Evidence map›Paper›PMID 37883924›Full record

ArticleCell reports methods2023

A robust pipeline for high-content, high-throughput immunophenotyping reveals age- and genetics-dependent changes in blood leukocytes.

Thomas Liechti, Sofie Van Gassen, Margaret Beddall, Reid Ballard, Yaser Iftikhar, Renguang Du, Thiagarajan Venkataraman, David Novak, Massimo Mangino, Stephen Perfetto and 4 more

Open access · goldAbstract read
In one paragraph

Article in Cell reports methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.2field-weighted citation impact, top 12% 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

11 citing papers in PubMed, 14 citations in OpenAlex.

  1. Review
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  4. Observational
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  10. Signatures of CD4Frontiers in immunology · 2024
    Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 4 institutions in 3 countries.

Thomas LiechtiImmunoTechnology Section, Vaccine Research Center, NIAID, NIH, Bethesda, MD, USA. Electronic address: liechti.thom@gmail.com.
Sofie Van GassenData Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
Margaret BeddallImmunoTechnology Section, Vaccine Research Center, NIAID, NIH, Bethesda, MD, USA.
Reid BallardImmunoTechnology Section, Vaccine Research Center, NIAID, NIH, Bethesda, MD, USA.
Yaser IftikharImmunoTechnology Section, Vaccine Research Center, NIAID, NIH, Bethesda, MD, USA.
Renguang DuImmunoTechnology Section, Vaccine Research Center, NIAID, NIH, Bethesda, MD, USA.
Thiagarajan VenkataramanInstitute for Cell Engineering, Division of Immunology, Department of Pathology, Johns Hopkins University, Baltimore, MD, USA.
David NovakData Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
Massimo ManginoDepartment of Twin Research & Genetic Epidemiology, King's College London, London, UK; National Heart and Lung Institute, Cardiovascular Science Division, Imperial College London, London, UK.
Stephen PerfettoImmunoTechnology Section, Vaccine Research Center, NIAID, NIH, Bethesda, MD, USA.
H Benjamin LarmanInstitute for Cell Engineering, Division of Immunology, Department of Pathology, Johns Hopkins University, Baltimore, MD, USA.
Tim SpectorDepartment of Twin Research & Genetic Epidemiology, King's College London, London, UK.
Yvan SaeysData Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
Mario RoedererImmunoTechnology Section, Vaccine Research Center, NIAID, NIH, Bethesda, MD, USA. Electronic address: marior@mail.nih.gov.
Ghent University · BEJohns Hopkins University · USKing's College London · GBOffice of Extramural Research · US

Funding

Wellcome Trust
6 · The paper itself

Abstract

High-dimensional flow cytometry is the gold standard to study the human immune system in large cohorts. However, large sample sizes increase inter-experimental variation because of technical and experimental inaccuracies introduced by batch variability. Our high-throughput sample processing pipeline in combination with 28-color flow cytometry focuses on increased throughput (192 samples/experiment) and high reproducibility. We implemented quality control checkpoints to reduce technical and experimental variation. Finally, we integrated FlowSOM clustering to facilitate automated data analysis and demonstrate the reproducibility of our pipeline in a study with 3,357 samples. We reveal age-associated immune dynamics in 2,300 individuals, signified by decreasing T and B cell subsets with age. In addition, by combining genetic analyses, our approach revealed unique immune signatures associated with a single nucleotide polymorphism (SNP) that abrogates CD45 isoform splicing. In summary, we provide a versatile and reliable high-throughput, flow cytometry-based pipeline for immune discovery and exploration in large cohorts.

Indexed as

B-Lymphocyte SubsetsLeukocytesFlow CytometryHumansImmunophenotypingReproducibility of ResultsCP: ImmunologyCP: Systems biologyflow cytometryhigh-dimensional flow cytometryhuman immunologyimmune agingimmune homeostasisimmune regulationimmunophenotyping studieslarge cohorts

Identifiers

PMID37883924
PMCPMC10626267
OpenAlexW4387940970

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.