How deep can single-cell biophysical morphological profiling go: from instrumentation to AI analytics (Invited Paper)

Pairing up optical microscopy and computer vision becomes a common strategy adopted in a broad spectrum of biological and biomedical screening applications. The common rationale is to generate the characteristic "fingerprint" profiles of cell morphology that could underpin the cell states/functions, but obscured through visual inspection or even in the molecular assay. However, it remains unachievable or affordable with current technologies to record, integrate, and analyze all relevant cell morphological data. The synergism between ultrafast imaging, microfluidics, and deep learning allows us to overcome some of these current limitations. This talk will highlight a few notable high-throughput, deep-learning-powered imaging techniques and analytical cytometry pipelines over the past few years. These platforms allow us to significantly scale up the single-cell biophysical/mechanical phenotyping throughput (beyond millions of cells per run) for rare-cancer-cell detection, T-cell subtyping and activation, and tumor biopsy analysis. The talk will also discuss the latest strategies for enriching biophysical phenotyping content by integrating with genetic-perturbation assay.