Biology subjects

Bateman, S.

Publications and source records attributed to Bateman, S..

3 recordsLinked to original sources

bioRxiv2026

dia-PASEF Enables Rapid Profiling of the Human Secretome for Deeper Insights into Cellular Dynamics and Inflammatory Mechanisms

Protein secretion is a fundamental mechanism for cellular coordination and signalling, with its dysregulation leading to widespread physiological dysfunction and disease. Immunoassay formats that utilise secondary antibody readouts are the current gold standard for measuring secreted proteins, offering high specificity and sensitivity, but relying on predefined protein panels that constrain the discovery of novel biology. We present a scalable mass spectrometry-based workflow that combines data-independent acquisition with ion mobility and parallel fragmentation to deliver rapid, global profiling of the secretome. Using a translationally relevant human iPSC-derived macrophage model, our approach identified over 1200 proteins in under 15 minutes of acquisition time, delivering exceptional reproducibility across a large sample set. We applied this approach to profile pro-inflammatory phenotypes, confirming robust identification of key cytokines and chemokines whilst revealing non-canonical immune responses absent from both targeted panels and the intracellular proteome. In particular, we identified a unique cholesterol efflux signature, marked by the secretion of APOA1 and PON1, in response to Mycobacterium Tuberculosis, consistent with the metabolic reprogramming that takes place during infection. Furthermore, temporal profiling of macrophage responses to lipopolysaccharide over 24 hours resolved dynamic secretion trajectories that distinguish between acute and chronic inflammatory states. The extended time period facilitated the observation of distinct cytokine-dependent secretion phenotypes, with early secretion of TNF and IL6 initiating downstream signalling cascades that resulted in the delayed secretion of chemokines such as CXCL10 and CCL8. Collectively, these findings establish a robust, scalable platform for global characterisation of secretory networks. Beyond macrophage biology, this workflow offers broad utility for biomarker discovery, mechanistic studies of disease progression and evaluation of new therapeutic interventions, providing a powerful tool for advancing precision medicine.

immunology↗

bioRxiv2025

MALDI-TOF mass spectrometry and proteomics as phenotypic screening tools for anti-inflammatory drugs

Phenotypic screening is a powerful technology to discover drug candidates in physiologically relevant systems without prior knowledge of molecular targets; however, mass spectrometry (MS) remains underutilised as readout strategy. In this proof-of-concept study, we developed and evaluated two complementary MS-based phenotypic screening approaches to identify anti-inflammatory compounds in human induced pluripotent stem cell-derived macrophages and compared them to a conventional targeted cytokine profiling assay. First, we established a novel MALDI-TOF MS fingerprinting strategy that effectively distinguished macrophage phenotypes, identified phenotype-specific biomarkers, and maintained high-throughput capabilities while reducing cost. Secondly, we performed an in-depth LC-MS proteomic analysis using low cell input on an Evosep-timsTOF HT setup, providing rich molecular detail. Both MS-based approaches demonstrated large comparability with the cytokine assay, with a large proportion of hits overlapping. Notably, the proteomics workflow uniquely enabled deeper insight into inflammation pathway engagement, off-target effects, compound potency, and cytotoxicity. Together, these findings highlight the potential of MS-driven phenotypic screening to enhance early drug discovery by enabling efficient, informative, and cost-effective hit selection. O_FIG O_LINKSMALLFIG WIDTH=196 HEIGHT=200 SRC="FIGDIR/small/691706v1_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@c83cb7org.highwire.dtl.DTLVardef@a14d4org.highwire.dtl.DTLVardef@1dda6d7org.highwire.dtl.DTLVardef@f47564_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

Big Data in Myoelectric Control: LargeMulti-User Models Enable Robust Zero-ShotEMG-based Discrete Gesture Recognition

Myoelectric control, the use of electromyogram (EMG) signals generated during muscle contractions to control a system or device, is a promising modality for enabling always-available control of emerging ubiquitous computing applications. However, its widespread use has historically been limited by the need for user-specific machine learning models because of behavioural and physiological differences between users. Leveraging the publicly available 612-user EMG-EPN612 dataset, this work dispels this notion, showing that true zero-shot cross-user myoelectric control is achievable without user-specific training. By taking a discrete approach to classification (i.e., recognizing the entire dynamic gesture as a single event), a classification accuracy of 93.0% for six gestures was achieved on a set of 306 unseen users (who provided no training data), showing that big data approaches (compared to most EMG studies, which typically employ only 10-20 users) can enable robust cross-user myoelectric control. By organizing the results into a series of mini-studies, this work provides an in-depth analysis of discrete cross-user models to answer unknown questions and uncover new research directions. In particular, this work explores the number of participants required to build cross-user models, the impact of transfer learning for fine-tuning these models, and the effects of under-represented end-user demographics in the training data, among other issues. Additionally, in order to further evaluate the performance of the created cross-user models, a completely new data set was created (using the same recording device) that includes known covariate factors such as cross-day use and limb-position variability. The results show that the large data models can effectively generalize to new datasets and mitigate the impact of common confounding factors that have historically limited the adoption of EMG-based inputs.

neuroscience↗