Karthik Shekhar

Therapeutic efficacy of potent neutralizing HIV-1-specific monoclonal antibodies in SHIV-infected rhesus monkeys

Human immunodeficiency virus type 1 (HIV-1)-specific monoclonal antibodies with extraordinary potency and breadth have recently been described. In humanized mice, combinations of monoclonal antibodies have been shown to suppress viraemia, but the therapeutic potential of these monoclonal antibodies has not yet been evaluated in primates with an intact immune system. Here we show that administration of a cocktail of HIV-1-specific monoclonal antibodies, as well as the single glycan-dependent monoclonal antibody PGT121, resulted in a rapid and precipitous decline of plasma viraemia to undetectable levels in rhesus monkeys chronically infected with the pathogenic simian–human immunodeficiency virus SHIV-SF162P3. A single monoclonal antibody infusion afforded up to a 3.1 log decline of plasma viral RNA in 7 days and also reduced proviral DNA in peripheral blood, gastrointestinal mucosa and lymph nodes without the development of viral resistance. Moreover, after monoclonal antibody administration, host Gag-specific T-lymphocyte responses showed improved functionality. Virus rebounded in most animals after a median of 56 days when serum monoclonal antibody titres had declined to undetectable levels, although, notably, a subset of animals maintained long-term virological control in the absence of further monoclonal antibody infusions. These data demonstrate a profound therapeutic effect of potent neutralizing HIV-1-specific monoclonal antibodies in SHIV-infected rhesus monkeys as well as an impact on host immune responses. Our findings strongly encourage the investigation of monoclonal antibody therapy for HIV-1 in humans.

Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors

Whats in a drop of blood? Blood contains many types of cells, including many immune system components. Immune cells used to be characterized by marker-based assays, but now classification relies on the genes that cells express. Villani et al. used deep sequencing at the single-cell level and unbiased clustering to define six dendritic cell and four monocyte populations. This refined analysis has identified, among others, a previously unknown dendritic cell population that potently activates T cells. Further cell culture revealed possible differentiation progenitors within the different cell populations. Science, this issue p. eaah4573 Discovery of additional immune cell subtypes will help identify functions and immune monitoring during disease. INTRODUCTION Dendritic cells (DCs) and monocytes consist of multiple specialized subtypes that play a central role in pathogen sensing, phagocytosis, and antigen presentation. However, their identities and interrelationships are not fully understood, as these populations have historically been defined by a combination of morphology, physical properties, localization, functions, developmental origins, and expression of a restricted set of surface markers. RATIONALE To overcome this inherently biased strategy for cell identification, we performed single-cell RNA sequencing of ~2400 cells isolated from healthy blood donors and enriched for HLA-DR+ lineage− cells. This single-cell profiling strategy and unbiased genomic classification, together with follow-up profiling and functional and phenotypic characterization of prospectively isolated subsets, led us to identify and validate six DC subtypes and four monocyte subtypes, and thus revise the taxonomy of these cells. RESULTS Our study reveals: 1) A new DC subset, representing 2 to 3% of the DC populations across all 10 donors tested, characterized by the expression of AXL, SIGLEC1, and SIGLEC6 antigens, named AS DCs. The AS DC population further divides into two populations captured in the traditionally defined plasmacytoid DC (pDC) and CD1C+ conventional DC (cDC) gates. This split is further reflected through AS DC gene expression signatures spanning a spectrum between cDC-like and pDC-like gene sets. Although AS DCs share properties with pDCs, they more potently activate T cells. This discovery led us to reclassify pDCs as the originally described “natural interferon-producing cells (IPCs)” with weaker T cell proliferation induction ability. 2) A new subdivision within the CD1C+ DC subset: one defined by a major histocompatibility complex class II–like gene set and one by a CD14+ monocyte–like prominent gene set. These CD1C+ DC subsets, which can be enriched by combining CD1C with CD32B, CD36, and CD163 antigens, can both potently induce T cell proliferation. 3) The existence of a circulating and dividing cDC progenitor giving rise to CD1C+ and CLEC9A+ DCs through in vitro differentiation assays. This blood precursor is defined by the expression of CD100+CD34int and observed at a frequency of ~0.02% of the LIN–HLA-DR+ fraction. 4) Two additional monocyte populations: one expressing classical monocyte genes and cytotoxic genes, and the other with unknown functions. 5) Evidence for a relationship between blastic plasmacytoid DC neoplasia (BPDCN) cells and healthy DCs. CONCLUSION Our revised taxonomy will enable more accurate functional and developmental analyses as well as immune monitoring in health and disease. The discovery of AS DCs within the traditionally defined pDC population explains many of the cDC properties previously assigned to pDCs, highlighting the need to revisit the definition of pDCs. Furthermore, the discovery of blood cDC progenitors represents a new therapeutic target readily accessible in the bloodstream for manipulation, as well as a new source for better in vitro DC generation. Although the current results focus on DCs and monocytes, a similar strategy can be applied to build a comprehensive human immune cell atlas. Establishing a human blood monocyte and dendritic cell atlas. We isolated ~2400 cells enriched from the healthy human blood lineage− HLA-DR+ compartment and subjected them to single-cell RNA sequencing. This strategy, together with follow-up profiling and functional and phenotypic characterization, led us to update the original cell classification to include six DCs, four monocyte subtypes, and one conventional DC progenitor. Dendritic cells (DCs) and monocytes play a central role in pathogen sensing, phagocytosis, and antigen presentation and consist of multiple specialized subtypes. However, their identities and interrelationships are not fully understood. Using unbiased single-cell RNA sequencing (RNA-seq) of ~2400 cells, we identified six human DCs and four monocyte subtypes in human blood. Our study reveals a new DC subset that shares properties with plasmacytoid DCs (pDCs) but potently activates T cells, thus redefining pDCs; a new subdivision within the CD1C+ subset of DCs; the relationship between blastic plasmacytoid DC neoplasia cells and healthy DCs; and circulating progenitor of conventional DCs (cDCs). Our revised taxonomy will enable more accurate functional and developmental analyses as well as immune monitoring in health and disease.

Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE)

Significance Mass cytometry enables the measurement of nearly 40 different proteins at the single-cell level, providing an unprecedented level of multidimensional information. Because of the complexity of these datasets across diverse populations of cells, new computational tools are needed to glean useful biological insights. Here we describe ACCENSE (Automatic Classification of Cellular Expression by Nonlinear Stochastic Embedding), a tool that computes a two-dimensional nonlinear distillation of the raw data, and automatically stratifies cells into phenotypic subpopulations based on their distribution of markers. Applying this tool to murine CD8+ T-cell data recovers known naive and memory subpopulations, and reveals additional diversity within these. In particular, we identify a novel subpopulation with a distinct multivariate phenotype, but which is not distinguishable on a biaxial plot of conventional markers. Mass cytometry enables an unprecedented number of parameters to be measured in individual cells at a high throughput, but the large dimensionality of the resulting data severely limits approaches relying on manual “gating.” Clustering cells based on phenotypic similarity comes at a loss of single-cell resolution and often the number of subpopulations is unknown a priori. Here we describe ACCENSE, a tool that combines nonlinear dimensionality reduction with density-based partitioning, and displays multivariate cellular phenotypes on a 2D plot. We apply ACCENSE to 35-parameter mass cytometry data from CD8+ T cells derived from specific pathogen-free and germ-free mice, and stratify cells into phenotypic subpopulations. Our results show significant heterogeneity within the known CD8+ T-cell subpopulations, and of particular note is that we find a large novel subpopulation in both specific pathogen-free and germ-free mice that has not been described previously. This subpopulation possesses a phenotypic signature that is distinct from conventional naive and memory subpopulations when analyzed by ACCENSE, but is not distinguishable on a biaxial plot of standard markers. We are able to automatically identify cellular subpopulations based on all proteins analyzed, thus aiding the full utilization of powerful new single-cell technologies such as mass cytometry.

A single-cell survey of the small intestinal epithelium

Intestinal epithelial cells absorb nutrients, respond to microbes, function as a barrier and help to coordinate immune responses. Here we report profiling of 53,193 individual epithelial cells from the small intestine and organoids of mice, which enabled the identification and characterization of previously unknown subtypes of intestinal epithelial cell and their gene signatures. We found unexpected diversity in hormone-secreting enteroendocrine cells and constructed the taxonomy of newly identified subtypes, and distinguished between two subtypes of tuft cell, one of which expresses the epithelial cytokine Tslp and the pan-immune marker CD45, which was not previously associated with non-haematopoietic cells. We also characterized the ways in which cell-intrinsic states and the proportions of different cell types respond to bacterial and helminth infections: Salmonella infection caused an increase in the abundance of Paneth cells and enterocytes, and broad activation of an antimicrobial program; Heligmosomoides polygyrus caused an increase in the abundance of goblet and tuft cells. Our survey highlights previously unidentified markers and programs, associates sensory molecules with cell types, and uncovers principles of gut homeostasis and response to pathogens.

Massively parallel single-nucleus RNA-seq with DroNc-seq

Single-nucleus RNA sequencing (sNuc-seq) profiles RNA from tissues that are preserved or cannot be dissociated, but it does not provide high throughput. Here, we develop DroNc-seq: massively parallel sNuc-seq with droplet technology. We profile 39,111 nuclei from mouse and human archived brain samples to demonstrate sensitive, efficient, and unbiased classification of cell types, paving the way for systematic charting of cell atlases.

Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis

Mapping the vertebrate developmental landscape As embryos develop, numerous cell types with distinct functions and morphologies arise from pluripotent cells. Three research groups have used single-cell RNA sequencing to analyze the transcriptional changes accompanying development of vertebrate embryos (see the Perspective by Harland). Wagner et al. sequenced the transcriptomes of more than 90,000 cells throughout zebrafish development to reveal how cells differentiate during axis patterning, germ layer formation, and early organogenesis. Farrell et al. profiled the transcriptomes of tens of thousands of embryonic cells and applied a computational approach to construct a branching tree describing the transcriptional trajectories that lead to 25 distinct zebrafish cell types. The branching tree revealed how cells change their gene expression as they become more and more specialized. Briggs et al. examined whole frog embryos, spanning zygotic genome activation through early organogenesis, to map cell states and differentiation across all cell lineages over time. These data and approaches pave the way for the comprehensive reconstruction of transcriptional trajectories during development. Science, this issue p. 981, p. eaar3131, p. eaar5780; see also p. 967 Single-cell RNA sequencing and a computational technique reveal cell trajectories that form the complex body plan of the zebrafish embryo. INTRODUCTION During embryogenesis, pluripotent cells gradually become specialized and acquire distinct functions and morphologies. Because much of the specification process is controlled through changes in gene expression, the identification of the transcriptional trajectories underlying cell fate acquisition is paramount to understanding and manipulating development. RATIONALE Traditional approaches have studied specific fate decisions by analyzing the transcription of a few selected marker genes or by profiling isolated, predefined cell populations. The advent of large-scale single-cell RNA sequencing (scRNA-seq) provides the means to comprehensively define the gene expression states of all embryonic cells as they acquire their fates. This technology raises the possibility of identifying the molecular trajectories that describe cell fate specification by sampling densely during embryogenesis and connecting the transcriptomes of cells that have similar gene expression profiles. However, the numerous transcriptional states and branch points, as well as the asynchrony in developmental processes, pose major challenges to the computational reconstruction of developmental trajectories from scRNA-seq data. RESULTS We generated single-cell transcriptomes from 38,731 cells during early zebrafish embryogenesis at high temporal resolution, spanning 12 stages from the onset of zygotic transcription through early somitogenesis. We took two complementary approaches to identify the transcriptional trajectories in the data. First, we developed a simulated diffusion-based computational approach, URD, which identified the trajectories describing the specification of 25 cell types in the form of a branching tree. Second, we identified modules of coexpressed genes and connected them across developmental time. Combining the reconstructed developmental trajectories with differential gene expression analysis uncovered gene expression cascades leading to each cell type, including previously unidentified markers and candidate regulators. Combining these trajectories with Seurat, which infers the spatial positions of cells on the basis of their transcriptomes, connected the earlier spatial position of progenitors to the later fate of their descendants. Inspection of the developmental tree led to new insights about molecular specification in zebrafish. For example, the first branch point in the tree indicated that the first molecular specification event may not only separate the germ layers but also define the axial versus nonaxial mesendoderm. Additionally, some developmental branch points contained intermediate cells that expressed genes characteristic of multiple downstream cell fates. Gene expression analysis at one such branch point (the axial mesoderm) suggested that the intermediate cells switch their specification from one fate (notochord) to another (prechordal plate). Last, analysis of single-cell transcriptomes from a Nodal-signaling mutant revealed that even at the whole-transcriptome level, mutant cells were canalized into a subset of wild-type states and did not adopt any transcriptional states not observed in wild type, despite abnormal developmental signaling. CONCLUSION These findings reconstruct the gene expression trajectories during the embryogenesis of a vertebrate and highlight the concurrent canalization and plasticity of cell type specification. The scRNA-seq data and developmental tree provide a rich resource for future studies in zebrafish: The raw and processed data and the URD software are available for download, and the data can be browsed interactively online. Last, this approach provides a broadly applicable framework with which to reconstruct complex developmental trajectories from single-cell transcriptomes. Developmental tree of early zebrafish embryogenesis. Single-cell transcriptomes were generated from zebrafish embryos at 12 developmental stages (six of which are shown). The transcriptional trajectories that describe the fate specification of 25 cell types were reconstructed from the data. Molecular specification is visualized with a force-directed layout, in which each cell is represented by a point (colored by developmental stage), proceeding from pluripotent cells (at the bottom center) outward to 25 distinct cell types. A subset of the identified trajectories are labeled in groups. During embryogenesis, cells acquire distinct fates by transitioning through transcriptional states. To uncover these transcriptional trajectories during zebrafish embryogenesis, we sequenced 38,731 cells and developed URD, a simulated diffusion-based computational reconstruction method. URD identified the trajectories of 25 cell types through early somitogenesis, gene expression along them, and their spatial origin in the blastula. Analysis of Nodal signaling mutants revealed that their transcriptomes were canalized into a subset of wild-type transcriptional trajectories. Some wild-type developmental branch points contained cells that express genes characteristic of multiple fates. These cells appeared to trans-specify from one fate to another. These findings reconstruct the transcriptional trajectories of a vertebrate embryo, highlight the concurrent canalization and plasticity of embryonic specification, and provide a framework with which to reconstruct complex developmental trees from single-cell transcriptomes.

Statistical Linkage Analysis of Substitutions in Patient-Derived Sequences of Genotype 1a Hepatitis C Virus Nonstructural Protein 3 Exposes Targets for Immunogen Design

ABSTRACT Chronic hepatitis C virus (HCV) infection is one of the leading causes of liver failure and liver cancer, affecting around 3% of the worlds population. The extreme sequence variability of the virus resulting from error-prone replication has thwarted the discovery of a universal prophylactic vaccine. It is known that vigorous and multispecific cellular immune responses, involving both helper CD4+ and cytotoxic CD8+ T cells, are associated with the spontaneous clearance of acute HCV infection. Escape mutations in viral epitopes can, however, abrogate protective T-cell responses, leading to viral persistence and associated pathologies. Despite the propensity of the virus to mutate, there might still exist substitutions that incur a fitness cost. In this paper, we identify groups of coevolving residues within HCV nonstructural protein 3 (NS3) by analyzing diverse sequences of this protein using ideas from random matrix theory and associated methods. Our analyses indicate that one of these groups comprises a large percentage of residues for which HCV appears to resist multiple simultaneous substitutions. Targeting multiple residues in this group through vaccine-induced immune responses should either lead to viral recognition or elicit escape substitutions that compromise viral fitness. Our predictions are supported by published clinical data, which suggested that immune genotypes associated with spontaneous clearance of HCV preferentially recognized and targeted this vulnerable group of residues. Moreover, mapping the sites of this group onto the available protein structure provided insight into its functional significance. An epitope-based immunogen is proposed as an alternative to the NS3 epitopes in the peptide-based vaccine IC41. IMPORTANCE Despite much experimental work on HCV, a thorough statistical study of the HCV sequences for the purpose of immunogen design was missing in the literature. Such a study is vital to identify epistatic couplings among residues that can provide useful insights for designing a potent vaccine. In this work, ideas from random matrix theory were applied to characterize the statistics of substitutions within the diverse publicly available sequences of the genotype 1a HCV NS3 protein, leading to a group of sites for which HCV appears to resist simultaneous substitutions possibly due to deleterious effect on viral fitness. Our analysis leads to completely novel immunogen designs for HCV. In addition, the NS3 epitopes used in the recently proposed peptide-based vaccine IC41 were analyzed in the context of our framework. Our analysis predicts that alternative NS3 epitopes may be worth exploring as they might be more efficacious.

Continuous immunotypes describe human immune variation and predict diverse responses

Significance The human immune system consists of many different white blood cells that coordinate their actions to fight infections. The balance between these cell populations is determined by direct interactions and soluble factors such as cytokines, which serve as messengers between cells. Understanding how the interactions between cell populations influence the function of the immune system as a whole will allow us to better distinguish patients most at risk for specific infections or immune-mediated diseases and inform vaccination strategies. Here, we determine key collective interactions between white blood cells present in blood samples taken from healthy individuals. This perspective allows us to predict functional responses and describe previously unappreciated differences between age groups and in individuals carrying cytomegalovirus. The immune system consists of many specialized cell populations that communicate with each other to achieve systemic immune responses. Our analyses of various measured immune cell population frequencies in healthy humans and their responses to diverse stimuli show that human immune variation is continuous in nature, rather than characterized by discrete groups of similar individuals. We show that the same three key combinations of immune cell population frequencies can define an individual’s immunotype and predict a diverse set of functional responses to cytokine stimulation. We find that, even though interindividual variations in specific cell population frequencies can be large, unrelated individuals of younger age have more homogeneous immunotypes than older individuals. Across age groups, cytomegalovirus seropositive individuals displayed immunotypes characteristic of older individuals. The conceptual framework for defining immunotypes suggested by our results could guide the development of better therapies that appropriately modulate collective immunotypes, rather than individual immune components.

Geometry-dependent functional changes in iPSC-derived cardiomyocytes probed by functional imaging and RNA sequencing

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) are a promising platform for cardiac studies in vitro, and possibly for tissue repair in humans. However, hiPSC-CM cells tend to retain morphology, metabolism, patterns of gene expression, and electrophysiology similar to that of embryonic cardiomyocytes. We grew hiPSC-CM in patterned islands of different sizes and shapes, and measured the effect of island geometry on action potential waveform and calcium dynamics using optical recordings of voltage and calcium from 970 islands of different sizes. hiPSC-CM in larger islands showed electrical and calcium dynamics indicative of greater functional maturity. We then compared transcriptional signatures of the small and large islands against a developmental time course of cardiac differentiation. Although island size had little effect on expression of most genes whose levels differed between hiPSC-CM and adult primary CM, we identified a subset of genes for which island size drove the majority (58%) of the changes associated with functional maturation. Finally, we patterned hiPSC-CM on islands with a variety of shapes to probe the relative contributions of soluble factors, electrical coupling, and direct cell-cell contacts to the functional maturation. Collectively, our data show that optical electrophysiology is a powerful tool for assaying hiPSC-CM maturation, and that island size powerfully drives activation of a subset of genes involved in cardiac maturation.

Single-Cell RNA Sequencing of Human T Cells

Understanding how populations of human T cells leverage cellular heterogeneity, plasticity, and diversity to achieve a wide range of functional flexibility, particularly during dynamic processes such as development, differentiation, and antigenic response, is a core challenge that is well suited for single-cell analysis. Hypothesis-free evaluation of cellular states and subpopulations by transcriptional profiling of single T cells can identify relationships that may be obscured by targeted approaches such as FACS sorting on cell-surface antigens, or bulk expression analysis. While this approach is relevant to all cell types, it is of particular interest in the study of T cells for which classical phenotypic criteria are now viewed as insufficient for distinguishing different T cell subtypes and transitional states, and defining the changes associated with dysfunctional T cell states in autoimmunity and tumor-related exhaustion. This unit describes a protocol to generate single-cell transcriptomic libraries of human blood CD4+ and CD8+ T cells, and also introduces the basic bioinformatic steps to process the resulting sequence data for further computational analysis. We show how cellular subpopulations can be identified from transcriptional data, and derive characteristic gene expression signatures that distinguish these states. We believe single-cell RNA-seq is a powerful technique to study the cellular heterogeneity in complex tissues, a paradigm that will be of great value for the immune system.