Journal: bioRxiv

Article Title: Improving drug discovery using image-based multiparametric analysis of the epigenetic landscape

doi: 10.1101/541151

Figure Lengend Snippet: (A) Hoechst 33342 stained (blue), and Sox2 (red) and Oct4 (green) immunofluorescence labeled fibroblasts (Sox2 - /Oct4 - ), iPSCs (Sox2 + /Oct4 + ) and NPCs (Sox2 + /Oct4 - ). Scale bar, 50 µm. (B, C) Quadratic discriminant analysis separating either cell fates or cell lines using texture features derived from images of fibroblasts, iPSCs, and NPC lines from 3 human donors (WT-61, WT-101 and WT-126; 3 technical replicates each); stained for H3K9me3 and H3K4me1. (B) Discriminant analysis separating the different cell types. Scatter plot depicts the first 2 discriminant factors for each cell population (2 replicate per cell line and cell type). Confusion matrix showing classification results for discriminant analysis (test set: 1 replicate per cell line and cell type). Numbers represent the percent of correctly (diagonal) and incorrectly (off the diagonal) classified cell populations. (C) Discriminant analysis attempts to separate different cell lines. Scatter plot depicts the first 2 discriminant factors for each cell population (2 replicates per cell line and cell type). Confusion matrix showing classification results for discriminant analysis (test set: 1 replicate per cell line and cell type). Numbers represent the percent of correctly (diagonal) and incorrectly (off the diagonal) classified cell populations. (D, E, F) TPC and DGC cell lines derived simultaneously from tumors of 3 human donors (MGG4, MGG6, MGG8; 3 technical replicates each); stained for H3K9me3, H3K4me1. (D) Quadratic discriminant analysis separating TPCs and DGCs using image texture features. Scatter plot depicts the first discriminant factor for each cell population (2 replicates per cell line). Confusion matrix showing results of discriminant analysis (test set: 1 replicate per cell line). Numbers represent the percent of correctly (diagonal) and incorrectly (off the diagonal) classified cell populations. (E) Pairwise classification of single TPC and DGC cells using an SVM classifier trained on texture features derived from images of H3K27me3, H3K9me3, H3K27ac, or H3K4me1. Numbers correspond to the percent of correctly classified cells for each line using indicated epigenetic marks. (F) Bar graph shows results of SVM classification for single TPC and DGC cells using a classifier trained on texture features derived from images of H3K27ac and H3K27me3 marks in the MGG4 line. (H) Quadratic discriminant analysis using texture features derived from images of untreated or 2 days serum or Bmp4 treated GBM2, 101A, SK262 and 454M cells (3 replicates per cell lines per treatment) and stained for H3K9me3, H3K4me1. Scatter plot depicts the first 2 discriminant factors for each cell population (2 replicates per cell lines per treatment). Confusion matrix shows classification results for discriminant analysis (test set: 1 replicate per cell line per treatment). Numbers represent the percent of correctly (diagonal) and incorrectly (off the diagonal) classified cell populations. (I) Distance map depicting the relative Euclidean distance between the transcriptomic profiles of DMSO-, Bmp4- and serum-treated GBM2 cells calculated using FPKM values of all expressed genes (14,376 genes; FPKM>1 in at least one sample). Each treatment in triplicates. (J) Distance map depicting the relative Euclidean distance between the multiparametric centroids of DMSO-, Bmp4- and serum-treated GBM2 cells calculated using texture features derived from images of H3K27ac and H3K27me3 marks. Each treatment in triplicates. R denotes Pearson correlation coefficient.

Article Snippet: The training set was used for a SVM classifier (MATLAB svmtrain function).

Techniques: Staining, Immunofluorescence, Labeling, Derivative Assay

Journal: bioRxiv

Article Title: Improving drug discovery using image-based multiparametric analysis of the epigenetic landscape

doi: 10.1101/541151

Figure Lengend Snippet: (A, B) Distance map depicting the relative Euclidean distance following MDS between the multiparametric centroids of 9 cell lines: 3 fibroblasts, 3 iPSCs, and 3 NPCs, calculated from texture feature values derived from images of (A) H3K9me3 and H3K4me1 or (B) H3K27ac and H3K27me3 marks. Each cell line appears as 3 technical triplicates. (C, D) Three-way classifications of the 9 cell lines using an SVM classifier trained on image texture features derived from images of pooled fibroblasts, iPSCs, and NPCs stained for (c) H3K9me3 and H3K4me1 or (d) H3K27ac and H3K27me3. (E, F) Quadratic discriminant analysis separating either cell fates or cell lines using texture features derived from images of fibroblasts, iPSCs, and NPC l lines from 3 human donors (WT-61, WT-101 and WT-126; 3 technical replicates each); stained for H3K27me3 and H3K27ac. (E) Discriminant analysis separating the different cell types. Scatter plot depict the first 2 discriminant factors for each cell population (2 replicate per cell line and cell type). Confusion matrixes showing results of classification for the discriminant analysis (test set: 1 replicate per cell line and cell type) Numbers represent the percent of correctly (diagonal) and incorrectly (off the diagonal) classified cell populations. (F) Discriminant analysis attempting to separate the different cell lines. Scatter plot depicting the first 2 discriminant factors for each cell population (2 replicate per cell line and cell type). Confusion matrixes showing results of classification for discriminant analysis (test set: 1 replicate per cell line and cell type). Numbers represent the percent of correctly (diagonal) and incorrectly (off the diagonal) classified cell populations. (G) Distance map depicting the relative Euclidean distance between the multiparametric centroids of 3 genetically distinct TCP and DGC lines calculated using texture features derived from images of H3K9me3 and H3K4me1 marks.

Article Snippet: The training set was used for a SVM classifier (MATLAB svmtrain function).

Techniques: Derivative Assay, Staining

Journal: bioRxiv

Article Title: Improving drug discovery using image-based multiparametric analysis of the epigenetic landscape

doi: 10.1101/541151

Figure Lengend Snippet: (A) Surface markers for isolation of hematopoietic cells by flow cytometry. (B) Distance map depicting the relative Euclidean distance between the multiparametric centroids of image texture features from immunofluorescence micrographs of 6 hematopoietic cell types. (C) Three-way classification of hematopoietic stem or progenitor cells, T and B lymphoid cells, and macrophages, using an SVM classifier trained on randomly selected subsets of MPPs, macrophages, and T-cells. (D) Accuracy of pairwise SVM classification between the 6 hematopoietic cell types.

Article Snippet: The training set was used for a SVM classifier (MATLAB svmtrain function).

Techniques: Isolation, Flow Cytometry, Immunofluorescence