (Fig C). The cluster specific genes were species conserved and share same functional themes. To obtain a detailed view on the cellular evolution from mouse to human in the HSPC system, we used mouse and human orthologous genes, and calculated an average of expression of cells in each population of human and mouse. A hierarchical cluster dendrogram indicated that cell types were highly conserved between human and mouse (Fig D). For example, MEP and GMP of mouse and human shared a very similar transcriptome pattern. Human HSC was firstly clustered with mouse LTHSC and then with mouse MPP. Further, MPP and LMPP in mouse had similar transcriptomes, which was observed in human already. We used monocle to examine the differentiation trajectory of hematopoiesis in human and mouse, and we defined the HSC and LTHSC as roots so that they located at starting points of the differentiation hierarchy. On graphic representation after application of Monocle, for both mouse and human cells three distinct branches emerged from HSC and LTHSC: Erythroid/Megakaryocytic, Myeloid, and Lymphoid (Fig E-F). We also examined levels of gene expression. As examples, for both mouse and human, Gata1 and Cd79a expression increased along the Erythroid branch and Lymphoid branch, respectively, while Procr expression decreased with differentiation. To understand the species conservation of cell populations of hematopoiesis in human and mouse, scmap was utilized to project human dataset on mouse and inferring cellular identity with the cell types defined in mouse. Most human MEP cells (85%) were mapped to mouse MEP cell types based on transcriptional similarity, suggesting likely functional similarity and species conservation. Further, 45% and 24% human HSC cells were mapped to mouse LTHSC and MPP cell types, respectively, indicating similarity of MPP and HSC (Fig G-H). Our analysis confirms evolutionary conservation between mouse and human hematopoietic systems. Figure Disclosures No relevant conflicts of interest to declare.

scholarly journals Mapping Human Hematopoietic Hierarchy at Single Cell Resolution by Microwell-seq

2017 ◽  
Author(s):  
Shujing Lai ◽  
Yang Xu ◽  
Wentao Huang ◽  
Mengmeng Jiang ◽  
Haide Chen ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Low Cost ◽  
Single Cells ◽  
Cell Types ◽  
Hematopoietic Stem ◽  
Adult Human ◽  
Type Definition ◽  
Cellular Hierarchy ◽  
Differentiation Pathways

SummaryThe classical hematopoietic hierarchy, which is mainly built with fluorescence-activated cell sorting (FACS) technology, proves to be inaccurate in recent studies. Single cell RNA-seq (scRNA-seq) analysis provides a solution to overcome the limit of FACS-based cell type definition system for the dissection of complex cellular hierarchy. However, large-scale scRNA-seq is constrained by the throughput and cost of traditional methods. Here, we developed Microwell-seq, a high-throughput and low-cost scRNA-seq platform using extremely simple devices. Using Microwell-seq, we constructed a single-cell resolution transcriptome atlas of human hematopoietic differentiation hierarchy by profiling more than 50,000 single cells throughout adult human hematopoietic system. We found that adult human hematopoietic stem and progenitor cell (HSPC) compartment is dominated by progenitors primed with lineage specific regulators. Our analysis revealed differentiation pathways for each cell types, through which HSPCs directly progress to lineage biased progenitors before differentiation. We propose a revised adult human hematopoietic hierarchy independent of oligopotent progenitors. Our study also demonstrates the broad applicability of Microwell-seq technology.

scholarly journals Shared regulation and functional relevance of local gene co-expression revealed by single cell analysis

2021 ◽  
Author(s):  
Diogo M Ribeiro ◽  
Chaymae Ziyani ◽  
Olivier Delaneau
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Regulatory Element ◽  
Single Cells ◽  
Cell Types ◽  
Regulatory Elements ◽  
Rna Seq ◽  
Regulatory Architecture ◽  
Gene Enhancer ◽  
Functional Relevance

Most human genes are co-expressed with a nearby gene. Yet, previous studies only reported this extensive local gene co-expression using bulk RNA-seq. Here, we leverage single cell datasets in >85 individuals to identify gene co-expression across cells, unbiased by cell type heterogeneity and benefiting from the co-occurrence of transcription events in single cells. We discover thousands of co-expressed genes in two cell types and (i) compare single cell to bulk RNA-seq in identifying local gene co-expression, (ii) show that many co-expressed genes – but not the majority – are composed of functionally-related genes and (iii) provide evidence that these genes are transcribed synchronously and their co-expression is maintained up to the protein level. Finally, we identify gene-enhancer associations using multimodal single cell data, which reveal that >95% of co-expressed gene pairs share regulatory elements. Our in-depth view of local gene co-expression and regulatory element co-activity advances our understanding of the shared regulatory architecture between genes.

scholarly journals A Meta-Analytic Single-Cell Atlas of Mouse Bone Marrow Hematopoietic Development

2021 ◽  
Author(s):  
Benjamin D Harris ◽  
John Lee ◽  
Jesse Gillis
Keyword(s):  
Bone Marrow ◽  
Single Cell ◽  
Progenitor Cells ◽  
Cell Types ◽  
Marker Genes ◽  
Mouse Bone Marrow ◽  
Clear Understanding ◽  
Hematopoietic Stem ◽  
Hematopoietic Development ◽  
Stem And Progenitor Cells

The clinical importance of the hematopoietic system makes it one of the most heavily studied lineages in all of biology. A clear understanding of the cell types and functional programs during hematopoietic development is central to research in aging, cancer, and infectious diseases. Known cell types are traditionally identified by the expression of proteins on the surface of the cells. Stem and progenitor cells defined based on these markers are assigned functions based on their lineage potential. The rapid growth of single cell RNA sequencing technologies (scRNAseq) provides a new modality for evaluating the cellular and functional landscape of hematopoietic stem and progenitor cells. The popularity of this technology among hematopoiesis researchers enables us to conduct a robust meta-analysis of mouse bone marrow scRNAseq data. Using over 300,000 cells across 12 datasets, we evaluate the classification and function of cell types based on discrete clustering, in silico FACS sorting, and a continuous trajectory. We identify replicable signatures that define cell types based on genes and known cellular functions. Additionally, we evaluate the conservation of signatures associated with erythroid and monocyte lineage development across species using co-expression networks. The co-expression networks predict the effectiveness of the signature at identifying erythroid and monocyte cells in zebrafish and human scRNAseq data. Together, this analysis provides a robust reference, particularly marker genes and functional annotations, for future experiments in hematopoietic development.

scholarly journals Combined single-cell gene and isoform expression analysis in haematopoietic stem and progenitor cells

2020 ◽  
Author(s):  
Laura Mincarelli ◽  
Vladimir Uzun ◽  
Stuart A. Rushworth ◽  
Wilfried Haerty ◽  
Iain C. Macaulay
Keyword(s):  
Single Cell ◽  
Progenitor Cells ◽  
Cell Types ◽  
Cell Phenotype ◽  
Hematopoietic Stem ◽  
Long Read ◽  
Stem And Progenitor Cells ◽  
Alternatively Spliced ◽  
Isoform Expression

AbstractSingle-cell RNA sequencing (scRNA-seq) enables gene expression profiling and characterization of novel cell types within heterogeneous cell populations. However, most approaches cannot detect alternatively spliced transcripts, which can profoundly shape cell phenotype by generating functionally distinct proteins from the same gene. Here, we integrate short- and long-read scRNA-seq of hematopoietic stem and progenitor cells to characterize changes in cell type abundance, gene and isoform expression during differentiation and ageing.

scholarly journals Predicting transcription factor binding in single cells through deep learning

2020 ◽  
Vol 6 (51) ◽  
pp. eaba9031
Author(s):  
Laiyi Fu ◽  
Lihua Zhang ◽  
Emmanuel Dollinger ◽  
Qinke Peng ◽  
Qing Nie ◽  
...  
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Sequence Motifs ◽  
Genome Wide ◽  
Chromatin Immunoprecipitation Sequencing ◽  
Deep Learning Model ◽  
Accessible Chromatin

Characterizing genome-wide binding profiles of transcription factors (TFs) is essential for understanding biological processes. Although techniques have been developed to assess binding profiles within a population of cells, determining them at a single-cell level remains elusive. Here, we report scFAN (single-cell factor analysis network), a deep learning model that predicts genome-wide TF binding profiles in individual cells. scFAN is pretrained on genome-wide bulk assay for transposase-accessible chromatin sequencing (ATAC-seq), DNA sequence, and chromatin immunoprecipitation sequencing (ChIP-seq) data and uses single-cell ATAC-seq to predict TF binding in individual cells. We demonstrate the efficacy of scFAN by both studying sequence motifs enriched within predicted binding peaks and using predicted TFs for discovering cell types. We develop a new metric “TF activity score” to characterize each cell and show that activity scores can reliably capture cell identities. scFAN allows us to discover and study cellular identities and heterogeneity based on chromatin accessibility profiles.

scholarly journals HSPCs display within-family homogeneity in differentiation and proliferation despite population heterogeneity

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Tamar Tak ◽  
Giulio Prevedello ◽  
Gaël Simon ◽  
Noémie Paillon ◽  
Camélia Benlabiod ◽  
...  
Keyword(s):  
Common Ancestor ◽  
Single Cells ◽  
Cell Types ◽  
Population Heterogeneity ◽  
Hematopoietic Stem ◽  
Stem And Progenitor Cells ◽  
Fate Decision ◽  
Differentiation Status ◽  
Differentiation And Proliferation ◽  
Substantial Heterogeneity

High-throughput single cell methods have uncovered substantial heterogeneity in the pool of hematopoietic stem and progenitor cells (HSPCs), but how much instruction is inherited by offspring from their heterogeneous ancestors remains unanswered. Using a method that enables simultaneous determination of common ancestor, division number, and differentiation status of a large collection of single cells, our data revealed that murine cells that derived from a common ancestor had significant similarities in their division progression and differentiation outcomes. Although each family diversifies, the overall collection of cell types observed is composed of homogeneous families. Heterogeneity between families could be explained, in part, by differences in ancestral expression of cell-surface markers. Our analyses demonstrate that fate decision by cells are largely inherited from ancestor cells, indicating the importance of common ancestor effects. These results may have ramifications for bone marrow transplantation and leukemia, where substantial heterogeneity in HSPC behavior is observed.

scholarly journals Single-cell phospho-specific flow cytometric analysis demonstrates biochemical and functional heterogeneity in human hematopoietic stem and progenitor compartments

Blood ◽  
2011 ◽  
Vol 117 (16) ◽  
pp. 4226-4233 ◽  
Author(s):  
Kenneth D. Gibbs ◽  
Penney M. Gilbert ◽  
Karen Sachs ◽  
Feifei Zhao ◽  
Helen M. Blau ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Flow Cytometric Analysis ◽  
Low Frequency ◽  
Specific Flow ◽  
Hematopoietic Stem ◽  
Stem And Progenitor Cells ◽  
Functional Consequences ◽  
Regulatory Effects

Abstract The low frequency of hematopoietic stem and progenitor cells (HSPCs) in human BM has precluded analysis of the direct biochemical effects elicited by cytokines in these populations, and their functional consequences. Here, single-cell phospho-specific flow cytometry was used to define the signaling networks active in 5 previously defined human HSPC subsets. This analysis revealed that the currently defined HSC compartment is composed of biochemically distinct subsets with the ability to respond rapidly and directly in vitro to a broader array of cytokines than previously appreciated, including G-CSF. The G-CSF response was physiologically relevant—driving cell-cycle entry and increased proliferation in a subset of single cells within the HSC compartment. The heterogeneity in the single-cell signaling and proliferation responses prompted subfractionation of the adult BM HSC compartment by expression of CD114 (G-CSF receptor). Xenotransplantation assays revealed that HSC activity is significantly enriched in the CD114neg/lo compartment, and almost completely absent in the CD114pos subfraction. The single-cell analyses used here can be adapted for further refinement of HSPC surface immunophenotypes, and for examining the direct regulatory effects of other factors on the homeostasis of stem and progenitor populations in normal or diseased states.

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