Biology subjects

Hasib, M. M.

Publications and source records attributed to Hasib, M. M..

4 recordsLinked to original sources

bioRxiv2026

Cell Cycle Regulation of Merkel Cell Polyomavirus Replication and Genome Inheritance in Single Cells

Merkel cell polyomavirus (MCV) establishes a near-ubiquitous, asymptomatic infection in humans but on rare occasions drives Merkel cell carcinoma, an aggressive skin cancer. How MCV replication is coordinated with host cell-cycle progression, and how this coordination shapes infected-cell fate, has remained unresolved. Here we combine reporter virus live-cell imaging, pharmacologic cell-cycle perturbation, flow cytometry, and single-cell transcriptomics to examine MCV replication dynamics. VP1 late gene expression occurs almost exclusively during late S or G2, is associated with prolonged G2 arrest, and frequently culminates in cell death. Strikingly, cells that divide before virus replication give rise to daughter cells that synchronously initiate viral replication and VP1 expression, revealing mitotic inheritance of silent viral genomes, a pattern seen for viruses with a known latency lifecycle. Single-cell RNA sequencing further identifies a transient antiviral and inflammatory response that is subsequently suppressed in surviving, cell cycle arrested cells. These findings define the cell-cycle logic, inheritance, and host consequences of MCV replication at single-cell resolution.

cell biology↗

bioRxiv2026

Spatially Resolved Microglial State Transitions Govern Strain-Specific Zika Neuropathogenesis

Neurotropic viruses disrupt brain homeostasis through complex interactions among infected cells, resident immune responses, and tissue architecture, yet how these processes unfold across space, time, and cell types, and how viral strain differences shape disease severity, remains poorly understood. Here, we integrate high-resolution spatial transcriptomics with infection-aware cell-type profiling to construct a spatiotemporal atlas of Zika virus (ZIKV) infection in the mouse brain. Comparing Asian and African ZIKV strains across early and late infection stages, we uncover structured, strain-dependent reorganization of immune and structural cell populations that defines discrete infection-associated tissue niches. Microglia undergo region-specific state transitions characterized by both cell-intrinsic antiviral programs and widespread bystander activation, producing tissue-wide immune amplification. In Asian strain infection, we identify disease-associated microglia (DAM) as critical mediators of infection containment: DAM accumulate in regions where viral burden stabilizes, are promoted by Apoe-Trem2 signaling from infected cells and are governed by transcription regulators that restrain inflammatory programs while preserving phagocytic and antiviral functions. In contrast, African strain infection is marked by impaired Apoe-Trem2 signaling, persistent inflammatory microglial activation, and failure of containment. Progressive infection leads to depletion of oligodendrocytes, astrocytes, and neurons, loss of local cellular diversity and disruption of tissue architecture concentrated in somatosensory and motor regions associated with myelination and synaptic programs. These architectural disruptions correlate with severe neurological phenotypes in African strain infection and are preceded by transcriptional dysregulation in infected glial cells, including sustained stress responses, inflammatory signaling, and suppression of myelination and homeostatic pathways. Together, our study establishes a spatially resolved framework linking viral strain-specific microglial states to tissue disorganization and neurological functional impairment, providing mechanistic insight into how neurotropic viruses reshape microenvironments to drive neurological disease.

cell biology↗

bioRxiv2026

spatiAlytica: Viewer-Grounded Multimodal Agentic System for Interactive Spatial Omics Analysis

Spatial transcriptomics and proteomics map tissue architecture and cellular interactions, but analysis remains limited by programming demands and text-centered AI agents that lack viewer grounding and cross-turn context. We present spatiAlytica, a viewer-centric multimodal interactive agentic system embedded in the Napari viewer that enables non-programmer biologists to perform iterative, hypothesis-driven spatial omics analysis via natural language. spatiAlytica couples viewer-state serialization, agentic memory, biological concept-to-data-field mapping, code generation and debugging, Spatial VQA, and grounded interpretation to support an exploratory analysis and interpretive reasoning workflow. We introduce spatiAlyticaBench, a comprehensive benchmark spanning 222 single-turn spatial analytical coding questions, 178 multi-turn sequential workflow questions, and 7,350 image-grounded reasoning questions. spatiAlytica outperformed strong agentic baselines, while using less time and tokens. Case studies across Kaposis sarcoma, colorectal cancer, and ovarian cancer recapitulated known spatial patterns and uncovered progressive CD8 T-cell dysfunction during KS progression.

bioinformatics↗

bioRxiv2024

Augment Single-cell RNA-seq data with Surface Protein Levels using Gene set-based Deep Learning and Transfer Learning Methods

As scRNA-seq becomes increasingly accessible, providing a cost-efficient method to augment surface protein levels from gene expression measurements are desirable. We proposed a machine learning approach that includes a novel geneset neural network (GS-NN) that aims to learn robust and biologically meaningful features and a highly efficient transfer learning strategy to address cross-dataset differences. We conducted comprehensive experiments to show the improvements of the proposed methods. Specifically, we demonstrate that GS-NN learns more robust features to achieve better cross-subject performance than other machine learning approaches. Transfer learning further improves that of GS-NN by reducing dataset differences through highly efficient fine-tuning. The unique genesets design of GS-NN also allows identification of functions contributing to the prediction and improvement of the proposed strategy. Overall, this study reports a novel approach to robustly augment. Key PointsO_LIThe article presents a machine learning approach, Geneset Neural Network(GS-NN) to augment surface protein levels from single-cell RNA sequencing(scRNA-seq) gene expression data. C_LIO_LIThe GS-NN aims to learn robust and biologically meaningful features, and the approach includes a highly efficient transfer learning strategy to address cross-dataset differences in scRNA-seq data. C_LIO_LIComprehensive experiments demonstrate that GS-NN learns more robust features using trasfer learning techniques achieving better cross-subject performance compared to other machine learning approaches. C_LIO_LIThe unique geneset-based architecture of GS-NN allows the identification and interpretion of biological functions contributing to the prediction of cell surface protein level. C_LIO_LIGS-NNs architecture is conveniently transferrable across datasets, making it valuable tool for researchers working with diverse scRNA-seq datasets. C_LI

bioinformatics↗