Comparative evaluation of reference-free transcriptomic deconvolution highlights the importance of biological validation in astrocytes across Alzheimer's disease.
Paper
Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC
The paper is loaded when this pane is shown.
The authors' code
Markdown · 30 lines · 4.7 KB · no license
- # Deconvolution-Astrocyte
- This repository contains the code and data supporting the study “Integrative Framework for Reference-Free Transcriptomic Deconvolution and Metabolic Profiling of Astrocytes across Alzheimer’s Disease Progression.” The project focuses on the integration of reference-free transcriptomic deconvolution methods with genome-scale metabolic modeling to characterize astrocyte-specific alterations along the continuum from mild cognitive impairment (MCI) to Alzheimer’s disease (AD).
- In this work, bulk hippocampal transcriptomic data are used to infer astrocyte-associated gene expression profiles across four biological states: Control, Incipient MCI, Moderate MCI, and Severe MCI/Alzheimer’s disease. Two complementary deconvolution approaches, CDSeq and DECODER, are applied to disentangle cell-type-specific signals from heterogeneous transcriptomic data without the need for predefined reference signatures. This strategy enables the reconstruction of astrocyte-relevant transcriptional programs directly from bulk data.
- The inferred astrocyte signatures are subsequently integrated into genome-scale metabolic models (GEMs) to generate condition-specific representations of astrocyte metabolism. These models are constrained using transcriptomic information and analyzed using flux-based approaches, allowing the identification of metabolic pathway alterations associated with disease progression. Through this framework, we aim to capture the progressive metabolic rewiring of astrocytes and identify key vulnerabilities linked to neurodegeneration.
- To ensure biological relevance, an external validation strategy is implemented using independent single-nucleus RNA sequencing (snRNA-seq) data from astrocytes in the entorhinal cortex. Pseudobulk profiles are generated using a donor-aware approach and compared with deconvolution-derived signatures. This validation demonstrates that CDSeq-derived profiles achieve moderate but biologically meaningful concordance with single-cell reference data (r ≈ 0.43–0.44), alongside the preservation of canonical astrocyte markers such as GFAP, AQP4, and SLC1A2, as well as enrichment of astrocyte-associated pathways. These results indicate that the inferred signals capture biologically relevant astrocyte programs despite cross-platform and cross-region differences.
- The analytical framework follows a systems biology approach in which transcriptomic data are first deconvolved to extract cell-type-associated signals and subsequently integrated into a generic astrocyte metabolic network. This enables the generation of context-specific models that can be analyzed to compare metabolic flux distributions across disease stages. The resulting outputs provide insight into pathway-level alterations, including dysregulation of lactate metabolism, the glutamine–glutamate cycle, and oxidative stress-related pathways.
- The repository includes the processed transcriptomic datasets, deconvolution outputs, condition-specific metabolic models, and scripts required to reproduce the full analytical workflow, from data preprocessing to validation and metabolic simulation. The structure of the repository is organized to facilitate reproducibility and reuse, allowing users to follow the complete pipeline and adapt it to other datasets or cell types.
- From a methodological perspective, this work highlights the importance of combining complementary computational approaches. CDSeq provides statistically robust and biologically coherent profiles through probabilistic modeling, whereas DECODER offers sensitivity to absolute transcriptional variation. The integration of both methods within a unified framework enables a more comprehensive characterization of astrocyte biology.
- From a biological standpoint, the results reinforce the central role of astrocytes in maintaining brain metabolic homeostasis and demonstrate that their metabolic function is progressively disrupted during neurodegeneration. The observed changes suggest a transition from homeostatic to reactive and ultimately dysfunctional astrocyte states, contributing to the metabolic vulnerability associated with Alzheimer’s disease.
- All data and scripts are made available to ensure transparency, reproducibility, and to support further methodological and biological exploration by the research community. This framework is designed to be extensible to other cell types, brain regions, and multi-omic datasets, providing a scalable platform for studying cell-type-specific metabolism in complex diseases.
- For questions or potential collaborations, please contact:
- Maria Andrea Angarita Rodríguez
- Doctoral Candidate in Biological Sciences
- MSc in Bioinformatics
- [email hidden]
- Laura Rodríguez
- [email hidden]
- Biologist
README.md at commit 5d7f2d0, no license · at the source
Overview
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, Colombia
- Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia Bogotá, Bogotá, Colombia
- Department of Biochemistry and Molecular Biology, Faculty of Biological Sciences, University of Concepción, Concepción, Chile
Abstract
Introduction: Astrocytes are central regulators of neuronal energy metabolism and redox homeostasis—processes that become progressively disrupted across the Alzheimer’s disease (AD) continuum. However, bulk transcriptomic data obscure cell-type–specific signals, and existing reference-free deconvolution methods often prioritize either statistical robustness or quantitative accuracy without fully integrating both dimensions.
Methods: Here, we present a comparative framework evaluating two complementary unsupervised approaches, CDSeq and DECODER, to reconstruct astrocyte-associated transcriptomic profiles from human hippocampal samples spanning control, mild cognitive impairment (incipient and moderate), and AD (severe) stages (GSE28146; n = 30). The inferred profiles were functionally contextualized through integration into a genome-scale metabolic model of human astrocytes, enabling the assessment of system-level metabolic alterations associated with disease progression.
Results: Our results reveal consistent dysregulation of key astrocytic pathways, including impairment of the astrocyte–neuron lactate shuttle, disruption of glutamine metabolism, and reduced glutathione-mediated an oxidant capacity. Methodological benchmarking showed distinct yet complementary performance profiles: DECODER achieved higher accuracy in reconstructing global expression magnitudes, whereas CDSeq exhibited greater stability and preservation of gene–gene relationships. Crucially, external validation using independent single-nucleus RNA-seq astrocyte data demonstrated that CDSeq-derived profiles achieve moderate but robust concordance with reference signatures (r ≈ 0.43–0.44), substantially exceeding DECODER-derived concordance (r ≈ 0.19–0.23), with higher concordance with astrocyte-associated signatures, alongside preservation of canonical astrocyte markers and enrichment of astrocyte-specific pathways, indicating superior biological coherence.
Discussion: Together, these findings demonstrate that technical accuracy does not necessarily translate into biological validity and highlight CDSeq as the method that more reliably captures astrocyte-specific transcriptional programs in this context. While DECODER remains valuable for detecting absolute expression changes, CDSeq provides a more consistent recovery of astrocyte-associated transcriptional patterns. More broadly, our results support the incorporation of biological validation alongside statistical benchmarking when selecting deconvolution methods for downstream systems biology and metabolic modeling applications. This framework establishes a reproducible strategy for evaluating deconvolution methods and their functional consequences, advancing the interpretation of bulk transcriptomic data in neurodegenerative disease.
Reproduced under the paper's license (CC BY), from the paper cited above.
Repository
Its files are read in the Code ↔ Paper reader above.
mangaritar/Deconvolution-Astrocyte
5d7f2d0924f0f1383c4d811ce38c64afc6b22cd4, 11 June 2026Availability: 1 check, the latest on 27 September 2026: the link answers
- 27 September 2026: the link answers
1 file
- README.md, Text, 30 lines
The paper's code and data availability statement is in the Data section.
Tracing map
Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.
What the map holds:
- 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
- 0 scripts, each with its path and the digest of its content;
- no match between paragraphs and code yet;
- neither the text of the paper nor the code itself.
Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.
Data
Datasets cited
- geo:GSE28146, at NCBI GEO; found in the text, “Statistical analysis”
Data availability statement
The data presented in this study are publicly available. The transcriptomic dataset analyzed is deposited in the NCBI Gene Expression Omnibus (GEO) repository under accession number GSE28146 (https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.
Version 2, 28 September 2026
- Funding: added Pontificia Universidad Javeriana: 2020000100357
Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, pages, dates, 6 authors, 7 keywords, 47 references.
Cite
This paper
Rodríguez-Millan, L., Angarita-Rodríguez, A., Vargas-López, V., Pinzón, A., Tarifeño-Saldivia, E., & González, J. (2026). Comparative evaluation of reference-free transcriptomic deconvolution highlights the importance of biological validation in astrocytes across Alzheimer's disease. Frontiers in bioinformatics, 6, 1858866. https://
BibTeX
@article{rodriguezmillan
author = {Rodríguez-Millan, Laura and Angarita-Rodríguez, Andrea and Vargas-López, Viviana and Pinzón, Andrés and Tarifeño-Saldivia, Estefania and González, Janneth},
title = {{Comparative evaluation of reference-free transcriptomic deconvolution highlights the importance of biological validation in astrocytes across Alzheimer's disease}},
journal = {Frontiers in bioinformatics},
year = {2026},
month = jul,
volume = {6},
pages = {1858866},
publisher = {Frontiers Media SA},
issn = {2673-7647},
doi = {10.3389/
url = {https://
pmid = {42516270},
pmcid = {PMC13402868}
}
RIS
TY - JOUR
AU - Rodríguez-Millan, Laura
AU - Angarita-Rodríguez, Andrea
AU - Vargas-López, Viviana
AU - Pinzón, Andrés
AU - Tarifeño-Saldivia, Estefania
AU - González, Janneth
TI - Comparative evaluation of reference-free transcriptomic deconvolution highlights the importance of biological validation in astrocytes across Alzheimer's disease
T2 - Frontiers in bioinformatics
J2 - Front Bioinform
PY - 2026
DA - 2026/
VL - 6
SP - 1858866
SN - 2673-7647
PB - Frontiers Media SA
DO - 10.3389/
UR - https://
LA - en
ER -
CSL-JSON
{
"id": "10.3389/
"type": "article-journal",
"title": "Comparative evaluation of reference-free transcriptomic deconvolution highlights the importance of biological validation in astrocytes across Alzheimer's disease",
"container-title": "Frontiers in bioinformatics",
"author": [
{
"family": "Rodríguez-Millan",
"given": "Laura"
},
{
"family": "Angarita-Rodríguez",
"given": "Andrea"
},
{
"family": "Vargas-López",
"given": "Viviana"
},
{
"family": "Pinzón",
"given": "Andrés"
},
{
"family": "Tarifeño-Saldivia",
"given": "Estefania"
},
{
"family": "González",
"given": "Janneth"
}
],
"container-title-short":
"volume": "6",
"page": "1858866",
"DOI": "10.3389/
"PMID": "42516270",
"PMCID": "PMC13402868",
"ISSN": "2673-7647",
"publisher": "Frontiers Media SA",
"URL": "https://
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
13
]
]
}
}
The tracing map gets a citation of its own once an author has validated it and it has a DOI.
Similar papers
The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.
- [1] doi:10.3389/fbinf.2026.1816121 [code]
- Transcriptome-informed metabolic modeling reveals astrocyte-specific vulnerabilities in mild cognitive impairment and Alzheimer's disease progression.Journal: Frontiers in bioinformaticsIn common: NCBI GEO GSE28146, Alzheimer's / dementia, genetics / omics, cellular / molecular, 9 references, author Viviana Vargas-López
- [2] doi:10.3389/fsysb.2026.1897648
- Integrative genome-scale metabolic model of GABAergic neurons reveals metabolic signatures across the mild cognitive impairment - Alzheimer disease continuum.Journal: Frontiers in systems biologyIn common: Alzheimer's / dementia, genetics / omics, 5 references
- [3] doi:10.3892/ijmm.2026.5973
- Effect of MALT1 inhibition by MI‑2 on the microglial phenotype switch, inflammatory cytokine secretion, neuronal loss and oxidative stress in Alzheimer's disease.Journal: International journal of molecular medicineIn common: NCBI GEO GSE28146, Alzheimer's / dementia, cellular / molecular
- [4] doi:10.1186/s12974-026-03837-9
- PTP1B in astrocytes drives pathogen-induced neurodegeneration.Journal: Journal of neuroinflammationIn common: NCBI GEO GSE28146, Alzheimer's / dementia, cellular / molecular
- [5] doi:10.1186/s40035-026-00567-z
- Transaldolase 1 contributes to pentose phosphate pathway disruption and synaptic dysfunction in Alzheimer's disease.Journal: Translational neurodegenerationIn common: Alzheimer's / dementia, genetics / omics, cellular / molecular, 2 references
- [6] doi:10.3389/fneur.2026.1822479 [code]
- Circulating neuron-derived cfDNA for blood-based detection of Alzheimer's and other neurodegenerative conditions.Journal: Frontiers in neurologyIn common: Alzheimer's / dementia, genetics / omics, 2 references
- [7] doi:10.1038/s41467-026-73474-6 [code]
- Dual platform spatial transcriptomics reveals parvalbumin interneuron subtype vulnerability in mouse models of Alzheimer's disease.Journal: Nature communicationsIn common: Alzheimer's / dementia, genetics / omics, cellular / molecular, 2 references
- [8] doi:10.1093/bib/bbag411 [code]
- Screening of cell-type-specific meta-programs for drug repurposing in Alzheimer's disease.Journal: Briefings in bioinformaticsIn common: Alzheimer's / dementia, genetics / omics, 2 references
- [9] doi:10.1038/s42003-026-10059-5 [code]
- Transcriptomic analysis in autism spectrum disorder suggests three molecular subtypes with distinct phenotypic profiles and functional pathways.Journal: Communications biologyIn common: genetics / omics, cellular / molecular, 2 references
- [10] doi:10.1038/s41398-026-04025-2 [code]
- Brain energetic landscapes shape state dysregulation in major depressive disorder: a morphological network controllability perspective.Journal: Translational psychiatryIn common: 2 references
Contribute
The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.
Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.
Claim this paper
Correct its record
Say what each link of this record is, remove the ones that are not the paper's, add the ones that are missing. The correction becomes a new version of the record, in its Versions section.
Validate its tracing map
You validate the map as this page shows it: 1 repository of the authors' code, each at its verified commit and with its license, 0 scripts, and 0 matches between paragraphs and code (see the Code and Map sections). It then receives a DOI on Zenodo, with you (your ORCID iD) and OSCR as its creators; the code itself is not deposited.
The map's fingerprint: sha256:472e8d084b249f6a…
Add the badge to its README
The badge links the code to this page. Copy one of these into the README of the paper's code: only you decide where it goes, and nothing is changed for you.
Markdown
[, paste the snippet at the top, then “Commit changes…” and, to review it first, “Create a new branch and start a pull request”. You open the pull request; OSCR asks for no permission.
Request its removal
To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).
Discussion, reproductions, activity
Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.
Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.
Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.