Designing Drug‐Response Experiments and Quantifying their Results

Marc Hafner1, Mario Niepel1, Kartik Subramanian1, Peter K. Sorger1

1 HMS LINCS Center, Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, Massachusetts
Publication Name:  Current Protocols in Chemical Biology
Unit Number:   
DOI:  10.1002/cpch.19
Online Posting Date:  June, 2017
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Abstract

We developed a Python package to help in performing drug‐response experiments at medium and high throughput and evaluating sensitivity metrics from the resulting data. In this article, we describe the steps involved in (1) generating files necessary for treating cells with the HP D300 drug dispenser, by pin transfer or by manual pipetting; (2) merging the data generated by high‐throughput slide scanners, such as the Perkin Elmer Operetta, with treatment annotations; and (3) analyzing the results to obtain data normalized to untreated controls and sensitivity metrics such as IC50 or GR50. These modules are available on GitHub and provide an automated pipeline for the design and analysis of high‐throughput drug response experiments, that helps to prevent errors that can arise from manually processing large data files. © 2017 by John Wiley & Sons, Inc.

Keywords: experimental design; drug response; data processing; computational pipeline

     
 
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Table of Contents

  • Introduction
  • Overview of the Method
  • Strategic Planning
  • Overview of the Software
  • Basic Protocol 1: Designing the Experiment
  • Basic Protocol 2: Processing Data Files
  • Basic Protocol 3: Evaluating Sensitivity Metrics
  • Commentary
  • Literature Cited
  • Figures
  • Tables
     
 
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Materials

Basic Protocol 1: Designing the Experiment

  Materials
  • The module experimental_design of the datarail Python package (https://github.com/datarail/datarail) containing:
    • Functions to generate various experimental designs such as single‐drug dose‐response or pairwise drug combinations across multiple concentrations
    • A function to randomize the positions of the treatments
    • Functions to plot the experimental design for error checking, troubleshooting, and subsequent data interpretation
    • Functions to export designs as either tsv files for manual pipetting and pin transfer or hpdd files (xml format) to drive the D300 drug dispenser
  • List of treatments including:
    • Treatment reagent names and any associated identifiers
    • Concentrations for each agent (μM by default)
    • Stock concentration for each agent (mM by default)
    • Vehicle for dissolution of treatment agent (typically DMSO for small molecules)
  • Information about plates
    • List of plate identifiers (barcodes or ID numbers)
    • Any plate‐based model variable that should be propagated to the downstream analysis (e.g., cell line, treatment duration)
    • Any plate‐based confounder variable (e.g., plate model and manufacturer, treatment date)
  • Jupyter notebook (or Python script) called experimental design notebook that will serve as a record of the experimental design. Exemplar notebooks are provided on the datarail GitHub repository (https://github.com/datarail/datarail).
  • An external reference database if the user wants to include links to reagent identifiers stored externally (e.g., PubChem, https://pubchem.ncbi.nlm.nih.gov; or LINCS, http://lincs.hms.harvard.edu/db/)

Basic Protocol 2: Processing Data Files

  Materials
  • The following modules of the datarail python package (https://github.com/datarail/datarail):
    • import_modules: functions to import and convert the output data file of a particular scanner into a processed file with standardized format
    • data_processing.drug_response: functions to merge the results and the design files into a single annotated result file
  • Output data from the scanner saved as a tsv file (other formats will require writing new import functions)
  • The plate description file and well annotation files generated in protocol 1 (or equivalent files generated manually or through other means)
  • Jupyter notebook (or Python script) called data processing notebook that will serve as a record of the data processing. Exemplar notebooks are provided on the datarail GitHub repository (https://github.com/datarail/datarail)

Basic Protocol 3: Evaluating Sensitivity Metrics

  Materials
  • The gr_metrics Python package (https://github.com/datarail/gr_metrics)
  • Annotated result file, which contains the model and readout variables for each sample. This file can be the output of protocol 2, or a file that has tabular format with the following columns:
    • Model variables (descriptions of the perturbation such as drug name and concentration)
    • Readout (cell count or a surrogate) in a column labeled cell_count
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Figures

Videos

Literature Cited

  Hafner, M., Niepel, M., Chung, M., & Sorger, P. K. (2016). Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs. Nature Methods, 13, 521–527. doi: 10.1038/nmeth.3853
  Lundholt, B. K., Scudder, K. M., & Pagliaro, L. (2003). A simple technique for reducing edge effect in cell‐based assays. Journal of Biomolecular Screening, 8, 566–570. doi: 10.1177/1087057103256465
  Niepel, M., Hafner, M., Chung, M., & Sorger, P. K. (2017). Measuring cancer drug sensitivity and resistance in cultured cells. Current Protocols in Chemical Biology, 9(2), 1–20.
  Powell, S. G., Baker, K. R., & Lawson, B. (2008). A critical review of the literature on spreadsheet errors. Decision Support Systems, 46, 128–138. doi: 10.1016/j.dss.2008.06.001
  Saez‐Rodriguez, J., Goldsipe, A., Muhlich, J., Alexopoulos, L. G., Millard, B., Lauffenburger, D. A., & Sorger, P. K. (2008). Flexible informatics for linking experimental data to mathematical models via DataRail. Bioinformatics, 24, 840–847. doi: 10.1093/bioinformatics/btn018
  Stocker, G., Fischer, M., Rieder, D., Bindea, G., Kainz, S., Oberstolz, M., … Trajanoski, Z. (2009). iLAP: A workflow‐driven software for experimental protocol development, data acquisition and analysis. BMC Bioinformatics, 10, 390–401. doi: 10.1186/1471‐2105‐10‐390
  Wu, T., & Zhou, Y. (2014). An intelligent automation platform for rapid bioprocess design. Journal of Laboratory Automation, 19, 381–393. doi: 10.1177/2211068213499756
  Zeeberg, B. R., Riss, J., Kane, D. W., Bussey, K. J., Uchio, E., Linehan, W. M., … Weinstein, J. N. (2004). Mistaken identifiers: Gene name errors can be introduced inadvertently when using Excel in bioinformatics. BMC Bioinformatics, 5, 80. doi: 10.1186/1471‐2105‐5‐80
  Ziemann, M., Eren, Y., El‐Osta, A., Zeeberg, B., Riss, J., Kane, D., … Smedley, D. (2016). Gene name errors are widespread in the scientific literature. Genome Biology, 17, 177. doi: 10.1186/s13059‐016‐1044‐7
Key References
  Hafner et al. (2016). See above.
  Paper describing the GR method.
  Niepel et al. (2017). See above.
  This article in Current Protocols in Chemical Biology is a companion article to the present article, including more experimental detail.
Internet Resources
  https://github.com/datarail/datarail
  GitHub repository with the scripts for the experimental design and data handling
  https://github.com/datarail/gr_metrics
  GitHub repository with the scripts for the evaluation of the GR values and metrics.
  http://www.GRcalculator.org
  GRcalculator: An online tool for calculating, visualizing, and mining drug response data designed by Clark, N. A., Hafner, M., Kouril, M., Williams, E. H., Muhlich, J. L., Niepel, M., Medvedovic, M.
  http://www.labautopedia.org/mw/Helpful_Hints_to_Manage_Edge_Effect_of_Cultured_Cells_for_High_Throughput_Screening
  Helpful hints to manage edge effect of cultured cells for high throughput screening. Corning Cell Culture Application and Technical Notes, 7–8 (author, Allison Tanner of Corning Life Sciences, 2001).
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