Advances in Characterizing Biological Tissues: A Focus on Spatially Resolved Transcriptomics
Since Rudolf Virchow described the cell as the central unit of life and disease in his Cellularpathologie of 1858 [1], researchers have been striving to develop new methods for characterizing the cellular composition of biological tissues. For centuries, cells have been identified by their morphology and function. In recent years, the discovery of biomolecules as the building blocks of cells has revolutionized the understanding of cell types [2] and they are now understood as manifestations of the cells’ molecular composition, adapted to their specific functions [3]. By allowing researchers to quantify different classes of biomolecules in their entirety, the emergence of omics technologies revolutionized our understanding of the molecular composition of tissues. In particular the investigation of RNA molecules, known as transcriptomics technologies has led to major breakthroughs in the identification of cell types. Technological advances have facilitated the study of the whole transcriptome of single cells and initiated consortia such as the Human Cell Atlas, which set itself the task to define all human cell types in terms of distinct molecular profiles and correlate this information with the spatial location of the cells, their developmental point in time as well as the disease state, the environmental exposure, and the lifestyle of the donor [4]. The success of such goals crucially depends on the development of technologies that enable multimodal and multiconditional measurements. At the forefront of these developments stands the field of spatially resolved transcriptomics (SRT), which has been widely recognized as one of the most promising biological technologies [5]. This review provides an overview of SRT technologies and discusses the technological and computational challenges of applying SRT in pathology.
Sequencing-based vs. Imaging-based Approaches
The SRT field can be largely divided into two methodological principles: Sequencing-based SRT and imaging-based SRT (Figure 1).
