Automatic Identification of Subcellular Phenotypes on Human Cell Arrays

Abstract

Light microscopic analysis of cell morphology provides a high-content readout of cell function and protein localization. Cell arrays and microwell transfection assays on cultured cells have made cell phenotype analysis accessible to high-throughput experiments. Both the localization of each protein in the proteome and the effect of RNAi knock-down of individual genes on cell morphology can be assayed by manual inspection of microscopic images. However, the use of morphological readouts for functional genomics requires fast and automatic identification of complex cellular phenotypes. Here, we present a fully automated platform for high-throughput cell phenotype screening combining human live cell arrays, screening microscopy, and machine-learning-based classification methods. Efficiency of this platform is demonstrated by classification of eleven subcellular patterns marked by GFP-tagged proteins. Our classification method can be adapted to virtually any microscopic assay based on cell morphology, opening a wide range of applications including large-scale RNAi screening in human cells.

Cell microarrays and RNA interference chip away at gene function

“…Since this work was done, a handful of overexpression cell microarray studies have been published that offer technological improvements on the basic reverse transfection principle 6,[23][24][25][26] . These studies have so far been limited to assay development or smaller screens.…”

“…For example, pilot overexpression microarrays using GFP reporters were developed for the cAMP and serum response element pathways 23,26 . Examination of the subcellular localization of tagged proteins, which can help in inferring gene function, has also been demonstrated in microarray format 24,25 . With overexpression cell microarrays established as a method, a new focus in the cell microarray field has been the adaptation of cell arrays for RNAi in a high-throughput format.…”

A quantitative measure for alterations in the actin cytoskeleton investigated with automated high‐throughput microscopy

“…Here, we suggest that image coherency is an adequate quantitative parameter for the problem at hand. In contrast to pattern recognition approaches like neural networks or vector-support machines based on motion-invariant image features like the Haralick texture features or Zernike moments (23)(24)(25), which are well-suited for classification of distinct subcellular phenotypes, the coherency measure appears to be particularly suited to characterize gradual changes in fibrous textures like the actin cytoskeleton. By its definition, coherency extracts the relative strength of the edges of structures compared to their surroundings (26).…”

High content image cytometry in the context of subnuclear organization

“…Automated and systematic analysis of cells and subcellular protein locations allows for an objective description of specific cellular phenotypes (4,5), which in turn can be related to cellular condition and by extension, to human health (6). A variety of single cell-based screens have been developed, for instance, for digital titration of viral (7) or bacterial infection (8), or for monitoring cell cycle progression and cell morphology in relation to cell adhesion and migration (9), angiogenesis (10) and carcinogenesis (11).…”