Biology subjects

Rao, V. R.

Publications and source records attributed to Rao, V. R..

4 recordsLinked to original sources

bioRxiv2026

Interpreting and Validating a Deep Learning Model Predictive of Spatial Morphologic-Molecular Patterns in Lung Adenocarcinoma, Using Ground Truth Immunohistochemistry

Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer, exhibits profound histological and molecular heterogeneity. While genomic profiling has identified key oncogenic drivers and immune signatures, its use is limited by cost, technical demands and tissue availability. In addition, spatial transcriptomics provides spatially resolved molecular insights but remains challenging and time-consuming. To address this gap, we developed XpressO-Lung, an explanatory deep learning model that predicts gene expression heterogeneity spatially in tumor and its microenvironment on hematoxylin and eosin based diagnostic (Dx) whole-slide images (WSIs) by learning associations between tissue morphology and the corresponding bulk-transcriptomic data. Utilizing 200 LUAD cases from The Cancer Genome Atlas, XpressO-Lung predicted spatial expression patterns of NAPSA, TP53I3, CD8A, TTF1, KRT7, CDKN2A, FOXO1, KEAP1, RB1 and TP53 on Dx-WSIs with AUCs ranging from 0.64 to 0.92. The predicted spatial gene expression patterns aligned with the known morphologic interactions of the tumor and its microenvironment, capturing biological events directly on Dx-WSIs. These spatio-morpho-molecular associations were further validated using immunohistochemistry on an external set of clinical samples at Dartmouth Health, demonstrating concordance between model-predicted spatial patterns and observed histomorphologic features. By coupling predictive performance with spatial interpretability of gene expression on Dx-WSIs, the XpressO-Lung model bridges histopathology and bulk-transcriptomics, enabling explainable spatio-morpho-genomic analyses to advance biomarker discovery, therapeutic stratification and precision oncology in LUAD.

pathology↗

bioRxiv2026

X-SPATIO: An Explanatory Deep Learning Pipeline for the Prediction and Visualization of Spatially Resolved Biomarker Expression in Triple-Negative Breast Cancer

Histopathologic evaluation remains central to cancer diagnosis and treatment planning, yet the molecular programs underlying distinct tissue morphologies are not routinely accessible in clinical workflows. Spatial transcriptomic/proteomic platforms provide region-specific molecular measurements but are limited by cost, throughput, and scalability. Most computational pathology models rely on either bulk tissue-based gene expression or a focused gene/protein expression-panel prediction, thereby obscuring subregion-specific morpho-molecular relationships and limiting spatial interpretation of a wider gene/protein expression network. This limitation is particularly significant in triple-negative breast cancer (TNBC), which exhibits pronounced spatial heterogeneity across tumor, stroma, and immune compartments. We developed X-SPATIO, a spatially compatible computational pipeline designed to directly link hematoxylin and eosin (H&E) morphology with region-matched mRNA and protein expression. The model was trained on H&E-defined regions of interest paired with spatially-resolved transcriptomic and proteomic data obtained from GeoMx Digital Spatial Profiler. Using a multiple-instance learning approach, X-SPATIO captures morpho-molecular associations, generating spatio-morphologic attention maps that indicate predictive tissue regions. X-SPATIO demonstrated strong performance across biologically relevant spatial biomarkers, achieving area under the curve values ranging [0.79, 0.97]. Attention maps revealed spatial patterns consistent with known biology, indicating alignment between learned features and tissue organization. By integrating spatial molecular ground truth with routine histopathology, X-SPATIO enables cost-effective inference of spatial biomarker expression and establishes a foundation for biologically grounded discovery and precision oncology in TNBC.

pathology↗

bioRxiv2024

Neurovirulent and non-neurovirulent strains of HIV-1 and their Tat proteins induce differential cytokine-chemokine profiles

HIV-1 enters the central nervous system (CNS) early in infection, and a significant proportion of people with HIV experience CNS complications despite anti-retroviral therapy. Chronic immune dysfunction, inflammatory cytokines and chemokines, and viral proteins like Tat and gp120 released by HIV-1-infected immune cells are implicated in the pathogenesis of HIV-1-associated neurocognitive disorders (HAND). To elucidate the contribution of non-viral factors to CNS complications in people with HIV-1, a comparative analysis of neurovirulent subtype B (HIV-1ADA) and non-neurovirulent subtype C (HIV-1Indie-C1) isolates was performed. Culture supernatants from HIV-1-infected PBMCs, either with or without immunodepletion of Tat and gp120, were used to treat SH-SY5Y neuroblastoma cells. HIV-1ADA-infected PBMC media showed significantly higher cytotoxicity than HIV-1IndieC1-infected PBMC media, notwithstanding the depletion of Tat and gp120, highlighting the role of non-viral factors contributing to neurotoxicity. A comparison of inflammatory profiles revealed that HIV-1ADA media contained elevated levels of cytokines (IL-1, IL-1{beta}, IL-6, TNF) and chemokines (CCL2, CCL3, CCL4, IP10). Given the involvement of Tat in upregulating immune mediators, PBMCs from healthy subjects were treated with recombinant purified Tat from subtype B or C. Subtype B Tat induced a stronger inflammatory response than subtype C Tat. These results confirm that both viral and non-viral immune factors mediate neuronal damage in people with HIV.

microbiology↗

bioRxiv2024

Human neural dynamics of real-world and imagined navigation

The ability to form episodic memories and later imagine them is integral to the human experience, influencing our recollection of the past and our ability to envision the future. While research on spatial navigation in rodents suggests the involvement of the medial temporal lobe (MTL), especially the hippocampus, in these cognitive functions, it is uncertain if these insights apply to the human MTL, especially regarding imagination and the reliving of events. Importantly, by involving human participants, imaginations can be explicitly instructed and their mental experiences verbally reported. In this study, we investigated the role of hippocampal theta oscillations in both real-world and imagined navigation, leveraging motion capture and intracranial electroencephalographic recordings from individuals with chronically implanted MTL electrodes who could move freely. Our results revealed intermittent theta dynamics, particularly within the hippocampus, which encoded spatial geometry and partitioned navigational routes into linear segments during real-world navigation. During imagined navigation, theta dynamics exhibited similar, repetitive patterns despite the absence of external environmental cues. Furthermore, a computational model, generalizing from real-world to imagined navigation, successfully reconstructed participants imagined positions using neural data. These findings offer unique insights into the neural mechanisms underlying human navigation and imagination, with implications for understanding episodic memory formation and retrieval in real-world settings.

neuroscience↗