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33/100

✓ Peer-reviewed alzhub usefulness score 33 out of 100. Genetics

Topic: Brain proteins, microglia and lab models

Spatial transcriptomics (ST) data provide spatially informed gene expression profiles. However, power is limited for spatially informed differential gene expression (DGE) of complex diseases such as Alzheimer disease (AD), due to small sample sizes of ST data. Conversely, single-nucleus RNA sequencing (snRNA-seq) data offer larger sample sizes for cell-type-specific (CTS) analyses but lack spatial information. Here, we integrated ST and snRNA-seq data to enhance the power of spatially informed CTS DGE analysis of AD-related phenotypes. We first utilized the CeLEry tool to infer six cortical layers of ∼1.5 million cells in the snRNA-seq data that were profiled from the dorsolateral prefrontal cortex (DLPFC) tissue of 436 postmortem brains. Then, we conducted cortical layer- and cell-type-specific (LCS) and CTS DGE analyses based on the linear mixed model, for β-amyloid, tangle density, and cognitive decline. We identified 138 LCS significant genes with false discovery rate (FDR) q <0.05, including 103 for β-amyloid, 24 for tangle density, and 25 for cognitive decline. The majority of these LCS significant genes, including known AD risk genes such as APOE, KCNIP3, and CTSD, cannot be detected by CTS analyses. We also identified 2 genes shared across all 3 phenotypes and 10 shared between 2 phenotypes. Gene set enrichment analyses with the LCS DGE results of microglia in cortical layer 6 of β-amyloid identified 12 significant AD-related pathways. In conclusion, incorporating spatial information with snRNA-seq data enhanced the power of spatially informed DGE analyses. These identified LCS significant genes not only help illustrate the pathogenesis of AD but they also provide potential targets for developing therapeutics of AD.

Key findings

We identified 138 LCS significant genes with false discovery rate (FDR) q <0.05, including 103 for β-amyloid, 24 for tangle density, and 25 for cognitive decline.
The majority of these LCS significant genes, including known AD risk genes such as APOE, KCNIP3, and CTSD, cannot be detected by CTS analyses.
Gene set enrichment analyses with the LCS DGE results of microglia in cortical layer 6 of β-amyloid identified 12 significant AD-related pathways.
In conclusion, incorporating spatial information with snRNA-seq data enhanced the power of spatially informed DGE analyses.
These identified LCS significant genes not only help illustrate the pathogenesis of AD but they also provide potential targets for developing therapeutics of AD.

Why it matters

By combining spatial and single-cell gene expression data, this study identified genes linked to Alzheimer's-related brain changes that other methods missed. This is a methodological advance for research and does not directly provide a treatment or cure.

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Study details

Evidence strengthLow

How this study scored 33/100

Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 32 of the 96 points we could judge it on, which is 33 out of 100.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Human Genetics and Genomics Advances)
Journal's citation record	0 / 4	Human Genetics and Genomics Advances: journal h-index 28 (OpenAlex, 2026-10-29)
Study design	0 / 12	No study design recorded — scores zero, not guessed (not stated in the record — scored 0)
Strength of evidence	1 / 5	Rated “low” in our summary
Peer-review validation	10 / 10	Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent	4 / 5	2025 — within 3 year(s) of the newest study here
Sample size	0 / 4	Participant count not stated — scores zero (not stated in the record — scored 0)
Effect reported	0 / 3	No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people	0 / 3	Not stated whether this was studied in people — scores zero (not stated in the record — scored 0)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	1 / 7	Cited 2 times (OpenAlex, 2026-10-10)
Citations per year	0 / 5	0.7 citations a year over 3 year(s) since 2025
Impact for its field and year	1 / 6	Cited 0.5× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	0 / 4	Cited 2 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	— / 4	OpenAlex's profile for last-listed author Jian Hu holds only 5 work(s), too few to judge a track record — left out of this score
Institution's track record	2 / 3	Columbia University: institution h-index 1,824 (OpenAlex)
Multi-centre collaboration	2 / 3	OpenAlex lists 4 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 40329537)
Free to read	2 / 2	Free to read
Points	32 / 96	Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 32 of the 96 points we could judge it on is 33 out of 100.
Total	33 / 100	—

What each of these 20 components measures, and what this score is not →

Read the original

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Cite this study APA, Vancouver, BibTeX · download .bib or .ris

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 7 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.

APA 7th

Tang, S., Liu, S., Buchman, A. S., Bennett, D. A., De Jager, P. L., Yang, J., et al. (2025). Integrating spatial transcriptomics and snRNA-seq data enhances differential gene expression analysis results of AD-related phenotypes. Human Genetics and Genomics Advances. https://doi.org/10.1016/j.xhgg.2025.100447

Vancouver

Tang S, Liu S, Buchman AS, Bennett DA, De Jager PL, Yang J, et al. Integrating spatial transcriptomics and snRNA-seq data enhances differential gene expression analysis results of AD-related phenotypes. Human Genetics and Genomics Advances. 2025. doi:10.1016/j.xhgg.2025.100447. PMID: 40329537.

BibTeX

@article{tang2025-40329537,
  author  = {Tang, Shizhen and Liu, Shihan and Buchman, Aron S. and Bennett, David Alan and De Jager, Philip L. and Yang, Jingjing and Hu, Jian},
  title   = {{Integrating spatial transcriptomics and snRNA-seq data enhances differential gene expression analysis results of AD-related phenotypes}},
  journal = {Human Genetics and Genomics Advances},
  year    = {2025},
  doi     = {10.1016/j.xhgg.2025.100447},
  pmid    = {40329537},
  url     = {https://doi.org/10.1016/j.xhgg.2025.100447}
}

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