{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T00:22:25Z","timestamp":1793146945078,"version":"4.5.0"},"reference-count":48,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioinform."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Astrocytes are central regulators of neuronal energy metabolism and redox homeostasis\u2014processes that become progressively disrupted across the Alzheimer\u2019s disease (AD) continuum. However, bulk transcriptomic data obscure cell-type\u2013specific signals, and existing reference-free deconvolution methods often prioritize either statistical robustness or quantitative accuracy without fully integrating both dimensions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our results reveal consistent dysregulation of key astrocytic pathways, including impairment of the astrocyte\u2013neuron 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\u2013gene 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 \u2248 0.43\u20130.44), substantially exceeding DECODER-derived concordance (r \u2248 0.19\u20130.23), with higher concordance with astrocyte-associated signatures, alongside preservation of canonical astrocyte markers and enrichment of astrocyte-specific pathways, indicating superior biological coherence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fbinf.2026.1858866","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T08:29:53Z","timestamp":1783931393000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Comparative evaluation of reference-free transcriptomic deconvolution highlights the importance of biological validation in astrocytes across Alzheimer\u2019s disease"],"prefix":"10.3389","volume":"6","author":[{"given":"Laura","family":"Rodr\u00edguez-Millan","sequence":"first","affiliation":[{"name":"Departamento de Nutrici\u00f3n y Bioqu\u00edmica, Facultad de Ciencias, Pontificia Universidad Javeriana","place":["Bogot\u00e1, Colombia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Angarita-Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Departamento de Nutrici\u00f3n y Bioqu\u00edmica, Facultad de Ciencias, Pontificia Universidad Javeriana","place":["Bogot\u00e1, Colombia"]},{"name":"Laboratorio de Bioinform\u00e1tica y Biolog\u00eda de Sistemas, Universidad Nacional de Colombia Bogot\u00e1","place":["Bogot\u00e1, Colombia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Viviana","family":"Vargas-L\u00f3pez","sequence":"additional","affiliation":[{"name":"Departamento de Nutrici\u00f3n y Bioqu\u00edmica, Facultad de Ciencias, Pontificia Universidad Javeriana","place":["Bogot\u00e1, Colombia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andr\u00e9s","family":"Pinz\u00f3n","sequence":"additional","affiliation":[{"name":"Laboratorio de Bioinform\u00e1tica y Biolog\u00eda de Sistemas, Universidad Nacional de Colombia Bogot\u00e1","place":["Bogot\u00e1, Colombia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Estefania","family":"Tarife\u00f1o-Saldivia","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, Faculty of Biological Sciences, University of Concepci\u00f3n","place":["Concepci\u00f3n, Chile"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janneth","family":"Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Departamento de Nutrici\u00f3n y Bioqu\u00edmica, Facultad de Ciencias, Pontificia Universidad Javeriana","place":["Bogot\u00e1, Colombia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,13]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"10765","DOI":"10.3390\/ijms241310765","article-title":"Comparative analysis of cell mixtures deconvolution and gene signatures generated for blood, immune and cancer cells","volume":"24","author":"Alonso-Moreda","year":"2023","journal-title":"Int. J. Mol. Sci."},{"key":"B2","doi-asserted-by":"publisher","first-page":"1500710","DOI":"10.3389\/fsysb.2024.1500710","article-title":"Improvement in the prediction power of an astrocyte genome-scale metabolic model using multi-omic data","volume":"4","author":"Angarita-Rodr\u00edguez","year":"2024","journal-title":"Front. Syst. Biol."},{"key":"B3","doi-asserted-by":"publisher","first-page":"1969","DOI":"10.1093\/bioinformatics\/bty019","article-title":"Computational deconvolution of transcriptomics data from mixed cell populations","volume":"34","author":"Avila Cobos","year":"2018","journal-title":"Bioinformatics"},{"key":"B4","doi-asserted-by":"publisher","first-page":"5650","DOI":"10.1038\/s41467-020-19015-1","article-title":"Benchmarking of cell type deconvolution pipelines for transcriptomics data","volume":"11","author":"Avila Cobos","year":"2020","journal-title":"Nat. Commun."},{"key":"B5","doi-asserted-by":"publisher","first-page":"281","DOI":"10.31887\/DCNS.2009.11.3\/mbelanger","article-title":"The role of astroglia in neuroprotection","volume":"11","author":"B\u00e9langer","year":"2009","journal-title":"Dialogues Clin. Neurosci."},{"key":"B6","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.jchemneu.2011.06.007","article-title":"Microarray analyses of laser-captured hippocampus reveal distinct gray and white matter signatures associated with incipient Alzheimer\u2019s disease","volume":"42","author":"Blalock","year":"2011","journal-title":"J. Chem. Neuroanat."},{"key":"B7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/alz.13450","article-title":"Astrocyte biomarkers GFAP and YKL-40 mediate early alzheimer's disease progression","volume":"20","author":"Brugulat-serrat","year":"2023","journal-title":"Alzheimers Dement."},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0308531101","article-title":"Metagenes and molecular pattern discovery using matrix factorization","author":"Brunet","year":"2003"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/0471142727.mb2201s101","article-title":"DNA microarrays: types, applications and their future","volume":"6137","author":"Bumgarner","year":"2014","journal-title":"DNA"},{"key":"B10","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1186\/s40478-022-01494-6","article-title":"Spatially resolved transcriptomics reveals genes associated with the vulnerability of middle temporal gyrus in Alzheimer\u2019s disease","volume":"10","author":"Chen","year":"2022","journal-title":"Acta Neuropathol. Commun."},{"key":"B11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/femsyr\/foac003","article-title":"Genome-scale modeling of yeast metabolism: retrospectives and perspectives","volume":"22","author":"Chen","year":"2022","journal-title":"FEMS Yeast Res."},{"key":"B12","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.neuropharm.2013.10.018","article-title":"Synaptic gene dysregulation within hippocampal CA1 pyramidal neurons in mild cognitive impairment","volume":"79","author":"Counts","year":"2014","journal-title":"Neuropharmacology"},{"key":"B13","doi-asserted-by":"publisher","first-page":"6077","DOI":"10.1038\/s41467-020-19894-4","article-title":"Muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data","volume":"11","author":"Crowell","year":"2020","journal-title":"Nat. Commun."},{"key":"B14","doi-asserted-by":"publisher","first-page":"2571","DOI":"10.1093\/bioinformatics\/btq406","article-title":"Probabilistic analysis of gene expression measurements from heterogeneous tissues","volume":"26","author":"Erkkil\u00e4","year":"2010","journal-title":"Bioinformatics"},{"key":"B15","doi-asserted-by":"publisher","first-page":"2221","DOI":"10.1002\/glia.23687","article-title":"Questions and (some) answers on reactive astrocytes","volume":"67","author":"Escartin","year":"2019","journal-title":"Glia"},{"key":"B16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1009550","article-title":"GPRuler: metabolic gene-protein-reaction rules automatic reconstruction","volume":"17","author":"Filippo","year":"2021","journal-title":"PLoS Comput. Biol."},{"key":"B17","doi-asserted-by":"publisher","first-page":"279","DOI":"10.2298\/sarh0908434p","article-title":"Mild cognitive impairment","author":"Fornazzari","year":"2001","journal-title":"Behav. Neurology Elder."},{"key":"B18","doi-asserted-by":"publisher","first-page":"2677","DOI":"10.1111\/ejn.16081","article-title":"Human hippocampal astrocytes: computational dissection of their transcriptome, sexual differences and exosomes across ageing and mild-cognitive impairment","volume":"58","author":"Guebel","year":"2023","journal-title":"Eur. J. Neurosci."},{"key":"B19","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1007\/s00401-015-1512-2","article-title":"Neuronal ceroid lipofuscinosis with DNAJC5\/CSP\u03b1 mutation has PPT1 pathology and exhibit aberrant protein palmitoylation","volume":"131","author":"Henderson","year":"2016","journal-title":"Acta Neuropathol."},{"key":"B20","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1186\/s13059-024-03292-w","article-title":"Heterogeneous pseudobulk simulation enables realistic benchmarking of cell-type deconvolution methods","volume":"25","author":"Hu","year":"2024","journal-title":"Genome Biol."},{"key":"B21","first-page":"685","volume-title":"Machine Learning and Deep Learning","author":"Janiesch","year":"2021"},{"key":"B22","doi-asserted-by":"publisher","first-page":"e1007510","DOI":"10.1371\/journal.pcbi.1007510","article-title":"CDSeq: a novel complete deconvolution method for dissecting heterogeneous samples using gene expression data","volume":"15","author":"Kang","year":"2019","journal-title":"PLoS Comput. Biol."},{"key":"B23","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1186\/s12859-021-04186-5","article-title":"CDSeqR: fast complete deconvolution for gene expression data from bulk tissues","volume":"22","author":"Kang","year":"2021","journal-title":"BMC Bioinforma."},{"key":"B24","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1186\/s12859-018-2382-0","article-title":"Expression analysis of RNA sequencing data from human neural and glial cell lines depends on technical replication and normalization methods","volume":"19","author":"Knight","year":"2018","journal-title":"BMC Bioinforma."},{"key":"B25","doi-asserted-by":"publisher","first-page":"S11","DOI":"10.1186\/1471-2105-14-S5-S11","article-title":"A mixture model for expression deconvolution from RNA-Seq in heterogeneous tissues","volume":"14","author":"Li","year":"2013","journal-title":"BMC Bioinforma."},{"key":"B26","doi-asserted-by":"publisher","first-page":"e1003123","DOI":"10.1371\/journal.pcbi.1003123","article-title":"Predicting network activity from high throughput metabolomics","volume":"9","author":"Li","year":"2013","journal-title":"PLoS Comput. Biol."},{"key":"B27","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/s12859-019-3312-5","article-title":"Optimization and expansion of non-negative matrix factorization","volume":"21","author":"Lin","year":"2020","journal-title":"BMC Bioinforma."},{"key":"B28","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1186\/s13073-022-01136-5","article-title":"Cell type-specific changes identified by single-cell transcriptomics in Alzheimer\u2019s disease","volume":"14","author":"Luquez","year":"2022","journal-title":"Genome Med."},{"key":"B29","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1016\/j.neuron.2015.03.035","article-title":"A cellular perspective on brain energy metabolism and functional imaging","volume":"86","author":"Magistretti","year":"2015","journal-title":"Neuron"},{"key":"B30","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1038\/nrn.2018.19","article-title":"Lactate in the brain: from metabolic end-product to signalling molecule","volume":"19","author":"Magistretti","year":"2018","journal-title":"Nat. Rev. Neurosci."},{"key":"B31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fnagi.2019.00059","article-title":"Astrocyte heterogeneity: impact to brain aging and disease","volume":"11","author":"Matias","year":"2019","journal-title":"Front. Aging Neurosci."},{"key":"B32","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s00401-011-0884-1","article-title":"Mild cognitive impairment: pathology and mechanisms","volume":"123","author":"Mufson","year":"2012","journal-title":"Acta Neuropathol."},{"key":"B33","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1186\/s13059-016-1062-5","article-title":"CellMapper: rapid and accurate inference of gene expression in difficult-to-isolate cell types","volume":"17","author":"Nelms","year":"2016","journal-title":"Genome Biol."},{"key":"B34","doi-asserted-by":"publisher","first-page":"4761","DOI":"10.1093\/nar\/gkae267","article-title":"Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges","volume":"52","author":"Nguyen","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"B35","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1038\/nbt.1614","article-title":"What is flux balance analysis?","volume":"28","author":"Orth","year":"2010","journal-title":"Nat. Biotechnol."},{"key":"B36","doi-asserted-by":"publisher","first-page":"4729","DOI":"10.1038\/s41467-019-12517-7","article-title":"De novo compartment deconvolution and weight estimation of tumor samples using DECODER","volume":"10","author":"Peng","year":"2019","journal-title":"Nat. Commun."},{"key":"B37","doi-asserted-by":"crossref","unstructured":"Biomarker discovery in heterogeneous tissue samples-taking the in-silico deconfounding approach\n          \n          \n            \n              Repsilber\n              D.\n            \n            \n              Kern\n              S.\n            \n            \n              Telaar\n              A.\n            \n            \n              Walzl\n              G.\n            \n            \n              Black\n              G. F.\n            \n            \n              Selbig\n              J.\n            \n          \n          10.1186\/1471-2105-11-27\n          BMC Bioinform.\n          11\n          2010","DOI":"10.1186\/1471-2105-11-27"},{"key":"B38","doi-asserted-by":"publisher","first-page":"2384","DOI":"10.1038\/s41593-024-01791-4","article-title":"Astrocyte transcriptomic changes along the spatiotemporal progression of Alzheimer\u2019s disease","volume":"27","author":"Serrano-Pozo","year":"2024","journal-title":"Nat. Neurosci."},{"key":"B39","doi-asserted-by":"crossref","DOI":"10.1002\/9781119970606","article-title":"Handbook of statistical systems biology","volume-title":"Handbook of Statistical Systems Biology","author":"Stumpf","year":"2011"},{"key":"B40","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1007\/s11192-009-0146-3","article-title":"Software survey: vosviewer, a computer program for bibliometric mapping","volume":"84","author":"van Eck","year":"2010","journal-title":"Scientometrics"},{"key":"B41","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1016\/j.isci.2018.10.028","article-title":"Transcriptome deconvolution of heterogeneous tumor samples with immune infiltration","volume":"9","author":"Wang","year":"2018","journal-title":"IScience"},{"key":"B42","doi-asserted-by":"publisher","first-page":"1622","DOI":"10.7150\/jca.27780","article-title":"The prospective value of dopamine receptors on bio-behavior of tumor","volume":"10","author":"Wang","year":"2019","journal-title":"J. Cancer"},{"key":"B43","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1002\/1531-8249(199909)46:3<425::AID-ANA22>3.0.CO;2-O","article-title":"Analysis of gene expression in multiple sclerosis lesions using cDNA microarrays","volume":"46","author":"Whitney","year":"1999","journal-title":"Ann. Neurology"},{"key":"B44","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1186\/s13195-022-01004-9","article-title":"Cerebrospinal fluid lactate levels along the Alzheimer\u2019s disease continuum and associations with blood-brain barrier integrity, age, cognition, and biomarkers","volume":"14","author":"Zebhauser","year":"2022","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"B45","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1186\/s12859-016-1333-x","article-title":"A network-based pathway-expanding approach for pathway analysis","volume":"17","author":"Zhang","year":"2016","journal-title":"BMC Bioinforma."},{"key":"B46","doi-asserted-by":"publisher","first-page":"1163","DOI":"10.1093\/jnen\/nly102","article-title":"Lactate deficit in an alzheimer disease mouse model: the relationship with neuronal damage","volume":"77","author":"Zhang","year":"2018","journal-title":"J. Neuropathology Exp. Neurology"},{"key":"B47","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1038\/s41392-023-01486-5","article-title":"Role of neuroinflammation in neurodegeneration development","volume":"8","author":"Zhang","year":"2023","journal-title":"Signal Transduct. Target. Ther."},{"key":"B48","doi-asserted-by":"publisher","first-page":"1523","DOI":"10.1038\/s41467-019-09234-6","article-title":"Metascape provides a biologist-oriented resource for the analysis of systems-level datasets","volume":"10","author":"Zhou","year":"2019","journal-title":"Nat. Commun."}],"container-title":["Frontiers in Bioinformatics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fbinf.2026.1858866\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T08:29:55Z","timestamp":1783931395000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fbinf.2026.1858866\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,13]]},"references-count":48,"alternative-id":["10.3389\/fbinf.2026.1858866"],"URL":"https:\/\/doi.org\/10.3389\/fbinf.2026.1858866","relation":{},"ISSN":["2673-7647"],"issn-type":[{"value":"2673-7647","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,13]]},"article-number":"1858866"}}