New research shows how artificial intelligence, combined with advanced experimental science, can help find “hidden” and unexplored proteins in the human body and reveal what they actually do, according to a new study published in Nature.

The discovery suggests a new way to study how cells communicate, survive stress and possibly contribute to disease.

Novel laser-fabricated plasmonic chip combines 185 nm Raman resolution and deep learning to identify and sort cancer cells with 96.2% accuracy.

Accurate and efficient isolation and recovery of interest cells allow detailed analyses of cellular function, growth and culture, and gene-expression profiling of cells within heterogeneous populations. Researchers have developed a novel microfluidic surface-enhanced Raman scattering (SERS) chip capable of performing deep-learning-assisted, label-free single-cell identification and sorting. The results could be used to guide future research on the design and development of non-invasive, efficient cell-sorting techniques with potential applications in clinical diagnostics and precision medicine.

Cell sorting is a basic yet vital procedure in precision biomedicine. Cell sorting separates mixed cells into pure subgroups based on their unique features. Biological tissues and blood contain diverse cell types, and mixed samples blur true cell signals in gene or protein tests. By sorting, researchers can get uniform cells to study cell functions, stem cell development, and disease mechanisms clearly. It also supports drug screening and cell product quality control. Without these techniques, accurate single-cell analysis and modern cell-based treatments would be impossible. The analysis of rare cells, such as cancer stem cells and circulating tumor cells, to assess their functional responses is rapidly gaining prominence given its relevance for its potential to address issues in the development of new drugs and clinical diagnostics.