基于生物信息学在单细胞维度解析人胚胎早期发育阶段造血系统的发生及其调控机制

doi:10.3877/cma.j.issn.2095-1221.2022.05.002

目的

解析人胚胎早期发育阶段造血系统的异质性及其潜在调控机制。

方法

利用生物信息学方法对早期人胚胎样本来源的单细胞转录组测序数据进行深度分析，包括复杂数据的降维分析、不同细胞亚群的鉴定及其相关性、非造血细胞对造血系统的潜在调控作用以及造血系统本身的发育路径和调控机制的解析；数据可视化由R编程语言、多种R包和Python包完成。

结果

人早期发育阶段存在多种细胞类型，并且早在卡内基分期（CS）7阶段就已经产生了造血系统；其他细胞类型可通过受配体的介导发挥对造血系统的潜在调控作用；造血的发生伴随着内皮细胞特征的弱化及各种特征性血细胞功能的增强；基于单细胞转录组数据，揭示了人胚胎发育早期造血系统的异质性，并鉴定出多种可能对特定细胞类型发挥作用的调控因子。

结论

人胚胎早期发育阶段的造血系统存在异质性，造血系统的发生可能受到其他细胞类型的调控，不同类型的血细胞在人胚胎发育早期已经存在对特定谱系具有特定功能的调控因子。

关键词: 造血系统, 单细胞转录组测序, 胚胎发育, 生物信息学, 造血调控

Objective

To decode the heterogeneity of human hematopoietic system in early development of human embryo and its potential regulatory mechanisms.

Methods

Bioinformatic methods were used to explore the single-cell transcriptome sequencing data of the earliest human embryo samples so far, including dimensional reduction analysis, the identification and correlation analysis of different cell subsets, the potential regulation of non-hematopoietic cells on hematopoietic system as well as the development pathway and the regulation mechanism of hematopoietic system itself; data visualization is conducted by R programming language and various R and Python packages.

Results

Many cell types were detected in early human embryonic development, and the hematopoietic system could be was traced as early as CS7 stage; non-hematopoietic cells played potential regulatory roles in hematopoietic system through the receptors-ligands; the development of hematopoiesis was accompanied by the attenuation of endothelial cell characteristics and the enhancement of the functions of specific blood cell types; based on this single-cell transcriptome dataset, the heterogeneity of hematopoietic system in the early stage of human embryonic development was revealed and a variety of regulatory factors that plays a role in specific cell types were identified.

Conclusions

Heterogeneitiesof the hematopoietic system in the early human embryo development were identified, and the hematopoietic system may be regulated by other cell types. Potential regulatory factors of specific blood celltypes in this early stage were revealed.

Key words: Haematopoiesis, Single-cell RNA sequencing, Embryonic development, Bioinformatics analysis, Regulation of haematopoiesis

图1 人原肠胚阶段单细胞图谱注：a图为CS7人原肠胚阶段细胞组成及异质性分析；b图为各细胞亚群相关性分析；c图为基于细胞亚群的拓扑结构分析；d图为细胞周期分析及组成

图2 非造血细胞对造血发育的互作分析注：a ~ b图为非造血细胞对造血细胞（生血内皮祖细胞和有核红细胞）的互作分析示意图，线的粗细代表富集受配体对的数量；c ~ d图为非造血细胞对生血内皮祖细胞和有核红细胞显著富集的受配体对，点的大小代表显著性，热图内部颜色代表分子表达水平，热图上部不同颜色圆点代表细胞类型

图3 造血发育的拟时序分析及伴随的生物学过程注：a图为造血发育的拟时序分析，箭头代表分化发育的方向；b图为拟时序分析路径上造血各细胞亚群的分布；c图为造血发育过程中相关基因的动态改变，根据其变化特征分为4个模块，颜色代表显著变化基因的相对表达高低；d图为4个模块基因富集的特征性生物学过程。点的大小代表该生物学过程富集到的基因比例，颜色代表其统计学显著性

图4 造血发育转录调控网络的构建注：a图为各血细胞亚群潜在调控因子活性的热图，颜色代表调控因子活性的高低；b图为有核红细胞转录调控网络的构建，线的粗细代表互作关系强弱；c图为有核红细胞富集转录因子下游靶基因富集的生物学过程

1	Cumano A, Godin I. Ontogeny of the hematopoietic system[J]. Annu Rev Immunol, 2007, 25:745-785.
2	Julien E, El Omar R, Tavian M. Origin of the hematopoietic system in the human embryo[J]. FEBS Lett, 2016, 590(22):3987-4001.
3	Orkin SH. Development of the hematopoietic system[J]. Curr Opin Genet Dev, 1996, 6(5):597-602.
4	Wang X, Yang L, Wang YC, et al. Comparative analysis of cell lineage differentiation during hepatogenesis in humans and mice at the single-cell transcriptome level[J]. Cell Res, 2020, 30(12):1109-1126.
5	Popescu D, Botting RA, Stephenson E, et al. Decoding human fetal liver haematopoiesis[J]. Nature, 2019, 574(7778):365-371.
6	Zeng Y, He J, Bai Z, et al. Tracing the first hematopoietic stem cell generation in human embryo by single-cell RNA sequencing[J]. Cell Res, 2019, 29(11):881-894.
7	Bian Z, Gong Y, Huang T, et al. Deciphering human macrophage development at single-cell resolution[J]. Nature, 2020, 582(7813):571-576.
8	Wang H, He J, Xu C, et al. Decoding human megakaryocyte development[J]. Cell Stem Cell, 2021, 28(3):535-549.e8.
9	Tyser RCV, Mahammadov E, Nakanoh S, et al. Single-cell transcriptomic characterization of a gastrulating human embryo[J]. Nature, 2021, 600(7888):285-289.
10	Picelli S, Faridani OR, Bjorklund AK, et al. Full-length RNA-seq from single cells using Smart-seq2[J]. Nat Protoc, 2014, 9(1):171-181.
11	Satija R, Farrell JA, Gennert D, et al. Spatial reconstruction of single-cell gene expression data[J]. Nat Biotechnol, 2015, 33(5):495-502.
12	Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis[J]. Genome Biol, 2018, 19(1):15. doi: 10.1186/s13059-017-1382-0.
13	Wolf FA, Hamey FK, Plass M, et al. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells[J]. Genome Biol, 2019, 20(1):59. doi: 10.1186/s13059-019-1663-x.
14	Efremova M, Vento-Tormo M, Teichmann SA, et al. CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes[J]. Nat Protoc, 2020, 15(4):1484-1506.
15	Trapnell C, Cacchiarelli D, Grimsby J, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells[J]. Nat Biotechnol, 2014, 32(4):381-386.
16	Wu T, Hu E, Xu S, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data[J]. Innovation (Camb), 2021, 2(3):100141.doi:10.1016/j.xinn.2021.100141.
17	Van de Sande B, Flerin C, Davie K, et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis[J]. Nat Protoc, 2020, 15(7):2247-2276.
18	Otasek D, Morris JH, Boucas J, et al. Cytoscape automation: empowering workflow-based network analysis[J]. Genome Biol, 2019, 20(1):185. doi: 10.1186/s13059-019-1758-4.
19	Yokomizo T, Dzierzak E. Three-dimensional cartography of hematopoietic clusters in the vasculature of whole mouse embryos[J]. Development, 2010, 137(21):3651-3661.
20	Kingsley PD, Greenfest-Allen E, Frame JM, et al. Ontogeny of erythroid gene expression[J]. Blood, 2013, 121(6):e5-e13.
21	Palis J. Hematopoietic stem cell-independent hematopoiesis: emergence of erythroid, megakaryocyte, and myeloid potential in the mammalian embryo[J]. FEBS Lett, 2016, 590(22):3965-3974.
22	Dzierzak E, Bigas A. Blood development: hematopoietic stem cell dependence and independence[J]. Cell Stem Cell, 2018, 22(5):639-651.
23	Tang F, Barbacioru C, Wang Y, et al. mRNA-Seq whole-transcriptome analysis of a single cell[J]. Nat Methods, 2009, 6(5):377-382.
24	Psaila B, Mead AJ. Single-cell approaches reveal novel cellular pathways for megakaryocyte and erythroid differentiation[J]. Blood, 2019, 133(13):1427-1435.
25	Watcham S, Kucinski I, Gottgens B. New insights into hematopoietic differentiation landscapes from single-cell RNA sequencing[J]. Blood, 2019, 133(13):1415-1426.
26	Cermenati S, Moleri S, Cimbro S, et al. Sox18 and Sox7 play redundant roles in vascular development[J]. Blood, 2008, 111(5):2657-2666.
27	Duong T, Koltowska K, Pichol-Thievend C, et al. VEGFD regulates blood vascular development by modulating SOX18 activity[J]. Blood, 2014, 123(7):1102-1112.
28	Moustaqil M, Fontaine F, Overman J, et al. Homodimerization regulates an endothelial specific signature of the SOX18 transcription factor[J]. Nucleic Acids Res, 2018, 46(21):11381-11395.
29	Duncan MT, Shin S, Wu JJ, et al. Dynamic transcription factor activity profiles reveal key regulatory interactions during megakaryocytic and erythroid differentiation[J]. Biotechnol Bioeng, 2014, 111(10):2082-2094.
30	Chen J, Zhou Q, Liu M, et al. FAM122A inhibits erythroid differentiation through GATA1[J]. Stem Cell Reports, 2020, 15(3):721-734.
31	Yan B, Yang J, Kim MY, et al. HDAC1 is required for GATA-1 transcription activity, global chromatin occupancy and hematopoiesis[J]. Nucleic Acids Res, 2021, 49(17):9783-9798.
32	Xue L, Galdass M, Gnanapragasam MN, et al. Extrinsic and intrinsic control by EKLF (KLF1) within a specialized erythroid niche[J]. Development, 2014, 141(11):2245-2254.
33	Abdel Ghani L, Yusenko MV, Frank D, et al. A synthetic covalent ligand of the C/EBPbeta transactivation domain inhibits acute myeloid leukemia cells[J]. Cancer Lett, 2022, 530:170-180.
34	Assouvie A, Rotival M, Hamroune J, et al. A genetic variant controls interferon-beta gene expression in human myeloid cells by preventing C/EBP-beta binding on a conserved enhancer[J]. PLoS Genet, 2020, 16(11):e1009090. doi: 10.1371/journal.pgen.1009090.
35	Johnson KD, Conn DJ, Shishkova E, et al. Constructing and deconstructing GATA2-regulated cell fate programs to establish developmental trajectories[J]. J Exp Med, 2020, 217(11):e20191526. doi: 10.1084/jem.20191526.
36	Zwifelhofer NM, Cai X, Liao R, et al. GATA factor-regulated solute carrier ensemble reveals a nucleoside transporter-dependent differentiation mechanism[J]. PLoS Genet, 2020, 16(12):e1009286. doi: 10.1371/journal.pgen.1009286.

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Dissecting the haematopoiesis and its regulatory mechanisms of the human early embryo in single cell resolution based on bioinformatic analysis

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Dissecting the haematopoiesis and its regulatory mechanisms of the human early embryo in single cell resolution based on bioinformatic analysis