Spatial Transcriptomics and Machine Learning Reveal Tissue-Specific Consequences of MYC Phase Separation Across Tumor Microenvironments

Abstract

Spatial transcriptomics has emerged as a transformative technology for mapping gene expression within tissue architecture, enabling the study of cellular heterogeneity and intercellular communication in situ. When combined with machine learning, these high-dimensional datasets can uncover tissue-specific regulatory mechanisms that govern tumor progression and response to therapy. This paper investigates the system-level consequences of MYC phase separation across diverse tumor microenvironments, leveraging spatial transcriptomic data and computational models to reveal how biomolecular condensates of the MYC transcription factor modulate transcriptional programs in a context-dependent manner. We argue that the integration of spatial omics and machine learning not only advances mechanistic understanding but also raises critical questions about the infrastructure, governance, and fairness of deploying such models in clinical and research settings. Through a cross-disciplinary analysis, we examine the architectural trade-offs inherent in processing massive spatial datasets, the robustness of machine learning predictions to batch effects and tissue heterogeneity, and the sustainability of computational pipelines that must balance precision with energy efficiency. We further discuss policy implications related to algorithmic transparency, equitable access to spatial profiling technologies, and the ethical governance of predictive models that may influence treatment decisions. By framing MYC phase separation as a case study in tissue-specific transcriptional regulation, we illustrate how systems thinking is essential for translating spatial transcriptomics and machine learning from discovery science to robust, fair, and sustainable biomedical applications.