Comparative evaluation of reference-free transcriptomic deconvolution highlights the importance of biological validation in astrocytes across Alzheimer’s disease
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, and AD stages establishes a reproducible strategy for evaluating deconvolution methods and their functional consequences.
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.
Mild cognitive impairment (MCI) represents a prodromal stage of Alzheimer’s disease (AD), but the metabolic mechanisms underlying early neuronal dysfunction remain incompletely understood. GABAergic neurons, which maintain excitatory–inhibitory balance and network stability, exhibit early vulnerability during neurodegeneration, although the metabolic alterations associated with their dysfunction remain poorly characterized.
We developed a context-specific genome-scale metabolic model (GEM) of human GABAergic neurons across the MCI–AD continuum using deconvolved hippocampal transcriptomic data. By integrating transcriptomic deconvolution with constraint-based modeling, including flux balance analysis (FBA) and flux variability analysis (FVA), we inferred disease-stage-associated metabolic alterations under Control, early MCI (E-MCI), advanced MCI (A-MCI), and AD conditions.
Our analyses suggest progressive remodeling of energy metabolism, the glutamate–glutamine–GABA cycle, redox homeostasis, lipid metabolism, and neuron–astrocyte metabolic interactions. FVA identified reaction-specific changes in feasible flux ranges, indicating remodeling of the feasible metabolic solution space rather than a uniform contraction across pathways. These predicted metabolic alterations were accompanied by transcriptional changes in GABAergic markers and showed qualitative agreement with independent metabolomic observations, supporting their biological plausibility.
Overall, this work provides a systems-level computational framework linking transcriptomic alterations with predicted metabolic remodeling in GABAergic neurons and generates experimentally testable hypotheses regarding metabolic dysfunction during progression from MCI to AD.
Andrea Angarita-Rodríguez, Johan H. Largo-González, Julián Pérez-Mejía et al.· Frontiers in Systems Biology· 0 citations
Background Cellular deconvolution methods estimate cell-type proportions from bulk RNA-seq data, typically using single-cell RNA-seq–derived signatures, enabling separation of disease-associated transcriptional changes into composition-driven and cell-intrinsic effects. However, these approaches depend on model assumptions and the stability of cell-type signatures, and it remains unclear how deconvolution-related uncertainties influence downstream analyses and biological conclusions. Results We systematically evaluated the effect of cell-type correction on disease-relevant transcriptomic insights, using Alzheimer’s disease (AD) as a model and the Mount Sinai Brain Bank cohort as a primary dataset. Applying dtangle, selected after comparison with another deconvolution approach, we estimated cell-type proportions across four brain regions and assessed how correction reshaped differential gene expression and pathway enrichment. Cell-type correction (CTC) markedly altered differentially expressed gene (DEG) profiles in a region-dependent manner: the superior temporal gyrus lost all significant signals, while the frontal pole gained DEGs with improved cross-region concordance. At the pathway level, correction shifted enrichment from synaptic loss and immune activation toward suppression of stress-response and immune regulatory programs, suggesting that composition changes partly obscure cell-intrinsic regulatory signals. Overlap with AD genome-wide association study loci and replication in an independent cohort indicated that cell-intrinsic changes are more consistently validated than composition-driven changes. Notably, KCNN2 and RIMS1, not currently recognized as canonical AD biomarkers, emerged as robust transcriptional signatures, potentially reflecting both composition-driven and cell-intrinsic dysregulation and warranting further investigation. Conclusions Parallel evaluation of uncorrected and CTC analyses distinguishes composition-driven from cell-intrinsic transcriptional effects and highlights robust disease signatures in heterogeneous tissues such as the brain.
AIMS
This study aims to systematically identify the cellular drivers of brain microenvironmental imbalance during Alzheimer's disease (AD) progression by integrating high-resolution single-nucleus RNA sequencing (snRNA-seq) with intercellular communication analyses. In the work, the functional transitions and metabolic mechanisms were specifically investigated for distinct astrocyte subpopulations underlying glutamate dysregulation.
MAIN METHODS
We analyzed ROSMAP snRNA-seq data from 427 individuals to identify astrocyte subpopulations, assess their associations with AD Braak stages, and infer intercellular communication using CellChat. For validation, primary mouse astrocytes were exposed to oligomeric tau, oligomeric Aβ₁₋₄₂, or activated microglia-conditioned medium (aMCM). Mdivi-1 was applied to aMCM-stimulated astrocytes to examine oxidative stress-related changes in glutamate-handling genes.
KEY FINDINGS
During disease progression, Ast.1 exhibited a transcriptional signature consistent with impaired glutamate clearance machinery and became the predominant source of inferred astrocyte-to-neuron glutamate signaling. Mechanistically, microglia-derived neuroinflammation robustly induces severe mitochondrial oxidative stress in astrocytes, specifically upregulating the SLC7A11 (xCT) antiporter. This compensatory antioxidant defense enforces a deleterious metabolic trade-off, directly coupling intracellular redox homeostasis with continuous pathological glutamate efflux. Crucially, targeted inhibition of oxidative stress via mdivi-1 significantly suppressed xCT hyperactivation and partially restored the expression of glutamate-handling genes.
SIGNIFICANCE
This study defines Ast.1 as a pivotal driver of microenvironmental collapse in AD, unveiling a novel microglia-astrocyte-neuron pathogenic cascade mediated by the ROS-xCT metabolic hub. Targeting this astrocytic vulnerability offers a promising therapeutic strategy for preventing neurodegeneration.
Xue-Man Xie, Wen-Jing Liu, Long-Chao Chen et al.· Life Science· 0 citations
BACKGROUND
Neuroinflammation, a key factor in aging and neurodegeneration, is characterized by the increased activation of microglia, the brain's resident immune cells. Microglia play a central role in maintaining brain homeostasis, and their dysregulation during aging is increasingly implicated in the onset and progression of Alzheimer's disease (AD). However, the molecular mechanisms underlying microglial state transitions across physiological and pathological aging remain poorly understood.
METHODS
To address this gap, we conducted an in silico comparative transcriptomic study using publicly available datasets from two murine bulk RNA-seq including wild-type (WT) and APP/PS1 transgenic (Tg) mice at multiple ages, one human scRNA-seq dataset with multiple ages, and data obtained from SCAD-Brain.
RESULT
Our analyses revealed that physiological microglial aging is characterized by dynamic, non-linear gene expression trajectories, whereby genes involved in mitochondrial function, lysosomal degradation, and immune response follow a mirror-like pattern across aging. This mirror-like behavior was conserved in human microglial data across ages. In contrast, this adaptive pattern was disrupted at late-stage pathological aging in Tg mice, where sustained alterations in inflammatory, mitochondrial, and lysosomal pathways became more pronounced. Consistent with these findings, genes dysregulated in Tg mice showed similar expression trends in AD patients in the SCAD-Brain database.
CONCLUSION
These results suggest that middle age may represent a critical transition stage preceding neuroinflammation and neurodegeneration, making it an attractive window to identify preventive or therapeutic targets in early AD. Collectively, this study identifies candidate pathways and genes that warrant further experimental validation in the context of AD and age-related neurodegeneration.
Andrés Muedano-Sosa, Magalli Trujillo-Pineda, Samuel Ruiz-Pérez et al.· Experimental Gerontology· 0 citations
Bulk transcriptomic studies of Alzheimer's disease are difficult to interpret because the diseased brain is not only transcriptionally altered, but structurally remodeled. Neuronal and synaptic loss, reactive gliosis, and vascular or extracellular-matrix changes can all shift measured expression.
To reanalyze GSE5281 with glial marker-derived adjustment and integrate residual gene sets with Alzheimer's disease S-PrediXcan/transcriptome-wide association study (TWAS) results to prioritize genes relevant to disease pathogenesis and target discovery.
We used age, composite glial score, and surrogate-variable adjustment to define residual upregulated and downregulated genes, then evaluated these sets in brain-tissue TWAS outputs using Stouffer meta-Z statistics, permutation/bootstrap set-level testing, and gene-level leave-one-out influence analysis.
The residual sets did not show significant set-level enrichment. AD_RESIDUAL_DOWN showed Stouffer Z = 0.862, permutation p = 0.6982, and bootstrap 95% CI = −3.18 to 5.38. AD_RESIDUAL_UP showed Stouffer Z = −1.564, permutation p = 0.5002, and bootstrap 95% CI = −5.43 to 2.26. However, gene-level analysis identified 41 influential gene-disease entries. GALNT6 was the strongest signal in the residual upregulated set and the strongest overall gene-level signal. C1QTNF4 and NTSR2 were the largest influence signals in the residual downregulated set, while EPHX2 ranked among the influential genes.
These findings support glial-adjusted residual transcriptomics with leave-one-out TWAS influence analysis as a computational prioritization strategy for Alzheimer's disease, rather than as evidence of broad pathway-level genetic enrichment.
Cheung Ngo· Journal of Alzheimer's Disea...· 0 citations
Interindividual heterogeneity in Alzheimer’s disease (AD) remains poorly understood, as disparate single-cell studies leave it unclear whether findings reflect shared architecture or dataset-specific idiosyncrasies. Here, we present panAD, a transcriptomic atlas of >3 million nuclei from 791 individuals across 13 studies, spanning AD, mild cognitive impairment, and cognitively normal aging. AD converges on a reproducible, cell-type-specific molecular architecture: co-expression modules track neuropathology and cognitive decline; GWAS risk genes act predominantly as downstream targets of transcription factor hubs such as microglial SPI1; intercellular communication is remodeled with disease stage; and sex differences concentrate in microglial immune-activation programs. To model patient-level transcriptomic heterogeneity, we developed the Multi-seed Optimization of Neural Embeddings for subTyping (MONET) framework, in which a masked variational autoencoder applied to covariate-adjusted, multi-cell-type profiles resolves four subtypes (Metal-Ion Stress, Neuroinflammatory, Synaptic Integrity, and Tissue Remodeling) that dissociate neuropathological burden from cognitive impairment and nominate predominantly non-overlapping candidate therapeutics. Finally, Stellar Atlas provides an AI-native conversational interface to the atlas.
Negin Rahimzadeh, S. Morabito, Saniya Khullar et al.· bioRxiv· 0 citations
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