Evidence map›Paper›PMID 42671929›Full record

ArticleCurrent protocols2026

Massively Parallel Profiling of Single-Cell RNA Dynamics Using Well-TEMP-seq.

Di Wang, Qiqi Lv, Shichao Lin

Abstract read
In one paragraph

Article in Current protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Di WangMOE Key Laboratory of Spectrochemical Analysis & Instrumentation, State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, Fujian Key Laboratory of Chemical Biology, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China.
Qiqi LvSchool of Life Sciences, Xiamen University, Xiamen, China.
Shichao LinMOE Key Laboratory of Spectrochemical Analysis & Instrumentation, State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, Fujian Key Laboratory of Chemical Biology, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China.

Funding

Natural Science Foundation of Fujian Province of China 2025J09060
6 · The paper itself

Abstract

Single-cell RNA sequencing (scRNA-seq) reveals the transcriptional heterogeneity of cells, revolutionizing our understanding of cellular processes. However, the static snapshots obtained from scRNA-seq fail to reveal the time-resolved dynamics of transcription, which impedes critical insights into various biological processes, such as cellular differentiation, embryonic development, disease progression, and responses to external stimuli. Here, we describe Well-TEMP-seq, a protocol for massively parallel profiling of the temporal dynamics of single-cell gene expression. Well-TEMP-seq combines metabolic RNA labeling with a microwell-based 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. Well-TEMP-seq is high-throughput, cost-effective, accurate, and provides a low cell loss rate and high single cell/bead pairing efficiency. More importantly, Well-TEMP-seq can be easily set up in other labs, and the loading of cells and beads can be easily accomplished by an optical microscope and a pipette. We believe that Well-TEMP-seq will be widely adopted and help researchers perform transformative research to unveil the dynamics of single-cell gene expression in diverse biological processes. © 2026 Wiley Periodicals LLC. Basic Protocol 1: Well-paired-seq chip fabrication Basic Protocol 2: Well-TEMP-seq sample processing Basic Protocol 3: Bioinformatics analysis.

Indexed as

Gene Expression ProfilingHigh-Throughput Nucleotide SequencingRNASequence Analysis, RNASingle-Cell AnalysisAnimalsHumansSingle-Cell Gene Expression AnalysisRNA4‐thiouridinegene expression dynamicsmetabolic RNA labelingmicrowellsingle‐cell RNA sequencing

Identifiers

PMID42671929
PMCPMC13528962

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.