Integrating Single Cell Proteomics with scRNA-seq: Methods and Tools
The rapid advancement of single-cell sequencing technologies has enabled researchers to dissect cellular heterogeneity at single-cell resolution. Among various approaches, single-cell RNA sequencing (scRNA-seq) primarily captures the transcriptomic landscape by profiling mRNA expression, whereas single cell proteomics (scProteomics) offers insights into protein-level expression, which more directly reflects functional cellular output. These two modalities represent distinct yet complementary dimensions of cellular biology, and their integrative application is increasingly recognized as a powerful strategy for deciphering complex biological systems.
Challenges for Multi-Omics Integration
Integrating scRNA-seq and single cell proteomics data at the single-cell level poses several major challenges:
1. Modality Heterogeneity
These two data types are derived from distinct experimental platforms, resulting in significant differences in noise levels, detection sensitivity, and data distributions.
2. Inconsistent Feature Spaces
Not all transcripts detected in scRNA-seq have corresponding protein products identified in single cell proteomics, leading to issues such as missing features and redundancy in feature representation.
3. Imbalanced Data Dimensionality
scRNA-seq typically yields expression profiles for tens of thousands of genes, while the number of proteins quantifiable per cell in current scProteomics is comparatively limited.
Strategies for Multi-Omics Integration
1. Anchor-Based Integration Using Shared Features
This class of methods identifies shared molecular features, such as paired mRNA and protein markers, as anchors to align cells across modalities and establish spatial correspondences.
Method: Seurat v4 Multimodal Integration
Seurat is a widely adopted toolkit for single-cell analysis. In version 4, it introduces an anchor-based framework for multimodal data integration. The method identifies cells or features with similar expression patterns across scRNA-seq and scProteomics, then uses a weighted nearest neighbor algorithm to map both datasets into a common low-dimensional space. This approach is particularly suitable when there is a substantial overlap in features and the goal is to achieve joint annotation of cell types.
2. Matrix Factorization-Based Dimensionality Reduction
These approaches perform linear dimensionality reduction to extract latent factors shared between modalities, thereby constructing a unified cell embedding space.
Method: MOFA+ (Multi-Omics Factor Analysis)
MOFA+ employs a variational Bayesian framework to decompose multi-omics data into latent factors that capture both shared and modality-specific sources of variation. It is especially effective in unsupervised settings, such as identifying disease subtypes or reconstructing multi-omics trajectories. With its ability to handle missing data, MOFA+ is well-suited for scenarios where scRNA-seq and scProteomics share limited overlap in measured features.
3. Deep Learning-Based Nonlinear Mapping
Deep neural networks offer enhanced capacity to model complex, nonlinear relationships and are well-suited for integrating noisy, high-dimensional single-cell data.
Method: totalVI (total Variational Inference)
totalVI is a probabilistic framework based on variational autoencoders (VAEs), designed to jointly model the distribution of mRNA and protein expression data. Although originally developed for RNA and antibody-derived tags (e.g., CITE-seq), totalVI can be extended to integrate scProteomics data. It supports multiple downstream tasks, including dimensionality reduction, batch effect correction, denoising, and clustering, making it highly suitable for large-scale single-cell analysis.
4. Graph-Based Cross-Modal Learning
Graph neural networks (GNNs) leverage relational structures among features or cells to capture intricate regulatory relationships across omics layers, representing a state-of-the-art direction in multimodal integration.
Method: GLUE (Graph-linked Unified Embedding)
GLUE constructs a gene regulatory graph that spans multiple omics modalities and embeds them into a shared latent space. Capable of integrating transcriptomic, proteomic, and epigenomic data, GLUE exhibits robust cross-modal generalization and is particularly effective for building single-cell atlases across species or tissue types. By combining graph structures with omics-specific features, GLUE significantly improves the biological interpretability of cell-cell similarity modeling.
The integration of scRNA-seq and single cell proteomics exemplifies a pivotal shift in single-cell omics, from simple dimensional coexistence to true functional complementarity. Multimodal fusion enhances the resolution and accuracy of cellular state characterization and offers novel perspectives for decoding gene regulatory networks, delineating developmental trajectories, and profiling disease microenvironments. In this evolving field, MtoZ Biolabs remains at the forefront of single-cell and multi-omics integration, dedicated to delivering reliable and high-throughput single-cell proteomics services for life science researchers.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
Related Services
How to order?
