The 10x Genomics Multiome assay represents a significant evolution in cellular analysis, moving beyond the static snapshot of gene expression to reveal the dynamic interplay between the genome and transcriptome within individual cells. This technology leverages the same underlying Chromium platform familiar to single-cell researchers but integrates a novel antibody-based strategy to simultaneously profile gene expression and chromatin accessibility from the same cell. By capturing these two layers of information in a single experiment, the Multiome provides an unprecedented, multi-dimensional view of cellular identity and regulation, allowing scientists to link genetic variants to their functional consequences with remarkable precision.
How the Multiome Technology Works
At its core, the 10x Genomics Multiome relies on a clever dual-indexing strategy within the Chromium Next GEM workflow. The process begins with the encapsulation of individual cells and barcoded nanoparticles into Gel Beads-in-Emulsion (GEMs). Inside each GEM, two distinct reactions occur in parallel. First, a conventional transcriptome library is constructed using mRNA capture via poly-T sequences on the bead. Simultaneously, a novel nuclear antibody, conjugated to a unique oligonucleotide barcode, permeates the nucleus and binds to regions of open chromatin. Subsequent steps involve tagging these accessible sites, fragmenting the DNA, and constructing a separate barcoded library for chromatin accessibility. The ingenuity lies in the shared barcode shared between the RNA and DNA molecules originating from the same cell, which is the key to merging these two modalities during subsequent bioinformatic analysis.
Unlinking and Computational Deconvolution
A critical innovation of the Multiome workflow is the "unlinking" step, where the physical connection between the RNA and DNA molecules is severed. This is necessary because the downstream amplification and sequencing processes would otherwise interfere with the ability to accurately quantify both molecules. After sequencing, sophisticated computational algorithms analyze the pooled data to computationally "deconvolute" the reads. By matching the unique dual barcodes, the software assigns each transcriptome read and each epigenomic read back to the same original cell. This process effectively creates two separate, high-quality single-cell datasets—gene expression and chromatin accessibility—that are intrinsically aligned at the cellular level, providing a cohesive picture of cellular state.
Advantages Over Single-Plex Methods
The primary advantage of the Multiome approach is its ability to acquire two distinct molecular profiles from the exact same cell population, eliminating the noise and biological variability introduced by performing separate experiments. Traditional methods require researchers to choose between measuring gene expression or chromatin accessibility, forcing a compromise or the costly and laborious process of running two parallel experiments on different cell populations. Because the data is co-located, it is vastly more powerful for identifying causal relationships, such as how a specific genetic variant identified through GWAS studies directly regulates a gene’s expression program. This correlation is far more accurate than inferring linkage from separate datasets, providing a direct line of evidence from genotype to phenotype.
Applications in Biomedical Research
The Multiome has found immense utility across a wide spectrum of biological and medical research. In immunology, it is a powerful tool for dissecting complex cell types within a tumor microenvironment or during an immune response, revealing not just which genes are active but how the local chromatin landscape permits or restricts that activity. In neuroscience, it helps classify distinct neuronal subtypes and understand the epigenetic regulation of genes involved in neurological disorders. Furthermore, the technology is instrumental in studying viral infection, where researchers can simultaneously track the host cell’s transcriptional response and the epigenetic changes occurring in the viral genome, offering a comprehensive view of pathogenesis.
Data Analysis and Interpretation
Analyzing Multiome data requires specialized bioinformatic pipelines and expertise, but the output is exceptionally rich. Standard dimensionality reduction techniques like UMAP or t-SNE can be applied to the combined dataset, often revealing more defined and biologically meaningful clusters than either modality alone. Researchers can then perform linkage analysis, overlaying gene expression data onto chromatin accessibility tracks to identify potential cis-regulatory elements for specific genes. This integrated view allows for the discovery of novel cell states, rare cell populations, and regulatory mechanisms that would remain hidden in single-plex analyses, making it a cornerstone technology for systems biology.
