Evidence map›Paper›PMID 39068182›Full record

ArticleScientific reports2024

Elucidating Microglial Heterogeneity and Functions in Alzheimer's Disease Using Single-cell Analysis and Convolutional Neural Network Disease Model Construction.

Xinyi Wu, Mingyu Liu, Xinyue Zhang, Xue Pan, Xiaotong Cui, Jiahui Jin, Huanan Sun, Chuyu Xiao, Xiangyi Tong, Liou Ren and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. 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
–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

11 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Review
  6. Review
  7. Characterization of Isolated Human Astrocytes from Aging Brain.International journal of molecular sciences · 2025
    Article
  8. Review
  9. Mapping microglial mechanisms in Alzheimer's disease: a comprehensive analysis.Experimental biology and medicine (Maywood, N.J.) · 2025
    Article
  10. Review
  11. 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

12 authors.

Xinyi Wu *Department of Anesthesiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, China.
Mingyu Liu *Department of Vascular and Thyroid Surgery, The First Hospital of China Medical University, Shenyang, China.
Xinyue ZhangDepartment of Anesthesiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, China.
Xue PanDepartment of Anesthesiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, China.
Xiaotong CuiDepartment of Anesthesiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, China.
Jiahui JinDepartment of Anesthesiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, China.
Huanan SunDepartment of Anesthesiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, China.
Chuyu XiaoDepartment of Anesthesiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, China.
Xiangyi TongDepartment of Anesthesiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, China.
Liou RenDepartment of Anesthesiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, China.
Yaxuan WangDepartment of Anesthesiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, China.
Xuezhao CaoDepartment of Anesthesiology, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, China. xuezhaocao2011@163.com.

Funding

National Natural Science Foundation of China (No. 81300938)Natural Science Foundation of Liaoning Province (No. 2022-MS-222)
6 · The paper itself

Abstract

In this study, we conducted an in-depth exploration of Alzheimer's Disease (AD) by integrating state-of-the-art methodologies, including single-cell RNA sequencing (scRNA-seq), weighted gene co-expression network analysis (WGCNA), and a convolutional neural network (CNN) model. Focusing on the pivotal role of microglia in AD pathology, our analysis revealed 11 distinct microglial subclusters, with 4 exhibiting obviously alterations in AD and HC groups. The investigation of cell-cell communication networks unveiled intricate interactions between AD-related microglia and various cell types within the central nervous system (CNS). Integration of WGCNA and scRNA-seq facilitated the identification of critical genes associated with AD-related microglia, providing insights into their involvement in processes such as peptide chain elongation, synapse-related functions, and cell adhesion. The identification of 9 hub genes, including USP3, through the least absolute shrinkage and selection operator (LASSO) and COX regression analyses, presents potential therapeutic targets. Furthermore, the development of a CNN-based model showcases the application of deep learning in enhancing diagnostic accuracy for AD. Overall, our findings significantly contribute to unraveling the molecular intricacies of microglial responses in AD, offering promising avenues for targeted therapeutic interventions and improved diagnostic precision.

Indexed as

Alzheimer DiseaseMicrogliaNeural Networks, ComputerSingle-Cell AnalysisDeep LearningGene Expression ProfilingGene Regulatory NetworksHumansAlzheimer’s diseaseConvolutional neural networkMicrogliascRNA-seq

Identifiers

PMID39068182
PMCPMC11283484

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.