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Kana, O. Z.

Publications and source records attributed to Kana, O. Z..

5 recordsLinked to original sources

Multiregional single-cell profiling reveals shared and specialized cellular vulnerability in Alzheimer's disease

Alzheimers disease (AD) is defined and staged by the stereotyped, progressive accumulation of amyloid-beta (A{beta}) plaques and hyperphosphorylated tau (pTau) tangles across brain regions. These regions differ substantially in their architecture and function but share largely conserved cellular composition, with some regional specialization. As pathology accumulates, specific neurons are lost and non-neuronal cells shift toward disease-associated states, but whether the cell types affected in any one region are the same across the others has remained unclear. Here we extended the Seattle Alzheimers Disease Brain Cell Atlas (SEA-AD) to ten neo- and allocortical regions spanning the cortical arc of canonical AD staging, profiling approximately seven million nuclei from 84 donors with single-nucleus RNA-seq, ATAC-seq, and Multiome alongside quantitative neuropathology and whole-genome sequencing. Nuclei were mapped to an expanded BRAIN Initiative reference taxonomy of 207 cell types, and a hierarchical pseudo-progression framework derived continuous, donor-level measures of AD pathological burden within each region and across the brain by jointly modeling A{beta} and pTau. Cellular changes were both highly selective and strikingly consistent: only [~]30% of cell types shifted in relative abundance, but those that did changed in a coherent direction across regions. Specific subsets of Sst, Lamp5, Vip, Sncg, and Pvalb inhibitory interneurons and myelinating oligodendrocytes were lost earliest in preclinical donors with minimal pathology, alongside initial emergence of AD-associated microglia; loss of L2/3 and selected deep-layer excitatory types, sharper microglial increases, and reactive astrocyte emergence followed in later-stage donors. Regionally specialized populations were also vulnerable, including expected allocortical types and, unexpectedly, primary visual cortex (V1C)-specialized layer 4 (L4 IT) excitatory neurons and intermixed Sst and Pvalb interneurons. Key changes replicated across three independent cohorts encompassing over 700 additional donors. We examined two complementary vulnerable populations in mechanistic detail: regionally specialized V1C L4 IT neurons lost late despite being widely considered resilient, and pan-cortical Sst interneurons lost earliest in disease. Applying a multi-agentic AI workflow that constructed literature-grounded hypotheses from differential expression to L4 IT neurons nominated hyperexcitability, mediated in part by high NMDA receptor expression, as a convergent vulnerability phenotype. Vulnerable Sst interneurons converged on hyperexcitability through partly distinct pathways, and were enriched for expression of AD GWAS-prioritized genes, linking their vulnerability to the genetic architecture of AD. These data, available at SEA-AD.org, provide a multiregional framework for the community to explore the molecular and cellular changes of AD progression.

neuroscience↗

The Caudate Nucleus Exhibits Distinct Pathology and Cell Type-Specific Responses Across Alzheimer's Disease

A{beta} presence in the caudate nucleus (Ca) partially defines Thal stage III in Alzheimers disease (AD), but little is known about ADs cellular impact on the region. Leveraging a public basal ganglia taxonomy of cellular populations, we generated a cellular resolution atlas of AD-associated pathological changes in Ca. Unlike cortex, we found that Ca AD pathology is dominated by two key features: phosphorylated tau (pTau)-containing neuropil threads enriched near oligodendrocytes in white matter tracts and amyloid-{beta} diffuse plaques enriched in gray matter. Although AD pathology in affected cortical regions results in neuronal loss, we find no AD-driven reductions in neuron proportions in Ca. However, there were observable changes in multiple cellular populations. Protoplasmic astrocytes and FLT1+/IL1B+ microglia increased in abundance with global pTau levels. We also observe gene expression changes in fast-spiking PTHLH-PVALB interneurons indicative of disrupted signaling pathways and altered intrinsic physiological properties. This work provides a cellular-resolution framework for understanding AD pathology in Ca.

neuroscience↗

A multiscale model of the mammalian liver circadian clock supports synchronization of autonomous oscillations by intercellular communication.

Expression of core circadian clock genes in hepatocytes across the liver lobule is temporally synchronized despite cell-autonomous oscillations in gene expression. This spatial synchronization has been attributed to an unknown intercellular coupling mechanism. Here we have developed multicellular computational models of the murine liver lobule with and without intercellular coupling to investigate the role of synchronization in circadian gene expression. Our models demonstrated that intercellular coupling was needed to generate sustained circadian oscillations with a near 24-hour period. Without coupling the simulated period was variable within the 21-28-hour range. Further model analysis revealed that a robust near-24-hour oscillation period can be generated with a wide range of circadian protein degradation rates. In contrast, only a small window of circadian gene transcription rates was able to generate realistic oscillatory periods. The coupled model accurately captured the temporal dynamics of circadian genes derived from single-nuclei transcriptomic data. Overall, this study provides novel insights into the mammalian hepatic circadian clock through modeling of spatial and temporal gene expression patterns and data-driven analysis.

systems biology↗

Generative Modeling of Single Cell Gene Expression for Dose-Dependent Chemical Perturbations

Single cell sequencing provides a new opportunity to study the heterogeneity of chemical perturbation within tissues. However, exploring the combinatorial space of all cell type-chemical combinations is experimentally and financially unfeasible. This space is significantly expanded by the dose axis of chemical perturbation. Thus, computational tools are needed to predict responses not only across tissues, but also across doses while capturing the nuances of cell type specific gene expression. Variational autoencoders simplify the single cell expression space allowing cross cell type predictions using simple vector arithmetic. However, differing sensitivities and non-linearities make cell type specific gene expression predictions following treatment at higher doses challenging. Here we introduce single cell Variational Inference of Dose-Response (scVIDR) which achieves high dose and cell type specific predictions better than other state of the art algorithms. scVIDR predicts in vivo and in vitro dose-dependent gene expression across cell types in mouse liver, peripheral blood mononuclear cells, and cancer cell lines. We use regression to interpret the outputs of scVIDR. Additionally, we use scVIDR to order individual cells based on their sensitivities to a particular chemical by assigning a pseudo-dose value to each cell. Taken together, we show that scVIDR can effectively predict the dose and cell state dependent changes associated with chemical perturbations.

bioinformatics↗

Predictive Models of Genome-wide Aryl Hydrocarbon Receptor DNA Binding Reveal Tissue Specific Binding Determinants

BackgroundThe Aryl Hydrocarbon Receptor (AhR) is an inducible transcription factor (TF) whose ligands include the environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). TCDD-mediated toxicity occurs through activation of AhR and its subsequent binding to the Dioxin Response Element (DRE), comprising the DNA motif 5-GCGTG-3. However, AhR binding in human tissues is highly dynamic and tissue specific. Approximately 50% of all experimentally verified AhR binding sites do not contain a DRE. Additionally, most accessible DREs are not bound by AhR. Identification of tissue specific AhR binding determinants is crucial for understanding downstream gene regulation and potential adverse outcomes of AhR activation. ResultsWe applied XGBoost, a supervised machine learning architecture, to predict the genome wide AhR binding status of DREs in open chromatin as a function of DNA sequence flanking the DRE, chromatin accessibility, histone modifications (HM), TF binding, and proximity of the DRE to gene promoters. We trained and validated our models using 5-fold cross validation to predict the binding status of DREs in AhR-activated MCF-7 breast cancer cells, primary human hepatocytes, and lymphoblastoid GM17212 cells, as well as AhR non-activated HepG2 hepatocellular carcinoma cells. Our results demonstrate highly accurate and robust models of AhR binding; and identify patterns of transcription factor binding and histone modifications predictive of AhR binding. These patterns are consistent within tissues but highly variable across tissues, which is suggestive of tissue-specific mechanisms of AhR binding. ConclusionsAhR binding is driven by a complex interplay of tissue-agnostic DNA sequence flanking its binding motif and tissue-specific local chromatin context.

genomics↗