Biology subjects

Wishart, C.

Publications and source records attributed to Wishart, C..

3 recordsLinked to original sources

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↗

bioRxiv2024

High fat low carbohydrate diet is linked to protection against CNS autoimmunity

Multiple sclerosis (MS) is a common central nervous system (CNS) autoimmune disease, and diets and nutrients are emerging as critical contributing factors. However, a comprehensive understanding of their impacts and the underlying mechanisms involved is lacking. Harnessing state-of-the-art nutritional geometry analytical methods, we first revealed that globally, increased carbohydrate supply was associated with increased MS disease burden, while fat supply had an opposite effect. Furthermore, in a preclinical MS mouse model, experimental autoimmune encephalomyelitis (EAE), we found that an isocaloric diet high in carbohydrate aggravated EAE, while a diet enriched in fat was fully protective. This was reflected by reduced neuroinflammation and skewing towards anti-inflammatory phenotypes, which involved transcriptomic, epigenetic and immunometabolic changes. We showcased that manipulating diets is a potentially efficient and cost-effective approach to prevent and/or ameliorate EAE. This exhibits translational potentials for intervention/prevention of MS and possibly other autoimmune diseases.

immunology↗

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↗