Researchers at Imperial College London and AstraZeneca have explored how combining two powerful techniques—single-cell RNA sequencing and ATAC sequencing—can enhance our ability to identify and classify different cell types. This step, called cell type annotation, is essential for understanding the roles individual cells play in health and disease.

While RNA sequencing provides valuable information about gene expression, ATAC sequencing gives insights into the DNA regions that are accessible for gene regulation. By merging these datasets, the researchers found that the annotation process could be significantly improved in certain types of cells, like human peripheral blood mononuclear cells (PBMCs). For example, when they combined RNA sequencing and ATAC-seq data, they achieved more accurate predictions of cell types, especially for CD4 T effector memory cells. However, this improvement wasn’t seen in neuronal cells with Alzheimer’s disease, which suggests that this approach may not work universally.

Overview of supervised annotation workflow

Fig. 1 illustrates an overview of the supervised annotation workflow, which involved three primary steps. Initially, a 10x Genomics multi-omic (RNA + ATAC) sequencing dataset detailing peripheral blood mononuclear cells (PBMC) or neuronal cells underwent rigorous quality control (QC) procedures. Subsequently, ground truth labels were derived through a pre-processing pipeline, followed by weighted nearest neighbors (WNN) clustering and marker gene-based annotation. These labels served as the ground truth for the supervised classification models. The final step involved generating 10 bootstrapped train and out-of-bag (OOB) test sets. This was followed by pre-processing and dimensionality reduction using principal component analysis (PCA) or single-cell Variational Inference (scVI). Classification was then performed using support vector machine (SVM), random forest (RF), or logistic regression (LR) models. Models utilizing RNA-only embeddings were compared to those utilizing RNA and ATAC

The research highlights the power of combining multiple types of data to get a clearer and more detailed picture of cell function, particularly when studying immune cells and diseases that involve complex cellular changes. This approach could lead to better diagnostic tools and treatments for conditions like Alzheimer’s and autoimmune disorders.