Humans are composed of roughly 37 trillion cells, each with distinct biological functions and molecular profiles. While single-cell sequencing has provided valuable insights, spatial omics technologies now enable measurement of molecular information within the original tissue context, revealing how cells are organized and communicate. Platforms such as Visium HD, Xenium, MERSCOPE, CosMx, and PhenoCycler provide complementary strengths in terms of resolution and molecular coverage, generating high-content maps of tissues at single-cell or even subcellular scale.
In parallel, histology imaging of the same tissue sections offers detailed structural information at subcellular resolution. These images provide a rich morphological backdrop that complements spatial omics data, allowing us to see how molecular patterns align with tissue architecture.
Our lab focuses on AI-driven spatial omics, developing machine learning approaches that integrate histology and molecular data. This paradigm enhances resolution, scalability, and interpretability, enabling deeper insights into tissue biology and disease mechanisms. We have developed a series of tools that aim to combine the strengths of spatial omics and pathology imaging so that we can achieve scalability, accessibility, and speed.