The human body contains over 35 trillion cells, with tissues and organs comprising of many different cell types constantly interacting with their neighbours and the surrounding microenvironment.
Understanding the heterogeneity and interplay between different cells is critical to understand the specialised and highly diverse functions performed by the body. It is equally important to understand what happens when things go wrong, with aberrant cellular processes leading to many diseases such as cancer and neurodegenerative disorders.
Traditional bulk sequencing analyses an average population of cells and has revealed many important biological insights. However, the development of single-cell sequencing improved on bulk sequencing dramatically, allowing the analysis of cellular functions at a fine-grained level and revealing the true cellular heterogeneity of tissues, organs and diseases.
Single-cell sequencing dissociates cells from their original tissue, meaning that the spatial context is lost. Now, this can be overcome by integrating a spatial aspect into analysis. This allows the full heterogeneity and function of cells to be observed in their original tissue context and the creation of data-rich high-resolution maps from complete tissue sections.
RNA and transcriptomics
Single-cell RNA sequencing
Single-cell transcriptomics, or single-cell RNA sequencing (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. 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).
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. Updated examples of FISH workflows include multiplexed error-robust fluorescence in situ hybridization (MERFISH), sequential fluorescence in situ hybridisation (seqFISH) and spatially-resolved transcript amplicon readout mapping (STARmap).
The other main method is spatially resolved transcriptomics, which was named Method of the Year by Nature in 2020. Building on scRNA-seq, spatial transcriptomics uses oligonucleotide microarrays to capture RNA transcripts across a tissue section, followed by next-generation sequencing. This enables high-resolution tissue maps with associated transcriptomic data.
Other established single-cell technologies
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 analyse 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.
scDNA-seq reveals the genetic heterogeneity of individual cells. The accumulation of genetic mutations is responsible for many biological processes and diseases, including aging, developmental diseases and cancer.
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.
Epigenomic function has been explored in single cells with chromatin accessibility, nucleosome positioning, histone tail modifications and enhancer-promoter interactions.
Single-cell and spatial multi-omics
Single-cell multi-omics technologies are fast developing, allowing the measurement of modalities such as DNA methylation, chromatin accessibility, RNA expression, protein abundance and spatial information to be analysed from the same cell. While the technology enables the measurement of these analytes, single-cell multi-omics presents significant computational challenges for the study and integration of these data types.
Commercial technologies and providers
10x Genomics are industry leaders in the single-cell and spatial analysis space. With their products published in over 3500 original research articles, 10x Genomics offer comprehensive workflows with everything from reagents to instruments and analysis software.
Their products include Xenium In Situ for subcellular mapping of RNA targets and Chromium X for single-cell analysis. The Visium range also offers spatial solutions for gene expression and proteogenomics.
Video: 10x Genomics YouTube
NanoString are pioneers within the field of spatial biology, providing solutions, workflows and products for spatial transcriptomics, spatial proteomics and single-cell spatial multi-omics.
The GeoMx® Digital Spatial Profiler from NanoString integrates with current histological or genomics workflows to generate spatial multi-omics data. The CosMx Spatial Molecular Imager is a high-plex in situ analysis platform that can perform spatial multi-omics using FFPE or fresh frozen tissue samples at a single cell and sub-cellular resolution.
Video: NanoString YouTube
Mission Bio are a single-cell commercial provider that offer multi-omic solutions for application within oncology and precision medicine. The Tapestri Platform from Mission Bio is designed for single-cell multi-omics, providing both genotype and phenotype data from the same cell.
Video: Mission Bio YouTube
Other suppliers of kits and reagents
- Bio-Rad Laboratories
- New England Biolabs
- Pacific Biosciences
- Takara Bio
- Thermo Fisher Scientific
Data and statistical analysis
High-throughput analysis methods have unlocked the potential of single cells. Now, multiple omics parameters can be analysed in an individual cell and hundreds of thousands of cells can be analysed in a single experiment. That generates a huge amount of data – requiring sophisticated and thorough data analysis methods to make sense of it all. Integrating a spatial aspect also adds another layer of complexity into the data analysis.
The main steps required to go from raw scRNA-seq data to visualising have seen standardisation and commercialisation, as detailed in Figure 1.
Many types of analysis can be performed using spatial transcriptomics data. Early tasks such as cell segmentation, shape and classification can be performed directly on the image. Clustering and annotation can be used to identify different cell types. The spatial distribution of different cell types can then be mapped back onto the tissue section and cell-to-cell interactions explored. Gene expression data and spatial co-ordinates can visualise spatial expression patterns. Finally, subcellular data can identify spatial dynamics of transcripts within a cell.
Single-cell & spatial analysis applications
Single-cell and spatial technologies can turn an interesting research area into a potentially revolutionary real-world application. How can single-cell and spatial analysis be applied to answer questions that couldn’t be answered before?
The Human Cell Atlas
The Human Cell Atlas (HCA) is a large-scale collaborative project involving a team of international researchers. The HCA uses a combination of single-cell and spatial technologies to create cellular reference maps with the position, function and characteristics of every cell type in the human body. The cellular reference maps generated can be used to analyse the underlying molecular mechanisms of development and activity of different cell types and how cells interact to form tissues.
Liquid biopsy and non-invasive diagnosis
The promise of liquid biopsy is a minimally invasive blood test that can be used to diagnose and monitor disease with a high level of sensitivity and specificity. Within cancer, this would avoid the use of invasive tissue biopsy procedures, which are the current standard for diagnosis and result in a large amount of patient discomfort.
Cells that are present in blood are already dissociated, making them easy to integrate into single-cell workflows. Within cancer, liquid biopsies typically target circulating tumour cells (CTCs) or circulating tumour DNA (ctDNA).
Drug discovery and development
Determining drug properties such as activity, toxicity, and exposure is a complex process that is essential in the design of drug candidates and their advancement to the development stage. There are significant variations in the biological and physiological function of different cells which can affect their response to drugs. Single-cell analysis has been applied in drug discovery and development to help unravel the cellular processes underlying drug response and refine the drug development process.
Emerging areas in single-cell and spatial analysis
Single-cell temporal analysis
Single-cell sequencing and spatial analysis provide huge amounts of data about individual cells and the context of cells within a tissue. However, these technologies only present a snapshot of what’s going on in tissues, fixed at one particular time point for each analysis. Researchers are now addressing this issue, in the emerging field of single-cell temporal analysis.
Several methods have been developed to achieve temporal analysis. The first is ‘pseudo-time’ sequencing, which refers to the partial ordering of cells in scRNA-seq data that represents predecessor and descendent cell state information. Metabolic labelling of newly made, nascent RNA can be used in pseudo-time sequencing.
The introduction of ‘RNA timestamps’ allows the transcriptional history of a single cell to be generated by directly analysing RNA. RNA timestamps are recorder RNA motifs that report RNA age through the gradual accumulation of adenosine-to-inosine (A-to-I) edits in each molecule. This system exploits RNA editing to infer the age of individual RNAs from RNA-seq data to an hour-scale accuracy.
Single-cell temporal analysis has recently been explored further. A paper, published in Nature, introduced Live-seq, a technology that is able to profile the transcriptome of an individual cell, while at the same time, keeping the cell alive. Live-seq uses fluidic force microscopy (FluidFM) to extract cytoplasmic mRNA from living single cells to use for RNA sequencing.
The majority of single-cell analysis studies focus on nucleic acids. The central dogma of molecular biology states that DNA is transcribed into RNA and RNA is translated into proteins, which are the functional molecules within a cell. However, biology is not that straightforward. Post-translational modifications can alter the structure and function of proteins, thereby altering cell phenotype. Studying proteins in their various forms has been suggested to provide a more faithful representation of cell phenotype. We therefore require proteomics to fully understand cellular functions.
At a single-cell level, there are two main approaches available for analysing the proteome. These are antibody-based methods and mass spectrometry-based methods (see Figure 2).
Spatial proteomics has been explored with the introduction of Deep Visual Proteomics (DVP), a method that incorporates advanced microscopy, artificial intelligence, and ultra-high-sensitivity proteomics to spatially characterise the proteome of individual cells.
By combining imaging technologies with unbiased proteomics to quantify the number of expressed proteins in a given cell, DVP is an important addition in the toolbox of spatial-omics technologies.