Biology subjects

Bielas, J. H.

Publications and source records attributed to Bielas, J. H..

4 recordsLinked to original sources

bioRxiv2023

MADDD-seq, a novel massively parallel sequencing tool for simultaneous detection of DNA damage and mutations

Our genome is exposed to a wide variety of DNA-damaging agents. If left unrepaired, this damage can be fixed into mutations that promote carcinogenesis and the development of genetically inherited diseases. As a result, it is crucial that we can detect DNA damage and mutations with exquisite sensitivity. Here, we describe a modified version of double barcoding sequencing technology termed Mutation And DNA Damage Detection-seq (MADDD-seq) that can detect DNA damage and mutations simultaneously, with a single assay. To demonstrate the utility of MADDD-seq as a multifunctional detection tool, we treated yeast cells with a DNA-damaging agent and tracked the presence of DNA damage and mutations over a 24-hour timespan. These experiments allowed us to identify thousands of adducts and mutations in a single sequencing run and expose the kinetics of DNA repair and mutagenesis in remarkable detail.

genetics↗

bioRxiv2022

Defining cellular population dynamics at single cell resolution during prostate cancer progression

Advanced prostate malignancies are a leading cause of cancer-related deaths in men, in large part due to our incomplete understanding of cellular drivers of disease progression. We investigated prostate cancer cell dynamics at single-cell resolution from disease onset to the development of androgen independence in vivo. We observe a dramatic expansion of a castration-resistant intermediate luminal cell type that correlates with treatment resistance and poor prognosis in human patients. Moreover, transformed epithelial cells and associated fibroblasts create a microenvironment conducive to pro-tumorigenic immune infiltration, which is in part androgen responsive. Androgen independent prostate cancer leads to significant diversification of intermediate luminal cell populations characterized by a range of androgen signaling activity inversely correlated with proliferation and mRNA translation. Accordingly, distinct epithelial populations are exquisitely sensitive to translation inhibition which leads to epithelial cell death, loss of pro-tumorigenic signaling, and decreased tumor heterogeneity. Our findings reveal a complex tumor environment largely dominated by castration-resistant luminal cells and immunosuppressive infiltrates.

cancer biology↗

bioRxiv2021

A faithful in vivo model of human macrophages in metastatic melanoma

AO_SCPLOWBSTRACTC_SCPLOWDespite recent therapeutic progress, advanced melanoma remains lethal for many patients. The composition of the immune tumor microenvironment (TME) has decisive impacts on therapy response and disease outcome. High dimensional analyses of patient samples can reveal the composition and heterogeneity of the immune TME. In particular, macrophages are known for their cancer-supportive role, but the underlying mechanisms are incompletely understood, and experimental in vivo systems are needed to test the functional properties of these cells. We characterized a humanized mouse model, reconstituted with a human immune system and a human melanoma, in which: (1) human macrophages support metastatic spread of the tumor; and (2) tumor-infiltrating macrophages have a specific transcriptional signature that faithfully represents the transcriptome of macrophages from patient melanoma samples and is associated with shorter survival. This model complements patient sample analyses, enabling the elucidation of fundamental principles in melanoma biology, and the development and evaluation of candidate therapies.

immunology↗

bioRxiv2020

BayesSpace enables the robust characterization of spatial gene expression architecture in tissue sections at increased resolution

Recently developed spatial gene expression technologies such as the Spatial Transcriptomics and Visium platforms allow for comprehensive measurement of transcriptomic profiles while retaining spatial context. However, existing methods for analyzing spatial gene expression data often do not efficiently leverage the spatial information and fail to address the limited resolution of the technology. Here, we introduce BayesSpace, a fully Bayesian statistical method for clustering analysis and resolution enhancement of spatial transcriptomics data that seamlessly integrates into current transcriptomics analysis workflows. We show that BayesSpace improves the identification of transcriptionally distinct tissues from spatial transcriptomics samples of the brain, of melanoma, and of squamous cell carcinoma. In particular, BayesSpaces improved resolution allows the identification of tissue structure that is not detectable at the original resolution and thus not recovered by other methods. Using an in silico dataset constructed from scRNA-seq, we demonstrate that BayesSpace can spatially resolve expression patterns to near single-cell resolution without the need for external single-cell sequencing data. In all, our results illustrate the utility BayesSpace has in facilitating the discovery of biological insights from a variety of spatial transcriptomics datasets.

bioinformatics↗