The Power of Integrating Single-Cell Multi-Omic Analysis to Accelerate Cell and Gene Therapy Biomanufacturing

Until recently, individual cells' lives have remained a secret, hidden from view in genomics and other “omics” techniques that look at bulk measurements. As a result, the unique signals in single cells get averaged out by larger signals shared by most cells. 

However, the rise of single-cell “omics” techniques has uncovered the complex cellular heterogeneity that underlies many biological processes and disease states. The first single-cell analysis methods, flow cytometry and fluorescence-activated cell sorting (FACS) have enabled researchers to track multiple protein biomarkers and rare subsets of cells with powerful impacts on immunological systems, tumor microenvironments, and hematopoietic stem cell populations.¹ 

Single-cell genomics, transcriptomics, epigenomics, and proteomics take it one step further, revealing a greater breadth of information and deeper insights into biology's heterogeneity. 

With the maturity of the single-cell field, the empowering rise of third-generation sequencing technologies, and the ability to integrate datasets for powerful single-cell multi-omics analysis, these techniques are uniquely positioned to accelerate the characterization and development of novel cell and gene therapies. For instance, measuring viral transduction rates, on-target editing efficiency, or zygosity is necessary for comprehensive genome-level characterization of a candidate therapeutic. Yet, this data is hidden when using bulk sequencing methods. There are also essential post-treatment questions – such as the consequences of in vivo editing or transgene expression and the monitoring of CAR-T cell dynamics and efficacy over time – that can only be answered using single-cell methods.¹‍

Below we discuss how single-cell methods and multi-omics are changing how the cell and gene therapy industry discovers and biomanufactures these important therapeutics.

What is Multi-Omics?

Now, single-cell tools (discussed in more detail below) are being combined to provide more insights into the lives of single cells at the genotypic and phenotypic levels. 

Multi-omics is a type of analysis that integrates two or more datasets from different levels of gene expression, including, most commonly, genomics, transcriptomics, proteomics, and epigenomics, to understand biological processes more comprehensively and holistically. 

By analyzing these in concert, researchers can uncover connections between what is happening at the molecular level and how it affects the phenotype of a single cell (i.e., single-cell multi-omics), untangling this complex interplay in greater detail than ever before.  

Multi-Omics Technology

Multi-omics has enabled researchers to take the various omics technologies developed and honed in the past decade and apply them to complicated biological questions in unprecedented detail. 

Multi-omics has helped bridge the gap between genotype and phenotype, uncovering scientific insights that single omics techniques fail to deliver when applied alone. Multi-omics technology has been applied in various R&D, diagnostics, and therapeutics applications. 

Single-Cell Analysis: The Different Methods and Applications in Cell and Gene Therapy Biomanufacturing

Discovering what single cells are capable of concerning a biological process or disease can be done with a broad range of single-cell platforms and techniques. Below, we discuss the most popular methods and how they interrogate current knowledge gaps in disease pathology or cell and gene therapy discovery, development, and biomanufacturing. 

Single-cell genomics

Single-cell genomics is the high-throughput sequencing of genomic DNA to understand the distinct populations of individual cells and their evolutionary relationships within a bulk community of cells.

Multiple single-cell whole genome amplification and sequencing (scWGS) methods have been developed with varying degrees of success. These methods face challenges, as researchers rely on only two copies of the genome in a single cell for amplification which can lead to amplification bias or allele dropout.² Several bioinformatics tools, such as SCcaller or Monovar, have been developed to deal with these issues.^3,4‍

There are now several well-validated, commercially available instruments for performing single-cell sequencing, making it a more reliable and streamlined process for untangling genetic heterogeneity. Using these single-cell sequencing, researchers have identified the mutational signatures of B-cell cancer and many other cancers.⁵

Single-cell genomics and applications in cell and gene therapy biomanufacturing

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ScWGS allow the untangling of different cancer cell lineages, including mutation co-occurrences and order of acquisition in multiple cancer types.^1,6 It also enables the identification of rare cell subtypes that can play a big part in the efficacy of or resistance to specific cell and gene therapies (or therapies, in general).

For viral vector-based therapies that introduce a transgene or gene editing technology, scWGS can be used for understanding transduction efficiency, copy number variation, aneuploidy, on- and off-target editing, and zygosity for either ex vivo and in vivo engineered cells. Researchers use these assays in the discovery and development phase or to monitor critical quality attributes during manufacturing.

Single-cell transcriptomics

Single-cell transcriptomics is the high-throughput sequencing of comprehensive or targeted RNA populations in individual cells to identify distinguishing gene expression profiles within a bulk population of cells.

Single-cell RNA-seq (scRNA-seq) is the most widely used method for single-cell analysis. Many microdroplet and microwell-based platforms have emerged, each with differing abilities to answer a wide range of research questions depending on the throughput, cost, and ease of use required.^1,7 As with single-cell genomics, single-cell whole transcriptome methods face quantitative challenges like amplification bias and RT efficiency issues.

Despite these challenges, single-cell RNA-seq has been used broadly to interrogate transcriptional responses of immune cells in the tumor microenvironments and to identify transcriptional responses to treatment resistance.² Bioinformatics tools, like the Seurat or Cellenics, have also been developed and offer an end-to-end single-cell RNA-seq data analysis tool.^8,9

Single-cell transcriptomic applications in cell and gene therapy biomanufacturing

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ScRNA-seq can identify active or inactive genes and pathways associated with the onset and progression of certain diseases.¹ This information can be used to determine relevant cell and gene therapy targets.

On the drug development side, scRNA-seq has been used to identify CAR-T cell populations associated with satisfactory and poor treatment outcomes.¹⁰ Also, scRNA-seq can be used as a lower-cost alternative to single nucleotide variants (SNVs) or splice variant analysis to examine treatment outcomes for gene editing technologies.¹

Single-cell epigenomics

Single-cell epigenomics is a technique for identifying the chemical changes in DNA or histones – called epigenetic modifications – in single cells that can impact gene expression without changing the underlying identity of the DNA sequence.

Researchers can use single-cell epigenomics to analyze individual cells' DNA methylation or chromatin modification status. Well-established bisulfite sequencing and chromatin immunoprecipitation are the foundation for these techniques, respectively.

One method, in particular, assay for transposase-accessible chromatin using sequencing (ATAC-seq), has been adopted by commercial single-cell platforms to provide single-cell ATAC-seq to a broad range of researchers.¹¹ These can be combined with single-cell RNA-seq data to validate and deconvolute complex transcriptional networks.²

Single-cell epigenomics applications in cell and gene therapy biomanufacturing

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Single-cell ATAC-seq (scATAC-seq) enables researchers to define chromatin accessibility in individual cells before and after cell or gene therapy treatment and further define specific cell subtypes most responsive to treatment.¹² These datasets can be further interrogated to identify regulatory elements in tumor, immune, or stromal cells that play a role in disease pathology.

ScATAC-seq has also been used to identify epigenetic signatures of cytotoxic and persistent CAR-T clones.¹³ 

Spatial transcriptomics

Another dimension of cellular heterogeneity is the location of select cells in 3-D space. While the above technologies provide insight into the genetic, epigenetic, and transcriptomic heterogeneity in biological systems, they discard precious structural information critical for understanding transcriptional responses, disease pathology, and normal biological functioning.

Spatial transcriptomics takes transcriptomics data and adds spatial context, creating a high-resolution, single-cell map of gene expression patterns across a biological sample. 

To collect spatial transcriptomic information, high-throughput fluorescence in situ hybridization (FISH)-based methods, such as MERFISH and Visium, have been developed to deliver spatially resolved gene expression profiles.^14,15 These technologies are still being developed and adopted, and technical and computational hurdles must be overcome to achieve whole-transcriptome coverage.¹ 

Spatial transcriptomics applications in cell and gene therapy biomanufacturing

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The application of spatial transcriptomics to cell and gene therapy development is in its infancy but holds much potential for monitoring treatment outcomes and identifying resistance mechanisms.¹ Thus far, it’s been primarily used to study tumor heterogeneity and gene expression changes within the tumor microenvironment.^1,16

Single-cell multi-omics

As described above, single-cell multi-omics combines multiple omics techniques and datasets to understand the molecular characteristics of single cells at the DNA sequence, RNA and/or protein expression, and epigenetic modification levels.

‍Multi-omics techniques that integrate independently isolated single-cell data sets using bioinformatics are used to uncover insights about complex disease states, such as cancer. Computational tools, such as Seurat Label Transfer and LIGER, enable the integration of single-cell transcriptomics, single-cell epigenomics, spatial transcriptomics, and single-cell proteomics datasets.^17,18 

Other single-cell multi-omics methods perform multilayered sequencing from the same individual cells. These techniques enable single-cell transcriptomics analysis in combination with single-cell genomics (e.g., G&T-seq), single-cell epigenomics (e.g., scM&T-seq), and single-cell proteomics (e.g., CITE-seq).^19,20,21‍

Both multi-omics strategies provide unprecedented insight into the various molecular dimensions that make up the cellular subtypes that control complex biological processes. Application to monitoring treatment efficacy of cell and gene therapy or target discovery will prove incredibly beneficial in the future.

The Future of Single-Cell Multi-Omic Analysis on Cell and Gene Therapy Biomanufacturing

The heterogeneity of diseases and responses to therapeutics, including cell and gene therapies, is now well accepted. Single-cell analysis has become a routine part of basic research, cell and gene therapy development and biomanufacturing design. The continued technical improvement to single-cell workflows will bring increased efficacy and safety to cell and gene therapy biomanufacturing, uncovering previously hidden insights on how the cell and gene therapy will work in real world settings.

With the increased accessibility of computational workflows for life science, single-cell multi-omic analysis is now at your fingertips, with no computational experience necessary. To see how we at Form Bio are making easy-to-use single-cell analysis in your hands, take a look at our “off-the-shelf” workflow solutions. 

‍AI Disclosure: Some of this content was generated with assistance from AI tools for copywriting.