Construction of a human cell landscape at single-cell level

Single-cell analysis is a valuable tool for dissecting cellular heterogeneity in complex systems 1 . However, a comprehensive single-cell atlas has not been achieved for humans. Here we use single-cell mRNA sequencing to determine the cell-type composition of all major human organs and construct a scheme for the human cell landscape (HCL). We have uncovered a single-cell hierarchy for many tissues that have not been well characterized. We established a ‘single-cell HCL analysis’ pipeline that helps to define human cell identity. Finally, we performed a single-cell comparative analysis of landscapes from human and mouse to identify conserved genetic networks. We found that stem and progenitor cells exhibit strong transcriptomic stochasticity, whereas differentiated cells are more distinct. Our results provide a useful resource for the study of human biology. Single-cell RNA sequencing is used to generate a dataset covering all major human organs in both adult and fetal stages, enabling comparison with similar datasets for mouse tissues.

[1]  P. Kharchenko,et al.  Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain , 2017, Nature Biotechnology.

[2]  H. Binder,et al.  Multilineage communication regulates human liver bud development from pluripotency , 2017, Nature.

[3]  J. Junker,et al.  Simultaneous lineage tracing and cell-type identification using CRISPR/Cas9-induced genetic scars , 2018, Nature Biotechnology.

[4]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[5]  Guo-Cheng Yuan,et al.  Revealing the Critical Regulators of Cell Identity in the Mouse Cell Atlas , 2018, bioRxiv.

[6]  James T. Webber,et al.  Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris , 2018, Nature.

[7]  Geoffrey J Maher,et al.  The adult human testis transcriptional cell atlas , 2018, Cell Research.

[8]  F. Tang,et al.  Single-Cell RNA Sequencing Analysis Reveals Sequential Cell Fate Transition during Human Spermatogenesis. , 2018, Cell stem cell.

[9]  J. Aerts,et al.  SCENIC: Single-cell regulatory network inference and clustering , 2017, Nature Methods.

[10]  A. Regev,et al.  Spatial reconstruction of single-cell gene expression , 2015, Nature Biotechnology.

[11]  A. Tanay,et al.  Cnidarian Cell Type Diversity and Regulation Revealed by Whole-Organism Single-Cell RNA-Seq , 2018, Cell.

[12]  Tracy M. Yamawaki,et al.  Evolution of pallium, hippocampus, and cortical cell types revealed by single-cell transcriptomics in reptiles , 2018, Science.

[13]  Fabian J Theis,et al.  Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics , 2018, Science.

[14]  N. Neff,et al.  Reconstructing lineage hierarchies of the distal lung epithelium using single cell RNA-seq , 2014, Nature.

[15]  R. Sandberg,et al.  Full-Length mRNA-Seq from single cell levels of RNA and individual circulating tumor cells , 2012, Nature Biotechnology.

[16]  Thomas R. Gingeras,et al.  STAR: ultrafast universal RNA-seq aligner , 2013, Bioinform..

[17]  Lu Wen,et al.  Tracing the temporal-spatial transcriptome landscapes of the human fetal digestive tract using single-cell RNA-sequencing , 2018, Nature Cell Biology.

[18]  N. Hacohen,et al.  Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors , 2017, Science.

[19]  F. Hamey,et al.  Heterogeneity of human lympho-myeloid progenitors at the single cell level , 2017, Nature Immunology.

[20]  Evan Z. Macosko,et al.  Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets , 2015, Cell.

[21]  Marcel H. Schulz,et al.  Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction , 2016, bioRxiv.

[22]  Lars E. Borm,et al.  Molecular Diversity of Midbrain Development in Mouse, Human, and Stem Cells , 2016, Cell.

[23]  Allan R. Jones,et al.  Conserved cell types with divergent features in human versus mouse cortex , 2019, Nature.

[24]  Mauro J. Muraro,et al.  A Single-Cell Transcriptome Atlas of the Human Pancreas , 2016, Cell systems.

[25]  Guo-Cheng Yuan,et al.  Mapping human pluripotent stem cell differentiation pathways using high throughput single-cell RNA-sequencing , 2018, Genome Biology.

[26]  Peiyong Jiang,et al.  Integrative single-cell and cell-free plasma RNA transcriptomics elucidates placental cellular dynamics , 2017, Proceedings of the National Academy of Sciences.

[27]  J. Thomson,et al.  Human embryonic stem cell-derived CD34+ cells: efficient production in the coculture with OP9 stromal cells and analysis of lymphohematopoietic potential. , 2005, Blood.

[28]  Allon M. Klein,et al.  A single cell atlas of the tracheal epithelium reveals the CFTR-rich pulmonary ionocyte , 2018, Nature.

[29]  Allon M. Klein,et al.  Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells , 2015, Cell.

[30]  R. Stewart,et al.  Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm , 2016, Genome Biology.

[31]  Dominic Grün,et al.  A Human Liver Cell Atlas reveals Heterogeneity and Epithelial Progenitors , 2019, Nature.

[32]  S. Orkin,et al.  Mapping the Mouse Cell Atlas by Microwell-Seq , 2018, Cell.

[33]  Samuel L. Wolock,et al.  A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. , 2016, Cell systems.

[34]  Madeline A. Lancaster,et al.  Human cerebral organoids recapitulate gene expression programs of fetal neocortex development , 2015, Proceedings of the National Academy of Sciences.

[35]  James D. Johnson,et al.  Reversal of diabetes with insulin-producing cells derived in vitro from human pluripotent stem cells , 2014, Nature Biotechnology.

[36]  Allon M. Klein,et al.  Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo , 2018, Science.

[37]  Catalin C. Barbacioru,et al.  mRNA-Seq whole-transcriptome analysis of a single cell , 2009, Nature Methods.

[38]  Principal Investigators,et al.  Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris , 2018 .

[39]  Rona S. Gertner,et al.  Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells , 2013, Nature.

[40]  A. Regev,et al.  Scaling single-cell genomics from phenomenology to mechanism , 2017, Nature.

[41]  A. Regev,et al.  Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis , 2018, Science.

[42]  Berthold Göttgens,et al.  A single-cell molecular map of mouse gastrulation and early organogenesis , 2019, Nature.

[43]  Mauro J. Muraro,et al.  De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data , 2016, Cell stem cell.

[44]  C. Myers,et al.  Using networks to measure similarity between genes: association index selection , 2013, Nature Methods.

[45]  M. Guo,et al.  SLICE: determining cell differentiation and lineage based on single cell entropy , 2016, Nucleic acids research.

[46]  Kay Elder,et al.  Defining the three cell lineages of the human blastocyst by single-cell RNA-seq , 2015, Development.

[47]  Roland Eils,et al.  circlize implements and enhances circular visualization in R , 2014, Bioinform..

[48]  Fabian J Theis,et al.  SCANPY: large-scale single-cell gene expression data analysis , 2018, Genome Biology.

[49]  M. Gerstein,et al.  Unlocking the secrets of the genome , 2009, Nature.

[50]  Omri Wurtzel,et al.  Cell type transcriptome atlas for the planarian Schmidtea mediterranea , 2018, Science.

[51]  Ruiqiang Li,et al.  Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells , 2013, Nature Structural &Molecular Biology.

[52]  Rong Li,et al.  Single-Cell RNA-Seq Analysis Maps Development of Human Germline Cells and Gonadal Niche Interactions. , 2017, Cell stem cell.

[53]  Fabian J Theis,et al.  Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells , 2015, Nature Biotechnology.

[54]  Sarah A. Teichmann,et al.  Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors , 2018, Science.

[55]  Andrew J. Hill,et al.  The single cell transcriptional landscape of mammalian organogenesis , 2019, Nature.

[56]  Jie Qiao,et al.  A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex , 2018, Nature.

[57]  Hans Clevers,et al.  Intra-tumour diversification in colorectal cancer at the single-cell level , 2018, Nature.

[58]  Tal Galili,et al.  dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering , 2015, Bioinform..

[59]  Fabian J Theis,et al.  PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells , 2019, Genome Biology.

[60]  Seema A. Khan,et al.  Profiling human breast epithelial cells using single cell RNA sequencing identifies cell diversity , 2018, Nature Communications.

[61]  L. Steinmetz,et al.  Human haematopoietic stem cell lineage commitment is a continuous process , 2017, Nature Cell Biology.

[62]  Kerstin B. Meyer,et al.  Single-cell reconstruction of the early maternal–fetal interface in humans , 2018, Nature.

[63]  Geoffrey J Maher,et al.  Chromatin and Single-Cell RNA-Seq Profiling Reveal Dynamic Signaling and Metabolic Transitions during Human Spermatogonial Stem Cell Development , 2017, Cell stem cell.

[64]  Sara Ballouz,et al.  Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor , 2018, Nature Communications.

[65]  Andrew C. Adey,et al.  Single-Cell Transcriptional Profiling of a Multicellular Organism , 2017 .

[66]  Erik Sundström,et al.  RNA velocity of single cells , 2018, Nature.

[67]  D. M. Smith,et al.  Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes , 2016, Cell metabolism.