Biology subjects

Watson, S. S.

Publications and source records attributed to Watson, S. S..

3 recordsLinked to original sources

bioRxiv2025

Multi-cellular phenotypic dynamics during the progression of breast tumors

In cancer, improving diagnostics and therapeutic interventions can benefit from understanding the cellular and phenotypic heterogeneity of the tumor microenvironment (TME). In recent years, the TME has been profiled at an unprecedented level of detail by performing single-cell RNA sequencing (scRNAseq) on patient samples. However, from patient samples, studying the temporal dynamics of the TME using patient samples has been challenging. Interrogating the temporal dynamics of the TME is critical to understand how inter-tumor heterogeneity is organized into a temporally ordered sequence of causes and consequences in cellular events. Here we survey the temporal dynamics of the TME by performing longitudinal scRNAseq on mouse breast tumors at different progression time points of tumor progression. We reveal multi-cellular phenotypic dynamics that follow one out of three possible temporal patterns: stable colonization, wave-like, or progressive increase. In particular, IFN-responsive cancer cells, GzmB + cytotoxic T cells, as well as C1q macrophages, progressively increase in parallel with tumors progression. These findings establish the single-cell types and phenotypes in a progressing breast tumor, and determine when these cellular players enter and leave the TME.

cancer biology↗

bioRxiv2021

Exploration and analysis of molecularly annotated, 3D models of breast cancer at single-cell resolution using virtual reality

A set of increasingly powerful approaches are enabling spatially resolved measurements of growing numbers of molecular features in biological samples. While important insights can be derived from the two-dimensional data that many of these technologies generate, it is clear that extending these approaches into the third and fourth dimensions will magnify their impact. Realizing biological insights from datasets where thousands to millions of cells are annotated with tens to hundreds of parameters in space will require the development of new computational and visualization strategies. Here, we describe Theia, a virtual reality-based platform, which enables exploration and analysis of either volumetric or segmented, molecularly-annotated, three-dimensional datasets, with the option to extend the analysis to time-series data. We also describe our pipeline for generating annotated 3D models of breast cancer and supply several datasets to enable users to explore the utility of Theia for understanding cancer biology in three dimensions.

cancer biology↗

bioRxiv2021

The Imaging and Molecular Annotation of Xenografts and Tumours (IMAXT) High Throughput Data and Analysis Infrastructure

With the aim of producing a 3D representation of tumours, IMAXT uses a large variety of modalities in order to acquire tumour samples and produce a map of every cell in the tumour and its host environment. With the large volume and variety of data produced in the project we develop automatic data workflows and analysis pipelines and introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located instead of bringing data to their local computers. Here we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumour models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT.

cancer biology↗