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Time-resolved single-cell RNA-seq using metabolic RNA labelling

Nature Reviews Methods Primers volume 2, Article number: 77 (2022) Cite this article

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Abstract

Single-cell RNA genomics technologies are revolutionizing biomedical science by profiling single cells with unprecedented resolution, providing fundamental insights into the role of different cellular states and intercellular heterogeneity in health and disease. The combination of single-cell RNA sequencing (scRNA-seq) with metabolic RNA labelling approaches now enables time-resolved monitoring of transcriptional responses for thousands of genes in thousands of individual cells in parallel. This facilitates and accelerates direct characterization of the temporal dimension of biological processes, which has been largely missing in current data. In this Primer, we provide an overview of the various metabolic RNA labelling approaches and their combination with currently available scRNA-seq and multi-omics platforms. We summarize the main challenges in the design of such experiments and discuss the various applications of time-resolved scRNA-seq in vitro and in vivo. We outline the computational tools and challenges to the analyses of the temporal dynamics of transcriptional responses at the single-cell level. We discuss the prospect of integrating data obtained by the respective time-resolved scRNA-seq approaches with complementary methods to elucidate gene regulatory networks that underlie molecular mechanisms. Finally, we discuss open questions and challenges in the field and give our thoughts for future development and applications.

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Fig. 1: Metabolic RNA labelling chemistries and approaches.
Fig. 2: Overview of workflows that allow recording of transcriptional activity at the single-cell level.
Fig. 3: scSLAM-seq data analysis and results.
Fig. 4: Applications of time-resolved scRNA-seq using metabolic RNA labelling across biological fields.
Fig. 5: Overview of methods compatible with RNA metabolic labelling amenable to multi-modal single-cell analysis.

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Acknowledgements

This work was supported by the European Research Council (ERC-2016-CoG 721016–HERPES and ERC-2022-CoG 101041177–DecipherHSV to L.D.), the Deutsche Forschungsgemeinschaft (DFG) to F.E. (ER 927/2-1) and C.C.F. (FR 2938/9-1) and the Amar Foundation to B.K.P. A.-E.S. is supported by the Bundesministerium für Bildung und Forschung (BMBF, HOPARL (COMPLS4-025)) and NIH NHGRI R01. F.E., A.-E.S. and L.D. are jointly supported by the DFG CRC1525 (453989101) and by the FOR-COVID (Bayerisches Staatsministerium für Wissenschaft und Kunst). E.A.M. is supported by a Wellcome Trust Senior Investigator award (219475/Z/19/Z) and CRUK awards (C13474 and A27826). I.A. is an Eden and Steven Romick Professorial Chair, supported by Merck KGaA, Darmstadt, Germany, the Chan Zuckerberg Initiative (CZI), the HHMI International Scholar award, the ERC Consolidator Grant (ERC-COG) 724471 HemTree2.0, an SCA award of the Wolfson Foundation and Family Charitable Trust, the Helen and Martin Kimmel award for innovative investigation, the NeuroMac DFG/Transregional Collaborative Research Center Grant. This work was supported by the Austrian Science Fund FWF (P31691 and F8011-B to R.M.; P33936 and F8009-B to A.L.). The Helmholtz Institute for RNA-based Infection Research (HIRI) supported this work with a seed grant through funds from the Bavarian Ministry of Economic Affairs and Media, Energy and Technology (grant allocation nos. 0703/68674/5/2017 and 0703/89374/3/2017).

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Authors and Affiliations

  1. Institute for Virology and Immunobiology, Julius-Maximilians-University Würzburg, Würzburg, Germany

    Florian Erhard, Thomas Hennig, Bhupesh K. Prusty & Lars Dölken

  2. Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz-Center for Infection Research (HZI), Würzburg, Germany

    Antoine-Emmanuel Saliba, Christophe Toussaint & Lars Dölken

  3. Institute of Molecular Biology, Biocenter, Medical University of Innsbruck, Innsbruck, Austria

    Alexandra Lusser

  4. Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel

    Daniel Kirschenbaum, Kathleen Abadie & Ido Amit

  5. Wellcome Trust Cancer Research UK Gurdon Institute and Departments of Biochemistry and Genetics, University of Cambridge, Cambridge, UK

    Eric A. Miska

  6. Institute of Informatics, Ludwig-Maximilians-Universität München, Munich, Germany

    Caroline C. Friedel

  7. University of Innsbruck, Institute of Organic Chemistry and Center for Molecular Biosciences (CMBI), Innsbruck, Austria

    Ronald Micura

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  1. Florian Erhard
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Contributions

Introduction (L.D., F.E., A.-E.S., A.L., T.H., B.K.P., C.C.F., I.A. and R.M.); Experimentation (L.D., F.E., A.-E.S., A.L. and R.M.); Results (L.D., F.E. and C.C.F.); Applications (L.D., F.E., A.-E.S., C.T., T.H., B.K.P., D.K., K.A., E.A.M. and I.A.); Reproducibility and data deposition (L.D., F.E. and C.C.F.); Limitations and optimizations (L.D., F.E., A.-E.S., A.L., C.T., D.K., K.A., E.A.M., C.C.F., I.A. and R.M.); Figures (R.M., F.E. and A.-E.S.); Outlook (L.D., F.E., A.-E.S., A.L., C.T., T.H., B.K.P., D.K., K.A., E.A.M., C.C.F., I.A. and R.M.); Overview of the Primer (L.D.).

Corresponding authors

Correspondence to Florian Erhard, Antoine-Emmanuel Saliba or Lars Dölken.

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Nature Reviews Methods Primers thanks Wei Chen, Xing Chen, Xiaohui Fan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Glossary

Bulk RNA-seq

Transcriptomics analysis of pooled cell populations, tissue sections or biopsy samples.

Nucleotide salvage pathways

Used to recover bases and nucleosides that are formed during degradation of RNA and DNA.

Prodrugs

Medications or compounds that are metabolized in the body into pharmacologically active drugs.

Click chemistry

A growing class of biocompatible chemical reactions for bioconjugation that are high in yield, wide in scope and simple to perform in easily removable or benign solvents under ambient temperatures.

Pulse-labelling

Metabolic labelling of a biological target molecule (in this case, RNA) with a chemically modified compound by administering the compound to the cell culture medium or injecting it into a living organism.

Pulse-chasing

Monitoring active degradation of a target molecule (in this case, RNA) after pulse-labelling to determine its decay rate.

Paired-end sequencing

Sequencing the cloned complementary DNA (cDNA) fragments from both ends enables the detection and removal of sequencing errors in the overlapping sequence and is necessary for reliable identification of nucleotide conversions in metabolically labelled RNA molecules.

Unique molecular identifiers

Random barcode sequences introduced during reverse transcription to distinguish multiple copies of the same RNA from PCR duplicates, providing an absolute quantification of transcript numbers of a given gene per single cell.

BAM format

Binary and compressed file format to store genomic coordinates along with specific information on each read — sequence and quality scores — and its alignment with the reference sequence.

RNA velocity

Temporal information contained in intronic reads (nascent RNA) of single-cell RNA sequencing data to predict future cell states and infer cell trajectories.

Off–on switches

Initiation (off–on) or abrogation (on–off) of transcription in individual cells in response to a stimulus that can be differentiated from a general increase (up) or decrease (down) in transcriptional activity in all cells.

Transcriptional bursting

A fundamental property of genes in which transcription from DNA to RNA occurs in pulses (‘bursts’).

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Erhard, F., Saliba, AE., Lusser, A. et al. Time-resolved single-cell RNA-seq using metabolic RNA labelling. Nat Rev Methods Primers 2, 77 (2022). https://doi.org/10.1038/s43586-022-00157-z

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