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Unprecedented technological advances in single-cell RNA-sequencing (scRNA-seq) technology have now made it possible to profile genome-wide expression in single cells at low cost and high throughput. There is substantial ongoing effort to use scRNA-seq measurements to identify the "cell types" that form components of a complex tissue, akin to taxonomizing species in ecology. Cell type classification from scRNA-seq data involves the application of computational tools rooted in dimensionality reduction and clustering, and statistical analysis to identify molecular signatures that are unique to each type. As datasets continue to grow in size and complexity, computational challenges abound, requiring analytical methods to be scalable, flexible, and robust. Moreover, careful consideration needs to be paid to experimental biases and statistical challenges that are unique to these measurements to avoid artifacts. This chapter introduces these topics in the context of cell-type identification, and outlines an instructive step-by-step example bioinformatic pipeline for researchers entering this field.

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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.

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.

Mutational escape from vaccine induced immune responses has thwarted the development of a successful vaccine against AIDS, whose causative agent is HIV, a highly mutable virus. Knowing the virus' fitness as a function of its proteomic sequence can enable rational design of potent vaccines, as this information can focus vaccine induced immune responses to target mutational vulnerabilities of the virus. Spin models have been proposed as a means to infer intrinsic fitness landscapes of HIV proteins from patient-derived viral protein sequences. These sequences are the product of non-equilibrium viral evolution driven by patient-specific immune responses, and are subject to phylogenetic constraints. How can such sequence data allow inference of intrinsic fitness landscapes? We combined computer simulations and variational theory \'{a} la Feynman to show that, in most circumstances, spin models inferred from patient-derived viral sequences reflect the correct rank order of the fitness of mutant viral strains. Our findings are relevant for diverse viruses., Comment: 10 pages, 4 figures and supplementary methods file

CD8[superscript +] T cells contribute to the control of HIV, but it is not clear whether initial immune responses modulate the viral set point. We screened high-risk uninfected women twice a week for plasma HIV RNA and identified 12 hyperacute infections. Onset of viremia elicited a massive HIV-specific CD8[superscript +] T cell response, with limited bystander activation of non-HIV memory CD8[superscript +] T cells. HIV-specific CD8[superscript +] T cells secreted little interferon-γ, underwent rapid apoptosis, and failed to upregulate the interleukin-7 receptor, known to be important for T cell survival. The rapidity to peak CD8[superscript +] T cell activation and the absolute magnitude of activation induced by the exponential rise in viremia were inversely correlated with set point viremia. These data indicate that rapid, high magnitude HIV-induced CD8[superscript +] T cell responses are crucial for subsequent immune control of acute infection, which has important implications for HIV vaccine design., Bill & Melinda Gates Foundation, Collaboration for AIDS Vaccine Discovery, Witten Family Foundation, Dan and Marjorie Sullivan, Ursula Brunner, Gary and Loren Cohen, Mark and Lisa Schwartz Foundation, International AIDS Vaccine Initiative (UKZNRSA1001), National Institute of Allergy and Infectious Diseases (U.S.) (R37AI067073), Center for AIDS Research (P30 AI060354)

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., National Institutes of Health (U.S.) (AI055332), National Institutes of Health (U.S.) (AI060354), National Institutes of Health (U.S.) (AI078526), National Institutes of Health (U.S.) (AI084794), National Institutes of Health (U.S.) (AI095985), National Institutes of Health (U.S.) (AI096040), National Institutes of Health (U.S.) (AI100148), National Institutes of Health (U.S.) (AI10063), Bill & Melinda Gates Foundation (OPP1033091), Bill & Melinda Gates Foundation (OPP1033115), Bill & Melinda Gates Foundation (OPP1040741), Bill & Melinda Gates Foundation (OPP1040753), Ragon Institute of MGH, MIT, and Harvard, Stavros S. Niarchos Foundation, Howard Hughes Medical Institute (Investigator)

Background: A recent analysis of mutational patterns within Gag revealed independently evolving groups of residues (termed sectors) whose mutations are collectively coordinated. Of these sectors, sector 3 is the least tolerant of multiple simultaneous mutations and therefore is proposed to be the most vulnerable to a targeted immune attack. We hypothesized that coordinated CTL targeting of sector 3 residues is associated with immune control. Methods: We completed a comprehensive evaluation of Gag-specific responses in a cohort of 9 Long-term non-progressors (LTNPs, VL 10,000 RNA copies/ml, untreated). A Gag peptide set of 11-mer peptides overlapping by 10 amino acids was generated to reflect all variants found in at least 5% of clade B sequences in the LANL HIV Sequence Database. This peptide set includes 1300 peptides and covers all 500 amino acids of Gag. All study subjects were screened for responses to all peptides by IFN-γ/IL-2 FluoroSpot. Results: We observed a trend in the preferential targeting of sector 3 residues by LTNPs (p=0.07). This trend was not observed for any other sector or in total breadth of responses. Supporting the importance of sector 3 targeting, we found a significant positive correlation in our cohort between the relative proportion of sector 3 responses and CD4 count (r=0.49, p=0.04). We found no significant differences between LTNPs and HIV-Progressors in either the targeting of conserved 11-mers or overall Gag epitope variant recognition. Interestingly, LTNPs demonstrated higher levels of variant recognition than HIV-progressors when considering only the variable regions containing sector 3 residues. Conclusion: We found that preferential targeting of sector 3 residues distinguished Gag-specific responses between LTNPs and HIV-progressors, and that coordinated targeting of sector 3 residues may require cross-reactive responses. Additional investigations are ongoing to elucidate the role of sector 3 targeting in immune control of HIV.