Comprehensive Integration of Single-Cell Data.

Single cell transcriptomics (scRNA-seq) has transformed our ability to discover and annotate cell types and states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, including high-dimensional immunophenotypes, chromatin accessibility, and spatial positioning, a key analytical challenge is to integrate these datasets into a harmonized atlas that can be used to better understand cellular identity and function. Here, we develop a computational strategy to "anchor" diverse datasets together, enabling us to integrate and compare single cell measurements not only across scRNA-seq technologies, but different modalities as well. After demonstrating substantial improvement over existing methods for data integration, we anchor scRNA-seq experiments with scATAC-seq datasets to explore chromatin differences in closely related interneuron subsets, and project single cell protein measurements onto a human bone marrow atlas to annotate and characterize lymphocyte populations. Lastly, we demonstrate how anchoring can harmonize in-situ gene expression and scRNA-seq datasets, allowing for the transcriptome-wide imputation of spatial gene expression patterns, and the identification of spatial relationships between mapped cell types in the visual cortex. Our work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets. Availability: Installation instructions, documentation, and tutorials are available at: https://www.satijalab.org/seurat

https://biorxiv.org/content/biorxiv/early/2018/11/02/460147.full-text.pdf

Comprehensive integration of single cell data

Spatial positions of cells in tissues strongly influence function, yet a high-throughput, genome-wide readout of gene expression with cellular resolution is lacking. We developed Slide-seq, a method for transferring RNA from tissue sections onto a surface covered in DNA-barcoded beads with known positions, allowing the locations of the RNA to be inferred by sequencing. Using Slide-seq, we localized cell types identified by single-cell RNA sequencing datasets within the cerebellum and hippocampus, characterized spatial gene expression patterns in the Purkinje layer of mouse cerebellum, and defined the temporal evolution of cell type-specific responses in a mouse model of traumatic brain injury. These studies highlight how Slide-seq provides a scalable method for obtaining spatially resolved gene expression data at resolutions comparable to the sizes of individual cells.

Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution

Single-cell RNA-seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single-cell analysis methods. As more analysis tools are becoming available, it is becoming increasingly difficult to navigate this landscape and produce an up-to-date workflow to analyse one's data. Here, we detail the steps of a typical single-cell RNA-seq analysis, including pre-processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell- and gene-level downstream analysis. We formulate current best-practice recommendations for these steps based on independent comparison studies. We have integrated these best-practice recommendations into a workflow, which we apply to a public dataset to further illustrate how these steps work in practice. Our documented case study can be found at https://www.github.com/theislab/single-cell-tutorial This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.

https://www.embopress.org/doi/epdf/10.15252/msb.20188746

Current best practices in single-cell RNA-seq analysis: a tutorial.

Single-Cell RNA Sequencing of Microglia throughout the Mouse Lifespan and in the Injured Brain Reveals Complex Cell-State Changes.

Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain.

The mammalian brain is composed of diverse, specialized cell populations, few of which we fully understand. To more systematically ascertain and learn from cellular specializations in the brain, we used Drop-seq to perform single-cell RNA sequencing of 690,000 cells sampled from nine regions of the adult mouse brain: frontal and posterior cortex (156,000 and 99,000 cells, respectively), hippocampus (113,000), thalamus (89,000), cerebellum (26,000), and all of the basal ganglia - the striatum (77,000), globus pallidus externus/nucleus basalis (66,000), entopeduncular/subthalamic nuclei (19,000), and the substantia nigra/ventral tegmental area (44,000). We developed computational approaches to distinguish biological from technical signals in single-cell data, then identified 565 transcriptionally distinct groups of cells, which we annotate and present through interactive online software we developed for visualizing and re-analyzing these data (DropViz). Comparison of cell classes and types across regions revealed features of brain organization. These included a neuronal gene-expression module for synthesizing axonal and presynaptic components; widely shared patterns in the combinatorial co-deployment of voltage-gated ion channels by diverse neuronal populations; functional distinctions among cells of the brain vasculature; and specialization of glutamatergic neurons across cortical regions to a degree not observed in other neuronal or non-neuronal populations. We describe systematic neuronal classifications for two complex, understudied regions of the basal ganglia, the globus pallidus externus and substantia nigra reticulata. In the striatum, where neuron types have been intensely researched, our data reveal a previously undescribed population of striatal spiny projection neurons (SPNs) comprising 4% of SPNs. The adult mouse brain cell atlas can serve as a reference for analyses of development, disease, and evolution.

https://www.biorxiv.org/content/biorxiv/early/2018/04/10/299081.full.pdf

Shuyu Wang

Papers

Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain.

A Single-Cell Atlas of Cell Types, States, and Other Transcriptional Patterns from Nine Regions of the Adult Mouse Brain