Imaging platforms for generating highly multiplexed histological images are being continually developed and improved. Significant improvements have also been made in the accuracy of methods for automated cell segmentation and classification. However, less attention has focused on the quantification and analysis of the resulting point clouds, which describe the spatial coordinates of individual cells. We focus here on a particular spatial statistical method, the cross-pair correlation function (cross-PCF), which can identify positive and negative spatial correlation between cells across a range of length scales. However, limitations of the cross-PCF hinder its widespread application to multiplexed histology. For example, it can only consider relations between pairs of cells, and cells must be classified using discrete categorical labels (rather than labeling continuous labels such as stain intensity). In this paper, we present three extensions to the cross-PCF which address these limitations and permit more detailed analysis of multiplex images: topographical correlation maps can visualize local clustering and exclusion between cells; neighbourhood correlation functions can identify colocalization of two or more cell types; and weighted-PCFs describe spatial correlation between points with continuous (rather than discrete) labels. We apply the extended PCFs to synthetic and biological datasets in order to demonstrate the insight that they can generate.

Whole slide imaging (WSI) can be used to quantify multiple responses within tissue sections during histological analysis. Feature Analysis on Consecutive Tissue Sections (FACTS®) allows the investigator to perform digital morphometric analysis (DMA) within specified regions of interest (ROI) across multiple serial sections at faster rates when compared with manual morphometry methods. Using FACTS® in conjunction with WSI is a powerful analysis tool, which allows DMA to target specific ROI across multiple tissue sections stained for different biomarkers. DMA may serve as an appropriate alternative to classic, manual, histologic morphometric measures, which have historically relied on the selection of high-powered fields of views and manual scoring (e.g., a gold standard). In the current study, existing preserved samples were used to determine if DMA would provide similar results to manual counting methods. Rodent hearts (n=14, left ventricles) were stained with Masson’s trichrome, and reacted for cluster of differentiation 68 (CD-68). This study found no statistical significant difference between a classic, manual method and the use of digital algorithms to perform the similar counts (p=0.38). DMA offers researchers the ability to accurately evaluate morphological characteristics in a reproducible fashion without investigator bias and with higher throughput.