Biology subjects

Bieniosek, M.

Publications and source records attributed to Bieniosek, M..

3 recordsLinked to original sources

bioRxiv2025

Spatial multi-omics and deep learning reveal fingerprints of immunotherapy response and resistance in hepatocellular carcinoma

Despite advances in immunotherapy treatment, nonresponse rates remain high, and mechanisms of resistance to checkpoint inhibition remain unclear. To address this gap, we performed spatial transcriptomic and proteomic profiling on human hepatocellular carcinoma tissues collected before and after immunotherapy. We developed an interpretable, multimodal deep learning framework to extract key cellular and molecular signatures from these data. Our graph neural network approach based on spatial proteomic inputs achieved outstanding performance (ROC-AUC > 0.9) in predicting patient treatment response. Key predictive features and associated spatial transcriptomic profiles revealed the multi-omic landscape of immunotherapy response and resistance. One such feature was an interface niche expressing restrictive extracellular matrix factors that physically separates tumor tissue and lymphoid aggregates in nonresponders. We integrate this and other spatially-resolved signatures into SPARC, a multi-omic "fingerprint" comprising scores for immunotherapy response and resistance mechanisms. This study lays groundwork for future patient stratification and treatment strategies in cancer immunotherapy.

cancer biology↗

bioRxiv2025

Learning single-cell spatial context through integrated spatial multiomics with CORAL

Cellular organization is central to tissue function and homeostasis, influencing development, disease progression, and therapeutic outcomes. The emergence of spatial omics technologies, including spatial transcriptomics and proteomics, has enabled the integration of molecular and histological features within tissues. Analyzing these multimodal data presents unique challenges, including variable resolutions, imperfect tissue alignment, and limited or variable spatial coverage. To address these issues, we introduce CORAL, a probabilistic deep generative model that leverages graph attention mechanisms to learn expressive, integrated representations of multimodal spatial omics data. CORAL deconvolves low-resolution spatial data into high-resolution single-cell profiles and detects functional spatial domains. It also characterizes cell-cell interactions and elucidates disease-relevant spatial features. Validated on synthetic data and experimental datasets, including Stero-CITE-seq data from mouse thymus, and paired CODEX and Visium data from hepatocellular carcinoma, CORAL demonstrates robustness and versatility. In hepatocellular carcinoma, CORAL uncovered key immune cell subsets that drive the failure of response to immunotherapy, highlighting its potential to advance spatial single-cell analyses and accelerate translational research.

bioinformatics↗

bioRxiv2024

ROSIE: AI generation of multiplex immunofluorescence staining from histopathology images

Hematoxylin and eosin (H&E) is a common and inexpensive histopathology assay. Though widely used and information-rich, it cannot directly inform about specific molecular markers, which require additional experiments to assess. To address this gap, we present ROSIE, a deep-learning framework that computationally imputes the expression and localization of dozens of proteins from H&E images. Our model is trained on a dataset of over 1000 paired and aligned H&E and multiplex immunofluorescence (mIF) samples from 20 tissues and disease conditions, spanning over 16 million cells. Validation of our in silico mIF staining method on held-out H&E samples demonstrates that the predicted biomarkers are effective in identifying cell phenotypes, particularly distinguishing lymphocytes such as B cells and T cells, which are not readily discernible with H&E staining alone. Additionally, ROSIE facilitates the robust identification of stromal and epithelial microenvironments and immune cell subtypes like tumor-infiltrating lymphocytes (TILs), which are important for understanding tumor-immune interactions and can help inform treatment strategies in cancer research.

pathology↗