Single-cell analysis enables deeper characterization of cell populations compared to bulk sequencing, with new experimental methods continuously being developed and applied. At the same time, multi-omics studies have become more and more popular, providing the opportunity to explore interactions between several different data layers, as opposed to just a single ‘ome’. Therefore, it is unsurprising that the recent emergence of single-cell multi-omics technologies has caused excitement across the industry.
Various experimental protocols for single-cell multi-omics analysis have been developed. Although these techniques are relatively new and are still at the ‘emerging’ stage of their journey, it is predicted that they will quickly become more sensitive and accurate. Furthermore, researchers are also working on adding a layer of spatial data to multi-omics. Linking a cell’s positional information to other ‘omes’ has the potential to help scientists map different cell types and functions within a tissue, transforming our understanding of in situ biology.
Multi-Omics: Exploring Inside Cells
Methods for single-cell multi-omics
The technologies used for cell isolation, barcoding and sequencing are core components of single-cell multi-omics analysis. The isolation of single cells involves mechanical or enzymatic dissociation to create a suspension. The individual cells are then captured by various methods, including laser capture microdissection, robotic micromanipulation, fluorescence activated cell sorting or microfluidic platforms.
Top Single Cell Isolation Techniques
Single-cell multi-omics protocols
- DNA-mRNA sequencing (DR-seq)
- Genome and transcriptome sequencing (G&T-seq)
- Simultaneous single-cell methylome and transcriptome sequencing (scM&T-seq)
- Single-cell nucleosome, methylation and transcription sequencing (scNMT-seq)
- Proximity ligation assay for RNA (PLAYR)
- Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq)
DNA-mRNA sequencing (DR-seq)
Measures the genome and transcriptome. The single cells are isolated and lysed. The resulting DNA and RNA are simultaneously amplified and the mixture is split in half – one for RNA sequencing and the other for genome sequencing.
Genome and transcriptome sequencing (G&T-seq)
Measures the genome and transcriptome. The mRNA and DNA are isolated by flow cytometry and physically separated from a lysed cell using magnetic beads. Then, they are amplified and sequenced separately, allowing researchers to use the protocol of their choice for analysing each.
Simultaneous single-cell methylome and transcriptome sequencing (scM&T-seq)
Measures methylated DNA and the transcriptome. Just like in G&T-seq, the mRNA and DNA are isolated by flow cytometry and physically separated from a lysed cell using magnetic beads. The RNA is amplified and sequenced. The DNA is treated with bisulfite to convert unmethylated cytosine into uracils and amplified, to enable sequencing of the methylome.
Single-cell nucleosome, methylation and transcription sequencing (scNMT-seq)
Measures chromatin accessibility, DNA methylation and the transcriptome.The same procedures as scM&T-seq are carried out – the RNA and DNA are isolated and amplified, and the DNA is treated with bisulfite. However, scNMT-seq also probes genome-wide chromatin accessibility.
Proximity ligation assay for RNA (PLAYR)
Measures the transcriptome and proteome. Proteins are labelled with antibodies linked to unique metal isotopes, and RNA transcripts are bound to isotope-labelled probes. Mass cytometry is used to measure the isotopes – more than 40 different mRNAs and proteins, in thousands of single cells, can be quantified per second.
Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq)
Measures the transcriptome and proteome. Oligonucleotides are tagged with antibodies and used to target cell-surface proteins. After the single cells have been isolated and lysed, the mRNA and oligo-tagged antibodies are bound to magnetic beads. They are then amplified and separated by size. The proteins are transcripts are quantified through sequencing.
A diagram showing single-cell multi-omics sequencing technologies and the expected outcomes. Image credit: J. Lee, H. Hyeon and D. Hwang, 2020
Subsequently, single-cell multi-omics data needs to be integrated and analysed. This can be done through various strategies, most of which are extensions of the methods used for single omics data.
Single-cell multi-omics data analysis methods
- Correlation analysis between single-cell mono-omics data
- Separate analysis of different types of single-cell data
- Integrative analysis of all types of single-cell omics data
Correlation analysis between single-cell mono-omics data
Correlation analysis is used to compare two sets of omics data, typically on a scatter plot, to determine the relationship between them. This is a popular method for examining the associations between DNA methylation levels with mRNA expression levels across single cells. It has also been applied to determining the relationship between mRNA and protein expression levels.
Separate analysis of different types of single-cell data
One set of omics data is analysed, followed by the integration of another single-cell data type. Single-cell RNA sequencing data is the most common type of data into which other omics are integrated. This is due to its higher coverage of the transcriptome.Typically, clustering is applied to the RNA data first to identify cell populations that the other omics data can be integrated into.
Integrative analysis of all types of single-cell omics data
This widespread integration is used to generate an overall single-cell map. This strategy is commonly used when the different omics data have comparable coverage, so it avoids potential biases. Several methods exist for integrative analysis of single-cell data, including linked inference of genomic experimental relationships (LIGER) and multi-omics factor analysis (MOFA). LIGER determines cell populations using mRNA expression and DNA methylation levels. MOFA generates cell populations by working out the degree in which cells belong to these clusters and how molecular features contribute towards this weighting.
Choosing the right multi-omics protocol
Researchers need to choose the optimum single-cell multi-omics protocol, based on the application of the study and how expensive, labour-intensive and technically demanding the technique is.
- Cost: The price of multi-omics protocols can vary greatly, depending on the number or complexity of the steps involved and the volume of reagents needed. Moreover, the cost of sequencing can be limiting.
- Time: The time taken to complete a batch of single-cell multi-omics experiments can differ, depending on techniques and the possibility of automating some of the steps. For example, workflows that involve manually separating the nucleus from the cytoplasm typically require more time due to the labour-intensive nature.
- Expertise: Most types of multi-omics methods will benefit from having a range of different skills and expertise, depending on the exact protocol chosen. Typically, a team will need technologists, a computational specialist and a biologist with knowledge of the experimental system.
The applications of single-cell multi-omics are still in their infancy. Therefore, numerous opportunities for expansion and novel paths of exploration are expected to arise in the near future. As a result of this evolution, many of the technical and computational limits in place today are likely to be overcome. In particular, most of the current methods for single-cell multi-omics experiments are only capable of integrating two layers at once. But for effective characterisation of an entire cell, the number of data types measured simultaneously will need to increase.
For information about the challenges facing single-cell multi-omics, checkout our report, titled ‘Multi-Omics: An Integrative Approach to Biomedical Research’. It includes several exclusive perspectives from experts in the field and various real-world case studies to demonstrate the usefulness of multi-omics. Download it here:
Image credit: NIH
