![cellprofiler analyst example properties file cellprofiler analyst example properties file](https://user-images.githubusercontent.com/1225595/29679781-91f10e5e-88d0-11e7-8460-dfb7933e2df7.png)
![cellprofiler analyst example properties file cellprofiler analyst example properties file](https://i.ytimg.com/vi/Odkk93UQZc8/maxresdefault.jpg)
( D) Box plot of relative abundance of events within each cluster follows the same color code used in ( A). ( C) Spearman’s correlation plot shows the average feature values of the images in each cluster to highlight morphological similarities and differences between events belonging to different clusters, such as cell size or cytoplasm granularity ( Supplementary file 1). Merge represents the overlay of brightfield (BF) and Draq5 (nuclear staining) signal. Representative cells for all the identified clusters are shown in Supplementary file 4. ( B) Representative cell images belonging to each cluster are shown to evaluate the homogeneity of the cluster and determine morphology of the cells for cluster annotation. Force-directed layout (FDL) graph visualizes 16 clusters, and each color represents a unique cell cluster. The selected images were processed through the pipeline described in Figure 1 and clustered based only on intrinsic morphological and fluorescent feature values. ( A) WKM tissue obtained from zebrafish is prepared for image-based flow cytometric analyses and run on the ImageStream X Mark II (n = 8). Standard gating of focused and nucleated events and manual outgating of most erythrocytes was performed using IDEAS software. The entire pipeline chart and step-by-step technical information, such as software used, time required for processing, and exported file format, are reported in the interactive map Figure 1-figure supplement 1 that automatically directs to the specific sections of the GitHub.
![cellprofiler analyst example properties file cellprofiler analyst example properties file](https://i.ytimg.com/vi/ViJxs_VmFqU/maxresdefault.jpg)
This provides high-throughput and unbiased way to compare different experiment sets without the requirement for pre-existing knowledge about the tissue cell types, cell biomarkers, or the need to cross-annotate clusters increasing the probability to introduce errors. ( G) This classifier is then used for deconvoluting data from new experimental sets and assigning each event to a CNN class with a given probability. This will generate a trained classifier with CNN classes based on FDL clusters. A CNN classifier is trained using the images obtained from homeostasis, naïve or wild-type (WT) cells, and already organized in clusters in an unbiased way through the first part of our method. (2) If the goal is integrating experiments and comparing cell type abundance between them, the use of steps ( F) and ( G) is suggested. Compare sets of clusters coming from multiple experiments and multiple rounds of analysis can be challenging without pre-existing knowledge of cell types, clearly different morphologies or biomarkers that would allow to establish a unique correlation between clusters coming from different FDL graphs.
![cellprofiler analyst example properties file cellprofiler analyst example properties file](https://mac-cdn.softpedia.com/screenshots/CellProfiler-Analyst_1.png)
This approach will produce a new set of clusters that will need to be reannotated. control), the steps described so far from ( A) to ( E) can be reapplied to the new dataset including a statistical analysis to compare cluster relative abundance. (1) If the goal is comparing samples belonging to the same experiment ( e.g., treatment vs. ( F) If new experiments are run and new data needs to be analyzed, two approaches can be taken. This heatmap shows feature similarities and differences between cells belonging to different clusters. ( E) Spearman’s correlation plot of feature values by clusters is one of the options available in Image3C for plotting integrated data. This step allows to evaluate the morphological homogeneity of the clusters, determine if the number of clusters is appropriate, and explore the phenotype/function of the cells based on visualization of individual channels. ( D) R integration in FCS Express Plus software allows the visualization of cell images by clusters or specifically selected with a gate. ( C) Images are clustered based on morphological and fluorescent feature values and visualized as a force-directed layout (FDL) graph where each dot represents one event. Samples that are outliers among replicates are also removed prior to the final normalization of the fluorescence intensities. R (or R studio) is used to calculate the correlation between features to allow to trim the features that are redundant with others. ( B) IDEAS software is used to open the raw images, compensate for correcting fluorescent spillover, subtract background, and quantify values for intrinsic morphological and fluorescent features. The samples are run on the ImageStream X Mark II, and 10,000 nucleated and focused events are saved for each sample as individual raw images. The signal can highlight specific cell components ( e.g., nuclei), metabolic cell states, or specific cell functions. ( A) A single cell suspension is prepared for image-based flow cytometric analyses. The cells can be labeled with any reagent working for the species of interest.