Calculating Sample Size Requirements for Temporal Dynamics in Single-Cell Proteomics

Single-cell measurements are uniquely capable of characterizing cell-to-cell heterogeneity and have been used to explore the large diversity of cell types and physiological functions present in tissues and other complex cell assemblies. An intriguing application of single-cell proteomics is the characterization of proteome dynamics during biological transitions, like cellular differentiation or disease progression. Time-course experiments, which regularly take measurements during state transitions, rely on the ability to detect dynamic trajectories in a data series. However, in a single-cell proteomics experiment, cell-to-cell heterogeneity complicates the confident identification of proteome dynamics as measurement variability may be higher than expected. Therefore, a critical question for these experiments is how many data points need to be acquired during the time course to enable robust statistical analysis. We present here an analysis of the most important variables that affect statistical confidence in the detection of proteome dynamics: fold change, measurement variability, and the number of cells measured during the time course. Importantly, we show that datasets with less than 16 measurements across the time domain suffer from low accuracy and also have a high false-positive rate. We also demonstrate how to balance competing demands in experimental design to achieve a desired result. [Display omitted] •Detecting temporal change in proteins depends on fold change and variability.•Replicate time courses improve reliability of detecting temporal dynamics.•Temporal experiments require a dense sampling of cells to track gradual transitions.•Time-course trajectory experiments require more samples than two-state comparisons. Cellular development and disease progression are gradual transitions between phenotypic stages. Time-course measurements that explicitly measure this transition are important to discover proteome dynamics. Single-cell measurements are a powerful tool for understanding heterogeneity, especially during phenotypic transitions. Single-cell proteomics measurements are emerging as an available tool to characterize the cellular state. We created a statistical method that predicts the success of an experimental design for temporal dynamics.