Biology subjects

Willetts, M.

Publications and source records attributed to Willetts, M..

5 recordsLinked to original sources

bioRxiv2026

Expanded Proteome Coverage Powered by Advanced Ion Processing Enables Deep Single-Cell Drug Response Subtyping in Human Stem Cell Derived Cardiomyocytes

Single-cell proteomics (SCP) enables the study of cellular heterogeneity at the functional level but remains limited by incomplete proteome coverage and high data missingness. Here, we present an enhanced label-free SCP workflow that leverages the timsUltra AIP mass spectrometry platform equipped with the Athena Ion Processor (AIP). Across a controlled dilution series of human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs), AIP-enabled acquisition consistently increased proteome depth and detection consistency across cells at all input levels. In single iPSC-CMs, the timsUltra AIP quantified up to 3,858 protein groups, averaging [~]1,300 proteins per cell, enabling robust proteome-level classification of cardiomyocyte subtypes. Using a reference-based protein classifier, cells were stratified into mature cardiomyocytes and less differentiated cell states, revealing substantial baseline heterogeneity. Importantly, increased single-cell sensitivity translated directly into biological insight, as approximately 30% of differentially expressed proteins associated with subtype-specific drug responses were detected exclusively by timsUltra AIP. Application of this workflow to PR-364 (a mitophagy boosting drug) dose-response experiment uncovered distinct, subtype-dependent pathway adaptations. Mature cardiomyocytes exhibited dose-dependent increases in mitochondrial and metabolic pathway activity, while immature cells showed enrichment of cytoskeletal and developmental programs. These effects were partially obscured in simulated bulk analyses, highlighting the value of single-cell resolution. Together, these results demonstrate that improved fragment ion transmission and utilization translate directly into enhanced biological insight, enabling more comprehensive and functionally relevant single-cell proteomics.

molecular biology↗

bioRxiv2025

Evaluation of Parallel Accumulation-Serial Fragmentation methods for metaproteomics using a model microbiome

Mass spectrometry-based metaproteomics allows for the identification and quantification of thousands of proteins from clinical and environmental samples and is rapidly gaining importance in microbiome sciences. Metaproteomics researchers can measure taxonomic and functional abundances of microbiomes, shedding light on mechanistic details of microbiome interactions with their environment. However, metaproteomic analysis suffers from limited depth of coverage due to the presence of millions of peptides at lower abundance levels. Recent advances in data-independent acquisition mass spectrometry coupled with Parallel Accumulation-Serial Fragmentation (PASEF) technology offer improved depth of coverage. PASEF technology enables simultaneous accumulation of ions from multiple co-eluting peptides by combining ion mobility separation with dynamic quadrupole isolation, allowing efficient and selective fragmentation in a single scan. This boosts ion sampling efficiency and resolves overlapping signals with high sensitivity. In this study, we assessed proteome coverage, quantitative precision, and accuracy of Data-dependent acquisition (DDA) and Data-independent acquisition (DIA) methods coupled with the PASEF method. For this, we used a ground-truth mock community containing 28 species (30 strains) from all three domains of life and bacteriophages with a 400-fold dynamic range of organism abundance. Our results showed that diaPASEF demonstrated superior performance, identifying 168% more peptide precursors, 155% more peptides, and 66% more protein groups compared to ddaPASEF. Quantitative measurements showed improved precision with diaPASEF, with 26 out of 28 organisms exhibiting coefficient of variation values below 20%, compared to 24 organisms with ddaPASEF. Both ddaPASEF and diaPASEF methods accurately quantified the 22 most abundant organisms, while measurements of low-abundance bacteriophages showed significant deviation from expected values. Our findings demonstrate that diaPASEF provides enhanced depth of coverage and quantitative reliability for metaproteomics analysis, particularly beneficial for clinical and environmental microbiome studies where deeper functional characterization is essential. This study provides valuable benchmark data to facilitate the development of advanced bioinformatic methods for quantitative metaproteomics.

biochemistry↗

bioRxiv2024

dia-PASEF Proteomics of Tumor and Stroma LMD Enriched from Archived HNSCC Samples

We employed laser microdissection to selectively harvest tumor cells and stroma from the microenvironment of formalin-fixed, paraffin-embedded head and neck squamous cell carcinoma (HNSCC) tissues. The captured HNSCC tissue fractions were analyzed by quantitative mass spectrometry-based proteomics using a data independent analysis approach. In paired samples, we achieved excellent proteome coverage having quantified 6,668 proteins with a median quantitative coefficient of variation under 10%. We observed significant differences in relevant functional pathways between the spatially resolved tumor and stroma regions. Our results identified extracellular matrix (ECM) as a major component enriched in the stroma, including many cancer associated fibroblast signature proteins in this compartment. We demonstrate the potential for comparative deep proteome analysis from very low starting input in a scalable format that is useful to decipher the alterations in tumor and the stromal microenvironment. Correlating such results with clinical features or disease progression will likely enable identification of novel targets for disease classification and interventions.

cancer biology↗

bioRxiv2022

Comparative analyses of the N-glycoproteomes in HCT116 cancer cells and their non-tumorigenic DNMT1/3b double knockout (DKO1) cells and insight into the Mannose-6-phosphate pathway

N-glycoproteomic analyses provide valuable resources for investigation of cancer mechanisms, biomarkers, and therapeutic targets. Here, we mapped and compared the site-specific N-glycoproteomes of colon cancer HCT116 cells and isogenic non-tumorigenic DNMT1/3b double knockout (DKO1) cells using Fbs1-GYR N-glycopeptide enrichment technology and trapped ion mobility spectrometry. Many significant changes in site-specific N-glycosylation were revealed, providing a molecular basis for further elucidation of the role of N-glycosylation in protein function. HCT116 cells display hypersialylation especially in cell surface membrane proteins. Both HCT116 and DKO1 show an abundance of paucimannose and 80% of paucimannose-rich proteins are annotated to reside in exosomes. The most striking N-glycosylation alteration was the degree of mannose-6-phosphate (M6P) modification. N-glycoproteomic analyses revealed that HCT116 display hyper-M6P modification, which was orthogonally validated by M6P immunodetection. Significant observed differences in N-glycosylation patterns of the major M6P receptor, CI-MPR in HCT116 and DKO1 may contribute to the hyper-M6P phenotype of HCT116 cells.

biochemistry↗

bioRxiv2017

Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,609 UK Biobank participants

Current public health guidelines on physical activity and sleep duration are limited by a reliance on subjective self-reported evidence. Using data from simple wrist-worn activity monitors, we developed a tailored machine learning model, using balanced random forests with Hidden Markov Models, to reliably detect a number of activity modes. We show that physical activity and sleep behaviours can be classified with 87% accuracy in 159,504 minutes of recorded free-living behaviours from 132 adults. These trained models can be used to infer fine resolution activity patterns at the population scale in 96,220 participants. For example, we find that men spend more time in both low- and high-intensity behaviours, while women spend more time in mixed behaviours. Walking time is highest in spring and sleep time lowest during the summer. This work opens the possibility of future public health guidelines informed by the health consequences associated with specific, objectively measured, physical activity and sleep behaviours.

bioinformatics↗