Exploring data-driven analysis of spatially resolved transcriptomes in situ and in single cells

RNA-sequencing offers the possibility to analyze the expression of all genes in a sample. However, the spatial information of gene expression is lost. In the pioneering work, a method was described that allowed studies of gene expression in tissue sections using RNA-sequencing to uncover transcriptional patterns in situ (Ståhl et al, Science, 2016). The basic concept is remarkably simple; by placing tissue sections on arrayed reverse transcription oligonucleotides with positional barcodes, cDNA for RNA-sequencing can be generated with maintained positional information within the tissue. The quality of the obtained cDNA libraries is as high as with the best protocols for homogenized tissue. Applying this strategy has been demonstrated to work remarkably well and allows visualizing and quantifying the transcriptome in regular histological tissue sections, i.e. tissue domains can be matched to precise gene expression patterns. Furthermore, data-driven methods can be applied to discover in an unsupervised manner transcriptomic patterns in space. Such patterns correspond to cell-types, microenvironments, or tissue components that allows for novel avenues of research.

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