201
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Almeida N, Chung MWH, Drudi EM, Engquist EN, Hamrud E, Isaacson A, Tsang VSK, Watt FM, Spagnoli FM. Employing core regulatory circuits to define cell identity. EMBO J 2021; 40:e106785. [PMID: 33934382 PMCID: PMC8126924 DOI: 10.15252/embj.2020106785] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 02/03/2021] [Accepted: 02/04/2021] [Indexed: 12/12/2022] Open
Abstract
The interplay between extrinsic signaling and downstream gene networks controls the establishment of cell identity during development and its maintenance in adult life. Advances in next-generation sequencing and single-cell technologies have revealed additional layers of complexity in cell identity. Here, we review our current understanding of transcription factor (TF) networks as key determinants of cell identity. We discuss the concept of the core regulatory circuit as a set of TFs and interacting factors that together define the gene expression profile of the cell. We propose the core regulatory circuit as a comprehensive conceptual framework for defining cellular identity and discuss its connections to cell function in different contexts.
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Affiliation(s)
- Nathalia Almeida
- Centre for Stem Cells and Regenerative MedicineGuy’s HospitalKing’s College LondonLondonUK
| | - Matthew W H Chung
- Centre for Stem Cells and Regenerative MedicineGuy’s HospitalKing’s College LondonLondonUK
| | - Elena M Drudi
- Centre for Stem Cells and Regenerative MedicineGuy’s HospitalKing’s College LondonLondonUK
| | - Elise N Engquist
- Centre for Stem Cells and Regenerative MedicineGuy’s HospitalKing’s College LondonLondonUK
| | - Eva Hamrud
- Centre for Stem Cells and Regenerative MedicineGuy’s HospitalKing’s College LondonLondonUK
| | - Abigail Isaacson
- Centre for Stem Cells and Regenerative MedicineGuy’s HospitalKing’s College LondonLondonUK
| | - Victoria S K Tsang
- Centre for Stem Cells and Regenerative MedicineGuy’s HospitalKing’s College LondonLondonUK
| | - Fiona M Watt
- Centre for Stem Cells and Regenerative MedicineGuy’s HospitalKing’s College LondonLondonUK
| | - Francesca M Spagnoli
- Centre for Stem Cells and Regenerative MedicineGuy’s HospitalKing’s College LondonLondonUK
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202
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Li L, Xiong F, Wang Y, Zhang S, Gong Z, Li X, He Y, Shi L, Wang F, Liao Q, Xiang B, Zhou M, Li X, Li Y, Li G, Zeng Z, Xiong W, Guo C. What are the applications of single-cell RNA sequencing in cancer research: a systematic review. JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH : CR 2021; 40:163. [PMID: 33975628 PMCID: PMC8111731 DOI: 10.1186/s13046-021-01955-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 04/20/2021] [Indexed: 12/18/2022]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a tool for studying gene expression at the single-cell level that has been widely used due to its unprecedented high resolution. In the present review, we outline the preparation process and sequencing platforms for the scRNA-seq analysis of solid tumor specimens and discuss the main steps and methods used during data analysis, including quality control, batch-effect correction, normalization, cell cycle phase assignment, clustering, cell trajectory and pseudo-time reconstruction, differential expression analysis and gene set enrichment analysis, as well as gene regulatory network inference. Traditional bulk RNA sequencing does not address the heterogeneity within and between tumors, and since the development of the first scRNA-seq technique, this approach has been widely used in cancer research to better understand cancer cell biology and pathogenetic mechanisms. ScRNA-seq has been of great significance for the development of targeted therapy and immunotherapy. In the second part of this review, we focus on the application of scRNA-seq in solid tumors, and summarize the findings and achievements in tumor research afforded by its use. ScRNA-seq holds promise for improving our understanding of the molecular characteristics of cancer, and potentially contributing to improved diagnosis, prognosis, and therapeutics.
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Affiliation(s)
- Lvyuan Li
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
| | - Fang Xiong
- Department of Stomatology, Xiangya Hospital, Central South University, Changsha, China
| | - Yumin Wang
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China.,Department of Stomatology, Xiangya Hospital, Central South University, Changsha, China
| | - Shanshan Zhang
- Department of Stomatology, Xiangya Hospital, Central South University, Changsha, China
| | - Zhaojian Gong
- Department of Oral and Maxillofacial Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Xiayu Li
- Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Disease Genome Research Center, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Yi He
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Lei Shi
- Department of Oral and Maxillofacial Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Fuyan Wang
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
| | - Qianjin Liao
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Bo Xiang
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
| | - Ming Zhou
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
| | - Xiaoling Li
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
| | - Yong Li
- Department of Medicine, Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Guiyuan Li
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
| | - Zhaoyang Zeng
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China
| | - Wei Xiong
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China. .,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China.
| | - Can Guo
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China. .,Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, China.
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203
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Abstract
With the increasingly accumulated bio-data, dynamics-based data-science has been progressing as an efficient way to reveal mechanisms of dynamical biological processes. We review three applications on detecting the tipping-points of diseases, quantifying cell's potency, and predicting time-series, to show the importance of dynamics-based data-science.
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Affiliation(s)
- Jifan Shi
- International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Japan
| | - Kazuyuki Aihara
- International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Japan
| | - Luonan Chen
- Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, China
- School of Life Science and Technology, ShanghaiTech University, China
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, China
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204
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Dani N, Herbst RH, McCabe C, Green GS, Kaiser K, Head JP, Cui J, Shipley FB, Jang A, Dionne D, Nguyen L, Rodman C, Riesenfeld SJ, Prochazka J, Prochazkova M, Sedlacek R, Zhang F, Bryja V, Rozenblatt-Rosen O, Habib N, Regev A, Lehtinen MK. A cellular and spatial map of the choroid plexus across brain ventricles and ages. Cell 2021; 184:3056-3074.e21. [PMID: 33932339 DOI: 10.1016/j.cell.2021.04.003] [Citation(s) in RCA: 142] [Impact Index Per Article: 47.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 12/15/2020] [Accepted: 04/02/2021] [Indexed: 02/06/2023]
Abstract
The choroid plexus (ChP) in each brain ventricle produces cerebrospinal fluid (CSF) and forms the blood-CSF barrier. Here, we construct a single-cell and spatial atlas of each ChP in the developing, adult, and aged mouse brain. We delineate diverse cell types, subtypes, cell states, and expression programs in epithelial and mesenchymal cells across ages and ventricles. In the developing ChP, we predict a common progenitor pool for epithelial and neuronal cells, validated by lineage tracing. Epithelial and fibroblast cells show regionalized expression by ventricle, starting at embryonic stages and persisting with age, with a dramatic transcriptional shift with maturation, and a smaller shift in each aged cell type. With aging, epithelial cells upregulate host-defense programs, and resident macrophages upregulate interleukin-1β (IL-1β) signaling genes. Our atlas reveals cellular diversity, architecture and signaling across ventricles during development, maturation, and aging of the ChP-brain barrier.
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Affiliation(s)
- Neil Dani
- Department of Pathology, Boston Children's Hospital, Boston, MA 02115, USA
| | - Rebecca H Herbst
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Cristin McCabe
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Gilad S Green
- Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Karol Kaiser
- Department of Experimental Biology, Faculty of Science, Masaryk University, Brno 611 37, Czech Republic
| | - Joshua P Head
- Department of Pathology, Boston Children's Hospital, Boston, MA 02115, USA
| | - Jin Cui
- Department of Pathology, Boston Children's Hospital, Boston, MA 02115, USA
| | - Frederick B Shipley
- Department of Pathology, Boston Children's Hospital, Boston, MA 02115, USA; Graduate Program in Biophysics, Harvard University, Cambridge, MA 02115, USA
| | - Ahram Jang
- Department of Pathology, Boston Children's Hospital, Boston, MA 02115, USA
| | - Danielle Dionne
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Lan Nguyen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Christopher Rodman
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Samantha J Riesenfeld
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jan Prochazka
- Czech Centre for Phenogenomics and Laboratory of Transgenic Models of Diseases, Institute of Molecular Genetics of the CAS, Prague 142 20, Czech Republic
| | - Michaela Prochazkova
- Czech Centre for Phenogenomics and Laboratory of Transgenic Models of Diseases, Institute of Molecular Genetics of the CAS, Prague 142 20, Czech Republic
| | - Radislav Sedlacek
- Czech Centre for Phenogenomics and Laboratory of Transgenic Models of Diseases, Institute of Molecular Genetics of the CAS, Prague 142 20, Czech Republic
| | - Feng Zhang
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Vitezslav Bryja
- Department of Experimental Biology, Faculty of Science, Masaryk University, Brno 611 37, Czech Republic
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Naomi Habib
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem 9190401, Israel.
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA; Koch Institute of Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
| | - Maria K Lehtinen
- Department of Pathology, Boston Children's Hospital, Boston, MA 02115, USA; Graduate Program in Biophysics, Harvard University, Cambridge, MA 02115, USA.
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205
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A single-embryo, single-cell time-resolved model for mouse gastrulation. Cell 2021; 184:2825-2842.e22. [PMID: 33932341 PMCID: PMC8162424 DOI: 10.1016/j.cell.2021.04.004] [Citation(s) in RCA: 96] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 01/26/2021] [Accepted: 04/05/2021] [Indexed: 12/11/2022]
Abstract
Mouse embryonic development is a canonical model system for studying mammalian cell fate acquisition. Recently, single-cell atlases comprehensively charted embryonic transcriptional landscapes, yet inference of the coordinated dynamics of cells over such atlases remains challenging. Here, we introduce a temporal model for mouse gastrulation, consisting of data from 153 individually sampled embryos spanning 36 h of molecular diversification. Using algorithms and precise timing, we infer differentiation flows and lineage specification dynamics over the embryonic transcriptional manifold. Rapid transcriptional bifurcations characterize the commitment of early specialized node and blood cells. However, for most lineages, we observe combinatorial multi-furcation dynamics rather than hierarchical transcriptional transitions. In the mesoderm, dozens of transcription factors combinatorially regulate multifurcations, as we exemplify using time-matched chimeric embryos of Foxc1/Foxc2 mutants. Our study rejects the notion of differentiation being governed by a series of binary choices, providing an alternative quantitative model for cell fate acquisition.
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206
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Basil MC, Katzen J, Engler AE, Guo M, Herriges MJ, Kathiriya JJ, Windmueller R, Ysasi AB, Zacharias WJ, Chapman HA, Kotton DN, Rock JR, Snoeck HW, Vunjak-Novakovic G, Whitsett JA, Morrisey EE. The Cellular and Physiological Basis for Lung Repair and Regeneration: Past, Present, and Future. Cell Stem Cell 2021; 26:482-502. [PMID: 32243808 PMCID: PMC7128675 DOI: 10.1016/j.stem.2020.03.009] [Citation(s) in RCA: 206] [Impact Index Per Article: 68.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The respiratory system, which includes the trachea, airways, and distal alveoli, is a complex multi-cellular organ that intimately links with the cardiovascular system to accomplish gas exchange. In this review and as members of the NIH/NHLBI-supported Progenitor Cell Translational Consortium, we discuss key aspects of lung repair and regeneration. We focus on the cellular compositions within functional niches, cell-cell signaling in homeostatic health, the responses to injury, and new methods to study lung repair and regeneration. We also provide future directions for an improved understanding of the cell biology of the respiratory system, as well as new therapeutic avenues.
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Affiliation(s)
- Maria C Basil
- Department of Medicine, Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jeremy Katzen
- Department of Medicine, Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Anna E Engler
- Center for Regenerative Medicine of Boston University and Boston Medical Center, The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA 02215, USA
| | - Minzhe Guo
- Division of Pulmonary Biology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH 45229, USA
| | - Michael J Herriges
- Center for Regenerative Medicine of Boston University and Boston Medical Center, The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA 02215, USA
| | - Jaymin J Kathiriya
- Division of Pulmonary Medicine, Department of Medicine, University of California-San Francisco, San Francisco, CA 94143, USA
| | - Rebecca Windmueller
- Department of Medicine, Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alexandra B Ysasi
- Center for Regenerative Medicine of Boston University and Boston Medical Center, The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA 02215, USA
| | - William J Zacharias
- Division of Pulmonary Biology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH 45229, USA
| | - Hal A Chapman
- Division of Pulmonary Medicine, Department of Medicine, University of California-San Francisco, San Francisco, CA 94143, USA
| | - Darrell N Kotton
- Center for Regenerative Medicine of Boston University and Boston Medical Center, The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA 02215, USA
| | - Jason R Rock
- Center for Regenerative Medicine of Boston University and Boston Medical Center, The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA 02215, USA
| | - Hans-Willem Snoeck
- Center for Human Development, Department of Medicine, Columbia University, New York, NY 10027, USA
| | - Gordana Vunjak-Novakovic
- Departments of Biomedical Engineering and Medicine, Columbia University, New York, NY 10027, USA
| | - Jeffrey A Whitsett
- Center for Regenerative Medicine of Boston University and Boston Medical Center, The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA 02215, USA
| | - Edward E Morrisey
- Department of Medicine, Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA 19104, USA.
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207
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Teschendorff AE, Feinberg AP. Statistical mechanics meets single-cell biology. Nat Rev Genet 2021; 22:459-476. [PMID: 33875884 PMCID: PMC10152720 DOI: 10.1038/s41576-021-00341-z] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/18/2021] [Indexed: 02/07/2023]
Abstract
Single-cell omics is transforming our understanding of cell biology and disease, yet the systems-level analysis and interpretation of single-cell data faces many challenges. In this Perspective, we describe the impact that fundamental concepts from statistical mechanics, notably entropy, stochastic processes and critical phenomena, are having on single-cell data analysis. We further advocate the need for more bottom-up modelling of single-cell data and to embrace a statistical mechanics analysis paradigm to help attain a deeper understanding of single-cell systems biology.
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Affiliation(s)
- Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China. .,UCL Cancer Institute, University College London, London, UK.
| | - Andrew P Feinberg
- Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.,Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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208
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Williams KL, Garza LA. Diverse cellular players orchestrate regeneration after wounding. Exp Dermatol 2021; 30:605-612. [PMID: 33251597 PMCID: PMC8059097 DOI: 10.1111/exd.14248] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/30/2020] [Accepted: 11/19/2020] [Indexed: 12/11/2022]
Abstract
Fibrosis is one of the largest sources of human morbidity. The skin is a complex organ where interplay between diverse cell types and signalling pathways is essential both in homeostasis and wound repair, which can result in fibrosis or regeneration. This makes skin a useful model to study fibrosis and regeneration. While fibrosis often occurs postinjury, both clinical and laboratory observations suggest skin regeneration, complete with reconstituted cell diversity and de novo hair follicles, is possible. Extensive research performed in pursuit of skin regeneration has elucidated the key players, both cellular and molecular. Interestingly, some cells known for their homeostatic function are not implicated in regeneration or wound-induced hair neogenesis (WIHN), suggesting regeneration harnesses separate functional pathways from embryogenesis or other non-homeostatic mechanisms. For example, classic bulge cells, noted for their role in normally cycling hair follicles, do not finally contribute to long-lived cells in the regenerated tissue. During healing, multiple populations of cells, among them specific epithelial lineages, mesenchymal cells, and immune cells promote regenerative outcomes in the wounded skin. Ultimately, targeting specific populations of cells will be essential in manipulating a postwound environment to favour regeneration in lieu of fibrosis.
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Affiliation(s)
- Kaitlin L Williams
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Luis A Garza
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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209
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210
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Demaree B, Delley CL, Vasudevan HN, Peretz CAC, Ruff D, Smith CC, Abate AR. Joint profiling of DNA and proteins in single cells to dissect genotype-phenotype associations in leukemia. Nat Commun 2021; 12:1583. [PMID: 33707421 PMCID: PMC7952600 DOI: 10.1038/s41467-021-21810-3] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 02/11/2021] [Indexed: 12/13/2022] Open
Abstract
Studies of acute myeloid leukemia rely on DNA sequencing and immunophenotyping by flow cytometry as primary tools for disease characterization. However, leukemia tumor heterogeneity complicates integration of DNA variants and immunophenotypes from separate measurements. Here we introduce DAb-seq, a technology for simultaneous capture of DNA genotype and cell surface phenotype from single cells at high throughput, enabling direct profiling of proteogenomic states in tens of thousands of cells. To demonstrate the approach, we analyze the disease of three patients with leukemia over multiple treatment timepoints and disease recurrences. We observe complex genotype-phenotype dynamics that illustrate the subtlety of the disease process and the degree of incongruity between blast cell genotype and phenotype in different clinical scenarios. Our results highlight the importance of combined single-cell DNA and protein measurements to fully characterize the heterogeneity of leukemia.
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Affiliation(s)
- Benjamin Demaree
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, San Francisco, CA, USA
| | - Cyrille L Delley
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Harish N Vasudevan
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
| | - Cheryl A C Peretz
- Division of Hematology/Oncology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Children's Hospital and Research Center Oakland, Oakland, CA, USA
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
| | - David Ruff
- Mission Bio, Inc., South San Francisco, CA, USA
| | - Catherine C Smith
- Division of Hematology/Oncology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Adam R Abate
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
- UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, San Francisco, CA, USA.
- Chan Zuckerberg Biohub, San Francisco, CA, USA.
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211
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Revealing lineage-related signals in single-cell gene expression using random matrix theory. Proc Natl Acad Sci U S A 2021; 118:1913931118. [PMID: 33836557 PMCID: PMC7980374 DOI: 10.1073/pnas.1913931118] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Gene expression profiles of a cellular population, generated by single-cell RNA sequencing, contains rich information about biological state, including cell type, cell cycle phase, gene regulatory patterns, and location within the tissue of origin. A major challenge is to disentangle information about these different biological states from each other, including distinguishing from cell lineage, since the correlation of cellular expression patterns is necessarily contaminated by ancestry. Here, we use a recent advance in random matrix theory, discovered in the context of protein phylogeny, to identify differentiation or ancestry-related processes in single-cell data. Qin and Colwell [C. Qin, L. J. Colwell, Proc. Natl. Acad. Sci. U.S.A. 115, 690-695 (2018)] showed that ancestral relationships in protein sequences create a power-law signature in the covariance eigenvalue distribution. We demonstrate the existence of such signatures in scRNA-seq data and that the genes driving them are indeed related to differentiation and developmental pathways. We predict the existence of similar power-law signatures for cells along linear trajectories and demonstrate this for linearly differentiating systems. Furthermore, we generalize to show that the same signatures can arise for cells along tissue-specific spatial trajectories. We illustrate these principles in diverse tissues and organisms, including the mammalian epidermis and lung, Drosophila whole-embryo, adult Hydra, dendritic cells, the intestinal epithelium, and cells undergoing induced pluripotent stem cells (iPSC) reprogramming. We show how these results can be used to interpret the gradual dynamics of lineage structure along iPSC reprogramming. Together, we provide a framework that can be used to identify signatures of specific biological processes in single-cell data without prior knowledge and identify candidate genes associated with these processes.
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212
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He J, Babarinde IA, Sun L, Xu S, Chen R, Shi J, Wei Y, Li Y, Ma G, Zhuang Q, Hutchins AP, Chen J. Identifying transposable element expression dynamics and heterogeneity during development at the single-cell level with a processing pipeline scTE. Nat Commun 2021; 12:1456. [PMID: 33674594 PMCID: PMC7935913 DOI: 10.1038/s41467-021-21808-x] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 02/04/2021] [Indexed: 12/16/2022] Open
Abstract
Transposable elements (TEs) make up a majority of a typical eukaryote’s genome, and contribute to cell heterogeneity in unclear ways. Single-cell sequencing technologies are powerful tools to explore cells, however analysis is typically gene-centric and TE expression has not been addressed. Here, we develop a single-cell TE processing pipeline, scTE, and report the expression of TEs in single cells in a range of biological contexts. Specific TE types are expressed in subpopulations of embryonic stem cells and are dynamically regulated during pluripotency reprogramming, differentiation, and embryogenesis. Unexpectedly, TEs are expressed in somatic cells, including human disease-specific TEs that are undetectable in bulk analyses. Finally, we apply scTE to single-cell ATAC-seq data, and demonstrate that scTE can discriminate cell type using chromatin accessibly of TEs alone. Overall, our results classify the dynamic patterns of TEs in single cells and their contributions to cell heterogeneity. How transposable elements (TE) contribute to cell fate changes is unclear. Here, the authors generate a pipeline to quantify TE expression from single cell data. They show the dynamic expression of TEs from gastrulation to somatic cell reprogramming and human disease
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Affiliation(s)
- Jiangping He
- Center for Cell Lineage and Atlas (CCLA), Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Isaac A Babarinde
- Department of Biology, Southern University of Science and Technology, Shenzhen, China
| | - Li Sun
- Department of Biology, Southern University of Science and Technology, Shenzhen, China
| | - Shuyang Xu
- Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Ruhai Chen
- Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Joint School of Life Sciences, Guangzhou Medical University and Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Junjie Shi
- Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Yuanjie Wei
- Center for Cell Lineage and Atlas (CCLA), Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Yuhao Li
- Department of Biology, Southern University of Science and Technology, Shenzhen, China
| | - Gang Ma
- Department of Biology, Southern University of Science and Technology, Shenzhen, China
| | - Qiang Zhuang
- Department of Biology, Southern University of Science and Technology, Shenzhen, China
| | - Andrew P Hutchins
- Department of Biology, Southern University of Science and Technology, Shenzhen, China.
| | - Jiekai Chen
- Center for Cell Lineage and Atlas (CCLA), Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China. .,Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China. .,Joint School of Life Sciences, Guangzhou Medical University and Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.
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213
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Atamian A, Cordón-Barris L, Quadrato G. Taming human brain organoids one cell at a time. Semin Cell Dev Biol 2021; 111:23-31. [PMID: 32718852 PMCID: PMC11268727 DOI: 10.1016/j.semcdb.2020.05.022] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 04/22/2020] [Accepted: 05/27/2020] [Indexed: 01/06/2023]
Abstract
Human brain organoids are self-organizing three-dimensional structures that emerge from human pluripotent stem cells and mimic aspects of the cellular composition and functionality of the developing human brain. Despite their impressive self-organizing capacity, organoids lack the stereotypic structural anatomy of their in vivo counterpart, making conventional analysis techniques underpowered to assess cellular composition and gene network regulation in organoids. Advances in single cell transcriptomics have recently allowed characterization and improvement of organoid protocols, as they continue to evolve, by enabling identification of cell types and states along with their developmental origins. In this review, we summarize recent approaches, progresses and challenges in resolving brain organoid's complexity through single-cell transcriptomics. We then discuss emerging technologies that may complement single-cell RNA sequencing by providing additional readouts of cellular states to generate an organ-level view of developmental processes. Altogether, these integrative technologies will allow monitoring of global gene regulation in thousands of individual cells and will offer an unprecedented opportunity to investigate features of human brain development and disease across multiple cellular modalities and with cell-type resolution.
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Affiliation(s)
- Alexander Atamian
- Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at USC, Los Angeles, CA 90033, USA
| | - Lluís Cordón-Barris
- Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at USC, Los Angeles, CA 90033, USA
| | - Giorgia Quadrato
- Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at USC, Los Angeles, CA 90033, USA.
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214
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Koehl P, Delarue M, Orland H. Physics approach to the variable-mass optimal-transport problem. Phys Rev E 2021; 103:012113. [PMID: 33601576 DOI: 10.1103/physreve.103.012113] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 12/21/2020] [Indexed: 11/07/2022]
Abstract
Optimal transport (OT) has become a discipline by itself that offers solutions to a wide range of theoretical problems in probability and mathematics with applications in several applied fields such as imaging sciences, machine learning, and in data sciences in general. The traditional OT problem suffers from a severe limitation: its balance condition imposes that the two distributions to be compared be normalized and have the same total mass. However, it is important for many applications to be able to relax this constraint and allow for mass creation and/or destruction. This is true, for example, in all problems requiring partial matching. In this paper, we propose an approach to solving a generalized version of the OT problem, which we refer to as the discrete variable-mass optimal-transport (VMOT) problem, using techniques adapted from statistical physics. Our first contribution is to fully describe this formalism, including all the proofs of its main claims. In particular, we derive a strongly concave effective free-energy function that captures the constraints of the VMOT problem at a finite temperature. From its maximum we derive a weak distance (i.e., a divergence) between possibly unbalanced distribution functions. The temperature-dependent OT distance decreases monotonically to the standard variable-mass OT distance, providing a robust framework for temperature annealing. Our second contribution is to show that the implementation of this formalism has the same properties as the regularized OT algorithms in time complexity, making it a competitive approach to solving the VMOT problem. We illustrate applications of the framework to the problem of partial two- and three-dimensional shape-matching problems.
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Affiliation(s)
- Patrice Koehl
- Department of Computer Science and Genome Center, University of California, Davis, California 95616, USA
| | - Marc Delarue
- Unité de Dynamique Structurale des Macromolécules, Department of Structural Biology and Chemistry, UMR 3528 du CNRS, Institut Pasteur, 75015 Paris, France
| | - Henri Orland
- Institut de Physique Théorique, Université Paris-Saclay, CEA, 91191 Gif/Yvette Cedex, France
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215
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Mattar P, Jolicoeur C, Dang T, Shah S, Clark BS, Cayouette M. A Casz1-NuRD complex regulates temporal identity transitions in neural progenitors. Sci Rep 2021; 11:3858. [PMID: 33594190 PMCID: PMC7886867 DOI: 10.1038/s41598-021-83395-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 02/02/2021] [Indexed: 12/14/2022] Open
Abstract
Neural progenitor cells undergo identity transitions during development to ensure the generation different types of neurons and glia in the correct sequence and proportions. A number of temporal identity factors that control these transitions in progenitor competence have been identified, but the molecular mechanisms underlying their function remain unclear. Here, we asked how Casz1, the mammalian orthologue of Drosophila castor, regulates competence during retinal development. We show that Casz1 is required to control the transition between neurogenesis and gliogenesis. Using BioID proteomics, we reveal that Casz1 interacts with the nucleosome remodeling and deacetylase (NuRD) complex in retinal cells. Finally, we show that both the NuRD and the polycomb repressor complexes are required for Casz1 to promote the rod fate and suppress gliogenesis. As additional temporal identity factors have been found to interact with the NuRD complex in other contexts, we propose that these factors might act through this common biochemical process to regulate neurogenesis.
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Affiliation(s)
- Pierre Mattar
- Cellular Neurobiology Research Unit, Institut de Recherches Cliniques de Montréal (IRCM), Montreal, QC, H2W 1R7, Canada. .,Department of Cell and Molecular Medicine, University of Ottawa, Ottawa, ON, K1H 8M5, Canada. .,Ottawa Health Research Institute (OHRI), Ottawa, ON, K1H 8L6, Canada.
| | - Christine Jolicoeur
- Cellular Neurobiology Research Unit, Institut de Recherches Cliniques de Montréal (IRCM), Montreal, QC, H2W 1R7, Canada
| | - Thanh Dang
- Department of Cell and Molecular Medicine, University of Ottawa, Ottawa, ON, K1H 8M5, Canada.,Ottawa Health Research Institute (OHRI), Ottawa, ON, K1H 8L6, Canada
| | - Sujay Shah
- Department of Cell and Molecular Medicine, University of Ottawa, Ottawa, ON, K1H 8M5, Canada.,Ottawa Health Research Institute (OHRI), Ottawa, ON, K1H 8L6, Canada
| | - Brian S Clark
- John F. Hardesty, MD, Department of Ophthalmology and Visual Sciences, Washington University School of Medicine, St. Louis, MO, 63110, USA.,Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Michel Cayouette
- Cellular Neurobiology Research Unit, Institut de Recherches Cliniques de Montréal (IRCM), Montreal, QC, H2W 1R7, Canada. .,Department of Anatomy and Cell Biology, and Division of Experimental Medicine, McGill University, Montreal, QC, H3A 0G4, Canada. .,Department of Medicine, Université de Montréal, Montreal, QC, H3T 1J4, Canada.
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216
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Armingol E, Officer A, Harismendy O, Lewis NE. Deciphering cell-cell interactions and communication from gene expression. Nat Rev Genet 2021; 22:71-88. [PMID: 33168968 PMCID: PMC7649713 DOI: 10.1038/s41576-020-00292-x] [Citation(s) in RCA: 488] [Impact Index Per Article: 162.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/25/2020] [Indexed: 12/13/2022]
Abstract
Cell-cell interactions orchestrate organismal development, homeostasis and single-cell functions. When cells do not properly interact or improperly decode molecular messages, disease ensues. Thus, the identification and quantification of intercellular signalling pathways has become a common analysis performed across diverse disciplines. The expansion of protein-protein interaction databases and recent advances in RNA sequencing technologies have enabled routine analyses of intercellular signalling from gene expression measurements of bulk and single-cell data sets. In particular, ligand-receptor pairs can be used to infer intercellular communication from the coordinated expression of their cognate genes. In this Review, we highlight discoveries enabled by analyses of cell-cell interactions from transcriptomic data and review the methods and tools used in this context.
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Affiliation(s)
- Erick Armingol
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | - Adam Officer
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA
| | - Olivier Harismendy
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA.
- Moores Cancer Center, University of California, San Diego, La Jolla, CA, USA.
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
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217
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Abstract
Developmental biology has grown into a data intensive science with the development of high-throughput imaging and multi-omics approaches. Machine learning is a versatile set of techniques that can help make sense of these large datasets with minimal human intervention, through tasks such as image segmentation, super-resolution microscopy and cell clustering. In this Spotlight, I introduce the key concepts, advantages and limitations of machine learning, and discuss how these methods are being applied to problems in developmental biology. Specifically, I focus on how machine learning is improving microscopy and single-cell 'omics' techniques and data analysis. Finally, I provide an outlook for the futures of these fields and suggest ways to foster new interdisciplinary developments.
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Affiliation(s)
- Paul Villoutreix
- LIS (UMR 7020), IBDM (UMR 7288), Turing Center For Living Systems, Aix-Marseille University, 13009, Marseille, France
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218
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Cahan P, Cacchiarelli D, Dunn SJ, Hemberg M, de Sousa Lopes SMC, Morris SA, Rackham OJL, Del Sol A, Wells CA. Computational Stem Cell Biology: Open Questions and Guiding Principles. Cell Stem Cell 2021; 28:20-32. [PMID: 33417869 PMCID: PMC7799393 DOI: 10.1016/j.stem.2020.12.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Computational biology is enabling an explosive growth in our understanding of stem cells and our ability to use them for disease modeling, regenerative medicine, and drug discovery. We discuss four topics that exemplify applications of computation to stem cell biology: cell typing, lineage tracing, trajectory inference, and regulatory networks. We use these examples to articulate principles that have guided computational biology broadly and call for renewed attention to these principles as computation becomes increasingly important in stem cell biology. We also discuss important challenges for this field with the hope that it will inspire more to join this exciting area.
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Affiliation(s)
- Patrick Cahan
- Institute for Cell Engineering, Department of Biomedical Engineering, Department of Molecular Biology and Genetics, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.
| | - Davide Cacchiarelli
- Telethon Institute of Genetics and Medicine (TIGEM), Armenise/Harvard Laboratory of Integrative Genomics, Pozzuoli, Italy d Department of Translational Medicine, University of Naples "Federico II," Naples, Italy
| | - Sara-Jane Dunn
- DeepMind, 14-18 Handyside Street, London N1C 4DN, UK; Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Jeffrey Cheah Biomedical Centre, Puddicombe Way, Cambridge Biomedical Campus, Cambridge CB2 0AW, UK
| | - Martin Hemberg
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK
| | | | - Samantha A Morris
- Department of Developmental Biology, Department of Genetics, Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Owen J L Rackham
- Centre for Computational Biology and The Program for Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, Singapore
| | - Antonio Del Sol
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 Avenue du Swing, Belvaux 4366, Luxembourg; CIC bioGUNE, Bizkaia Technology Park, 801 Building, 48160 Derio, Spain; IKERBASQUE, Basque Foundation for Science, Bilbao 48013, Spain
| | - Christine A Wells
- Centre for Stem Cell Systems, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC 3010, Australia
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219
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Li H, Long C, Xiang J, Liang P, Li X, Zuo Y. Dppa2/4 as a trigger of signaling pathways to promote zygote genome activation by binding to CG-rich region. Brief Bioinform 2020; 22:6034044. [PMID: 33316032 DOI: 10.1093/bib/bbaa342] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 09/30/2020] [Accepted: 10/27/2020] [Indexed: 12/23/2022] Open
Abstract
Developmental pluripotency-associated 2 (Dppa2) and developmental pluripotency-associated 4 (Dppa4) as positive drivers were helpful for transcriptional regulation of zygotic genome activation (ZGA). Here, we systematically assessed the cooperative interplay of Dppa2 and Dppa4 in regulating cell pluripotency and found that simultaneous overexpression of Dppa2/4 can make induced pluripotent stem cells closer to embryonic stem cells (ESCs). Compared with other pluripotency transcription factors, Dppa2/4 can regulate majorities of signaling pathways by binding on CG-rich region of proximal promoter (0-500 bp), of which 85% and 77% signaling pathways were significantly activated by Dppa2 and Dppa4, respectively. Notably, Dppa2/4 also can dramatically trigger the decisive signaling pathways for facilitating ZGA, including Hippo, MAPK and TGF-beta signaling pathways and so on. At last, we found alkaline phosphatase, placental-like 2 (Alppl2) was completely silenced when Dppa2 and 4 single- or double-knockout in ESC, which is consistent with Dux. Moreover, Alppl2 was significantly activated in mouse 2-cell embryos and 4-8 cells stage of human embryos, further predicted that Alppl2 was directly regulated by Dppa2/4 as a ZGA candidate driver to facilitate pre-embryonic development.
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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
| | - Jinzhu Xiang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Pengfei Liang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Xueling Li
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, 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
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220
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An S, Huang J, Wan L. D-EE: Distributed software for visualizing intrinsic structure of large-scale single-cell data. Gigascience 2020; 9:giaa126. [PMID: 33179041 PMCID: PMC7657844 DOI: 10.1093/gigascience/giaa126] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 09/29/2020] [Accepted: 10/20/2020] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Dimensionality reduction and visualization play vital roles in single-cell RNA sequencing (scRNA-seq) data analysis. While they have been extensively studied, state-of-the-art dimensionality reduction algorithms are often unable to preserve the global structures underlying data. Elastic embedding (EE), a nonlinear dimensionality reduction method, has shown promise in revealing low-dimensional intrinsic local and global data structure. However, the current implementation of the EE algorithm lacks scalability to large-scale scRNA-seq data. RESULTS We present a distributed optimization implementation of the EE algorithm, termed distributed elastic embedding (D-EE). D-EE reveals the low-dimensional intrinsic structures of data with accuracy equal to that of elastic embedding, and it is scalable to large-scale scRNA-seq data. It leverages distributed storage and distributed computation, achieving memory efficiency and high-performance computing simultaneously. In addition, an extended version of D-EE, termed distributed optimization implementation of time-series elastic embedding (D-TSEE), enables the user to visualize large-scale time-series scRNA-seq data by incorporating experimentally temporal information. Results with large-scale scRNA-seq data indicate that D-TSEE can uncover oscillatory gene expression patterns by using experimentally temporal information. CONCLUSIONS D-EE is a distributed dimensionality reduction and visualization tool. Its distributed storage and distributed computation technique allow us to efficiently analyze large-scale single-cell data at the cost of constant time speedup. The source code for D-EE algorithm based on C and MPI tailored to a high-performance computing cluster is available at https://github.com/ShaokunAn/D-EE.
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Affiliation(s)
- Shaokun An
- NCMIS, LSEC, LSC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Zhongguancun East Road, Haidian District, Beijing, 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Yuquan Road, Shijingshan District, Beijing, 100049, China
| | - Jizu Huang
- NCMIS, LSEC, LSC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Zhongguancun East Road, Haidian District, Beijing, 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Yuquan Road, Shijingshan District, Beijing, 100049, China
| | - Lin Wan
- NCMIS, LSEC, LSC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Zhongguancun East Road, Haidian District, Beijing, 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Yuquan Road, Shijingshan District, Beijing, 100049, China
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221
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Montoro DT, Haber AL, Rood JE, Regev A, Rajagopal J. A Synthesis Concerning Conservation and Divergence of Cell Types across Epithelia. Cold Spring Harb Perspect Biol 2020; 12:cshperspect.a035733. [PMID: 32122885 DOI: 10.1101/cshperspect.a035733] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Advances in single-cell RNA-seq (scRNA-seq) and computational analysis have enabled the systematic interrogation of the cellular composition of tissues. Combined with tools from developmental biology, cell biology, and genetics, these approaches are revealing fundamental aspects of tissue geometry and physiology, including the distribution, origins, and inferred functions of specialized cell types, and the dynamics of cellular turnover and differentiation. By comparing different tissues, such studies can delineate shared and specialized features of cell types and their lineage. Here, we compare two developmentally related murine epithelia, the airway and the small intestinal epithelia, which are both derived from the embryonic endodermal gut tube. We examine how airway and intestine generate and functionalize common archetypal cell types to fulfill similar shared physiologic functionalities. We point to cases in which similar cell types are repurposed to accommodate each tissue's unique physiologic role, and highlight tissue-specific cells whose specializations contribute to the distinct functional roles of each organ. We discuss how archetypal and unique cell types are incorporated within a cellular lineage, and how the regulation of the proportions of these cell types enables tissue-level organization to meet functional demands and maintain homeostasis.
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Affiliation(s)
- Daniel T Montoro
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA
| | - Adam L Haber
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA
| | - Jennifer E Rood
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA.,Howard Hughes Medical Institute and Koch Institute for Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Jayaraj Rajagopal
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA.,Center for Regenerative Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.,Harvard Stem Cell Institute, Cambridge, Massachusetts 02138, USA.,Departments of Internal Medicine and Pediatrics, Pulmonary and Critical Care Division, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.,Howard Hughes Medical Institute Faculty Scholar, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
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222
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Yang KY, Ku M, Lui KO. Single-cell transcriptomics uncover distinct innate and adaptive cell subsets during tissue homeostasis and regeneration. J Leukoc Biol 2020; 108:1593-1602. [PMID: 33070367 DOI: 10.1002/jlb.6mr0720-131r] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Revised: 07/30/2020] [Accepted: 08/10/2020] [Indexed: 02/06/2023] Open
Abstract
Recently, immune cell-mediated tissue repair and regeneration has been an emerging paradigm of regenerative medicine. Immune cells form an essential part of the wound as induction of inflammation is a necessary step to elicit tissue healing. Rapid progress in transcriptomic analyses by high-throughput next-generation sequencing has been developed to study gene regulatory network and establish molecular signatures of immune cells that could potentially predict their functional roles in tissue repair and regeneration. However, the identification of cellular heterogeneity especially on the rare cell subsets has been limited in transcriptomic analyses of bulk cell populations. Therefore, genome-wide, single-cell RNA sequencing (scRNA-Seq) has offered an unprecedented approach to unravel cellular diversity and to study novel immune cell populations involved in tissue repair and regeneration through unsupervised sampling of individual cells without the need to rely on prior knowledge about cell-specific markers. The analysis of gene expression patterns at a single-cell resolution also holds promises to uncover the mechanisms and therefore the development of therapeutic strategy promoting immunoregenerative medicine. In this review, we will discuss how scRNA-Seq facilitates the characterization of immune cells, including macrophages, innate lymphoid cells and T and B lymphocytes, discovery of immune cell heterogeneity, identification of novel subsets, and tracking of developmental trajectories of distinct immune cells during tissue homeostasis, repair, and regeneration.
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Affiliation(s)
- Kevin Y Yang
- Department of Chemical Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
| | - Manching Ku
- Division of Pediatric Hematology and Oncology, Department of Pediatrics and Adolescent Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Kathy O Lui
- Department of Chemical Pathology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China.,Li Ka Shing Institute of Health Sciences, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
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223
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Liang S, Dou J, Iqbal R, Chen K. Batch-Corrected Distance Mitigates Temporal and Spatial Variability for Clustering and Visualization of Single-Cell Gene Expression Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.10.08.332080. [PMID: 33052339 PMCID: PMC7553164 DOI: 10.1101/2020.10.08.332080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Clustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. Batch effect, i.e., the variability among samples gathered from different times, tissues, and patients, introduces large between-group distance and obscures the true identities of cells. To solve this problem, we introduce Batch-Corrected Distance (BCD), a metric using temporal/spatial locality of the batch effect to control for such factors. We validate BCD on a simulated data as well as applied it to a mouse retina development dataset and a lung dataset. We also found the utility of our approach in understanding the progression of the Coronavirus Disease 2019 (COVID-19). BCD achieves more accurate clusters and better visualizations than state-of-the-art batch correction methods on longitudinal datasets. BCD can be directly integrated with most clustering and visualization methods to enable more scientific findings.
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Affiliation(s)
- Shaoheng Liang
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center
- Department of Computer Science, Rice University
| | - Jinzhuang Dou
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center
| | - Ramiz Iqbal
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center
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224
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Huang Y, Zhang H, Wang L, Tang C, Qin X, Wu X, Pan M, Tang Y, Yang Z, Babarinde IA, Lin R, Ji G, Lai Y, Xu X, Su J, Wen X, Satoh T, Ahmed T, Malik V, Ward C, Volpe G, Guo L, Chen J, Sun L, Li Y, Huang X, Bao X, Gao F, Liu B, Zheng H, Jauch R, Lai L, Pan G, Chen J, Testa G, Akira S, Hu J, Pei D, Hutchins AP, Esteban MA, Qin B. JMJD3 acts in tandem with KLF4 to facilitate reprogramming to pluripotency. Nat Commun 2020; 11:5061. [PMID: 33033262 PMCID: PMC7545202 DOI: 10.1038/s41467-020-18900-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 09/16/2020] [Indexed: 12/13/2022] Open
Abstract
The interplay between the Yamanaka factors (OCT4, SOX2, KLF4 and c-MYC) and transcriptional/epigenetic co-regulators in somatic cell reprogramming is incompletely understood. Here, we demonstrate that the histone H3 lysine 27 trimethylation (H3K27me3) demethylase JMJD3 plays conflicting roles in mouse reprogramming. On one side, JMJD3 induces the pro-senescence factor Ink4a and degrades the pluripotency regulator PHF20 in a reprogramming factor-independent manner. On the other side, JMJD3 is specifically recruited by KLF4 to reduce H3K27me3 at both enhancers and promoters of epithelial and pluripotency genes. JMJD3 also promotes enhancer-promoter looping through the cohesin loading factor NIPBL and ultimately transcriptional elongation. This competition of forces can be shifted towards improved reprogramming by using early passage fibroblasts or boosting JMJD3’s catalytic activity with vitamin C. Our work, thus, establishes a multifaceted role for JMJD3, placing it as a key partner of KLF4 and a scaffold that assists chromatin interactions and activates gene transcription. Previous work suggested that histone demethylase JMJD3 is detrimental to somatic cell reprogramming. Here, the authors show that while JMJD3 has a context-independent detrimental effect on early stages of reprogramming, during late stages it activates epithelial and pluripotency genes together with Klf4.
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Affiliation(s)
- Yinghua Huang
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China.,Laboratory of Metabolism and Cell Fate, GIBH, CAS, 510530, Guangzhou, China
| | - Hui Zhang
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), 510005, Guangzhou, China
| | - Lulu Wang
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China.,Laboratory of Metabolism and Cell Fate, GIBH, CAS, 510530, Guangzhou, China
| | - Chuanqing Tang
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China.,Laboratory of Metabolism and Cell Fate, GIBH, CAS, 510530, Guangzhou, China
| | - Xiaogan Qin
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China.,Laboratory of Metabolism and Cell Fate, GIBH, CAS, 510530, Guangzhou, China
| | - Xinyu Wu
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China.,Laboratory of Metabolism and Cell Fate, GIBH, CAS, 510530, Guangzhou, China.,University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Meifang Pan
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China.,Laboratory of Metabolism and Cell Fate, GIBH, CAS, 510530, Guangzhou, China.,University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Yujia Tang
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China.,Laboratory of Metabolism and Cell Fate, GIBH, CAS, 510530, Guangzhou, China
| | - Zhongzhou Yang
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China
| | - Isaac A Babarinde
- Department of Biology, Southern University of Science and Technology, 518055, Shenzhen, China
| | - Runxia Lin
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China.,Laboratory of Metabolism and Cell Fate, GIBH, CAS, 510530, Guangzhou, China.,University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Guanyu Ji
- Science and Technology Department, E-GENE, 518118, Shenzhen, China
| | - Yiwei Lai
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China.,Laboratory of Integrative Biology, GIBH, CAS, 510530, Guangzhou, China
| | - Xueting Xu
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China.,Laboratory of Metabolism and Cell Fate, GIBH, CAS, 510530, Guangzhou, China.,School of Life Sciences, University of Science and Technology of China, 230027, Hefei, China
| | - Jianbin Su
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China.,Laboratory of Metabolism and Cell Fate, GIBH, CAS, 510530, Guangzhou, China
| | - Xue Wen
- Stem Cell and Cancer Center, First Affiliated Hospital, Jilin University, 130061, Changchun, China
| | - Takashi Satoh
- Department of Host Defense, Research Institute for Microbial Diseases, Osaka University, Osaka, 565-0871, Japan
| | - Tanveer Ahmed
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China.,Laboratory of Metabolism and Cell Fate, GIBH, CAS, 510530, Guangzhou, China
| | - Vikas Malik
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China.,Laboratory of Integrative Biology, GIBH, CAS, 510530, Guangzhou, China
| | - Carl Ward
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China.,Laboratory of Integrative Biology, GIBH, CAS, 510530, Guangzhou, China
| | - Giacomo Volpe
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China.,Laboratory of Integrative Biology, GIBH, CAS, 510530, Guangzhou, China
| | - Lin Guo
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China
| | - Jinlong Chen
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China
| | - Li Sun
- Department of Biology, Southern University of Science and Technology, 518055, Shenzhen, China
| | - Yingying Li
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), 510005, Guangzhou, China
| | - Xiaofen Huang
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China
| | - Xichen Bao
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), 510005, Guangzhou, China.,Joint School of Life Sciences, GIBH and Guangzhou Medical University, 511436, Guangzhou, China
| | - Fei Gao
- Science and Technology Department, E-GENE, 518118, Shenzhen, China.,Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, 518120, Shenzhen, China
| | - Baohua Liu
- Health Science Center, Shenzhen University, 518060, Shenzhen, China
| | - Hui Zheng
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), 510005, Guangzhou, China.,Joint School of Life Sciences, GIBH and Guangzhou Medical University, 511436, Guangzhou, China
| | - Ralf Jauch
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Liangxue Lai
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), 510005, Guangzhou, China.,Joint School of Life Sciences, GIBH and Guangzhou Medical University, 511436, Guangzhou, China
| | - Guangjin Pan
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), 510005, Guangzhou, China.,Joint School of Life Sciences, GIBH and Guangzhou Medical University, 511436, Guangzhou, China
| | - Jiekai Chen
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), 510005, Guangzhou, China.,Joint School of Life Sciences, GIBH and Guangzhou Medical University, 511436, Guangzhou, China
| | - Giuseppe Testa
- Department of Experimental Oncology, European Institute of Oncology, Milan, 20139, Italy
| | - Shizuo Akira
- Department of Host Defense, Research Institute for Microbial Diseases, Osaka University, Osaka, 565-0871, Japan
| | - Jifan Hu
- Stem Cell and Cancer Center, First Affiliated Hospital, Jilin University, 130061, Changchun, China
| | - Duanqing Pei
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), 510005, Guangzhou, China
| | - Andrew P Hutchins
- Department of Biology, Southern University of Science and Technology, 518055, Shenzhen, China
| | - Miguel A Esteban
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China. .,Laboratory of Integrative Biology, GIBH, CAS, 510530, Guangzhou, China. .,Joint School of Life Sciences, GIBH and Guangzhou Medical University, 511436, Guangzhou, China. .,Institute for Stem Cells and Regeneration, CAS, 100101, Beijing, China.
| | - Baoming Qin
- Chinese Academy of Sciences (CAS) Key Laboratory of Regenerative Biology and Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health (GIBH), CAS, 510530, Guangzhou, China. .,Laboratory of Metabolism and Cell Fate, GIBH, CAS, 510530, Guangzhou, China. .,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), 510005, Guangzhou, China. .,Joint School of Life Sciences, GIBH and Guangzhou Medical University, 511436, Guangzhou, China.
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225
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Gladyshev VN. The Ground Zero of Organismal Life and Aging. Trends Mol Med 2020; 27:11-19. [PMID: 32980264 DOI: 10.1016/j.molmed.2020.08.012] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 08/25/2020] [Accepted: 08/28/2020] [Indexed: 02/07/2023]
Abstract
Cells may naturally proceed or be forced to transition to a state with a radically lower biological age, that is, be rejuvenated. Examples are the conversion of somatic cells to induced pluripotent stem cells and rejuvenation of the germline with each generation. We posit that these processes converge to the same 'ground zero', the mid-embryonic state characterized by the lowest biological age where both organismal life and aging begin. It may also be related to the phylotypic state. The ground zero model clarifies the relationship between aging, development, rejuvenation, and de-differentiation, which are distinct throughout life. By extending the rejuvenation phase during early embryogenesis and editing the genome, it may be possible to achieve the biological age at the ground zero lower than that achieved naturally.
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Affiliation(s)
- Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
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226
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Massri AJ, Schiebinger GR, Berrio A, Wang L, Wray GA, McClay DR. Methodologies for Following EMT In Vivo at Single Cell Resolution. Methods Mol Biol 2020; 2179:303-314. [PMID: 32939729 DOI: 10.1007/978-1-0716-0779-4_23] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
An epithelial-mesenchymal transition (EMT) occurs in almost every metazoan embryo at the time mesoderm begins to differentiate. Several embryos have a long record as models for studying an EMT given that a known population of cells enters the EMT at a known time thereby enabling a detailed study of the process. Often, however, it is difficult to learn the molecular details of these model EMT systems because the transitioning cells are a minority of the population of cells in the embryo and in most cases there is an inability to isolate that population. Here we provide a method that enables an examination of genes expressed before, during, and after the EMT with a focus on just the cells that undergo the transition. Single cell RNA-seq (scRNA-seq) has advanced as a technology making it feasible to study the trajectory of gene expression specifically in the cells of interest, in vivo, and without the background noise of other cell populations. The sea urchin skeletogenic cells constitute only 5% of the total number of cells in the embryo yet with scRNA-seq it is possible to study the genes expressed by these cells without background noise. This approach, though not perfect, adds a new tool for uncovering the mechanism of EMT in this cell type.
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Affiliation(s)
| | | | | | - Lingyu Wang
- Department of Biology, Duke University, Durham, NC, USA
| | | | - David R McClay
- Department of Biology, Duke University, Durham, NC, USA.
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227
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Single-cell multiomics: technologies and data analysis methods. Exp Mol Med 2020; 52:1428-1442. [PMID: 32929225 PMCID: PMC8080692 DOI: 10.1038/s12276-020-0420-2] [Citation(s) in RCA: 236] [Impact Index Per Article: 59.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 02/14/2020] [Accepted: 02/28/2020] [Indexed: 01/31/2023] Open
Abstract
Advances in single-cell isolation and barcoding technologies offer unprecedented opportunities to profile DNA, mRNA, and proteins at a single-cell resolution. Recently, bulk multiomics analyses, such as multidimensional genomic and proteogenomic analyses, have proven beneficial for obtaining a comprehensive understanding of cellular events. This benefit has facilitated the development of single-cell multiomics analysis, which enables cell type-specific gene regulation to be examined. The cardinal features of single-cell multiomics analysis include (1) technologies for single-cell isolation, barcoding, and sequencing to measure multiple types of molecules from individual cells and (2) the integrative analysis of molecules to characterize cell types and their functions regarding pathophysiological processes based on molecular signatures. Here, we summarize the technologies for single-cell multiomics analyses (mRNA-genome, mRNA-DNA methylation, mRNA-chromatin accessibility, and mRNA-protein) as well as the methods for the integrative analysis of single-cell multiomics data. The expansion of single-cell profiling technologies will provide unprecedented insights into the molecular mechanisms inherent in disease. Novel technologies known collectively as ‘single-cell multiomics’ enable systematic, high-resolution profiling of DNA, RNA and proteins in individual cells. This provides valuable data about gene regulation and molecular populations, and cellular processes during disease development and progression. Daehee Hwang and co-workers at Seoul National University, Seoul, South Korea, reviewed existing single-cell multiomics technologies and highlighted ways to integrate the data generated. Analytical features of multiomics allow scientists to isolate, sequence and label (or ‘barcode’) multiple molecules in single cells. Different sequencing techniques can be used for different purposes, such as exploring gene mutation coverage or measuring RNA transcripts. Combining these sequencing data will help identify links between significant features during disease.
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228
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Tran TN, Bader GD. Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data. PLoS Comput Biol 2020; 16:e1008205. [PMID: 32903255 PMCID: PMC7505465 DOI: 10.1371/journal.pcbi.1008205] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 09/21/2020] [Accepted: 07/29/2020] [Indexed: 12/21/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) can map cell types, states and transitions during dynamic biological processes such as tissue development and regeneration. Many trajectory inference methods have been developed to order cells by their progression through a dynamic process. However, when time series data is available, most of these methods do not consider the available time information when ordering cells and are instead designed to work only on a single scRNA-seq data snapshot. We present Tempora, a novel cell trajectory inference method that orders cells using time information from time-series scRNA-seq data. In performance comparison tests, Tempora inferred known developmental lineages from three diverse tissue development time series data sets, beating state of the art methods in accuracy and speed. Tempora works at the level of cell clusters (types) and uses biological pathway information to help identify cell type relationships. This approach increases gene expression signal from single cells, processing speed, and interpretability of the inferred trajectory. Our results demonstrate the utility of a combination of time and pathway information to supervise trajectory inference for scRNA-seq based analysis. Single-cell RNA sequencing (scRNA-seq) enables an unparalleled ability to map the heterogeneity of dynamic multicellular processes, such as tissue development, tumor growth, wound response and repair, and inflammation. Multiple methods have been developed to order cells along a pseudotime axis that represents a trajectory through such processes using the concept that cells that are closely related in a lineage will have similar transcriptomes. However, time series experiments provide another useful information source to order cells, from earlier to later time point. By introducing a novel use of biological pathway prior information, our Tempora algorithm improves the accuracy and speed of cell trajectory inference from time-series scRNA-seq data as measured by reconstructing known developmental trajectories from three diverse data sets. By analyzing scRNA-seq data at the cluster (cell type) level instead of at the single-cell level and by using known pathway information, Tempora amplifies gene expression signals from one cell using similar cells in a cluster and similar genes within a pathway. This approach also reduces computational time and resources needed to analyze large data sets because it works with a relatively small number of clusters instead of a potentially large number of cells. Finally, it eases interpretation, via operating on a relatively small number of clusters which usually represent known cell types, as well as by identifying time-dependent pathways. Tempora is useful for finding novel insights in dynamic processes.
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Affiliation(s)
- Thinh N. Tran
- Department of Molecular Genetics, University of Toronto, Ontario, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Ontario, Canada
| | - Gary D. Bader
- Department of Molecular Genetics, University of Toronto, Ontario, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Ontario, Canada
- * E-mail:
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229
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Xu Y, Zhang Z, You L, Liu J, Fan Z, Zhou X. scIGANs: single-cell RNA-seq imputation using generative adversarial networks. Nucleic Acids Res 2020; 48:e85. [PMID: 32588900 PMCID: PMC7470961 DOI: 10.1093/nar/gkaa506] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 05/11/2020] [Accepted: 06/17/2020] [Indexed: 12/21/2022] Open
Abstract
Single-cell RNA-sequencing (scRNA-seq) enables the characterization of transcriptomic profiles at the single-cell resolution with increasingly high throughput. However, it suffers from many sources of technical noises, including insufficient mRNA molecules that lead to excess false zero values, termed dropouts. Computational approaches have been proposed to recover the biologically meaningful expression by borrowing information from similar cells in the observed dataset. However, these methods suffer from oversmoothing and removal of natural cell-to-cell stochasticity in gene expression. Here, we propose the generative adversarial networks (GANs) for scRNA-seq imputation (scIGANs), which uses generated cells rather than observed cells to avoid these limitations and balances the performance between major and rare cell populations. Evaluations based on a variety of simulated and real scRNA-seq datasets show that scIGANs is effective for dropout imputation and enhances various downstream analysis. ScIGANs is robust to small datasets that have very few genes with low expression and/or cell-to-cell variance. ScIGANs works equally well on datasets from different scRNA-seq protocols and is scalable to datasets with over 100 000 cells. We demonstrated in many ways with compelling evidence that scIGANs is not only an application of GANs in omics data but also represents a competing imputation method for the scRNA-seq data.
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Affiliation(s)
- Yungang Xu
- Centre for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Centre at Houston, TX 77030, USA
| | - Zhigang Zhang
- School of Information Management and Statistics, Hubei University of Economics, Wuhan, Hubei 430205, China
- Hubei Centre for Data and Analysis, Hubei University of Economics, Wuhan, Hubei 430205, China
| | - Lei You
- Centre for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Centre at Houston, TX 77030, USA
| | - Jiajia Liu
- Centre for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Centre at Houston, TX 77030, USA
- College of Electronic and Information Engineering, Tongji University, Shanghai, Shanghai 201804, China
| | - Zhiwei Fan
- Centre for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Centre at Houston, TX 77030, USA
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Chengdu 610040, China
| | - Xiaobo Zhou
- Centre for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Centre at Houston, TX 77030, USA
- Department of Paediatric Surgery, McGovern Medical School, The University of Texas Health Science Centre at Houston, Houston, TX 77030, USA
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230
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Cao P, Li H, Zuo Y, Nashun B. Characterization of DNA Methylation Patterns and Mining of Epigenetic Markers During Genomic Reprogramming in SCNT Embryos. Front Cell Dev Biol 2020; 8:570107. [PMID: 32984351 PMCID: PMC7492385 DOI: 10.3389/fcell.2020.570107] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Accepted: 08/13/2020] [Indexed: 12/15/2022] Open
Abstract
Somatic cell nuclear transfer (SCNT), also known as somatic cell cloning, is a commonly used technique to study epigenetic reprogramming. Although SCNT has the advantages of being safe and able to obtain pluripotent cells, early developmental arrest happens in most SCNT embryos. Overcoming epigenetic barriers is currently the primary strategy for improving reprogramming efficiency and improving developmental rate in SCNT embryos. In this study, we analyzed DNA methylation profiles of in vivo fertilized embryos and SCNT embryos with different developmental fates. Overall DNA methylation level was higher in SCNT embryos during global de-methylation process compared to in vivo fertilized embryos. In addition, promoter region, first intron and 3′UTR were found to be the major genomic regions that were hyper-methylated in SCNT embryos. Surprisingly, we found the length of re-methylated region was directly related to the change of methylation level. Furthermore, a number of genes including Dppa2 and Dppa4 which are important for early zygotic genome activation (ZGA) were not properly activated in SCNT embryos. This study comprehensively analyzed genome-wide DNA methylation patterns in SCNT embryos and provided candidate target genes for improving efficiency of genomic reprogramming in SCNT embryos. Since SCNT technology has been widely used in agricultural and pastoral production, protection of endangered animals, and therapeutic cloning, the findings of this study have significant importance for all these fields.
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Affiliation(s)
- Pengbo Cao
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Hanshuang Li
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Buhe Nashun
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
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231
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Chu X, Wang J. Conformational state switching and pathways of chromosome dynamics in cell cycle. APPLIED PHYSICS REVIEWS 2020; 7:031403. [PMID: 32884608 PMCID: PMC7376616 DOI: 10.1063/5.0007316] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 06/11/2020] [Indexed: 05/02/2023]
Abstract
The cell cycle is a process and function of a cell with different phases essential for cell growth, proliferation, and replication. It depends on the structure and dynamics of the underlying DNA molecule, which underpins the genome function. A microscopic structural-level understanding of how a genome or its functional module chromosome performs the cell cycle in terms of large-scale conformational transformation between different phases, such as the interphase and the mitotic phase, is still challenging. Here, we develop a non-equilibrium, excitation-relaxation energy landscape-switching model to quantify the underlying chromosome conformational transitions through (de-)condensation for a complete microscopic understanding of the cell cycle. We show that the chromosome conformational transition mechanism from the interphase to the mitotic phase follows a two-stage scenario, in good agreement with the experiments. In contrast, the mitotic exit pathways show the existence of an over-expanded chromosome that recapitulates the chromosome in the experimentally identified intermediate state at the telophase. We find the conformational pathways are heterogeneous and irreversible as a result of the non-equilibrium dynamics of the cell cycle from both structural and kinetic perspectives. We suggest that the irreversibility is mainly due to the distinct participation of the ATP-dependent structural maintenance of chromosomal protein complexes during the cell cycle. Our findings provide crucial insights into the microscopic molecular structural and dynamical physical mechanism for the cell cycle beyond the previous more macroscopic descriptions. Our non-equilibrium landscape framework is general and applicable to study diverse non-equilibrium physical and biological processes such as active matter, differentiation/development, and cancer.
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Affiliation(s)
- Xiakun Chu
- Department of Chemistry, State University of New York at
Stony Brook, Stony Brook, New York 11794, USA
| | - Jin Wang
- Author to whom correspondence should be addressed:
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232
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Xing QR, El Farran CA, Gautam P, Chuah YS, Warrier T, Toh CXD, Kang NY, Sugii S, Chang YT, Xu J, Collins JJ, Daley GQ, Li H, Zhang LF, Loh YH. Diversification of reprogramming trajectories revealed by parallel single-cell transcriptome and chromatin accessibility sequencing. SCIENCE ADVANCES 2020; 6:eaba1190. [PMID: 32917699 PMCID: PMC7486102 DOI: 10.1126/sciadv.aba1190] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 07/30/2020] [Indexed: 05/16/2023]
Abstract
Cellular reprogramming suffers from low efficiency especially for the human cells. To deconstruct the heterogeneity and unravel the mechanisms for successful reprogramming, we adopted single-cell RNA sequencing (scRNA-Seq) and single-cell assay for transposase-accessible chromatin (scATAC-Seq) to profile reprogramming cells across various time points. Our analysis revealed that reprogramming cells proceed in an asynchronous trajectory and diversify into heterogeneous subpopulations. We identified fluorescent probes and surface markers to enrich for the early reprogrammed human cells. Furthermore, combinatory usage of the surface markers enabled the fine segregation of the early-intermediate cells with diverse reprogramming propensities. scATAC-Seq analysis further uncovered the genomic partitions and transcription factors responsible for the regulatory phasing of reprogramming process. Binary choice between a FOSL1 and a TEAD4-centric regulatory network determines the outcome of a successful reprogramming. Together, our study illuminates the multitude of diverse routes transversed by individual reprogramming cells and presents an integrative roadmap for identifying the mechanistic part list of the reprogramming machinery.
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Affiliation(s)
- Q R Xing
- Epigenetics and Cell Fates Laboratory, Programme in Stem Cell, Regenerative Medicine and Aging, Institute of Molecular and Cell Biology, A*STAR, Singapore 138673, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore
| | - Chadi A El Farran
- Epigenetics and Cell Fates Laboratory, Programme in Stem Cell, Regenerative Medicine and Aging, Institute of Molecular and Cell Biology, A*STAR, Singapore 138673, Singapore
- Department of Biological Sciences, National University of Singapore, Singapore 117558, Singapore
| | - Pradeep Gautam
- Epigenetics and Cell Fates Laboratory, Programme in Stem Cell, Regenerative Medicine and Aging, Institute of Molecular and Cell Biology, A*STAR, Singapore 138673, Singapore
- Department of Biological Sciences, National University of Singapore, Singapore 117558, Singapore
| | - Yu Song Chuah
- Epigenetics and Cell Fates Laboratory, Programme in Stem Cell, Regenerative Medicine and Aging, Institute of Molecular and Cell Biology, A*STAR, Singapore 138673, Singapore
| | - Tushar Warrier
- Epigenetics and Cell Fates Laboratory, Programme in Stem Cell, Regenerative Medicine and Aging, Institute of Molecular and Cell Biology, A*STAR, Singapore 138673, Singapore
- Department of Biological Sciences, National University of Singapore, Singapore 117558, Singapore
| | - Cheng-Xu Delon Toh
- Epigenetics and Cell Fates Laboratory, Programme in Stem Cell, Regenerative Medicine and Aging, Institute of Molecular and Cell Biology, A*STAR, Singapore 138673, Singapore
| | - Nam-Young Kang
- Laboratory of Bioimaging Probe Development, Singapore Bioimaging Consortium, A*STAR, Singapore 138667, Singapore
- Department of Creative IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
| | - Shigeki Sugii
- Institute of Bioengineering and Nanotechnology, A*STAR, Singapore 138669, Singapore
- Cardiovascular and Metabolic Disorders Program, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Young-Tae Chang
- Laboratory of Bioimaging Probe Development, Singapore Bioimaging Consortium, A*STAR, Singapore 138667, Singapore
- Department of Chemistry, National University of Singapore, Singapore 117543, Singapore
- Center for Self-assembly and Complexity, Institute for Basic Science (IBS), Pohang 37673, Republic of Korea
- Department of Chemistry, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
| | - Jian Xu
- Department of Biological Sciences, National University of Singapore, Singapore 117558, Singapore
- Department of Plant Systems Physiology, Institute for Water and Wetland Research, Radboud University, Heyendaalseweg 135, 6525 AJ, Nijmegen, Netherlands
| | - James J Collins
- Institute for Medical Engineering and Science, Department of Biological Engineering, and Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - George Q Daley
- Stem Cell Program, Division of Pediatric Hematology and Oncology, Boston Children's Hospital and Dana-Farber Cancer Institute, Boston, MA 02115, USA
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
- Harvard Stem Cell Institute, Cambridge, MA 02138, USA
- Manton Center for Orphan Disease Research, Boston, MA 02115, USA
| | - Hu Li
- Center for Individualized Medicine, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA.
| | - Li-Feng Zhang
- School of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore.
| | - Yuin-Han Loh
- Epigenetics and Cell Fates Laboratory, Programme in Stem Cell, Regenerative Medicine and Aging, Institute of Molecular and Cell Biology, A*STAR, Singapore 138673, Singapore.
- Department of Biological Sciences, National University of Singapore, Singapore 117558, Singapore
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore 119077, Singapore
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
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233
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Raimundo F, Vallot C, Vert JP. Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis. Genome Biol 2020; 21:212. [PMID: 32831127 PMCID: PMC7444048 DOI: 10.1186/s13059-020-02128-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 08/03/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Many computational methods have been developed recently to analyze single-cell RNA-seq (scRNA-seq) data. Several benchmark studies have compared these methods on their ability for dimensionality reduction, clustering, or differential analysis, often relying on default parameters. Yet, given the biological diversity of scRNA-seq datasets, parameter tuning might be essential for the optimal usage of methods, and determining how to tune parameters remains an unmet need. RESULTS Here, we propose a benchmark to assess the performance of five methods, systematically varying their tunable parameters, for dimension reduction of scRNA-seq data, a common first step to many downstream applications such as cell type identification or trajectory inference. We run a total of 1.5 million experiments to assess the influence of parameter changes on the performance of each method, and propose two strategies to automatically tune parameters for methods that need it. CONCLUSIONS We find that principal component analysis (PCA)-based methods like scran and Seurat are competitive with default parameters but do not benefit much from parameter tuning, while more complex models like ZinbWave, DCA, and scVI can reach better performance but after parameter tuning.
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Affiliation(s)
| | - Celine Vallot
- CNRS UMR3244, Institut Curie, PSL Research University, Paris, 75005 France
- Translational Research Department, Institut Curie, PSL Research University, Paris, 75005 France
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234
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Abstract
Human pluripotent stem cells harbor the capacity to differentiate into cells from the three embryonic germ layers, and this ability grants them a central role in modeling human disorders and in the field of regenerative medicine. Here, we review pluripotency in human cells with respect to four different aspects: (1) embryonic development, (2) transcriptomes of pluripotent cell stages, (3) genes and pathways that reprogram somatic cells into pluripotent stem cells, and finally (4) the recent identification of the human pluripotent stem cell essentialome. These four aspects of pluripotency collectively culminate in a broader understanding of what makes a cell pluripotent.
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235
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Marjanovic ND, Hofree M, Chan JE, Canner D, Wu K, Trakala M, Hartmann GG, Smith OC, Kim JY, Evans KV, Hudson A, Ashenberg O, Porter CBM, Bejnood A, Subramanian A, Pitter K, Yan Y, Delorey T, Phillips DR, Shah N, Chaudhary O, Tsankov A, Hollmann T, Rekhtman N, Massion PP, Poirier JT, Mazutis L, Li R, Lee JH, Amon A, Rudin CM, Jacks T, Regev A, Tammela T. Emergence of a High-Plasticity Cell State during Lung Cancer Evolution. Cancer Cell 2020; 38:229-246.e13. [PMID: 32707077 PMCID: PMC7745838 DOI: 10.1016/j.ccell.2020.06.012] [Citation(s) in RCA: 184] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 03/13/2020] [Accepted: 06/18/2020] [Indexed: 12/13/2022]
Abstract
Tumor evolution from a single cell into a malignant, heterogeneous tissue remains poorly understood. Here, we profile single-cell transcriptomes of genetically engineered mouse lung tumors at seven stages, from pre-neoplastic hyperplasia to adenocarcinoma. The diversity of transcriptional states increases over time and is reproducible across tumors and mice. Cancer cells progressively adopt alternate lineage identities, computationally predicted to be mediated through a common transitional, high-plasticity cell state (HPCS). Accordingly, HPCS cells prospectively isolated from mouse tumors and human patient-derived xenografts display high capacity for differentiation and proliferation. The HPCS program is associated with poor survival across human cancers and demonstrates chemoresistance in mice. Our study reveals a central principle underpinning intra-tumoral heterogeneity and motivates therapeutic targeting of the HPCS.
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Affiliation(s)
- Nemanja Despot Marjanovic
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Computational and Systems Biology PhD Program, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Matan Hofree
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jason E Chan
- Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - David Canner
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Katherine Wu
- Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Marianna Trakala
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Griffin G Hartmann
- Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Olivia C Smith
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Jonathan Y Kim
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Kelly Victoria Evans
- Wellcome - MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge CB2 0AW, UK; Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge CB2 3DY, UK
| | - Anna Hudson
- Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Orr Ashenberg
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Caroline B M Porter
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alborz Bejnood
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ayshwarya Subramanian
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kenneth Pitter
- Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Yan Yan
- Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Toni Delorey
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Devan R Phillips
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Nisargbhai Shah
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ojasvi Chaudhary
- The Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Alexander Tsankov
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Travis Hollmann
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Natasha Rekhtman
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Pierre P Massion
- Department of Medicine and Cancer Early Detection and Prevention Initiative, Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - John T Poirier
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Linas Mazutis
- The Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ruifang Li
- Epigenetics Technology Innovation Lab, Center for Epigenetics Research, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Joo-Hyeon Lee
- Wellcome - MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge CB2 0AW, UK; Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge CB2 3DY, UK
| | - Angelika Amon
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Charles M Rudin
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; The Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Tyler Jacks
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Tuomas Tammela
- Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Cell and Developmental Biology, Weill-Cornell Medical College, New York, NY 10065, USA.
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236
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Caponegro MD, Thompson KK, Tayyab M, Tsirka SE. A Rigorous Quantitative Approach to Analyzing Phagocytosis Assays. Bio Protoc 2020; 10:e3698. [PMID: 33209965 DOI: 10.21769/bioprotoc.3698] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
Abstract
Studying monocytic cells in isolated systems in vitro contributes significantly to the understanding of innate immune physiology. Functional assays produce read outs which can be used to measure responses to selected stimuli, such as pathogen exposure, antigen loading, and cytokine stimulation. Integration of these results with high quality in vivo models allows for the development of therapeutics which target these cell populations. Current methodologies to quantify phagocytic function of monocytic cells in vitro either measure phagocytic activity of individual cells (average number of beads or particles/cell), or a population outcome (% cells that contain phagocytosed material). Here we address technical challenges and shortcomings of these methods and present a protocol for collecting and analyzing data derived from a functional assay which measures phagocytic activity of macrophage and macrophage-like cells. We apply this method to two different experimental conditions, and compare to existing work flows. We also provide an online tool for users to upload and analyze data using this method.
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Affiliation(s)
- Michael D Caponegro
- Molecular and Cellular Pharmacology Program, Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY 11794-8651, USA
| | - Kaitlyn Koenig Thompson
- Molecular and Cellular Pharmacology Program, Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY 11794-8651, USA
| | - Maryam Tayyab
- Molecular and Cellular Pharmacology Program, Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY 11794-8651, USA
| | - Stella E Tsirka
- Molecular and Cellular Pharmacology Program, Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY 11794-8651, USA
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237
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Li B, Gould J, Yang Y, Sarkizova S, Tabaka M, Ashenberg O, Rosen Y, Slyper M, Kowalczyk MS, Villani AC, Tickle T, Hacohen N, Rozenblatt-Rosen O, Regev A. Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq. Nat Methods 2020; 17:793-798. [PMID: 32719530 PMCID: PMC7437817 DOI: 10.1038/s41592-020-0905-x] [Citation(s) in RCA: 104] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 06/18/2020] [Indexed: 11/10/2022]
Abstract
Massively parallel single-cell and single-nucleus RNA sequencing has opened the way to systematic tissue atlases in health and disease, but as the scale of data generation is growing, so is the need for computational pipelines for scaled analysis. Here we developed Cumulus-a cloud-based framework for analyzing large-scale single-cell and single-nucleus RNA sequencing datasets. Cumulus combines the power of cloud computing with improvements in algorithm and implementation to achieve high scalability, low cost, user-friendliness and integrated support for a comprehensive set of features. We benchmark Cumulus on the Human Cell Atlas Census of Immune Cells dataset of bone marrow cells and show that it substantially improves efficiency over conventional frameworks, while maintaining or improving the quality of results, enabling large-scale studies.
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Affiliation(s)
- Bo Li
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Division of Rheumatology, Allergy, and Immunology, Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
| | - Joshua Gould
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yiming Yang
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Division of Rheumatology, Allergy, and Immunology, Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Boston, MA, USA
| | - Siranush Sarkizova
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Marcin Tabaka
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Orr Ashenberg
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yanay Rosen
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Michal Slyper
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Monika S Kowalczyk
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Alexandra-Chloé Villani
- Division of Rheumatology, Allergy, and Immunology, Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
| | - Timothy Tickle
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Nir Hacohen
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
| | | | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Koch Institute of Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
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238
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Solovey M, Scialdone A. COMUNET: a tool to explore and visualize intercellular communication. Bioinformatics 2020; 36:4296-4300. [PMID: 32399572 PMCID: PMC7520036 DOI: 10.1093/bioinformatics/btaa482] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 04/16/2020] [Accepted: 05/05/2020] [Indexed: 01/30/2023] Open
Abstract
MOTIVATION Intercellular communication plays an essential role in multicellular organisms and several algorithms to analyze it from single-cell transcriptional data have been recently published, but the results are often hard to visualize and interpret. RESULTS We developed Cell cOmmunication exploration with MUltiplex NETworks (COMUNET), a tool that streamlines the interpretation of the results from cell-cell communication analyses. COMUNET uses multiplex networks to represent and cluster all potential communication patterns between cell types. The algorithm also enables the search for specific patterns of communication and can perform comparative analysis between two biological conditions. To exemplify its use, here we apply COMUNET to investigate cell communication patterns in single-cell transcriptomic datasets from mouse embryos and from an acute myeloid leukemia patient at diagnosis and after treatment. AVAILABILITY AND IMPLEMENTATION Our algorithm is implemented in an R package available from https://github.com/ScialdoneLab/COMUNET, along with all the code to perform the analyses reported here. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Maria Solovey
- Institute of Computational Biology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg 85764, Germany
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München – German Research Center for Environmental Health, München 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg 85764, Germany
| | - Antonio Scialdone
- Institute of Computational Biology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg 85764, Germany
- Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München – German Research Center for Environmental Health, München 81377, Germany
- Institute of Functional Epigenetics, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg 85764, Germany
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239
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Hie B, Peters J, Nyquist SK, Shalek AK, Berger B, Bryson BD. Computational Methods for Single-Cell RNA Sequencing. Annu Rev Biomed Data Sci 2020. [DOI: 10.1146/annurev-biodatasci-012220-100601] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Single-cell RNA sequencing (scRNA-seq) has provided a high-dimensional catalog of millions of cells across species and diseases. These data have spurred the development of hundreds of computational tools to derive novel biological insights. Here, we outline the components of scRNA-seq analytical pipelines and the computational methods that underlie these steps. We describe available methods, highlight well-executed benchmarking studies, and identify opportunities for additional benchmarking studies and computational methods. As the biochemical approaches for single-cell omics advance, we propose coupled development of robust analytical pipelines suited for the challenges that new data present and principled selection of analytical methods that are suited for the biological questions to be addressed.
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Affiliation(s)
- Brian Hie
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Joshua Peters
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, Massachusetts 02139, USA
| | - Sarah K. Nyquist
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, Massachusetts 02139, USA
- Program in Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Alex K. Shalek
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, Massachusetts 02139, USA
- Department of Chemistry, Institute for Medical Engineering & Science (IMES), and Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Bryan D. Bryson
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, Massachusetts 02139, USA
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240
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Abstract
Cortical interneurons display striking differences in shape, physiology, and other attributes, challenging us to appropriately classify them. We previously suggested that interneuron types should be defined by their role in cortical processing. Here, we revisit the question of how to codify their diversity based upon their division of labor and function as controllers of cortical information flow. We suggest that developmental trajectories provide a guide for appreciating interneuron diversity and argue that subtype identity is generated using a configurational (rather than combinatorial) code of transcription factors that produce attractor states in the underlying gene regulatory network. We present our updated three-stage model for interneuron specification: an initial cardinal step, allocating interneurons into a few major classes, followed by definitive refinement, creating subclasses upon settling within the cortex, and lastly, state determination, reflecting the incorporation of interneurons into functional circuit ensembles. We close by discussing findings indicating that major interneuron classes are both evolutionarily ancient and conserved. We propose that the complexity of cortical circuits is generated by phylogenetically old interneuron types, complemented by an evolutionary increase in principal neuron diversity. This suggests that a natural neurobiological definition of interneuron types might be derived from a match between their developmental origin and computational function.
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Affiliation(s)
- Gord Fishell
- Department of Neurobiology, Blavatnik Institute, Harvard Medical School, Boston, Massachusetts 02115, USA;
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, Massachusetts 02142, USA
- Center for Genomics and Systems Biology, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Adam Kepecs
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
- Department of Neuroscience, Washington University in St. Louis, St. Louis, Missouri 63130, USA;
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241
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Kirita Y, Wu H, Uchimura K, Wilson PC, Humphreys BD. Cell profiling of mouse acute kidney injury reveals conserved cellular responses to injury. Proc Natl Acad Sci U S A 2020; 117:15874-15883. [PMID: 32571916 PMCID: PMC7355049 DOI: 10.1073/pnas.2005477117] [Citation(s) in RCA: 283] [Impact Index Per Article: 70.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
After acute kidney injury (AKI), patients either recover or alternatively develop fibrosis and chronic kidney disease. Interactions between injured epithelia, stroma, and inflammatory cells determine whether kidneys repair or undergo fibrosis, but the molecular events that drive these processes are poorly understood. Here, we use single nucleus RNA sequencing of a mouse model of AKI to characterize cell states during repair from acute injury. We identify a distinct proinflammatory and profibrotic proximal tubule cell state that fails to repair. Deconvolution of bulk RNA-seq datasets indicates that this failed-repair proximal tubule cell (FR-PTC) state can be detected in other models of kidney injury, increasing during aging in rat kidney and over time in human kidney allografts. We also describe dynamic intercellular communication networks and discern transcriptional pathways driving successful vs. failed repair. Our study provides a detailed description of cellular responses after injury and suggests that the FR-PTC state may represent a therapeutic target to improve repair.
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Affiliation(s)
- Yuhei Kirita
- Division of Nephrology, Department of Medicine, Washington University in Saint Louis School of Medicine, St. Louis, MO 63110
- Department of Nephrology, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
| | - Haojia Wu
- Division of Nephrology, Department of Medicine, Washington University in Saint Louis School of Medicine, St. Louis, MO 63110
| | - Kohei Uchimura
- Division of Nephrology, Department of Medicine, Washington University in Saint Louis School of Medicine, St. Louis, MO 63110
| | - Parker C Wilson
- Division of Anatomic and Molecular Pathology, Department of Pathology and Immunology, Washington University in Saint Louis School of Medicine, St. Louis, MO 63110
| | - Benjamin D Humphreys
- Division of Nephrology, Department of Medicine, Washington University in Saint Louis School of Medicine, St. Louis, MO 63110;
- Department of Developmental Biology, Washington University in Saint Louis School of Medicine, St. Louis, MO 63110
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242
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Ton MLN, Guibentif C, Göttgens B. Single cell genomics and developmental biology: moving beyond the generation of cell type catalogues. Curr Opin Genet Dev 2020; 64:66-71. [PMID: 32629366 DOI: 10.1016/j.gde.2020.05.033] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 05/25/2020] [Indexed: 11/17/2022]
Abstract
Major developmental processes such as gastrulation and early embryogenesis rely on a complex network of cell-cell interactions, chromatin remodeling, and transcriptional regulators. This makes it challenging to study early development when using bulk populations of cells. Recent advances in single-cell technologies have allowed researchers to better understand the interactions between different molecular modalities and the heterogeneities within classically defined cell types. As new single-cell technologies mature, they have the potential of providing a step-change in our understanding of embryogenesis. In this review, we summarize recent advances in single-cell technologies with particular focus on those that lend insight into early organogenesis. We then discuss current pitfalls and implications for future research.
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Affiliation(s)
- Mai-Linh N Ton
- Department of Haematology, University of Cambridge, Cambridge, UK; Wellcome-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Carolina Guibentif
- Department of Haematology, University of Cambridge, Cambridge, UK; Wellcome-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK; Sahlgrenska Cancer Center, Department of Microbiology and Immunology, University of Gothenburg, Gothenburg, Sweden
| | - Berthold Göttgens
- Department of Haematology, University of Cambridge, Cambridge, UK; Wellcome-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
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243
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Townes FW, Irizarry RA. Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers. Genome Biol 2020; 21:160. [PMID: 32620142 PMCID: PMC7333325 DOI: 10.1186/s13059-020-02078-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 06/19/2020] [Indexed: 12/15/2022] Open
Abstract
Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. Unique molecular identifiers (UMIs) remove duplicates in read counts resulting from polymerase chain reaction, a major source of noise. For scRNA-seq data lacking UMIs, we propose quasi-UMIs: quantile normalization of read counts to a compound Poisson distribution empirically derived from UMI datasets. When applied to ground-truth datasets having both reads and UMIs, quasi-UMI normalization has higher accuracy than competing methods. Using quasi-UMIs enables methods designed specifically for UMI data to be applied to non-UMI scRNA-seq datasets.
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Affiliation(s)
- F. William Townes
- Department of Computer Science, Princeton University, Princeton, NJ USA
| | - Rafael A. Irizarry
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA USA
- Department of Biostatistics, Harvard University, Cambridge, MA USA
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244
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Ray TA, Cochran K, Kozlowski C, Wang J, Alexander G, Cady MA, Spencer WJ, Ruzycki PA, Clark BS, Laeremans A, He MX, Wang X, Park E, Hao Y, Iannaccone A, Hu G, Fedrigo O, Skiba NP, Arshavsky VY, Kay JN. Comprehensive identification of mRNA isoforms reveals the diversity of neural cell-surface molecules with roles in retinal development and disease. Nat Commun 2020; 11:3328. [PMID: 32620864 PMCID: PMC7335077 DOI: 10.1038/s41467-020-17009-7] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 05/30/2020] [Indexed: 02/08/2023] Open
Abstract
Genes encoding cell-surface proteins control nervous system development and are implicated in neurological disorders. These genes produce alternative mRNA isoforms which remain poorly characterized, impeding understanding of how disease-associated mutations cause pathology. Here we introduce a strategy to define complete portfolios of full-length isoforms encoded by individual genes. Applying this approach to neural cell-surface molecules, we identify thousands of unannotated isoforms expressed in retina and brain. By mass spectrometry we confirm expression of newly-discovered proteins on the cell surface in vivo. Remarkably, we discover that the major isoform of a retinal degeneration gene, CRB1, was previously overlooked. This CRB1 isoform is the only one expressed by photoreceptors, the affected cells in CRB1 disease. Using mouse mutants, we identify a function for this isoform at photoreceptor-glial junctions and demonstrate that loss of this isoform accelerates photoreceptor death. Therefore, our isoform identification strategy enables discovery of new gene functions relevant to disease.
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Affiliation(s)
- Thomas A Ray
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, 27710, USA
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Kelly Cochran
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, 27710, USA
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Chris Kozlowski
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, 27710, USA
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Jingjing Wang
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, 27710, USA
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Graham Alexander
- Center for Genomic and Computational Biology, Duke University, Durham, NC, 27710, USA
| | | | | | - Philip A Ruzycki
- John F. Hardesty, M.D. Department of Ophthalmology and Visual Sciences, Washington University, St. Louis, MO, 63110, USA
| | - Brian S Clark
- John F. Hardesty, M.D. Department of Ophthalmology and Visual Sciences, Washington University, St. Louis, MO, 63110, USA
- Department of Developmental Biology, Washington University, St. Louis, MO, 63110, USA
| | | | - Ming-Xiao He
- Advanced Cell Diagnostics, Newark, CA, 94560, USA
| | | | - Emily Park
- Advanced Cell Diagnostics, Newark, CA, 94560, USA
| | - Ying Hao
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Alessandro Iannaccone
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Gary Hu
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, 27710, USA
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Olivier Fedrigo
- Center for Genomic and Computational Biology, Duke University, Durham, NC, 27710, USA
- The Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA
| | - Nikolai P Skiba
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Vadim Y Arshavsky
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Jeremy N Kay
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, 27710, USA.
- Department of Ophthalmology, Duke University School of Medicine, Durham, NC, 27710, USA.
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245
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Tong A, Huang J, Wolf G, van Dijk D, Krishnaswamy S. TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics. PROCEEDINGS OF MACHINE LEARNING RESEARCH 2020; 119:9526-9536. [PMID: 34337419 PMCID: PMC8320749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
It is increasingly common to encounter data from dynamic processes captured by static cross-sectional measurements over time, particularly in biomedical settings. Recent attempts to model individual trajectories from this data use optimal transport to create pairwise matchings between time points. However, these methods cannot model continuous dynamics and non-linear paths that entities can take in these systems. To address this issue, we establish a link between continuous normalizing flows and dynamic optimal transport, that allows us to model the expected paths of points over time. Continuous normalizing flows are generally under constrained, as they are allowed to take an arbitrary path from the source to the target distribution. We present TrajectoryNet, which controls the continuous paths taken between distributions to produce dynamic optimal transport. We show how this is particularly applicable for studying cellular dynamics in data from single-cell RNA sequencing (scRNA-seq) technologies, and that TrajectoryNet improves upon recently proposed static optimal transport-based models that can be used for interpolating cellular distributions.
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Affiliation(s)
- Alexander Tong
- Department of Computer Science, Yale University, New Haven, CT, USA
| | - Jessie Huang
- Department of Computer Science, Yale University, New Haven, CT, USA
| | - Guy Wolf
- Department of Mathematics & Statistics, Université de Montréal, Montréal, QC, Canada
- Mila – Quebec AI Institute, Montréal, QC, Canada
| | - David van Dijk
- Department of Computer Science, Yale University, New Haven, CT, USA
- Internal Medicine, Cardiology, Yale University, New Haven, CT, USA
| | - Smita Krishnaswamy
- Department of Computer Science, Yale University, New Haven, CT, USA
- Department of Genetics, Yale University, New Haven, CT, USA
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246
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Defining Reprogramming Checkpoints from Single-Cell Analyses of Induced Pluripotency. Cell Rep 2020; 27:1726-1741.e5. [PMID: 31067459 PMCID: PMC6555151 DOI: 10.1016/j.celrep.2019.04.056] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Revised: 03/04/2019] [Accepted: 04/10/2019] [Indexed: 12/14/2022] Open
Abstract
Elucidating the mechanism of reprogramming is confounded by heterogeneity due to the low efficiency and differential kinetics of obtaining induced pluripotent stem cells (iPSCs) from somatic cells. Therefore, we increased the efficiency with a combination of epigenomic modifiers and signaling molecules and profiled the transcriptomes of individual reprogramming cells. Contrary to the established temporal order, somatic gene inactivation and upregulation of cell cycle, epithelial, and early pluripotency genes can be triggered independently such that any combination of these events can occur in single cells. Sustained co-expression of Epcam, Nanog, and Sox2 with other genes is required to progress toward iPSCs. Ehf, Phlda2, and translation initiation factor Eif4a1 play functional roles in robust iPSC generation. Using regulatory network analysis, we identify a critical role for signaling inhibition by 2i in repressing somatic expression and synergy between the epigenomic modifiers ascorbic acid and a Dot1L inhibitor for pluripotency gene activation.
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247
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Kucinski I, Gottgens B. Advancing Stem Cell Research through Multimodal Single-Cell Analysis. Cold Spring Harb Perspect Biol 2020; 12:a035725. [PMID: 31932320 PMCID: PMC7328456 DOI: 10.1101/cshperspect.a035725] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Technological advances play a key role in furthering our understanding of stem cell biology, and advancing the prospects of regenerative therapies. Highly parallelized methods, developed in the last decade, can profile DNA, RNA, or proteins in thousands of cells and even capture data across two or more modalities (multiomics). This allows unbiased and precise definition of molecular cell states, thus allowing classification of cell types, tracking of differentiation trajectories, and discovery of underlying mechanisms. Despite being based on destructive techniques, novel experimental and bioinformatic approaches enable embedding and extraction of temporal information, which is essential for deconvolution of complex data and establishing cause and effect relationships. Here, we provide an overview of recent studies pertinent to stem cell biology, followed by an outlook on how further advances in single-cell molecular profiling and computational analysis have the potential to shape the future of both basic and translational research.
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Affiliation(s)
- Iwo Kucinski
- Wellcome-MRC Cambridge Stem Cell Institute and Department of Haematology, University of Cambridge, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, Cambridge CB2 0AW, United Kingdom
| | - Berthold Gottgens
- Wellcome-MRC Cambridge Stem Cell Institute and Department of Haematology, University of Cambridge, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, Cambridge CB2 0AW, United Kingdom
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248
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Chen J. Perspectives on somatic reprogramming: spotlighting epigenetic regulation and cellular heterogeneity. Curr Opin Genet Dev 2020; 64:21-25. [PMID: 32599300 DOI: 10.1016/j.gde.2020.05.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Revised: 05/11/2020] [Accepted: 05/12/2020] [Indexed: 01/08/2023]
Abstract
Induction of pluripotency in somatic cells via ectopic expression of defined factors demonstrates the reversibility of developmental programming, thus serves as an ideal model to investigate the underlying principles of cell fate determination, which drives the differentiation of various cell types from an identical genome. Over the last decade, our understanding has grown considerably regarding how somatic reprogramming initiates and be regulated. Recent mechanistic investigations on chromatin architecture regulation and cell heterogeneity significantly contribute to the understanding of somatic reprogramming. Specifically, it is found that chromatin dynamics which are regulated by the orchestration of transcriptional factors and epigenetic regulation, play an important role during somatic reprogramming. Moreover, the widespread application of single-cell technologies reveals latent cell fate transition trajectories during the reprogramming process. Here, we highlighted several mechanistic studies on somatic reprogramming, particularly from epigenetic regulation and cell heterogeneity perspectives, and summarized their contribution to our current understanding of cell fate determination.
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Affiliation(s)
- Jiekai Chen
- Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, 510530 Guangzhou, China; Guangzhou Regenerative Medicine and Health-Guangdong Laboratory (GRMH-GDL), 510530 Guangzhou, China.
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249
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TFAP2C facilitates somatic cell reprogramming by inhibiting c-Myc-dependent apoptosis and promoting mesenchymal-to-epithelial transition. Cell Death Dis 2020; 11:482. [PMID: 32587258 PMCID: PMC7316975 DOI: 10.1038/s41419-020-2684-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 06/08/2020] [Accepted: 06/12/2020] [Indexed: 02/05/2023]
Abstract
Transcription factors are known to mediate the conversion of somatic cells to induced pluripotent stem cells (iPSCs). Transcription factor TFAP2C plays important roles in the regulation of embryonic development and carcinogenesis; however, the roles of Tfap2c in regulating somatic cell reprogramming are not well understood. Here we demonstrate Tfap2c is induced during the generation of iPSCs from mouse fibroblasts and acts as a facilitator for iPSCs formation. Mechanistically, the c-Myc-dependent apoptosis, which is a roadblock to reprogramming, can be significantly mitigated by Tfap2c overexpression. Meanwhile, Tfap2c can greatly promote mesenchymal-to-epithelial transition (MET) at initiation stage of OSKM-induced reprogramming. Further analysis of gene expression and targets of Tfap2c during reprogramming by RNA-sequencing (RNA-seq) and ChIP-qPCR indicates that TFAP2C can promote epithelial gene expression by binding to their promoters directly. Finally, knockdown of E-cadherin (Cdh1), an important downstream target of TFAP2C and a critical regulator of MET antagonizes Tfap2c-mediated reprogramming. Taken together, we conclude that Tfap2c serves as a strong activator for somatic cell reprogramming through promoting the MET and inhibiting c-Myc-dependent apoptosis.
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250
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Abstract
Cells adapt to changing environments. Perturb a cell and it returns to a point of homeostasis. Perturb a population and it evolves toward a fitness peak. We review quantitative models of the forces of adaptation and their visualizations on landscapes. While some adaptations result from single mutations or few-gene effects, others are more cooperative, more delocalized in the genome, and more universal and physical. For example, homeostasis and evolution depend on protein folding and aggregation, energy and protein production, protein diffusion, molecular motor speeds and efficiencies, and protein expression levels. Models provide a way to learn about the fitness of cells and cell populations by making and testing hypotheses.
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Affiliation(s)
- Luca Agozzino
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA; .,Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, USA
| | - Gábor Balázsi
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA; .,Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York 11794, USA
| | - Jin Wang
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA; .,Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, USA.,Department of Chemistry, Stony Brook University, Stony Brook, New York 11790, USA
| | - Ken A Dill
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA; .,Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, USA.,Department of Chemistry, Stony Brook University, Stony Brook, New York 11790, USA
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