Advanced Tools for Single-Cell Multiomics Data Interpretation

Single-cell sequencing consists of a diverse range of techniques used to investigate the genome, transcriptome, epigenome, and other omics domains. While the different techniques used to study these cellular components are insightful on their own, their combined analysis, known as multiomics, offers a fuller picture. Despite the significant advances that single-cell multiomics has brought to cell biology and translational research, data integration and analysis remain a challenge for many scientists. We'll review the fundamentals of single-cell multiomics and cover some of the computational tools available to facilitate the integrative analysis of the data.

Single-cell multiomics

“Single-cell multiomics involves the assessment of multiple cellular features on a single-cell level,” stated Angela Churchill, Ph.D., Product Marketing Manager of Single Cell Applications at 10x Genomics. She explained how this approach offers an in-depth perspective of biology, facilitating a more detailed understanding of cell types and conditions, as well as enabling new research questions that were once out of reach.

Multiomic analysis, however, is not limited to single-cell research. “Multiomics can be applied to either bulk or single-cell sequencing, and the method that you choose depends on your research goals,” stated Dalia Daujotyte, Director of Product Marketing at Illumina. “Bulk sequencing can be an incredibly accessible, straightforward approach for identifying differences between tissues.” When studying uniform tissue or abundant cell types, Daujotyte noted that bulk sequencing is typically the most cost-effective solution. Conversely, for heterogenous cell populations of rarer cell interests, single-cell multiomics is effective at profiling these different cells and layers. “This increases the probability that rarer phenomena are captured. It also allows for increased potential to determine whether cellular identity matters within the context of a heterogeneous microenvironment,” Daujotyte added.

Progress and milestones

Over the years, the advancement of more comprehensive assays has enhanced the ability of researchers to conduct single-cell multiomic research. Churchill explained that expansions in assays, like those for the 10x Genomics Chromium Single Cell platform, have opened up the possibilities to assess many cellular features in different combinations, including whole transcriptome gene expression, protein expression, paired full-length T- and B-cell receptor sequencing, antigen specificity, open chromatin profiling, and CRISPR guide RNA (gRNA) detection. Other notable advancements include the improvements to sequencing technologies. “Innovations like Illumina’s NovaSeq X substantially increase throughput and decrease the workflow cost and time, allowing for more ‘-omes’ to be sequenced, furthering the growth of single-cell multiomics,” stated Daujotyte.

Among the many types of research benefiting from these advancements, cancer research has seen remarkable progress. “Single-cell and cancer research are a perfect fit because cancers don’t consist of one cell type,” Daujotyte noted. This stems from the fact that single-cell sequencing excels at revealing cellular diversity, which is essential for comprehending the complex tumor microenvironment. Within cancer research, Daujotyte explained that spatial multiomic sequencing is now a rapidly expanding method, which applies genome, transcriptome, epigenome, or proteome sequencing to cells without disrupting their tissue structure. “Current methods of spatial sequencing aren’t quite single-cell resolution, so single-cell sequencing isn’t going anywhere anytime soon,” Daujotyte pointed out. “But as resolution increases and NGS enables untargeted, comprehensive profiling of “-omes” within the context of tissue, spatial sequencing is in a good place for growth.”

Tools for data integration

Single-cell experiments typically require considerable effort, but the analysis often proves to be the most challenging aspect of the research. “Single-cell multiomic datasets are inherently large and highly dimensional, which can lead to limitations with data processing, management, and interpretation,” emphasized Churchill. She mentioned that a range of comprehensive dataset analysis tools exists, including robust options from 10x Genomics like Cell Ranger, Cloud Analysis, and Loupe Browser.

As a set of freely available pipelines, Cell Ranger streamlines the process of analyzing Chromium single-cell data and can process raw data, perform alignments, gene counting, and more. Cell Ranger can also be integrated with the Cloud Analysis platform to monitor, manage, and process the data using optimized cloud clusters. In addition, researchers can use Loupe Browser, a visualization software that assists with the interpretation of single-cell multiomic datasets. “No bioinformatics expertise is required to utilize this software and it enables streamlined exploration of multiomic datasets, including integrated views of gene expression, V(D)J data, open chromatin regions, and more,” Churchill added.

There are also open-source tools like MOFA2 (Multi-Omics Factor Analysis v2)1 and LIGER (Linked Inference of Genomic Experimental Relationships)2 that are commonly used for integrating multiomic datasets. These tools typically offer flexibility to integrate different types of omic data. The major downside is that many require advanced bioinformatics skills, which restricts their usage from scientists without extensive coding and analysis experience.

Fortunately, Daujotyte noted that the field is transitioning to programs that are on-instrument, on-server, or app-based, providing researchers with a more streamlined, comprehensive, and of course, user-friendly workflow. These programs include Illumina’s DRAGEN secondary analysis apps that can move data from different omics through common analysis pipelines, such as RNA sequencing data normalization. Additionally, Daujotyte explained that apps like Illumina Correlation Engine can take data from multiple approaches and perform comparisons to over 246 thousand curated public datasets, allowing important patterns like cellular pathways and genetic perturbations to be revealed.

The development of these various bioinformatics tools and software will continually increase the ways that researchers can interpret their complex multiomics datasets. As highlighted by both Churchill and Daujotyte, the shift toward more intuitive programs or app-based platforms allows researchers without an extensive bioinformatics background to efficiently analyze and gain meaningful insights from their datasets.

Recommended Further Readings for Multiomic Analysis

1. Hao Y, Hao S, Andersen-Nissen E, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184(13):3573-3587.e29. doi:https://doi.org/10.1016/j.cell.2021.04.048

2. Dimitriu MA, Lazar-Contes I, Roszkowski M, Mansuy IM. Single-cell multiomics techniques: From conception to applications. Frontiers in Cell and Developmental Biology. 2022;10. doi:https://doi.org/10.3389/fcell.2022.854317

3. Stanojevic S, Li Y, Ristivojevic A, Garmire LX. Computational methods for single-cell multi-omics integration and alignment. Genomics, Proteomics and Bioinformatics. 2022;20:836-849. doi:https://doi.org/10.1016/j.gpb.2022.11.013

4. Baysoy A, Bai Z, Satija R, Fan R. The technological landscape and applications of single-cell multi-omics. Nature Reviews Molecular Cell Biology. 2023;24(10):695-713. doi:https://doi.org/10.1038/s41580-023-00615-w

References

1. Argelaguet R, Velten B, Arnol D, et al. Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets. Molecular Systems Biology. 2018;14(6):e8124. doi:https://doi.org/10.15252/msb.20178124

2. Welch JD, Kozareva V, Ferreira A, et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell. 2019;177(7):1873-1887. doi:https://doi.org/10.1016/j.cell.2019.05.006