Evidence map›Paper›PMID 34685587›Full record

ReviewCells2021

Tutorial: Guidelines for Single-Cell RT-qPCR.

Daniel Zucha, Mikael Kubista, Lukas Valihrach

Abstract readReview
In one paragraph

Review in Cells, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Decoding Diabetes: Hub Genes as Pivotal Players in Cardiomyopathy and Kidney Disease.Endocrine, metabolic & immune disorders drug targets · 2026
    Article
  5. Article
  6. Review
  7. Article
  8. Review
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  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. 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.

Daniel ZuchaLaboratory of Gene Expression, Institute of Biotechnology CAS, 252 50 Vestec, Czech Republic.ORCID 0000-0002-2469-1315
Mikael KubistaLaboratory of Gene Expression, Institute of Biotechnology CAS, 252 50 Vestec, Czech Republic.
Lukas ValihrachLaboratory of Gene Expression, Institute of Biotechnology CAS, 252 50 Vestec, Czech Republic.ORCID 0000-0002-6704-4337

Funding

Grantová Agentura České Republiky GACR 19-02046SGrantová Agentura České Republiky GACR 20-05770SHorizon 2020 EJP RD COFUND-EJP N° 825575Ministerstvo Školství, Mládeže a Tělovýchovy CZ.1.05/1.1.00/02.0109Ministerstvo Školství, Mládeže a Tělovýchovy RVO 86652036
6 · The paper itself

Abstract

Reverse transcription quantitative PCR (RT-qPCR) has delivered significant insights in understanding the gene expression landscape. Thanks to its precision, sensitivity, flexibility, and cost effectiveness, RT-qPCR has also found utility in advanced single-cell analysis. Single-cell RT-qPCR now represents a well-established method, suitable for an efficient screening prior to single-cell RNA sequencing (scRNA-Seq) experiments, or, oppositely, for validation of hypotheses formulated from high-throughput approaches. Here, we aim to provide a comprehensive summary of the scRT-qPCR method by discussing the limitations of single-cell collection methods, describing the importance of reverse transcription, providing recommendations for the preamplification and primer design, and summarizing essential data processing steps. With the detailed protocol attached in the appendix, this tutorial provides a set of guidelines that allow any researcher to perform scRT-qPCR measurements of the highest standard.

Indexed as

Gene Expression ProfilingHumansReal-Time Polymerase Chain ReactionReverse TranscriptionSensitivity and SpecificitySingle-Cell Analysisgene expressionpreamplificationquantitative PCRreverse transcriptionRT-qPCRsample collectionsingle cell

Identifiers

PMID34685587
PMCPMC8534298

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.