Biology subjects

Jarzabek, V.

Publications and source records attributed to Jarzabek, V..

3 recordsLinked to original sources

bioRxiv2026

dbverse scales spatial omics analysis with embedded analytical databases

Spatial omics datasets are increasing in size and complexity, exceeding the memory of standard computers and thereby limiting data analysis. Here we present dbverse, a framework for larger-than-memory matrix, spatial and genomic data analysis in embedded analytical databases. Benchmarks show dbverse provides orders of magnitude runtime improvements relative to established in-memory and file-backed methods for core operations in single-cell and spatial omics analysis. We integrated dbverse with Giotto Suite, scaling end-to-end preprocessing of millions of cells and enabling spatial alternative polyadenylation analysis as demonstrated on a Visium HD 3' ovarian clear cell carcinoma sample. The dbverse framework provides an interoperable database foundation for larger-than-memory spatial omics analysis on ordinary computers.

bioinformatics↗

bioRxiv2025

Clinical feasibility of spatial transcriptomics using discarded tissue from diagnostic breast biopsies

Spatial transcriptomics holds potential to transform cancer diagnostics, yet significant barriers still limit its clinical translation. First, access to primary patient tissue is often restricted by logistical challenges and patient hesitancy. Second, it remains uncertain whether high-quality spatial transcriptomics data can be generated from clinical biopsy sections as these are collected primarily for diagnostic purposes that do not prioritize RNA-quality. We investigated whether discarded tissue slices, generated during standard pathology procedures, could be repurposed for spatial transcriptomics and alleviate concerns about both tissue availability and quality. Here, we established a pipeline to collect and perform spatial in situ transcriptomics on discarded biopsy material from a breast cancer patient, and digitized matched pathology images, including hematoxylin and eosin and traditional histochemistry stains from adjacent sections. Our results show that spatial transcriptomics data from discarded tissue are concordant with the original pathology report, while also providing additional insights such as accurate cell type annotation, detailed spatial architecture, and quantification of biological processes relevant to breast cancer progression. Altogether, our approach using discarded pathology tissue sections provides a practical and scalable solution that would maximize the scientific value of existing clinical specimens and enable high-resolution tumor microenvironment mapping.

genomics↗

bioRxiv2025

High resolution spatial profiling of the hematopoietic landscape of the murine lung

Our current understanding of blood cell development and functionality stems primarily from the investigation of adult bone marrow (BM) and the fetal liver prenatally. However, emerging evidence highlights the lung as a previously underappreciated residence for hematopoietic cells. While a diversity of cells specific to the BM are known to promote the maturation and trafficking of hematopoietic cells, how the lung niche influences the development and functionality of resident cells is not known. Spatial in situ transcriptomics enables accurate mapping of cell identities and interactions within intact tissue, providing insights not accessible by dissociated single-cell profiling. Here, we present a high-resolution spatial transcriptomic atlas of the healthy adult murine lung placing specific emphasis on the hemato-endothelial landscape of this organ. As a case study, we developed a semi-automatic workflow to explicitly identify and curate rare - often multinucleated - megakaryocytes, requiring a combination of hex-binning spatial enrichment of canonical markers, expert curation, and cell boundary merging to correct for segmentation artifacts. We then characterized the spatial neighborhoods of megakaryocytes, illustrating their topological embedding within vascular, stromal, and immune microenvironments. Finally, we demonstrated the utility of this dataset for hypothesis-driven signaling studies by examining ligand-receptor interactions across pathways including BMP, VEGF, and ECM-integrin signaling. Together, this work defines the lung-blood niche and advances our understanding of the organ-specific properties of blood cells. We also provide a high-resolution spatial reference for the murine lung and demonstrate how targeted spatial in situ transcriptomics enable focused case studies of rare hematopoietic niches. KEY POINTSO_LIThis work represents the highest resolution gene expression mapping of the spatial symbiosis between the hematopoietic and pulmonary systems. C_LIO_LIPulmonary megakaryocytes localize within distinct vascular and stromal neighborhoods. C_LI

cell biology↗