Can single cell RNA sequencing reshape the clinical biochemistry of hematology: New clusters of circulating blood cells

Clin Transl Med. 2021 Dec;11(12):e671. doi: 10.1002/ctm2.671.

Abstract

scRNA-seq is on track for use as a routine measurement of clinical biochemistry and to assist in clinical decision-making and guide the performance of molecular medicine, but there are still a large number of challenges to be overcome. In conclusion, scRNA-seq-based clusters and differentiation of circulating blood cells have been examined and informative in patients with various diseases, although the information generated from scRNA-seq varies between different conditions, technologies, and diseases. Most of the clinical studies published have focused on the landscape of circulating immune cells, disease-specific patterns of new clusters, understanding of potential mechanisms, and potential correlation between cell clusters, differentiations, cell interactions, and circulating and migrated cells. It is clear that the information from scRNA-seq advances the understanding of the disease, identifies disease-specific target panels, and suggests new therapeutic strategies. The adaptation of scRNA-seq as a routine clinical measurement will require standardization and normalization of scRNA-seq-based comprehensive information and validation in a large population of healthy and diseased patients. The integration of public databases on human circulating cell clusters and differentiations with an application of artificial intelligence and computational science will accelerate the application of scRNA-seq for clinical practice. Thus, we call special attention from scientists and clinicians to the clinical and translational discovery, validation, and medicine opportunities of scRNA-seq development.

Publication types

  • Editorial
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Artificial Intelligence / standards
  • Artificial Intelligence / trends
  • Chemistry, Clinical
  • Hematology / methods
  • Hematology / trends*
  • Humans
  • Single-Cell Analysis / methods
  • Single-Cell Analysis / statistics & numerical data