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Cited by 6Alzheimer's disease research and treatmentsDementia and Cognitive Impairment ResearchBioinformatics and Genomic Networks

Cross study transcriptomic investigation of Alzheimer’s brain tissue discoveries and limitations

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Tsurukawa, Fernando Koiti, Mao, Yixiang, Sanchez-Villalobos, Cesar, Khanna, Nishtha, Crasto, Chiquito J., Lawrence, J. Josh, Pal, Ranadip·2025·Scientific Reports
DOI 10.1038/s41598-025-01017-yPMC PMC12062235
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Abstract

Developing effective treatments for Alzheimer’s disease (AD) likely requires a deep understanding of molecular mechanisms. Integration of transcriptomic datasets and developing innovative computational analyses may yield novel molecular targets with broad applicability. The motivation for this study was conceived from two main observations: (a) most transcriptomic analyses of AD data consider univariate differential expression analysis, and (b) insights are often not transferable across studies. We designed a machine learning-based framework that can elucidate interpretable multivariate relationships from multiple human AD studies to discover robust transcriptomic AD biomarkers transferable across multiple studies. Our analysis of three human hippocampus datasets revealed multiple robust synergistic associations from unrelated pathways along with inconsistencies of gene associations across different studies. Our study underscores the utility of developing AI-assisted next-gen metrics for integration, robustness, and generalization and also highlights the potential benefit of elucidating molecular mechanisms and pathways that are important in targeting a single population.

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  • AI-driven analyses of transcriptomic and proteomic datasets have facilitated the identification of key synaptic genes and network disruptions associated with early cognitive decline.

    “Recent AI-driven analyses of transcriptomic and proteomic datasets have facilitated the identification of key synaptic genes and network disruptions associated with early cognitive decline41.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [41]

    The Abstract and Discussion describe a machine-learning analysis of Alzheimer’s transcriptomic datasets that identified gene associations, and the Introduction discusses downregulated synaptic pathways. The Results specify that the datasets were RNA-seq and microarray data, not proteomic data. The paper also does not establish that its identified genes or network disruptions are associated specifically with early cognitive decline. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Machine learning techniques integrating transcriptomic and proteomic datasets reveal widespread dysregulation within neurotrophic signaling networks in AD.

    “To investigate these alterations, machine learning techniques have been employed to integrate transcriptomic and proteomic datasets, revealing widespread dysregulation within neurotrophic signaling networks in AD41,58.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [41]

    The Abstract describes a machine-learning framework integrating transcriptomic data from multiple human AD studies. The Results identify the datasets as RNA-Seq and microarray gene-expression data; the paper does not report integrating proteomic data or finding widespread dysregulation of neurotrophic signaling networks. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Predictive accuracy declined in ethnically diverse cohorts, underscoring the importance of ancestry-inclusive validation.

    “However, predictive accuracy declined in ethnically diverse cohorts, underscoring the importance of ancestry-inclusive validation41,159,284”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [41]

    The Results (Bivariate analysis; page unavailable) report that gene pairs selected in the VR dataset had lower average accuracy in MAYO and GSE, and the Discussion calls for validation across independent datasets. The paper does not establish that those cohorts were ethnically diverse or attribute the decline to ancestry, so it does not specifically support the call for ancestry-inclusive validation. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • One study combining proteomic and metabolomic data identified dysregulated sphingolipid and ceramide metabolism in APOE ε4 carriers, findings supported by post-mortem analyses.

    “One study combining proteomic and metabolomic data identified dysregulated sphingolipid and ceramide metabolism in APOE ε4 carriers, findings supported by post-mortem analyses.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [41]

    The Introduction describes RNA-seq and transcriptomic analysis, and the Results section on potential biomarkers reports gene-ranking findings. Searches for APOE ε4, sphingolipids, ceramides, combined proteomic and metabolomic data, and post-mortem validation found no passage addressing the claimed finding. The cited paper’s discussion of a lipid-transport protein does not establish it. (LLM, conf=0.75)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Tsurukawa, F. K., Mao, Y., Sanchez-Villalobos, C., Khanna, N., Crasto, C. J., Lawrence, J. J., & Pal, R.
Affiliations
Texas Tech University (US)
Corresponding author
  • Lawrence, J. Josh
  • Pal, Ranadip
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2025-05-08
Volume
15
Issue
1
Pages
16041-16041
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-025-01017-y
PMC
PMC12062235
OpenAlex
W4410212220
Cited by
6
References
2
Citations by year
2026: 4 · 2025: 2
FWCI
3.86
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Institutes of Health (R01 AG073826-01A1)
Topics
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
Alzheimer's disease, transcriptomics, hippocampus, differential gene expression, machine learning, brain tissue
MeSH terms
Machine Learning, Alzheimer Disease, Brain, Hippocampus, Humans, Biomarkers, Computational Biology, Gene Expression Profiling, Transcriptome

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