Phenotypic cell-based assays have proven to be efficient at discovering first-in-class therapeutic drugs mainly because they allow for scanning a wide spectrum of possible targets at once. However, despite compelling methodological advances, posterior identification of a compound's mechanism of action (MOA) has remained difficult and highly refractory to automated analyses. Methods such as the cell painting assay and multiplexing fluorescent dyes to reveal broadly relevant cellular components were recently suggested for MOA prediction. We demonstrated that adding fluorescent dyes to a single assay has limited impact on MOA prediction accuracy, as monitoring only the nuclei stain could reach compelling levels of accuracy. This observation suggested that multiplexed measurements are highly correlated and nuclei stain could possibly reflect the general state of the cell. We then hypothesized that combining unrelated and possibly simple cell-based assays could bring a solution that would be biologically and technically more relevant to predict a drug target than using a single assay multiplexing dyes. We show that such a combination of past screen data could rationally be reused in screening facilities to train an ensemble classifier to predict drug targets and prioritize a possibly large list of unknown compound hits at once.
Technologies such as microscopy, sequential hybridization, and mass spectrometry enable quantitative single‐cell phenotypic and molecular measurements in situ. Deciphering spatial phenotypic and molecular effects on the single‐cell level is one of the grand challenges and a key to understanding the effects of cell–cell interactions and microenvironment. However, spatial information is usually overlooked by downstream data analyses, which usually consider single‐cell read‐out values as independent measurements for further averaging or clustering, thus disregarding spatial locations. With this work, we attempt to fill this gap. We developed a toolbox that allows one to test for the presence of a spatial effect in microscopy images of adherent cells and estimate the spatial scale of this effect. The proposed Python module can be used for any light microscopy images of cells as well as other types of single‐cell data such as in situ transcriptomics or metabolomics. The input format of our package matches standard output formats from image analysis tools such as CellProfiler, Fiji, or Icy and thus makes our toolbox easy and straightforward to use, yet offering a powerful statistical approach for a wide range of applications. © 2019 International Society for Advancement of Cytometry
BackgroundCell culture on printed micropatterns slides combined with automated fluorescent microscopy allows for extraction of tens of thousands of videos of small isolated growing cell clusters. The analysis of such large dataset in space and time is of great interest to the community in order to identify factors involved in cell growth, cell division or tissue formation by testing multiples conditions. However, cells growing on a micropattern tend to be tightly packed and to overlap with each other. Consequently, image analysis of those large dynamic datasets with no possible human intervention has proven impossible using state of the art automated cell detection methods.ResultsHere, we propose a fully automated image analysis approach to estimate the number, the location and the shape of each cell nucleus, in clusters at high throughput. The method is based on a robust fit of Gaussian mixture models with two and three components on each frame followed by an analysis over time of the fitting residual and two other relevant features. We use it to identify with high precision the very first frame containing three cells. This allows in our case to measure a cell division angle on each video and to construct division angle distributions for each tested condition. We demonstrate the accuracy of our method by validating it against manual annotation on about 4000 videos of cell clusters.ConclusionsThe proposed approach enables the high throughput analysis of video sequences of isolated cell clusters obtained using micropatterns. It relies only on two parameters that can be set robustly as they reduce to the average cell size and intensity.
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