Biology subjects

Sargeant, C. J.

Publications and source records attributed to Sargeant, C. J..

5 recordsLinked to original sources

bioRxiv2026

SpatialBench: Comparative cross-platform benchmarking of high-resolution spatial transcriptomics using matched mouse lymphoid tissue

Spatial transcriptomics (ST) has rapidly expanded with the introduction of multiple high-resolution platforms, yet cross-platform benchmarking remains limited and largely focused on technical performance. Here we present SpatialBench, a matched multi-platform resource comprising Visium HD, Xenium and MERSCOPE data together with single-cell and single-nucleus references from a malaria-challenged wild-type and B cell-specific Tbx21 knockout mouse spleen model. In this system, loss of T-bet in B cells disrupts germinal center (GC) polarization and antibody maturation, providing a biologically grounded benchmark for technology comparison. We leveraged this system to systematically evaluate ST platform performance using technical and biological readouts. Across platforms, immune organization and Tbx21-associated programs were consistently recovered, indicating robustness of major biological signals. Platforms instead differed in the level of biological resolution accessible. Visium HD enabled transcriptome-scale GC characterization and, together with Xenium, resolved dark and light zone organization, whereas GC zonation was not resolved in MERSCOPE, consistent with differences in transcript detection sensitivity. SpatialBench provides a biologically defined reference dataset for evaluation of ST technologies, method development, computational benchmarking, and studies of GC spatial organization in lymphoid tissue.

bioinformatics↗

bioRxiv2026

CountESS: a flexible, graphical pipeline tool for deep mutational scanning analysis

Deep Mutational Scanning (DMS) experiments generate large volumes of sequencing data that must be processed through multi-step computational pipelines to yield interpretable variant scores. At least twelve dedicated tools have been published for this purpose, yet the diversity of experimental designs, scoring strategies, and software implementations has produced a fragmented landscape in which no single tool accommodates the full range of workflows encountered in practice. Here we present CountESS (Count-based Experiment Scoring and Statistics), an open-source pipeline tool that provides a modular, graphical interface for constructing flexible DMS analysis workflows. CountESS supports a wide range of input formats, barcode translation, HGVS variant calling, and user-defined scoring functions, enabling it to accommodate diverse experimental designs including selection assays, time-series experiments, and bin-based assays such as VAMP-seq. Implemented in Python with DuckDB as a computational backend, the software provides high-performance, memory-efficient processing suitable for large datasets. CountESS is freely available at https://github.com/CountESS-Project/CountESS under the 3-Clause BSD Licence. Supplementary data, including demonstration pipelines and example datasets, are available at https://github.com/CountESS-Project/countess-demo.

bioinformatics↗

bioRxiv2025

Orchestrating Spatial Transcriptomics Analysis with Bioconductor

Spatial transcriptomics technologies provide spatially-resolved measurements of gene expression through assays that can either target selected genes or capture transcriptome-wide expression profiles. The complexity and variability of these technologies and their associated data necessitate multi-step workflows integrating diverse computational methods and software packages. We provide a freely accessible, open-source, continuously updated and tested online book containing reproducible code examples, datasets, and discussion about data analysis workflows for spatial omics data using Bioconductor in R, including interoperability with Python.

bioinformatics↗

bioRxiv2025

stPipe: A flexible and streamlined R/Bioconductor pipeline for preprocessing sequencing-based spatial transcriptomics data

Spatial transcriptomics technology has developed rapidly in recent years, with various sequencing-based platforms such as 10x Visium, Slide-seq and Stereo-seq becoming widely used by researchers. Each platform brings its own set of protocols and customised data analysis pipelines which presents challenges when the goal is to obtain uniformly preprocessed data that is conveniently formatted for downstream analysis. To address the need for simpler, open-source solutions that deal with sequencing-based spatial transcriptomics (sST) data from different platforms, we present stPipe, a comprehensive and modular pipeline for analysing sST data. stPipe handles various analysis steps including (i) data processing from raw paired end FASTQ files to create a spatially resolved gene count matrix; (ii) the collation of relevant quality control metrics during preprocessing to ensure unwanted artefacts can be filtered from further analysis; and (iii) the adoption of standardised data storage containers to allow results to be easily passed on to a wide range of downstream analysis packages tailored to different goals (such as clustering, cell-cell communication analysis and differential expression analysis). stPipe is implemented as an R/Bioconductor package that builds upon functionality in the scPipe software, and offers a flexible preprocessing pipeline that can manage data from all current main-stream sST plaforms. A key use case for stPipe is in methods benchmarking, and we demonstrate how the uniform processing of sST data collected on reference tissue samples from the cadasSTre and SpatialBenchVisium projects is made easier, allowing comparisons between different technology platforms and downstream analysis tools. Our framework thus aims to advance the standardization and optimization of spatial transcriptomics analyses, fostering collaboration and innovation within the research community.

bioinformatics↗

bioRxiv2024

Spotlight on 10x Visium: a multi-sample protocol comparison of spatial technologies

BackgroundSpatial transcriptomics allows gene expression to be measured within complex tissue contexts. Among the array of spatial capture technologies available is 10x Genomics Visium platform, a popular method which enables transcriptomewide profiling of tissue sections. Visium offers a range of sample handling and library construction methods which introduces a need for benchmarking to compare data quality and assess how well the technology can recover expected tissue features and biological signatures. ResultsHere we present SpatialBench, a unique reference dataset generated from spleen tissue of mice responding to malaria infection spanning several tissue preparation protocols (both fresh frozen and FFPE samples, with and without CytAssist tissue placement). We noted better quality control metrics in reference samples prepared using probe-based capture methods, particularly those processed with CytAssist, validating the improvement in data quality produced with the platform. Our analysis of replicate samples extends to explore spatially variable gene detection, the outcomes of clustering and cell deconvolution using matched single-cell RNA-sequencing data and publicly available reference data to identify cell types and tissue regions expected in the spleen. Multi-sample differential expression analysis recovered known gene signatures related to biological sex or gene knockout. ConclusionsWe framed a comprehensive multi-sample analysis workflow that allowed us to generate consistent results both within and between different subsets of replicate samples, enabling broader comparisons and interpretations to be made at the group-level. Our SpatialBench dataset, analysis, and workflow can serve as a practical guide for Visium users and may prove valuable in other benchmarking studies.

genomics↗