Consensus scHPF Identifies Cell Type-Specific Drug Responses in Glioma by Integrating Large-Scale scRNA-seq.

Single-cell transcriptomic analyses now frequently involve elaborate study designs including samples from multiple individuals, experimental conditions, perturbations, and batches from complex tissues. Dimensionality reduction is required to facilitate integration, interpretation, and statistical analysis. However, these datasets often include subtly different cellular subpopulations or state transitions, which are poorly described by clustering.

Deconvolution of cell type-specific drug responses in human tumor tissue with single-cell RNA-seq.

Background: Preclinical studies require models that recapitulate the cellular diversity of human tumors and provide insight into the drug sensitivities of specific cellular populations. The ideal platform would enable rapid screening of cell type-specific drug sensitivities directly in patient tumor tissue and reveal strategies to overcome intratumoral heterogeneity.

Methods: We combine multiplexed drug perturbation in acute slice culture from freshly resected tumors with single-cell RNA sequencing (scRNA-seq) to profile transcriptome-wide drug responses in individual patients.

Single-cell transcriptomics of human T cells reveals tissue and activation signatures in health and disease.

Human T cells coordinate adaptive immunity in diverse anatomic compartments through production of cytokines and effector molecules, but it is unclear how tissue site influences T cell persistence and function. Here, we use single cell RNA-sequencing (scRNA-seq) to define the heterogeneity of human T cells isolated from lungs, lymph nodes, bone marrow and blood, and their functional responses following stimulation. Through analysis of >50,000 resting and activated T cells, we reveal tissue T cell signatures in mucosal and lymphoid sites, and lineage-specific activation states across all sites including distinct effector states for CD8 T cells and an interferon-response state for CD4 T cells.

gene signature identification from single-cell RNA-seq with hierarchical Poisson factorization.

Common approaches to gene signature discovery in single-cell RNA-sequencing (scRNA-seq) depend upon predefined structures like clusters or pseudo-temporal order, require prior normalization, or do not account for the sparsity of single-cell data. We present single-cell hierarchical Poisson factorization (scHPF), a Bayesian factorization method that adapts hierarchical Poisson factorization (Gopalan , 2015, , 326) for discovery of both continuous and discrete expression patterns from scRNA-seq. scHPF does not require prior normalization and captures statistical properties of single-cell data better than other methods in benchmark datasets.

Single-Cell Analysis of Regional Differences in Adult V-SVZ Neural Stem Cell Lineages.

The ventricular-subventricular zone (V-SVZ) harbors adult neural stem cells. V-SVZ neural stem cells exhibit features of astrocytes, have a regional identity, and depending on their location in the lateral or septal wall of the lateral ventricle, generate different types of neuronal and glial progeny. We performed large-scale single-cell RNA sequencing to provide a molecular atlas of cells from the lateral and septal adult V-SVZ of male and female mice.

Single-cell transcriptome analysis of lineage diversity in high-grade glioma.

Background: Despite extensive molecular characterization, we lack a comprehensive understanding of lineage identity, differentiation, and proliferation in high-grade gliomas (HGGs).

Methods: We sampled the cellular milieu of HGGs by profiling dissociated human surgical specimens with a high-density microwell system for massively parallel single-cell RNA-Seq. We analyzed the resulting profiles to identify subpopulations of both HGG and microenvironmental cells and applied graph-based methods to infer structural features of the malignantly transformed populations.

Single-Cell Transcriptomic Analysis of Tumor Heterogeneity.

Intratumoral heterogeneity is among the greatest challenges in precision cancer therapy. However, developments in high-throughput single-cell RNA sequencing (scRNA-seq) may now provide the statistical power to dissect the diverse cellular populations of tumors. In the future these technologies might inform the selection of targeted combination therapies and enrollment criteria for clinical trials.