Biology subjects

McGuire, H.

Publications and source records attributed to McGuire, H..

3 recordsLinked to original sources

bioRxiv2025

dioscRi enables transferable prediction of clinical outcomes in multi-parameter cytometry data

Multi-parameter cytometry technologies enable high-dimensional analysis of immune cell populations at single-cell resolution. Deep learning has emerged as a transformative tool for analyzing these datasets, but existing methods often struggle with transferability across datasets due to technical variability, batch effects and identification of biologically relevant cell populations, limiting their utility in clinical research. We present dioscRi, a transferable deep learning framework that integrates a maximum mean discrepancy variational autoencoder for normalization and de-noising, enhancing cross-dataset compatibility. Changes in cell type proportions and marker expression are identified by structuring these features within biologically or empirically derived cell type hierarchies. These hierarchies are incorporated directly into an overlapping group LASSO model, improving the prediction of clinical outcomes. When applied to a coronary artery disease study, dioscRi recapitulated several known immune associations. Benchmarking across multiple datasets demonstrated dioscRis ability to generalize and outperform existing methods, establishing it as a versatile and interpretable tool for cytometry data analysis.

bioinformatics↗

bioRxiv2024

Neuromorphic Imaging Cytometry on Human Blood Cells

AO_SCPLOWBSTRACTC_SCPLOWImage-enhanced cytometry and sorting are powerful technologies that provide single-cell resolution and, where possible, cell actuation based on spatial and fluorescence characterisation. With the emergence of deep learning (DL), numerous cytometry-related works incorporate DL to assist their research in handling data-intensive and repetitive workloads. The rich spatial information provided by single-cell images has exceptional use with DL models to classify cells, detect rare cell events, disclose irregularity and achieve higher sample purity than a conventional feature-gating strategy. One of the significant challenges in these image-enable technologies is the constrained throughput owing to the data-expensive image acquisition and balancing between speed and resolution. This work introduces a novel paradigm by adopting a bio-inspired neuromorphic photosensor to capture fast-moving cell events. It facilitates a data-efficient, fluorescence-sensitive, fast inference approach to establish a foundation for neuromorphic-enabled cytometry/sorting applications. We have also curated the first neuromorphic-encoded cell dataset, including human blood cells (red blood cells, neutrophils, lymphocytes, thrombocytes), endothelial cells and polystyrene-based microparticles. To evaluate the data quality and potential of DL-based gating, we have directly trained a hybrid classification model based on this dataset, accomplishing a promising performance of 97% accuracy and F1 score with a significant reduction in memory usage and power consumption. Combining neuromorphic imaging and DL holds substantial potential to develop into a next-generation AI-assisted cytometry and sorting application.

cell biology↗

bioRxiv2023

Neuromorphic Cytometry: Implementation on cell counting and size estimation

Flow cytometry is a widespread and high-throughput technology that can measure the features of cells and can be combined with fluorescence analysis for additional phenotypical characterisations but only provide low-dimensional output and spatial resolution. Imaging flow cytometry is another technology that offers rich spatial information, allowing more profound insight into single-cell analysis. However, offering such high-resolution, full-frame feedback can compromise speed and has become a significant trade-off challenge to tackle during development. In addition, the current dynamic range offered by conventional photosensors can only capture limited fluorescence signals, exacerbating the difficulties in elevating performance speed. Neuromorphic photo-sensing architecture focuses on the events of interest via individual-firing pixels to reduce data redundancy and provide low latency in data processing. With the inherent high dynamic range, this architecture has the potential to drastically elevate the performance in throughput by incorporating motion-activated spatial resolution. Herein, we presented an early demonstration of neuromorphic cytometry with the implementation of object counting and size estimation to measure 8 m and 15 m polystyrene-based microparticles and human monocytic cell line (THP-1). In this work, our platform has achieved highly consistent outputs with a widely adopted flow cytometer (CytoFLEX) in detecting the total number and size of the microparticles. Although the current platform cannot deliver multiparametric measurements on cells, future endeavours will include further functionalities and increase the measurement parameters (granularity, cell condition, fluorescence analysis) to enrich cell interpretation.

cell biology↗