![]() ![]() In Understanding Results, we provide insights on how visualizing example cells from the samples and linking them to the correlations between samples will provide extensive information that can be used to formulate new hypotheses and interpretations from the data. In addition, representative images of each sample can be retrieved to understand how the cells are distributed across representative fields of view (e.g., those captured from different sites within a sample well), which can give insights into treatment toxicity and/or growth-stimulating effects. This notebook contains Python scripts to help crop representative or random single cells from each treatment and group the cropped images based on correlations of interest. In Basic Protocol 2, the goal is to help biologists create intuitions about differences between treatments by examining example cells. Exploring the data is essential to gain insights into the biological interpretation of the profiles. Using Morpheus, the data can be grouped in different ways, revealing how features and samples are correlated. In Basic Protocol 1, we show how to explore the overall large-scale associations of the data (after feature extraction and cleaning) using the free web-based software Morpheus. Here, we present two protocols: exploratory analysis using Morpheus software (Basic Protocol 1) and image and single-cell visualization following profile interpretation (Basic Protocol 2). In Basic Protocol 2, biologists can examine representative cells from each sample. In Basic Protocol 1, based on sample clustering, biologists can understand the underlying morphology that makes certain samples cluster in a certain way. This leads to a common bottleneck: given a sample or cluster of samples, how do you interpret what a given profile means biologically? However, the biological meaning behind clusters is difficult to interpret because there are thousands of features in the profile. Afterward, samples can be grouped into clusters based on their image-based profiles (Fig. ![]() It is then possible to analyze whether features are modified in a treated sample of cells compared to controls. The collection of features for a cell is called a profile (sometimes described as a morphological profile or image-based profile), and typically a thousand or more features are measured per cell. Diverse stains are used (as in the Cell Painting assay, which stains eight cell components Bray et al., 2016 Cimini et al., 2022) and then image analysis software segments the cells and measures all possible morphological features from single cells. By contrast, in image-based profiling, the aim is to let the cells speak for themselves. In a typical quantitative microscopy experiment, biologists select fluorescent biomarkers (such as antibodies or dyes for specific proteins or cell compartments) and measure only the features they hypothesize will be perturbed in the experiment. Current Protocols published by Wiley Periodicals LLC.īasic Protocol 1: Exploratory analysis of profile similarities and driving featuresīasic Protocol 2: Image and single-cell visualization following profile interpretationĪutomated microscopy allows biologists to acquire thousands of images from cells perturbed with drugs, small interfering RNA (siRNA), CRISPR-Cas9, and more. Together, these two tutorials help researchers interpret image-based data to speed up research. In the second protocol, we show how to interactively explore images together with the numerical data, and we provide scripts to create visualizations of representative single cells and image sites to understand how changes in features are reflected in the images. The protocol includes steps to examine feature-driving differences between samples and to visualize correlations between features and treatments to create interpretable heatmaps using the open-source web tool Morpheus. In the first protocol, we examine the similarity among perturbed cell samples using data from compounds that cluster by their mechanisms of action. Here, we provide two complementary protocols to help explore and interpret data from image-based profiling experiments. Image-based profiling quantitatively assesses the effects of perturbations on cells by capturing a breadth of changes via microscopy. ![]()
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