Image-Based Live Cell Sorting

Single-Cell Analysis and Sorting Paves the Way for Biomedical Breakthroughs The ability to analyze individual cells has revolutionized biomedical research and clinical medicine, leading to innovations that encompass genetic engineering, regenerative medicine, and cancer immunotherapies. While cell population data provide a wealth of information, there is an inherent loss of information from bulk cell samples. Small variations between adjacent cells are known to lead to profound physiologic differences, for example, immune cells vary in their ability to combat infection, neighboring hematopoietic stem cells differentiate into distinct cell lineages, and cancer Technologies capable of cell separation based on cell images provide powerful tools enabling cell selection criteria that rely on spatially or temporally varying properties. Image-based cell sorting (IBCS) systems utilize microfluidic or microarray platforms, each having unique characteristics and applications. The advent of IBCS marks a new paradigm in which cell phenotype and behavior can be explored with high resolution and tied to cellular physiological and omics data, providing a deeper understanding of single-cell physiology and the creation of cell lines with unique properties. Cell sorting guided by high-content image information has far-reaching implications in biomedical research, clinical medicine, and pharmaceutical development.

Automated summary

Single-cell analysis and sorting have revolutionized biomedical research and clinical medicine, allowing for a deeper exploration of cell physiology and behavior. These technologies provide powerful tools for genetic engineering, regenerative medicine, and cancer immunotherapies by enabling more precise cell selection criteria and the creation of cell lines with unique properties.