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

View ORCID ProfileSamuel H. Friedman, View ORCID ProfilePaul Macklin
doi: https://doi.org/10.1101/175703
Samuel H. Friedman
1Opto-Knowledge Systems, Inc., Torrance, CA USA
2Formerly: Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA USA
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  • For correspondence: sam@oksi.com
Paul Macklin
2Formerly: Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA USA
3Intelligent Systems Engineering, Indiana University, Bloomington, IN USA
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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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted August 13, 2017.
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Computational investigation of biological and technical variability in high throughput phenotyping and cell line identification
Samuel H. Friedman, Paul Macklin
bioRxiv 175703; doi: https://doi.org/10.1101/175703
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Computational investigation of biological and technical variability in high throughput phenotyping and cell line identification
Samuel H. Friedman, Paul Macklin
bioRxiv 175703; doi: https://doi.org/10.1101/175703

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