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

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

Published: 2021-01-10 DOI: 10.1038/s41596-020-00432-x

Quantification

Machine-learning

Cell morphology

Morphological heterogeneity

VAMPIRE algorithm

AI 解读

Efficient and safe single-cell cloning of human pluripotent stem cells using the CEPT cocktail

Profiling native pulmonary basement membrane stiffness using atomic force microscopy

Genetic circuit design automation with Cello 2.0

Synthesis of siRNA nanoparticles to silence plaque-destabilizing gene in atherosclerotic lesional macrophages

Scalable dual-omics profiling with single-nucleus chromatin accessibility and mRNA expression sequencing 2 (SNARE-seq2)

A practical guide to scanning light-field microscopy with digital adaptive optics

Effective high-throughput isolation of fully human antibodies targeting infectious pathogens

Improved sampling and DNA extraction procedures for microbiome analysis in food-processing environments

MOF-derived nanoporous carbons with diverse tunable nanoarchitectures

Tutorial: a statistical genetics guide to identifying HLA alleles driving complex disease

查看全部

Sections

Figures

References

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 tremen
Introduction
Materials
- Equipment
- Software
Procedure
Troubleshooting
Timing
Anticipated results
References
ACKNOWLEDGMENTS
ACKNOWLEDGMENTS

Wu, P. H. et al. Evolution of cellular morpho-phenotypes in cancer metastasis. Sci. Rep. 5, 1–10 (2015).
Chen, W.-C. et al. Functional interplay between the cell cycle and cell phenotypes. Integr. Biol. 5, 523–34 (2013).
Chambliss, A. B., Wu, P. H., Chen, W. C., Sun, S. X. & Wirtz, D. Simultaneously defining cell phenotypes, cell cycle, and chromatin modifications at single-cell resolution. FASEB J 27, 2667–2676 (2013).
Bakal, C., Aach, J., Church, G. & Perrimon, N. Quantitative morphological signatures define local signaling networks regulating cell morphology. Science 316, 1753–1756 (2007).
Rohban, M. H. et al. Systematic morphological profiling of human gene and allele function via cell painting. eLife 6, e24060 (2017).
Wu, P.-H. et al. Single-cell morphology encodes metastatic potential. Sci. Adv. http://doi.org/10.1126/sciadv.aaw6938 (2020).
Driscoll, M. K. et al. Robust and automated detection of subcellular morphological motifs in 3D microscopy images. Nat. Methods http://doi.org/10.1038/s41592-019-0539-z (2019).
Yeung, T. et al. Effects of substrate stiffness on cell morphology, cytoskeletal structure, and adhesion. Cell Motil. Cytoskeleton http://doi.org/10.1002/cm.20041 (2005).
Guo, Q. et al. Modulation of keratocyte phenotype by collagen fibril nanoarchitecture in membranes for corneal repair. Biomaterials 34, 9365–9372 (2013).
Sero, J. E. et al. Cell shape and the microenvironment regulate nuclear trans of NF-κB in breast epithelial and tumor cells. Mol. Syst. Biol. 11, 790 (2015).
Simm, J. et al. Repurposing high-throughput image assays enables biological activity prediction for drug discovery. Cell Chem. Biol. http://doi.org/10.1016/j.chembiol.2018.01.015 (2018).
Bray, M.-A. et al. A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay. Gigascience 6, 1–5 (2017).
Wawer, M. J. et al. Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling. Proc. Natl Acad. Sci. USA 111, 10911–10916 (2014).
Bray, M. A. et al. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat. Protoc. 11, 1757–1774 (2016).
Beghin, A. et al. Localization-based super-resolution imaging meets high-content screening. Nat. Methods 14, 1184–1190 (2017).
Meijering, E., Carpenter, A. E., Peng, H., Hamprecht, F. A. & Olivo-Marin, J. C. Imagining the future of bioimage analysis. Nat. Biotechnol. 34, 1250–1255 (2016).
Ruan, X. & Murphy, R. F. Evaluation of methods for generative modeling of cell and nuclear shape. Bioinformatics http://doi.org/10.1093/bioinformatics/bty983 (2019).
Piccinini, F. et al. Advanced cell classifier: user-friendly machine-learning-based software for discovering phenotypes in high-content imaging data. Cell Syst 4, 651–655.e5 (2017).
Danuser, G. Computer vision in cell biology. Cell 147, 973–978 (2011).
Falk, T. et al. U-Net: deep learning for cell counting, detection, and morphometry. Nat. Methods 16, 67–70 (2019).
Chicco, D. Ten quick tips for machine learning in computational biology. BioData Mining http://doi.org/10.1186/s13040-017-0155-3 (2017).
Gabril, M. Y. & Yousef, G. M. Informatics for practicing anatomical pathologists: Marking a new era in pathology practice. Modern Pathol. 23, 349–358 (2010).
Fuchs, T. J. & Buhmann, J. M. Computational pathology: challenges and promises for tissue analysis. Computer. Med. Imag. Graphics 35, 515–530 (2011).
Sarnecki, J. S. et al. A robust nonlinear tissue-component discrimination method for computational pathology. Lab. Investig 96, 450–458 (2016).
Beck, A. H. et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci. Transl. Med. 3, 108ra113–108ra113 (2011).
Phillip, J. M. et al. Biophysical and biomolecular determination of cellular age in humans. Nat. Biomed. Eng. 1, 0093 (2017).
Pegoraro, G. & Misteli, T. High-throughput imaging for the discovery of cellular mechanisms of disease. Trends Genet. 33, 604–615 (2017).
Lang, P., Yeow, K., Nichols, A. & Scheer, A. Cellular imaging in drug discovery. Nat. Rev. Drug Discov. 5, 343–356 (2006).
Loo, L. H., Wu, L. F. & Altschuler, S. J. Image-based multivariate profiling of drug responses from single cells. Nat. Methods 4, 445–453 (2007).
Sailem, H. Z., Sero, J. E. & Bakal, C. Visualizing cellular imaging data using PhenoPlot. Nat. Commun. 6, 1–6 (2015).
McQuin, C. et al. CellProfiler 3.0: Next-generation image processing for biology. PLoS Biol. http://doi.org/10.1371/journal.pbio.2005970 (2018).
Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. http://doi.org/10.1186/gb-2006-7-10-r100 (2016).
Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis. Nat. Methods http://doi.org/10.1038/nmeth.2019 (2012).
Jayatilaka, H. et al. EB1 and cytoplasmic dynein mediate protrusion dynamics for efficient 3-dimensional cell migration. FASEB J. http://doi.org/10.1096/fj.201700444RR (2018).
Jayatilaka, H. et al. Synergistic IL-6 and IL-8 paracrine signalling pathway infers a strategy to inhibit tumour cell migration. Nat. Commun. 8, 15584 (2017).
Jayatilaka, H. et al. Tumor cell density regulates matrix metalloproteinases for enhanced migration. Oncotarget 9, 32556–32569 (2018).
Phillip, J. M., Aifuwa, I., Walston, J. & Wirtz, D. The mechanobiology of aging. Annu. Rev. Biomed. Eng. 17, 113–141 (2015).
Kim, D.-H. et al. Volume regulation and shape bifurcation in the cell nucleus. J. Cell Sci. 129, 457–457 (2016).
Yu, Y. et al. Inhibition of spleen tyrosine kinase potentiates paclitaxel-induced cytotoxicity in ovarian cancer cells by stabilizing microtubules. Cancer Cell 28, 82–96 (2015).
Driscoll, M. K. et al. Automated image analysis of nuclear shape: What can we learn from a prematurely aged cell? Aging 4, 119–132 (2012).
Bookstein, F. L. Landmark methods for forms without landmarks: Morphometrics of group differences in outline shape. Med. Image Anal. http://doi.org/10.1016/S1361-8415(97)85012-8 (1997).
Dryden, I. L. & Mardia, K. V. Statistical Shape Analysis, with Applications in R 2nd edn. http://doi.org/10.1002/9781119072492 (2016).
Keren, K. et al. Mechanism of shape determination in motile cells. Nature 453, 475–480 (2008).
Pincus, Z. & Theriot, J. A. Comparison of quantitative methods for cell-shape analysis. J. Microsc. 227, 140–156 (2007).
MacLeod, N. Generalizing and extending the eigenshape method of shape space visualization and analysis. Paleobiology 25, 107–138 (1999).
Tsai, A. et al. A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans. Med. Imaging http://doi.org/10.1109/TMI.2002.808355 (2003).
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. (2011).
Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. A Density-based algorithm for discovering clusters in large spatial databases with noise. in Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (1996).
Ankerst, M., Breunig, M. M., Kriegel, H. P. & Sander, J. OPTICS: ordering points to identify the clustering structure. SIGMOD Rec. 28, 49–60 (1999).
Kim, D. H. & Wirtz, D. Focal adhesion size uniquely predicts cell migration. FASEB J. 27, 1351–1361 (2013).
Kim, J.-K. et al. Nuclear lamin A/C harnesses the perinuclear apical actin cables to protect nuclear morphology. Nat. Commun. 8, 2123 (2017).
Zheng, W., Thorne, N. & McKew, J. C. Phenotypic screens as a renewed approach for drug discovery. Drug Discov. Today http://doi.org/10.1016/j.drudis.2013.07.001 (2003).
Kashyap, A., Jain, M., Shukla, S. & Andley, M. Role of nuclear morphometry in breast cancer and its correlation with cytomorphological grading of breast cancer: a study of 64 cases. J. Cytol. http://doi.org/10.4103/JOC.JOC_237_16 (2003).
Seethala, R. R. et al. Noninvasive follicular thyroid neoplasm with papillary-like nuclear features: a review for pathologists. Mod. Pathol. http://doi.org/10.1038/modpathol.2017.130 (2018).
Legland, D., Arganda-Carreras, I. & Andrey, P. MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ. Bioinformatics http://doi.org/10.1093/bioinformatics/btw413 (2016).
Abdi, H. & Williams, L. J. Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics 2, 433–459 (2010).
Shlens, J. A tutorial on principal component analysis. Preprint at http://arxiv.org/abs/1404.1100 (2014).
Lee, H. C., Liao, T., Zhang, Y. J. & Yang, G. Shape component analysis: Structure-preserving dimension reduction on biological shape spaces. Bioinformatics http://doi.org/10.1093/bioinformatics/btv648 (2016).
Hinton, G. E. & Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science http://doi.org/10.1126/science.1127647 (2006).
Goodfellow, I. J. et al. Generative adversarial nets. GitHub http://www.github.com/goodfeli/adversarial.
Osokin, A., Chessel, A., Salas, R. E. C. & Vaggi, F. GANs for biological image synthesis. Proc. IEEE Int. Conf. Comput. Vis. 2017, 2252–2261 (2017).
Johnson, G. R., Donovan-Maiye, R. M. & Maleckar, M. M. Generative modeling with conditional autoencoders: building an integrated cell. Preprint at arXiv http://arxiv.org/abs/1705.00092 (2017).
Liberti, L. Distance geometry and data science. TOP 28, 271–339 (2020).
Donaldson, J. G. Immunofluorescence staining. Curr. Protoc. Cell Biol. 60, 4.3.1–4.3.6 (1998).
Giri, A. et al. The Arp2/3 complex mediates multigeneration dendritic protrusions for efficient 3-dimensional cancer cell migration. FASEB J 27, 4089–4099 (2013).
Fraley, S. I. et al. A distinctive role for focal adhesion proteins in three-dimensional cell motility. Nat. Cell Biol. http://doi.org/10.1038/ncb2062 (2010).
Artym, V. V. & Matsumoto, K. Imaging cells in three-dimensional collagen matrix. Curr. Protoc. Cell Biol. http://doi.org/10.1002/0471143030.cb1018s48 (2010).
Fischer, A. H., Jacobson, K. A., Rose, J. & Zeller, R. Hematoxylin and eosin staining of tissueand cell sections. Cold Spring Harb. Protoc. 3, pdb.prot4986 (2008).
Kim, S. W., Roh, J. & Park, C. S. Immunohistochemistry for pathologists: Protocols, pitfalls, and tips. J. Pathol. Transl. Med. 50, 411–418 (2016).
Hale, C. M. et al. SMRT analysis of MTOC and nuclear positioning reveals the role of EB1 and LIC1 in single-cell polarization. J. Cell Sci. 124, 4267–4285 (2011).
Kim, D. H. & Wirtz, D. Cytoskeletal tension induces the polarized architecture of the nucleus. Biomaterials 48, 161–172 (2015).
Hale, C. M. et al. SMRT analysis of MTOC and nuclear positioning reveals the role of EB1 and LIC1 in single-cell polarization. J. Cell Sci. 124, 4267–4285 (2011).

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

Supplementary information

Supplementary Information

Reporting Summary

Supplementary Data 1

推荐阅读