Biology subjects

Adeleke, D.

Publications and source records attributed to Adeleke, D..

3 recordsLinked to original sources

bioRxiv2025

MisTIC: Missegmented Transcript Inference Correction for Improved Spatial Transcriptomics Analysis

Imaging-based spatially resolved transcriptomics (SRT) technologies, such as 10X Xenium, MERSCOPE, and CosMx, have revolutionized our ability to study gene expression within the spatial context of tissues at single-cell resolution. The acquisition of such data relies heavily on cell segmentation algorithms, which often produce imperfect boundaries, leading to transcript misassignment. These misassignments can significantly affect downstream analyses, including cell type identification, differential expression analysis, cell-cell communication, and RNA localization. We present MisTIC (Missegmented Transcript Inference Correction), a variational Bayesian model designed to correct transcript misassignment errors without requiring resegmentation. In benchmarking analyses using synthetic data with simulated transcript misassignment, MisTIC demonstrated high sensitivity and specificity in removing misassigned transcripts. In real data applications, MisTIC effectively enhances cell type identification, reduces ambiguity in differential expression analysis, and improves the detection of cell-cell communication. Furthermore, RNA localization analysis based on data corrected by MisTIC revealed that, in T cells located near cancer-associated fibroblasts compared to those farther away, genes involved in T cell activation, inflammation, and cytotoxicity were depleted from cytoplasmic regions despite not being differentially expressed between the two T cell subsets. In conclusion, MisTIC is a powerful tool for correcting transcript misassignment in SRT data. It not only improves the accuracy of routine analyses but also enables novel investigations that provide deeper insights into the dynamics of gene expression.

bioinformatics↗

bioRxiv2025

AlphaMissense pathogenicity scores predict response to immunotherapy and enhances the predictive capability of tumor mutation burden

Tumor Mutational Burden (TMB) is a widely used biomarker for selecting cancer patients for immune checkpoint inhibitor (ICI) therapy. However, TMB alone has limited predictive power, as it fails to account for the functional impact of mutations. We introduce AlphaTMB, a composite biomarker that integrates the quantity of mutations (TMB) with the qualitative assessment of their pathogenicity using AlphaMissense, a deep learning model that predicts the deleteriousness of missense variants. Using a pan-cancer cohort of 1,662 patients from the MSK-IMPACT study who received ICI therapy, we computed three scores per patient: TMB, Alpha (sum of AlphaMissense scores), and AlphaTMB (product of TMB and Alpha). Patients were stratified using both cancer-specific and pan-cancer quantiles. Survival outcomes were evaluated using Kaplan-Meier and multivariate Cox proportional hazards models, controlling for cancer type, age, and ICI regimen. AlphaTMB showed strong correlation with TMB (Spearman {rho} = 0.866, p < 0.001), but offered improved prognostic accuracy. Patients in the bottom 80% AlphaTMB group had significantly poorer survival than those in the top 10% (HR < 2.51, p < 0.001), outperforming TMB and Alpha alone. AlphaTMB reclassified borderline cases, identifying subsets with low TMB but high deleterious mutation load, and vice versa. Gene mutation heatmaps and co-occurrence analysis confirmed that to 10% AlphaTMB-high tumors were enriched in mismatch repair and POLE mutations, reflecting a neoantigen-rich, immunotherapy-responsive phenotype. AlphaTMB improves survival prediction beyond TMB alone, better captures immunogenic tumor profiles, and reflects more accurate patient stratification. This AI derived somatic mutations pathogenicity scoring represents a step toward personalized immuno-oncology and merits further validation in prospective studies.

bioinformatics↗

bioRxiv2025

Macrophages drive a fibrogenic gene program of periductal fibroblasts in pediatric primary sclerosing cholangitis

Primary sclerosing cholangitis (PSC) is an autoimmune, cholestatic liver disease characterized by inflammation and fibrosis surrounding bile ducts. The cellular crosstalk driving periductal fibrosis remains poorly defined. This study applied a multi-omics approach integrating MERSCOPE spatial transcriptomics, bulk RNA-seq, and SomaScan proteomics to characterize fibrotic periductal regions and their cell-cell communications. Macrophages (MP) expressing moderate-to-high CD163 were found co-localized with cholangiocytes, T cells, and collagen-producing hepatic stellate cells (HSC). Cell niche analysis identified periductal regions with elevated fibrotic signals, in which cell-cell communication analysis revealed MP-HSC interactions involving 17 fibrotic driver genes in MP (e.g., IFNGR1, CSF1R, CD163) and six fibrotic effector genes in HSC (e.g., COL1A2, VCAN, MMP2). In validation analyses, bulk RNA-seq data showed higher driver and effector gene scores in PSC with established fibrosis compared to early-stage PSC and autoimmune hepatitis (AIH). Plasma proteins encoded by MP driver genes were elevated in autoimmune liver disease (AILD) and in patients with elevated ([≥]3.29 kPa) liver stiffness on MR elastography. These findings suggest that macrophages engage in localized crosstalk with HSC, activating fibrotic gene programs and contributing to periductal fibrosis in PSC, thereby identifying potential molecular targets for therapeutic intervention.

immunology↗