Comprehensive and unbiased multiparameter high-throughput screening by compaRe finds effective and subtle drug responses in AML models

  1. Morteza Chalabi Hajkarim
  2. Ella Karjalainen
  3. Mikhail Osipovitch
  4. Konstantinos Dimopoulos
  5. Sandra L Gordon
  6. Francesca Ambri
  7. Kasper Dindler Rasmussen
  8. Kirsten Grønbæk
  9. Kristian Helin
  10. Krister Wennerberg  Is a corresponding author
  11. Kyoung-Jae Won  Is a corresponding author
  1. University of Copenhagen, Denmark
  2. University of Helsinki, Finland
  3. Rigshospitalet, Denmark
  4. University of Dundee, United Kingdom
  5. Memorial Sloan Kettering Cancer Center, United States

Abstract

Large-scale multiparameter screening has become increasingly feasible and straightforward to perform thanks to developments in technologies such as high-content microscopy and high-throughput flow cytometry. The automated toolkits for analyzing similarities and differences between large numbers of tested conditions have not kept pace with these technological developments. Thus, effective analysis of multiparameter screening datasets becomes a bottleneck and a limiting factor in unbiased interpretation of results. Here we introduce compaRe, a toolkit for large-scale multiparameter data analysis, which integrates quality control, data bias correction, and data visualization methods with a mass-aware gridding algorithm-based similarity analysis providing a much faster and more robust analyses than existing methods. Using mass and flow cytometry data from acute myeloid leukemia and myelodysplastic syndrome patients, we show that compaRe can reveal interpatient heterogeneity and recognizable phenotypic profiles. By applying compaRe to high-throughput flow cytometry drug response data in AML models, we robustly identified multiple types of both deep and subtle phenotypic response patterns, highlighting how this analysis could be used for therapeutic discoveries. In conclusion, compaRe is a toolkit that uniquely allows for automated, rapid, and precise comparisons of large-scale multiparameter datasets, including high-throughput screens.

Data availability

Mass cytometry datasets were downloaded from Cytobank Community with the experiment ID 44185. AML mouse and human high-throughput flow cytometry data have been deposited in FLOWRepository with the repository IDs FR-FCM-Z357 and FR-FCM-Z3DP respectively. Flow cytometry data of AML and MDS patients have been deposited in FLOWRepository with the repository ID FR-FCM-Z3ET. Acquisition, installation and more technical details are available in compaRe's online tutorial on (https://github.com/morchalabi/COMPARE-suite). Similarity measurement and clustering modules as stand-alone tools have been merged into a separate R package and are available for download at (https://github.com/morchalabi/compaRe).

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Morteza Chalabi Hajkarim

    Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2039-2676
  2. Ella Karjalainen

    Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
    Competing interests
    The authors declare that no competing interests exist.
  3. Mikhail Osipovitch

    Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  4. Konstantinos Dimopoulos

    Rigshospitalet, Copenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  5. Sandra L Gordon

    Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0270-8291
  6. Francesca Ambri

    Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  7. Kasper Dindler Rasmussen

    School of Life Sciences, University of Dundee, Dundee, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  8. Kirsten Grønbæk

    Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
    Competing interests
    The authors declare that no competing interests exist.
  9. Kristian Helin

    Cell Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Krister Wennerberg

    Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
    For correspondence
    krister.wennerberg@bric.ku.dk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1352-4220
  11. Kyoung-Jae Won

    Biotech Research and Innovation Centre (BRIC), University of Copenhagen, Copenhagen, Denmark
    For correspondence
    kyoung.won@bric.ku.dk
    Competing interests
    The authors declare that no competing interests exist.

Funding

Novo Nordisk Foundation center for Stem Cell Biology (NNF17CC0027852)

  • Kirsten Grønbæk
  • Kristian Helin
  • Krister Wennerberg
  • Kyoung-Jae Won

Kræftens Bekæmpelse (R223‐A13071)

  • Kirsten Grønbæk
  • Kristian Helin
  • Krister Wennerberg
  • Kyoung-Jae Won

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: The informed consent, and consent to publish of patient samples in this study has been approved by the Danish National Science Ethics Committee/National Videnskabsetisk Komite: Målrettet behandling af patienter med blodsygdomme, license no. 1705391.

Reviewing Editor

  1. Aleksandra M Walczak, CNRS LPENS, France

Publication history

  1. Preprint posted: January 9, 2021 (view preprint)
  2. Received: September 9, 2021
  3. Accepted: February 14, 2022
  4. Accepted Manuscript published: February 15, 2022 (version 1)
  5. Version of Record published: April 20, 2022 (version 2)
  6. Version of Record updated: May 3, 2022 (version 3)

Copyright

© 2022, Chalabi Hajkarim et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 910
    Page views
  • 172
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Morteza Chalabi Hajkarim
  2. Ella Karjalainen
  3. Mikhail Osipovitch
  4. Konstantinos Dimopoulos
  5. Sandra L Gordon
  6. Francesca Ambri
  7. Kasper Dindler Rasmussen
  8. Kirsten Grønbæk
  9. Kristian Helin
  10. Krister Wennerberg
  11. Kyoung-Jae Won
(2022)
Comprehensive and unbiased multiparameter high-throughput screening by compaRe finds effective and subtle drug responses in AML models
eLife 11:e73760.
https://doi.org/10.7554/eLife.73760

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Stella Tamana, Maria Xenophontos ... Petros Kountouris
    Research Article

    Haemoglobinopathies are the commonest monogenic diseases worldwide and are caused by variants in the globin gene clusters. With over 2400 variants detected to date, their interpretation using the ACMG/AMP guidelines is challenging and computational evidence can provide valuable input about their functional annotation. While many in silico predictors have already been developed, their performance varies for different genes and diseases. In this study, we evaluate 31 in silico predictors using a dataset of 1627 variants in HBA1, HBA2, and HBB. By varying the decision threshold for each tool, we analyse their performance (a) as binary classifiers of pathogenicity, and (b) by using different non-overlapping pathogenic and benign thresholds for their optimal use in the ACMG/AMP framework. Our results show that CADD, Eigen-PC, and REVEL are the overall top performers, with the former reaching moderate strength level for pathogenic prediction. Eigen-PC and REVEL achieve the highest accuracies for missense variants, while CADD is also a reliable predictor of non-missense variants. Moreover, SpliceAI is the top performing splicing predictor, reaching strong level of evidence, while GERP++ and phyloP are the most accurate conservation tools. This study provides evidence about the optimal use of computational tools in globin gene clusters under the ACMG/AMP framework.

    1. Computational and Systems Biology
    2. Physics of Living Systems
    Mario García-Navarrete, Merisa Avdovic ... Krzysztof Wabnik
    Research Article Updated

    Cells convert electrical signals into chemical outputs to facilitate the active transport of information across larger distances. This electrical-to-chemical conversion requires a tightly regulated expression of ion channels. Alterations of ion channel expression provide landmarks of numerous pathological diseases, such as cardiac arrhythmia, epilepsy, or cancer. Although the activity of ion channels can be locally regulated by external light or chemical stimulus, it remains challenging to coordinate the expression of ion channels on extended spatial–temporal scales. Here, we engineered yeast Saccharomyces cerevisiae to read and convert chemical concentrations into a dynamic potassium channel expression. A synthetic dual-feedback circuit controls the expression of engineered potassium channels through phytohormones auxin and salicylate to produce a macroscopically coordinated pulses of the plasma membrane potential. Our study provides a compact experimental model to control electrical activity through gene expression in eukaryotic cell populations setting grounds for various cellular engineering, synthetic biology, and potential therapeutic applications.