Biology subjects

Marsh-Wakefield, F.

Publications and source records attributed to Marsh-Wakefield, F..

6 recordsLinked to original sources

bioRxiv2026

An open multimodal spatial resource integrating same-tissue transcriptomics, proteomics, and histology

Spatial transcriptomic and proteomic technologies provide complementary insights into tissue organisation, cellular phenotype and function, yet integrating these modalities on the same tissue section remains technically challenging. Sequential workflows must preserve RNA integrity, antigenicity and tissue morphology while maintaining accurate spatial registration. At present, publicly available multimodal datasets suitable for computational method development remain limited. Here, we present a workflow for sequential 10x Genomics Xenium spatial transcriptomics, COMET cyclic immunofluorescence, and haematoxylin and eosin (H&E) histological staining on the same formalin-fixed paraffin-embedded tissue section. We demonstrate this approach across multiple biologically distinct human tissues, including tonsil, hepatocellular adenoma, and matched tumour and non-tumour hepatocellular carcinoma, illustrating the widespread applicability of the workflow beyond a single tissue type. Following image registration, Xenium-derived cell segmentations were applied to protein images to generate integrated single-cell transcriptomic and proteomic measurements for downstream analyses. To facilitate community reuse, we publicly release four representative aligned tissue cores together with transcript coordinates, multiplex protein images, H&E images, cell segmentations, and integrated single-cell datasets. We additionally introduce UnumLocalia, an open-source visualisation and data extraction tool that enables interactive exploration of aligned multimodal images, supports user-defined cell segmentation, and allows export of integrated single-cell data for downstream analyses. Together, this technical protocol, workflow, software, and openly available dataset provide a reusable resource for multimodal spatial biology, supporting advances in biological discovery, computational method development, multimodal data integration, and validation of emerging analytical approaches across complementary spatial technologies.

immunology↗

bioRxiv2025

Identifying the Minimal Number of Protein Markers for Cell Type Annotation Using MiniMarS

Over the past decade, there has been an explosion in the characterisation and discovery of cell populations using single-cell technologies. Single-cell multi-omics data, particularly those incorporating gene and protein expression, are increasingly commonplace and can lead to more refined characterisation of cell types. A common challenge for biologists is to isolate cells of interest using a minimal number of markers for cytometry experiments. Although several methods exist for marker selection, there is limited guidance on the relative performance of these methods, and a wrapper package that combines multiple methods is lacking. The method that performs best can vary depending on the dataset and it can be challenging for researchers to test multiple methods for a given dataset. To address these issues, we present MiniMarS (Minimal Marker Selection), an R package that serves as a wrapper for 10 different algorithms. It allows users to determine the best-performing algorithm for identifying the optimal number of markers that will delineate cell populations in their dataset. MiniMarS uses pre-annotated cells with protein features from CyTOF or sequencing-based assays such as CITE-seq and Abseq as input. Outputs include 1) the minimum number of protein markers required to identify the annotated cell populations using a range of marker selection algorithms, and 2) a range of metrics to evaluate the performance of each algorithm. MiniMarS effectively differentiated populations across various datasets, including those from human blood, bone marrow, thymus, mouse spleen, and lymph nodes, even after subsampling over 41,000 cells to 2,500 cells. MiniMarS also identified 15 markers from CITE-seq data, which were then used to successfully identify the same 11 cell subsets in a CyTOF dataset (F1 score>0.9). Additionally, we showed that by appropriately combining clusters, MiniMarS improves the F1 score of a rare population identification (<1% of total cells) by 28.7%. Together, these findings highlight the broad applicability of MiniMarS in identifying appropriate markers for distinguishing cell populations.

bioinformatics↗

bioRxiv2023

SuperCellCyto: enabling efficient analysis of large scale cytometry datasets

Advancements in cytometry technologies have enabled quantification of up to 50 proteins across millions of cells at single cell resolution. Analysis of cytometry data routinely involves tasks such as data integration, clustering, and dimensionality reduction. While numerous tools exist, many require extensive run times when processing large cytometry data containing millions of cells. Existing solutions, such as random subsampling, are inadequate as they risk excluding rare cell subsets. To address this, we propose SuperCellCyto, an R package that builds on the SuperCell tool which groups highly similar cells into supercells. SuperCellCyto is available on GitHub (https://github.com/phipsonlab/SuperCellCyto).

bioinformatics↗

bioRxiv2023

A unique human cord blood CD8+CD45RA+CD27+CD161+ T cell subset identified by flow cytometric data analysis using Seurat

Advances in single cell analysis, especially cytometric approaches, have profoundly innovated immunological research. This has resulted in an expansion of high dimensional data, posing great challenges for comprehensive and unbiased analysis. Conventional manual analysis thus becomes untenable, while most computational methods lack flexibility and interoperability, hampering usability. Here, for the first time, we adapted Seurat, a single cell RNA sequencing (scRNA-seq) analysis package, for end-to-end flow cytometric data analysis. We showcased its robust analytical capacity by analyzing the adult blood and cord blood T cell profiles, which was validated by Spectre, another cytometric data analysis package, and manual analysis. Importantly, a unique CD8+CD45RA+CD27+CD161+ T cell subset, was identified in cord blood and characterized using flow cytometry and scRNA-seq analysis from a published dataset. Collectively, Seurat possesses great potential for cytometric data analysis. It facilitates thorough interpretations of high dimensional data using a single pipeline, implementing data-driven investigation in clinical immunology.

immunology↗

bioRxiv2021

TrackSOM: mapping immune response dynamics through sequential clustering of time- and disease-course single-cell cytometry data

Mapping the dynamics of immune cell populations over time or disease-course is key to understanding immunopathogenesis and devising putative interventions. We present TrackSOM, an algorithm which delineates cellular populations and tracks their development over a time- or disease-course of cytometry datasets. We demonstrate TrackSOM-enabled elucidation of the immune response to West Nile Virus infection in mice, uncovering heterogeneous sub-populations of immune cells and relating their functional evolution to disease severity. TrackSOM is easy to use, encompasses few parameters, is quick to execute, and enables an integrative and dynamic overview of the immune system kinetics that underlie disease progression and/or resolution.

bioinformatics↗

bioRxiv2020

Integration, exploration, and analysis of high-dimensional single-cell cytometry data using Spectre

As the size and complexity of high-dimensional cytometry data continue to expand, comprehensive, scalable, and methodical computational analysis approaches are essential. Yet, contemporary clustering and dimensionality reduction tools alone are insufficient to analyze or reproduce analyses across large numbers of samples, batches, or experiments. Moreover, approaches that allow for the integration of data across batches or experiments are not well incorporated into computational toolkits to allow for streamlined workflows. Here we present Spectre, an R package that enables comprehensive end-to-end integration and analysis of high-dimensional cytometry data from different batches or experiments. Spectre streamlines the analytical stages of raw data pre-processing, batch alignment, data integration, clustering, dimensionality reduction, visualization and population labelling, as well as quantitative and statistical analysis. Critically, the fundamental data structures used within Spectre, along with the implementation of machine learning classifiers, allow for the scalable analysis of very large high-dimensional datasets, generated by flow cytometry, mass cytometry (CyTOF), or spectral cytometry. Using open and flexible data structures, Spectre can also be used to analyze data generated by single-cell RNA sequencing (scRNAseq) or high-dimensional imaging technologies, such as Imaging Mass Cytometry (IMC). The simple, clear, and modular design of analysis workflows allow these tools to be used by bioinformaticians and laboratory scientists alike. Spectre is available as an R package or Docker container. R code is available on Github (https://github.com/immunedynamics/spectre).

immunology↗