High throughput single cell expression profiling and characterization of circulating tumor cells

Mikael Kubista1,*

1TATAA Biocenter, Göteborg, Sweden

Biological samples are composed of large number of cells of different types. When studying traditional samples containing many cells only the collective response of all the cells present is measured. However, the cells may respond differently and a small subpopulation may be critical. Today, these systems can be studied using single cell expression profiling. Here we apply single cell profiling to study the response of astrocytes to brain trauma using mouse model, to study asymmetric cell division during early embryonic development, for the stratification of breast cancer patients by profiling circulating tumor cells (CTCs).


Astrocytes were collected from mouse brains at different time points after the induction of focal ischemia. Each cell was profiled for the expression of 47 genes. Classification revealed astrocyte reactivation with the formation of distinct subtypes (Figure). Single cell and subcellular blastomere profiling revealed asymmetric cell division is induced by distribution of key cell fate determinants already in the fertilized cell. Profiling of CTCs show promise for predicting response to therapy of breast cancer patients.


Single cell profiling is most powerful to study complex biological samples and shows promise to stratify patients based on properties of a critical minority cell population. Power and robust flows for experimental and analytical analysis are available, as well as highly optimized reagents.


M. Bengtsson, A. Ståhlberg, P. Rorsman, and M. Kubista.


Anders Ståhlberg, Mikael Kubista. The workflow of single cell profiling using qPCR. Exp. Rev. Mol. Diagn.

Gene expression profiling in single cells from the pancreatic islets of Langerhans reveals lognormal distribution of mRNA levels. Genome Research 15, 1388, 2005.14. 2014.Heterogeneity of Astrocytes: From Development to Injury-Single Cell Gene Expression. PloS One 8(8), e69734 (2013).


Vendula Rusnakova, Pavel Honsa, David Dzamba, Anders Ståhlberg, Mikael Kubista, Miroslava Anderova.



Keywords: Biomarker development, Cancer, RNA