Biology subjects

Dionne, J. A.

Publications and source records attributed to Dionne, J. A..

5 recordsLinked to original sources

bioRxiv2026

Dynamic, single-cell monitoring of CAR T cell identity and activation with Raman spectroscopy

Chimeric antigen receptor (CAR) T cell therapies have reshaped treatment for cancers and immune-mediated diseases, yet their safety and efficacy depend on both the proliferation of engineered cells and their dynamic functional state -- features that remain challenging to monitor in real-time clinical settings. Current methods require targeted labels, extensive processing, and provide only static snapshots of cell identity and activation. Here, we introduce a surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) approach that enables single-cell identification of engineered CAR T cells without molecularly-targeted labels and time-resolved, semi-continuous monitoring of their functional activation state through a single physical readout spanning donor-derived cells and patient blood. From intrinsic vibrational signatures of live cells, we detect spectral differences resulting from engineered receptor expression in donor-derived CD19- and GD2-targeted CAR T cells (nine and five donors, respectively) with 81-85% donor-level accuracy, and resolve dynamic antigen-specific activation trajectories with temporal precision. Applying this same SERS-ML method to longitudinal samples from a four-patient CD19-CAR T therapy cohort, we classify patient peripheral blood mononuclear cells across pre-and post-infusion timepoints with a mean patient-level accuracy of 84%, and show that isolated CAR-positive T cells are distinguishable from both CAR-negative T cells and background populations with average 86-88% accuracies. These capabilities stem from biochemical signatures consistent with processes such as receptor expression, tonic signalling, and immune synapse formation, demonstrating a single method that reports both cellular identity and activation state with biochemical specificity across cell engineering and clinical monitoring contexts. Our results extend CAR T cell monitoring beyond static phenotyping and support the potential of SERS-ML analysis for rapid, point-of-care assessment of engineered immune cells across the therapeutic lifecycle.

bioengineering↗

bioRxiv2025

Predicting targeted- and immunotherapeutic response outcomes in melanoma with single-cell Raman Spectroscopy and AI

PURPOSEIdentifying reliable predictors of immunotherapeutic response in melanoma remains an outstanding challenge. Existing transcriptomic and proteomic profiling methods for the tumor-immune microenvironment (TIME) are costly and may not faithfully capture modifications actively impacting tumor behavior. Here, we present a non-destructive, single-cell approach combining Raman spectroscopy and machine learning (ML) that enables rapid cell profiling and therapeutic response prediction. METHODSWe analyzed single-cell Raman spectra of mouse and human melanoma cell lines alongside nine melanoma patient-derived samples with known resistance profiles to targeted and immunotherapeutic inhibitors bemcentinib, cabozantinib, dabrafenib, nivolumab, and a combination of nivolumab and relatlimab. We assessed cell phenotyping classification and treatment resistance using random forests and feature importance analysis. For patient samples, we constructed a two-stage evaluation workflow to determine clinical drug resistance through aggregated single-cell predictions and identified corresponding highly variant spectral signatures using computational methods adapted from single-cell RNA sequencing methods. RESULTSIn cell lines, our approach achieved >96% differentiation accuracy across tumor microenvironment cell types and induced functional phenotypes. Persistent (drug-resistant) cells formed subclusters based on genetic mutations rather than sample origin, with Raman signatures reflecting biochemical changes relevant to therapeutic pathways. For patient samples, our workflow correctly inferred resistance likelihoods for 30 of 33 clinically-relevant patient-drug combinations (91% accuracy). CONCLUSIONSingle-cell Raman spectroscopy combined with machine learning offers a scalable, prognostic platform to predict therapeutic resistance likelihood, with further potential to advance clinical, multi-omic biomarker efforts for melanoma. Our approach may improve first-and second-line therapy selection assessments for precision medicine by providing rapid, non-destructive prediction of therapeutic response based on cellular spectral profiles. Context SummaryO_ST_ABSKey objectiveC_ST_ABSCan label-free, single-cell Raman spectroscopy and machine learning approach accurately profile melanoma cell states and therapeutic resistance likelihood to targeted and immunotherapeutic agents? Knowledge generatedRaman spectroscopy with machine learning differentiated tumor microenvironment cell types and functional phenotypes with >96% accuracy in cell lines. When applied to patient-derived metastatic melanoma samples, the approach correctly inferred patient response to a panel of targeted and immunotherapeutic inhibitors with 91% accuracy (30 of 33 cases). High-likelihood persistent and sensitive cells across diverse patients exhibited recurrent spectral features. RelevanceSingle-cell Raman-based profiling supports functional-diagnostic assessment or resistance likelihood and may contribute to improved therapeutic selection and precision oncology strategies for melanoma patients.

cancer biology↗

bioRxiv2024

p53 Regulates Nuclear Architecture to Reduce Carcinogen Sensitivity and Mutagenic Potential

The p53 tumor suppressor is an indispensable regulator of DNA damage responses that accelerates carcinogenesis when mutated. In this report, we uncover a new mechanism by which p53 maintains genomic integrity in the absence of canonical DNA damage response activation. Specifically, loss of p53 dramatically alters chromatin structure at the nuclear periphery, allowing increased transmission of an environmental carcinogen, ultraviolet (UV) radiation, into the nucleus. Genome-wide mapping of UV-induced DNA lesions in p53-deficient primary cells reveals elevated lesion abundance in regions corresponding to locations of high mutation burden in malignant melanomas. These findings uncover a novel role of p53 in the suppression of mutations that contribute to cancer and highlight the critical influence of nuclear architecture in regulating sensitivity to carcinogens. One-Sentence SummaryThe p53 tumor suppressor reduces carcinogen sensitivity and mutagenic potential by maintaining nuclear architecture.

cancer biology↗

bioRxiv2024

Bacterial Wastewater-Based Epidemiology Using Surface-Enhanced Raman Spectroscopy and Machine Learning

Wastewater-based epidemiology (WBE) is a powerful tool for monitoring community disease occurrence, but current methods for bacterial detection suffer from limited scalability, the need for a priori knowledge of the target organism, and the high degree of genetic similarity between different strains of the same species. Here, we show that surface-enhanced Raman spectroscopy (SERS) can be a scalable, label-free method for detection of bacteria in wastewater. We preferentially enhance Raman signal from bacteria in wastewater using positively-charged plasmonic gold nanorods (AuNRs) that electrostatically bind to the bacterial surface. Transmission cryoelectron microscopy (cryoEM) confirms that AuNRs bind selectively to bacteria in this wastewater matrix. We spike the bacterial species Staphylococcus epidermidis, Staphylococcus aureus, Serratia marcescens, and Escerichia coli and AuNRs into filter-sterilized wastewater, varying the AuNR concentration to achieve maximum signal across all pathogens. We then collect 540 spectra from each species, and train a machine learning (ML) model to identify bacterial species in wastewater. For bacterial concentrations of 109 cells/mL, we achieve an accuracy exceeding 85%. We also demonstrate that this system is effective at environmentally-realistic bacterial concentrations, with a limit of bacterial detection of 104 cells/mL. These results are a key first step toward a label-free, high-throughput platform for bacterial WBE.

bioengineering↗

bioRxiv2022

Heart cockle shells transmit sunlight for photosynthesis using bundled fiber optic cables and condensing lenses

Many animals convergently evolved photosynthetic symbioses. In bivalves, giant clams (Cardiidae: Tridacninae) gape open to irradiate their symbionts, but heart cockles (Cardiidae: Fraginae) stay closed because sunlight passes through transparent windows in their shells. Here, we show that heart cockles (Corculum cardissa and spp.) use biophotonic adaptations to transmit sunlight for photosynthesis. Heart cockles transmit 11-62% of photosynthetically active radiation (mean=31%) but only 5-28% of potentially harmful UV radiation (mean=14%) to their symbionts. Beneath each window, microlenses condense light to penetrate more deeply into the symbiont-rich tissue. Within each window, aragonite forms narrow fibrous prisms perpendicular to the surface. These bundled "fiber optic cables project images through the shell with a resolution of >100 lines/mm. Parameter sweeps show that the aragonite fibers size ([~]1{micro}m diameter), morphology (long fibers rather than plates), and orientation (along the optical c-axis) transmit more light than many other possible designs. Heart cockle shell windows are thus: (i) the first instance of fiber optic cable bundles in an organism to our knowledge; (ii) a second evolution, with epidermal cells in angiosperm plants, of condensing lenses for photosynthesis; and (iii) a photonic system that efficiently transmits useful light while protecting photosymbionts from UV radiation.

evolutionary biology↗