Visualization and quality control tools for large-scale multiplex tissue analysis in TissUUmaps3

Abstract

Large-scale multiplex tissue analysis aims to understand processes such as development and tumor formation by studying the occurrence and interaction of cells in local environments in, for example, tissue samples from patient cohorts. A typical procedure in the analysis is to delineate individual cells, classify them into cell types, and analyze their spatial relationships. All steps come with a number of challenges, and to address them and identify the bottlenecks of the analysis, it is necessary to include quality control tools in the analysis workflow. This makes it possible to optimize the steps and adjust settings in order to get better and more precise results. Additionally, the development of automated approaches for tissue analysis requires visual verification to reduce skepticism with regard to the accuracy of the results. Quality control tools could be used to build users' trust in automated approaches. In this paper, we present three plugins for visualization and quality control in large-scale multiplex tissue analysis of microscopy images. The first plugin focuses on the quality of cell staining, the second one was made for interactive evaluation and comparison of different cell classification results, and the third one serves for reviewing interactions of different cell types.

Keywords: Cell classification; quality control; spatial omics; visualization.

Figure 1.

Diagram of the workflow. The left column describes the steps of the analysis, while the center column describes some of the associated quality control questions, and the right column lists the associated visualization and quality control plugins: (a) Plugin for visualization comparison and quality control of cell staining. (b) Plugin for visualization comparison and quality control of cell classification. (c) Plugin for visualization and quality control of cell–cell interactions.

Figure 2.

Workflow before using plugin StainV&QC. (a) Multiplexed microscopy data with segmented cells, (b) Table of extracted features from all the segmented cells, (c) Plugin StainV&QC.

Figure 3.

Workflow for using plugin ClassV&QC. (a) Features extracted from the microscopy images are input to manual or automated classification step, (b) Manual, semiautomated, or automated cell classification, (c) Plugin ClassV&QC containing interactive confusion matrix where the user can click on the elements of the matrix and only cells counted in that matrix element are displayed on the Spatial viewport.

Figure 4.

Workflow for using the InteractionV&QC plugin. (a) Cell classification results, (b) Accumulation scores (as quantified using NET), blue bars represent the distribution of the randomized counts of connections and the black line represent the actual count of connections, (c) Plugin InteractionV&QC containing interactive matrix where the user can click on the elements of the matrix and only those two corresponding cell types are displayed on the Spatial viewport.

Figure 5.

Main screen of StainV&QC plugin, comparing two cores with corresponding data in feature space before and after normalization. Colors represent individual tissue cores.

Figure 6.

Main screen of the ClassV&QC plugin, comparing two classification techniques in the Spatial viewport with a corresponding confusion matrix. Circles represent the results of manual annotations colored by cell type and stars stand for FCNN classification colored by cell type.

Figure 7.

Main screen of ClassV&QC plugin, comparing two classification techniques applied to human U2OS cell cultures. Discs represent CellProfiler results colored by the cell category and stars stand for SimSearch results colored by the cell category.

Figure 8.

The main screen of the InteractionV&QC plugin. (a) neighborhood enrichment calculated by NET, (b) neighborhood enrichment calculated by Squidpy.