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Multimodal Single-Cell Integration Across Time, Individuals, and Batches

ABSTRACT: In the past decade, the advent of single-cell genomics has enabled the measurement of DNA, RNA, and proteins in single cells. These technologies allow the study of biology at an unprecedented scale and resolution. Among the outcomes have been detailed maps of early human embryonic development, the discovery of new disease-associated cell types, and cell-targeted therapeutic interventions. Moreover, with recent advances in experimental techniques it is now possible to measure multiple genomic modalities in the same cell. While multimodal single-cell data is increasingly available, data analysis methods are still scarce. Due to the small volume of a single cell, measurements are sparse and noisy. Differences in molecular sampling depths between cells (sequencing depth) and technical effects from handling cells in batches (batch effects) can often overwhelm biological differences. When analyzing multimodal data, one must account for different feature spaces, as well as shared and unique variation between modalities and between batches. Furthermore, current pipelines for single-cell data analysis treat cells as static snapshots, even when there is an underlying dynamical biological process. Accounting for temporal dynamics alongside state changes over time is an open challenge in single-cell data science. Generally, genetic information flows from DNA to RNA to proteins. DNA must be accessible (ATAC data) to produce RNA (GEX data), and RNA in turn is used as a template to produce protein (ADT data). These processes are regulated by feedback: for example, a protein may bind DNA to prevent the production of more RNA. This genetic regulation is the foundation for dynamic cellular processes that allow organisms to develop and adapt to changing environments. In single-cell data science, dynamic processes have been modeled by so-called pseudotime algorithms that capture the progression of the biological process. Yet, generalizing these algorithms to account for both pseudotime and real time is still an open problem.

ORGANISM(S): Homo sapiens

PROVIDER: GSE305370 | GEO | 2025/10/23

REPOSITORIES: GEO

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Project description:Single-cell RNA sequencing (scRNA-seq) is an invaluable tool for profiling cells in complex tissues and dissecting activation states that lack well-defined surface protein expression. For immune cells, the transcriptomic profile captured by scRNA- seq cannot always identify cell states and subsets defined by conventional flow cytometry. Emerging technologies have enabled multimodal sequencing of single cells, such as paired sequencing of the transcriptome and surface proteome by CITE-seq, but integrating these high dimensional modalities for accurate cell type annotation remains a challenge in the field. Here, we describe a machine learning tool called MultiModal Classifier Hierarchy (MMoCHi) for the cell-type annotation of CITE-seq data. Our classifier involves several steps: 1) we use landmark registration to remove batch-related staining artifacts in CITE-Seq protein expression, 2) the user defines a hierarchy of classifications based on cell type similarity and ontology and provides markers (protein or gene expression) for the identification of ground truth populations within the dataset by threshold gating, 3) progressing through this user-defined hierarchy, we train a random forest classifier using all available modalities (surface proteome and transcriptome data), and 4) we use these forests to predict cell types across the entire dataset. Applying MMoCHi to CITE-seq data of immune cells isolated from eight distinct tissue sites of two human organ donors yields high-purity cell type annotations encompassing the broad array of immune cell states in the dataset. This includes T and B cell memory subsets, macrophages and monocytes, and natural killer cells, as well as rare populations of plasmacytoid dendritic cells, innate T cells, and innate lymphoid cell subsets. We validate the use of feature importances extracted from the classifier hierarchy to select robust genes for improved identification of T cell memory subsets by scRNA-seq. Together, MMoCHi provides a comprehensive system of tools for the batch-correction and cell- type annotation of CITE-seq data. Moreover, this tool provides flexibility in classification hierarchy design allowing for cell type annotations to reflect a researcher’s specific experimental design. This flexibility also renders MMoCHi readily extendable beyond immune cell annotation, and potentially adaptable to other sequencing modalities.

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Multimodal Single-Cell Integration Across Time, Individuals, and Batches

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