Evidence map›Paper›PMID 34389057›Full record

ReviewMilitary Medical Research2021

Advances in single-cell sequencing: insights from organ transplantation.

Ying Wang, Jian-Ye Wang, Angelika Schnieke, Konrad Fischer

Open access · diamondAbstract readReview
In one paragraph

Review in Military Medical Research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
1.7field-weighted citation impact, top 15% 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

23 citing papers in PubMed, 31 citations in OpenAlex.

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  15. Molecular monitoring of lung allograft health: is it ready for routine clinical use?European respiratory review : an official journal of the European Respiratory Society · 2023
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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

4 authors at 1 institution in 1 country.

Ying Wang *Chair of Livestock Biotechnology, School of Life Sciences Weihenstephan, Technical University of Munich, Liesel Beckman Str 1, 85354, Freising, Germany.
Jian-Ye Wang *Department of Surgery, School of Medicine, Technical University of Munich, 81675, Munich, Germany.
Angelika SchniekeChair of Livestock Biotechnology, School of Life Sciences Weihenstephan, Technical University of Munich, Liesel Beckman Str 1, 85354, Freising, Germany.
Konrad FischerChair of Livestock Biotechnology, School of Life Sciences Weihenstephan, Technical University of Munich, Liesel Beckman Str 1, 85354, Freising, Germany. konrad.fischer@wzw.tum.de.
Technical University of Munich · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single-cell RNA sequencing (scRNA-seq) is a comprehensive technical tool to analyze intracellular and intercellular interaction data by whole transcriptional profile analysis. Here, we describe the application in biomedical research, focusing on the immune system during organ transplantation and rejection. Unlike conventional transcriptome analysis, this method provides a full map of multiple cell populations in one specific tissue and presents a dynamic and transient unbiased method to explore the progression of allograft dysfunction, starting from the stress response to final graft failure. This promising sequencing technology remarkably improves individualized organ rejection treatment by identifying decisive cellular subgroups and cell-specific interactions.

Indexed as

Gene Expression ProfilingHumansOrgan TransplantationSequence Analysis, RNASingle-Cell Analysis10× Genomics chromiumImmune cell interactionsSingle-cell RNA sequencingTranscriptional profilingTransplant rejection

Identifiers

PMID34389057
PMCPMC8361611
OpenAlexW3188035986

What OpenQuestion holds

Textmetadata
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.