Induction of a colitogenic phenotype in Th1-like cells depends on interleukin-23 receptor signaling.

Interleukin-23 receptor plays a critical role in inducing inflammation and autoimmunity. Here, we report that Th1-like cells differentiated in vitro with IL-12 + IL-21 showed similar IL-23R expression to that of pathogenic Th17 cells using eGFP reporter mice. Fate mapping established that these cells did not transition through a Th17 cell state prior to becoming Th1-like cells, and we observed their emergence in vivo in the T cell adoptive transfer colitis model. Using IL-23R-deficient Th1-like cells, we demonstrated that IL-23R was required for the development of a highly colitogenic phenotype. Single-cell RNA sequencing analysis of intestinal T cells identified IL-23R-dependent genes in Th1-like cells that differed from those expressed in Th17 cells. The perturbation of one of these regulators (CD160) in Th1-like cells inhibited the induction of colitis. We thus uncouple IL-23R as a purely Th17 cell-specific factor and implicate IL-23R signaling as a pathogenic driver in Th1-like cells inducing tissue inflammation.

Metabolic modeling of single Th17 cells reveals regulators of autoimmunity.

Metabolism is a major regulator of immune cell function, but it remains difficult to study the metabolic status of individual cells. Here, we present Compass, an algorithm to characterize cellular metabolic states based on single-cell RNA sequencing and flux balance analysis. We applied Compass to associate metabolic states with T helper 17 (Th17) functional variability (pathogenic potential) and recovered a metabolic switch between glycolysis and fatty acid oxidation, akin to known Th17/regulatory T cell (Treg) differences, which we validated by metabolic assays. Compass also predicted that Th17 pathogenicity was associated with arginine and downstream polyamine metabolism. Indeed, polyamine-related enzyme expression was enhanced in pathogenic Th17 and suppressed in Treg cells. Chemical and genetic perturbation of polyamine metabolism inhibited Th17 cytokines, promoted Foxp3 expression, and remodeled the transcriptome and epigenome of Th17 cells toward a Treg-like state. In vivo perturbations of the polyamine pathway altered the phenotype of encephalitogenic T cells and attenuated tissue inflammation in CNS autoimmunity.

Hotspot identifies informative gene modules across modalities of single-cell genomics.

Two fundamental aims that emerge when analyzing single-cell RNA-seq data are identifying which genes vary in an informative manner and determining how these genes organize into modules. Here, we propose a general approach to these problems, called "Hotspot," that operates directly on a given metric of cell-cell similarity, allowing for its integration with any method (linear or non-linear) for identifying the primary axes of transcriptional variation between cells. In addition, we show that when using multimodal data, Hotspot can be used to identify genes whose expression reflects alternative notions of similarity between cells, such as physical proximity in a tissue or clonal relatedness in a cell lineage tree. In this manner, we demonstrate that while Hotspot is capable of identifying genes that reflect nuanced transcriptional variability between T helper cells, it can also identify spatially dependent patterns of gene expression in the cerebellum as well as developmentally heritable expression programs during embryogenesis. Hotspot is implemented as an open-source Python package and is available for use at http://www.github.com/yoseflab/hotspot. A record of this paper's transparent peer review process is included in the supplemental information.

Functional interpretation of single cell similarity maps.

We present Vision, a tool for annotating the sources of variation in single cell RNA-seq data in an automated and scalable manner. Vision operates directly on the manifold of cell-cell similarity and employs a flexible annotation approach that can operate either with or without preconceived stratification of the cells into groups or along a continuum. We demonstrate the utility of Vision in several case studies and show that it can derive important sources of cellular variation and link them to experimental meta-data even with relatively homogeneous sets of cells. Vision produces an interactive, low latency and feature rich web-based report that can be easily shared among researchers, thus facilitating data dissemination and collaboration.

Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq.

Systematic measurement biases make normalization an essential step in single-cell RNA sequencing (scRNA-seq) analysis. There may be multiple competing considerations behind the assessment of normalization performance, of which some may be study specific. We have developed "scone"- a flexible framework for assessing performance based on a comprehensive panel of data-driven metrics. Through graphical summaries and quantitative reports, scone summarizes trade-offs and ranks large numbers of normalization methods by panel performance. The method is implemented in the open-source Bioconductor R software package scone. We show that top-performing normalization methods lead to better agreement with independent validation data for a collection of scRNA-seq datasets. scone can be downloaded at http://bioconductor.org/packages/scone/.

FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data.

BACKGROUND: A key challenge in the emerging field of single-cell RNA-Seq is to characterize phenotypic diversity between cells and visualize this information in an informative manner. A common technique when dealing with high-dimensional data is to project the data to 2 or 3 dimensions for visualization. However, there are a variety of methods to achieve this result and once projected, it can be difficult to ascribe biological significance to the observed features. Additionally, when analyzing single-cell data, the relationship between cells can be obscured by technical confounders such as variable gene capture rates. RESULTS: To aid in the analysis and interpretation of single-cell RNA-Seq data, we have developed FastProject, a software tool which analyzes a gene expression matrix and produces a dynamic output report in which two-dimensional projections of the data can be explored. Annotated gene sets (referred to as gene 'signatures') are incorporated so that features in the projections can be understood in relation to the biological processes they might represent. FastProject provides a novel method of scoring each cell against a gene signature so as to minimize the effect of missed transcripts as well as a method to rank signature-projection pairings so that meaningful associations can be quickly identified. Additionally, FastProject is written with a modular architecture and designed to serve as a platform for incorporating and comparing new projection methods and gene selection algorithms. CONCLUSIONS: Here we present FastProject, a software package for two-dimensional visualization of single cell data, which utilizes a plethora of projection methods and provides a way to systematically investigate the biological relevance of these low dimensional representations by incorporating domain knowledge.

Protein Sialylation Regulates a Gene Expression Signature that Promotes Breast Cancer Cell Pathogenicity.

Many mechanisms have been proposed for how heightened aerobic glycolytic metabolism fuels cancer pathogenicity, but there are still many unexplored pathways. Here, we have performed metabolomic profiling to map glucose incorporation into metabolic pathways upon transformation of mammary epithelial cells by 11 commonly mutated human oncogenes. We show that transformation of mammary epithelial cells by oncogenic stimuli commonly shunts glucose-derived carbons into synthesis of sialic acid, a hexosamine pathway metabolite that is converted to CMP-sialic acid by cytidine monophosphate N-acetylneuraminic acid synthase (CMAS) as a precursor to glycoprotein and glycolipid sialylation. We show that CMAS knockdown leads to elevations in intracellular sialic acid levels, a depletion of cellular sialylation, and alterations in the expression of many cancer-relevant genes to impair breast cancer pathogenicity. Our study reveals the heretofore unrecognized role of sialic acid metabolism and protein sialylation in regulating the expression of genes that maintain breast cancer pathogenicity.