Unparalleled perspective on the biology of single cells
Single-cell Gene-expression analysis has been chosen by SCIENCE editors as “Breakthrough of the Year 2018”. Now, this technique has become available for any researcher to study the processes behind cellular development and disease.
Standard RNA-seq measures average gene-expression levels, which is inadequate for heterogenous samples. Single cell sequencing does not require sorting your cells into small populations based on known markers. It allows you to administer any cell population – as a whole or pre-sorted – and create gene-expression profiles on each cell individually. Unsupervised clustering algorithms then visualize your cells in plots, placing cells with similar expression profiles together. You can clearly discern between cell types and study the heterogeneity within those clusters. Visualizing cell-to-cell variability has never been more attainable.