Computational investigation of biological and technical variability in high throughput phenotyping and cell line identification

Abstract

Abstract:High-throughput cell profiling experiments are characterizing cell phenotype under a broad variety of microenvironmental and therapeutic conditions. However, biological and technical variability are contributing to wide ranges of reported parameter values, even for standard cell lines grown in identical conditions. In this paper, we develop a mathematical model of cell proliferation assays that account for biological and technical variability and limitations of the experimental platforms, including (1) cell confluency effects, (2) biological variability and technical errors in pipetting, (3) biological variability in proliferation characteristics, (4) technical variability and uncertainty in measurement timing, (5) cell counting errors, and (6) the impact of limited temporal sampling. We use this model to create synthetic datasets with growth rates and measurement times typical of cancer cell cultures, and investigate the impact of the initial cell seeding density and the common practice of fitting exponential growth curves to three cell count measurements. We find that the combined sources of variability mask the sub-exponential growth characteristics of the synthetic datasets, and that researchers profiling the same cell lines under different seeding characteristics can find significant (p < 0.05) differences in the measured growth rates. Even seeding the cells at 1% of the confluent limit can cause significant (p < 0.05) differences in the measured growth rate from the ground truth. We explored the effect of reducing errors in each part of the virtual experimental system, and found the best improvements from reducing timing errors, reducing cell counting errors, or reducing the interval between measurements (to reduce the inaccuracy of the exponential growth assumption when fitting curves). Reducing biological variability and pipetting errors had the least impact, because any improvements are still masked by cell counting errors. We close with a discussion of recommended practices for high-throughput cell phenotyping and cell line identification systems.

High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow

“…We close by noting that this framework has applications beyond cancer. In general, testing multiscale hypotheses in high throughput is valuable in determining the rules underlying (often puzzling) experimental data, and even to evaluate the limitations of experiments themselves [52,53]. Moreover, we envision that the PhysiCell-EMEWS framework could be used as a multicellular design tool: for any given multicellular design including single-cell and cellcell interaction rules (which map onto hypotheses in this framework), PhysiCell-EMEWS can test the emergent multicellular behavior against the target behavior (the design goal), and iteratively tune the cell rules to achieve the design goal.…”

When Seeing Isn't Believing: How Math Can Guide Our Interpretation of Measurements and Experiments

“…Poleszczuk et al (2015) used mathematical models of clonal dynamics to understand the impact of limited biopsies in characterizing clones. We simulated the impact of variability in pipetting, seeding conditions, and limited observations on fitting cell proliferation rates; in some cases, measuring two identical lines with differing seeding conditions can yield significant (p < 0.05) but false differences in the estimated growth rates (Friedman and Macklin, 2017). Baker's group has used simulations to investigate the impact of assay geometry on estimates of cell motility and proliferation (Treloar et al, 2014), and more recently to evaluate the impact of temporal sampling limits on measuring motility and biotransport (Harrison and Baker, 2017).…”