Biology subjects

Lytle, A.

Publications and source records attributed to Lytle, A..

3 recordsLinked to original sources

bioRxiv2026

Single-cell spatial multi-omic characterization of the tumour microenvironment in transformed follicular lymphoma

Histological examination of follicular lymphoma (FL) biopsies remains the cornerstone for diagnostic grading of FL. Single-cell sequencing approaches, while transcriptomically rich, require tissue dissociation and lose the native spatial context that underpins FL transformation to diffuse large B-cell lymphoma (DLBCL). To investigate the spatial interplay between malignant B-cells and the tumour microenvironment (TME) across disease states, we performed subcellular single-cell spatial transcriptomics and spatial proteomics on 12 paired pre/post-transformation samples and 10 non-transforming FL controls, integrated with matched single-cell whole genome sequencing (scWGS). Our analysis reveals that transformation is accompanied by a shift toward B-cell-predominant stromal and immunosuppressive cellular neighbourhoods, where the magnitude of expansion correlates with time to transformation. Prior to transformation, immunomodulatory Galectin-9 interactions move from the intra-follicular core to the extra-follicular space. Integration with scWGS demonstrates that high copy-number instability in malignant B-cells is associated with reduced supportive T-cell niches and intensified immunoregulatory crosstalk at the transformed state. Collectively, our multi-omic analysis characterizes TME remodeling during FL transformation, contributing to a refined disease evolution model.

bioinformatics↗

bioRxiv2025

Multidimensional characterization of cellular ecosystems in Hodgkin lymphoma

The tissue architecture of classic Hodgkin Lymphoma (CHL) is unique among cancers and characterized by rare malignant Hodgkin and Reed-Sternberg cells that co-evolve with a complex ecosystem of immune cells in the tumor microenvironment (TME). The lack of a comprehensive systems-level interrogation has hindered the description of disease heterogeneity and clinically relevant molecular subtypes. Here, we employed an integrative, multimodal approach to characterize CHL tumors using malignant cell sequencing, spatial transcriptomics and imaging mass cytometry. We identified four molecular subtypes (CST, CN913, STB, and CN2P), each characterized by distinct clinical features, mutational patterns, malignant cell gene expression profiles, and spatial architecture involving immune cell populations. Functional modeling of CSF2RB mutations, a characteristic feature of the CST subtype, revealed dysregulated oncogenic signaling and unique TME crosstalk. These findings highlight the significance of multi-dimensional profiling in elucidating patterns of molecular alterations that drive immune ecosystems and underlie therapeutically exploitable vulnerabilities.

cancer biology↗

bioRxiv2023

ESQmodel: biologically informed evaluation of 2-D cell segmentation quality in multiplexed tissue images

MotivationSingle cell segmentation is critical in the processing of spatial omics data to accurately perform cell type identification and analyze spatial expression patterns. Segmentation methods often rely on semi-supervised annotation or labeled training data which are highly dependent on user expertise. To ensure the quality of segmentation, current evaluation strategies quantify accuracy by assessing cellular masks or through iterative inspection by pathologists. While these strategies each address either the statistical or biological aspects of segmentation, there lacks an unified approach to evaluating segmentation accuracy. ResultsIn this paper, we present ESQmodel, a Bayesian probabilistic method to evaluate single cell segmentation using expression data. By using the extracted cellular data from segmentation and a prior belief of cellular composition as input, ESQmodel computes per cell entropy to assess segmentation quality by how consistent cellular expression profiles match with cell type expectations. Availability and implementationSource code is available on Github at: https://github.com/Roth-Lab/ESQmodel under the MIT license.

bioinformatics↗