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A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei

Nature Protocols volume 16, pages 754–774 (2021)Cite this article

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Abstract

Cell morphology encodes essential information on many underlying biological processes. It is commonly used by clinicians and researchers in the study, diagnosis, prognosis, and treatment of human diseases. Quantification of cell morphology has seen tremendous advances in recent years. However, effectively defining morphological shapes and evaluating the extent of morphological heterogeneity within cell populations remain challenging. Here we present a protocol and software for the analysis of cell and nuclear morphology from fluorescence or bright-field images using the VAMPIRE algorithm (https://github.com/kukionfr/VAMPIRE_open). This algorithm enables the profiling and classification of cells into shape modes based on equidistant points along cell and nuclear contours. Examining the distributions of cell morphologies across automatically identified shape modes provides an effective visualization scheme that relates cell shapes to cellular subtypes based on endogenous and exogenous cellular conditions. In addition, these shape mode distributions offer a direct and quantitative way to measure the extent of morphological heterogeneity within cell populations. This protocol is highly automated and fast, with the ability to quantify the morphologies from 2D projections of cells seeded both on 2D substrates or embedded within 3D microenvironments, such as hydrogels and tissues. The complete analysis pipeline can be completed within 60 minutes for a dataset of ~20,000 cells/2,400 images.

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Fig. 1: Cells confined to narrow ranges of traditional morphological parameters still exhibit highly variable shapes.
Fig. 2: Overview of VAMPIRE analysis, from the extraction of contour coordinates to the automatic generation of shape modes.
Fig. 3: Overview of VAMPIRE implementation with the VAMPIRE GUI.
Fig. 4: Determinants of cluster coherence in the shape mode distributions.
Fig. 5: VAMPIRE analysis of LMNA+/+ and LMNA−/− mouse embryonic fibroblasts.
Fig. 6: VAMPIRE analysis of mouse embryonic fibroblasts seeded on adhesive micro-patterned surfaces.
Fig. 7: VAMPIRE analysis of human dermal fibroblasts from donors of different ages.
Fig. 8: Analysis of nuclear shape in H&E stained tissue sections with VAMPIRE.

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Data availability

The datasets generated and/or analyzed during the current study are available from GitHub: Micropattern Data (https://github.com/kukionfr/Micropattern_MEF_LMNA_Image) and Aging Data (https://github.com/kukionfr/Aging_human_dermal_fibroblast_nucleus). A smaller example dataset is provided as Supplementary Data 1 and is also deposited on GitHub: https://github.com/kukionfr/VAMPIRE_open/releases/download/v1.0/Supplementary.Data.zip.

Code availability

The VAMPIRE source code is available on GitHub: https://github.com/kukionfr/VAMPIRE_open. The code can be accessed and used by readers without restriction.

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Acknowledgements

This work was supported in part by National Institutes of Health grants U54CA143868 (D.W.), R01CA174388 (D.W.), P30AG021334 (P.H.W. and J.M.P.) and U01AG060903 (D.W., J.M.P. and P.H.W.).

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Author notes

  1. These authors contributed equally: Jude M. Phillip, Kyu-Sang Han.

Authors and Affiliations

  1. Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences Oncology Center, Johns Hopkins Institute for Nanobiotechnology (INBT), Johns Hopkins University, Baltimore, MD, USA

    Jude M. Phillip, Kyu-Sang Han, Wei-Chiang Chen, Denis Wirtz & Pei-Hsun Wu

  2. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Jude M. Phillip

  3. Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA

    Denis Wirtz

  4. Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD, USA

    Denis Wirtz

  5. Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA

    Denis Wirtz

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Contributions

J.M.P. and P.H.W. designed and conducted experiments; P.H.W., J.M.P., D.W. and W.C. conceived analysis and workflow of VAMPIRE; P.H.W. developed the original VAMPIRE software; K.S.H. converted the VAMPIRE software from MATLAB to Python; K.S.H. developed the graphical user interface of VAMPIRE; K.S.H. and J.M.P. analyzed and plotted data; P.H.W. and D.W. supervised the study; J.M.P., D.W., K.S.H. and P.H.W. wrote and edited the protocol; D.W., J.M.P., and P.H.W. secured funding.

Corresponding authors

Correspondence to Denis Wirtz or Pei-Hsun Wu.

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The authors declare no competing interests.

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Key references using this protocol

Wu, P. -H. et al. Sci. Rep. 5, 1–10 (2015): https://www.nature.com/articles/srep18437

Wu, P.-H. et al. Sci. Adv. 6, eaaw6938 (2020): https://advances.sciencemag.org/content/6/4/eaaw6938

Phillip, J. M. et al. Nat. Biomed. Eng. 1, 0093 (2017): https://www.nature.com/articles/s41551-017-0093

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2.

Reporting Summary

Supplementary Data 1

Example input files for the protocol procedures, including fluorescence images of phalloidin-stained mouse embryonic fibroblast, their segmented images, CellProfiler segmentation workflow pipeline file, lists of segmented images for building and applying the model in CSV format, and example output files generated during the analysis.

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Phillip, J.M., Han, KS., Chen, WC. et al. A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei. Nat Protoc 16, 754–774 (2021). https://doi.org/10.1038/s41596-020-00432-x

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