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Single cell sequencing of the placenta: Professor Steve Charnock-Jones

Single cell sequencing of the placenta

Tissue function is critically dependent the cells within a given tissue. Such populations are heterogeneous dynamic. The importance of interactions among such cells is most clearly recognised in tumours and situations where immune cells are prevalent. Recent technical technological advances have permitted the detailed phenotyping of heterogeneous cellular populations and most recently enabled the mRNA sequencing of single sales. In many instances a few 100s for cells have been sequenced and novel cell types identified for the first time. A recently published protocol “Drop-Seq” describe the microfluidic processing of many thousands of single cells allowing high throughput and relatively cheap sequencing of tissue cell populations. Such methods have revealed new cell types, specific markers to identify them and given insight to tissue heterogeneity. (Macosko et al. 2015; Shekhar et al. 2016)

The placenta is similar to other tissues in that it is composed of multiple cell types derived from several different embryonic lineages. While we have a good understanding of some of these there are many subpopulations of which are very poorly characterised and likely some that us yet to be identified. We have recently developed the necessary expertise to apply drop-seq to placental tissue. In this project placental heterogeneity will be investigated in both mouse and human tissues.

For further information, please contact:
Steve Charnock-Jones, dscj1@cam.ac.uk

 Macosko, Evan Z, Anindita Basu, Rahul Satija, James Nemesh, Karthik Shekhar, Melissa Goldman, Itay Tirosh, et al. 2015. “Highly Parallel Genome-Wide Expression Profiling of Individual Cells Using Nanoliter Droplets.” Cell 161 (5). Elsevier: 1202–14. doi:10.1016/j.cell.2015.05.002.

 Shekhar, Karthik, Sylvain W Lapan, Irene E Whitney, Nicholas M Tran, Evan Z Macosko, Monika Kowalczyk, Xian Adiconis, et al. 2016. “Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics.” Cell 166 (5). Elsevier Inc.: 1308–30. doi:10.1016/j.cell.2016.07.054.