Evidence map›Paper›PMID 30894605›Full record

ArticleScientific reports2019

In situ 10-cell RNA sequencing in tissue and tumor biopsy samples.

Shambhavi Singh, Lixin Wang, Dylan L Schaff, Matthew D Sutcliffe, Alex F Koeppel, Jungeun Kim, Suna Onengut-Gumuscu, Kwon-Sik Park, Hui Zong, Kevin A Janes

Abstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Laser capture microdissection.Nature reviews. Methods primers · 2025
    Article
  2. Article
  3. Review
  4. Single-Cell RNA Sequencing with Spatial Transcriptomics of Cancer Tissues.International journal of molecular sciences · 2022
    Review
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  6. Review
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  8. Article
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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

10 authors.

Shambhavi SinghDepartment of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA.
Lixin WangDepartment of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA.
Dylan L SchaffDepartment of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA.
Matthew D SutcliffeDepartment of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA.ORCID 0000-0002-2139-6732
Alex F KoeppelBioinformatics Core, University of Virginia, Charlottesville, VA, 22908, USA.
Jungeun KimDepartment of Microbiology, Immunology & Cancer Biology, University of Virginia, Charlottesville, VA, 22908, USA.
Suna Onengut-GumuscuCenter for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA.ORCID 0000-0002-6563-8334
Kwon-Sik ParkDepartment of Microbiology, Immunology & Cancer Biology, University of Virginia, Charlottesville, VA, 22908, USA.
Hui ZongDepartment of Microbiology, Immunology & Cancer Biology, University of Virginia, Charlottesville, VA, 22908, USA.
Kevin A JanesDepartment of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA. kjanes@virginia.edu.

Funding

Women's Oncology Program - WONP30CA044579 · NCI · UNIVERSITY OF VIRGINIA CHARLOTTESVILLE · PI Thomas P. Loughran · 1987 to 2026
$72.1M
MEDICAL SCIENTIST TRAINING PROGRAMT32GM007267 · NIGMS · UNIVERSITY OF VIRGINIA CHARLOTTESVILLE · PI GARCIA-BLANCO, MARIANO A. · 1985 to 2024
$13.4M
An Integrated Systems Approach for Incompletely Penetrant Onco-phenotypes.U01CA215794 · NCI · UNIVERSITY OF VIRGINIA · PI JANES, KEVIN A · 2017 to 2021
$2.4M
(PQB4) Stochastic Profiling of Functional Single-Cell States Within Solid TumorsR01CA194470 · NCI · UNIVERSITY OF VIRGINIA · PI JANES, KEVIN A · 2015 to 2018
$1.8M
NCI NIH HHS P30 CA044579NCI NIH HHS R01 CA194470NCI NIH HHS U01 CA215794NIGMS NIH HHS T32 GM007267
6 · The paper itself

Abstract

Single-cell transcriptomic methods classify new and existing cell types very effectively, but alternative approaches are needed to quantify the individual regulatory states of cells in their native tissue context. We combined the tissue preservation and single-cell resolution of laser capture with an improved preamplification procedure enabling RNA sequencing of 10 microdissected cells. This in situ 10-cell RNA sequencing (10cRNA-seq) can exploit fluorescent reporters of cell type in genetically engineered mice and is compatible with freshly cryoembedded clinical biopsies from patients. Through recombinant RNA spike-ins, we estimate dropout-free technical reliability as low as ~250 copies and a 50% detection sensitivity of ~45 copies per 10-cell reaction. By using small pools of microdissected cells, 10cRNA-seq improves technical per-cell reliability and sensitivity beyond existing approaches for single-cell RNA sequencing (scRNA-seq). Detection of low-abundance transcripts by 10cRNA-seq is comparable to random 10-cell groups of scRNA-seq data, suggesting no loss of gene recovery when cells are isolated in situ. Combined with existing approaches to deconvolve small pools of cells, 10cRNA-seq offers a reliable, unbiased, and sensitive way to measure cell-state heterogeneity in tissues and tumors.

Indexed as

AnimalsBiopsyCell Line, TumorGene Expression ProfilingHumansMiceNeoplasmsReproducibility of ResultsRNARNA, Small CytoplasmicSequence Analysis, RNASingle-Cell AnalysisSoftwareTranscriptomeRNARNA, recombinantRNA, Small Cytoplasmic

Identifiers

PMID30894605
PMCPMC6426952

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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.