Tissues are composed of cells of different types, which are organized spatially and structurally to perform their roles. To be able to fully understand cellular mechanisms and functions, it is essential to resolve this complex spatial architecture of the heterogeneous tissue environment. Deep exploration of the transcriptional landscape in tissues can provide new insights into biological functions in the homeostatic state and how this is affected in disease (1). One of the ways to extract information from heterogeneous cell populations is single-cell RNA sequencing (scRNA-seq), which is a method that attracted considerable attention in the past decades thanks to its capability to identify quantitative expression of RNA on a single cell level. scRNA-seq technologies have provided many valuable insights into complex biological systems, including for example the characterization of malignant cells and tumor-infiltrating immune cells. One of the main drawbacks of scRNA-seq is the lack of spatial context (2). Utilizing spatial transcriptomics, which is an umbrella term for different methods that link transcriptomics data to the original location and context within the tissue, is a solution to circumvent the limitations of scRNA-seq (3). Such approaches include microdissection analysis, in silico reconstruction of sequencing data, and offline analysis of in situ captured targets. Particularly interesting are methods that allow true localization of gene expression at single-cell level, including in situ sequencing or hybridization (4). The potential impact of spatial transcriptomics is rising, as evidenced by Nature Methods selecting it as the method of the year for 2020 (5). However, applying these technologies at scale is currently hampered by the lack of automated workflow solutions, including the execution of molecular assays and the associated imaging.
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