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An overview of single-cell RNA sequencing and spatial transcriptomics

Single-cell RNA sequencing (scRNA-seq) has become the most established and widely used technique within single-cell analysis. Spatial transcriptomics is quickly following in the footsteps of scRNA-seq, with spatially resolved transcriptomics named Method of the Year by Nature in 2020.

Single-cell RNA sequencing

Single-cell transcriptomics, or scRNA-seq, is widely used for analysing the transcriptome of single-cell populations. With scRNA-seq, gene expression profiling can explore genotype-phenotype relationships at the fine-grained single-cell level.

Minute changes in a single cell can often lead to system-wide changes. For example, a cancer cell can undergo mutations that render it resistant to therapy, leading to changes in the composition of the entire cellular population of the tumour during treatment.

Recent years have seen scRNA-seq integrated into biomedical research, and huge scope still remains for the application of this technology in the future (see Table 1).

Table 1: Current and future applications of scRNA-seq analysis. Adapted from: A hitchhiker’s guide to single-cell transcriptomics and data analysis pipelines (Nayak & Hasija, 2021).

scRNA-seq methods

The first step in scRNA-seq protocols is preparing the samples for analysis – single cells need to be dissociated from their original tissue samples. Tissues are digested enzymatically, or cells released mechanically, before capturing individual cells from the single cell suspension. Several capture methods are commonly used including multi-plate methods (combined with FACS), microfluidics and laser capture microdissection.

Once single cells have been obtained, RNA needs to be isolated and the same steps as bulk RNA sequencing can be followed. These steps involve the reverse transcription of mRNA to synthesise cDNA, the addition of unique molecular identifiers and finally cDNA amplification and sequencing.

Choosing single-cell and spatial analysis technologies

Spatial analysis

Spatial transcriptomics enables researchers to measure all gene activity in a sample, and map where each gene activity is occurring relative to all other activity. Retaining spatial context when studying the molecular information of a tissue allows researchers to visualise changes happening in situ and begin to piece together complex cause and-effect relationships between cellular changes.

There are two main methods of spatial analysis. Firstly, fluorescence in situ hybridisation (FISH)-methods, where transcripts are directly labelled in tissue sections to enable single-cell locations to be visualised. The second method is based on scRNA-seq, which profiles whole transcriptomes after cellular dissociation. This method is associated with the additional challenge of linking the transcriptomes back to their original location.

Next generation FISH technologies

Recent years have seen FISH methods updated and integrated into single-cell and spatial workflows. Examples include multiplexed error-robust fluorescence in situ hybridization (MERFISH), sequential fluorescence in situ hybridisation (seqFISH) and spatially-resolved transcript amplicon readout mapping (STARmap).

Interview: A Spotlight On: Spatial Transcriptomics – Jeffrey Moffitt, Assistant Professor, Harvard Medical School

Spatial transcriptomics

Building on scRNA-seq, spatial transcriptomic techniques use oligonucleotide microarrays to capture RNA transcripts across a tissue section, followed by next-generation sequencing (see Figure 1). These workflows have enabled high-resolution tissue maps with associated transcriptomic data.

Figure 1: Spatial transcriptomics experimental procedure and example results. (a) Oligonucleotide capture of RNA transcripts across tissue sections followed by detachment and sequencing. (b) Spatial map of human squamous cell carcinoma using capture spot RNA mixtures devolved by cell type. Taken from: Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics (Longo et al., 2021).

Examples of spatial transcriptomic techniques include Visium spatial gene expression (10x Genomics), high-definition spatial transcriptomics (HDST) and slide-seq.

Integrating spatial technology adds another layer of complexity into single-cell analysis. It is essential that the sample preparation and cell capture methods are capable of retaining the original spatial localisation of each cell within a tissue. The analysis can then be mapped back to the original tissue section, allowing visualisation of the molecular information in situ.

Report: The 2023 Spatial and Single-cell Analysis Playbook

A guide to single-cell sequencing and spatial analysis