Evidence map›Paper›PMID 29319504›Full record

ArticleeLife2018

Automated cell-type classification in intact tissues by single-cell molecular profiling.

Monica Nagendran, Daniel P Riordan, Pehr B Harbury, Tushar J Desai

Abstract read
In one paragraph

Article in eLife, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.

0numbers the graph read from it
0cells of the map it votes in
68citing 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

68 citing papers in PubMed.

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8 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

4 authors.

Monica Nagendran *Department of Internal Medicine, Division of Pulmonary & Critical Care, Stanford University School of Medicine, Stanford, United States.
Daniel P Riordan *Department of Biochemistry, Stanford University School of Medicine, Stanford, United States.
Pehr B HarburyDepartment of Biochemistry, Stanford University School of Medicine, Stanford, United States.ORCID 0000-0002-3657-5485
Tushar J DesaiDepartment of Internal Medicine, Division of Pulmonary & Critical Care, Stanford University School of Medicine, Stanford, United States.ORCID 0000-0002-8794-5319

Funding

NHLBI NIH HHS 1R56HL1274701NHLBI NIH HHS 5U01HL09999507
6 · The paper itself

Abstract

A major challenge in biology is identifying distinct cell classes and mapping their interactions in vivo. Tissue-dissociative technologies enable deep single cell molecular profiling but do not provide spatial information. We developed a proximity ligation in situ hybridization technology (PLISH) with exceptional signal strength, specificity, and sensitivity in tissue. Multiplexed data sets can be acquired using barcoded probes and rapid label-image-erase cycles, with automated calculation of single cell profiles, enabling clustering and anatomical re-mapping of cells. We apply PLISH to expression profile ~2900 cells in intact mouse lung, which identifies and localizes known cell types, including rare ones. Unsupervised classification of the cells indicates differential expression of 'housekeeping' genes between cell types, and re-mapping of two sub-classes of Club cells highlights their segregated spatial domains in terminal airways. By enabling single cell profiling of various RNA species in situ, PLISH can impact many areas of basic and medical research.

Indexed as

AnimalsAutomation, LaboratoryCellsDNA FingerprintingIn Situ HybridizationLungMice, Inbred C57BLPathology, MolecularSensitivity and SpecificitySingle-Cell Analysiscell biologyhumanhuman biologyIdiopathic Pulmonary Fibrosisin situ hybridizationlung cell type classificationmedicinemousesingle cell expression profiling

Identifiers

PMID29319504
PMCPMC5802843

What OpenQuestion holds

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LicenceCC BY
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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.