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An overview of single-cell and spatial analysis technologies

Single-cell and spatial analysis technologies have enabled the study of biology and disease like never before, revealing a whole world of information in each individual cell. Being able to study single cells within their original tissue context can reveal more insights about the inner workings of tissues and organs and create data-rich high-resolution tissue maps.


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 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.

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.

An overview of single-cell RNA sequencing and spatial transcriptomics

scRNA-seq and spatial transcriptomics dominant the current single-cell and spatial landscape. However, other types of molecular analysis have also been established in the single-cell and spatial world.


Single-cell genome sequencing (scDNA-seq) can be used for the analysis of germline or somatic mutations present in a population of single cells. As only two copies of genomic DNA are present in every human cell, scDNA-seq poses a greater technical challenge than scRNA-seq.

Whole genome analysis solutions have been introduced, including multiple displacement amplification (MDA), multiple annealing and looping-based amplification cycles (MALBAC) and degenerate oligonucleotide-primed PCR (DOP-PCR).

scDNA-seq can be used to reveal the genetic heterogeneity of individual cells. The accumulation of genetic mutations is the basis of many biological processes and diseases, including aging, developmental diseases and cancer. scDNA-seq facilitates the research of these processes at the single-cell level, potentially identifying low-level mutations that could be acted on therapeutically.

Spatial genomics is an example of this: slide-DNA-seq is one of the first technologies that can spatially capture DNA from tissues at high resolution, allowing for the direct detection of genetic alteration.

Report: Advances in Single-Cell and Spatial Analysis 3rd Edition


Single-cell analysis methods can be used to study more than the genome and transcriptome. Epigenomic function has been explored in single cells with chromatin accessibility, nucleosome positioning, histone tail modifications and enhancer-promoter interactions. Single-cell proteomics has also emerged to analyse the complete proteome of individual cells.

An overview of single-cell and spatial proteomics

By combining data from multiple -omics technologies, multi-omics can achieve a layered molecular analysis at the single cell level. Due to the complementary nature of different -omics technologies, multi-omics studies show that this type of data integration can provide more comprehensive insights into cellular biology than studying them alone. As such, a number of different multi-omics approaches have emerged over recent years (see Table 1).

Table 1: Examples of single-cell multi-omics technologies. Adapted from Single-cell multiomics: technologies and data analysis methods (Lee et al., 2020).

Report: Advances in Single-Cell and Spatial Analysis 3rd Edition

Choosing single-cell & spatial technologies

A guide to single-cell sequencing and spatial analysis