Evidence map›Paper›PMID 28932696›Full record

ArticleMethodsX2017

An automated workflow for quantifying RNA transcripts in individual cells in large data-sets.

Matthew C Pharris, Tzu-Ching Wu, Xinping Chen, Xu Wang, David M Umulis, Vikki M Weake, Tamara L Kinzer-Ursem

Abstract read
In one paragraph

Article in MethodsX, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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.

Matthew C PharrisWeldon School of Biomedical Engineering, Purdue University, United States.
Tzu-Ching WuWeldon School of Biomedical Engineering, Purdue University, United States.
Xinping ChenDepartment of Biochemistry, Purdue University, United States.
Xu WangWeldon School of Biomedical Engineering, Purdue University, United States.
David M UmulisWeldon School of Biomedical Engineering, Purdue University, United States.
Vikki M WeakeDepartment of Biochemistry, Purdue University, United States.
Tamara L Kinzer-UrsemWeldon School of Biomedical Engineering, Purdue University, United States.

Funding

Indiana Clinical and Translational Sciences InstituteUL1TR001108 · NCATS · INDIANA UNIVERSITY INDIANAPOLIS · PI DENNE, SCOTT C., SHEKHAR, ANANTHA · 2013 to 2017
$23.3M
Resource Component: Acquisition, maintenance and distribution of Drosophila stocksP40OD018537 · OD · TRUSTEES OF INDIANA UNIVERSITY · PI Annette L. Parks · 2014 to 2026
$13.5M
Epigenetic Regulation of Gene Expression in the Aging EyeR01EY024905 · NEI · PURDUE UNIVERSITY · PI WEAKE, VIKKI MARIE · 2015 to 2020
$2.3M
Systems Analysis of BMP Regulation in Developing Zebrafish EmbryosR01HD073156 · NICHD · PURDUE UNIVERSITY · PI UMULIS, DAVID MICHAEL · 2013 to 2017
$1.5M
NCATS NIH HHS UL1 TR001108NEI NIH HHS R01 EY024905NICHD NIH HHS R01 HD073156NIH HHS P40 OD018537
6 · The paper itself

Abstract

Advanced molecular probing techniques such as single molecule fluorescence in situ hybridization (smFISH) or RNAscope can be used to assess the quantity and spatial location of mRNA transcripts within cells. Quantifying mRNA expression in large image sets usually involves automated counting of fluorescent spots. Though conventional spot counting algorithms may suffice, they often lack high-throughput capacity and accuracy in cases of crowded signal or excessive noise. Automatic identification of cells and processing of many images is still a challenge. We have developed a method to perform automatic cell boundary identification while providing quantitative data about mRNA transcript levels across many images. Comparisons of mRNA transcript levels identified by the method highly correlate to qPCR measurements of mRNA expression in

Indexed as

Cell-by-cell relative integrated transcript (CCRIT) quantificationMachine learningmRNA transcriptionRNAscopesmFISH

Identifiers

PMID28932696
PMCPMC5596354

What OpenQuestion holds

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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.