We live in an era of data-driven science in which we can sequence the genomes of individual cells and measure the full complement of proteins contained within. How do we acquire and measure such data? The vast world of the “-omics,” including genomics and proteomics, has become accessible to research scientists in basic and clinical research sectors because of the rise of new tools and techniques that have improved the speed, accuracy, and affordability of the methods. Flow cytometry has been a critical technique in this revolution as it allows for the detection and sorting of individual cells. Check out the main features of each of these “-omics” fields and how they complement your current research projects.
Genomic sequencing technology has transformed significantly in the last several decades, and most recently through the development of next-generation sequencing (NGS) technology. Now the genomes of individual cells can be sequenced using NGS, which generates large numbers of short reads for superior genome coverage. Similarly, epigenomic analysis can measure epigenetic modifications to genomic DNA, and transcriptomic (also known as RNA-seq) analysis can measure mRNA transcripts at the single-cell level.
Genomic studies can give us insight into what genes are present within a cell, but proteomic analysis is a critical tool that measures which proteins are expressed by a cell. Highly efficient protein purification and mass spectrometry methods have made proteomic analysis more accessible to researchers and now proteomics-based diagnostics are also being explored by biotechnology and pharmaceutical companies.
Flow Cytometry’s Fit
Flow cytometry techniques allow for individual cells to be identified based on staining with different fluorescent antibodies specific to various proteins. This means that flow cytometry can be used to identify and sort different cell subsets or individual cells for downstream analysis, including genomic or proteomic analysis.
Genomic and proteomic analyses are here to stay, so consider revolutionizing your understanding of your current research question by using these data-driven approaches in connection with flow cytometry.