Biology subjects

Chernysheva, A.

Publications and source records attributed to Chernysheva, A..

3 recordsLinked to original sources

bioRxiv2026

Modeling pancreatic cancer tumor stroma co-evolution in an in ovo model

Pancreatic ductal adenocarcinoma (PDAC) is characterized by a dense, desmoplastic microenvironment that drives disease progression, yet conventional models fail to capture this complex tumor-stroma coevolution. Here, we utilize the chick chorioallantoic membrane (CAM) platform to investigate tumor-stroma interactions using murine PDAC cell lines and patient-derived organoids (PDOs). Integrating single-cell RNA sequencing and spatial transcriptomics, we show that the CAM microenvironment supports the emergence of complex tumor ecosystems while preserving patient-specific characteristics. Within five days, in ovo tumors faithfully recapitulated the structural and molecular features of parental tumors. Histological analysis revealed the rapid recruitment and spatial organization of heterogeneous host cancer-associated fibroblast (CAF) populations, showcasing distinct myofibroblastic and inflammatory stromal states. Crucially, the model preserved intrinsic tumor heterogeneity and permitted functional interrogation of subtype-specific extracellular matrix remodeling and metastatic dissemination. Together, our findings demonstrate that the CAM provides a highly permissive niche for tumor-stroma coevolution. As a rapid, scalable, and biologically relevant platform, this in ovo model offers a powerful approach for studying stromal composition, metastatic progression, and patient-specific tumor biology in pancreatic cancer.

cancer biology↗

bioRxiv2025

InSituPy -- A framework for histology-guided, multi-sample analysis of single-cell spatial transcriptomics data

Single-cell spatial transcriptomics (scST) methodologies allow, in combination with histological stainings, an unprecedented view on disease progression. To comprehensively analyze scST data, bioinformatic analysis frameworks need to integrate the diverse set of data modalities and, just as importantly, enable the joint analysis of multiple datasets from clinical or experimental cohorts together with its corresponding metadata. Here, we present the InSituPy framework to comprehensively analyze single-cell spatial transcriptomic data from a multi-sample level down to the cellular and subcellular level. The framework contains analysis workflows for the integration of image data as well as pathological and biological expert knowledge. Increasing the accessibility of the data for non-bioinformaticians, the framework opens new ways of generating hypotheses, especially in the context of translational research.

bioinformatics↗

bioRxiv2024

Benchmarking of T-Cell Receptor - Epitope Predictors with ePytope-TCR

Understanding the recognition of disease-derived epitopes through T-cell receptors (TCRs) has the potential to serve as a stepping stone for the development of efficient immunotherapies and vaccines. While a plethora of sequence-based prediction methods for TCR-epitope binding exists, their available pre-trained models have not been comparatively evaluated on standardized datasets and evaluation settings. Furthermore, technical problems such as non-standardized input and output formats of these prediction tools hinder interoperability and broad usage in applied research. To alleviate these shortcomings, we introduce ePytope-TCR, an extension of the vaccine design and immuno-prediction framework ePytope. We integrated 18 TCR-epitope prediction methods into this common framework offering interoperable interfaces with standard TCR repertoire data formats. We showcase the applicability of ePytope-TCR by evaluating the performance of the prediction methods on two challenging datasets for annotating single-cell repertoires and predicting TCR cross-reactivity towards mutated epitopes. While novel predictors successfully predicted binding to frequently observed epitopes, all methods failed for less observed epitopes. Further, we detected a strong bias in the prediction scores between different epitope classes. We envision this benchmark to guide researchers in their choice of a predictor for a given setting. Further, we aspire to accelerate the development of novel prediction models by allowing fast benchmarking against existing approaches through common interfaces and defining standardized evaluation settings.

bioinformatics↗