Automated inference procedure for the determination of cell growth parameters

Inferring time-dependent population growth rates in cell cultures undergoing adaptation

Background

The population growth rate is an important characteristic of any cell culture. During sustained experiments, the growth rate may vary due to competition or adaptation. For instance, in presence of a toxin or a drug, an increasing growth rate indicates that the cells adapt and become resistant. Consequently, time-dependent growth rates are fundamental to follow on the adaptation of cells to a changing evolutionary landscape. However, as there are no tools to calculate the time-dependent growth rate directly by cell counting, it is common to use only end point measurements of growth rather than tracking the growth rate continuously.

Results

We present a computer program for inferring the growth rate over time in suspension cells using nothing but cell counts, which can be measured non-destructively. The program was tested on simulated and experimental data. Changes were observed in the initial and absolute growth rates, betraying resistance and adaptation.

Conclusions

For experiments where adaptation is expected to occur over a longer time, our method provides a means of tracking growth rates using data that is normally collected anyhow for monitoring purposes. The program and its documentation are freely available at https://github.com/Sandalmoth/ratrack under the permissive zlib license.

A Multi-center Study on the Reproducibility of Drug-Response Assays in Mammalian Cell Lines

Evidence that some high-impact biomedical results cannot be repeated has stimulated interest in practices that generate findable, accessible, interoperable, and reusable (FAIR) data. Multiple papers have identified specific examples of irreproducibility, but practical ways to make data more reproducible have not been widely studied. Here, five research centers in the NIH LINCS Program Consortium investigate the reproducibility of a prototypical perturbational assay: quantifying the responsiveness of cultured cells to anti-cancer drugs. Such assays are important for drug development, studying cellular networks, and patient stratification. While many experimental and computational factors impact intra- and inter-center reproducibility, the factors most difficult to identify and control are those with a strong dependency on biological context. These factors often vary in magnitude with the drug being analyzed and with growth conditions. We provide ways to identify such context-sensitive factors, thereby improving both the theory and practice of reproducible cell-based assays.

Factors that impact the reproducibility of experimental data are poorly understood. Five NIH-LINCS centers performed the same set of drug-response measurements and compared results. Technical and biological variables that impact precision and reproducibility and are also sensitive to biological context were the most problematic.