TDAExplore: Quantitative analysis of fluorescence microscopy images through topology-based machine learning

Abstract

Recent advances in machine learning have greatly enhanced automatic methods to extract information from fluorescence microscopy data. However, current machine-learning-based models can require hundreds to thousands of images to train, and the most readily accessible models classify images without describing which parts of an image contributed to classification. Here, we introduce TDAExplore, a machine learning image analysis pipeline based on topological data analysis. It can classify different types of cellular perturbations after training with only 20-30 high-resolution images and performs robustly on images from multiple subjects and microscopy modes. Using only images and whole-image labels for training, TDAExplore provides quantitative, spatial information, characterizing which image regions contribute to classification. Computational requirements to train TDAExplore models are modest and a standard PC can perform training with minimal user input. TDAExplore is therefore an accessible, powerful option for obtaining quantitative information about imaging data in a wide variety of applications.

Keywords: actin cytoskeleton; fluorescence microscopy; image classification; image segmentation; machine learning; persistence landscapes; persistent homology; topological data analysis.

Figure 1

Persistent homology-based image analysis pipeline to extract topological features (A) Computation of a persistence landscape from a selected image patch. (i) Representative image patch of 75 pixel radius. (ii) Points are sampled from the spatial coordinates of fluorescence signals. (iii–vi) Sequence of simplicial complexes generated by connecting neighboring points within a distance that increases from (iii) to (vi). (iv) Four cycles that represent persistent homology classes in degree one are colored. (v) The pink cycle has been filled in. (vi) All cycles have been filled in. (vii) Persistence diagram plotting the birth radius and death radius of each of the persistent homology classes in degree one. Colored points correspond to the colored cycles in (iv). (vii) Persistence landscape gives a vector encoding of the persistent homology. (B) Persistence landscapes generated from each selected patch in the input image. Three representative patches and their landscapes are shown. (C) Histogram of t-SNE scores generated from persistence landscapes. Colorized pixels representing t-SNE scores overlaid on the original image as a mask.

Figure 2

TDAExplore analysis of the actin cytoskeleton (A) Performance evaluation for classification of cells treated with the Arp2/3 inhibitor CK-666 or its inactive control CK-689. N = 25 and 32 for CK-689 and CK-666, respectively. From left to right, distribution of patch features, topological score per cell, and confusion matrix displaying classification summaries. Topological scores are generated through five separate rounds of testing; rounds are designated by color. (B) Performance evaluation for classification of control and PFN1 KO cells, n = 41 and 27, respectively. From left to right, distribution of patch features, topological score per cell, and confusion matrix displaying classification summaries. Topological scores are generated through five separate rounds of testing; rounds are designated by color. (C) Distribution of patch values after treatment with CK-689 and CK-666, where CK-689 patches are values <0 and colored red, while CK-666 is classified as patch values > 0 and colored blue. White represents intermediate values. Below are computed feature masks of patch values for representative images from CK-689- and CK-666-treated cells. (D) Average topological score based on distance from the leading edge to the cell center. Transparent bands depict 95% confidence intervals. Representative cell regions from the leading edge to center are shown above for comparison. (E) Distribution of patch values for control and PFN1 KO cells where control patches are values <0 and colored red, while PFN1 KO is classified as patch values > 0 and colored blue. White represents intermediate values. Below are computed feature masks of patch values for representative images from control and PFN1 KO cells. (F) Average topological score based on distance from the leading edge to the cell center. Transparent bands depict 95% confidence intervals. Representative cell regions from the leading edge to center are shown above for comparison. Box and whisker plots in (A and B) denote 95th (top whisker), 75th (top edge of box), 25th (bottom edge of box), and 10th (bottom whisker) percentiles and the median (bold line in box). ∗∗∗∗p ≤ 0.0001. p values were generated by a two-sided permutation test using the mean score. Scale bar represents 10 μm.