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.