a | An overview of the single-cell RNA sequencing (RNA-seq) workflow. Single-cell sequencing begins with the isolation of single cells from a sample, such as dissociated skin tissue, by any one of a number of methods, including micropipetting into individual microfuge tubes161 or flow sorting into 96 or 384 well plates39,166 containing a lysis buffer, capture in a microfluidic chip167, distribution in nanowells168, microfluidic isolation in reagent-filled droplets169,170 or marking cells with in situ barcodes171,172. Cells are reverse transcribed in order to produce cDNA (usually tagged with unique molecular identifiers (UMIs)) for RNA-seq library preparation and sequencing. Quality control (QC), differential gene expression (DGE) and 2D visualization (t-distributed stochastic neighbour embedding (tSNE)), along with unsupervised clustering and network analysis, of the single-cell RNA-seq data are used to determine discrete cell populations. The number of cells usually profiled is indicated alongside each technology, as is the RNA-seq strategy — for example, 3΄ or 5΄ mRNA or full-length cDNA. b | An overview of the spatialomics workflow. Spatial encoding requires a frozen tissue section to be applied to oligo-arrayed microarray slides184 or to ‘pucks’ of densely packed oligo-coated beads185. The mRNA diffuses to the slide surface and hybridizes to oligo-dT cDNA synthesis primers that encode UMIs and spatial barcodes. It is then reverse transcribed to produce cDNA, which is pooled for library preparation and sequencing. Computational analysis of the spatialomics data maps sequence reads back to their spatial coordinates after DGE analysis and allows differential spatial expression to be visualized. Single-cell and spatialomics RNA-seq data are usually generated on short-read sequencers. Part a is adapted from ref.163, Springer Nature Limited.