Enhancing Cell Sorting Accuracy in Flow Cytometry Using High-Dimensional Computing

Flow cytometry is a powerful analytical technique that enables the rapid analysis of cells/particles, facilitating the differentiation of various cell types based on multiple characteristics [1]. Effective and high-throughput cell sorting is critical, especially for single-cell analysis. Among label-free cell sorting methods, microfluidic techniques have gained significant attention due to their advantages over traditional approaches, such as reduced sample and reagent consumption, high sensitivity, and seamless integration with other technologies. However, a major challenge in flow cytometry is the large volume of data generated during the process. Processing such extensive datasets poses challenges in terms of efficient data interpretation and feature extraction, potentially affecting overall system performance and accuracy.