Sopa: a technology-invariant pipeline for analyses of image-based spatial omics.

Spatial omics data allow in-depth analysis of tissue architectures, opening new opportunities for biological discovery. In particular, imaging techniques offer single-cell resolutions, providing essential insights into cellular organizations and dynamics. Yet, the complexity of such data presents analytical challenges and demands substantial computing resources. Moreover, the proliferation of diverse spatial omics technologies, such as Xenium, MERSCOPE, CosMX in spatial-transcriptomics, and MACSima and PhenoCycler in multiplex imaging, hinders the generality of existing tools. We introduce Sopa ( https://github.com/gustaveroussy/sopa ), a technology-invariant, memory-efficient pipeline with a unified visualizer for all image-based spatial omics. Built upon the universal SpatialData framework, Sopa optimizes tasks like segmentation, transcript/channel aggregation, annotation, and geometric/spatial analysis. Its output includes user-friendly web reports and visualizer files, as well as comprehensive data files for in-depth analysis. Overall, Sopa represents a significant step toward unifying spatial data analysis, enabling a more comprehensive understanding of cellular interactions and tissue organization in biological systems.

Timing and location dictate monocyte fate and their transition to tumor-associated macrophages.

Tumor-associated macrophages (TAMs) are a heterogeneous population of cells whose phenotypes and functions are shaped by factors that are incompletely understood. Herein, we asked when and where TAMs arise from blood monocytes and how they evolve during tumor development. We initiated pancreatic ductal adenocarcinoma (PDAC) in inducible monocyte fate-mapping mice and combined single-cell transcriptomics and high-dimensional flow cytometry to profile the monocyte-to-TAM transition. We revealed that monocytes differentiate first into a transient intermediate population of TAMs that generates two longer-lived lineages of terminally differentiated TAMs with distinct gene expression profiles, phenotypes, and intratumoral localization. Transcriptome datasets and tumor samples from patients with PDAC evidenced parallel TAM populations in humans and their prognostic associations. These insights will support the design of new therapeutic strategies targeting TAMs in PDAC.