Single-cell Multimodal Profiling: Expanding Our View of Cell Biology

Understanding the Need for Multimodal Profiling

Understanding how cells function requires more than measuring a single layer of molecular information. Traditional scRNA-seq captures gene expression, but important regulators like chromatin state and protein activity remain hidden.

Single-cell multimodal profiling aims to bridge these gaps by simultaneously measuring different types of information - RNA, chromatin accessibility, and surface proteins - from the same cells. This expanded view helps researchers reveal complex regulatory mechanisms, identify subtle cell states, and study processes like immune activation or differentiation with greater precision.

In recent years, multimodal single-cell technologies have become increasingly important in both basic research and translational studies.

Challenges in Integrating Different Modalities

While exciting, analyzing multimodal datasets is not simple. Each modality has distinct technical and statistical properties. Common challenges include:

- Data sparsity: Chromatin accessibility (ATAC) data are much sparser than RNA expression data.
- Normalization: RNA counts and antibody-derived tag (ADT) counts require different normalization methods.
- Batch effects: Different modalities can be affected differently by batch artifacts.
- Dropout events: Incomplete capture of molecules introduces noise, especially for lowly expressed genes.
- Modality-specific biases: Such as non-specific antibody binding in CITE-seq experiments.

Careful quality control and thoughtful analysis design are necessary to handle these issues.

Popular Platforms for Multimodal Single-cell Data

Platform	Modalities Captured	Notes
10X Genomics Multiome	scRNA + scATAC	Gene expression and chromatin accessibility from the same nucleus
CITE-seq	scRNA + ADT (surface proteins)	Uses oligo-tagged antibodies to capture protein abundance
ASAP-seq	scATAC + ADT	Chromatin accessibility combined with protein measurement

Each method offers unique advantages depending on study goals. Multiome links open chromatin to transcription, while CITE-seq is excellent for immunophenotyping.

Strategies for Multimodal Data Analysis

- Weighted Nearest Neighbor (WNN) Analysis: Used by Seurat v4, combining RNA and ATAC modalities.
- totalVI: A deep generative model handling RNA and ADT data naturally.
- MOFA+ (Multi-Omics Factor Analysis Plus): Factorizes multimodal data into latent factors.

Choosing the right method depends on data quality, study goals, and balance between interpretability and modeling power.

Typical Difficulties Encountered

- High background in ADT channels (non-specific binding).
- Sparse ATAC matrices making clustering unstable.
- RNA and ATAC modalities clustering differently.
- Overfitting with small cell numbers in predictive models.

Starting with careful single-modality QC before integration often leads to more robust results.

About the Author: Justin T. Li received his Ph.D. in Neurobiology from the University of Wisconsin-Madison in 2000 and a M.S. in Computer Science from the University of Houston in 2001. Between 2004 and 2009, he served as an Assistant Professor in the Medical School at the University of Minnesota Twin Cities campus. From 2009 to 2013, he was the Chief Bioinformatics Officer at LC Sciences in Houston. In June 2013, Justin joined AccuraScience as a Lead Bioinformatician. He has published over 50 research articles in bioinformatics, computational biology, and related fields. More information about Justin can be found at https://www.accurascience.com/our_team.html.

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