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Single-cell RNA and protein profiling of immune cells from the mouse brain and its border tissues

ABSTRACT: Brain-immune crosstalk and neuroinflammation critically shape brain physiology in health and disease. A detailed understanding of the brain immune landscape is essential for developing new treatments for many neurological disorders. Single-cell technologies offer an unbiased assessment of the heterogeneity, dynamics and functions of immune cells. Here, we provide a protocol that outlines all the steps involved for performing single-cell multi-omic analysis of the brain immune compartment. This includes a step-by-step description on how to micro-dissect the border regions of the mouse brain, together with dissociation protocols tailored to each of these tissues. These combine a high-yield with minimal dissociation-induced gene expression changes. Next, we outline the steps involved for droplet-based single-cell RNA sequencing (scRNA-seq), Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), or high-dimensional flow cytometry. Each of these single-cell modalities has specific strengths and limitations, but they are highly complementary. Importantly, we detail how to implement CITE-seq with large antibody panels to obtain unbiased protein-expression screening coupled to transcriptome analysis. Finally, we describe the main steps involved in the analysis and interpretation of the data. This optimized workflow allows for a detailed assessment of immune cell heterogeneity and activation in the whole brain or specific border regions, at RNA and protein level. The wetlab workflow can be completed by properly trained researchers (with basic proficiency in cell and molecular biology) and takes between 6 and 11 hours, depending on the chosen procedures. The computational analysis requires a background in bioinformatics and programming in R.

ORGANISM(S): Mus musculus

PROVIDER: GSE191075 | GEO | 2021/12/23

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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Single-cell RNA and protein profiling of immune cells from the mouse brain and its border tissues

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