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Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity., Graphical abstract, Highlights • “Weighted nearest neighbor” analysis integrates multimodal single-cell data • A multimodal reference “atlas” of the circulating human immune system • Identification and validation of novel sources of lymphoid heterogeneity • “Reference-based” mapping of query datasets onto a multimodal atlas, A framework that allows for the integration of multiple data types using single cells is applied to understand distinct immune cell states, previously unidentified immune populations, and to interpret immune responses to vaccinations.

Much of the current effort in HIV-1 vaccine design is directed at achieving T-cell immunity that will result in enough immunological memory to contain HIV-1 infection after acquisition. However, antigenic diversity, plus a lack of understanding of HIV-1 vaccine immunology, have hindered the development of a globally effective cytotoxic T-lymphocyte (CTL)-based vaccine. Cellular response, in using a finite immune system to recognize an infinite number of potential pathogens, exhibits a series of parsimonious features. These features are considered critical in modulating HIV-1 vaccine multiple specificities. We took the features into consideration when the potential epitope coverage (E(c)) to circulating strains by current vaccine strategies was analyzed. Based on these analyses, several approaches are proposed to enhance the breadth of vaccine responses and, hence, the potential protective efficacy.

Dozens of human immunodeficiency virus-type 1 (HIV-1) vaccine candidates specifically designed to elicit cytotoxic T-lymphocyte (CTL) responses have entered the pipeline of clinical trials. Evaluating the immunogenicity and potential efficacy of these HIV-1 vaccine candidates is challenging in the face of the extensive viral genetic diversity of circulating strains. Standardized peptide reagents to define the magnitude and potential breadth of the T-cell response, especially to circulating strains of HIV-1, are needed. For this purpose we developed a biometric approach based on T-cell recognition pattern for defining standardized reagents. Circulating strains in the Los Alamos database were evaluated and standardized algorithms to define all potential T-cell epitopes (PTEs) were generated. While many unique PTEs could be identified, a finite number based upon prevalence of circulating strains in the database, which we define as vaccine-important PTEs (VIPs), were used to select a common standardized panel of HIV-1 peptides for CTL-based vaccine evaluation. The usability of PTE peptide set was manifested by detection of Nef-specific CTL responses in HIV-1 subtype B infections.

Prime-boost immunization with heterologous vaccines elicits potent cellular immunity. In this study, we assessed the influence of various TLR ligands on SIV Gag–specific T cell immunity and protection following prime-boost immunization. Rhesus macaques (RMs) were primed with SIV Gag protein emulsified in Montanide ISA51 with or without TLR3 (polyinosinic-polycytidylic acid [poly-IC]), TLR4 (monophosphoryl lipid A), TLR7/8 (3M-012), TLR9 (CpG), or TLR3 (poly-IC) combined with TLR7/8 ligands, then boosted with replication defective adenovirus 5 expressing SIV Gag (rAd5-Gag). After priming, RMs that received SIV Gag protein plus poly-IC developed significantly higher frequencies of SIV Gag–specific CD4+ Th1 responses in blood and bronchoalveolar lavage (BAL) fluid lymphocytes compared with all other adjuvants, and low-level SIV Gag–specific CD8+ T cell responses. After the rAd5-Gag boost, the magnitude and breadth of SIV Gag–specific CD8+ T cell responses were significantly increased in RM primed with SIV Gag protein plus poly-IC, with or without the TLR7/8 ligand, or CpG. However, the anamnestic, SIV Gag–specific CD8+ T cell response to SIVmac251 challenge was not significantly enhanced by SIV Gag protein priming with any of the adjuvants. In contrast, the anamnestic SIV Gag–specific CD4+ T cell response in BAL was enhanced by SIV Gag protein priming with poly-IC or CpG, which correlated with partial control of early viral replication after SIVmac251 challenge. These results demonstrate that prime-boost vaccination with SIV Gag protein/poly-IC improves magnitude, breadth, and durability of CD4+ T cell immune responses, which could have a role in the control of SIV viral replication.

The cellular immune system is characterized by flexibility with respect to epitope recognition at the level of peptide binding to HLA molecules and HLA-peptide complexes to T-cell receptors (TCRs). For epitopes recognized by cytotoxic T-lymphocytes (CTLs), amino acid substitutions at different positions have varying impact on recognition. By analyzing the frequencies of specific amino acid substitutions at each position in conjunction with HLA-peptide binding and immune-response data, we have developed new methods to predict cross-reactive recognition of epitope variants by CTLs. We derived position-specific substitution matrices (EPSSMs) through the analysis of known HLA ligands and achieved relatively accurate prediction of detrimental and tolerated amino acid substitutions. Initial analysis of amino acid substitutions in CTL epitopes with degenerate recognition showed strong position-specific preferences. This first systematic analysis further suggested that spatial constraint may be the major molecular factor determining the degenerate epitope recognition. As the data cumulates, we anticipate that eventually EPSSMs will be available for prediction of degenerate T-cell epitope recognition.