Histopathological data remain one of the most trusted tools in science when doctors or researchers want to understand what’s happening inside a tissue. Today, they have largely gone digital. These images, therefore, contain enormous information about tissues from different scales. However, these images remain separate from modern multimodal and single-cell frameworks. While genetics and single-cell biology have developed effective ways for sharing and comparing data, digital pathology images are hard to incorporate—stored in proprietary formats, processed with incompatible tools, and hard to connect to molecular information like RNA sequencing. Thus, the valuable resources of digitalized tissue images are largely underutilized in many research and clinical settings. Now, a new study introduces LazySlide, an open-source Python package built on “the scverse ecosystem for efficient whole-slide image analysis and multimodal integration” designed to make whole-slide image analysis more accessible, interoperable, and ready to plug into the same computational workflows that already drive modern genomics. Through the power of foundation models, LazySlide aims to democratize digital pathology analysis by bridging histopathology with omics workflows. “Histology contains an enormous amount of biological information, but it is often difficult to access computationally,” says Yimin Zheng, PhD, a postdoc in the lab of André Rendeiro, PhD, at the CeMM Research Center for Molecular Medicine in Vienna, Austria. “With LazySlide, we wanted to provide a tool that allows researchers to explore tissue images in a systematic, quantitative way and to connect what they see under the microscope with underlying molecular processes.” The study, published in Nature Methods…
