Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data

Abstract

ThiNew multiplexed tissue imaging technologies have enabled the study of normal and diseased tissues in unprecedented detail. These methods are increasingly being applied to understand how cancer cells and immune response change during tumor development, progression, and metastasis as well as following treatment. Yet, existing analysis approaches focus on investigating small tissue samples on a per-cell basis, not taking into account the spatial proximity of cells, which indicates cell-cell interaction and specific biological processes in the larger cancer microenvironment. We present Visinity, a scalable visual analytics system to analyze cell interaction patterns across cohorts of whole-slide multiplexed tissue images. Our approach is based on a fast regional neighborhood computation, leveraging unsupervised learning to quantify, compare, and group cells by their surrounding cellular neighborhood. These neighborhoods can be visually analyzed in an exploratory and confirmatory workflow. Users can explore spatial patterns present across tissues through a scalable image viewer and coordinated views highlighting the neighborhood composition and spatial arrangements of cells. To verify or refine existing hypotheses, users can query for specific patterns to determine their presence and statistical significance. Findings can be interactively annotated, ranked, and compared in the form of small multiples. In two case studies with biomedical experts, we demonstrate that Visinity can identify common biological processes within a human tonsil and uncover novel white-blood networks and immune-tumor interactions.

Consensus tissue domain detection in spatial multi-omics data using MILWRM

“…Pixel-based tissue domain detection forms the basis of the top-down approach to spatial data analysis. Current methods of tissue domain detection are either based on a bottom-up approach, that is, building cellular neighborhoods using segmented single-cell data (20,34,59) and/or lack scalability across samples (8,30,55,63). Here, we addressed this gap by developing MILWRM, an algorithm to detect spatial domains across samples through a top-down, pixel-based approach.…”

“…Cellular segmentation and annotation are the most challenging step in this kind of approach. There are various methods available for cellular segmentation (25,40), annotation (39) and neighborhood analysis (20,34,59). Widely used lower resolution imaging data such as spatial transcriptomics (ST) and imaging mass spectrometry data are analyzed using cellular deconvolution algorithms to approximate singlecell composition.…”

DASS Good: Explainable Data Mining of Spatial Cohort Data

“…Specialized approaches have been developed to explore cohort features in other domains such as tissue imaging [FYTL18, JKW * 22, WKN * 12], neuroscience [JBB * 08, JBF * 20, ASO * 16, MPL * 18], and lumbar spine features [CLL * 21,KOJL * 14].…”