Evidence map›Paper›PMID 31937773›Full record

ArticleNature communications2020

Integrated single cell analysis of blood and cerebrospinal fluid leukocytes in multiple sclerosis.

David Schafflick, Chenling A Xu, Maike Hartlehnert, Michael Cole, Andreas Schulte-Mecklenbeck, Tobias Lautwein, Jolien Wolbert, Michael Heming, Sven G Meuth, Tanja Kuhlmann and 4 more

Open access · goldAbstract readComparative Study
In one paragraph

Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 280 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
280citing papers in PubMed, 6 pooled it
22.7field-weighted citation impact, top 1% 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

280 citing papers in PubMed, 6 syntheses or guidelines pooled it, 437 citations in OpenAlex.

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220 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

14 authors at 3 institutions in 2 countries.

David SchafflickDepartment of Neurology with Institute of Translational Neurology, University Hospital Münster, Münster, Germany.
Chenling A XuDepartment of Electrical Engineering & Computer Science, Center for Computational Biology, University of California, Berkeley, CA, USA.ORCID http://orcid.org/0000-0001-9610-7627
Maike HartlehnertDepartment of Neurology with Institute of Translational Neurology, University Hospital Münster, Münster, Germany.
Michael ColeDepartment of Electrical Engineering & Computer Science, Center for Computational Biology, University of California, Berkeley, CA, USA.ORCID http://orcid.org/0000-0002-5026-1714
Andreas Schulte-MecklenbeckDepartment of Neurology with Institute of Translational Neurology, University Hospital Münster, Münster, Germany.ORCID http://orcid.org/0000-0003-3855-4706
Tobias LautweinDepartment of Neurology with Institute of Translational Neurology, University Hospital Münster, Münster, Germany.
Jolien WolbertDepartment of Neurology with Institute of Translational Neurology, University Hospital Münster, Münster, Germany.
Michael HemingDepartment of Neurology with Institute of Translational Neurology, University Hospital Münster, Münster, Germany.ORCID http://orcid.org/0000-0002-9568-2790
Sven G MeuthDepartment of Neurology with Institute of Translational Neurology, University Hospital Münster, Münster, Germany.ORCID http://orcid.org/0000-0003-2571-3501
Tanja KuhlmannInstitute of Neuropathology, University Hospital Münster, Münster, Germany.ORCID http://orcid.org/0000-0002-0174-5042
Catharina C GrossDepartment of Neurology with Institute of Translational Neurology, University Hospital Münster, Münster, Germany.ORCID http://orcid.org/0000-0002-4872-9189
Heinz WiendlDepartment of Neurology with Institute of Translational Neurology, University Hospital Münster, Münster, Germany.
Nir YosefDepartment of Physics, University of California, Berkeley, CA, USA. niryosef@berkeley.edu.ORCID http://orcid.org/0000-0001-9004-1225
Gerd Meyer Zu HorsteDepartment of Neurology with Institute of Translational Neurology, University Hospital Münster, Münster, Germany. gerd.meyerzuhoerste@ukmuenster.de.ORCID http://orcid.org/0000-0002-4341-4719
University Hospital Münster · DEUniversity of California, Berkeley · USRagon Institute of MGH, MIT and Harvard · US

Funding

Tool and Technology Development CoreU19MH114821 · NIMH · COLD SPRING HARBOR LABORATORY · PI HUANG, Z JOSH · 2017 to 2021
$64.5M
NIMH NIH HHS U19 MH114821
6 · The paper itself

Abstract

Cerebrospinal fluid (CSF) protects the central nervous system (CNS) and analyzing CSF aids the diagnosis of CNS diseases, but our understanding of CSF leukocytes remains superficial. Here, using single cell transcriptomics, we identify a specific location-associated composition and transcriptome of CSF leukocytes. Multiple sclerosis (MS) - an autoimmune disease of the CNS - increases transcriptional diversity in blood, but increases cell type diversity in CSF including a higher abundance of cytotoxic phenotype T helper cells. An analytical approach, named cell set enrichment analysis (CSEA) identifies a cluster-independent increase of follicular (TFH) cells potentially driving the known expansion of B lineage cells in the CSF in MS. In mice, TFH cells accordingly promote B cell infiltration into the CNS and the severity of MS animal models. Immune mechanisms in MS are thus highly compartmentalized and indicate ongoing local T/B cell interaction.

Indexed as

AnimalsBlood CellsB-LymphocytesCentral Nervous SystemCerebrospinal FluidEncephalomyelitis, Autoimmune, ExperimentalGene Expression ProfilingHumansLeukocytesMiceMultiple SclerosisPhenotypeSingle-Cell AnalysisT-Lymphocytes, Helper-InducerT-Lymphocyte Subsets

Identifiers

PMID31937773
PMCPMC6959356
OpenAlexW2999689705

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.