Evidence map›Paper›PMID 33762663›Full record

ArticleScientific reports2021

Microdroplet-based one-step RT-PCR for ultrahigh throughput single-cell multiplex gene expression analysis and rare cell detection.

Jennifer Ma, Gary Tran, Alwin M D Wan, Edmond W K Young, Eugenia Kumacheva, Norman N Iscove, Peter W Zandstra

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.9field-weighted citation impact, top 14% of its field
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

10 citing papers in PubMed, 29 citations in OpenAlex.

  1. Article
  2. Review
  3. Single-cell Technology in Stem Cell Research.Current stem cell research & therapy · 2025
    Review
  4. Article
  5. Article
  6. Fluorescence-activated droplet sequencing (FAD-seq) directly provides sequences of screening hits in antibody discovery.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  7. Article
  8. Review
  9. Article
  10. 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

7 authors at 3 institutions in 1 country.

Jennifer MaInstitute of Biomedical Engineering, University of Toronto, Toronto, ON, M5S 3G9, Canada.
Gary TranDepartment of Medical Biophysics, University of Toronto, Toronto, ON, M5G 1L7, Canada.
Alwin M D WanDepartment of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, M5S 3G8, Canada.
Edmond W K YoungInstitute of Biomedical Engineering, University of Toronto, Toronto, ON, M5S 3G9, Canada.
Eugenia KumachevaInstitute of Biomedical Engineering, University of Toronto, Toronto, ON, M5S 3G9, Canada.
Norman N IscoveDepartment of Medical Biophysics, University of Toronto, Toronto, ON, M5G 1L7, Canada.
Peter W ZandstraSchool of Biomedical Engineering, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC, V6T 1Z3, Canada. peter.zandstra@ubc.ca.
University of Toronto · CAPrincess Margaret Cancer Centre · CAUniversity of British Columbia · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gene expression analysis of individual cells enables characterization of heterogeneous and rare cell populations, yet widespread implementation of existing single-cell gene analysis techniques has been hindered due to limitations in scale, ease, and cost. Here, we present a novel microdroplet-based, one-step reverse-transcriptase polymerase chain reaction (RT-PCR) platform and demonstrate the detection of three targets simultaneously in over 100,000 single cells in a single experiment with a rapid read-out. Our customized reagent cocktail incorporates the bacteriophage T7 gene 2.5 protein to overcome cell lysate-mediated inhibition and allows for one-step RT-PCR of single cells encapsulated in nanoliter droplets. Fluorescent signals indicative of gene expressions are analyzed using a probabilistic deconvolution method to account for ambient RNA and cell doublets and produce single-cell gene signature profiles, as well as predict cell frequencies within heterogeneous samples. We also developed a simulation model to guide experimental design and optimize the accuracy and precision of the assay. Using mixtures of in vitro transcripts and murine cell lines, we demonstrated the detection of single RNA molecules and rare cell populations at a frequency of 0.1%. This low cost, sensitive, and adaptable technique will provide an accessible platform for high throughput single-cell analysis and enable a wide range of research and clinical applications.

Indexed as

Computational BiologyGene Expression ProfilingHigh-Throughput Screening AssaysLab-On-A-Chip DevicesMicrofluidic Analytical TechniquesMultiplex Polymerase Chain ReactionReverse Transcriptase Polymerase Chain ReactionSingle-Cell AnalysisTranscriptomeWorkflow

Identifiers

PMID33762663
PMCPMC7990930
OpenAlexW3136963597

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.