Eddy, E. | bioRxiv Science

bioRxiv2026

Autoimmune-like CD8⁺ T Cell Responses Drive Atherosclerotic Plaque Instability and Predict Cardiovascular Events

Background and aimsAtherosclerotic plaque rupture is a major cause of myocardial infarction and stroke. However, the precise drivers of plaque destabilisation remain elusive. We hypothesised that antigen-driven, autoimmune-like T cell responses are central to the destabilisation and rupture of atherosclerotic plaques. MethodsTo dissect T cell responses specifically in unstable compared to stable plaques, we leveraged near-infrared autofluorescence (NIRAF) imaging-guided dissection of human carotid plaques. We also used our tandem stenosis model reflecting plaque instability as seen in patients to differentiate between unstable and stable plaques in mice. To explore T cell involvement, we studied T cell differentiation states and T cell receptor (TCR) repertoires by single-cell multi-omics. Then, testing if antigen-driven CD8+ T cell responses drive plaque instability in mice, we applied a combination of AAV8-PCSK9-induced atherosclerosis, tandem stenosis and TCR transgenic mice. Finally, we leveraged data from the AtheroExpress Biobank Study to link T cell immunity to histology-defined instability and cardiovascular outcomes. ResultsT cell responses in unstable versus stable atherosclerosis were distinct. Unstable human plaques contained highly expanded, autoimmune-like CD8 T cells with markedly increased cytotoxic signatures, reduced exhaustion and distinct clonal repertoires compared to stable regions. Most plaque CD8 T cells exhibited a pronounced tissue-resident transcriptional program. Moreover, the transcriptional signature of these plaque resident T cells was distinct from multiple other human tissues. Autoimmune-like cytotoxic and tissue-resident CD8+ T cell responses were also evident in murine atherosclerosis, where restricting the activation of antigen-driven CD8+ T cells prevented plaque destabilisation. Importantly, analysis of carotid endarterectomy samples from >1000 patients identified that intraplaque cytotoxic CD8 T cell gene signatures strongly correlated with histological instability and predicted future strokes. ConclusionsIntegrated human, murine and clinical analyses demonstrate that autoimmune-like, cytotoxic CD8 T cell responses are central drivers of plaque instability and major cardiovascular events. Targeting pathogenic CD8 T cell responses may thus offer a compelling immunomodulatory strategy to stabilise plaques and reduce the risks of stroke and myocardial infarction. Graphical AbstractO_ST_ABSKey QuestionC_ST_ABSRupture of unstable atherosclerotic plaques is a typical cause of myocardial infarction and stroke. To understand the underlying cause and to prevent plaque rupture, we addressed the central hypothesis that autoimmune-like T cell responses drive plaque destabilisation and rupture. Key FindingsCD8+ T cells are clonally expanded with increased cytotoxic signatures in unstable versus stable plaques (mice and humans) and require antigen recognition to drive plaque instability. Cytotoxic CD8+ T cell signatures in excised plaques correlate with increased future cardiovascular events. Take Home MessageAutoimmune-like adaptive immune reactions, dominated by CD8+ T cells, are a major driver of plaque instability/rupture. Therefore, targeting pathogenic CD8 T cell responses offers a compelling immunomodulatory strategy to stabilise plaques and reduce the risk of myocardial infarction and stroke. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/720043v1_ufig1.gif" ALT="Figure 1"> View larger version (58K): org.highwire.dtl.DTLVardef@bd83dcorg.highwire.dtl.DTLVardef@1bee3borg.highwire.dtl.DTLVardef@1b56bc0org.highwire.dtl.DTLVardef@1b5176f_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

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↗