Multiome Approaches and Single-Cell Complexities

Advances in single-cell technologies have transformed how researchers study cellular complexity, allowing for the simultaneous measurement of multiple molecular layers within individual cells. This multiome approach integrates different types of biological data, providing a comprehensive view of cellular function.

By capturing transcriptomic, epigenomic, and proteomic information together, scientists can uncover intricate regulatory mechanisms and cell-to-cell variability with unprecedented resolution.

Concept In Single-Cell Multiome Analysis

Single-cell multiome analysis advances the understanding of cellular heterogeneity by capturing multiple molecular modalities from the same cell. Unlike traditional single-omics approaches, which examine one layer of biological information at a time, multiome techniques integrate transcriptomic, epigenomic, and proteomic data, offering a complete picture of cellular function. This integration enables researchers to correlate gene expression with chromatin accessibility and protein abundance, revealing regulatory interactions that would otherwise remain obscured.

A key challenge in multiome analysis is the technical difficulty of capturing multiple molecular layers simultaneously without compromising data quality. Each modality requires distinct biochemical processing steps, and ensuring compatibility between these workflows is a major hurdle. Advances in microfluidics, droplet-based sequencing, and combinatorial indexing have helped overcome these limitations, enabling researchers to profile thousands of cells in parallel while preserving molecular integrity. For example, 10x Genomics’ Chromium Single Cell Multiome ATAC + Gene Expression allows concurrent measurement of chromatin accessibility and transcriptomic data, providing insights into how regulatory elements influence gene activity at a single-cell resolution.

Data integration presents another challenge, as transcriptomic, epigenomic, and proteomic data exist in different formats and scales. Machine learning algorithms and probabilistic models align these datasets in a biologically meaningful way. Integrative frameworks such as Seurat and Signac facilitate joint analysis of single-cell RNA sequencing (scRNA-seq) and assay for transposase-accessible chromatin sequencing (scATAC-seq) data, identifying regulatory networks that drive cellular identity and function.

Molecular Layers

Single-cell multiome analysis relies on measuring multiple molecular layers to provide a comprehensive view of cellular function. These layers—transcriptome, epigenome, and proteome—each offer distinct yet interconnected insights into gene regulation and cellular behavior.

Transcriptome

The transcriptome represents the complete set of RNA molecules expressed within a cell at a given time, providing a snapshot of gene activity. Single-cell RNA sequencing (scRNA-seq) is the primary method for profiling transcriptomes at high resolution, enabling researchers to identify cell types, states, and dynamic gene expression changes.

One challenge in transcriptomic analysis is the sparsity of single-cell RNA data, where many genes exhibit dropout effects due to low RNA capture efficiency. Computational methods such as imputation algorithms and probabilistic modeling help reconstruct missing expression values. Multiome techniques like 10x Genomics’ Chromium Single Cell Multiome ATAC + Gene Expression link gene expression with chromatin accessibility, revealing transcription factor and enhancer regulation. These approaches have uncovered novel regulatory programs in diverse biological contexts, from embryonic development to disease progression.

Epigenome

The epigenome encompasses chemical modifications to DNA and histones that influence gene expression without altering genetic sequence. The most commonly profiled epigenomic feature in single-cell multiome analysis is chromatin accessibility, which reflects the openness of DNA regions to transcription factor binding. Techniques such as scATAC-seq map these accessible regions, providing insights into regulatory elements controlling gene expression.

Integrating epigenomic with transcriptomic data helps infer causal relationships between chromatin state and gene activity. Studies show that enhancer accessibility often precedes gene expression changes, suggesting a regulatory hierarchy in cellular differentiation. Computational tools like Signac facilitate joint analysis of scRNA-seq and scATAC-seq data, identifying transcription factor motifs and regulatory networks. Emerging methods such as single-cell DNA methylation sequencing add further layers of epigenomic information, refining gene regulation insights.

Proteome

The proteome represents the complete set of proteins expressed within a cell, reflecting the functional output of gene expression. Unlike transcriptomic and epigenomic data, which provide indirect insights into cellular function, proteomic analysis directly measures protein abundance and modifications. Single-cell proteomics techniques, such as mass cytometry (CyTOF) and single-cell western blotting, enable high-resolution protein profiling, allowing researchers to study post-translational modifications and protein-protein interactions.

One challenge in single-cell proteomics is the limited sensitivity of current technologies, as proteins lack the amplification potential of nucleic acids. Advances in proximity extension assays (PEA) and single-molecule protein sequencing have improved detection capabilities. When integrated with transcriptomic and epigenomic data, proteomic analysis reveals how gene expression patterns translate into functional protein networks. Studies combining single-cell RNA sequencing with CyTOF have identified distinct protein expression signatures defining cellular states in complex tissues.

Data Generation Approaches

Generating high-quality data in single-cell multiome analysis requires methodologies that accurately capture multiple molecular layers within individual cells. The complexity arises from the need to profile distinct biomolecules—RNA, chromatin accessibility, and proteins—without compromising data integrity. Advances in microfluidics, combinatorial indexing, and droplet-based sequencing have improved throughput, sensitivity, and reproducibility, enabling large-scale multiome datasets with minimal technical noise.

Droplet-based sequencing, used by platforms like 10x Genomics Chromium, encapsulates single cells in nanoliter-sized droplets with barcoded beads. This approach allows simultaneous capture of transcriptomic and epigenomic information, revealing regulatory interactions. However, challenges such as cell doublets—instances where multiple cells are encapsulated in the same droplet—can contaminate data. Computational tools filter out doublets, preserving data fidelity.

Combinatorial indexing eliminates the need for physical cell isolation by uniquely tagging nucleic acids at multiple processing steps. Methods like sci-ATAC-seq and SHARE-seq use this strategy to profile chromatin accessibility and gene expression in a cost-effective manner. Unlike droplet-based methods, combinatorial indexing allows profiling of fixed cells and nuclei, expanding the range of sample types that can be analyzed. This flexibility benefits studies of rare cell populations or archived tissue samples, where traditional single-cell methods may be impractical.

Mass cytometry and single-cell proteomics contribute to multiome data generation by enabling protein expression measurement alongside transcriptomic and epigenomic data. Techniques such as CITE-seq integrate antibody-based protein quantification with single-cell RNA sequencing, bridging transcriptomic signals with functional protein expression. Despite advancements, proteomic data generation at the single-cell level remains challenging due to the lack of amplification mechanisms for proteins, making signal detection more difficult than for nucleic acids.

Regression Methods

Analyzing complex multiome datasets requires statistical techniques that model relationships between molecular layers while accounting for noise and sparsity. Regression methods quantify associations between gene expression, chromatin accessibility, and protein abundance. Unlike traditional bulk-level approaches, single-cell regression accommodates high-dimensional data with nonlinear dependencies and stochastic variation.

Generalized linear models (GLMs) extend traditional regression frameworks to count-based data like RNA sequencing reads, assessing how gene expression varies with epigenomic and proteomic features. Negative binomial regression addresses overdispersion in single-cell RNA sequencing by adjusting for skewed transcript count distribution. Machine learning-based regression techniques, including Gaussian process regression and deep learning models, enhance predictive accuracy by capturing nonlinear dependencies, uncovering gene regulatory relationships.

Biological Interpretations

Extracting insights from single-cell multiome data requires analytical frameworks that decipher relationships between molecular layers. The complexity of these datasets necessitates computational strategies that distinguish true biological variation from technical noise.

A key aspect of biological interpretation is identifying cell states and trajectories by integrating transcriptomic, epigenomic, and proteomic signals. Dimensionality reduction techniques such as Uniform Manifold Approximation and Projection (UMAP) or t-Distributed Stochastic Neighbor Embedding (t-SNE) visualize cellular clusters and transitions, revealing lineage hierarchies and differentiation pathways. These insights have been particularly valuable in developmental biology, mapping how stem cells commit to specific fates based on coordinated gene expression and chromatin accessibility changes.

Multiome approaches also reconstruct gene regulatory networks governing cellular identity. Researchers infer transcription factor activity by correlating chromatin accessibility with downstream gene expression, providing mechanistic insights into regulatory circuits. Methods such as SCENIC integrate single-cell RNA and ATAC sequencing data to predict regulatory interactions, shedding light on how transcription factors orchestrate cellular responses. This has led to discoveries in disease biology, such as identifying dysregulated gene regulatory networks in cancer and neurodegenerative disorders. Integrating proteomic data enhances functional interpretations by linking molecular signals to phenotypic outcomes, uncovering post-translational modifications and protein-protein interactions that influence cellular behavior.