Article Text
PDF
Abstract
Background Recent progress in cancer immunotherapy has revolutionised the treatment paradigm for many cancer patients. Single cell profiling has been pivotal in the development of cancer immunotherapies. However, while traditional single cell technologies have yielded extensive ‘omic datasets enabling users to infer biological function, they have not yet empowered the direct functional analysis at single cell level. To that end, we describe a platform that harnesses droplet microfluidics and optical electrowetting-on-dielectric (oEWOD) to perform highly-controlled sequential and multiplexed single cell assays in massively parallelised workflows to enable complex cell profiling during screening.
Methods Soluble reagents or objects, such as cells or beads, are encapsulated into droplets of media in fluorous oil and are actively filtered based on size and content ensuring only desirable droplets are retained for analysis, thereby overcoming the Poisson distribution. Droplets are stored on a temperature-controlled chip and the history of individual droplets is logged from the point of filter until completion of the workflow. On chip, droplets are subject to an automated and flexible suite of operations including the merging of droplets and the fluorescent acquisition of assay readouts to enable complex sequential assay workflows (figure 1).
Results To demonstrate the utility of the platform in the immuno-oncology space, we show examples of single-cell functional workflows for antibody discovery and cell and gene therapy (figure 2). In the latter, droplets containing single effector cells (e.g. T-cells) undergo two rounds of merging with droplets containing single target cells (figure 3). Similar workflows may be used to explore T-cell exhaustion or the specificity of cell killing.
Conclusions The Lightcast platform enables complex cell screening to understand true functional biology of single cells, allowing greater insights to understanding mechanisms of cell health and disease.
The lightcast platform
The filter function
An example workflow applicable to immuno-oncology and cell and gene therapy
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See http://creativecommons.org/licenses/by-nc/4.0/.