Evidence map›Paper›PMID 32804550›Full record

ArticleAmerican journal of respiratory cell and molecular biology2020

Single-Nucleus RNA-Sequencing Profiling of Mouse Lung. Reduced Dissociation Bias and Improved Rare Cell-Type Detection Compared with Single-Cell RNA Sequencing.

Jeffrey R Koenitzer, Haojia Wu, Jeffrey J Atkinson, Steven L Brody, Benjamin D Humphreys

Open access · greenAbstract read
In one paragraph

Article in American journal of respiratory cell and molecular biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed
5.1field-weighted citation impact, top 3% 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

62 citing papers in PubMed, 82 citations in OpenAlex.

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2 more citing papers are in PubMed but not listed here.

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

5 authors at 2 institutions in 1 country.

Jeffrey R KoenitzerDivision of Pulmonary and Critical Care, Department of Medicine.
Haojia WuDivision of Nephrology, Department of Medicine, and.ORCID 0000-0002-7866-2544
Jeffrey J AtkinsonDivision of Pulmonary and Critical Care, Department of Medicine.
Steven L BrodyDivision of Pulmonary and Critical Care, Department of Medicine.
Benjamin D HumphreysDivision of Nephrology, Department of Medicine, and.
Pulmonary and Critical Care Associates · USWashington University in St. Louis · US

Funding

Washington University Center for Cellular ImagingP30CA091842 · NCI · WASHINGTON UNIVERSITY · PI TIMOTHY J. EBERLEIN · 2001 to 2026
$128.0M
PRINCIPLES IN PULMONARY RESEARCHT32HL007317 · NHLBI · WASHINGTON UNIVERSITY · PI Steven Brody, Michael J Holtzman · 1985 to 2026
$16.1M
Understanding Myofibroblast Progenitor Fate and Function in Renal FibrosisR01DK103740 · NIDDK · WASHINGTON UNIVERSITY · PI BENJAMIN D. HUMPHREYS · 2015 to 2026
$3.9M
Adult Progenitors in Kidney TubulointerstitiumUH3DK107374 · NIDDK · WASHINGTON UNIVERSITY · PI HUMPHREYS, BENJAMIN D. · 2017 to 2019
$948k
Adult Progenitors in Kidney TubulointerstitiumUH2DK107374 · NIDDK · WASHINGTON UNIVERSITY · PI HUMPHREYS, BENJAMIN D. · 2015 to 2016
$626k
NCI NIH HHS P30 CA091842NHLBI NIH HHS T32 HL007317NIDDK NIH HHS R01 DK103740NIDDK NIH HHS UH2 DK107374NIDDK NIH HHS UH3 DK107374
6 · The paper itself

Abstract

Single-cell RNA sequencing (scRNASeq) has advanced our understanding of lung biology, but its utility is limited by the need for fresh samples, loss of cell types by death or inadequate dissociation, and transcriptional stress responses induced during tissue digestion. Single-nucleus RNA sequencing (snRNASeq) has addressed these deficiencies in other tissues, but no protocol exists for lung tissue. We present a snRNASeq protocol and compare its results with those of scRNASeq. Two nuclear suspensions were prepared in lysis buffer on ice while one cell suspension was generated using enzymatic and mechanical dissociation. Cells and nuclei were processed using the 10× Genomics platform, and sequencing data were analyzed by Seurat. A total of 16,110 single-nucleus and 11,934 single-cell transcriptomes were generated. Gene detection rates were equivalent in snRNASeq and scRNASeq (∼1,700 genes and 3,000 unique molecular identifiers per cell) when mapping intronic and exonic reads. In the combined data, 89% of epithelial cells were identified by snRNASeq versus 22.2% of immune cells. snRNASeq transcriptomes are enriched for transcription factors and signaling proteins, with reduction in mitochondrial and stress-response genes. Both techniques improved mesenchymal cell detection over previous studies. Homeostatic signaling relationships among alveolar cell types were defined by receptor-ligand mapping using snRNASeq data, revealing interplay among epithelial, mesenchymal, and capillary endothelial cells. snRNASeq can be applied to archival murine lung samples, improves dissociation bias, eliminates artifactual gene expression, and provides similar gene detection compared with scRNASeq.

Indexed as

Gene Expression ProfilingSequence Analysis, RNAAnimalsCell NucleusDissociative DisordersEndothelial CellsEpithelial CellsLungMice, Inbred C57BLbioinformaticssingle celltranscriptomics

Identifiers

PMID32804550
PMCPMC7790136
OpenAlexW3078409957

What OpenQuestion holds

Textmetadata
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.