1
|
Xiong H, Wang Q, Li CC, He A. Single-cell joint profiling of multiple epigenetic proteins and gene transcription. SCIENCE ADVANCES 2024; 10:eadi3664. [PMID: 38170774 PMCID: PMC10796078 DOI: 10.1126/sciadv.adi3664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024]
Abstract
Sculpting the epigenome with a combination of histone modifications and transcription factor occupancy determines gene transcription and cell fate specification. Here, we first develop uCoTarget, utilizing a split-pool barcoding strategy for realizing ultrahigh-throughput single-cell joint profiling of multiple epigenetic proteins. Through extensive optimization for sensitivity and multimodality resolution, we demonstrate that uCoTarget enables simultaneous detection of five histone modifications (H3K27ac, H3K4me3, H3K4me1, H3K36me3, and H3K27me3) in 19,860 single cells. We applied uCoTarget to the in vitro generation of hematopoietic stem/progenitor cells (HSPCs) from human embryonic stem cells, presenting multimodal epigenomic profiles in 26,418 single cells. uCoTarget reveals establishment of pairing of HSPC enhancers (H3K27ac) and promoters (H3K4me3) and RUNX1 engagement priming for H3K27ac activation along the HSPC path. We then develop uCoTargetX, an expansion of uCoTarget to simultaneously measure transcriptome and multiple epigenome targets. Together, our methods enable generalizable, versatile multimodal profiles for reconstructing comprehensive epigenome and transcriptome landscapes and analyzing the regulatory interplay at single-cell level.
Collapse
Affiliation(s)
- Haiqing Xiong
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - Qianhao Wang
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Chen C. Li
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Aibin He
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
- Key laboratory of Carcinogenesis and Translational Research of Ministry of Education of China, Peking University Cancer Hospital & Institute, Peking University, Beijing 100142, China
| |
Collapse
|
2
|
Wang Y, Liu J, Du LY, Wyss JL, Farrell JA, Schier AF. Gene module reconstruction elucidates cellular differentiation processes and the regulatory logic of specialized secretion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.29.573643. [PMID: 38234833 PMCID: PMC10793473 DOI: 10.1101/2023.12.29.573643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
During differentiation, cells become structurally and functionally specialized, but comprehensive views of the underlying remodeling processes are elusive. Here, we leverage scRNA-seq developmental trajectories to reconstruct differentiation using two secretory tissues as a model system - the zebrafish notochord and hatching gland. First, we present an approach to integrate expression and functional similarities for gene module identification, revealing dozens of gene modules representing known and newly associated differentiation processes and their temporal ordering. Second, we focused on the unfolded protein response (UPR) transducer module to study how general versus cell-type specific secretory functions are regulated. By profiling loss- and gain-of-function embryos, we found that the UPR transcription factors creb3l1, creb3l2, and xbp1 are master regulators of a general secretion program. creb3l1/creb3l2 additionally activate an extracellular matrix secretion program, while xbp1 partners with bhlha15 to activate a gland-specific secretion program. Our study offers a multi-source integrated approach for functional gene module identification and illustrates how transcription factors confer general and specialized cellular functions.
Collapse
Affiliation(s)
- Yiqun Wang
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, 02138, USA
- Biozentrum, University of Basel, Basel, 4056, Switzerland
| | - Jialin Liu
- Biozentrum, University of Basel, Basel, 4056, Switzerland
- Allen Discovery Center for Cell Lineage Tracing, University of Washington, Seattle, WA, 98195, USA
| | - Lucia Y. Du
- Biozentrum, University of Basel, Basel, 4056, Switzerland
- Allen Discovery Center for Cell Lineage Tracing, University of Washington, Seattle, WA, 98195, USA
| | - Jannik L. Wyss
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Jeffrey A. Farrell
- Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, 20892, USA
| | - Alexander F. Schier
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, 02138, USA
- Biozentrum, University of Basel, Basel, 4056, Switzerland
- Allen Discovery Center for Cell Lineage Tracing, University of Washington, Seattle, WA, 98195, USA
- Lead contact
| |
Collapse
|
3
|
Kim IS. DNA Barcoding Technology for Lineage Recording and Tracing to Resolve Cell Fate Determination. Cells 2023; 13:27. [PMID: 38201231 PMCID: PMC10778210 DOI: 10.3390/cells13010027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 12/18/2023] [Accepted: 12/20/2023] [Indexed: 01/12/2024] Open
Abstract
In various biological contexts, cells receive signals and stimuli that prompt them to change their current state, leading to transitions into a future state. This change underlies the processes of development, tissue maintenance, immune response, and the pathogenesis of various diseases. Following the path of cells from their initial identity to their current state reveals how cells adapt to their surroundings and undergo transformations to attain adjusted cellular states. DNA-based molecular barcoding technology enables the documentation of a phylogenetic tree and the deterministic events of cell lineages, providing the mechanisms and timing of cell lineage commitment that can either promote homeostasis or lead to cellular dysregulation. This review comprehensively presents recently emerging molecular recording technologies that utilize CRISPR/Cas systems, base editing, recombination, and innate variable sequences in the genome. Detailing their underlying principles, applications, and constraints paves the way for the lineage tracing of every cell within complex biological systems, encompassing the hidden steps and intermediate states of organism development and disease progression.
Collapse
Affiliation(s)
- Ik Soo Kim
- Department of Microbiology, Gachon University College of Medicine, Incheon 21999, Republic of Korea
| |
Collapse
|
4
|
Katkar G, Ghosh P. Macrophage states: there's a method in the madness. Trends Immunol 2023; 44:954-964. [PMID: 37945504 PMCID: PMC11266835 DOI: 10.1016/j.it.2023.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 11/12/2023]
Abstract
Single-cell approaches have shone a spotlight on discrete context-specific tissue macrophage states, deconstructed to their most minute details. Machine-learning (ML) approaches have recently challenged that dogma by revealing a context-agnostic continuum of states shared across tissues. Both approaches agree that 'brake' and 'accelerator' macrophage subpopulations must be balanced to achieve homeostasis. Both approaches also highlight the importance of ensemble fluidity as subpopulations switch between wide ranges of accelerator and brake phenotypes to mount the most optimal wholistic response to any threat. A full comprehension of the rules that govern these brake and accelerator states is a promising avenue because it can help formulate precise macrophage re-education therapeutic strategies that might selectively boost or suppress disease-associated states and phenotypes across various tissues.
Collapse
Affiliation(s)
- Gajanan Katkar
- Department of Cellular and Molecular Medicine, University of California, San Diego, CA, 92093, USA.
| | - Pradipta Ghosh
- Department of Cellular and Molecular Medicine, University of California, San Diego, CA, 92093, USA; Department of Medicine, University of California, San Diego, CA, 92093, USA.
| |
Collapse
|
5
|
Sun J, Lin Y, Ha N, Zhang J, Wang W, Wang X, Bian Q. Single-cell RNA-Seq reveals transcriptional regulatory networks directing the development of mouse maxillary prominence. J Genet Genomics 2023; 50:676-687. [PMID: 36841529 DOI: 10.1016/j.jgg.2023.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 01/15/2023] [Accepted: 02/08/2023] [Indexed: 02/27/2023]
Abstract
During vertebrate embryonic development, neural crest-derived ectomesenchyme within the maxillary prominences undergoes precisely coordinated proliferation and differentiation to give rise to diverse craniofacial structures, such as tooth and palate. However, the transcriptional regulatory networks underpinning such an intricate process have not been fully elucidated. Here, we perform single-cell RNA-Seq to comprehensively characterize the transcriptional dynamics during mouse maxillary development from embryonic day (E) 10.5-E14.5. Our single-cell transcriptome atlas of ∼28,000 cells uncovers mesenchymal cell populations representing distinct differentiating states and reveals their developmental trajectory, suggesting that the segregation of dental from the palatal mesenchyme occurs at E11.5. Moreover, we identify a series of key transcription factors (TFs) associated with mesenchymal fate transitions and deduce the gene regulatory networks directed by these TFs. Collectively, our study provides important resources and insights for achieving a systems-level understanding of craniofacial morphogenesis and abnormality.
Collapse
Affiliation(s)
- Jian Sun
- Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China; National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Shanghai 200011, China
| | - Yijun Lin
- Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China; Shanghai Institute of Precision Medicine, Shanghai 200125, China
| | - Nayoung Ha
- Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China; National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Shanghai 200011, China
| | - Jianfei Zhang
- Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China; National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Shanghai 200011, China
| | - Weiqi Wang
- Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China; National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Shanghai 200011, China
| | - Xudong Wang
- Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China; National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Shanghai 200011, China.
| | - Qian Bian
- Department of Oral and Cranio-Maxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China; Shanghai Institute of Precision Medicine, Shanghai 200125, China; Shanghai Key Laboratory of Reproductive Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
| |
Collapse
|
6
|
Deng Q, Wang S, Huang Z, Lan Q, Lai G, Xu J, Yuan Y, Liu C, Lin X, Feng W, Ma W, Cheng M, Hao S, Duan S, Zheng H, Chen X, Hou Y, Luo Y, Liu L, Liu C. Single-cell chromatin accessibility profiling of cell-state-specific gene regulatory programs during mouse organogenesis. Front Neurosci 2023; 17:1170355. [PMID: 37440917 PMCID: PMC10333525 DOI: 10.3389/fnins.2023.1170355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 06/07/2023] [Indexed: 07/15/2023] Open
Abstract
In mammals, early organogenesis begins soon after gastrulation, accompanied by specification of various type of progenitor/precusor cells. In order to reveal dynamic chromatin landscape of precursor cells and decipher the underlying molecular mechanism driving early mouse organogenesis, we performed single-cell ATAC-seq of E8.5-E10.5 mouse embryos. We profiled a total of 101,599 single cells and identified 41 specific cell types at these stages. Besides, by performing integrated analysis of scATAC-seq and public scRNA-seq data, we identified the critical cis-regulatory elements and key transcription factors which drving development of spinal cord and somitogenesis. Furthermore, we intersected accessible peaks with human diseases/traits-related loci and found potential clinical associated single nucleotide variants (SNPs). Overall, our work provides a fundamental source for understanding cell fate determination and revealing the underlying mechanism during postimplantation embryonic development, and expand our knowledge of pathology for human developmental malformations.
Collapse
Affiliation(s)
- Qiuting Deng
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
- BGI-Hangzhou, Hangzhou, China
| | - Shengpeng Wang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
- BGI-Hangzhou, Hangzhou, China
| | - Zijie Huang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
- BGI-Shenzhen, Shenzhen, China
| | | | | | | | | | | | - Xiumei Lin
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
- BGI-Hangzhou, Hangzhou, China
| | - Weimin Feng
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
- BGI-Hangzhou, Hangzhou, China
| | - Wen Ma
- BGI-Shenzhen, Shenzhen, China
| | | | - Shijie Hao
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
- BGI-Hangzhou, Hangzhou, China
| | - Shanshan Duan
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
- BGI-Hangzhou, Hangzhou, China
| | | | | | - Yong Hou
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
- BGI-Shenzhen, Shenzhen, China
| | | | - Longqi Liu
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
- BGI-Hangzhou, Hangzhou, China
- BGI-Shenzhen, Shenzhen, China
- Shenzhen Bay Laboratory, Shenzhen, China
| | - Chuanyu Liu
- BGI-Shenzhen, Shenzhen, China
- Shenzhen Bay Laboratory, Shenzhen, China
| |
Collapse
|
7
|
Rosales-Alvarez RE, Rettkowski J, Herman JS, Dumbović G, Cabezas-Wallscheid N, Grün D. VarID2 quantifies gene expression noise dynamics and unveils functional heterogeneity of ageing hematopoietic stem cells. Genome Biol 2023; 24:148. [PMID: 37353813 PMCID: PMC10290360 DOI: 10.1186/s13059-023-02974-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 05/18/2023] [Indexed: 06/25/2023] Open
Abstract
Variability of gene expression due to stochasticity of transcription or variation of extrinsic signals, termed biological noise, is a potential driving force of cellular differentiation. Utilizing single-cell RNA-sequencing, we develop VarID2 for the quantification of biological noise at single-cell resolution. VarID2 reveals enhanced nuclear versus cytoplasmic noise, and distinct regulatory modes stratified by correlation between noise, expression, and chromatin accessibility. Noise levels are minimal in murine hematopoietic stem cells (HSCs) and increase during differentiation and ageing. Differential noise identifies myeloid-biased Dlk1+ long-term HSCs in aged mice with enhanced quiescence and self-renewal capacity. VarID2 reveals noise dynamics invisible to conventional single-cell transcriptome analysis.
Collapse
Affiliation(s)
- Reyna Edith Rosales-Alvarez
- Würzburg Institute of Systems Immunology, Max Planck Research Group at the Julius-Maximilians-Universität Würzburg, Würzburg, Germany
- International Max Planck Research School for Immunobiology, Epigenetics, and Metabolism (IMPRS-IEM), Freiburg, Germany
- Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Jasmin Rettkowski
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
- Spemann Graduate School of Biology and Medicine (SGBM), Freiburg, Germany
| | - Josip Stefan Herman
- Würzburg Institute of Systems Immunology, Max Planck Research Group at the Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Gabrijela Dumbović
- Würzburg Institute of Systems Immunology, Max Planck Research Group at the Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Nina Cabezas-Wallscheid
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
- CIBSS-Centre for Integrative Biological Signaling Studies, University of Freiburg, Freiburg, Germany
| | - Dominic Grün
- Würzburg Institute of Systems Immunology, Max Planck Research Group at the Julius-Maximilians-Universität Würzburg, Würzburg, Germany.
- Helmholtz Institute for RNA-Based Infection Research (HIRI), Helmholtz-Center for Infection Research (HZI), Würzburg, Germany.
| |
Collapse
|
8
|
Adler M, Moriel N, Goeva A, Avraham-Davidi I, Mages S, Adams TS, Kaminski N, Macosko EZ, Regev A, Medzhitov R, Nitzan M. Emergence of division of labor in tissues through cell interactions and spatial cues. Cell Rep 2023; 42:112412. [PMID: 37086403 PMCID: PMC10242439 DOI: 10.1016/j.celrep.2023.112412] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 01/26/2023] [Accepted: 04/03/2023] [Indexed: 04/23/2023] Open
Abstract
Most cell types in multicellular organisms can perform multiple functions. However, not all functions can be optimally performed simultaneously by the same cells. Functions incompatible at the level of individual cells can be performed at the cell population level, where cells divide labor and specialize in different functions. Division of labor can arise due to instruction by tissue environment or through self-organization. Here, we develop a computational framework to investigate the contribution of these mechanisms to division of labor within a cell-type population. By optimizing collective cellular task performance under trade-offs, we find that distinguishable expression patterns can emerge from cell-cell interactions versus instructive signals. We propose a method to construct ligand-receptor networks between specialist cells and use it to infer division-of-labor mechanisms from single-cell RNA sequencing (RNA-seq) and spatial transcriptomics data of stromal, epithelial, and immune cells. Our framework can be used to characterize the complexity of cell interactions within tissues.
Collapse
Affiliation(s)
- Miri Adler
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA; Tananbaum Center for Theoretical and Analytical Human Biology, Yale University School of Medicine, New Haven, CT, USA
| | - Noa Moriel
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Aleksandrina Goeva
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Inbal Avraham-Davidi
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Simon Mages
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA; Gene Center and Department of Biochemistry, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Taylor S Adams
- Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Naftali Kaminski
- Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Evan Z Macosko
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA; Massachusetts General Hospital, Department of Psychiatry, Boston, MA, USA
| | - Aviv Regev
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Ruslan Medzhitov
- Tananbaum Center for Theoretical and Analytical Human Biology, Yale University School of Medicine, New Haven, CT, USA; Howard Hughes Medical Institute, Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA.
| | - Mor Nitzan
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel; Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem, Israel; Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel.
| |
Collapse
|
9
|
Li H, Long C, Hong Y, Luo L, Zuo Y. Characterizing Cellular Differentiation Potency and Waddington Landscape via Energy Indicator. RESEARCH (WASHINGTON, D.C.) 2023; 6:0118. [PMID: 37223479 PMCID: PMC10202187 DOI: 10.34133/research.0118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 03/20/2023] [Indexed: 05/25/2023]
Abstract
The precise characterization of cellular differentiation potency remains an open question, which is fundamentally important for deciphering the dynamics mechanism related to cell fate transition. We quantitatively evaluated the differentiation potency of different stem cells based on the Hopfield neural network (HNN). The results emphasized that cellular differentiation potency can be approximated by Hopfield energy values. We then profiled the Waddington energy landscape of embryogenesis and cell reprogramming processes. The energy landscape at single-cell resolution further confirmed that cell fate decision is progressively specified in a continuous process. Moreover, the transition of cells from one steady state to another in embryogenesis and cell reprogramming processes was dynamically simulated on the energy ladder. These two processes can be metaphorized as the motion of descending and ascending ladders, respectively. We further deciphered the dynamics of the gene regulatory network (GRN) for driving cell fate transition. Our study proposes a new energy indicator to quantitatively characterize cellular differentiation potency without prior knowledge, facilitating the further exploration of the potential mechanism of cellular plasticity.
Collapse
Affiliation(s)
- Hanshuang Li
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences,
Inner Mongolia University, Hohhot 010070, China
| | - Chunshen Long
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences,
Inner Mongolia University, Hohhot 010070, China
| | - Yan Hong
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences,
Inner Mongolia University, Hohhot 010070, China
| | - Liaofu Luo
- Department of Physics,
Inner Mongolia University, Hohhot 010070, China
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences,
Inner Mongolia University, Hohhot 010070, China
| |
Collapse
|
10
|
Jiang S, Huang Z, Li Y, Yu C, Yu H, Ke Y, Jiang L, Liu J. Single-cell chromatin accessibility and transcriptome atlas of mouse embryos. Cell Rep 2023; 42:112210. [PMID: 36881507 DOI: 10.1016/j.celrep.2023.112210] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 11/08/2022] [Accepted: 02/16/2023] [Indexed: 03/08/2023] Open
Abstract
Cis-regulatory elements regulate gene expression and lineage specification. However, the potential regulation of cis-elements on mammalian embryogenesis remains largely unexplored. To address this question, we perform single-cell assay for transposase-accessible chromatin using sequencing (ATAC-seq) and RNA-seq in embryonic day 7.5 (E7.5) and E13.5 mouse embryos. We construct the chromatin accessibility landscapes with cell spatial information in E7.5 embryos, showing the spatial patterns of cis-elements and the spatial distribution of potentially functional transcription factors (TFs). We further show that many germ-layer-specific cis-elements and TFs in E7.5 embryos are maintained in the cell types derived from the corresponding germ layers at later stages, suggesting that these cis-elements and TFs are important during cell differentiation. We also find a potential progenitor for Sertoli and granulosa cells in gonads. Interestingly, both Sertoli and granulosa cells exist in male gonads and female gonads during gonad development. Collectively, we provide a valuable resource to understand organogenesis in mammals.
Collapse
Affiliation(s)
- Shan Jiang
- China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Zheng Huang
- China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yun Li
- China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Chengwei Yu
- China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; College of Future Technology College, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hao Yu
- China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; College of Future Technology College, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuwen Ke
- College of Biological Science, China Agricultural University, Beijing 100193, China
| | - Lan Jiang
- China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; College of Future Technology College, University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Jiang Liu
- China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; College of Future Technology College, University of Chinese Academy of Sciences, Beijing 100049, China; CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China.
| |
Collapse
|
11
|
Correlated gene modules uncovered by high-precision single-cell transcriptomics. Proc Natl Acad Sci U S A 2022; 119:e2206938119. [PMID: 36508663 PMCID: PMC9907105 DOI: 10.1073/pnas.2206938119] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Correlations in gene expression are used to infer functional and regulatory relationships between genes. However, correlations are often calculated across different cell types or perturbations, causing genes with unrelated functions to be correlated. Here, we demonstrate that correlated modules can be better captured by measuring correlations of steady-state gene expression fluctuations in single cells. We report a high-precision single-cell RNA-seq method called MALBAC-DT to measure the correlation between any pair of genes in a homogenous cell population. Using this method, we were able to identify numerous cell-type specific and functionally enriched correlated gene modules. We confirmed through knockdown that a module enriched for p53 signaling predicted p53 regulatory targets more accurately than a consensus of ChIP-seq studies and that steady-state correlations were predictive of transcriptome-wide response patterns to perturbations. This approach provides a powerful way to advance our functional understanding of the genome.
Collapse
|
12
|
Doyle JJ. Cell types as species: Exploring a metaphor. FRONTIERS IN PLANT SCIENCE 2022; 13:868565. [PMID: 36072310 PMCID: PMC9444152 DOI: 10.3389/fpls.2022.868565] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 07/29/2022] [Indexed: 06/05/2023]
Abstract
The concept of "cell type," though fundamental to cell biology, is controversial. Cells have historically been classified into types based on morphology, physiology, or location. More recently, single cell transcriptomic studies have revealed fine-scale differences among cells with similar gross phenotypes. Transcriptomic snapshots of cells at various stages of differentiation, and of cells under different physiological conditions, have shown that in many cases variation is more continuous than discrete, raising questions about the relationship between cell type and cell state. Some researchers have rejected the notion of fixed types altogether. Throughout the history of discussions on cell type, cell biologists have compared the problem of defining cell type with the interminable and often contentious debate over the definition of arguably the most important concept in systematics and evolutionary biology, "species." In the last decades, systematics, like cell biology, has been transformed by the increasing availability of molecular data, and the fine-grained resolution of genetic relationships have generated new ideas about how that variation should be classified. There are numerous parallels between the two fields that make exploration of the "cell types as species" metaphor timely. These parallels begin with philosophy, with discussion of both cell types and species as being either individuals, groups, or something in between (e.g., homeostatic property clusters). In each field there are various different types of lineages that form trees or networks that can (and in some cases do) provide criteria for grouping. Developing and refining models for evolutionary divergence of species and for cell type differentiation are parallel goals of the two fields. The goal of this essay is to highlight such parallels with the hope of inspiring biologists in both fields to look for new solutions to similar problems outside of their own field.
Collapse
|
13
|
Gan Y, Guo C, Guo W, Xu G, Zou G. Entropy-based inference of transition states and cellular trajectory for single-cell transcriptomics. Brief Bioinform 2022; 23:6607748. [PMID: 35696651 DOI: 10.1093/bib/bbac225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 05/11/2022] [Accepted: 05/12/2022] [Indexed: 11/12/2022] Open
Abstract
The development of single-cell RNA-seq (scRNA-seq) technology allows researchers to characterize the cell types, states and transitions during dynamic biological processes at single-cell resolution. One of the critical tasks is to infer pseudo-time trajectory. However, the existence of transition cells in the intermediate state of complex biological processes poses a challenge for the trajectory inference. Here, we propose a new single-cell trajectory inference method based on transition entropy, named scTite, to identify transitional states and reconstruct cell trajectory from scRNA-seq data. Taking into account the continuity of cellular processes, we introduce a new metric called transition entropy to measure the uncertainty of a cell belonging to different cell clusters, and then identify cell states and transition cells. Specifically, we adopt different strategies to infer the trajectory for the identified cell states and transition cells, and combine them to obtain a detailed cell trajectory. For the identified cell clusters, we utilize the Wasserstein distance based on the probability distribution to calculate distance between clusters, and construct the minimum spanning tree. Meanwhile, we adopt the signaling entropy and partial correlation coefficient to determine transition paths, which contain a group of transition cells with the largest similarity. Then the transitional paths and the MST are combined to infer a refined cell trajectory. We apply scTite to four real scRNA-seq datasets and an integrated dataset, and conduct extensive performance comparison with nine existing trajectory inference methods. The experimental results demonstrate that the proposed method can reconstruct the cell trajectory more accurately than the compared algorithms. The scTite software package is available at https://github.com/dblab2022/scTite.
Collapse
Affiliation(s)
- Yanglan Gan
- School of Computer Science and Technology, Donghua University, 201600, Shanghai, China
| | - Cheng Guo
- School of Computer Science and Technology, Donghua University, 201600, Shanghai, China
| | - Wenjing Guo
- School of Computer Science and Technology, Donghua University, 201600, Shanghai, China
| | - Guangwei Xu
- School of Computer Science and Technology, Donghua University, 201600, Shanghai, China
| | - Guobing Zou
- School of Computer Science and Technology, Shanghai University, 200444, Shanghai, China
| |
Collapse
|
14
|
Swift J, Greenham K, Ecker JR, Coruzzi GM, McClung CR. The biology of time: dynamic responses of cell types to developmental, circadian and environmental cues. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 109:764-778. [PMID: 34797944 PMCID: PMC9215356 DOI: 10.1111/tpj.15589] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 11/10/2021] [Accepted: 11/15/2021] [Indexed: 05/26/2023]
Abstract
As sessile organisms, plants are finely tuned to respond dynamically to developmental, circadian and environmental cues. Genome-wide studies investigating these types of cues have uncovered the intrinsically different ways they can impact gene expression over time. Recent advances in single-cell sequencing and time-based bioinformatic algorithms are now beginning to reveal the dynamics of these time-based responses within individual cells and plant tissues. Here, we review what these techniques have revealed about the spatiotemporal nature of gene regulation, paying particular attention to the three distinct ways in which plant tissues are time sensitive. (i) First, we discuss how studying plant cell identity can reveal developmental trajectories hidden in pseudotime. (ii) Next, we present evidence that indicates that plant cell types keep their own local time through tissue-specific regulation of the circadian clock. (iii) Finally, we review what determines the speed of environmental signaling responses, and how they can be contingent on developmental and circadian time. By these means, this review sheds light on how these different scales of time-based responses can act with tissue and cell-type specificity to elicit changes in whole plant systems.
Collapse
Affiliation(s)
- Joseph Swift
- Plant Biology Laboratory, The Salk Institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA 92037, USA
| | - Kathleen Greenham
- Department of Plant and Microbial Biology, University of Minnesota, St Paul, MN 55108, USA
| | - Joseph R. Ecker
- Plant Biology Laboratory, The Salk Institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA 92037, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA 92037, USA
| | - Gloria M. Coruzzi
- Department of Biology, Center for Genomics and Systems Biology, New York University, NY, USA
| | | |
Collapse
|
15
|
Mircea M, Semrau S. How a cell decides its own fate: a single-cell view of molecular mechanisms and dynamics of cell-type specification. Biochem Soc Trans 2021; 49:2509-2525. [PMID: 34854897 PMCID: PMC8786291 DOI: 10.1042/bst20210135] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 11/06/2021] [Accepted: 11/08/2021] [Indexed: 12/13/2022]
Abstract
On its path from a fertilized egg to one of the many cell types in a multicellular organism, a cell turns the blank canvas of its early embryonic state into a molecular profile fine-tuned to achieve a vital organismal function. This remarkable transformation emerges from the interplay between dynamically changing external signals, the cell's internal, variable state, and tremendously complex molecular machinery; we are only beginning to understand. Recently developed single-cell omics techniques have started to provide an unprecedented, comprehensive view of the molecular changes during cell-type specification and promise to reveal the underlying gene regulatory mechanism. The exponentially increasing amount of quantitative molecular data being created at the moment is slated to inform predictive, mathematical models. Such models can suggest novel ways to manipulate cell types experimentally, which has important biomedical applications. This review is meant to give the reader a starting point to participate in this exciting phase of molecular developmental biology. We first introduce some of the principal molecular players involved in cell-type specification and discuss the important organizing ability of biomolecular condensates, which has been discovered recently. We then review some of the most important single-cell omics methods and relevant findings they produced. We devote special attention to the dynamics of the molecular changes and discuss methods to measure them, most importantly lineage tracing. Finally, we introduce a conceptual framework that connects all molecular agents in a mathematical model and helps us make sense of the experimental data.
Collapse
Affiliation(s)
- Maria Mircea
- Leiden Institute of Physics, Leiden University, Leiden, The Netherlands
| | - Stefan Semrau
- Leiden Institute of Physics, Leiden University, Leiden, The Netherlands
| |
Collapse
|
16
|
Zhao C, Xiu W, Hua Y, Zhang N, Zhang Y. CStreet: a computed Cell State trajectory inference method for time-series single-cell RNA sequencing data. Bioinformatics 2021; 37:3774-3780. [PMID: 34196686 DOI: 10.1093/bioinformatics/btab488] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 06/24/2021] [Accepted: 06/30/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION The increasing amount of time-series single-cell RNA sequencing (scRNA-seq) data raises the key issue of connecting cell states (i.e., cell clusters or cell types) to obtain the continuous temporal dynamics of transcription, which can highlight the unified biological mechanisms involved in cell state transitions. However, most existing trajectory methods are specifically designed for individual cells, so they can hardly meet the needs of accurately inferring the trajectory topology of the cell state, which usually contains cells assigned to different branches. RESULTS Here, we present CStreet, a computed Cell State trajectory inference method for time-series scRNA-seq data. It uses time-series information to construct the k-nearest neighbors connections between cells within each time point and between adjacent time points. Then, CStreet estimates the connection probabilities of the cell states and visualizes the trajectory, which may include multiple starting points and paths, using a force-directed graph. By comparing the performance of CStreet with that of six commonly used cell state trajectory reconstruction methods on simulated data and real data, we demonstrate the high accuracy and high tolerance of CStreet. AVAILABILITY AND IMPLEMENTATION CStreet is written in Python and freely available on the web at https://github.com/TongjiZhanglab/CStreet and https://doi.org/10.5281/zenodo.4483205. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Chengchen Zhao
- Institute for Regenerative Medicine, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Science and Technology, Tongji University, Shanghai, 200092, China
| | - Wenchao Xiu
- Institute for Regenerative Medicine, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Science and Technology, Tongji University, Shanghai, 200092, China
| | - Yuwei Hua
- Institute for Regenerative Medicine, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Science and Technology, Tongji University, Shanghai, 200092, China
| | - Naiqian Zhang
- School of Mathematics and Statistics, Shandong University at Weihai, Weihai, 264209, China
| | - Yong Zhang
- Institute for Regenerative Medicine, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Science and Technology, Tongji University, Shanghai, 200092, China
| |
Collapse
|
17
|
Westerman EL, Bowman SEJ, Davidson B, Davis MC, Larson ER, Sanford CPJ. Deploying Big Data to Crack the Genotype to Phenotype Code. Integr Comp Biol 2021; 60:385-396. [PMID: 32492136 DOI: 10.1093/icb/icaa055] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Mechanistically connecting genotypes to phenotypes is a longstanding and central mission of biology. Deciphering these connections will unite questions and datasets across all scales from molecules to ecosystems. Although high-throughput sequencing has provided a rich platform on which to launch this effort, tools for deciphering mechanisms further along the genome to phenome pipeline remain limited. Machine learning approaches and other emerging computational tools hold the promise of augmenting human efforts to overcome these obstacles. This vision paper is the result of a Reintegrating Biology Workshop, bringing together the perspectives of integrative and comparative biologists to survey challenges and opportunities in cracking the genotype to phenotype code and thereby generating predictive frameworks across biological scales. Key recommendations include promoting the development of minimum "best practices" for the experimental design and collection of data; fostering sustained and long-term data repositories; promoting programs that recruit, train, and retain a diversity of talent; and providing funding to effectively support these highly cross-disciplinary efforts. We follow this discussion by highlighting a few specific transformative research opportunities that will be advanced by these efforts.
Collapse
Affiliation(s)
- Erica L Westerman
- Department of Biological Sciences, University of Arkansas, Fayetteville, AR 72701, USA
| | - Sarah E J Bowman
- High-Throughput Crystallization Screening Center, Hauptman-Woodward Medical Research Institute, Buffalo, NY 14203, USA.,Department of Biochemistry, Jacobs School of Medicine & Biomedical Sciences at the University at Buffalo, Buffalo, NY 14203, USA
| | - Bradley Davidson
- Department of Biology, Swarthmore College, Swarthmore, PA 19081, USA
| | - Marcus C Davis
- Department of Biology, James Madison University, Harrisonburg, VA 22807, USA
| | - Eric R Larson
- Department of Natural Resources and Environmental Sciences, University of Illinois, Urbana, IL 61801, USA
| | - Christopher P J Sanford
- Department of Ecology, Evolution and Organismal Biology, Kennesaw State University, Kennesaw, GA 30144, USA
| |
Collapse
|
18
|
Chen Y, Song J, Ruan Q, Zeng X, Wu L, Cai L, Wang X, Yang C. Single-Cell Sequencing Methodologies: From Transcriptome to Multi-Dimensional Measurement. SMALL METHODS 2021; 5:e2100111. [PMID: 34927917 DOI: 10.1002/smtd.202100111] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/26/2021] [Indexed: 06/14/2023]
Abstract
Cells are the basic building blocks of biological systems, with inherent unique molecular features and development trajectories. The study of single cells facilitates in-depth understanding of cellular diversity, disease processes, and organization of multicellular organisms. Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for the interrogation of gene expression patterns and the dynamics of single cells, allowing cellular heterogeneity to be dissected at unprecedented resolution. Nevertheless, measuring at only transcriptome level or 1D is incomplete; the cellular heterogeneity reflects in multiple dimensions, including the genome, epigenome, transcriptome, spatial, and even temporal dimensions. Hence, integrative single cell analysis is highly desired. In addition, the way to interpret sequencing data by virtue of bioinformatic tools also exerts critical roles in revealing differential gene expression. Here, a comprehensive review that summarizes the cutting-edge single-cell transcriptome sequencing methodologies, including scRNA-seq, spatial and temporal transcriptome profiling, multi-omics sequencing and computational methods developed for scRNA-seq data analysis is provided. Finally, the challenges and perspectives of this field are discussed.
Collapse
Affiliation(s)
- Yingwen Chen
- The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, The Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Jia Song
- Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Qingyu Ruan
- The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, The Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Xi Zeng
- The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, The Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Lingling Wu
- Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Linfeng Cai
- The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, The Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Xuanqun Wang
- The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, The Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Chaoyong Yang
- The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, The Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
- Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| |
Collapse
|
19
|
Lopez-Anido CB, Vatén A, Smoot NK, Sharma N, Guo V, Gong Y, Anleu Gil MX, Weimer AK, Bergmann DC. Single-cell resolution of lineage trajectories in the Arabidopsis stomatal lineage and developing leaf. Dev Cell 2021; 56:1043-1055.e4. [PMID: 33823130 DOI: 10.1101/2020.09.08.288498] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 01/19/2021] [Accepted: 03/09/2021] [Indexed: 05/22/2023]
Abstract
Dynamic cell identities underlie flexible developmental programs. The stomatal lineage in the Arabidopsis leaf epidermis features asynchronous and indeterminate divisions that can be modulated by environmental cues. The products of the lineage, stomatal guard cells and pavement cells, regulate plant-atmosphere exchanges, and the epidermis as a whole influences overall leaf growth. How flexibility is encoded in development of the stomatal lineage and how cell fates are coordinated in the leaf are open questions. Here, by leveraging single-cell transcriptomics and molecular genetics, we uncovered models of cell differentiation within Arabidopsis leaf tissue. Profiles across leaf tissues identified points of regulatory congruence. In the stomatal lineage, single-cell resolution resolved underlying cell heterogeneity within early stages and provided a fine-grained profile of guard cell differentiation. Through integration of genome-scale datasets and spatiotemporally precise functional manipulations, we also identified an extended role for the transcriptional regulator SPEECHLESS in reinforcing cell fate commitment.
Collapse
Affiliation(s)
- Camila B Lopez-Anido
- Department of Biology, Stanford University, Stanford, CA 94305-5020, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305-5020, USA
| | - Anne Vatén
- Department of Biology, Stanford University, Stanford, CA 94305-5020, USA
| | - Nicole K Smoot
- Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305-5020, USA
| | - Nidhi Sharma
- Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305-5020, USA
| | - Victoria Guo
- Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305-5020, USA
| | - Yan Gong
- Department of Biology, Stanford University, Stanford, CA 94305-5020, USA
| | - M Ximena Anleu Gil
- Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305-5020, USA
| | - Annika K Weimer
- Department of Biology, Stanford University, Stanford, CA 94305-5020, USA
| | - Dominique C Bergmann
- Department of Biology, Stanford University, Stanford, CA 94305-5020, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305-5020, USA.
| |
Collapse
|
20
|
Acosta J, Ssozi D, van Galen P. Single-Cell RNA Sequencing to Disentangle the Blood System. Arterioscler Thromb Vasc Biol 2021; 41:1012-1018. [PMID: 33441024 PMCID: PMC7901535 DOI: 10.1161/atvbaha.120.314654] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The blood system is often represented as a tree-like structure with stem cells that give rise to mature blood cell types through a series of demarcated steps. Although this representation has served as a model of hierarchical tissue organization for decades, single-cell technologies are shedding new light on the abundance of cell type intermediates and the molecular mechanisms that ensure balanced replenishment of differentiated cells. In this Brief Review, we exemplify new insights into blood cell differentiation generated by single-cell RNA sequencing, summarize considerations for the application of this technology, and highlight innovations that are leading the way to understand hematopoiesis at the resolution of single cells. Graphic Abstract: A graphic abstract is available for this article.
Collapse
Affiliation(s)
- Jean Acosta
- Division of Hematology, Brigham and Women's Hospital, Boston, MA. Department of Medicine, Harvard Medical School, Boston, MA. Broad Institute of MIT and Harvard, Cambridge, MA
| | - Daniel Ssozi
- Division of Hematology, Brigham and Women's Hospital, Boston, MA. Department of Medicine, Harvard Medical School, Boston, MA. Broad Institute of MIT and Harvard, Cambridge, MA
| | - Peter van Galen
- Division of Hematology, Brigham and Women's Hospital, Boston, MA. Department of Medicine, Harvard Medical School, Boston, MA. Broad Institute of MIT and Harvard, Cambridge, MA
| |
Collapse
|
21
|
Romitti M, Eski SE, Fonseca BF, Gillotay P, Singh SP, Costagliola S. Single-Cell Trajectory Inference Guided Enhancement of Thyroid Maturation In Vitro Using TGF-Beta Inhibition. Front Endocrinol (Lausanne) 2021; 12:657195. [PMID: 34135860 PMCID: PMC8202408 DOI: 10.3389/fendo.2021.657195] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 05/10/2021] [Indexed: 12/30/2022] Open
Abstract
The thyroid gland regulates metabolism and growth via secretion of thyroid hormones by thyroid follicular cells (TFCs). Loss of TFCs, by cellular dysfunction, autoimmune destruction or surgical resection, underlies hypothyroidism. Recovery of thyroid hormone levels by transplantation of mature TFCs derived from stem cells in vitro holds great therapeutic promise. However, the utilization of in vitro derived tissue for regenerative medicine is restricted by the efficiency of differentiation protocols to generate mature organoids. Here, to improve the differentiation efficiency for thyroid organoids, we utilized single-cell RNA-Seq to chart the molecular steps undertaken by individual cells during the in vitro transformation of mouse embryonic stem cells to TFCs. Our single-cell atlas of mouse organoid systematically and comprehensively identifies, for the first time, the cell types generated during production of thyroid organoids. Using pseudotime analysis, we identify TGF-beta as a negative regulator of thyroid maturation in vitro. Using pharmacological inhibition of TGF-beta pathway, we improve the level of thyroid maturation, in particular the induction of Nis expression. This in turn, leads to an enhancement of iodide organification in vitro, suggesting functional improvement of the thyroid organoid. Our study highlights the potential of single-cell molecular characterization in understanding and improving thyroid maturation and paves the way for identification of therapeutic targets against thyroid disorders.
Collapse
Affiliation(s)
- Mírian Romitti
- *Correspondence: Mírian Romitti, ; Sumeet Pal Singh, ; Sabine Costagliola,
| | | | | | | | - Sumeet Pal Singh
- *Correspondence: Mírian Romitti, ; Sumeet Pal Singh, ; Sabine Costagliola,
| | - Sabine Costagliola
- *Correspondence: Mírian Romitti, ; Sumeet Pal Singh, ; Sabine Costagliola,
| |
Collapse
|
22
|
Savulescu AF, Jacobs C, Negishi Y, Davignon L, Mhlanga MM. Pinpointing Cell Identity in Time and Space. Front Mol Biosci 2020; 7:209. [PMID: 32923457 PMCID: PMC7456825 DOI: 10.3389/fmolb.2020.00209] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 07/30/2020] [Indexed: 01/15/2023] Open
Abstract
Mammalian cells display a broad spectrum of phenotypes, morphologies, and functional niches within biological systems. Our understanding of mechanisms at the individual cellular level, and how cells function in concert to form tissues, organs and systems, has been greatly facilitated by centuries of extensive work to classify and characterize cell types. Classic histological approaches are now complemented with advanced single-cell sequencing and spatial transcriptomics for cell identity studies. Emerging data suggests that additional levels of information should be considered, including the subcellular spatial distribution of molecules such as RNA and protein, when classifying cells. In this Perspective piece we describe the importance of integrating cell transcriptional state with tissue and subcellular spatial and temporal information for thorough characterization of cell type and state. We refer to recent studies making use of single cell RNA-seq and/or image-based cell characterization, which highlight a need for such in-depth characterization of cell populations. We also describe the advances required in experimental, imaging and analytical methods to address these questions. This Perspective concludes by framing this argument in the context of projects such as the Human Cell Atlas, and related fields of cancer research and developmental biology.
Collapse
Affiliation(s)
- Anca F. Savulescu
- Division of Chemical, Systems & Synthetic Biology, Faculty of Health Sciences, Institute of Infectious Disease & Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Caron Jacobs
- Division of Chemical, Systems & Synthetic Biology, Faculty of Health Sciences, Institute of Infectious Disease & Molecular Medicine, University of Cape Town, Cape Town, South Africa
- SAMRC/NHLS/UCT Molecular Mycobacteriology Research Unit, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
- Wellcome Centre for Infectious Diseases Research in Africa, University of Cape Town, Cape Town, South Africa
| | - Yutaka Negishi
- Division of Chemical, Systems & Synthetic Biology, Faculty of Health Sciences, Institute of Infectious Disease & Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Laurianne Davignon
- Division of Chemical, Systems & Synthetic Biology, Faculty of Health Sciences, Institute of Infectious Disease & Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Musa M. Mhlanga
- Division of Chemical, Systems & Synthetic Biology, Faculty of Health Sciences, Institute of Infectious Disease & Molecular Medicine, University of Cape Town, Cape Town, South Africa
- Wellcome Centre for Infectious Diseases Research in Africa, University of Cape Town, Cape Town, South Africa
- Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal
| |
Collapse
|