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Transcriptomics: RNA analysis goes spatial
RNA analysis traditionally involves aggregating the gene expression profiles of hundreds of thousands or even millions of cells at once. The result is an average – a snapshot of the sample as a whole. The development of single-cell RNA sequencing methods, such as Drop-seq, provided researchers with the tools to query the gene expression patterns of individual cells, to tease apart cell-to-cell differences. Such analyses can identify rare and unknown cell types, clarify developmental pathways, and illuminate disease processes. But they still involve cells that have been removed from their tissue context. Thus, researchers can identify that a new cell type exists, for instance, but not where it was located in the sample. Now a growing collection of sequencing- and microscopy-based ‘spatial transcriptomics’ methods is changing all that.
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