How to Build an Image‐Processing Pipeline for Automating Multiparameter Histocytometry Analysis

Abstract

Until relatively recently, analysis of imaging data has been primarily quantitative and limited to 3‐4 markers. The advancement of various technologies overcoming this marker limitation provided the capability of analyzing multiparameter imaging data down to the single cell level, termed histocytometry. Currently, most published end‐to‐end histocytometric analysis of imaging data is performed using expensive commercial programs or freely available analysis packages that require significant knowledge of programming languages for execution. Here we present a protocol that performs cell segmentation, phenotyping and spatial analysis, using software with easy‐to‐use GUIs (graphical user interfaces). These protocols allow the user to derive spatial and phenotypical data for the analysis of multiparameter microscopic images from most imaging platforms in a low‐cost manner. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Cell Segmentation and generation of histocytometric .csv file Basic Protocol 2: Phenotyping of cell populations Basic Protocol 3: Spatial relationship analyses of phenotyped populations Support Protocol 1: Nuclei Segmentation Accuracy Test Support Protocol 2: Correcting y‐axis Inversion of Histocytometry Data Relative to Original Image File

Creation of High‐Dimensional Reduction Analysis‐Compatible Histocytometry Files from Images of Densely‐Packed Cells and/or Variable Stain Intensity

“…The previous protocol pipeline (Munoz-Erazo et al, 2022) will work well with images where nuclei have considerable spatial separation as well as good and fairly consistent nuclei stain intensity. It is also suitable when phenotyping cells which are defined by clear expression or absence of a marker.…”

“…The number of required training regions within ilastik (Basic Protocol 1, steps 9‐12) can vary; minimization of training regions required depends on the variability of nuclei intensity, density, and morphology within a sample and across the samples of interest. Assessing the efficiency of probability map can be done in a similar manner as outlined in Support Protocol 2 in Munoz‐Erazo et al (2022).…”

“…As a result, we are restricted to single images containing no more than 65535 segmentable cells with the use of 16-bit images. It is strongly recommended for users to read the previous protocol paper ( Munoz-Erazo et al, 2022), as it will aid in comprehension of the workflow of this protocol paper, as well as understanding the advantages of this protocol paper over the former.…”

“…It is strongly recommended for users to read the previous protocol paper (Munoz‐Erazo et al., 2022), as it will aid in comprehension of the workflow of this protocol paper, as well as understanding the advantages of this protocol paper over the former.…”

“…Previously, we published a protocol for constructing a software pipeline to perform histocytometric and spatial analysis of imaging data (Munoz-Erazo, Schmidt, Shinko, & Price, 2022). While suitable for images derived from a variety of different tissues and/or preparation techniques, it is affected by two shortfalls.…”