Optimised cell capture explained
Express your interest
Single-cell genomics workflows require compartmentalisation of individual particles (cells and barcoding beads) in lipid droplets or microwells. Generally, the occupancy of particles in microwells or lipid droplets is dictated by a Poisson distribution, limiting the efficiency of scRNAseq experiments. Thus, particle concentration is the main factor determining particle occupancy; however other elements can influence the efficiency of single-cell RNAseq experiments, including cell size and morphology, low quality (dying) cells or cell debris, medium viscosity, particle density and magnetic properties.
This project aims to characterise the influence of these parameters in the efficiency of scRNAseq experiments and produce an optimised protocol for different cell types, including cancer cell lines and primary cells. Cell type characterisation will be achieved by fluorescent tags and labels.
Some of the skills to take part in this project include:
- Experience/interest in microfluidics and flow cytometry.
- Experience/interest in cellular and molecular biology.
- Having a positive ‘can do’ attitude.
Learning opportunities
- Characterise the influence of physical properties of different cell types for single-cell capture using microfluidic devices and high-density microwell plates.
- Benchmark of current multiplexing protocols based on immunolabelling and lipid tagging.
- Develop protocols for sample and cell multiplexing.
Applicable knowledge
This project is suitable for 3rd or 4th year students.
How many places are available?
There are two (2) student positions available.
Why is this important?
This project is designed to provide a comprehensive background on the utilisation of state-of-the-art single-cell genomic experiments and has the potential to contribute to R&D pipelines currently explored in the context of industry collaboration, for example optimising and developing new protocols for the use of the Rhapsody instrument.
Project leaders
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Associate Prof & Director, Single Cell Genomics Facility