Acosta, P. | Search bioRxiv

bioRxiv2025

Resolving phenotyping discordance with SPACEMAP, an integrated machine learning framework

Multiplex imaging technologies have revolutionized our ability to study cellular behavior within the tissue microenvironment. Translating this complex data into meaningful biological insights requires a unified analytical framework. To address this, we developed SPACEMAP (Spatial Phenotyping And Classification with Enhanced Multiplex Analysis Pipeline), a comprehensive Python and Qupath-based platform for multiplex imaging analysis. SPACEMAP integrates image registration, segmentation, artifact removal, tissue and zone classification, spatial feature extraction, and a consolidated phenotyping approach into a single system. A core feature of SPACEMAP is its high-fidelity phenotyping. To evaluate classification performance, we benchmarked our method RESOLVE, against three established approaches, Leiden clustering, Self-Organizing Maps, and SCIMAP revealing substantial disagreement among them. SPACEMAP overcomes this through two complementary workflows: a machine learning model trained on expert-labeled cells, and a consensus classifier that integrates high-confidence cells across methods. Here, we validated SPACEMAP on in-house colorectal cancer samples and a public dataset, demonstrating its robustness.

bioinformatics↗

bioRxiv2024

Tryptophan metabolite atlas uncovers organ, age, and sex-specific variations

Although tryptophan (Trp) is the largest and most structurally complex amino acid, it is the least abundant in the proteome. Its distinct indole ring and high carbon content enable it to generate various biologically active metabolites such as serotonin, kynurenine (Kyn), and indole-3-pyruvate (I3P). Dysregulation of Trp metabolism has been implicated in diseases ranging from depression to cancer. Investigating Trp and its metabolites in healthy tissues offers pathways to target disease-associated disruptions selectively, while preserving essential functions. In this study, we comprehensively mapped Trp metabolites across the Kyn, serotonin, and I3P pathways, as well as the microbiome-derived metabolite tryptamine, in C57BL/6 mice. Our comprehensive analysis covered 12 peripheral organs, the central nervous system, and serum in both male and female mice at three life stages: young (3 weeks), adult (54 weeks), and aged (74 weeks). We found significant tissue-, sex-, and age-specific variations in Trp metabolism, with notably higher levels of the oncometabolites I3P and Kyn in aging males. These findings emphasize the value of organ-specific analysis of Trp metabolism for understanding its role in disease progression and identifying targeted therapeutic opportunities. AUTHOR SUMMARYTrp metabolism has primarily been studied in cell lines, often leading to generalized assumptions about its role in health and disease. However, how Trp and its metabolites are allocated across tissues, sexes, and life stages has remained poorly understood. This gap is critical, as Trp is the largest amino acid, minimally used for protein synthesis, and largely metabolized in the liver, yet its distribution and metabolism in other tissues are unknown. Misconceptions, such as the idea that all cancers universally increase Kyn production, have contributed to therapeutic failures, highlighting the need for rigorous, tissue-specific studies. Our study systematically quantifies Trp metabolites across organs and tissues in vivo, revealing significant organ-, sex-, and age-specific variations. These findings provide a foundational resource for understanding Trp metabolism in normal physiology and disease, with potential applications in cancer, neurodegeneration, and other metabolic disorders.

biochemistry↗