In vivo profiling of astrocyte secretome reveals brain-region specific regulatory networks in a mouse model of amyloid pathology.
Paper
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The authors' code
R · 47 lines · 1.6 KB · CC-BY-4.0
- # 01_data_preprocessing.R
- # Data input, missing value imputation, sample aggregation (technical replicates)
- rm(list = ls())
- library(openxlsx)
- source("scripts/utils.R") # if needed for later, but not used in this script
- # Input
- all_protein_imput <- read.xlsx("data/protein_Samplematrix_imputeNA.xlsx", rowNames = TRUE)
- all_protein <- read.xlsx("data/Protein_Matrix_annotation.xlsx", rowNames = TRUE)
- # Align column order
- all_protein <- all_protein[, colnames(all_protein_imput)]
- rownames(all_protein) <- rownames(all_protein_imput)
- colnames(all_protein) <- colnames(all_protein)[order(colnames(all_protein), decreasing = TRUE)]
- all_protein <- as.matrix(all_protein)
- all_protein[is.na(all_protein)] <- 0
- # Separate replicates and non-replicates
- rep <- all_protein[, c(4,5,27,28)]
- all_protein_nonrep <- all_protein[, -c(4,27)]
- # Average triplicates
- nonrep <- matrix(nrow = nrow(all_protein_nonrep), ncol = ncol(all_protein_nonrep)/3)
- rownames(nonrep) <- rownames(all_protein_nonrep)
- for (i in 1:nrow(nonrep)) {
- m <- c()
- for (j in seq(1, 36, 3)) {
- m <- c(m, mean(all_protein_nonrep[i, j], all_protein_nonrep[i, j+1], all_protein_nonrep[i, j+2]))
- }
- nonrep[i, ] <- m
- }
- colnames(nonrep) <- sapply(strsplit(colnames(all_protein_nonrep)[seq(1,36,3)], "-"), "[", 1)
- # Extract non-zero proteins per sample
- samples <- list()
- for (i in 1:ncol(nonrep)) {
- colname <- colnames(nonrep)
- rowname <- rownames(nonrep)
- samples[[colname[i]]] <- rowname[which(nonrep[, i] != 0)]
- }
- # Save samples list as RDS for later scripts
- saveRDS(samples, "data/samples_list.rds")
- # Note: samples2 is loaded from EXO_DEPs.xlsx in subsequent scripts
01_data_preprocessing.R, under CC-BY-4.0 · at the source
Overview
- Department of Neurology and Centre for Clinical Neuroscience, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, 400042 China
- Chongqing Key Laboratory of Ageing and Brain Diseases, Chongqing, 400042 China
- Shigatse Branch, Xinqiao Hospital, Army Medical University (Third Military Medical University), Tibet, 857012 China
- Department of Cognitive Impairment Research, Chongqing Institute for Brain and Intelligence, Guangyang Bay Laboratory, Chongqing, 401336 China
Abstract
Coordinated cell-to-cell communications is crucial for the proper functioning and maintenance of brain activities, and its disruption contributes to neurological disorders, including Alzheimer’s disease (AD). Altered astrocyte-neuron communications have been implicated in AD progression, yet the underlying regulatory networks remain poorly understood. Given that secretory proteins mediate both local and long-range intercellular signaling, we constructed a spatiotemporal profile of the astrocyte-derived secretome using in vivo TurboID proximity labeling in mice of amyloid pathology. Early alterations in the entorhinal cortex secretome were identified and enriched in metabolic pathways, whereas changes in the hippocampus were observed later, correlating with neuronal and synaptic maintenance. These findings suggest that early remodeling of the astrocyte secretome in the entorhinal cortex may be involved in AD pathogenesis, while later changes in the hippocampus contribute to neurodegeneration and cognitive decline. This work provides a systematic map of the dynamic, region-specific remodeling of the astrocyte secretome in AD, identifying novel spatiotemporal vulnerabilities and potential therapeutic targets.
Supplementary Information: The online version contains supplementary material available at 10.1186/
Reproduced under the paper's license (CC BY), from the paper cited above.
Repository
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Zenodo 20131790
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
- 28 September 2026: the link answers (HTTP 200)
8 files
- 01_data_preprocessing.R, R, 47 lines
- 02_secretory_protein_ven
n.R , R, 73 lines - 03_differential_expressi
on.R , R, 93 lines - 04_trend_clustering.R, R, 52 lines
- 05_go_bubble_heatmap.R, R, 54 lines
- 06_pca_analysis.R, R, 40 lines
- utils.R, R, 85 lines
- README.md, Text, 1 line
The paper's code and data availability statement is in the Data section.
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Data
Datasets cited
- figshare:33066625, at figshare; found in DataCite
- figshare:33066628, at figshare; found in DataCite
- figshare:33066631, at figshare; found in DataCite
Data availability
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 28 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 6 keywords, 11 MeSH terms, 2 funders, 75 references.
Cite
This paper
Jiang, Q., Hu, J.-N., Dong, H.-M., Xin, J.-Y., Shi, A.-Y., Zeng, G.-H., Liu, J., & Wang, Y.-J. (2026). In vivo profiling of astrocyte secretome reveals brain-region specific regulatory networks in a mouse model of amyloid pathology. Molecular neurodegeneration, 21(1), 39. https://
BibTeX
@article{jiang2026vivo,
author = {Jiang, Qiu and Hu, Jian-Ni and Dong, Hao-Min and Xin, Jia-Yan and Shi, An-Yu and Zeng, Gui-Hua and Liu, Jie and Wang, Yan-Jiang},
title = {{In vivo profiling of astrocyte secretome reveals brain-region specific regulatory networks in a mouse model of amyloid pathology}},
journal = {Molecular neurodegeneration},
year = {2026},
month = may,
volume = {21},
number = {1},
pages = {39},
publisher = {BMC},
issn = {1750-1326},
doi = {10.1186/
url = {https://
pmid = {42177534},
pmcid = {PMC13393950}
}
RIS
TY - JOUR
AU - Jiang, Qiu
AU - Hu, Jian-Ni
AU - Dong, Hao-Min
AU - Xin, Jia-Yan
AU - Shi, An-Yu
AU - Zeng, Gui-Hua
AU - Liu, Jie
AU - Wang, Yan-Jiang
TI - In vivo profiling of astrocyte secretome reveals brain-region specific regulatory networks in a mouse model of amyloid pathology
T2 - Molecular neurodegeneration
J2 - Mol Neurodegener
PY - 2026
DA - 2026/
VL - 21
IS - 1
SP - 39
SN - 1750-1326
PB - BMC
DO - 10.1186/
UR - https://
LA - en
ER -
CSL-JSON
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