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Anti-PD-1 therapy yields objective clinical responses in 30–40% of advanced melanoma patients. Since most patients do not respond, predictive biomarkers to guide treatment selection are needed. We hypothesize that MHC-I/II expression is required for tumour antigen presentation and may predict anti-PD-1 therapy response. In this study, across 60 melanoma cell lines, we find bimodal expression patterns of MHC-II, while MHC-I expression was ubiquitous. A unique subset of melanomas are capable of expressing MHC-II under basal or IFNγ-stimulated conditions. Using pathway analysis, we show that MHC-II(+) cell lines demonstrate signatures of ‘PD-1 signalling’, ‘allograft rejection’ and ‘T-cell receptor signalling’, among others. In two independent cohorts of anti-PD-1-treated melanoma patients, MHC-II positivity on tumour cells is associated with therapeutic response, progression-free and overall survival, as well as CD4 + and CD8 + tumour infiltrate. MHC-II + tumours can be identified by melanoma-specific immunohistochemistry using commercially available antibodies for HLA-DR to improve anti-PD-1 patient selection. Immunotherapy is used to treat melanoma, however patient responses vary widely highlighting the need for factors that can predict therapeutic success. Here, the authors show that MHC-II molecules expressed by tumour cells are positively correlated with a good response to therapy and overall patient survival.

Marker enrichment modeling (MEM) provides an objective metric for characterizing cell populations from high-content single-cell analysis. The MEM score outperforms standard metrics and provides a machine-readeable label for cell subsets. Learning cell identity from high-content single-cell data presently relies on human experts. We present marker enrichment modeling (MEM), an algorithm that objectively describes cells by quantifying contextual feature enrichment and reporting a human- and machine-readable text label. MEM outperforms traditional metrics in describing immune and cancer cell subsets from fluorescence and mass cytometry. MEM provides a quantitative language to communicate characteristics of new and established cytotypes observed in complex tissues.

RNA-based vaccines against SARS-CoV-2 have proven critical to limiting COVID-19 disease severity and spread. Cellular mechanisms driving antigen-specific responses to these vaccines, however, remain uncertain. Here we identify and characterize antigen-specific cells and antibody responses to the RNA vaccine BNT162b2 using multiple single-cell technologies for in depth analysis of longitudinal samples from a cohort of healthy participants. Mass cytometry and unbiased machine learning pinpoint an expanding, population of antigen-specific memory CD4 + and CD8 + T cells with characteristics of follicular or peripheral helper cells. B cell receptor sequencing suggest progression from IgM, with apparent cross-reactivity to endemic coronaviruses, to SARS-CoV-2-specific IgA and IgG memory B cells and plasmablasts. Responding lymphocyte populations correlate with eventual SARS-CoV-2 IgG, and a participant lacking these cell populations failed to sustain SARS-CoV-2-specific antibodies and experienced breakthrough infection. These integrated proteomic and genomic platforms identify an antigen-specific cellular basis of RNA vaccine-based immunity. Vaccination against COVID-19 has shown activation of different immune cell types. Here the authors characterise the immune response to the SARS-CoV-2 mRNA vaccine using longitudinal CyTOF single cell approaches to characterise antigen specific B and T-cell responses promoted by this vaccine.

Lung adenocarcinoma (ADC) is a heterogeneous group of tumors associated with different survival rates, even when detected at an early stage. Here, we aim to investigate whether CyTOF identifies cellular and molecular predictors of tumor behavior. We developed and validated a CyTOF panel of 34 antibodies in four ADC cell lines and PBMC. We tested our panel in a set of 10 ADCs, classified into long- (LPS) (n = 4) and short-predicted survival (SPS) (n = 6) based on radiomics features. We identified cellular subpopulations of epithelial cancer cells (ECC) and their microenvironment and validated our results by multiplex immunofluorescence (mIF) applied to a tissue microarray (TMA) of LPS and SPS ADCs. The antibody panel captured the phenotypical differences in ADC cell lines and PBMC. LPS ADCs had a higher proportion of immune cells. ECC clusters (ECCc) were identified and uncovered two ADC groups. ECCc with high HLA-DR expression were correlated with CD4+ and CD8+ T cells, with LPS samples being enriched for those clusters. We confirmed a positive correlation between HLA-DR expression on ECC and T cell number by mIF staining on TMA slides. Spatial analysis demonstrated shorter distances from T cells to the nearest ECC in LPS. Our results demonstrate a distinctive cellular profile of ECC and their microenvironment in ADC. We showed that HLA-DR expression in ECC is correlated with T cell infiltration, and that a set of ADCs with high abundance of HLA-DR+ ECCc and T cells is enriched in LPS samples. This suggests new insights into the role of antigen presenting tumor cells in tumorigenesis.

Discovering bioactive metabolites within a metabolome is challenging because there is generally little foreknowledge of metabolite molecular and cell-targeting activities. Here, single-cell response profiles and primary human tissue comprise a response platform used to discover novel microbial metabolites with cell-type-selective effector properties in untargeted metabolomic inventories. Metabolites display diverse effector mechanisms, including targeting protein synthesis, cell cycle status, DNA damage repair, necrosis, apoptosis, or phosphoprotein signaling. Arrayed metabolites are tested against acute myeloid leukemia patient bone marrow and molecules that specifically targeted blast cells or nonleukemic immune cell subsets within the same tissue biopsy are revealed. Cell-targeting polyketides are identified in extracts from biosynthetically prolific bacteria, including a previously unreported leukemia blast-targeting anthracycline and a polyene macrolactam that alternates between targeting blasts or nonmalignant cells by way of light-triggered photochemical isomerization. High-resolution cell profiling with mass cytometry confirms response mechanisms and is used to validate initial observations. Bioactive metabolites from plant and microbial extracts hold therapeutic potential. Here, the authors combine untargeted metabolomic arrays with flow cytometry-based single cell response profiling and identify metabolites with cell subset-specific activities in the bone marrow from an AML patient.

Chemical and biological stimulus screening in a hypogean actinomycete was used to elicit secondary metabolism. Optimal biosynthesis of bioactive natural products was identified using Multiplexed Activity Profiling for determining dose-dependent activity via six single-cell biological readouts. Bioactive extracts were fractioned to establish candidate compounds for isolation using Multiplexed Activity Metabolomics by correlating microtiter well-isolated phenotypes and extracted ion current peaks. This guided the isolation of four filipin polyene macrolides including a new metabolite filipin XV, an alkyl side-chain hydroxylated congener of the filipin chainin, with substantially attenuated cytotoxicity. Filipin-specific cytotoxicity was confirmed using flow cytometry and fluorescence microscopy.

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease-specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders EXpanding (T-REX) was created to identify changes in both rare and common cells across human immune monitoring settings. T-REX identified cells with highly similar phenotypes that localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized MHCII tetramer reagents that mark rhinovirus-specific CD4+ cells were left out during analysis and then used to test whether T-REX identified biologically significant cells. T-REX identified rhinovirus-specific CD4+ T cells based on phenotypically homogeneous cells expanding by ≥95% following infection. T-REX successfully identified hotspots of virus-specific T cells by comparing infection (day 7) to either pre-infection (day 0) or post-infection (day 28) samples. Plotting the direction and degree of change for each individual donor provided a useful summary view and revealed patterns of immune system behavior across immune monitoring settings. For example, the magnitude and direction of change in some COVID-19 patients was comparable to blast crisis acute myeloid leukemia patients undergoing a complete response to chemotherapy. Other COVID-19 patients instead displayed an immune trajectory like that seen in rhinovirus infection or checkpoint inhibitor therapy for melanoma. The T-REX algorithm thus rapidly identifies and characterizes mechanistically significant cells and places emerging diseases into a systems immunology context for comparison to well-studied immune changes.

Human tumors contain populations of both cancerous and host immune cells whose malignant signaling interactions may define each patient's disease trajectory. We used multiplexed phospho-flow cytometry to profile single cells within human follicular lymphoma tumors and discovered a subpopulation of lymphoma cells with impaired B cell antigen receptor (BCR) signaling. The abundance of BCR-insensitive cells in each tumor negatively correlated with overall patient survival. These lymphoma negative prognostic (LNP) cells increased as tumors relapsed following chemotherapy. Loss of antigen receptor expression did not explain the absence of BCR signaling in LNP tumor cells, and other signaling responses were intact in these cells. Furthermore, BCR signaling responses could be reactivated in LNP cells, indicating that BCR signaling is not missing but rather specifically suppressed. LNP cells were also associated with changes to signaling interactions in the tumor microenvironment. Lower IL-7 signaling in tumor infiltrating T cells was observed in tumors with high LNP cell counts. The strength of signaling through T cell mediator of B cell function CD40 also stratified patient survival, particularly for those whose tumors contained few LNP cells. Thus, analysis of cell–cell interactions in heterogeneous primary tumors using signaling network profiles can identify and mechanistically define new populations of rare and clinically significant cells. Both the existence of these LNP cells and their aberrant signaling profiles provide targets for new therapies for follicular lymphoma.

A goal of cancer research is to reveal cell subsets linked to continuous clinical outcomes to generate new therapeutic and biomarker hypotheses. We introduce a machine learning algorithm, Risk Assessment Population IDentification (RAPID), that is unsupervised and automated, identifies phenotypically distinct cell populations, and determines whether these populations stratify patient survival. With a pilot mass cytometry dataset of 2 million cells from 28 glioblastomas, RAPID identified tumor cells whose abundance independently and continuously stratified patient survival. Statistical validation within the workflow included repeated runs of stochastic steps and cell subsampling. Biological validation used an orthogonal platform, immunohistochemistry, and a larger cohort of 73 glioblastoma patients to confirm the findings from the pilot cohort. RAPID was also validated to find known risk stratifying cells and features using published data from blood cancer. Thus, RAPID provides an automated, unsupervised approach for finding statistically and biologically significant cells using cytometry data from patient samples.

Metabolic reprogramming dictates the fate and function of stimulated T cells, yet these pathways can be suppressed in T cells in tumor microenvironments. We previously showed that glycolytic and mitochondrial adaptations directly contribute to reducing the effector function of renal cell carcinoma (RCC) CD8+ tumor-infiltrating lymphocytes (TILs). Here we define the role of these metabolic pathways in the activation and effector functions of CD8+ RCC TILs. CD28 costimulation plays a key role in augmenting T cell activation and metabolism, and is antagonized by the inhibitory and checkpoint immunotherapy receptors CTLA4 and PD-1. While RCC CD8+ TILs were activated at a low level when stimulated through the T cell receptor alone, addition of CD28 costimulation greatly enhanced activation, function, and proliferation. CD28 costimulation reprogrammed RCC CD8+ TIL metabolism with increased glycolysis and mitochondrial oxidative metabolism, possibly through upregulation of GLUT3. Mitochondria also fused to a greater degree, with higher membrane potential and overall mass. These phenotypes were dependent on glucose metabolism, as the glycolytic inhibitor 2-deoxyglucose both prevented changes to mitochondria and suppressed RCC CD8+ TIL activation and function. These data show that CD28 costimulation can restore RCC CD8+ TIL metabolism and function through rescue of T cell glycolysis that supports mitochondrial mass and activity.