Models of Throughput for Multi-Cell, Multi-Type Droplet Microfluidics

Abstract

New experimental platforms encapsulate multiple cells per microfluidic droplet, with each cell belonging to one of multiple possible types. The motivating example comes from microbial ecology, where we want to observe the interactions of microbial strains. Because droplets are formed randomly, we want to accurately predict the data throughput, the numbers of droplets containing desired combinations of cell types. Herein I identify the default statistical model for predicting the data throughput of multi-cell, multi-type droplet microfluidics experiments, which fits to cell type count data. I explain the assumptions behind this model and issues that in practice may cause these assumptions to fail. One such issue, "compositional heterogeneity", is unique to multi-type experiments. I show how to modify the default statistical model to describe the consequences of these issues, without needing to mechanistically model their causes. In practice, only two of these issues may substantially change the data throughput predictions. The changes depend on both (1) which combination of these issues are present, and (2) the precise definition of data throughput. Finally, I show that for a given experimental platform one can estimate the severity of these two issues, enabling more accurate data throughput predictions that account for these two issues.