A single cell RNA sequencing resource for early sea urchin development.

Identifying cell states during development from their mRNA profiles provides insight into their gene regulatory network. Here, we leverage the sea urchin embryo for its well-established gene regulatory network to interrogate the embryo using single cell RNA sequencing. We tested eight developmental stages in Strongylocentrotus purpuratus, from the eight-cell stage to late in gastrulation. We used these datasets to parse out 22 major cell states of the embryo, focusing on key transition stages for cell type specification of each germ layer. Subclustering of these major embryonic domains revealed over 50 cell states with distinct transcript profiles. Furthermore, we identified the transcript profile of two cell states expressing germ cell factors, one we conclude represents the primordial germ cells and the other state is transiently present during gastrulation. We hypothesize that these cells of the Veg2 tier of the early embryo represent a lineage that converts to the germ line when the primordial germ cells are deleted. This broad resource will hopefully enable the community to identify other cell states and genes of interest to expose the underpinning of developmental mechanisms.

Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign.

Single-cell measurement techniques can now probe gene expression in heterogeneous cell populations from the human body across a range of environmental and physiological conditions. However, new mathematical and computational methods are required to represent and analyze gene-expression changes that occur in complex mixtures of single cells as they respond to signals, drugs, or disease states. Here, we introduce a mathematical modeling platform, PopAlign, that automatically identifies subpopulations of cells within a heterogeneous mixture and tracks gene-expression and cell-abundance changes across subpopulations by constructing and comparing probabilistic models. Probabilistic models provide a low-error, compressed representation of single-cell data that enables efficient large-scale computations. We apply PopAlign to analyze the impact of 40 different immunomodulatory compounds on a heterogeneous population of donor-derived human immune cells as well as patient-specific disease signatures in multiple myeloma. PopAlign scales to comparisons involving tens to hundreds of samples, enabling large-scale studies of natural and engineered cell populations as they respond to drugs, signals, or physiological change.

Dissecting the Landscape of Activated CMV-Stimulated CD4+ T Cells in Humans by Linking Single-Cell RNA-Seq With T-Cell Receptor Sequencing.

CD4+ T cells are crucial in cytomegalovirus (CMV) infection, but their role in infection remains unclear. The heterogeneity and potential functions of CMVpp65-reactivated CD4+ T cell subsets isolated from human peripheral blood, as well as their potential interactions, were analyzed by single-cell RNA-seq and T cell receptor (TCR) sequencing. Tregs comprised the largest population of these reactivated cells, and analysis of Treg gene expression showed transcripts associated with both inflammatory and inhibitory functions. The detailed phenotypes of CMV-reactivated CD4+ cytotoxic T1 (CD4+ CTL1), CD4+ cytotoxic T2 (CD4+ CTL2), and recently activated CD4+ T (Tra) cells were analyzed in single cells. Assessment of the TCR repertoire of CMV-reactivated CD4+ T cells confirmed the clonal expansion of stimulated CD4+ CTL1 and CD4+ CTL2 cells, which share a large number of TCR repertoires. This study provides clues for resolving the functions of CD4+ T cell subsets and their interactions during CMV infection. The specific cell groups defined in this study can provide resources for understanding T cell responses to CMV infection.

Identifying gene expression from single cells to single genes.

Gene regulatory networks reveal how transcription factors contribute to a dynamic cascade of cellular information processing. Recent advances in technologies have enhanced the toolkit for testing GRN mechanisms and connections. Here we emphasize three approaches that we have found important for interrogating transcriptional mechanisms in echinoderms: single cell mRNA sequencing (drop-seq), nascent RNA detection and identification, and chromatin immunoprecipitation (ChIP). We present these applications in order since it is a logical experimental protocol. With preliminary information from bulk mRNA transcriptome analysis and differential gene expression studies (DE-seq), one may need to test in what specific cells important genes may be expressed and to use single cell sequencing to define such links. Nascent RNA analysis with the Click-iT chemistry allows the investigator to deduce when the RNA was transcribed, not just identify its presence, and ChIP allows the investigator to study direct interactions of putative transcriptional regulators with the gene promoter of interest. This flow of thinking, and the technologies to support it, is presented here for echinoderms. While many of the procedures are general and applicable to many organisms and cell types, we emphasize unique aspects of the protocols for consideration in using echinoderm embryos, larvae, and adult tissues.

The effects of restricted glycolysis on stem-cell like characteristics of breast cancer cells.

Altered glycolysis is a characteristic of many cancers, and can also be associated with changes in stem cell-like cancer (SCLC) cell populations. We therefore set out to directly examine the effect of glycolysis on SCLC cell phenotype, using a model where glycolysis is stably reduced by adapting the cells to a sugar source other than glucose. Restricting glycolysis using this approach consistently resulted in cells with increased oncogenic potential; including an increase in SCLC cells, proliferation in 3D matrigel, invasiveness, chemoresistance, and altered global gene expression. Tumorigenicity in vivo was also markedly increased. SCLC cells exhibited increased dependence upon alternate metabolic pathways. They also became c-KIT dependent, indicating that their apparent state of maturation is regulated by glycolysis. Single-cell mRNA sequencing identified altered networks of metabolic-, stem- and signaling- gene expression within SCLC-enriched populations in response to glycolytic restriction. Therefore, reduced glycolysis, which may occur in niches within tumors where glucose availability is limiting, can promote tumor aggressiveness by increasing SCLC cell populations, but can also introduce novel, potentially exploitable, vulnerabilities in SCLC cells.

Spatial Transcriptomics: Constructing a Single-Cell Resolution Transcriptome-Wide Expression Atlas.

The method described here aims at the construction of a single-cell resolution gene expression atlas for an animal or tissue, combining in situ hybridization (ISH) and single-cell mRNA-sequencing (scRNAseq).A high resolution and medium-coverage gene expression atlas of an animal or tissue of interest can be obtained by performing a series of ISH experiments, followed by a process of image registration and gene expression averaging. Using the overlapping fraction of the genes, concomitantly obtained scRNAseq data can be fitted into the spatial context of the gene expression atlas, complementing the coverage by genes.