Evidence map›Paper›PMID 36209092›Full record

ArticleImmunity & ageing : I & A2022

Composition of the infiltrating immune cells in the brain of healthy individuals: effect of aging.

Tapio Nevalainen, Arttu Autio, Mikko Hurme

Open access · goldAbstract read
In one paragraph

Article in Immunity & ageing : I & A, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
1.6field-weighted citation impact, top 16% 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

19 citing papers in PubMed, 22 citations in OpenAlex.

  1. Article
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  11. Review
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  13. Novel strategies in Parkinson's disease treatment: a review.Frontiers in molecular neuroscience · 2024
    Review
  14. Article
  15. Article
  16. Review
  17. Article
  18. Aging microglia.Cellular and molecular life sciences : CMLS · 2023
    Review
  19. Article
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

3 authors at 1 institution in 1 country.

Tapio Nevalainen *Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön katu 34, 33520, Tampere, Finland. tapio.nevalainen@tuni.fi.
Arttu Autio *Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön katu 34, 33520, Tampere, Finland.
Mikko HurmeFaculty of Medicine and Health Technology, Tampere University, Arvo Ylpön katu 34, 33520, Tampere, Finland.
Tampere University · FI

Funding

LIMBIC &MEDULLARY MECH. IN COCAINE-RELATED SUDDEN DEATHR01DA006227 · NIDA · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI MASH, DEBORAH C. · 1990 to 2012
$3.7M
Methods for high-resolution analysis of genetic effects on gene expressionR01MH101814 · NIMH · UNIVERSITY OF GENEVA · PI BUSTAMANTE, CARLOS DANIEL, DERMITZAKIS, EMMANOUIL · 2013 to 2016
$2.3M
Harnessing GTEx to Create Transcriptome Knowledge and Inform Disease BiologyR01MH101820 · NIMH · UNIVERSITY OF CHICAGO · PI COX, NANCY J, NICOLAE, DAN LIVIU · 2013 to 2015
$2.3M
Identification and validation of cell specific eQTLs by Bayesian modelingR01MH101822 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI BROWN, CHRISTOPHER DAVID, ENGELHARDT, BARBARA · 2013 to 2016
$1.9M
Genetic Regulation of Gene Expression and its Impact on Phenotypes - SupplementR01MH101782 · NIMH · STANFORD UNIVERSITY · PI SABATTI, CHIARA · 2013 to 2016
$1.3M
MODELING THE EFFECTS OF STRUCTURAL VARIATION IN GTEX DATA AND MENDELIAN DISEASER01MH101810 · NIMH · WASHINGTON UNIVERSITY · PI CONRAD, DONALD F. · 2013 to 2016
$1.3M
Systems approaches to link tissue-specific expression to diseaseR01MH101819 · NIMH · NORTH CAROLINA STATE UNIVERSITY RALEIGH · PI NOBEL, ANDREW B, WRIGHT, FRED A. · 2013 to 2015
$1.3M
Statistical analysis of gene expression quantitative trait loci (eQTL)R01MH101825 · NIMH · UNIVERSITY OF CHICAGO · PI STEPHENS, MATTHEW · 2013 to 2015
$1.1M
Statistical analysis of gene expression quantitative trait loci (eQTL)R01MH090951 · NIMH · UNIVERSITY OF CHICAGO · PI PRITCHARD, JONATHAN K · 2010 to 2012
$873k
Methods for high-resolution analysis of genetic effects on gene expressionR01MH090941 · NIMH · UNIVERSITY OF GENEVA · PI DERMITZAKIS, EMMANOUIL, GUIGO, RODERIC · 2010 to 2012
$863k
FACILITATING GTEx, DISEASE, AND GxE ANALYSES VIA FAST EXPRESSION (e)QTL MAPPINGR01MH090936 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI NOBEL, ANDREW B, RUSYN, IVAN · 2010 to 2012
$847k
Using the Transcriptome for SNP and Gene AnnotationR01MH090937 · NIMH · UNIVERSITY OF CHICAGO · PI COX, NANCY J, NICOLAE, DAN LIVIU · 2010 to 2012
$825k
CCR NIH HHS HHSN261200800001CNHLBI NIH HHS HHSN268201000029CNIDA NIH HHS R01 DA006227NIH HHS HHSN261200800001ENIMH NIH HHS R01 MH090936NIMH NIH HHS R01 MH090937NIMH NIH HHS R01 MH090941NIMH NIH HHS R01 MH090948NIMH NIH HHS R01 MH090951NIMH NIH HHS R01 MH101782NIMH NIH HHS R01 MH101810NIMH NIH HHS R01 MH101814NIMH NIH HHS R01 MH101819NIMH NIH HHS R01 MH101820NIMH NIH HHS R01 MH101822NIMH NIH HHS R01 MH101825
6 · The paper itself

Abstract

Immune cells infiltrating the central nervous system (CNS) are involved in the defense against invading microbes as well as in the pathogenesis of neuroinflammatory diseases. In these conditions, the presence of several types of immune and inflammatory cells have been demonstrated. However, some studies have also reported low amounts of immune cells that have been detected in the CNS of healthy individuals, but the cell types present have not been systematically analyzed. To do this, we now used brain samples from The Genotype- Tissue Expression (GTEx) project to analyze the relative abundance of 22 infiltrating leukocyte types using a digital cytometry tool (CIBERSORTx). To characterize cell proportions in different parts of the CNS, samples from 13 different anatomic brain regions were used. The data obtained demonstrated that several leukocyte types were present in the CNS. Six leukocyte types (CD4 memory resting T cells, M0 macrophages, plasma cells, CD8 T cells, CD4 memory activated T cells, and monocytes) were present with a proportion higher than 0.05, i.e. 5%. These six cell types were present in most brain regions with only insignificant variation. A consistent association with age was seen with monocytes, CD8 T cells, and follicular helper T cells. Taken together, these data show that several infiltrating immune cell types are present in the non-diseased CNS tissue and that the proportions of infiltrating cells are affected by age in a manner that is consistent with literature on immunosenecence and inflammaging.

Indexed as

AgingBrainInfiltrating immune cellsInflammaging

Identifiers

PMID36209092
PMCPMC9547407
OpenAlexW4303644446

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.