Biology subjects

Kana, O.

Publications and source records attributed to Kana, O..

3 recordsLinked to original sources

bioRxiv2026

scVIP: personalized modeling of single-cell transcriptomes for developmental and disease phenotypes

Single-cell transcriptomics resolves cellular heterogeneity within individuals, but connecting molecular states to individual-level phenotypes requires frameworks that explicitly bridge these scales. We present scVIP, a generative model that links gene expression, cell-type composition, and phenotypic measurements within a single probabilistic model, which enables accurate phenotype prediction and interpretable trajectory inference. A cell-type-aware multi-instance learning architecture learns donor embeddings that capture progression while localizing phenotype-associated signals to specific cell populations. Applied across four settings, scVIP accurately predicts cortical developmental age (Pearson r = 0.95), characterizes Huntingtons disease progression (concordance correlation coefficient = 0.90), integrates two Alzheimers disease cohorts recovering disease-relevant microglial and astrocytic programs, and distinguishes healthy from ACPA-positive individuals and non-progressors from early RA individuals, identifying inflammatory T cell programs associated with disease. scVIP enables principled analysis of how cellular states collectively shape organism-level phenotypes across development and disease.

bioinformatics↗

bioRxiv2025

A cross-species spatial transcriptomic atlas of the human and non-human primate basal ganglia

The basal ganglia are interconnected subcortical nuclei with complex topographical organization that orchestrate goal-directed behaviors and are implicated in neurodegenerative movement disorders. We generated a cellular-resolution, spatial transcriptomic atlas of the basal ganglia in human, rhesus macaque, and common marmoset, sampling over one million cells in each species. By integrating spatial data with a cross-species, consensus snRNA-seq cell type taxonomy, this atlas reveals conserved principles of molecular organization within and across structures. The cellular architecture is complex but highly stereotyped, with gene expression gradients superimposed onto discrete compartments. Extensive spatial sampling illuminates 3D gradients of molecular organization in the striatum and reveals cell type-specific core and shell compartments in the primate internal globus pallidus, which is conserved with mouse. This unified, cross-species spatial transcriptomic atlas will be a foundational resource for characterizing the molecular and functional organization of the basal ganglia and their roles in health and disease.

neuroscience↗

bioRxiv2022

Prediction of mammalian tissue-specific CLOCK-BMAL1 binding to E-box motifs

The mammalian circadian clock is based on a core intracellular gene regulatory network, coordinated by communication between the central nervous system and peripheral tissues like the liver. Transcriptional and translational feedback loops underlie the molecular mechanism of circadian oscillation and generate its 24 h periodicity. The Brain and muscle Arnt-like protein-1 (Bmal1) forms a heterodimer with Circadian Locomotor Output Cycles Kaput (Clock) that binds to E-box gene regulatory elements, activating transcription of clock genes. In this work we aimed to develop a predictive model of genome-wide CLOCK-BMAL1 binding to E-box motifs. We found over-representation of the canonical E-box motif CACGTG in BMAL1-bound regions in accessible chromatin of the mouse liver, heart and kidney. We developed three different tissue-specific machine learning models based on DNA sequence, DNA sequence plus DNA shape, and DNA sequence and shape plus histone modifications. Combining DNA sequence with DNA shape and histone modification features yielded improved transcription factor binding site prediction. Further, we identified the genomic and epigenomic features that best correlate to the binding of BMAL1 to DNA. The DNA shape features Electrostatic Potential, Minor Groove Width and Propeller Twist together with the histone modifications H3K27ac, H3K4me1, H3K36me3, and H3K4me3 were the features most highly predictive of DNA binding by BMAL1 across all three tissues.

bioinformatics↗