1
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Ramsköld D, Hendriks GJ, Larsson AJM, Mayr JV, Ziegenhain C, Hagemann-Jensen M, Hartmanis L, Sandberg R. Single-cell new RNA sequencing reveals principles of transcription at the resolution of individual bursts. Nat Cell Biol 2024:10.1038/s41556-024-01486-9. [PMID: 39198695 DOI: 10.1038/s41556-024-01486-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Accepted: 07/15/2024] [Indexed: 09/01/2024]
Abstract
Analyses of transcriptional bursting from single-cell RNA-sequencing data have revealed patterns of variation and regulation in the kinetic parameters that could be inferred. Here we profiled newly transcribed (4-thiouridine-labelled) RNA across 10,000 individual primary mouse fibroblasts to more broadly infer bursting kinetics and coordination. We demonstrate that inference from new RNA profiles could separate the kinetic parameters that together specify the burst size, and that the synthesis rate (and not the transcriptional off rate) controls the burst size. Importantly, transcriptome-wide inference of transcriptional on and off rates provided conclusive evidence that RNA polymerase II transcribes genes in bursts. Recent reports identified examples of transcriptional co-bursting, yet no global analyses have been performed. The deep new RNA profiles we generated with allelic resolution demonstrated that co-bursting rarely appears more frequently than expected by chance, except for certain gene pairs, notably paralogues located in close genomic proximity. Altogether, new RNA single-cell profiling critically improves the inference of transcriptional bursting and provides strong evidence for independent transcriptional bursting of mammalian genes.
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Affiliation(s)
- Daniel Ramsköld
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
| | - Gert-Jan Hendriks
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
| | - Anton J M Larsson
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
| | - Juliane V Mayr
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
| | - Christoph Ziegenhain
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
| | | | - Leonard Hartmanis
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
| | - Rickard Sandberg
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden.
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2
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Volteras D, Shahrezaei V, Thomas P. Global transcription regulation revealed from dynamical correlations in time-resolved single-cell RNA sequencing. Cell Syst 2024; 15:694-708.e12. [PMID: 39121860 DOI: 10.1016/j.cels.2024.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/29/2024] [Accepted: 07/11/2024] [Indexed: 08/12/2024]
Abstract
Single-cell transcriptomics reveals significant variations in transcriptional activity across cells. Yet, it remains challenging to identify mechanisms of transcription dynamics from static snapshots. It is thus still unknown what drives global transcription dynamics in single cells. We present a stochastic model of gene expression with cell size- and cell cycle-dependent rates in growing and dividing cells that harnesses temporal dimensions of single-cell RNA sequencing through metabolic labeling protocols and cel lcycle reporters. We develop a parallel and highly scalable approximate Bayesian computation method that corrects for technical variation and accurately quantifies absolute burst frequency, burst size, and degradation rate along the cell cycle at a transcriptome-wide scale. Using Bayesian model selection, we reveal scaling between transcription rates and cell size and unveil waves of gene regulation across the cell cycle-dependent transcriptome. Our study shows that stochastic modeling of dynamical correlations identifies global mechanisms of transcription regulation. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Dimitris Volteras
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Vahid Shahrezaei
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London, SW7 2AZ, UK.
| | - Philipp Thomas
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London, SW7 2AZ, UK.
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3
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Gulati GS, D'Silva JP, Liu Y, Wang L, Newman AM. Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics. Nat Rev Mol Cell Biol 2024:10.1038/s41580-024-00768-2. [PMID: 39169166 DOI: 10.1038/s41580-024-00768-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/16/2024] [Indexed: 08/23/2024]
Abstract
Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.
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Affiliation(s)
- Gunsagar S Gulati
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Yunhe Liu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Aaron M Newman
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
- Chan Zuckerberg Biohub - San Francisco, San Francisco, CA, USA.
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4
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Xu Z, Sziraki A, Lee J, Zhou W, Cao J. Dissecting key regulators of transcriptome kinetics through scalable single-cell RNA profiling of pooled CRISPR screens. Nat Biotechnol 2024; 42:1218-1223. [PMID: 37749268 PMCID: PMC10961254 DOI: 10.1038/s41587-023-01948-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 08/15/2023] [Indexed: 09/27/2023]
Abstract
We present a combinatorial indexing method, PerturbSci-Kinetics, for capturing whole transcriptomes, nascent transcriptomes and single guide RNA (sgRNA) identities across hundreds of genetic perturbations at the single-cell level. Profiling a pooled CRISPR screen targeting various biological processes, we show the gene expression regulation during RNA synthesis, processing and degradation, miRNA biogenesis and mitochondrial mRNA processing, systematically decoding the genome-wide regulatory network that underlies RNA temporal dynamics at scale.
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Affiliation(s)
- Zihan Xu
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
- The David Rockefeller Graduate Program in Bioscience, The Rockefeller University, New York, NY, USA
| | - Andras Sziraki
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
- The David Rockefeller Graduate Program in Bioscience, The Rockefeller University, New York, NY, USA
| | - Jasper Lee
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
| | - Wei Zhou
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
| | - Junyue Cao
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA.
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5
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Ding LY, Chang CJ, Chen SY, Chen KL, Li YS, Wu YC, Hsu TY, Ying HY, Wu HY, Hughes MW, Wang CY, Chang CH, Tang MJ, Chuang WJ, Shan YS, Chang CJ, Huang PH. Stromal Rigidity Stress Accelerates Pancreatic Intraepithelial Neoplasia Progression and Chromosomal Instability via Nuclear Protein Tyrosine Kinase 2 Localization. THE AMERICAN JOURNAL OF PATHOLOGY 2024; 194:1346-1373. [PMID: 38631549 DOI: 10.1016/j.ajpath.2024.02.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 01/11/2024] [Accepted: 02/21/2024] [Indexed: 04/19/2024]
Abstract
Because the mechanotransduction by stromal stiffness stimulates the rupture and repair of the nuclear envelope in pancreatic progenitor cells, accumulated genomic aberrations are under selection in the tumor microenvironment. Analysis of cell growth, micronuclei, and phosphorylated Ser-139 residue of the histone variant H2AX (γH2AX) foci linked to mechanotransduction pressure in vivo during serial orthotopic passages of mouse KrasLSL-G12D/+;Trp53flox/flox;Pdx1-Cre (KPC) cancer cells in the tumor and in migrating through the size-restricted 3-μm micropores. To search for pancreatic cancer cell-of-origin, analysis of single-cell data sets revealed that the extracellular matrix shaped an alternate route of acinar-ductal transdifferentiation of acinar cells into topoisomerase II α (TOP2A)-overexpressing cancer cells and derived subclusters with copy number amplifications in MYC-PTK2 (protein tyrosine kinase 2) locus and PIK3CA. High-PTK2 expression is associated with 171 differentially methylated CpG loci, 319 differentially expressed genes, and poor overall survival in The Cancer Genome Atlas-Pancreatic Adenocarcinoma cohort. Abolished RGD-integrin signaling by disintegrin KG blocked the PTK2 phosphorylation, increased cancer apoptosis, decreased vav guanine nucleotide exchange factor 1 (VAV1) expression, and prolonged overall survival in the KPC mice. Reduction of α-smooth muscle actin deposition in the CD248 knockout KPC mice remodeled the tissue stroma and down-regulated TOP2A expression in the epithelium. In summary, stromal stiffness induced the onset of cancer cells-of-origin by ectopic TOP2A expression, and the genomic amplification of MYC-PTK2 locus via alternative transdifferentiation of pancreatic progenitor cells is the vulnerability useful for disintegrin KG treatment.
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Affiliation(s)
- Li-Yun Ding
- Institute of Basic Medical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Chia-Jung Chang
- Department of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Szu-Ying Chen
- Institute of Basic Medical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Kuan-Lin Chen
- Department of Biochemistry and Molecular Biology, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Yueh-Shan Li
- Department of Biochemistry and Molecular Biology, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Yun-Chieh Wu
- Department of Biochemistry and Molecular Biology, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Ting-Yi Hsu
- Department of Biochemistry and Molecular Biology, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Hsin-Yu Ying
- Department of Biochemistry and Molecular Biology, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Hsin-Yi Wu
- Instrumentation Center, College of Science, National Taiwan University, Taipei, Taiwan
| | - Michael W Hughes
- Institute of Clinical Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Department of Life Sciences, College of Bioscience and Biotechnology, National Cheng Kung University, Tainan, Taiwan; International Center for Wound Repair and Regeneration, National Cheng Kung University, Tainan, Taiwan
| | - Chia-Yih Wang
- Institute of Basic Medical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Department of Cell Biology and Anatomy, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Chih-Han Chang
- Department of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan; Medical Device Innovation Center, National Cheng Kung University, Tainan, Taiwan
| | - Ming-Jer Tang
- Institute of Basic Medical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan; International Center for Wound Repair and Regeneration, National Cheng Kung University, Tainan, Taiwan; Department of Physiology, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Woei-Jer Chuang
- Institute of Basic Medical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Department of Biochemistry and Molecular Biology, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Center of Cell Therapy, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Yan-Shen Shan
- Institute of Clinical Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Center of Cell Therapy, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Division of General Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Chia-Jung Chang
- Department of Internal Medicine, Ditmanson Medical Foundation, Chia-Yi Christian Hospital, Chia-Yi, Taiwan.
| | - Po-Hsien Huang
- Institute of Basic Medical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Department of Biochemistry and Molecular Biology, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
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6
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Weiler P, Lange M, Klein M, Pe'er D, Theis F. CellRank 2: unified fate mapping in multiview single-cell data. Nat Methods 2024; 21:1196-1205. [PMID: 38871986 PMCID: PMC11239496 DOI: 10.1038/s41592-024-02303-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 05/09/2024] [Indexed: 06/15/2024]
Abstract
Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or RNA velocity to reconstruct state-change trajectories; however, trajectory inference does not incorporate valuable time point information or utilize additional modalities, whereas methods that address these different data views cannot be combined or do not scale. Here we present CellRank 2, a versatile and scalable framework to study cellular fate using multiview single-cell data of up to millions of cells in a unified fashion. CellRank 2 consistently recovers terminal states and fate probabilities across data modalities in human hematopoiesis and endodermal development. Our framework also allows combining transitions within and across experimental time points, a feature we use to recover genes promoting medullary thymic epithelial cell formation during pharyngeal endoderm development. Moreover, we enable estimating cell-specific transcription and degradation rates from metabolic-labeling data, which we apply to an intestinal organoid system to delineate differentiation trajectories and pinpoint regulatory strategies.
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Affiliation(s)
- Philipp Weiler
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Marius Lange
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Michal Klein
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Machine Learning Research, Apple, Paris, France
| | - Dana Pe'er
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Fabian Theis
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
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7
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Maizels RJ, Snell DM, Briscoe J. Reconstructing developmental trajectories using latent dynamical systems and time-resolved transcriptomics. Cell Syst 2024; 15:411-424.e9. [PMID: 38754365 DOI: 10.1016/j.cels.2024.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 02/01/2024] [Accepted: 04/17/2024] [Indexed: 05/18/2024]
Abstract
The snapshot nature of single-cell transcriptomics presents a challenge for studying the dynamics of cell fate decisions. Metabolic labeling and splicing can provide temporal information at single-cell level, but current methods have limitations. Here, we present a framework that overcomes these limitations: experimentally, we developed sci-FATE2, an optimized method for metabolic labeling with increased data quality, which we used to profile 45,000 embryonic stem (ES) cells differentiating into neural tube identities. Computationally, we developed a two-stage framework for dynamical modeling: VelvetVAE, a variational autoencoder (VAE) for velocity inference that outperforms all other tools tested, and VelvetSDE, a neural stochastic differential equation (nSDE) framework for simulating trajectory distributions. These recapitulate underlying dataset distributions and capture features such as decision boundaries between alternative fates and fate-specific gene expression. These methods recast single-cell analyses from descriptions of observed data to models of the dynamics that generated them, providing a framework for investigating developmental fate decisions.
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Affiliation(s)
- Rory J Maizels
- The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK; University College, London, UK
| | - Daniel M Snell
- The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - James Briscoe
- The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK.
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8
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Maizels RJ. A dynamical perspective: moving towards mechanism in single-cell transcriptomics. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230049. [PMID: 38432314 PMCID: PMC10909508 DOI: 10.1098/rstb.2023.0049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 10/31/2023] [Indexed: 03/05/2024] Open
Abstract
As the field of single-cell transcriptomics matures, research is shifting focus from phenomenological descriptions of cellular phenotypes to a mechanistic understanding of the gene regulation underneath. This perspective considers the value of capturing dynamical information at single-cell resolution for gaining mechanistic insight; reviews the available technologies for recording and inferring temporal information in single cells; and explores whether better dynamical resolution is sufficient to adequately capture the causal relationships driving complex biological systems. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.
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Affiliation(s)
- Rory J. Maizels
- The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
- University College London, London WC1E 6BT, UK
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9
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Wei C, Kesner B, Yin H, Lee JT. Imprinted X chromosome inactivation at the gamete-to-embryo transition. Mol Cell 2024; 84:1442-1459.e7. [PMID: 38458200 PMCID: PMC11031340 DOI: 10.1016/j.molcel.2024.02.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 12/23/2023] [Accepted: 02/13/2024] [Indexed: 03/10/2024]
Abstract
In mammals, dosage compensation involves two parallel processes: (1) X inactivation, which equalizes X chromosome dosage between males and females, and (2) X hyperactivation, which upregulates the active X for X-autosome balance. The field currently favors models whereby dosage compensation initiates "de novo" during mouse development. Here, we develop "So-Smart-seq" to revisit the question and interrogate a comprehensive transcriptome including noncoding genes and repeats in mice. Intriguingly, de novo silencing pertains only to a subset of Xp genes. Evolutionarily older genes and repetitive elements demonstrate constitutive Xp silencing, adopt distinct signatures, and do not require Xist to initiate silencing. We trace Xp silencing backward in developmental time to meiotic sex chromosome inactivation in the male germ line and observe that Xm hyperactivation is timed to Xp silencing on a gene-by-gene basis. Thus, during the gamete-to-embryo transition, older Xp genes are transmitted in a "pre-inactivated" state. These findings have implications for the evolution of imprinting.
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Affiliation(s)
- Chunyao Wei
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA; Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Barry Kesner
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA; Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Hao Yin
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA; Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Jeannie T Lee
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA; Department of Genetics, Harvard Medical School, Boston, MA, USA.
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10
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Fishman L, Modak A, Nechooshtan G, Razin T, Erhard F, Regev A, Farrell JA, Rabani M. Cell-type-specific mRNA transcription and degradation kinetics in zebrafish embryogenesis from metabolically labeled single-cell RNA-seq. Nat Commun 2024; 15:3104. [PMID: 38600066 PMCID: PMC11006943 DOI: 10.1038/s41467-024-47290-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 03/27/2024] [Indexed: 04/12/2024] Open
Abstract
During embryonic development, pluripotent cells assume specialized identities by adopting particular gene expression profiles. However, systematically dissecting the relative contributions of mRNA transcription and degradation to shaping those profiles remains challenging, especially within embryos with diverse cellular identities. Here, we combine single-cell RNA-Seq and metabolic labeling to capture temporal cellular transcriptomes of zebrafish embryos where newly-transcribed (zygotic) and pre-existing (maternal) mRNA can be distinguished. We introduce kinetic models to quantify mRNA transcription and degradation rates within individual cell types during their specification. These models reveal highly varied regulatory rates across thousands of genes, coordinated transcription and destruction rates for many transcripts, and link differences in degradation to specific sequence elements. They also identify cell-type-specific differences in degradation, namely selective retention of maternal transcripts within primordial germ cells and enveloping layer cells, two of the earliest specified cell types. Our study provides a quantitative approach to study mRNA regulation during a dynamic spatio-temporal response.
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Affiliation(s)
- Lior Fishman
- Department of Genetics, Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Jerusalem, 9190401, Israel
| | - Avani Modak
- Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, 20814, USA
| | - Gal Nechooshtan
- Department of Genetics, Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Jerusalem, 9190401, Israel
| | - Talya Razin
- Department of Genetics, Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Jerusalem, 9190401, Israel
| | - Florian Erhard
- Institute for Virology and Immunobiology, University of Würzburg, Würzburg, Germany
- Chair of Computational Immunology, University of Regensburg, Regensburg, Germany
| | - Aviv Regev
- Department of Biology, MIT, Cambridge, MA, 02139, USA
- Klarman Cell Observatory Broad Institute of MIT and Harvard Cambridge, Cambridge, MA, 02142, USA
| | - Jeffrey A Farrell
- Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, 20814, USA.
| | - Michal Rabani
- Department of Genetics, Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Jerusalem, 9190401, Israel.
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11
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Liu Y, Huang K, Chen W. Resolving cellular dynamics using single-cell temporal transcriptomics. Curr Opin Biotechnol 2024; 85:103060. [PMID: 38194753 DOI: 10.1016/j.copbio.2023.103060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 12/04/2023] [Accepted: 12/10/2023] [Indexed: 01/11/2024]
Abstract
Cellular dynamics, the transition of a cell from one state to another, is central to understanding developmental processes and disease progression. Single-cell transcriptomics has been pushing the frontiers of cellular dynamics studies into a genome-wide and single-cell level. While most single-cell RNA sequencing approaches are disruptive and only provide a snapshot of cell states, the dynamics of a cell could be reconstructed by either exploiting temporal information hiding in the transcriptomics data or integrating additional information. In this review, we describe these approaches, highlighting their underlying principles, key assumptions, and the rationality to interpret the results as models. We also discuss the recently emerging nondisruptive live-cell transcriptomics methods, which are highly complementary to the computational models for their assumption-free nature.
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Affiliation(s)
- Yifei Liu
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Kai Huang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Wanze Chen
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
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12
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Liu H, Arsiè R, Schwabe D, Schilling M, Minia I, Alles J, Boltengagen A, Kocks C, Falcke M, Friedman N, Landthaler M, Rajewsky N. SLAM-Drop-seq reveals mRNA kinetic rates throughout the cell cycle. Mol Syst Biol 2023; 19:1-23. [PMID: 38778223 PMCID: PMC10568207 DOI: 10.15252/msb.202211427] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 07/24/2023] [Accepted: 08/04/2023] [Indexed: 05/25/2024] Open
Abstract
RNA abundance is tightly regulated in eukaryotic cells by modulating the kinetic rates of RNA production, processing, and degradation. To date, little is known about time‐dependent kinetic rates during dynamic processes. Here, we present SLAM‐Drop‐seq, a method that combines RNA metabolic labeling and alkylation of modified nucleotides in methanol‐fixed cells with droplet‐based sequencing to detect newly synthesized and preexisting mRNAs in single cells. As a first application, we sequenced 7280 HEK293 cells and calculated gene‐specific kinetic rates during the cell cycle using the novel package Eskrate. Of the 377 robust‐cycling genes that we identified, only a minor fraction is regulated solely by either dynamic transcription or degradation (6 and 4%, respectively). By contrast, the vast majority (89%) exhibit dynamically regulated transcription and degradation rates during the cell cycle. Our study thus shows that temporally regulated mRNA degradation is fundamental for the correct expression of a majority of cycling genes. SLAM‐Drop‐seq, combined with Eskrate, is a powerful approach to understanding the underlying mRNA kinetics of single‐cell gene expression dynamics in continuous biological processes.
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Affiliation(s)
- Haiyue Liu
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Roberto Arsiè
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Daniel Schwabe
- Mathematical Cell Physiology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Marcel Schilling
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
| | - Igor Minia
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Jonathan Alles
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Anastasiya Boltengagen
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Christine Kocks
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Martin Falcke
- Mathematical Cell Physiology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Department of Physics, Humboldt University Berlin, Berlin, Germany
| | - Nir Friedman
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
- The Lautenberg Center for Immunology and Cancer Research, Institute of Medical Research Israel-Canada (IMRIC), Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
- The Center for Computational Medicine, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Markus Landthaler
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
- Institut für Biologie, Humboldt Universität zu Berlin, Berlin, Germany.
| | - Nikolaus Rajewsky
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- German Center for Cardiovascular Research (DZHK), Berlin, Germany.
- NeuroCure Cluster of Excellence, Berlin, Germany.
- German Cancer Consortium (DKTK), Berlin, Germany.
- National Center for Tumor Diseases (NCT), Berlin, Germany.
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13
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Shi Q, Chen X, Zhang Z. Decoding Human Biology and Disease Using Single-cell Omics Technologies. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:926-949. [PMID: 37739168 PMCID: PMC10928380 DOI: 10.1016/j.gpb.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 05/22/2023] [Accepted: 06/08/2023] [Indexed: 09/24/2023]
Abstract
Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.
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Affiliation(s)
- Qiang Shi
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Xueyan Chen
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Zemin Zhang
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Changping Laboratory, Beijing 102206, China.
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14
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Erbe R, Stein-O’Brien G, Fertig EJ. Transcriptomic forecasting with neural ordinary differential equations. PATTERNS (NEW YORK, N.Y.) 2023; 4:100793. [PMID: 37602211 PMCID: PMC10435954 DOI: 10.1016/j.patter.2023.100793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 04/03/2023] [Accepted: 06/13/2023] [Indexed: 08/22/2023]
Abstract
Single-cell transcriptomics technologies can uncover changes in the molecular states that underlie cellular phenotypes. However, understanding the dynamic cellular processes requires extending from inferring trajectories from snapshots of cellular states to estimating temporal changes in cellular gene expression. To address this challenge, we have developed a neural ordinary differential-equation-based method, RNAForecaster, for predicting gene expression states in single cells for multiple future time steps in an embedding-independent manner. We demonstrate that RNAForecaster can accurately predict future expression states in simulated single-cell transcriptomic data with cellular tracking over time. We then show that by using metabolic labeling single-cell RNA sequencing (scRNA-seq) data from constitutively dividing cells, RNAForecaster accurately recapitulates many of the expected changes in gene expression during progression through the cell cycle over a 3-day period. Thus, RNAForecaster enables short-term estimation of future expression states in biological systems from high-throughput datasets with temporal information.
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Affiliation(s)
- Rossin Erbe
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Johns Hopkins Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA
| | - Genevieve Stein-O’Brien
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Johns Hopkins Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Kavli Neurodiscovery Institute, Baltimore, MD, USA
- Single Cell Training and Analysis Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Elana J. Fertig
- Johns Hopkins Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA
- Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
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15
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Zeng Q, Mousa M, Nadukkandy AS, Franssens L, Alnaqbi H, Alshamsi FY, Safar HA, Carmeliet P. Understanding tumour endothelial cell heterogeneity and function from single-cell omics. Nat Rev Cancer 2023:10.1038/s41568-023-00591-5. [PMID: 37349410 DOI: 10.1038/s41568-023-00591-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/22/2023] [Indexed: 06/24/2023]
Abstract
Anti-angiogenic therapies (AATs) are used to treat different types of cancers. However, their success is limited owing to insufficient efficacy and resistance. Recently, single-cell omics studies of tumour endothelial cells (TECs) have provided new mechanistic insight. Here, we overview the heterogeneity of human TECs of all tumour types studied to date, at the single-cell level. Notably, most human tumour types contain varying numbers but only a small population of angiogenic TECs, the presumed targets of AATs, possibly contributing to the limited efficacy of and resistance to AATs. In general, TECs are heterogeneous within and across all tumour types, but comparing TEC phenotypes across tumours is currently challenging, owing to the lack of a uniform nomenclature for endothelial cells and consistent single-cell analysis protocols, urgently raising the need for a more consistent approach. Nonetheless, across most tumour types, universal TEC markers (ACKR1, PLVAP and IGFBP3) can be identified. Besides angiogenesis, biological processes such as immunomodulation and extracellular matrix organization are among the most commonly predicted enriched signatures of TECs across different tumour types. Although angiogenesis and extracellular matrix targets have been considered for AAT (without the hoped success), the immunomodulatory properties of TECs have not been fully considered as a novel anticancer therapeutic approach. Therefore, we also discuss progress, limitations, solutions and novel targets for AAT development.
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Affiliation(s)
- Qun Zeng
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven and Center for Cancer Biology, VIB, Leuven, Belgium
| | - Mira Mousa
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, UAE
| | - Aisha Shigna Nadukkandy
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven and Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory of Angiogenesis and Vascular Heterogeneity, Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Lies Franssens
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven and Center for Cancer Biology, VIB, Leuven, Belgium
| | - Halima Alnaqbi
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, UAE
| | - Fatima Yousif Alshamsi
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE
| | - Habiba Al Safar
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, UAE.
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE.
| | - Peter Carmeliet
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven and Center for Cancer Biology, VIB, Leuven, Belgium.
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, UAE.
- Laboratory of Angiogenesis and Vascular Heterogeneity, Department of Biomedicine, Aarhus University, Aarhus, Denmark.
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16
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Edwards DM, Davies P, Hebenstreit D. Synergising single-cell resolution and 4sU labelling boosts inference of transcriptional bursting. Genome Biol 2023; 24:138. [PMID: 37328900 PMCID: PMC10276402 DOI: 10.1186/s13059-023-02977-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 05/25/2023] [Indexed: 06/18/2023] Open
Abstract
Despite the recent rise of RNA-seq datasets combining single-cell (sc) resolution with 4-thiouridine (4sU) labelling, analytical methods exploiting their power to dissect transcriptional bursting are lacking. Here, we present a mathematical model and Bayesian inference implementation to facilitate genome-wide joint parameter estimation and confidence quantification (R package: burstMCMC). We demonstrate that, unlike conventional scRNA-seq, 4sU scRNA-seq resolves temporal parameters and furthermore boosts inference of dimensionless parameters via a synergy between single-cell resolution and 4sU labelling. We apply our method to published 4sU scRNA-seq data and linked with ChIP-seq data, we uncover previously obscured associations between different parameters and histone modifications.
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Affiliation(s)
| | - Philip Davies
- School of Life Sciences, University of Warwick, Coventry, UK
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17
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Rummel T, Sakellaridi L, Erhard F. grandR: a comprehensive package for nucleotide conversion RNA-seq data analysis. Nat Commun 2023; 14:3559. [PMID: 37321987 PMCID: PMC10272207 DOI: 10.1038/s41467-023-39163-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 06/01/2023] [Indexed: 06/17/2023] Open
Abstract
Metabolic labeling of RNA is a powerful technique for studying the temporal dynamics of gene expression. Nucleotide conversion approaches greatly facilitate the generation of data but introduce challenges for their analysis. Here we present grandR, a comprehensive package for quality control, differential gene expression analysis, kinetic modeling, and visualization of such data. We compare several existing methods for inference of RNA synthesis rates and half-lives using progressive labeling time courses. We demonstrate the need for recalibration of effective labeling times and introduce a Bayesian approach to study the temporal dynamics of RNA using snapshot experiments.
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Affiliation(s)
- Teresa Rummel
- Institute for Virology and Immunobiology, University of Würzburg, Versbacher Str. 7, 97078, Würzburg, Germany
| | - Lygeri Sakellaridi
- Institute for Virology and Immunobiology, University of Würzburg, Versbacher Str. 7, 97078, Würzburg, Germany
| | - Florian Erhard
- Institute for Virology and Immunobiology, University of Würzburg, Versbacher Str. 7, 97078, Würzburg, Germany.
- Faculty for Informatics and Data Science, University of Regensburg, Bajuwarenstr. 4, 93053, Regensburg, Germany.
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18
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Xu Z, Sziraki A, Lee J, Zhou W, Cao J. PerturbSci-Kinetics: Dissecting key regulators of transcriptome kinetics through scalable single-cell RNA profiling of pooled CRISPR screens. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.29.526143. [PMID: 36778497 PMCID: PMC9915486 DOI: 10.1101/2023.01.29.526143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Here we described PerturbSci-Kinetics, a novel combinatorial indexing method for capturing three-layer single-cell readout (i.e., whole transcriptomes, nascent transcriptomes, sgRNA identities) across hundreds of genetic perturbations. Through PerturbSci-Kinetics profiling of pooled CRISPR screens targeting a variety of biological processes, we were able to decipher the complexity of RNA regulations at multiple levels (e.g., synthesis, processing, degradation), and revealed key regulators involved in miRNA and mitochondrial RNA processing pathways. Our technique opens the possibility of systematically decoding the genome-wide regulatory network underlying RNA temporal dynamics at scale and cost-effectively.
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19
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Ren J, Zhou H, Zeng H, Wang CK, Huang J, Qiu X, Sui X, Li Q, Wu X, Lin Z, Lo JA, Maher K, He Y, Tang X, Lam J, Chen H, Li B, Fisher DE, Liu J, Wang X. Spatiotemporally resolved transcriptomics reveals the subcellular RNA kinetic landscape. Nat Methods 2023; 20:695-705. [PMID: 37038000 PMCID: PMC10172111 DOI: 10.1038/s41592-023-01829-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 02/22/2023] [Indexed: 04/12/2023]
Abstract
Spatiotemporal regulation of the cellular transcriptome is crucial for proper protein expression and cellular function. However, the intricate subcellular dynamics of RNA remain obscured due to the limitations of existing transcriptomics methods. Here, we report TEMPOmap-a method that uncovers subcellular RNA profiles across time and space at the single-cell level. TEMPOmap integrates pulse-chase metabolic labeling with highly multiplexed three-dimensional in situ sequencing to simultaneously profile the age and location of individual RNA molecules. Using TEMPOmap, we constructed the subcellular RNA kinetic landscape in various human cells from transcription and translocation to degradation. Clustering analysis of RNA kinetic parameters across single cells revealed 'kinetic gene clusters' whose expression patterns were shaped by multistep kinetic sculpting. Importantly, these kinetic gene clusters are functionally segregated, suggesting that subcellular RNA kinetics are differentially regulated in a cell-state- and cell-type-dependent manner. Spatiotemporally resolved transcriptomics provides a gateway to uncovering new spatiotemporal gene regulation principles.
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Affiliation(s)
- Jingyi Ren
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Haowen Zhou
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hu Zeng
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Jiahao Huang
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Xiaojie Qiu
- Whitehead Institute for Biomedical Research Cambridge, Cambridge, MA, USA
| | - Xin Sui
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Qiang Li
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Xunwei Wu
- Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Zuwan Lin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Jennifer A Lo
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kamal Maher
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yichun He
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Xin Tang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Judson Lam
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hongyu Chen
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Brian Li
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - David E Fisher
- Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Jia Liu
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Xiao Wang
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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20
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Fishman L, Nechooshtan G, Erhard F, Regev A, Farrell JA, Rabani M. Single-cell temporal dynamics reveals the relative contributions of transcription and degradation to cell-type specific gene expression in zebrafish embryos. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.20.537620. [PMID: 37131717 PMCID: PMC10153228 DOI: 10.1101/2023.04.20.537620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
During embryonic development, pluripotent cells assume specialized identities by adopting particular gene expression profiles. However, systematically dissecting the underlying regulation of mRNA transcription and degradation remains a challenge, especially within whole embryos with diverse cellular identities. Here, we collect temporal cellular transcriptomes of zebrafish embryos, and decompose them into their newly-transcribed (zygotic) and pre-existing (maternal) mRNA components by combining single-cell RNA-Seq and metabolic labeling. We introduce kinetic models capable of quantifying regulatory rates of mRNA transcription and degradation within individual cell types during their specification. These reveal different regulatory rates between thousands of genes, and sometimes between cell types, that shape spatio-temporal expression patterns. Transcription drives most cell-type restricted gene expression. However, selective retention of maternal transcripts helps to define the gene expression profiles of germ cells and enveloping layer cells, two of the earliest specified cell-types. Coordination between transcription and degradation restricts expression of maternal-zygotic genes to specific cell types or times, and allows the emergence of spatio-temporal patterns when overall mRNA levels are held relatively constant. Sequence-based analysis links differences in degradation to specific sequence motifs. Our study reveals mRNA transcription and degradation events that control embryonic gene expression, and provides a quantitative approach to study mRNA regulation during a dynamic spatio-temporal response.
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Affiliation(s)
- Lior Fishman
- Department of Genetics, Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Jerusalem, 9190401, Israel
| | - Gal Nechooshtan
- Department of Genetics, Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Jerusalem, 9190401, Israel
| | - Florian Erhard
- Institute for Virology and Immunobiology, University of Würzburg, Würzburg, Germany
| | - Aviv Regev
- Department of Biology, MIT, Cambridge MA 02139, USA; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Jeffrey A. Farrell
- Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, 20814, USA
| | - Michal Rabani
- Department of Genetics, Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Jerusalem, 9190401, Israel
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21
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Papež M, Jiménez Lancho V, Eisenhut P, Motheramgari K, Borth N. SLAM-seq reveals early transcriptomic response mechanisms upon glutamine deprivation in Chinese hamster ovary cells. Biotechnol Bioeng 2023; 120:970-986. [PMID: 36575109 DOI: 10.1002/bit.28320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/30/2022] [Accepted: 12/25/2022] [Indexed: 12/29/2022]
Abstract
Mammalian cells frequently encounter subtle perturbations during recombinant protein production. Identifying the genetic factors that govern the cellular stress response can facilitate targeted genetic engineering to obtain production cell lines that demonstrate a higher stress tolerance. To simulate nutrient stress, Chinese hamster ovary (CHO) cells were transferred into a glutamine(Q)-free medium and transcriptional dynamics using thiol(SH)-linked alkylation for the metabolic sequencing of RNA (SLAM-seq) along with standard RNA-seq of stressed and unstressed cells were investigated. The SLAM-seq method allows differentiation between actively transcribed, nascent mRNA, and total (previously present) mRNA in the sample, adding an additional, time-resolved layer to classic RNA-sequencing. The cells tackle amino acid (AA) limitation by inducing the integrated stress response (ISR) signaling pathway, reflected in Atf4 overexpression in the early hours post Q deprivation, leading to subsequent activation of its targets, Asns, Atf3, Ddit3, Eif4ebp1, Gpt2, Herpud1, Slc7a1, Slc7a11, Slc38a2, Trib3, and Vegfa. The GCN2-eIF2α-ATF4 pathway is confirmed by a significant halt in transcription of translation-related genes at 24 h post Q deprivation. The downregulation of lipid synthesis indicates the inhibition of the mTOR pathway, further confirmed by overexpression of Sesn2. Furthermore, SLAM-seq detects short-lived transcription factors, such as Egr1, that would have been missed in standard experimental designs with RNA-seq. Our results describe the successful establishment of SLAM-seq in CHO cells and therefore facilitate its future use in other scenarios where dynamic transcriptome profiling in CHO cells is essential.
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Affiliation(s)
- Maja Papež
- Austrian Centre of Industrial Biotechnology (acib GmbH), Graz, Austria
| | | | - Peter Eisenhut
- Austrian Centre of Industrial Biotechnology (acib GmbH), Graz, Austria
| | | | - Nicole Borth
- Austrian Centre of Industrial Biotechnology (acib GmbH), Graz, Austria
- University of Natural Resources and Life Sciences (BOKU), Vienna, Austria
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22
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Well-TEMP-seq as a microwell-based strategy for massively parallel profiling of single-cell temporal RNA dynamics. Nat Commun 2023; 14:1272. [PMID: 36882403 PMCID: PMC9992361 DOI: 10.1038/s41467-023-36902-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 02/21/2023] [Indexed: 03/09/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) reveals the transcriptional heterogeneity of cells, but the static snapshots fail to reveal the time-resolved dynamics of transcription. Herein, we develop Well-TEMP-seq, a high-throughput, cost-effective, accurate, and efficient method for massively parallel profiling the temporal dynamics of single-cell gene expression. Well-TEMP-seq combines metabolic RNA labeling with scRNA-seq method Well-paired-seq to distinguish newly transcribed RNAs marked by T-to-C substitutions from pre-existing RNAs in each of thousands of single cells. The Well-paired-seq chip ensures a high single cell/barcoded bead pairing rate (~80%) and the improved alkylation chemistry on beads greatly alleviates chemical conversion-induced cell loss (~67.5% recovery). We further apply Well-TEMP-seq to profile the transcriptional dynamics of colorectal cancer cells exposed to 5-AZA-CdR, a DNA-demethylating drug. Well-TEMP-seq unbiasedly captures the RNA dynamics and outperforms the splicing-based RNA velocity method. We anticipate that Well-TEMP-seq will be broadly applicable to unveil the dynamics of single-cell gene expression in diverse biological processes.
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23
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Neuschulz A, Bakina O, Badillo-Lisakowski V, Olivares-Chauvet P, Conrad T, Gotthardt M, Kettenmann H, Junker JP. A single-cell RNA labeling strategy for measuring stress response upon tissue dissociation. Mol Syst Biol 2023; 19:e11147. [PMID: 36573354 PMCID: PMC9912023 DOI: 10.15252/msb.202211147] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 12/12/2022] [Accepted: 12/13/2022] [Indexed: 12/28/2022] Open
Abstract
Tissue dissociation, a crucial step in single-cell sample preparation, can alter the transcriptional state of a sample through the intrinsic cellular stress response. Here we demonstrate a general approach for measuring transcriptional response during sample preparation. In our method, transcripts made during dissociation are labeled for later identification upon sequencing. We found general as well as cell-type-specific dissociation response programs in zebrafish larvae, and we observed sample-to-sample variation in the dissociation response of mouse cardiomyocytes despite well-controlled experimental conditions. Finally, we showed that dissociation of the mouse hippocampus can lead to the artificial activation of microglia. In summary, our approach facilitates experimental optimization of dissociation procedures as well as computational removal of transcriptional perturbation response.
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Affiliation(s)
- Anika Neuschulz
- Quantitative Developmental Biology, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany.,Humboldt-Universität zu Berlin, Berlin, Germany
| | - Olga Bakina
- Cellular Neurosciences, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Victor Badillo-Lisakowski
- Humboldt-Universität zu Berlin, Berlin, Germany.,Neuromuscular and Cardiovascular Cell Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany.,DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
| | - Pedro Olivares-Chauvet
- Quantitative Developmental Biology, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Thomas Conrad
- BIH/MDC Genomics Technology Platform, Berlin, Germany
| | - Michael Gotthardt
- Neuromuscular and Cardiovascular Cell Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany.,DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany.,Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Helmut Kettenmann
- Cellular Neurosciences, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Jan Philipp Junker
- Quantitative Developmental Biology, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany.,DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany.,Charité Universitätsmedizin Berlin, Berlin, Germany
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24
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Bao Z, Li T, Liu J. Determining RNA Natural Modifications and Nucleoside Analog-Labeled Sites by a Chemical/Enzyme-Induced Base Mutation Principle. Molecules 2023; 28:1517. [PMID: 36838506 PMCID: PMC9958784 DOI: 10.3390/molecules28041517] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/30/2023] [Accepted: 02/02/2023] [Indexed: 02/09/2023] Open
Abstract
The natural chemical modifications of messenger RNA (mRNA) in living organisms have shown essential roles in both physiology and pathology. The mapping of mRNA modifications is critical for interpreting their biological functions. In another dimension, the synthesized nucleoside analogs can enable chemical labeling of cellular mRNA through a metabolic pathway, which facilitates the study of RNA dynamics in a pulse-chase manner. In this regard, the sequencing tools for mapping both natural modifications and nucleoside tags on mRNA at single base resolution are highly necessary. In this work, we review the progress of chemical sequencing technology for determining both a variety of naturally occurring base modifications mainly on mRNA and a few on transfer RNA and metabolically incorporated artificial base analogs on mRNA, and further discuss the problems and prospects in the field.
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Affiliation(s)
- Ziming Bao
- MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310058, China
| | - Tengwei Li
- MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310058, China
| | - Jianzhao Liu
- MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310058, China
- Life Sciences Institute, Zhejiang University, Hangzhou 310058, China
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25
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Zheng M, Lin Y, Wang W, Zhao Y, Bao X. Application of nucleoside or nucleotide analogues in RNA dynamics and RNA-binding protein analysis. WILEY INTERDISCIPLINARY REVIEWS. RNA 2022; 13:e1722. [PMID: 35218164 DOI: 10.1002/wrna.1722] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 01/07/2022] [Accepted: 01/26/2022] [Indexed: 06/14/2023]
Abstract
Cellular RNAs undergo dynamic changes during RNA biological processes, which are tightly orchestrated by RNA-binding proteins (RBPs). Yet, the investigation of RNA dynamics is hurdled by highly abundant steady-state RNAs, which make the signals of dynamic RNAs less detectable. Notably, the exert of nucleoside or nucleotide analogue-based RNA technologies has provided a remarkable platform for RNA dynamics research, revealing diverse unnoticed features in RNA metabolism. In this review, we focus on the application of two types of analogue-based RNA sequencing, antigen-/antibody- and click chemistry-based methodologies, and summarize the RNA dynamics features revealed. Moreover, we discuss emerging single-cell newly transcribed RNA sequencing methodologies based on nucleoside analogue labeling, which provides novel insights into RNA dynamics regulation at single-cell resolution. On the other hand, we also emphasize the identification of RBPs that interact with polyA, non-polyA RNAs, or newly transcribed RNAs and also their associated RNA-binding domains at genomewide level through ultraviolet crosslinking and mass spectrometry in different contexts. We anticipated that further modification and development of these analogue-based RNA and RBP capture technologies will aid in obtaining an unprecedented understanding of RNA biology. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry RNA Methods > RNA Analyses in Cells.
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Affiliation(s)
- Meifeng Zheng
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yingying Lin
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- The Center for Infection and Immunity Study, School of Medicine, Sun Yat-sen University, Guangming Science City, Shenzhen, China
| | - Wei Wang
- Center for Biosafety, Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
| | - Yu Zhao
- Molecular Cancer Research Center, School of Medicine, Sun Yat-sen University, Shenzhen, China
| | - Xichen Bao
- Center for Cell Lineage and Development, CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- University of Chinese Academy of Sciences, Beijing, China
- Center for Cell Lineage and Atlas, Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
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26
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Xing J. Reconstructing data-driven governing equations for cell phenotypic transitions: integration of data science and systems biology. Phys Biol 2022; 19:10.1088/1478-3975/ac8c16. [PMID: 35998617 PMCID: PMC9585661 DOI: 10.1088/1478-3975/ac8c16] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 08/23/2022] [Indexed: 11/11/2022]
Abstract
Cells with the same genome can exist in different phenotypes and can change between distinct phenotypes when subject to specific stimuli and microenvironments. Some examples include cell differentiation during development, reprogramming for induced pluripotent stem cells and transdifferentiation, cancer metastasis and fibrosis progression. The regulation and dynamics of cell phenotypic conversion is a fundamental problem in biology, and has a long history of being studied within the formalism of dynamical systems. A main challenge for mechanism-driven modeling studies is acquiring sufficient amount of quantitative information for constraining model parameters. Advances in quantitative experimental approaches, especially high throughput single-cell techniques, have accelerated the emergence of a new direction for reconstructing the governing dynamical equations of a cellular system from quantitative single-cell data, beyond the dominant statistical approaches. Here I review a selected number of recent studies using live- and fixed-cell data and provide my perspective on future development.
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Affiliation(s)
- Jianhua Xing
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15232, USA
- Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15232, USA
- UPMC-Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
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27
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Xing J. Reconstructing data-driven governing equations for cell phenotypic transitions: integration of data science and systems biology. Phys Biol 2022. [PMID: 35998617 DOI: 10.48550/arxiv.2203.14964] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Cells with the same genome can exist in different phenotypes and can change between distinct phenotypes when subject to specific stimuli and microenvironments. Some examples include cell differentiation during development, reprogramming for induced pluripotent stem cells and transdifferentiation, cancer metastasis and fibrosis progression. The regulation and dynamics of cell phenotypic conversion is a fundamental problem in biology, and has a long history of being studied within the formalism of dynamical systems. A main challenge for mechanism-driven modeling studies is acquiring sufficient amount of quantitative information for constraining model parameters. Advances in quantitative experimental approaches, especially high throughput single-cell techniques, have accelerated the emergence of a new direction for reconstructing the governing dynamical equations of a cellular system from quantitative single-cell data, beyond the dominant statistical approaches. Here I review a selected number of recent studies using live- and fixed-cell data and provide my perspective on future development.
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Affiliation(s)
- Jianhua Xing
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15232, United States of America.,Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15232, United States of America.,UPMC-Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, United States of America
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28
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Gupta A, Martin-Rufino JD, Jones TR, Subramanian V, Qiu X, Grody EI, Bloemendal A, Weng C, Niu SY, Min KH, Mehta A, Zhang K, Siraj L, Al' Khafaji A, Sankaran VG, Raychaudhuri S, Cleary B, Grossman S, Lander ES. Inferring gene regulation from stochastic transcriptional variation across single cells at steady state. Proc Natl Acad Sci U S A 2022; 119:e2207392119. [PMID: 35969771 PMCID: PMC9407670 DOI: 10.1073/pnas.2207392119] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 07/20/2022] [Indexed: 12/24/2022] Open
Abstract
Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful cofluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential.
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Affiliation(s)
- Anika Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115
| | - Jorge D. Martin-Rufino
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115
- Dana-Farber Cancer Institute, Boston, MA 02215
| | | | | | - Xiaojie Qiu
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142
- HHMI, Massachusetts Institute of Technology, Cambridge, MA 02139
| | | | | | - Chen Weng
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115
- Dana-Farber Cancer Institute, Boston, MA 02215
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142
| | | | - Kyung Hoi Min
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Arnav Mehta
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Dana-Farber Cancer Institute, Boston, MA 02215
- Department of Medicine, Massachusetts General Hospital, Boston, MA 02114
| | - Kaite Zhang
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | - Layla Siraj
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | | | - Vijay G. Sankaran
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115
- Dana-Farber Cancer Institute, Boston, MA 02215
| | - Soumya Raychaudhuri
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA 02115
| | - Brian Cleary
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | | | - Eric S. Lander
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115
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29
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Chen W, Guillaume-Gentil O, Rainer PY, Gäbelein CG, Saelens W, Gardeux V, Klaeger A, Dainese R, Zachara M, Zambelli T, Vorholt JA, Deplancke B. Live-seq enables temporal transcriptomic recording of single cells. Nature 2022; 608:733-740. [PMID: 35978187 PMCID: PMC9402441 DOI: 10.1038/s41586-022-05046-9] [Citation(s) in RCA: 84] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 06/29/2022] [Indexed: 11/26/2022]
Abstract
Single-cell transcriptomics (scRNA-seq) has greatly advanced our ability to characterize cellular heterogeneity1. However, scRNA-seq requires lysing cells, which impedes further molecular or functional analyses on the same cells. Here, we established Live-seq, a single-cell transcriptome profiling approach that preserves cell viability during RNA extraction using fluidic force microscopy2,3, thus allowing to couple a cell's ground-state transcriptome to its downstream molecular or phenotypic behaviour. To benchmark Live-seq, we used cell growth, functional responses and whole-cell transcriptome read-outs to demonstrate that Live-seq can accurately stratify diverse cell types and states without inducing major cellular perturbations. As a proof of concept, we show that Live-seq can be used to directly map a cell's trajectory by sequentially profiling the transcriptomes of individual macrophages before and after lipopolysaccharide (LPS) stimulation, and of adipose stromal cells pre- and post-differentiation. In addition, we demonstrate that Live-seq can function as a transcriptomic recorder by preregistering the transcriptomes of individual macrophages that were subsequently monitored by time-lapse imaging after LPS exposure. This enabled the unsupervised, genome-wide ranking of genes on the basis of their ability to affect macrophage LPS response heterogeneity, revealing basal Nfkbia expression level and cell cycle state as important phenotypic determinants, which we experimentally validated. Thus, Live-seq can address a broad range of biological questions by transforming scRNA-seq from an end-point to a temporal analysis approach.
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Affiliation(s)
- Wanze Chen
- Laboratory of Systems Biology and Genetics, Institute of Bio-engineering and Global Health Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | | | - Pernille Yde Rainer
- Laboratory of Systems Biology and Genetics, Institute of Bio-engineering and Global Health Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Christoph G Gäbelein
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich, Switzerland
| | - Wouter Saelens
- Laboratory of Systems Biology and Genetics, Institute of Bio-engineering and Global Health Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Vincent Gardeux
- Laboratory of Systems Biology and Genetics, Institute of Bio-engineering and Global Health Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Amanda Klaeger
- Laboratory of Systems Biology and Genetics, Institute of Bio-engineering and Global Health Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Riccardo Dainese
- Laboratory of Systems Biology and Genetics, Institute of Bio-engineering and Global Health Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Magda Zachara
- Laboratory of Systems Biology and Genetics, Institute of Bio-engineering and Global Health Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Tomaso Zambelli
- Laboratory of Biosensors and Bioelectronics, Institute for Biomedical Engineering, ETH Zurich, Zurich, Switzerland
| | - Julia A Vorholt
- Department of Biology, Institute of Microbiology, ETH Zurich, Zurich, Switzerland.
| | - Bart Deplancke
- Laboratory of Systems Biology and Genetics, Institute of Bio-engineering and Global Health Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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30
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Hahaut V, Pavlinic D, Carbone W, Schuierer S, Balmer P, Quinodoz M, Renner M, Roma G, Cowan CS, Picelli S. Fast and highly sensitive full-length single-cell RNA sequencing using FLASH-seq. Nat Biotechnol 2022; 40:1447-1451. [PMID: 35637419 PMCID: PMC9546769 DOI: 10.1038/s41587-022-01312-3] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 04/08/2022] [Indexed: 12/29/2022]
Abstract
We present FLASH-seq (FS), a full-length single-cell RNA sequencing (scRNA-seq) method with increased sensitivity and reduced hands-on time compared to Smart-seq3. The entire FS protocol can be performed in ~4.5 hours, is simple to automate and can be easily miniaturized to decrease resource consumption. The FS protocol can also use unique molecular identifiers (UMIs) for molecule counting while displaying reduced strand-invasion artifacts. FS will be especially useful for characterizing gene expression at high resolution across multiple samples. FLASH-seq speeds up high-sensitivity scRNA-seq while sparing resources.
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31
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Ginhoux F, Yalin A, Dutertre CA, Amit I. Single-cell immunology: Past, present, and future. Immunity 2022; 55:393-404. [PMID: 35263567 DOI: 10.1016/j.immuni.2022.02.006] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/30/2021] [Accepted: 02/09/2022] [Indexed: 02/08/2023]
Abstract
The immune system is a complex, dynamic, and plastic ecosystem composed of multiple cell types that constantly sense and interact with their local microenvironment to protect from infection and maintain homeostasis. For over a century, great efforts and ingenuity have been applied to the characterization of immune cells and their microenvironments, but traditional marker-based and bulk technologies left key questions unanswered. In the past decade, the advent of single-cell genomic approaches has revolutionized our knowledge of the cellular and molecular makeup of the immune system. In this perspective, we outline the past, present, and future applications of single-cell genomics in immunology and discuss how the integration of multiomics at the single-cell level will pave the way for future advances in immunology research and clinical translation.
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Affiliation(s)
- Florent Ginhoux
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A(∗)STAR), Singapore 138648, Singapore; Gustave Roussy Cancer Campus, Villejuif 94800, France; Inserm U1015, Gustave Roussy, Villejuif 94800, France; Shanghai Institute of Immunology, Shanghai JiaoTong University School of Medicine, Shanghai 200025, China; Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore 169856, Singapore.
| | - Adam Yalin
- Department of Immunology, Weizmann Institute, Rehovot 7610001, Israel.
| | - Charles Antoine Dutertre
- Gustave Roussy Cancer Campus, Villejuif 94800, France; Inserm U1015, Gustave Roussy, Villejuif 94800, France.
| | - Ido Amit
- Department of Immunology, Weizmann Institute, Rehovot 7610001, Israel.
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32
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Qiu X, Zhang Y, Martin-Rufino JD, Weng C, Hosseinzadeh S, Yang D, Pogson AN, Hein MY, Hoi Joseph Min K, Wang L, Grody EI, Shurtleff MJ, Yuan R, Xu S, Ma Y, Replogle JM, Lander ES, Darmanis S, Bahar I, Sankaran VG, Xing J, Weissman JS. Mapping transcriptomic vector fields of single cells. Cell 2022; 185:690-711.e45. [PMID: 35108499 PMCID: PMC9332140 DOI: 10.1016/j.cell.2021.12.045] [Citation(s) in RCA: 138] [Impact Index Per Article: 69.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 10/08/2021] [Accepted: 12/28/2021] [Indexed: 01/03/2023]
Abstract
Single-cell (sc)-RNA-seq, together with RNA-velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. Here, we introduce an analytical framework dynamo, that infers absolute RNA velocity, reconstructs continuous vector-field functions that predict cell fates, employs differential geometry to extract underlying regulations, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo’s power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically-labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1–GATA1 circuit. Leveraging the Least-Action-Path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo thus represents an important step in advancing quantitative and predictive theories of cell-state transitions.
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Affiliation(s)
- Xiaojie Qiu
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Yan Zhang
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA; Joint CMU-Pitt Ph.D. Program in Computational Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jorge D Martin-Rufino
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Chen Weng
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA; Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Shayan Hosseinzadeh
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Dian Yang
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Angela N Pogson
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Marco Y Hein
- Chan Zuckerberg Biohub, 499 Illinois St, San Francisco, CA 94158, USA
| | - Kyung Hoi Joseph Min
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Li Wang
- Department of Mathematics, University of Texas at Arlington, Arlington, TX, USA
| | | | | | - Ruoshi Yuan
- California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA
| | | | - Yian Ma
- Halıcıoğlu Data Science Institute, University of California San Diego, San Diego, CA, USA
| | - Joseph M Replogle
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA; Medical Scientist Training Program, University of California, San Francisco, CA, USA
| | - Eric S Lander
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Systems Biology Harvard Medical School, Boston, MA 02125, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | | | - Ivet Bahar
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA; Joint CMU-Pitt Ph.D. Program in Computational Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Vijay G Sankaran
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Jianhua Xing
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA; Joint CMU-Pitt Ph.D. Program in Computational Biology, University of Pittsburgh, Pittsburgh, PA, USA; UPMC-Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA; Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Jonathan S Weissman
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA; Koch Institute For Integrative Cancer Research at MIT, MIT, Cambridge, MA, USA.
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33
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Ding J, Sharon N, Bar-Joseph Z. Temporal modelling using single-cell transcriptomics. Nat Rev Genet 2022; 23:355-368. [PMID: 35102309 DOI: 10.1038/s41576-021-00444-7] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/14/2021] [Indexed: 12/16/2022]
Abstract
Methods for profiling genes at the single-cell level have revolutionized our ability to study several biological processes and systems including development, differentiation, response programmes and disease progression. In many of these studies, cells are profiled over time in order to infer dynamic changes in cell states and types, sets of expressed genes, active pathways and key regulators. However, time-series single-cell RNA sequencing (scRNA-seq) also raises several new analysis and modelling issues. These issues range from determining when and how deep to profile cells, linking cells within and between time points, learning continuous trajectories, and integrating bulk and single-cell data for reconstructing models of dynamic networks. In this Review, we discuss several approaches for the analysis and modelling of time-series scRNA-seq, highlighting their steps, key assumptions, and the types of data and biological questions they are most appropriate for.
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34
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Shang J, He L, Wang J, Tong A, Xiang Y. In Situ Visualizing Nascent RNA by Exploring DNA-Templated Oxidative Amination of 4-Thiouridine. Bioconjug Chem 2022; 33:164-171. [PMID: 34910465 DOI: 10.1021/acs.bioconjchem.1c00524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Tracking and mapping the nascent RNA molecules in cells is essential for deciphering embryonic development and neuronal differentiation. Here, we utilized 4-thiouridine (s4U) as a metabolic tag to label nascent RNA and developed a fluorescence imaging method based on the DNA-templated oxidative amination (DTOA) reaction of s4U. The DTOA reaction occurred between amine-modified DNA and s4U-containing RNA with high sequence specificity and chemical selectivity. Target nascent mRNAs in HeLa cells, including those encoding green fluorescent proteins (GFPs) and endogenous BAG-1, were thus lit up selectively by DTOA-based fluorescence in situ hybridization (DTOA FISH). We believe the DTOA conjugation chemistry shown in this study could be generally applied to investigate the spatial distribution of nascent transcription dynamics in cellular processes.
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Affiliation(s)
- Jiachen Shang
- Department of Chemistry, Beijing Key Laboratory for Microanalytical Methods and Instrumentation, Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education), Tsinghua University, Beijing 100084, China
| | - Luo He
- Department of Chemistry, Beijing Key Laboratory for Microanalytical Methods and Instrumentation, Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education), Tsinghua University, Beijing 100084, China
| | - Jingyi Wang
- Department of Chemistry, Beijing Key Laboratory for Microanalytical Methods and Instrumentation, Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education), Tsinghua University, Beijing 100084, China
| | - Aijun Tong
- Department of Chemistry, Beijing Key Laboratory for Microanalytical Methods and Instrumentation, Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education), Tsinghua University, Beijing 100084, China
| | - Yu Xiang
- Department of Chemistry, Beijing Key Laboratory for Microanalytical Methods and Instrumentation, Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education), Tsinghua University, Beijing 100084, China
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35
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Lange M, Bergen V, Klein M, Setty M, Reuter B, Bakhti M, Lickert H, Ansari M, Schniering J, Schiller HB, Pe'er D, Theis FJ. CellRank for directed single-cell fate mapping. Nat Methods 2022; 19:159-170. [PMID: 35027767 PMCID: PMC8828480 DOI: 10.1038/s41592-021-01346-6] [Citation(s) in RCA: 231] [Impact Index Per Article: 115.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 11/07/2021] [Indexed: 12/20/2022]
Abstract
Computational trajectory inference enables the reconstruction of cell state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for single-cell fate mapping in diverse scenarios, including regeneration, reprogramming and disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, taking into account the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in velocity vectors. On pancreas development data, CellRank automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. Applied to lineage-traced cellular reprogramming data, predicted fate probabilities correctly recover reprogramming outcomes. CellRank also predicts a new dedifferentiation trajectory during postinjury lung regeneration, including previously unknown intermediate cell states, which we confirm experimentally. CellRank infers directed cell state transitions and cell fates incorporating RNA velocity information into a graph based Markov process.
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Affiliation(s)
- Marius Lange
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.,Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Volker Bergen
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.,Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Michal Klein
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
| | - Manu Setty
- Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Basic Sciences Division and Translational Data Science IRC, Fred Hutchinson Cancer Research Center, Seattle WA, USA
| | - Bernhard Reuter
- Department of Computer Science, University of Tübingen, Tübingen, Germany.,Zuse Institute Berlin (ZIB), Berlin, Germany
| | - Mostafa Bakhti
- Institute of Diabetes and Regeneration Research, Helmholtz Center Munich, Munich, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Heiko Lickert
- Institute of Diabetes and Regeneration Research, Helmholtz Center Munich, Munich, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Meshal Ansari
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.,Comprehensive Pneumology Center (CPC) / Institute of Lung Biology and Disease (ILBD), Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Janine Schniering
- Comprehensive Pneumology Center (CPC) / Institute of Lung Biology and Disease (ILBD), Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Herbert B Schiller
- Comprehensive Pneumology Center (CPC) / Institute of Lung Biology and Disease (ILBD), Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Dana Pe'er
- Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany. .,Department of Mathematics, Technical University of Munich, Munich, Germany. .,TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
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36
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Braun C, Knüppel R, Perez-Fernandez J, Ferreira-Cerca S. Non-radioactive In Vivo Labeling of RNA with 4-Thiouracil. Methods Mol Biol 2022; 2533:199-213. [PMID: 35796990 PMCID: PMC9761907 DOI: 10.1007/978-1-0716-2501-9_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
RNA molecules and their expression dynamics play essential roles in the establishment of complex cellular phenotypes and/or in the rapid cellular adaption to environmental changes. Accordingly, analyzing RNA expression remains an important step to understand the molecular basis controlling the formation of cellular phenotypes, cellular homeostasis or disease progression. Steady-state RNA levels in the cells are controlled by the sum of highly dynamic molecular processes contributing to RNA expression and can be classified in transcription, maturation and degradation. The main goal of analyzing RNA dynamics is to disentangle the individual contribution of these molecular processes to the life cycle of a given RNA under different physiological conditions. In the recent years, the use of nonradioactive nucleotide/nucleoside analogs and improved chemistry, in combination with time-dependent and high-throughput analysis, have greatly expanded our understanding of RNA metabolism across various cell types, organisms, and growth conditions.In this chapter, we describe a step-by-step protocol allowing pulse labeling of RNA with the nonradioactive nucleotide analog, 4-thiouracil , in the eukaryotic model organism Saccharomyces cerevisiae and the model archaeon Haloferax volcanii .
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Affiliation(s)
- Christina Braun
- Biochemistry III-Institute for Biochemistry, Genetics and Microbiology, University of Regensburg, Regensburg, Germany
| | - Robert Knüppel
- Biochemistry III-Institute for Biochemistry, Genetics and Microbiology, University of Regensburg, Regensburg, Germany
| | - Jorge Perez-Fernandez
- Biochemistry III-Institute for Biochemistry, Genetics and Microbiology, University of Regensburg, Regensburg, Germany.
- Department of Experimental Biology, University of Jaen, Jaén, Spain.
| | - Sébastien Ferreira-Cerca
- Biochemistry III-Institute for Biochemistry, Genetics and Microbiology, University of Regensburg, Regensburg, Germany.
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37
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Moreno S, Brunner M, Delazer I, Rieder D, Lusser A, Micura R. Synthesis of 4-thiouridines with prodrug functionalization for RNA metabolic labeling. RSC Chem Biol 2022; 3:447-455. [PMID: 35441143 PMCID: PMC8985182 DOI: 10.1039/d2cb00001f] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 02/18/2022] [Indexed: 12/22/2022] Open
Abstract
Metabolic labeling has emerged as a powerful tool to endow RNA with reactive handles allowing for subsequent chemical derivatization and processing. Recently, thiolated nucleosides, such as 4-thiouridine (4sU), have attracted great interest in metabolic labeling-based RNA sequencing approaches (TUC-seq, SLAM-seq, TimeLapse-seq) to study cellular RNA expression and decay dynamics. For these and other applications (e.g. PAR-CLIP), thus far only the naked nucleoside 4sU has been applied. Here we examined the concept of derivatizing 4sU into a 5′-monophosphate prodrug that would allow for cell permeation and potentially improve labeling efficiency by bypassing the rate-limiting first step of 5′ phosphorylation of the nucleoside into the ultimately bioactive 4sU triphosphate (4sUTP). To this end, we developed robust synthetic routes towards diverse 4sU monophosphate prodrugs. Using metabolic labeling assays, we found that most of the newly introduced 4sU prodrugs were well tolerated by the cells. One derivative, the bis(4-acetyloxybenzyl) 5′-monophosphate of 4sU, was also efficiently incorporated into nascent RNA. Synthetic access to 4-thiouridine (4sU) derivatives with monophosphate prodrug patterns creates additional possibilities for metabolic labeling of RNA for different applications.![]()
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Affiliation(s)
- Sarah Moreno
- Institute of Organic Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Melanie Brunner
- Institute of Molecular Biology, Biocenter, Medical University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Isabel Delazer
- Institute of Molecular Biology, Biocenter, Medical University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Dietmar Rieder
- Institute of Bioinformatics, Biocenter, Medical University of Innsbruck, Innrain 82, 6020 Innsbruck, Austria
| | - Alexandra Lusser
- Institute of Molecular Biology, Biocenter, Medical University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Ronald Micura
- Institute of Organic Chemistry, Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
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38
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Zhang Z, Zhang X. Inference of high-resolution trajectories in single-cell RNA-seq data by using RNA velocity. CELL REPORTS METHODS 2021; 1:100095. [PMID: 35474895 PMCID: PMC9017235 DOI: 10.1016/j.crmeth.2021.100095] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 08/29/2021] [Accepted: 09/20/2021] [Indexed: 12/19/2022]
Abstract
Trajectory inference (TI) methods infer cell developmental trajectory from single-cell RNA sequencing data. Current TI methods can be categorized into those using RNA velocity information and those using only single-cell gene expression data. The latter type of methods are restricted to certain trajectory structures, and cannot determine cell developmental direction. Recently proposed TI methods using RNA velocity information have limited accuracy. We present CellPath, a method that infers cell trajectories by integrating single-cell gene expression and RNA velocity information. CellPath overcomes the restrictions of TI methods that do not use RNA velocity information: it can find multiple high-resolution trajectories without constraints on the trajectory structure, and can automatically detect the direction of each trajectory path. We evaluate CellPath on both real and simulated datasets and show that CellPath finds more accurate and detailed trajectories than the state-of-the-art TI methods using or not using RNA velocity information.
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Affiliation(s)
- Ziqi Zhang
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Xiuwei Zhang
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
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39
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Longo SK, Guo MG, Ji AL, Khavari PA. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat Rev Genet 2021; 22:627-644. [PMID: 34145435 PMCID: PMC9888017 DOI: 10.1038/s41576-021-00370-8] [Citation(s) in RCA: 380] [Impact Index Per Article: 126.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/29/2021] [Indexed: 02/07/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but does not capture their spatial distribution nor reveal local networks of intercellular communication acting in situ. A suite of recently developed techniques that localize RNA within tissue, including multiplexed in situ hybridization and in situ sequencing (here defined as high-plex RNA imaging) and spatial barcoding, can help address this issue. However, no method currently provides as complete a scope of the transcriptome as does scRNA-seq, underscoring the need for approaches to integrate single-cell and spatial data. Here, we review efforts to integrate scRNA-seq with spatial transcriptomics, including emerging integrative computational methods, and propose ways to effectively combine current methodologies.
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Affiliation(s)
- Sophia K. Longo
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA,Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Margaret G. Guo
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA,Stanford Cancer Institute, Stanford University, Stanford, CA, USA,Program in Biomedical Informatics, Stanford University, Stanford, CA, USA
| | - Andrew L. Ji
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA,Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Paul A. Khavari
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA,Stanford Cancer Institute, Stanford University, Stanford, CA, USA,Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA
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40
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Huang Y, Sanguinetti G. BRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments. Genome Biol 2021; 22:251. [PMID: 34452629 PMCID: PMC8393734 DOI: 10.1186/s13059-021-02461-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 08/10/2021] [Indexed: 02/06/2023] Open
Abstract
RNA splicing is an important driver of heterogeneity in single cells through the expression of alternative transcripts and as a determinant of transcriptional kinetics. However, the intrinsic coverage limitations of scRNA-seq technologies make it challenging to associate specific splicing events to cell-level phenotypes. BRIE2 is a scalable computational method that resolves these issues by regressing single-cell transcriptomic data against cell-level features. We show that BRIE2 effectively identifies differential disease-associated alternative splicing events and allows a principled selection of genes that capture heterogeneity in transcriptional kinetics and improve RNA velocity analyses, enabling the identification of splicing phenotypes associated with biological changes.
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Affiliation(s)
- Yuanhua Huang
- School of Biomedical Sciences, University of Hong Kong, Hong Kong SAR, Pok Fu Lam, China.
- Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong SAR, Pok Fu Lam, China.
| | - Guido Sanguinetti
- School of Informatics, University of Edinburgh, Edinburgh, UK.
- SISSA, International School of Advanced Studies, Trieste, Italy.
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41
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Furlan M, de Pretis S, Pelizzola M. Dynamics of transcriptional and post-transcriptional regulation. Brief Bioinform 2021; 22:bbaa389. [PMID: 33348360 PMCID: PMC8294512 DOI: 10.1093/bib/bbaa389] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 11/12/2020] [Accepted: 11/27/2020] [Indexed: 02/07/2023] Open
Abstract
Despite gene expression programs being notoriously complex, RNA abundance is usually assumed as a proxy for transcriptional activity. Recently developed approaches, able to disentangle transcriptional and post-transcriptional regulatory processes, have revealed a more complex scenario. It is now possible to work out how synthesis, processing and degradation kinetic rates collectively determine the abundance of each gene's RNA. It has become clear that the same transcriptional output can correspond to different combinations of the kinetic rates. This underscores the fact that markedly different modes of gene expression regulation exist, each with profound effects on a gene's ability to modulate its own expression. This review describes the development of the experimental and computational approaches, including RNA metabolic labeling and mathematical modeling, that have been disclosing the mechanisms underlying complex transcriptional programs. Current limitations and future perspectives in the field are also discussed.
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Affiliation(s)
- Mattia Furlan
- Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), 20139 Milan, Italy
| | - Stefano de Pretis
- Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), 20139 Milan, Italy
| | - Mattia Pelizzola
- Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), 20139 Milan, Italy
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42
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Boileau E, Altmüller J, Naarmann-de Vries IS, Dieterich C. A comparison of metabolic labeling and statistical methods to infer genome-wide dynamics of RNA turnover. Brief Bioinform 2021; 22:6315814. [PMID: 34228787 PMCID: PMC8574959 DOI: 10.1093/bib/bbab219] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 05/18/2021] [Accepted: 05/21/2021] [Indexed: 12/27/2022] Open
Abstract
Metabolic labeling of newly transcribed RNAs coupled with RNA-seq is being increasingly used for genome-wide analysis of RNA dynamics. Methods including standard biochemical enrichment and recent nucleotide conversion protocols each require special experimental and computational treatment. Despite their immediate relevance, these technologies have not yet been assessed and benchmarked, and no data are currently available to advance reproducible research and the development of better inference tools. Here, we present a systematic evaluation and comparison of four RNA labeling protocols: 4sU-tagging biochemical enrichment, including spike-in RNA controls, SLAM-seq, TimeLapse-seq and TUC-seq. All protocols are evaluated based on practical considerations, conversion efficiency and wet lab requirements to handle hazardous substances. We also compute decay rate estimates and confidence intervals for each protocol using two alternative statistical frameworks, pulseR and GRAND-SLAM, for over 11 600 human genes and evaluate the underlying computational workflows for their robustness and ease of use. Overall, we demonstrate a high inter-method reliability across eight use case scenarios. Our results and data will facilitate reproducible research and serve as a resource contributing to a fuller understanding of RNA biology.
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Affiliation(s)
- Etienne Boileau
- Section of Bioinformatics and Systems Cardiology, Klaus Tschira Institute for Integrative Computational Cardiology, Im Neuenheimer Feld 669, 69120, Heidelberg, Germany.,Department of Internal Medicine III (Cardiology, Angiology, and Pneumology), University Hospital Heidelberg, Im Neuenheimer Feld 669, 69120, Heidelberg, Germany.,DZHK (German Centre for Cardiovascular Research) Partner Site Heidelberg/Mannheim
| | - Janine Altmüller
- Cologne Center for Genomics (CCG), University of Cologne, Weyertal 115b, 50931, Kön, Germany.,Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Core Facility Genomics, Charitéplatz 1, 10117 Berlin, Germany.,Max Delbrük Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Isabel S Naarmann-de Vries
- Section of Bioinformatics and Systems Cardiology, Klaus Tschira Institute for Integrative Computational Cardiology, Im Neuenheimer Feld 669, 69120, Heidelberg, Germany.,Department of Internal Medicine III (Cardiology, Angiology, and Pneumology), University Hospital Heidelberg, Im Neuenheimer Feld 669, 69120, Heidelberg, Germany.,Department of Intensive Care Medicine, University Hospital Aachen, Pauwelsstrasse 30, 52074, Aachen, Germany
| | - Christoph Dieterich
- Section of Bioinformatics and Systems Cardiology, Klaus Tschira Institute for Integrative Computational Cardiology, Im Neuenheimer Feld 669, 69120, Heidelberg, Germany.,Department of Internal Medicine III (Cardiology, Angiology, and Pneumology), University Hospital Heidelberg, Im Neuenheimer Feld 669, 69120, Heidelberg, Germany.,DZHK (German Centre for Cardiovascular Research) Partner Site Heidelberg/Mannheim
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43
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Holler K, Neuschulz A, Drewe-Boß P, Mintcheva J, Spanjaard B, Arsiè R, Ohler U, Landthaler M, Junker JP. Spatio-temporal mRNA tracking in the early zebrafish embryo. Nat Commun 2021; 12:3358. [PMID: 34099733 PMCID: PMC8184788 DOI: 10.1038/s41467-021-23834-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 05/18/2021] [Indexed: 01/17/2023] Open
Abstract
Early stages of embryogenesis depend on subcellular localization and transport of maternal mRNA. However, systematic analysis of these processes is hindered by a lack of spatio-temporal information in single-cell RNA sequencing. Here, we combine spatially-resolved transcriptomics and single-cell RNA labeling to perform a spatio-temporal analysis of the transcriptome during early zebrafish development. We measure spatial localization of mRNA molecules within the one-cell stage embryo, which allows us to identify a class of mRNAs that are specifically localized at an extraembryonic position, the vegetal pole. Furthermore, we establish a method for high-throughput single-cell RNA labeling in early zebrafish embryos, which enables us to follow the fate of individual maternal transcripts until gastrulation. This approach reveals that many localized transcripts are specifically transported to the primordial germ cells. Finally, we acquire spatial transcriptomes of two xenopus species and compare evolutionary conservation of localized genes as well as enriched sequence motifs.
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Affiliation(s)
- Karoline Holler
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Anika Neuschulz
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Philipp Drewe-Boß
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Janita Mintcheva
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Bastiaan Spanjaard
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Roberto Arsiè
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Uwe Ohler
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
- Department of Biology, Humboldt University, Berlin, Germany
| | - Markus Landthaler
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
- IRI Life Science, Institute of Biology, Humboldt University, Berlin, Germany
| | - Jan Philipp Junker
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany.
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44
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Chen Y, Song J, Ruan Q, Zeng X, Wu L, Cai L, Wang X, Yang C. Single-Cell Sequencing Methodologies: From Transcriptome to Multi-Dimensional Measurement. SMALL METHODS 2021; 5:e2100111. [PMID: 34927917 DOI: 10.1002/smtd.202100111] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/26/2021] [Indexed: 06/14/2023]
Abstract
Cells are the basic building blocks of biological systems, with inherent unique molecular features and development trajectories. The study of single cells facilitates in-depth understanding of cellular diversity, disease processes, and organization of multicellular organisms. Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for the interrogation of gene expression patterns and the dynamics of single cells, allowing cellular heterogeneity to be dissected at unprecedented resolution. Nevertheless, measuring at only transcriptome level or 1D is incomplete; the cellular heterogeneity reflects in multiple dimensions, including the genome, epigenome, transcriptome, spatial, and even temporal dimensions. Hence, integrative single cell analysis is highly desired. In addition, the way to interpret sequencing data by virtue of bioinformatic tools also exerts critical roles in revealing differential gene expression. Here, a comprehensive review that summarizes the cutting-edge single-cell transcriptome sequencing methodologies, including scRNA-seq, spatial and temporal transcriptome profiling, multi-omics sequencing and computational methods developed for scRNA-seq data analysis is provided. Finally, the challenges and perspectives of this field are discussed.
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Affiliation(s)
- Yingwen Chen
- The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, The Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Jia Song
- Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Qingyu Ruan
- The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, The Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Xi Zeng
- The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, The Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Lingling Wu
- Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Linfeng Cai
- The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, The Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Xuanqun Wang
- The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, The Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Chaoyong Yang
- The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, The Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
- Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
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45
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Single-cell technologies and analyses in hematopoiesis and hematological malignancies. Exp Hematol 2021; 98:1-13. [PMID: 33979683 DOI: 10.1016/j.exphem.2021.05.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/29/2021] [Accepted: 05/03/2021] [Indexed: 01/03/2023]
Abstract
In recent years, single-cell technologies have emerged as breakthrough techniques that enable the characterization of hematopoietic cell populations of normal and malignant tissue samples and will be combined in the near future with bulk technologies, currently used in clinical practice, to improve diagnosis, prognosis, and the search for novel molecular targets. These single-cell methods have the advantage of not masking cell-to-cell variation features and involve the study of genetic, epigenetic, transcriptional, and proteomic landscapes from a single-cell perspective. Latest advances in this field have enabled the development of novel strategies that significantly increase both sensitivity and high throughput. In this review, we emphasize emerging techniques aimed at assessing individual or multiomic parameters at single-cell resolution and analyze how these technologies have helped us understand hematopoietic variability and identify unknown and/or rare subpopulations. We also summarize the impact of these single-cell profiling strategies on the characterization of cell diversity within the tumor and the clonal evolution of multiple hematological malignancies in samples from untreated and treated patients, which provide valuable information for diagnosis, prognosis, and future treatments and explain why current therapies may fail. However, despite these improvements, new challenges lie ahead.
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46
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Jaeger MG, Winter GE. Fast-acting chemical tools to delineate causality in transcriptional control. Mol Cell 2021; 81:1617-1630. [PMID: 33689749 DOI: 10.1016/j.molcel.2021.02.015] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 01/20/2021] [Accepted: 02/11/2021] [Indexed: 12/11/2022]
Abstract
Multi-dimensional omics profiling continues to illuminate the complexity of cellular processes. Because of difficult mechanistic interpretation of phenotypes induced by slow perturbation, fast experimental setups are increasingly used to dissect causal interactions directly in cells. Here we review a growing body of studies that leverage rapid pharmacological perturbation to delineate causality in gene control. When coupled with kinetically matched readouts, fast chemical genetic tools allow recording of primary phenotypes before confounding secondary effects manifest. The toolbox encompasses directly acting probes, such as active-site inhibitors and proteolysis-targeting chimeras, as well as strategies using genetic engineering to render target proteins chemically tractable, such as analog-sensitive and degron systems. We anticipate that extrapolation of these concepts to single-cell setups will further transform our mechanistic understanding of transcriptional control in the future. Importantly, the concept of leveraging speed to derive causality should be broadly applicable to many aspects of biological regulation.
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Affiliation(s)
- Martin G Jaeger
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Georg E Winter
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
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47
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Su S, Li X. Dive into Single, Seek Out Multiple: Probing Cancer Metastases via Single-Cell Sequencing and Imaging Techniques. Cancers (Basel) 2021; 13:1067. [PMID: 33802312 PMCID: PMC7959126 DOI: 10.3390/cancers13051067] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 02/25/2021] [Accepted: 02/27/2021] [Indexed: 02/08/2023] Open
Abstract
Metastasis is the cause of most cancer deaths and continues to be the biggest challenge in clinical practice and laboratory investigation. The challenge is largely due to the intrinsic heterogeneity of primary and metastatic tumor populations and the complex interactions among cancer cells and cells in the tumor microenvironment. Therefore, it is important to determine the genotype and phenotype of individual cells so that the metastasis-driving events can be precisely identified, understood, and targeted in future therapies. Single-cell sequencing techniques have allowed the direct comparison of the genomic and transcriptomic changes among different stages of metastatic samples. Single-cell imaging approaches have enabled the live visualization of the heterogeneous behaviors of malignant and non-malignant cells in the tumor microenvironment. By applying these technologies, we are achieving a spatiotemporal precision understanding of cancer metastases and clinical therapeutic translations.
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Affiliation(s)
| | - Xiaohong Li
- Department of Cancer Biology, College of Medicine and Life Sciences, The University of Toledo, Toledo, OH 43614, USA;
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48
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Ohnuki H, Venzon DJ, Lobanov A, Tosato G. Iterative epigenomic analyses in the same single cell. Genome Res 2021; 31:1819-1830. [PMID: 33627472 DOI: 10.1101/gr.269068.120] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 01/14/2021] [Indexed: 11/24/2022]
Abstract
Gene expression in individual cells is epigenetically regulated by DNA modifications, histone modifications, transcription factors, and other DNA-binding proteins. It has been shown that multiple histone modifications can predict gene expression and reflect future responses of bulk cells to extracellular cues. However, the predictive ability of epigenomic analysis is still limited for mechanistic research at a single cell level. To overcome this limitation, it would be useful to acquire reliable signals from multiple epigenetic marks in the same single cell. Here, we propose a new approach and a new method for analysis of several components of the epigenome in the same single cell. The new method allows reanalysis of the same single cell. We found that reanalysis of the same single cell is feasible, provides confirmation of the epigenetic signals, and allows application of statistical analysis to identify reproduced reads using data sets generated only from the single cell. Reanalysis of the same single cell is also useful to acquire multiple epigenetic marks from the same single cells. The method can acquire at least five epigenetic marks: H3K27ac, H3K27me3, mediator complex subunit 1, a DNA modification, and a DNA-interacting protein. We can predict active signaling pathways in K562 single cells using the epigenetic data and confirm that the predicted results strongly correlate with actual active signaling pathways identified by RNA-seq results. These results suggest that the new method provides mechanistic insights for cellular phenotypes through multilayered epigenome analysis in the same single cells.
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Affiliation(s)
- Hidetaka Ohnuki
- Laboratory of Cellular Oncology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - David J Venzon
- Biostatistics and Data Management Section, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Rockville, Maryland 20850, USA
| | - Alexei Lobanov
- CCR Collaborative Bioinformatics Resource (CCBR), Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.,Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, Maryland 21702, USA
| | - Giovanna Tosato
- Laboratory of Cellular Oncology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
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49
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Lu C. Decoding the function of an oncogenic transcription factor: finding the first responders. Mol Cell 2021; 81:418-420. [PMID: 33545056 DOI: 10.1016/j.molcel.2021.01.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Transcription factors (TFs) are frequently altered in human diseases. Identifying the direct and immediate target genes of TFs is critical to understanding their role in pathophysiology. Stengel et al. (2020) applied chemogenetic and nascent transcriptome mapping technologies to define the core gene set regulated by AML1-ETO-an oncogenic TF fusion protein frequently found in acute myeloid leukemia (AML).
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Affiliation(s)
- Chao Lu
- Department of Genetics and Development and Herbert Irving Comprehensive Cancer Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA.
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50
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Xue W, Ma XK, Yang L. Fast and furious: insights of back splicing regulation during nascent RNA synthesis. SCIENCE CHINA-LIFE SCIENCES 2021; 64:1050-1061. [PMID: 33580425 DOI: 10.1007/s11427-020-1881-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 01/07/2021] [Indexed: 12/15/2022]
Abstract
Alternative splicing of eukaryotic precursor (messenger) RNAs in the nucleus not only increases transcriptomic complexity, but also expands proteomic and functional diversity. In addition to basic types of alternative splicing, recent transcriptome-wide analyses have also suggested other new types of non-canonical splicing, such as back splicing and recursive splicing, and their widespread expression across species Increasing lines of evidence have suggested mechanisms for back splicing, including insights from analyses of nascent RNA sequencing. In this review, we discuss our current understanding of back splicing regulation, and highlight its distinct characteristics in processing during nascent RNA synthesis by taking advantage of metabolic tagging nascent RNA sequencing. Features of recursive splicing are also discussed in the perspective of nascent RNA sequencing.
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Affiliation(s)
- Wei Xue
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xu-Kai Ma
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Li Yang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China. .,School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
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