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. The combination of single-cell RNA sequencing with metabolic RNA labelling enables a time-resolved view of transcriptional responses in individual cells. In this Primer, Erhard and Saliba et al. discuss metabolic labelling approaches and how to assess the temporal dynamics of transcriptional responses in different conditions. 

https://www.zora.uzh.ch/id/eprint/224580/1/nihms_1613929.pdf

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

A bifunctional chemical signature enabling RNA 4-thiouridine enrichment sequencing with single-base resolution.

A fundamental objective of genomics is to track variations in gene expression program. While metabolic RNA labeling-based single-cell RNA sequencing offers insights into temporal biological processes, its limited applicability only to in vitro models challenges the study of in vivo gene expression dynamics. Herein, we introduce Dyna-vivo-seq, a strategy that enables time-resolved dynamic transcription profiling in vivo at the single-cell level by examining new and old RNAs. The new RNAs can offer an additional dimension to reveal cellular heterogeneity. Leveraging new RNAs, we discern two distinct high and low metabolic labeling populations among proximal tubular (PT) cells. Furthermore, we identify 90 rapidly responding transcription factors during the acute kidney injury in female mice, highlighting that high metabolic labeling PT cells exhibit heightened susceptibility to injury. Dyna-vivo-seq provides a powerful tool for the characterization of dynamic transcriptome at the single-cell level in living organism and holds great promise for biomedical applications. 

Dyna-vivo-seq unveils cellular RNA dynamics during acute kidney injury via in vivo metabolic RNA labeling-based scRNA-seq

Dynamic information of intracellular transcripts is essential to understand their functional roles. Routine RNA-sequencing (RNA-seq) methods only measure RNA species at a steady state and do not provide RNA dynamic information. Here, we develop addition-elimination mechanism-activated nucleotide transition sequencing (AENT-seq) for transcriptome-wide profiling of RNA dynamics. In AENT-seq, nascent transcripts are metabolically labeled with 4-thiouridine (4sU). The total RNA is treated with N2H4·H2O under aqueous conditions. N2H4·H2O is demonstrated to convert 4sU to 4-hydrazino cytosine (C*) based on an addition-elimination chemistry. C* is regarded as cytosine (C) during the DNA extension process. This 4sU-to-C transition marks nascent transcripts, so it enables sequencing analysis of RNA dynamics. We apply our AENT-seq to investigate transcript dynamic information of several genes involved in cancer progression and metastasis. This method uses a simple chemical reaction in aqueous solutions and will be rapidly disseminated with extensive applications.

Addition-Elimination Mechanism-Activated Nucleotide Transition Sequencing for RNA Dynamics Profiling

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Hao Yan

Author Tools

Chat about Author

Papers

Addition-Elimination Mechanism-Activated Nucleotide Transition Sequencing for RNA Dynamics Profiling