Evidence map›Paper›PMID 40827686›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Identifying Tipping Points during Healthy Brain Aging through Single-Nucleus Transcriptomic Analysis.

Peiru Wu, Xuyu Zhao, Zixin Chen, Jingying Huang, Tengteng Dai, Jianxin Zhou, Luyao Xiao, Luonan Chen, Robert Chunhua Zhao, Jiao Wang

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. 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

10 authors.

Peiru WuSchool of Life Sciences, Shanghai University, Shanghai, 200444, China.
Xuyu ZhaoSchool of Life Sciences, Shanghai University, Shanghai, 200444, China.
Zixin ChenSchool of Life Sciences, Shanghai University, Shanghai, 200444, China.
Jingying HuangSchool of Life Sciences, Shanghai University, Shanghai, 200444, China.
Tengteng DaiSchool of Life Sciences, Shanghai University, Shanghai, 200444, China.
Jianxin ZhouSchool of Life Sciences, Shanghai University, Shanghai, 200444, China.
Luyao XiaoSchool of Life Sciences, Shanghai University, Shanghai, 200444, China.
Luonan ChenSchool of Mathematical Sciences and School of AI, Shanghai Jiao Tong University, Shanghai, 200230, China.ORCID https://orcid.org/0000-0002-3960-0068
Robert Chunhua ZhaoSchool of Life Sciences, Shanghai University, Shanghai, 200444, China.ORCID https://orcid.org/0000-0002-4609-8424
Jiao WangSchool of Life Sciences, Shanghai University, Shanghai, 200444, China.ORCID https://orcid.org/0000-0001-9875-5425

Funding

Basic Research Program of Shanghai 20JC1412200Chinese Academy of Medical Sciences Initiative for Innovative Medicine 2022-I2M-1-012JST Moonshot R&D JPMJMS2021National Natural Science Foundation of China 12131020National Natural Science Foundation of China 12326614National Natural Science Foundation of China 12426310National Natural Science Foundation of China 42450084National Natural Science Foundation of China 42450135National Natural Science Foundation of China 82474345National Natural Science Foundation of China T2341007National Natural Science Foundation of China T2350003Science and Technology Commission of Shanghai Municipality 23JS1401300Science and Technology Commission of Shanghai Municipality 24JS2810400Science and Technology Commission of Shanghai Municipality 25J22800700Zhejiang Province Vanguard Goose-Leading Initiative 2025C01114
6 · The paper itself

Abstract

Brain aging significantly impairs cognitive and behavioral functions. While some nonlinear aging studies have identified age-specific aging peaks at certain ages, the influence of different cell types on brain aging fluctuations across the lifespan remains unclear. This study, approaching from the interdisciplinary perspective of brain aging and systems dynamics, extends the nonlinear aging analysis to the cellular level, using single-cell transcriptomic data to analyze 45 healthy elderly brain samples aged 29-94 years. Describing cellular and molecular differences in the aging process, neuron proportion is downregulated but relatively stable with low variability after aging, while glial cells are significantly upregulated and highly unstable. Notably, peaks in nonlinear molecular fluctuations are observed in aging at ages 60, 70, and 79. The nonlinear features prompted the introduction of a dynamic network biomarker and the identification of 56-60 years as the tipping point in the brain's healthy aging process, then suggesting that glia predominantly mediate this process and exploring the underlying features and mechanisms. This work investigates the tipping point of aging at single-cell resolution and provides new research strategies for early diagnosis and intervention of aging-related neurological diseases.

Indexed as

AgingBrainHealthy AgingTranscriptomeAdultAgedAged, 80 and overFemaleGene Expression ProfilingHumansMaleMiddle AgedNeurogliaNeuronsSingle-Cell Analysisageingbrain cellsdynamic network biomarkersnonlinearsingle‐cell RNA sequencingtipping point

Identifiers

PMID40827686
PMCPMC12591157

What OpenQuestion holds

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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.