Preparation of Single-Cell RNA-Seq Libraries for Next Generation Sequencing

For the past several decades, due to technical limitations, the field of transcriptomics has focused on population-level measurements that can mask significant differences between individual cells. With the advent of single-cell RNA-Seq, it is now possible to profile the responses of individual cells at unprecedented depth and thereby uncover, transcriptome-wide, the heterogeneity that exists within these populations. This unit describes a method that merges several important technologies to produce, in high-throughput, single-cell RNA-Seq libraries. Complementary DNA (cDNA) is made from full-length mRNA transcripts using a reverse transcriptase that has terminal transferase activity. This, when combined with a second “template-switch” primer, allows for cDNAs to be constructed that have two universal priming sequences. Following preamplification from these common sequences, Nextera XT is used to prepare a pool of 96 uniquely indexed samples ready for Illumina sequencing.National Institutes of Health (U.S.) (Centers of Excellence in Genomic Science 1P50HG006193-01)National Institutes of Health (U.S.) (Pioneer Award DP1OD003958-01)Broad Institute of MIT and HarvardHoward Hughes Medical InstituteKlarman Cell Observator

Transcript-indexed ATAC-seq for precision immune profiling.

T cells create vast amounts of diversity in the genes that encode their T cell receptors (TCRs), which enables individual clones to recognize specific peptide-major histocompatibility complex (MHC) ligands. Here we combined sequencing of the TCR-encoding genes with assay for transposase-accessible chromatin with sequencing (ATAC-seq) analysis at the single-cell level to provide information on the TCR specificity and epigenomic state of individual T cells. By using this approach, termed transcript-indexed ATAC-seq (T-ATAC-seq), we identified epigenomic signatures in immortalized leukemic T cells, primary human T cells from healthy volunteers and primary leukemic T cells from patient samples. In peripheral blood CD4+ T cells from healthy individuals, we identified cis and trans regulators of naive and memory T cell states and found substantial heterogeneity in surface-marker-defined T cell populations. In patients with a leukemic form of cutaneous T cell lymphoma, T-ATAC-seq enabled identification of leukemic and nonleukemic regulatory pathways in T cells from the same individual by allowing separation of the signals that arose from the malignant clone from the background T cell noise. Thus, T-ATAC-seq is a new tool that enables analysis of epigenomic landscapes in clonal T cells and should be valuable for studies of T cell malignancy, immunity and immunotherapy

Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

Recent molecular studies have shown that, even when derived from a seemingly homogenous population, individual cells can exhibit substantial differences in gene expression, protein levels and phenotypic output1, 2, 3, 4, 5, with important functional consequences4, 5. Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected RNAs1, 2 or proteins5, 6 simultaneously, because genomic profiling methods3 could not be applied to single cells until very recently7, 8, 9, 10. Here we use single-cell RNA sequencing to investigate heterogeneity in the response of mouse bone-marrow-derived dendritic cells (BMDCs) to lipopolysaccharide. We find extensive, and previously unobserved, bimodal variation in messenger RNA abundance and splicing patterns, which we validate by RNA-fluorescence in situ hybridization for select transcripts. In particular, hundreds of key immune genes are bimodally expressed across cells, surprisingly even for genes that are very highly expressed at the population average. Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. Some of the observed bimodality can be attributed to closely related, yet distinct, known maturity states of BMDCs; other portions reflect differences in the usage of key regulatory circuits. For example, we identify a module of 137 highly variable, yet co-regulated, antiviral response genes. Using cells from knockout mice, we show that variability in this module may be propagated through an interferon feedback circuit, involving the transcriptional regulators Stat2 and Irf7. Our study demonstrates the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits.National Institutes of Health (U.S.) (NIH Postdoctoral Fellowship (1F32HD075541-01))Charles H. Hood Foundation (Postdoctoral Fellowship)National Institutes of Health (U.S.) (NIH grant U54 AI057159)National Institutes of Health (U.S.) (NIH New Innovator Award (DP2 OD002230))National Institutes of Health (U.S.) (NIH CEGS Award (1P50HG006193-01))National Institutes of Health (U.S.) (NIH Pioneer Awards (5DP1OD003893-03))National Institutes of Health (U.S.) (NIH Pioneer Awards (DP1OD003958-01))Broad Institute of MIT and HarvardBroad Institute of MIT and Harvard (Klarman Cell Observatory

A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells

Reversing the dysfunctional T cell state that arises in cancer and chronic viral infections is the focus of therapeutic interventions; however, current therapies are effective in only some patients and some tumor types. To gain a deeper molecular understanding of the dysfunctional T cell state, we analyzed population and single-cell RNA profiles of CD8+tumor-infiltrating lymphocytes (TILs) and used genetic perturbations to identify a distinct gene module for T cell dysfunction that can be uncoupled from T cell activation. This distinct dysfunction module is downstream of intracellular metallothioneins that regulate zinc metabolism and can be identified at single-cell resolution. We further identify Gata-3, a zinc-finger transcription factor in the dysfunctional module, as a regulator of dysfunction, and we use CRISPR-Cas9 genome editing to show that it drives a dysfunctional phenotype in CD8+TILs. Our results open novel avenues for targeting dysfunctional T cell states while leaving activation programs intact

Single-cell RNA-seq reveals dynamic paracrine control of cellular variation

High-throughput single-cell transcriptomics offers an unbiased approach for understanding the extent, basis and function of gene expression variation between seemingly identical cells. Here we sequence single-cell RNA-seq libraries prepared from over 1,700 primary mouse bone-marrow-derived dendritic cells spanning several experimental conditions. We find substantial variation between identically stimulated dendritic cells, in both the fraction of cells detectably expressing a given messenger RNA and the transcript’s level within expressing cells. Distinct gene modules are characterized by different temporal heterogeneity profiles. In particular, a ‘core’ module of antiviral genes is expressed very early by a few ‘precocious’ cells in response to uniform stimulation with a pathogenic component, but is later activated in all cells. By stimulating cells individually in sealed microfluidic chambers, analysing dendritic cells from knockout mice, and modulating secretion and extracellular signalling, we show that this response is coordinated by interferon-mediated paracrine signalling from these precocious cells. Notably, preventing cell-to-cell communication also substantially reduces variability between cells in the expression of an early-induced ‘peaked’ inflammatory module, suggesting that paracrine signalling additionally represses part of the inflammatory program. Our study highlights the importance of cell-to-cell communication in controlling cellular heterogeneity and reveals general strategies that multicellular populations can use to establish complex dynamic responses.National Human Genome Research Institute (U.S.). Centers of Excellence in Genomic Science (1P50HG006193-01)National Institutes of Health (U.S.). Pioneer Award (DP1OD003958-01)Howard Hughes Medical InstituteBroad Institute of MIT and Harvard. Klarman Cell Observator

Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma

Although human tumours are shaped by the genetic evolution of cancer cells, evidence also suggests that they display hierarchies related to developmental pathways and epigenetic programs in which cancer stem cells (CSCs) can drive tumour growth and give rise to differentiated progeny. Yet, unbiased evidence for CSCs in solid human malignancies remains elusive. Here we profile 4,347 single cells from six IDH1 or IDH2 mutant human oligodendrogliomas by RNA sequencing (RNA-seq) and reconstruct their developmental programs from genome-wide expression signatures. We infer that most cancer cells are differentiated along two specialized glial programs, whereas a rare subpopulation of cells is undifferentiated and associated with a neural stem cell expression program. Cells with expression signatures for proliferation are highly enriched in this rare subpopulation, consistent with a model in which CSCs are primarily responsible for fuelling the growth of oligodendroglioma in humans. Analysis of copy number variation (CNV) shows that distinct CNV sub-clones within tumours display similar cellular hierarchies, suggesting that the architecture of oligodendroglioma is primarily dictated by developmental programs. Subclonal point mutation analysis supports a similar model, although a full phylogenetic tree would be required to definitively determine the effect of genetic evolution on the inferred hierarchies. Our single-cell analyses provide insight into the cellular architecture of oligodendrogliomas at single-cell resolution and support the cancer stem cell model, with substantial implications for disease management

Spatial reconstruction of single-cell gene expression data

Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. RNA staining methods assay only a small number of transcripts, whereas single-cell RNA-seq, which measures global gene expression, separates cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos and generated a transcriptome-wide map of spatial patterning. We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers. Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.Howard Hughes Medical InstituteKlarman Cell ObservatoryNational Human Genome Research Institute (U.S.) (Centers for Excellence in Genomics Science 1P50HG006193

Spatial reconstruction of single-cell gene expression

Spatial localization is a key determinant of cellular fate and behavior, but spatial RNA assays traditionally rely on staining for a limited number of RNA species. In contrast, single-cell RNA-seq allows for deep profiling of cellular gene expression, but established methods separate cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos, inferring a transcriptome-wide map of spatial patterning. We confirmed Seurat’s accuracy using several experimental approaches, and used it to identify a set of archetypal expression patterns and spatial markers. Additionally, Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems