Biology subjects

Cruchaga, C.

Publications and source records attributed to Cruchaga, C..

At least 19 recordsLinked to original sources

bioRxiv2026

Parietal Cortex Transcriptomics Refines Parkinson Disease GWAS Nomination and Highlights STAT3 as a Putative Upstream Glial Regulator

Parkinson disease (PD) affects more than 1.1 million individuals in the United States and around 12 million worldwide. Although Genome Wide Association Studies (GWAS) have substantially advanced our understanding of PD genetic architecture, the regulatory mechanisms linking PD risk loci to disease-relevant gene expression remain incompletely characterized, limiting our ability to infer disease mechanisms from genetic associations. Here, we integrated disease-state parietal cortex transcriptomics with the International Parkinsons Disease Genomics Consortium (iPDGC) locus prioritization to refine PD gene nomination and identify biologically plausible candidates missed by GWAS-only approaches. Using bulk RNA-seq from 99 neuropathologically confirmed PD cases and 30 neuropathologically confirmed controls, we prioritized candidate genes across 78 loci and classified them according to concordance between genetic evidence and differential expression in diseased cortices. This integrative approach recovered candidate genes not captured by external GWAS-based prioritization methods and highlighted synaptic, lysosomal, and proteostasis pathways as major components of PD risk biology. Network and transcription factor analyses further suggested coordinated regulation of these genes, with STAT3 emerging as a putative upstream glial regulator. Together, these findings suggest that integrating disease-state transcriptomics with genetic prioritization can refine PD risk-gene nomination and uncover regulatory programs that may be missed by GWAS alone.

neuroscience↗

bioRxiv2026

Biomarker Variability Limits Individualized Amyloid Time Estimation in Alzheimer Disease

ObjectiveDisease progression modeling (DPM) or "amyloid time" is increasingly used to stage Alzheimer disease (AD). DPM performance depends on within-individual heterogeneity in rates of pathological accumulation as well as test-retest reliability of the biomarker. The relative contributions of these variabilities have not been systematically assessed. This would be particularly relevant if extrapolations from DPM were to be used to make individual-level predictions for research, clinical trials, or potentially future clinical practice. MethodsWe conducted simulation studies incorporating empirically-derived noise properties from amyloid biomarkers to assess the contributions of inter- and intra-individual variability. Findings generalized in an autosomal dominant AD cohort with amyloid positron emission tomography (PET), cerebrospinal fluid (CSF), and plasma biomarkers and in a sporadic AD cohort with both amyloid PET and plasma biomarkers. We assessed group level DPM performance via mean average error (MAE) and root mean squared error (RMSE). At the individual level, we evaluated distinctness of distributions of biomarker levels associated with specific disease timings. ResultsInter-individual variability was the dominant source of error in temporal estimates. Intra-individual variability reduced estimate stability. Optimal performance occurred in biomarkers with positive average accumulation rates where a subset of individuals had exceptionally high levels of accumulation. In research study data, amyloid PET outperformed CSF and plasma biomarkers. InterpretationDPM is fundamentally constrained by dynamic range, variability, and test-retest reliability of the biomarker of interest. Current DPM approaches are more robust at the group level, particularly when applied to biomarkers with more than 10-15% variability like fluid biomarkers. FundingNational Institute on Aging, Alzheimers Association, German Center for Neurodegenerative Diseases, Raul Carrea Institute for Neurological Research, Japan Agency for Medical Research and Development, Korean Ministry of Health & Welfare and Ministry of Science and ICT, Spanish Institute of Health.

bioinformatics↗

bioRxiv2026

The NORAD-pumilio regulatory axis links lncRNA dysregulation to tau propagation-associated phenotypes

Long non-coding RNAs (lncRNAs) are increasingly implicated in neurodegenerative disease, yet their roles in tauopathy remain poorly understood. Here, we defined the lncRNA landscape across iPSC-derived neurons, astrocytes, and microglia harboring the frontotemporal dementia-associated MAPT IVS10+16 mutation and investigated how lncRNA dysregulation interfaces with tau pathology. Transcriptomic analyses revealed extensive cell-type specific lncRNA expression changes, with neurons exhibiting the greatest degree of mutation-associated remodeling. Comparative analyses with MAPT IVS10+16 patient brain tissue identified NORAD and MIR22HG as lncRNAs significantly dysregulated across all three cell types and human brains. NORAD was also altered in Alzheimers disease and Parkinsons disease brains, suggesting a broader role in neurodegenerative disease. Mechanistically, NORAD-associated protein networks converged on pathways related to RNA regulation, cytoskeletal organization, proteostasis, and tau interaction networks. Given the established role of NORAD in regulating PUM1 and PUM2 RNA-binding (pumilio) proteins, we examined the NORAD-pumilio axis and identified enrichment of pumilio-associated pathways linked to autophagy, endocytosis, proteostasis, and cytoskeletal regulation. NORAD depletion reduced tau seeding and uptake, whereas functional depletion of PUM1 or PUM2 increased both processes, supporting an antagonistic relationship between NORAD and pumilio signaling in modulation of tau aggregation. Together, these findings identify widespread lncRNA dysregulation across neural cell types in the setting of a MAPT mutation and nominate the NORAD-pumilio axis as a regulatory pathway linking RNA homeostasis and tau propagation biology.

neuroscience↗

bioRxiv2026

Reduced LACTB expression in myeloid cells is associated with elevated succinylcarnitine levels and reduced Alzheimers disease risk.

BackgroundLactamase {beta} (LACTB) is a serine {beta}-lactamase-like mitochondrial enzyme associated with cancer progression, obesity, and lipid metabolism. LACTB is located in an Alzheimers Disease (AD) risk locus and has been associated with AD in a proteomic study. MethodsWe performed Mendelian Randomization (MR) analysis to estimate the association between LACTB expression, succinylcarnitine levels, and AD risk. We generated LACTB knock-down (KD) THP1 macrophages, LACTB knock-out (KO) iPSC-derived microglia and LACTB enzymatically-dead (ED) mice. The impact of LACTB loss-of-function in myeloid cells was characterized via transcriptomics, metabolomics, lipidomics, and functional assays. Finally, human LACTB KO microglia precursors were xenotransplanted into the brains of mice with amyloid pathology to assess in vivo interactions with amyloid plaques. ResultsMR analyses revealed that lower LACTB expression in myeloid cells may lead to reduced AD risk and higher levels of succinylcarnitine, a metabolite associated with AD risk. We identified LACTB as a primary enzyme responsible for succinylcarnitine hydrolysis. Transcriptional and functional studies showed that loss of LACTB enhances OXPHOS, and reduces protein synthesis and triglycerides. LACTB expression was upregulated following interferon or TNF stimulation, and its loss modified efferocytosis- related functions under inflammatory conditions. In vivo, xenotransplanted human LACTB KO microglia exhibited enhanced association with amyloid plaques. ConclusionsOur findings define a previously unrecognized axis linking LACTB and succinylcarnitine to myeloid cell function and AD susceptibility. Given the druggability of LACTB and the potential for succinylcarnitine to serve as a translational biomarker, this enzyme represents a promising therapeutic target for modulation of neuroinflammation in AD.

neuroscience↗

bioRxiv2026

Cellular Aging Signatures in the Plasma Proteome Record Human Health and Disease

Aging is asynchronous across cells and organs, but whether plasma proteins can capture cell type-specific aging and predict disease and mortality remains unknown. We developed machine learning models to estimate the biological age of more than 40 distinct cell types--spanning neuronal, immune, glial, endocrine, epithelial, and musculoskeletal origins--using over 7,000 plasma proteins measured in 60,000 individuals across three cohorts, comprising the largest human plasma proteomics aging study to date. Individuals showed heterogeneous aging profiles, with 20-25% exhibiting accelerated aging in a single cell type and 1-3% across ten or more cell types. APOE genotype showed antagonistic aging effects in different cell types: APOE4 carriers exhibited older astrocytes but younger macrophages, while APOE2 carriers showed the inverse. Cellular aging signatures were uniquely associated with disease status and predicted incident disease and mortality over 15 years of follow-up. Amyotrophic lateral sclerosis (ALS) showed the strongest association with skeletal myocyte aging (hazard ratio = 12.7 for extreme accelerated versus youthful aging). In Alzheimers disease (AD), prevalent cases showed accelerated aging across multiple neural and peripheral cell types, with extreme astrocyte aging conferring AD risk comparable to APOE4 carrier status. Moreover, extreme astrocyte aging increased AD risk in APOE4/4 carriers threefold, while youthful astrocytes strikingly reduced risk. Beyond neurodegeneration, respiratory cell aging identified smokers at 58% higher lung cancer risk, and myeloid aging identified normoglycemic individuals at higher diabetes risk. Both specific cellular vulnerabilities and cumulative aging burden influenced survival, wherein youthful immune or neuronal profiles were protective. A polycellular aging risk score provided robust mortality risk stratification across platforms and cohorts. These findings establish a framework for quantifying biological aging at the cellular resolution using plasma proteomics, revealing heterogeneity in aging trajectories and their impact on disease susceptibility and resilience.

neuroscience↗

bioRxiv2026

Structural Brain Indicators of Cognitive Performance in Middle and Late Adulthood: The Human Connectome Project in Aging/Aging Adult Brain Connectome Cohort

IntroductionSignificant effort has been put towards mapping patterns of atrophy in the cerebral cortex that are related to pathological aging, including the characteristic patterns of neurodegeneration in Alzheimers disease (AD). In contrast, brain structural patterns that support preserved or even exceptional cognition throughout the adult age-span and especially in later life are much less known. It is possible that superior cognitive performance in late life is supported by a preservation of brain structures vulnerable to typical aging. Alternatively, elevated performance could be related to preservation of brain regions vulnerable to age-associated pathology. Examination of individuals that exhibit superior cognition throughout the adult lifespan may provide unique insights into neural mechanisms that support cognitive resilience in late life. MethodsWe examined cross-sectional associations between cortical brain structure and cognitive performance across three stages of adulthood: midlife (36-59), young-old (60-79), and older adults (80+) in typically aging individuals enrolled as part of the Human Connectome Project Lifespan-Aging (HCP-A)/Aging Adult Brain Connectome (AABC) studies. Participants were considered generally healthy and excluded for significant and/or atypical health conditions for their demographic category, including a clinical diagnosis of cognitive impairment or dementia. Domain-specific cognitive factor scores representing memory, fluid intelligence, and crystalized intelligence were sex-stratified and residualized relative to age, and participants were classified as high, middle, or low performers based on their unique performance relative to the study sample. ResultsIn the full sample, high performers demonstrated greater cortical thickness in regions of somatomotor, visual, and auditory cortices, as well as cortical areas in the frontal, parietal, and insular cortices (e.g., 5m, LIPv, MBelt). We also found associations between medial temporal and cingulate cortical thickness and cognitive performance, but only for select analyses. Group differences in cortical thickness were greatest when contrasting high and low performers for fluid intelligence compared to the other cognitive factors and were most prominent in the midlife participants compared to the other age strata. These group differences were primarily driven by reduced cortical thickness in the low performing individuals in the younger age bins relative to the typically performing sample. Effects were rather limited when contrasting high and low performers in the 80+ age group. The cortical areas of high statistical significance appeared to show a cross-sectional convergence effect, such that the differences in cortical thickness between high vs. low cognitive performance groups diminished with increasing age. Despite lower statistical power, effect sizes were greatest when contrasting individuals at the extremes of performance (e.g., top 10% vs. bottom 10% performers). These effects were robust to subsample replications using longitudinally defined cognitive classifications. DiscussionElevated cognitive performance was cross-sectionally associated with increased regional cortical thickness and effects were most prominent in mid-life compared to later ages. Notably, contrary to brain regions that may be expected to support such high-order cognitive performance, a significant portion of primary sensory, motor, and insular cortical areas exhibited group differences between the high- and low-performing groups in this relatively younger age group. Group differences were due to lower thickness in these regions in the low performing group relative to the typical performers. In contrast to the younger portion of the sample, regions typically considered vulnerable to Alzheimers disease (i.e., regions in the medial temporal lobe) were only infrequently implicated. These results suggest that specific patterns of cortical brain structural integrity including preserved thickness of primary motor and sensory cortical regions may be a necessary but not sufficient mechanism supporting superior cognitive abilities in earlier adulthood, while alternate neural mechanisms may support cognitive resilience later in life. These results must be interpreted with caution given the cross-sectional nature of this study. Cognitive capacity can only be estimated from a single timepoint, and several factors contribute to inter-individual variation that will not be accounted for in the models applied here. Longitudinal assessment of cognitive resilience in the HCP-A/AABC cohort will be performed in future work.

neuroscience↗

bioRxiv2025

A proteomic signature of vascular dysfunction linked to tauopathy and degeneration in the aging brain

Small vessel disease (SVD) impacts healthy aging of organs across the body, yet its contributions to adverse brain aging remain poorly defined. Here we show thromboinflammation, a core feature of SVD, as a driver of adverse brain aging. We identify cerebrospinal fluid fibrinogen as a marker of brain thromboinflammation and screen neurovascular biosignatures mediating its impact on synaptic vulnerability along the full spectrum of brain aging from cognitively typical, amyloid-negative to cognitively impaired, amyloid-positive older adults. We identified 53 proteins mediating fibrinogens effects on synaptic markers in 1,655 donors from three independent cohorts. Single-cell transcriptomic mapping revealed mediator enrichment in neurovascular unit cells. Pathway analysis demonstrated dysregulation of angiogenesis, fibrosis, and immune signaling. Vascular and microglial-enriched biosignatures associated with compromised white matter integrity. These findings indicate thromboinflammation as an early, amyloid-independent pathway to neurodegeneration and tauopathy, establishing vascular health as fundamental to preserving brain healthspan.

neuroscience↗

bioRxiv2025

OmniCellAgent: Towards AI Co-Scientists for Scientific Discovery in Precision Medicine

Real-world biomedical scientific discovery operates as an iterative lifecycle integrating four core pillars: the targeted identification and analysis of question-specific omics datasets; the context-aware interpretation of molecular data using rich biomedical prior knowledge database; comprehensive literature reviews; and the subjective creativity and expert intuition of human scientists. Together, these pillars drive robust evidence synthesis and novel hypothesis generation. The recently reported AI agents support automated omics analysis and literature review, which typically require users to predefine and curate disease-specific datasets, which is a process that remains challenging and time-consuming. In this study, we present OmniCellAgent, a novel multi-agent AI framework built on large-scale single-cell RNA sequencing (scRNA-seq) datasets, and can autonomously retrieve and analyze disease and control-related scRNAseq datasets of diverse cell types across tissues and conditions. Moreover, it incorporates a biomedical prior knowledge and literature review agents, and disease domain-specific expert agents to systematically annotate omic data-derived targets. By aggregating evidence across agents, the framework generates structured analytical reports and potential scientific hypotheses. We evaluated OmniCellAgent across multiple disease settings, demonstrating its ability to identify relevant datasets, generate omic data analysis results, and produce structured reports and scientific hypotheses. Our findings demonstrate that multi-agent AI systems lower the technical barriers to omics-driven research, thereby accelerating scientific discovery and hypothesis generation in biomedical research and precision medicine. The source code is publicly available at: https://github.com/FuhaiLiAiLab/OmniCellAgent.

bioinformatics↗

bioRxiv2025

Ms4a4a deficiency ameliorates plaque pathology in a mouse model of amyloid accumulation

Genome-wide association studies for Alzheimer disease (AD) risk have identified a number of genes enriched in microglia, including MS4A4A. Common variants in MS4A4A influence AD risk, MS4A4A expression, TREM2 signaling, and a specific microglial transcriptional state, though the exact role of MS4A4A in AD remains unclear. Using a mouse model of amyloid beta (A{beta}) accumulation (5xFAD), we examined the impact of Ms4a4a loss on A{beta} pathology. Before A{beta} accumulation, Ms4a4a loss reduces steady-state A{beta} levels and shortens A{beta} half-life in brain interstitial fluid. In aged 5xFAD Ms4a4a-deficient mice, plaques are more compact with reduced overall plaque burden. Microglia lacking Ms4a4a are more pro-inflammatory and produce more MMP-9, which may promote degradation of A{beta} and A{beta} fibrils. Human subjects that carry a variant near MS4A4A (rs1582763) that confers resilience to AD also exhibit significantly elevated levels of MMP-9 in their cerebrospinal fluid. Together, our results suggest that loss of Ms4a4a improves A{beta} pathology by altering A{beta} clearance, offering insights for therapeutic interventions in AD.

neuroscience↗

bioRxiv2024

BioMedGraphica: An All-in-One Platform for Biomedical Prior Knowledge and Omic Signaling Graph Generation

Multi-omic data analysis is essential for scientific discovery in precision medicine. However, translating statistical results of omic data analysis into novel scientific hypothesis remains a significant challenge. Human experts must manually review analysis results and generate new hypothesis based on extensive and inter-connected biomedical prior knowledge, which is subjective and not scalable. While large language models (LLMs) can accelerate the discovery, their reasoning improves when grounded in structured, auditable and comprehensive biomedical prior knowledge. Biomedical knowledge, however, is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of AI systems to fully leverage biomedical data for scientific discovery. To address these challenges, we developed BioMedGraphica, an all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified knowledge graph containing 2,306,921 entities and 27,232,091 relations. In addition, to the best of our knowledge, this is the first work to propose a novel Textual-Numeric Graph (TNG) data-structure for multi-omics data analysis. In TNG, textual information captures prior biological knowledge (e.g., transcription start sites, functions, mechanisms), while numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data-structure for the development of graph foundation models, with the potential to improve prediction performance and interpretability, while also augmenting LLMs by supplying graph-structured mechanistic context to strengthen reasoning. The details for BioMedGraphica code can be accessed by github link: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph data can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica

bioinformatics↗

bioRxiv2024

iSODA: A Comprehensive Tool for Integrative Omics Data Analysis in Single- and Multi-Omics Experiments

Omics technologies including genomics, proteomics, metabolomics, and lipidomics allow profound insights into health and disease. Thanks to plummeting costs of continuously evolving omics analytical platforms, research centers collect multi-omics data more routinely. They are, however, confronted with the lack of a versatile software solution to harmoniously analyze single-omics data and merge and interpret multi-omics data. We have developed iSODA, an interactive web-based application for the analysis of single-as well as multi-omics omics data. The software tool emphasizes intuitive, interactive visualizations designed for user-driven data exploration. Researchers can filter and normalize their datasets and access a variety of functions ranging from simple data visualization like volcano plots and PCA, to advanced functional analyses like enrichment analysis for proteomics and saturation analysis for lipidomics. For insights from integrated multi-omics, iSODA incorporates Multi-Omics Factor Analysis - MOFA, and Similarity Network Fusion - SNF. All results are presented in interactive plots with the possibility of downloading plots and associated data. The ability to adapt the imported data on-the-fly allows for tasks such as removal of outlier samples or failed features, various imputation strategies, or data normalization. The modular design allows for extensions with new analyses and plots. The software is accessible under http://isoda.online/. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/605811v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@1eb61c8org.highwire.dtl.DTLVardef@60a7feorg.highwire.dtl.DTLVardef@1b54b41org.highwire.dtl.DTLVardef@15fd1b2_HPS_FORMAT_FIGEXP M_FIG C_FIG Graphical summary for iSODA showcasing some application examples, the data import, the single-omics and multi-omics modules.

bioinformatics↗

bioRxiv2024

Synapse protein signatures in cerebrospinal fluid and plasma predict cognitive maintenance versus decline in Alzheimers disease

Rates of cognitive decline in Alzheimers disease (AD) are extremely heterogeneous, with ages of symptom onset ranging from age 40-100 years and conversion from mild cognitive impairment to AD dementia taking 2-20 years. Development of biomarkers for amyloid-beta (A{beta}) and tau protein aggregates, the hallmark pathologies of AD, have improved patient monitoring/stratification and drug development, but they still only explain 20-40% of the variance in cognitive impairment (CI) in AD. To discover additional molecular drivers and biomarkers of AD dementia, we perform cerebrospinal fluid (CSF) proteomics on 3,416 individuals from six deeply phenotyped prospective AD case-control cohorts. We identify synapse proteins as the strongest correlates of CI, independent of A{beta} and tau. Using machine learning we derive the CSF YWHAG:NPTX2 synapse protein ratio, a robust correlate of CI, which explains 27% of the variance in CI beyond CSF PTau181:A{beta}42, 10% beyond tau PET, and 50% beyond CSF NfL in A{beta} positive individuals. We find YWHAG:NPTX2 also increases with normal aging as early as age 20 and increases at a faster rate in APOE4 carriers and autosomal dominant-AD mutation carriers. Most notably, YWHAG:NPTX2+ individuals (top 25th percentile) are 15-times (HR=15.4 [10.6-22.2]) more likely to experience cognitive decline over 15 years compared to YWHAG:NPTX2- individuals (bottom 25th percentile), and this rises to 19-times (HR=18.9 [10.83-32.9]) with additional stratification by A{beta} and phosphorylated tau status. Lastly, we perform plasma proteomics on 4,245 individuals to develop a plasma-based signature of CI which partly recapitulates CSF YWHAG:NPTX2. Overall, our findings underscore CSF YWHAG:NPTX2 and the corresponding plasma signature as robust prognostic biomarkers for AD onset and progression beyond gold-standard biomarkers of A{beta}, tau, and neurodegeneration and implicate synapse dysfunction as a core driver of AD dementia.

neuroscience↗

bioRxiv2024

mosGraphGen: a novel tool to generate multi-omic signaling graphs to facilitate integrative and interpretable graph AI model development

Multi-omics data, i.e., genomics, epigenomics, transcriptomics, proteomics, characterize cellular complex signaling systems from multi-level and multi-view and provide a holistic view of complex cellular signaling pathways. However, it remains challenging to integrate and interpret multi-omics data for mining key disease targets and signaling pathways. Graph AI models have been widely used to analyze graph-structure datasets, and are ideal for integrative multi-omics data analysis because they can naturally integrate and represent multi-omics data as a biologically meaningful multi-level signaling graph and interpret multi-omics data via graph node and edge ranking analysis. However, it is non-trivial for graph-AI model developers to pre-analyze multi-omics data and convert the data into biologically meaningful graphs, which can be directly fed into graph-AI models. To resolve this challenge, we developed mosGraphGen (multi-omics signaling graph generator), generating Multi-omics Signaling graphs (mos-graph) of individual samples by mapping multi-omics data onto a biologically meaningful multi-level background signaling network with data normalization by aggregating measurements and aligning to the reference genome. With mosGraphGen, AI model developers can directly apply and evaluate their models using these mos-graphs. In the results, mosGraphGen was used and illustrated using two widely used multi-omics datasets of TCGA and Alzheimers disease (AD) samples. The code of mosGraphGen is open-source and publicly available via GitHub: https://github.com/FuhaiLiAiLab/mosGraphGen

systems biology↗

bioRxiv2023

Differential chromatin accessibility and transcriptional dynamics define breast cancer subtypes and their lineages

Breast cancer is a heterogeneous disease, and treatment is guided by biomarker profiles representing distinct molecular subtypes. Breast cancer arises from the breast ductal epithelium, and experimental data suggests breast cancer subtypes have different cells of origin within that lineage. The precise cells of origin for each subtype and the transcriptional networks that characterize these tumor-normal lineages are not established. In this work, we applied bulk, single-cell (sc), and single-nucleus (sn) multi-omic techniques as well as spatial transcriptomics and multiplex imaging on 61 samples from 37 breast cancer patients to show characteristic links in gene expression and chromatin accessibility between breast cancer subtypes and their putative cells of origin. We applied the PAM50 subtyping algorithm in tandem with bulk RNA-seq and snRNA-seq to reliably subtype even low-purity tumor samples and confirm promoter accessibility using snATAC. Trajectory analysis of chromatin accessibility and differentially accessible motifs clearly connected progenitor populations with breast cancer subtypes supporting the cell of origin for basal-like and luminal A and B tumors. Regulatory network analysis of transcription factors underscored the importance of BHLHE40 in luminal breast cancer and luminal mature cells, and KLF5 in basal-like tumors and luminal progenitor cells. Furthermore, we identify key genes defining the basal-like (PRKCA, SOX6, RGS6, KCNQ3) and luminal A/B (FAM155A, LRP1B) lineages, with expression in both precursor and cancer cells and further upregulation in tumors. Exhausted CTLA4-expressing CD8+ T cells were enriched in basal-like breast cancer, suggesting altered means of immune dysfunction among breast cancer subtypes. We used spatial transcriptomics and multiplex imaging to provide spatial detail for key markers of benign and malignant cell types and immune cell colocation. These findings demonstrate analysis of paired transcription and chromatin accessibility at the single cell level is a powerful tool for investigating breast cancer lineage development and highlight transcriptional networks that define basal and luminal breast cancer lineages.

cancer biology↗

bioRxiv2023

Harmonization of CSF and imaging biomarkers for Alzheimer's disease biomarkers: need and practical applications for genetics studies and preclinicalclassification

INTRODUCTIONIn Alzheimers disease (AD) research, cerebrospinal fluid (CSF) Amyloid beta (A{beta}), Tau and pTau are the most accepted and well validated biomarkers. Several methods and platforms exist to measure those biomarkers which leads to challenges in combining data across studies. Thus, there is a need to identify methods that harmonize and standardize these values. METHODSWe used a Z-score based approach to harmonize CSF and amyloid imaging data from multiple cohorts and compared GWAS result using this method with currently accepted methods. We also used a generalized mixture modelling to calculate the threshold for biomarker-positivity. RESULTSZ-scores method performed as well as meta-analysis and did not lead to any spurious results. Cutoffs calculated with this approach were found to be very similar to those reported previously. DISCUSSIONThis approach can be applied to heterogeneous platforms and provides biomarker cut-offs consistent with the classical approaches without requiring any additional data.

bioinformatics↗

bioRxiv2022

Systematic characterization of brain cellular crosstalk signaling networks in Alzheimer's disease reveals a novel role for SEMA6D in TREM2-dependent microglial activation

Cellular crosstalk, mediated by membrane receptors and their ligands, is crucial for brain homeostasis and can contribute to neurodegenerative diseases such as Alzheimers disease (AD). To discover crosstalk dysregulations in AD, we reconstructed crosstalk networks from single-nucleus transcriptional profiles from 67 clinically and neuropathologically well-characterized controls and AD brain donors. We predicted a significant role for TREM2 and additional AD risk genes mediating neuron-microglia crosstalk in AD. The gene sub-network mediating SEMA6D-TREM2 crosstalk is activated near A{beta} plaques and SEMA6D-expressing cells and is disrupted in late AD stages. Using CRISPR-modified human induced pluripotent stem cell-derived microglia, we demonstrated that SEMA6D induces microglial activation in a TREM2-dependent manner. In summary, we demonstrate that characterizing cellular crosstalk networks can yield novel insights into AD biology. One Sentence SummaryWe investigate cell-to-cell communication in Alzheimers disease to characterize disease biology and suggest new avenues for therapeutic intervention.

neuroscience↗

bioRxiv2022

Data Descriptor: Human whole exome genotype data for Alzheimer's Disease

Bigger sample size can help to identify new genetic variants contributing to an increased risk of developing Alzheimers disease. However, the heterogeneity of the whole-exome sequencing (WES) data generation methods presents a challenge to a joint analysis. Here we present a bioinformatics strategy for joint calling 20,504 WES samples collected across nine studies and sequenced using ten different capture kits in fourteen sequencing centers in the Alzheimers Disease Sequencing Project. gVCFs of samples were joint-called by the Genome Center for Alzheimers Disease into a single VCF, containing only positions within the union of capture kits. The VCF was then processed using specific strategies to account for the batch effects arising from the use of different capture kits from different studies. We identified 8.2 million autosomal variants. 96.82% of the variants are high-quality, and are located in 28,579 Ensembl transcripts. 41% of the variants are intronic and 15% are missense variants. 1.8% of the variants are with CADD>30. Our new strategy for processing these diversely generated WES samples has shown to generate high-quality data. The improved ability to combine data sequenced in different batches benefits the whole genomics research community. The WES data are accessible to the scientific community via https://dss.niagads.org/.

genetics↗

bioRxiv2022

Loss of estrogen unleashing neuro-inflammation increases the risk of Alzheimer's disease in women

The risk of Alzheimers disease (AD) in women is about 2 times greater than in men. The estrogen hypothesis is being accepted as the essential sex factor causing the sex difference in AD. Also, the recent meta-analysis using large-scale medical records data indicated estrogen replacement therapy. However, the underlying molecular targets and mechanisms explaining this sex difference in AD disease development remain unclear. In this study, we identified that estrogen treatment can strongly inhibition of neuro-inflammation signaling targets, using the systems pharmacology model; and identified ESR1/ESR2 (the receptors of estrogen) are topologically close to the neuroinflammation biomarker genes using signaling network analysis. Moreover, the estrogen level in women decreased to an extremely lower level than in men after age 55. Pooling together the multiple pieces of evidence, it is concluded that the loss of estrogen unleashing neuro-inflammation increases the womens risk of Alzheimers disease. These analysis results provide novel supporting evidence explaining the potential mechanism of the anti-neuroinflammation role of estrogen causing the sex difference of AD. Medications boosting the direct downstream signaling of ESR1/ESR2, or inhibiting upstream signaling targets of neuroinflammation, like JAK2 inhibitors, on the signaling network can be potentially effective or synergistic combined with estrogen for AD prevention and treatment.

neuroscience↗