51
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Waylen LN, Nim HT, Martelotto LG, Ramialison M. From whole-mount to single-cell spatial assessment of gene expression in 3D. Commun Biol 2020; 3:602. [PMID: 33097816 PMCID: PMC7584572 DOI: 10.1038/s42003-020-01341-1] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 09/10/2020] [Indexed: 12/31/2022] Open
Abstract
Unravelling spatio-temporal patterns of gene expression is crucial to understanding core biological principles from embryogenesis to disease. Here we review emerging technologies, providing automated, high-throughput, spatially resolved quantitative gene expression data. Novel techniques expand on current benchmark protocols, expediting their incorporation into ongoing research. These approaches digitally reconstruct patterns of embryonic expression in three dimensions, and have successfully identified novel domains of expression, cell types, and tissue features. Such technologies pave the way for unbiased and exhaustive recapitulation of gene expression levels in spatial and quantitative terms, promoting understanding of the molecular origin of developmental defects, and improving medical diagnostics.
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Affiliation(s)
- Lisa N Waylen
- Australian Regenerative Medicine Institute and Systems Biology Institute, Monash University, Clayton, VIC, Australia
| | - Hieu T Nim
- Australian Regenerative Medicine Institute and Systems Biology Institute, Monash University, Clayton, VIC, Australia
- Transcriptomics and Bioinformatics Group, Murdoch Children's Research Institute, Parkville, VIC, Australia
| | - Luciano G Martelotto
- Single Cell Core Laboratory, Harvard Medical School, Department of System Biology, Boston, MA, USA
| | - Mirana Ramialison
- Australian Regenerative Medicine Institute and Systems Biology Institute, Monash University, Clayton, VIC, Australia.
- Transcriptomics and Bioinformatics Group, Murdoch Children's Research Institute, Parkville, VIC, Australia.
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52
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Qiu Q, Hu P, Qiu X, Govek KW, Cámara PG, Wu H. Massively parallel and time-resolved RNA sequencing in single cells with scNT-seq. Nat Methods 2020; 17:991-1001. [PMID: 32868927 PMCID: PMC8103797 DOI: 10.1038/s41592-020-0935-4] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 07/22/2020] [Indexed: 12/31/2022]
Abstract
Single-cell RNA sequencing offers snapshots of whole transcriptomes but obscures the temporal RNA dynamics. Here we present single-cell metabolically labeled new RNA tagging sequencing (scNT-seq), a method for massively parallel analysis of newly transcribed and pre-existing mRNAs from the same cell. This droplet microfluidics-based method enables high-throughput chemical conversion on barcoded beads, efficiently marking newly transcribed mRNAs with T-to-C substitutions. Using scNT-seq, we jointly profiled new and old transcriptomes in ~55,000 single cells. These data revealed time-resolved transcription factor activities and cell-state trajectories at the single-cell level in response to neuronal activation. We further determined rates of RNA biogenesis and decay to uncover RNA regulatory strategies during stepwise conversion between pluripotent and rare totipotent two-cell embryo (2C)-like stem cell states. Finally, integrating scNT-seq with genetic perturbation identifies DNA methylcytosine dioxygenase as an epigenetic barrier into the 2C-like cell state. Time-resolved single-cell transcriptomic analysis thus opens new lines of inquiry regarding cell-type-specific RNA regulatory mechanisms.
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Affiliation(s)
- Qi Qiu
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA.,Penn Epigenetics Institute, University of Pennsylvania, Philadelphia, PA, USA.,Institute of Regenerative Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Peng Hu
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA.,Penn Epigenetics Institute, University of Pennsylvania, Philadelphia, PA, USA.,Institute of Regenerative Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Xiaojie Qiu
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.,Whitehead Institute, Cambridge, MA, USA
| | - Kiya W Govek
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA.,Institute of Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Pablo G Cámara
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA.,Institute of Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Hao Wu
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA. .,Penn Epigenetics Institute, University of Pennsylvania, Philadelphia, PA, USA. .,Institute of Regenerative Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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53
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Cao J, Zhou W, Steemers F, Trapnell C, Shendure J. Sci-fate characterizes the dynamics of gene expression in single cells. Nat Biotechnol 2020; 38:980-988. [PMID: 32284584 PMCID: PMC7416490 DOI: 10.1038/s41587-020-0480-9] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2019] [Accepted: 03/06/2020] [Indexed: 02/07/2023]
Abstract
Gene expression programs change over time, differentiation and development, and in response to stimuli. However, nearly all techniques for profiling gene expression in single cells do not directly capture transcriptional dynamics. In the present study, we present a method for combined single-cell combinatorial indexing and messenger RNA labeling (sci-fate), which uses combinatorial cell indexing and 4-thiouridine labeling of newly synthesized mRNA to concurrently profile the whole and newly synthesized transcriptome in each of many single cells. We used sci-fate to study the cortisol response in >6,000 single cultured cells. From these data, we quantified the dynamics of the cell cycle and glucocorticoid receptor activation, and explored their intersection. Finally, we developed software to infer and analyze cell-state transitions. We anticipate that sci-fate will be broadly applicable to quantitatively characterize transcriptional dynamics in diverse systems.
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Affiliation(s)
- Junyue Cao
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
| | - Wei Zhou
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | | | - Cole Trapnell
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA.
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA.
- Howard Hughes Medical Institute, Seattle, WA, USA.
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54
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Tong A, Huang J, Wolf G, van Dijk D, Krishnaswamy S. TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics. PROCEEDINGS OF MACHINE LEARNING RESEARCH 2020; 119:9526-9536. [PMID: 34337419 PMCID: PMC8320749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
It is increasingly common to encounter data from dynamic processes captured by static cross-sectional measurements over time, particularly in biomedical settings. Recent attempts to model individual trajectories from this data use optimal transport to create pairwise matchings between time points. However, these methods cannot model continuous dynamics and non-linear paths that entities can take in these systems. To address this issue, we establish a link between continuous normalizing flows and dynamic optimal transport, that allows us to model the expected paths of points over time. Continuous normalizing flows are generally under constrained, as they are allowed to take an arbitrary path from the source to the target distribution. We present TrajectoryNet, which controls the continuous paths taken between distributions to produce dynamic optimal transport. We show how this is particularly applicable for studying cellular dynamics in data from single-cell RNA sequencing (scRNA-seq) technologies, and that TrajectoryNet improves upon recently proposed static optimal transport-based models that can be used for interpolating cellular distributions.
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Affiliation(s)
- Alexander Tong
- Department of Computer Science, Yale University, New Haven, CT, USA
| | - Jessie Huang
- Department of Computer Science, Yale University, New Haven, CT, USA
| | - Guy Wolf
- Department of Mathematics & Statistics, Université de Montréal, Montréal, QC, Canada
- Mila – Quebec AI Institute, Montréal, QC, Canada
| | - David van Dijk
- Department of Computer Science, Yale University, New Haven, CT, USA
- Internal Medicine, Cardiology, Yale University, New Haven, CT, USA
| | - Smita Krishnaswamy
- Department of Computer Science, Yale University, New Haven, CT, USA
- Department of Genetics, Yale University, New Haven, CT, USA
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55
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Wagner DE, Klein AM. Lineage tracing meets single-cell omics: opportunities and challenges. Nat Rev Genet 2020; 21:410-427. [PMID: 32235876 PMCID: PMC7307462 DOI: 10.1038/s41576-020-0223-2] [Citation(s) in RCA: 295] [Impact Index Per Article: 73.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/03/2020] [Indexed: 12/20/2022]
Abstract
A fundamental goal of developmental and stem cell biology is to map the developmental history (ontogeny) of differentiated cell types. Recent advances in high-throughput single-cell sequencing technologies have enabled the construction of comprehensive transcriptional atlases of adult tissues and of developing embryos from measurements of up to millions of individual cells. Parallel advances in sequencing-based lineage-tracing methods now facilitate the mapping of clonal relationships onto these landscapes and enable detailed comparisons between molecular and mitotic histories. Here we review recent progress and challenges, as well as the opportunities that emerge when these two complementary representations of cellular history are synthesized into integrated models of cell differentiation.
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Affiliation(s)
- Daniel E Wagner
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Obstetrics, Gynecology and Reproductive Science, Center for Reproductive Sciences, Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA, USA.
| | - Allon M Klein
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
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56
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Chu KJ, Ma YS, Jiang XH, Wu TM, Wu ZJ, Li ZZ, Wang JH, Gao QX, Yi B, Shi Y, Wang HM, Gu LP, Zhang SQ, Wang GR, Liu JB, Fu D, Jiang XQ. Whole-Transcriptome Sequencing Identifies Key Differentially Expressed mRNAs, miRNAs, lncRNAs, and circRNAs Associated with CHOL. MOLECULAR THERAPY. NUCLEIC ACIDS 2020; 21:592-603. [PMID: 32721879 PMCID: PMC7390861 DOI: 10.1016/j.omtn.2020.06.025] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 06/19/2020] [Accepted: 06/23/2020] [Indexed: 12/11/2022]
Abstract
To systematically evaluate the whole-transcriptome sequencing data of cholangiocarcinoma (CHOL) to gain more insights into the transcriptomic landscape and molecular mechanism of this cancer, we performed whole-transcriptome sequencing based on the tumorous (C) and their corresponding non-tumorous adjacent to the tumors (CP) from eight CHOL patients. Subsequently, differential expression analysis was performed on the C and CP groups, followed by functional interaction prediction analysis to investigate gene-regulatory circuits in CHOL. In addition, The Cancer Genome Atlas (TCGA) for CHOL data was used to validate the results. In total, 2,895 differentially expressed messenger RNAs (dif-mRNAs), 56 differentially expressed microRNAs (dif-miRNAs), 151 differentially expressed long non-coding RNAs (dif-lncRNAs), and 110 differentially expressed circular RNAs (dif-circRNAs) were found in CHOL samples compared with controls. Enrichment analysis on those differentially expressed genes (DEGs) related to miRNA, lncRNA, and circRNA also identified the function of spliceosome. The downregulated hsa-miR-144-3p were significantly enriched in the competing endogenous RNA (ceRNA) complex network, which also included 7 upregulated and 13 downregulated circRNAs, 7 upregulated lncRNAs, and 90 upregulated and 40 downregulated mRNAs. Moreover, most of the DEGs and a few of the miRNAs (such as hsa-miR-144-3p) were successfully validated by TCGA data. The genes involved in RNA splicing and protein degradation processes and miR-144-3p may play fundamental roles in the pathogenesis of CHOL.
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Affiliation(s)
- Kai-Jian Chu
- Department of Biliary Tract Surgery I, Third Affiliated Hospital of Second Military Medical University, Shanghai 200438, China
| | - Yu-Shui Ma
- Cancer Institute, Nantong Tumor Hospital, Nantong 226631, China; Department of Radiology, The Fourth Affiliated Hospital of Anhui Medical University, Hefei 230012, China; Central Laboratory for Medical Research, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Xiao-Hui Jiang
- General Surgery, Nantong Tumor Hospital, Nantong 226631, China
| | - Ting-Miao Wu
- Department of Radiology, The Fourth Affiliated Hospital of Anhui Medical University, Hefei 230012, China
| | - Zhi-Jun Wu
- Department of Oncology, Nantong Second People's Hospital, Nantong 226002, China
| | - Zhi-Zhen Li
- Department of Biliary Tract Surgery I, Third Affiliated Hospital of Second Military Medical University, Shanghai 200438, China
| | - Jing-Han Wang
- Department of Biliary Tract Surgery I, Third Affiliated Hospital of Second Military Medical University, Shanghai 200438, China
| | - Qing-Xiang Gao
- Department of Biliary Tract Surgery I, Third Affiliated Hospital of Second Military Medical University, Shanghai 200438, China
| | - Bin Yi
- Department of Biliary Tract Surgery I, Third Affiliated Hospital of Second Military Medical University, Shanghai 200438, China
| | - Yi Shi
- Cancer Institute, Nantong Tumor Hospital, Nantong 226631, China
| | - Hui-Min Wang
- Cancer Institute, Nantong Tumor Hospital, Nantong 226631, China
| | - Li-Peng Gu
- Central Laboratory for Medical Research, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Su-Qing Zhang
- Department of Hepatobiliary Surgery, Nantong Tumor Hospital, Nantong 226631, China
| | - Gao-Ren Wang
- Cancer Institute, Nantong Tumor Hospital, Nantong 226631, China
| | - Ji-Bin Liu
- Cancer Institute, Nantong Tumor Hospital, Nantong 226631, China.
| | - Da Fu
- Department of Radiology, The Fourth Affiliated Hospital of Anhui Medical University, Hefei 230012, China; Central Laboratory for Medical Research, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China.
| | - Xiao-Qing Jiang
- Department of Biliary Tract Surgery I, Third Affiliated Hospital of Second Military Medical University, Shanghai 200438, China.
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57
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Lederer AR, La Manno G. The emergence and promise of single-cell temporal-omics approaches. Curr Opin Biotechnol 2020; 63:70-78. [DOI: 10.1016/j.copbio.2019.12.005] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Revised: 12/03/2019] [Accepted: 12/08/2019] [Indexed: 12/13/2022]
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58
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McKinley KL, Castillo-Azofeifa D, Klein OD. Tools and Concepts for Interrogating and Defining Cellular Identity. Cell Stem Cell 2020; 26:632-656. [PMID: 32386555 PMCID: PMC7250495 DOI: 10.1016/j.stem.2020.03.015] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Defining the mechanisms that generate specialized cell types and coordinate their functions is critical for understanding organ development and renewal. New tools and discoveries are challenging and refining our definitions of a cell type. A rapidly growing toolkit for single-cell analyses has expanded the number of markers that can be assigned to a cell simultaneously, revealing heterogeneity within cell types that were previously regarded as homogeneous populations. Additionally, cell types defined by specific molecular markers can exhibit distinct, context-dependent functions; for example, between tissues in homeostasis and those responding to damage. Here we review the current technologies used to identify and characterize cells, and we discuss how experimental and pathological perturbations are adding increasing complexity to our definitions of cell identity.
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Affiliation(s)
- Kara L McKinley
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
| | - David Castillo-Azofeifa
- Department of Orofacial Sciences, University of California, San Francisco, San Francisco, CA, USA; Program in Craniofacial Biology, University of California, San Francisco, San Francisco, CA, USA
| | - Ophir D Klein
- Department of Orofacial Sciences, University of California, San Francisco, San Francisco, CA, USA; Program in Craniofacial Biology, University of California, San Francisco, San Francisco, CA, USA; Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
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59
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Gasser C, Delazer I, Neuner E, Pascher K, Brillet K, Klotz S, Trixl L, Himmelstoß M, Ennifar E, Rieder D, Lusser A, Micura R. Thioguanosine Conversion Enables mRNA‐Lifetime Evaluation by RNA Sequencing Using Double Metabolic Labeling (TUC‐seq DUAL). Angew Chem Int Ed Engl 2020. [DOI: 10.1002/ange.201916272] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Catherina Gasser
- Institute of Organic Chemistry and Center for Molecular BiosciencesLeopold-Franzens University Innrain 80 6020 Innsbruck Austria
| | - Isabel Delazer
- Institute of Molecular BiologyBiocenterMedical University of Innsbruck Innrain 82 6020 Innsbruck Austria
| | - Eva Neuner
- Institute of Organic Chemistry and Center for Molecular BiosciencesLeopold-Franzens University Innrain 80 6020 Innsbruck Austria
| | - Katharina Pascher
- Institute of Molecular BiologyBiocenterMedical University of Innsbruck Innrain 82 6020 Innsbruck Austria
| | - Karl Brillet
- Université de StrasbourgArchitecture et Réactivité de l'ARN—CNRS UPR 9002Institut de Biologie Moléculaire et Cellulaire 67000 Strasbourg France
| | - Sarah Klotz
- Institute of Organic Chemistry and Center for Molecular BiosciencesLeopold-Franzens University Innrain 80 6020 Innsbruck Austria
| | - Lukas Trixl
- Institute of Molecular BiologyBiocenterMedical University of Innsbruck Innrain 82 6020 Innsbruck Austria
| | - Maximilian Himmelstoß
- Institute of Organic Chemistry and Center for Molecular BiosciencesLeopold-Franzens University Innrain 80 6020 Innsbruck Austria
| | - Eric Ennifar
- Université de StrasbourgArchitecture et Réactivité de l'ARN—CNRS UPR 9002Institut de Biologie Moléculaire et Cellulaire 67000 Strasbourg France
| | - Dietmar Rieder
- Institute of BioinformaticsBiocenterMedical University of Innsbruck Innrain 82 6020 Innsbruck Austria
| | - Alexandra Lusser
- Institute of Molecular BiologyBiocenterMedical University of Innsbruck Innrain 82 6020 Innsbruck Austria
| | - Ronald Micura
- Institute of Organic Chemistry and Center for Molecular BiosciencesLeopold-Franzens University Innrain 80 6020 Innsbruck Austria
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60
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Olivares-Chauvet P, Junker JP. Inclusion of temporal information in single cell transcriptomics. Int J Biochem Cell Biol 2020; 122:105745. [PMID: 32283227 DOI: 10.1016/j.biocel.2020.105745] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Revised: 03/25/2020] [Accepted: 04/06/2020] [Indexed: 10/24/2022]
Abstract
Single cell transcriptomics has emerged as a powerful method for dissecting cell type diversity and for understanding mechanisms of cell fate decisions. However, inclusion of temporal information remains challenging, since each cell can be measured only once by sequencing analysis. Here, we discuss recent progress and current efforts towards inclusion of temporal information in single cell transcriptomics. Even from snapshot data, temporal dynamics can be computationally inferred via pseudo-temporal ordering of single cell transcriptomes. Temporal information can also come from analysis of intronic reads or from RNA metabolic labeling, which can provide additional evidence for pseudo-time trajectories and enable more fine-grained analysis of gene regulatory interactions. These approaches measure dynamics on short timescales of hours. Emerging methods for high-throughput lineage tracing now enable information storage over long timescales by using CRISPR/Cas9 to record information in the genome, which can later be read out by sequencing.
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Affiliation(s)
- Pedro Olivares-Chauvet
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany.
| | - Jan Philipp Junker
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany.
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61
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Qiu X, Rahimzamani A, Wang L, Ren B, Mao Q, Durham T, McFaline-Figueroa JL, Saunders L, Trapnell C, Kannan S. Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe. Cell Syst 2020; 10:265-274.e11. [PMID: 32135093 PMCID: PMC7223477 DOI: 10.1016/j.cels.2020.02.003] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 06/08/2019] [Accepted: 02/05/2020] [Indexed: 01/13/2023]
Abstract
Here, we present Scribe (https://github.com/aristoteleo/Scribe-py), a toolkit for detecting and visualizing causal regulatory interactions between genes and explore the potential for single-cell experiments to power network reconstruction. Scribe employs restricted directed information to determine causality by estimating the strength of information transferred from a potential regulator to its downstream target. We apply Scribe and other leading approaches for causal network reconstruction to several types of single-cell measurements and show that there is a dramatic drop in performance for "pseudotime"-ordered single-cell data compared with true time-series data. We demonstrate that performing causal inference requires temporal coupling between measurements. We show that methods such as "RNA velocity" restore some degree of coupling through an analysis of chromaffin cell fate commitment. These analyses highlight a shortcoming in experimental and computational methods for analyzing gene regulation at single-cell resolution and suggest ways of overcoming it.
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Affiliation(s)
- Xiaojie Qiu
- Molecular & Cellular Biology Program, University of Washington, Seattle, WA, USA; Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Arman Rahimzamani
- Department of Electrical Engineering, University of Washington, Seattle, WA, USA
| | - Li Wang
- Department of Mathematics, University of Texas at Arlington, Arlington, TX, USA
| | - Bingcheng Ren
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Qi Mao
- HERE company, Chicago, IL 60606, USA
| | - Timothy Durham
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | | | - Lauren Saunders
- Molecular & Cellular Biology Program, University of Washington, Seattle, WA, USA; Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Cole Trapnell
- Molecular & Cellular Biology Program, University of Washington, Seattle, WA, USA; Department of Genome Sciences, University of Washington, Seattle, WA, USA; Brotman-Baty Institute for Precision Medicine, Seattle, WA, USA.
| | - Sreeram Kannan
- Department of Electrical Engineering, University of Washington, Seattle, WA, USA.
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62
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Gasser C, Delazer I, Neuner E, Pascher K, Brillet K, Klotz S, Trixl L, Himmelstoß M, Ennifar E, Rieder D, Lusser A, Micura R. Thioguanosine Conversion Enables mRNA-Lifetime Evaluation by RNA Sequencing Using Double Metabolic Labeling (TUC-seq DUAL). Angew Chem Int Ed Engl 2020; 59:6881-6886. [PMID: 31999864 PMCID: PMC7186826 DOI: 10.1002/anie.201916272] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Indexed: 12/24/2022]
Abstract
Temporal information about cellular RNA populations is essential to understand the functional roles of RNA. We have developed the hydrazine/NH4 Cl/OsO4 -based conversion of 6-thioguanosine (6sG) into A', where A' constitutes a 6-hydrazino purine derivative. A' retains the Watson-Crick base-pair mode and is efficiently decoded as adenosine in primer extension assays and in RNA sequencing. Because 6sG is applicable to metabolic labeling of freshly synthesized RNA and because the conversion chemistry is fully compatible with the conversion of the frequently used metabolic label 4-thiouridine (4sU) into C, the combination of both modified nucleosides in dual-labeling setups enables high accuracy measurements of RNA decay. This approach, termed TUC-seq DUAL, uses the two modified nucleosides in subsequent pulses and their simultaneous detection, enabling mRNA-lifetime evaluation with unprecedented precision.
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Affiliation(s)
- Catherina Gasser
- Institute of Organic Chemistry and Center for Molecular Biosciences, Leopold-Franzens University, Innrain 80, 6020, Innsbruck, Austria
| | - Isabel Delazer
- Institute of Molecular Biology, Biocenter, Medical University of Innsbruck, Innrain 82, 6020, Innsbruck, Austria
| | - Eva Neuner
- Institute of Organic Chemistry and Center for Molecular Biosciences, Leopold-Franzens University, Innrain 80, 6020, Innsbruck, Austria
| | - Katharina Pascher
- Institute of Molecular Biology, Biocenter, Medical University of Innsbruck, Innrain 82, 6020, Innsbruck, Austria
| | - Karl Brillet
- Université de Strasbourg, Architecture et Réactivité de l'ARN-CNRS UPR 9002, Institut de Biologie Moléculaire et Cellulaire, 67000, Strasbourg, France
| | - Sarah Klotz
- Institute of Organic Chemistry and Center for Molecular Biosciences, Leopold-Franzens University, Innrain 80, 6020, Innsbruck, Austria
| | - Lukas Trixl
- Institute of Molecular Biology, Biocenter, Medical University of Innsbruck, Innrain 82, 6020, Innsbruck, Austria
| | - Maximilian Himmelstoß
- Institute of Organic Chemistry and Center for Molecular Biosciences, Leopold-Franzens University, Innrain 80, 6020, Innsbruck, Austria
| | - Eric Ennifar
- Université de Strasbourg, Architecture et Réactivité de l'ARN-CNRS UPR 9002, Institut de Biologie Moléculaire et Cellulaire, 67000, Strasbourg, France
| | - 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 82, 6020, Innsbruck, Austria
| | - Ronald Micura
- Institute of Organic Chemistry and Center for Molecular Biosciences, Leopold-Franzens University, Innrain 80, 6020, Innsbruck, Austria
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63
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Evers TMJ, Hochane M, Tans SJ, Heeren RMA, Semrau S, Nemes P, Mashaghi A. Deciphering Metabolic Heterogeneity by Single-Cell Analysis. Anal Chem 2019; 91:13314-13323. [PMID: 31549807 PMCID: PMC6922888 DOI: 10.1021/acs.analchem.9b02410] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Single-cell analysis provides insights into cellular heterogeneity and dynamics of individual cells. This Feature highlights recent developments in key analytical techniques suited for single-cell metabolic analysis with a special focus on mass spectrometry-based analytical platforms and RNA-seq as well as imaging techniques that reveal stochasticity in metabolism.
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Affiliation(s)
- Tom MJ Evers
- Medical Systems Biophysics and Bioengineering, Leiden Academic Centre for Drug Research, Faculty of Mathematics and Natural Sciences, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
| | - Mazène Hochane
- Leiden Institute of Physics, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
| | - Sander J Tans
- AMOLF Institute, Science Park 104 1098 XG Amsterdam, The Netherlands
| | - Ron MA Heeren
- The Maastricht MultiModal Molecular Imaging Institute (M4I), Division of Imaging Mass Spectrometry, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands
| | - Stefan Semrau
- Leiden Institute of Physics, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
| | - Peter Nemes
- Department of Chemistry & Biochemistry, University of Maryland, College Park, MD 20742, USA
| | - Alireza Mashaghi
- Medical Systems Biophysics and Bioengineering, Leiden Academic Centre for Drug Research, Faculty of Mathematics and Natural Sciences, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
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