2024/12/10 by Madeleine Torcasso, Junting Ai, Gabriel Casella +6 · 1 voice
Biochemistry, Genetics and Molecular Biology · #Advanced Fluorescence Microscopy Techniques #Cell Image Analysis Techniques #Single-cell and spatial transcriptomics
paper · pdf · doi:10.1117/1.jmi.11.6.067502
openalex publication_date 2024/12/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
PurposeThe rapid development of highly multiplexed microscopy has enabled the study of cells embedded within their native tissue. The rich spatial data provided by these techniques have yielded exciting insights into the spatial features of human disease. However, computational methods for analyzing these high-content images are still emerging; there is a need for more robust and generalizable tools for evaluating the cellular constituents and stroma captured by high-plex imaging. To address this need, we have adapted spectral angle mapping—an algorithm developed for hyperspectral image analysis—to compress the channel dimension of high-plex immunofluorescence (IF) images.ApproachHere, we present pseudo-spectral angle mapping (pSAM), a robust and flexible method for determining the most likely class of each pixel in a high-plex image. The class maps calculated through pSAM yield pixel classifications which can be combined with instance segmentation algorithms to classify individual cells.ResultsIn a dataset of colon biopsies imaged with a 13-plex staining panel, 16 pSAM class maps were computed to generate pixel classifications. Instance segmentations of cells with Cellpose2.0 (F1-score of 0.83±0.13) were combined with these class maps to provide cell class predictions for 13 cell classes. In addition, in a separate unseen dataset of kidney biopsies imaged with a 44-plex staining panel, pSAM plus Cellpose2.0 (F1-score of 0.86±0.11) detected a diverse set of 38 classes of structural and immune cells.ConclusionsIn summary, pSAM is a powerful and generalizable tool for evaluating high-plex IF image data and classifying cells in these high-dimensional images.