Biology subjects

Samorodnitsky, S.

Publications and source records attributed to Samorodnitsky, S..

2 recordsLinked to original sources

bioRxiv2026

Ecological determinants of disease and immunity in myelodysplastic syndromes

Myelodysplastic syndromes (MDS) are clonal hematopoietic malignancies characterized by ineffective hematopoiesis, dysplastic morphology, and risk of progression to acute myeloid leukemia. While genomic alterations intrinsic to malignant MDS disease-initiating cells have been well-characterized, molecular assessment of the bone marrow in situ has been limited. Here we present single cell spatial assessment of gene expression, T cell receptors, as well as MDS-defining mutations and RNA isoforms within fixed, decalcified human bone marrow core biopsies (41 MDS, 15 controls) paired with single cell immunogenomic analysis of bone marrow aspirates (35 MDS, 6 controls). Bone marrow spatial analyses of >7.47x106 cells identified hematopoietic and non-hematopoietic populations not readily captured in dissociated tissue. We developed computational methods to compare ecological niche structures, revealing enriched hematopoietic niches and reorganization of T cell immunity in MDS patient bone marrow. In situ genotyping of mutant cells revealed increased TGF{beta} expression in malignant megakaryocytes suppressing local T cell cytotoxicity. By contrast, TGF{beta} signaling was disrupted in mutant cells due to aberrant splicing of multiple TGF{beta} signaling components. This study provides a spatially resolved landscape of human MDS bone marrow and uncovers mechanisms by which malignant cells simultaneously promote intrinsic clonal persistence while rewiring the microenvironment for immune escape.

cancer biology↗

bioRxiv2024

Detecting Clinically Relevant Topological Structures in Multiplexed Spatial Proteomics Imaging Using TopKAT

Novel multiplexed spatial proteomics imaging platforms expose the spatial architecture of cells in the tumor microenvironment (TME). The diverse cell population in the TME, including its spatial context, has been shown to have important clinical implications, correlating with disease prognosis and treatment response. The accelerating implementation of spatial proteomic technologies motivates new statistical models to test if cell-level images associate with patient-level endpoints. Few existing methods can robustly characterize the geometry of the spatial arrangement of cells and also yield both a valid and powerful test for association with patient-level outcomes. We propose a topology-based approach that combines persistent homology with kernel testing to determine if topological structures created by cells predict continuous, binary, or survival clinical endpoints. We term our method TopKAT (Topological Kernel Association Test) and show that it can be more powerful than statistical tests grounded in the spatial point process model, particularly when cells arise along the boundary of a ring. We demonstrate the properties of TopKAT through simulation studies and apply it to two studies of triple negative breast cancer where we show that TopKAT recovers clinically relevant topological structures in the spatial distribution of immune and tumor cells.

bioinformatics↗