Biology subjects

Zaborsky, N.

Publications and source records attributed to Zaborsky, N..

4 recordsLinked to original sources

bioRxiv2025

spatialgater: A shiny webtool for in situ gating of cells in SpatialExperiments

Multiplexed imaging techniques generate high-dimensional datasets that contain their molecular profiles of cells combined with spatial coordinates, which can be stored in SpatialExperiment objects. Current analysis workflows separate cells after clustering them by their bio-molecule expression levels without considering their spatial context within the tissue. While patch-/neighbourhood detection methods exist, there is no option to select single cells by their location at will. By introducing spatialgater, we aim to boost interactivity and reduce programming efforts of image analysis by enabling spatial gating of cells from SpatialExperiment objects through an intuitive web-based interface. The package displays cells as dots on a zoom-able image and allows users to draw polygon gates directly on individual cells. All selected cell identifiers can be exported as.csv and/or written back as logical gate column in the colData() of the original SpatialExperiment. spatialgater provides an accessible, interactive addition to spatial analysis pipelines. By using a publicly available imaging mass cytometry dataset from breast cancer tissue, we demon-strate its effectiveness in characterizing and comparing T-cells by their spatial location.

bioinformatics↗

bioRxiv2024

Hedgehog-Hippo pathway interactions promote T cell exclusion from the tumor microenvironment in basal cell carcinoma

Basal cell carcinoma (BCC) is the most common non-melanoma skin cancer driven primarily by genetic activation of the Hedgehog/GLI (HH/GLI) signaling pathway. BCC also displays a high mutational burden, with frequent co-occurrence of mutations in the TP53, NOTCH, N- MYC, and Hippo/YAP pathways, though the impact of these mutations on HH/GLI-driven tumorigenesis remains poorly understood. This study examines the interaction between the HH/GLI and Hippo/YAP signaling pathways in the pathogenesis of BCC and the establishment of an immunosuppressive tumor microenvironment (TME). The study used 3D human organotypic skin models and humanized mouse models to demonstrate that co-activation of HH/GLI and Hippo/YAP in epidermal cells suppresses T cell chemotaxis. Transcriptome analysis revealed a significant downregulation of chemotactic and inflammatory genes, particularly under combined GLI-YAP activation. Analysis of primary human BCC biopsies confirmed these findings and demonstrated a marked suppression of T cell chemo-attractants, including CCL22 and CCL27. Spatial profiling of immune cells in BCC tissues revealed preferential exclusion of CD8+ T cells from the TME, which correlated with high HH/GLI and Hippo/YAP activity. Further, functional assays in humanized mice showed that the GLI-YAP interaction controls immune cell distribution by inhibiting T cell recruitment and migration into the skin. These findings suggest that cooperation between oncogenic HH/GLI and Hippo/YAP signaling contributes to the development of an immunosuppressive TME in BCC by disrupting local chemokine signaling. Understanding these mechanisms provides a basis for the development of targeted combination therapies to enhance the efficacy of existing treatments, such as Smoothened (SMO) inhibitors and immune checkpoint blockers, by promoting T cell infiltration and activation within the TME.

cancer biology↗

bioRxiv2022

Validation of genetic variants from NGS data using Deep Convolutional Neural Networks

Accurate somatic variant calling from next-generation sequencing data is one most important tasks in personalised cancer therapy. The sophistication of the available technologies is ever-increasing, yet, manual candidate refinement is still a necessary step in state-of-the-art processing pipelines. This limits reproducibility and introduces a bottleneck with respect to scalability. We demonstrate that the validation of genetic variants can be improved using a machine learning approach resting on a Convolutional Neural Network, trained using existing human annotation. In contrast to existing approaches, we introduce a way in which contextual data from sequencing tracks can be included into the automated assessment. A rigorous evaluation shows that the resulting model is robust and performs on par with trained researchers following published standard operating procedure.

bioinformatics↗

bioRxiv2019

Epidermal activation of Hedgehog signaling establishes an immunosuppressive microenvironment in basal cell carcinoma by modulating skin immunity

Genetic activation of Hedgehog (HH)/GLI signaling causes basal cell carcinoma (BCC), a very frequent non-melanoma skin cancer. Small molecule targeting of the essential HH effector Smoothened (SMO) proved an efficient medical therapy of BCC, although lack of durable responses and frequent development of drug resistance pose major challenges to anti-HH treatments. In light of the recent breakthroughs in cancer immunotherapy, we analyzed in detail the possible immunosuppressive mechanisms in HH/GLI-induced BCC. Using a genetic mouse model of BCC, we identified profound differences in the infiltration of BCC lesions with cells of the adaptive and innate immune system. Epidermal activation of HH/GLI led to an accumulation of immunosuppressive regulatory T cells, and to an increased expression of immune checkpoint molecules including PD-1/PD-L1. Anti-PD1 monotherapy, however, did not reduce tumor growth, presumably due to the lack of immunogenic mutations in common BCC mouse models, as shown by whole-exome sequencing. BCC lesions also displayed a marked infiltration with neutrophils, the depletion of which unexpectedly promoted BCC growth. The results provide a comprehensive survey of the immune status of murine BCC and provide a basis for the design of efficacious rational combination treatments. This study also underlines the need for predictive immunogenic mouse models of BCC to evaluate in vivo the efficacy of immunotherapeutic strategies.

cancer biology↗