Morphological Clustering of Cell Cultures Based on Size, Shape, and Texture Features. (2nd April 2016)

• Record Type: Journal Article
• Authors: Ahonen, Ilmari; Härmä, Ville; Schukov, Hannu-Pekka; Nees, Matthias; Nevalainen, Jaakko
• Is Part Of: Statistics in biopharmaceutical research. Volume 8:Number 2(2016)
• Page Start: 217
• Page End: 228
• Publication Date: 2016-04-02
• Subjects: Drug discovery -- High throughput screening -- Imaging -- Principal curve -- 3D cell cultures; Pharmacy -- Statistical methods -- Periodicals; Pharmaceutical biotechnology -- Statistical methods -- Periodicals; Biopharmaceutics -- Periodicals; Biometry -- Periodicals; Pharmacy -- Statistical methods; Periodicals; 615.190727
• DOI: 10.1080/19466315.2016.1146162
• Languages: English
• ISSNs: 1946-6315

Abstract

ABSTRACT: High content screening for drug discovery in cancer research relies increasingly on cell-based models, using microscopic imaging as a primary readout. In combination, microscopic imaging and cell culturing provide powerful tools for studying cancer-relevant cell biology in vitro. As a result, an enormous amount of complex biometric image data is generated that can be used for high throughput and high content analyses. We present a method for computationally efficient and flexible quantification of multicellular structures or tumor spheroids, conducted in a semi-unsupervised manner. Our phenotypic clustering approach is based on morphological features, in particular, on size and novel shape and texture features. It consists of multiple automated steps in which the information characterizing the most relevant morphological features is first extracted from the images, the dimension of the features is reduced, and finally, structures are clustered into biologically meaningful groups. Local central moments and local binary operators characterize the texture, whereas shape features are obtained by an alignment to elliptical and smooth reference shapes. Using simulation studies, we show that the cluster identification performs well and demonstrates good repeatability in the presence of random orientation, size, rescaling, and texture. We show how the method can be applied to an actual high-content imaging dataset to find an intuitive and flexible summary of high content screens, not achievable with existing tools. Supplementary materials for this article are available online.