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.
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.
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.
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•Bioinformatics strategy for analysis of high-dimensional single cell data.•Unsupervised approaches reveal and characterize cells with unexpected phenotypes.•Modular approach ...facilitates development and testing of new tools.•Sequential use of machine learning tools combines complementary strengths of each.
The flood of high-dimensional data resulting from mass cytometry experiments that measure more than 40 features of individual cells has stimulated creation of new single cell computational biology tools. These tools draw on advances in the field of machine learning to capture multi-parametric relationships and reveal cells that are easily overlooked in traditional analysis. Here, we introduce a workflow for high dimensional mass cytometry data that emphasizes unsupervised approaches and visualizes data in both single cell and population level views. This workflow includes three central components that are common across mass cytometry analysis approaches: (1) distinguishing initial populations, (2) revealing cell subsets, and (3) characterizing subset features. In the implementation described here, viSNE, SPADE, and heatmaps were used sequentially to comprehensively characterize and compare healthy and malignant human tissue samples. The use of multiple methods helps provide a comprehensive view of results, and the largely unsupervised workflow facilitates automation and helps researchers avoid missing cell populations with unusual or unexpected phenotypes. Together, these methods develop a framework for future machine learning of cell identity.
Follicular lymphoma (FL) is currently incurable using conventional chemotherapy or immunotherapy regimes, compelling new strategies. Advances in high-throughput sequencing technologies that can ...reveal oncogenic pathways have stimulated interest in tailoring therapies toward actionable somatic mutations. However, for mutation-directed therapies to be most effective, the mutations must be uniformly present in evolved tumor cells as well as in the self-renewing tumor-cell precursors. Here, we show striking intratumoral clonal diversity within FL tumors in the representation of mutations in the majority of genes as revealed by whole exome sequencing of subpopulations. This diversity captures a clonal hierarchy, resolved using immunoglobulin somatic mutations and IGH-BCL2 translocations as a frame of reference and by comparing diagnosis and relapse tumor pairs, allowing us to distinguish early versus late genetic eventsduring lymphomagenesis. We provide evidence that IGH-BCL2 translocations and CREBBP mutations are early events, whereas MLL2 and TNFRSF14 mutations probably represent late events during disease evolution. These observations provide insight into which of the genetic lesions represent suitable candidates for targeted therapies.
•Analysis of coding genomes of FL tumor subpopulations reveals striking clonal diversity at diagnosis and progression.•Within a hierarchy of somatic evolution of FL coding genomes, many recurrent mutations are subclonal at diagnosis.
We set out to determine the accuracy of 3D-navigated mandibular and maxillary osteotomies with the ultimate aim to integrate virtual cutting guides and 3D-navigation into ablative and reconstructive ...head and neck surgery.
Four surgeons (two attending, two clinical fellows) completed 224 unnavigated and 224 3D-navigated osteotomies on anatomical models according to preoperative 3D plans. The osteotomized bones were scanned and analyzed.
Median distance from the virtual plan was 2.1 mm unnavigated (IQR 2.6 mm, ≥3 mm in 33%) and 1.2 mm 3D-navigated (IQR 1.1 mm, ≥3 mm in 6%) (P<0.0001); median pitch was 4.5° unnavigated (IQR 7.1°) and 3.5° 3D-navigated (IQR 4.0°) (P<0.0001); median roll was 7.4° unnavigated (IQR 8.5°) and 2.6° 3D-navigated (IQR 3.8°) (P<0.0001).
3D-rendering enables osteotomy navigation. 3 mm is an appropriate planning distance. The next steps are translating virtual cutting guides to free bone flap reconstruction and clinical use.
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.
Memory T cells are thought to rely on oxidative phosphorylation and short-lived effector T cells on glycolysis. Here, we investigated how T cells arrive at these states during an immune response. To ...understand the metabolic state of rare, early-activated T cells, we adapted mass cytometry to quantify metabolic regulators at single-cell resolution in parallel with cell signaling, proliferation, and effector function. We interrogated CD8+ T cell activation in vitro and in response to Listeria monocytogenes infection in vivo. This approach revealed a distinct metabolic state in early-activated T cells characterized by maximal expression of glycolytic and oxidative metabolic proteins. Cells in this transient state were most abundant 5 days post-infection before rapidly decreasing metabolic protein expression. Analogous findings were observed in chimeric antigen receptor (CAR) T cells interrogated longitudinally in advanced lymphoma patients. Our study demonstrates the utility of single-cell metabolic analysis by mass cytometry to identify metabolic adaptations of immune cell populations in vivo and provides a resource for investigations of metabolic regulation of immune responses across a variety of applications.
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•A mass cytometry approach quantifies metabolic proteins in single cells in vivo•Early-activated T cells exhibit simultaneous peak oxidative and glycolytic activity•CD8+ T cells transit through this transient state prior to differentiation•CAR T cells exhibit an analogous transient program upon infusion into patients
Levine, Hiam-Galvez, et al. develop a mass-cytometry-based approach to quantify metabolic protein expression in single cells in vivo, revealing a distinct metabolic state early after CD8+ T cell activation characterized by simultaneous expression of glycolytic and oxidative proteins. This approach provides a resource for the study of metabolic regulation across a variety of applications.
Cytobank is a Web-based application for storage, analysis, and sharing of flow cytometry experiments. Researchers use a Web browser to log in and use a wide range of tools developed for basic and ...advanced flow cytometry. In addition to providing access to standard cytometry tools from any computer, Cytobank creates a platform and community for developing new analysis and publication tools. Figure layouts created on Cytobank are designed to allow transparent access to the underlying experiment annotation and data processing steps. Since all flow cytometry files and analysis data are stored on a central server, experiments and figures can be viewed or edited by anyone with the proper permission, from any computer with Internet access. Once a primary researcher has performed the initial analysis of the data, collaborators can engage in experiment analysis and make their own figure layouts using the gated, compensated experiment files. Cytobank is available to the scientific community at http://www.cytobank.org.
Sepsis is the leading cause of death in adult ICUs. At present, sepsis diagnosis relies on nonspecific clinical features. It could transform clinical care to have immune-cell biomarkers that could ...predict sepsis diagnosis and guide treatment. For decades, neutrophil phenotypes have been studied in sepsis, but a diagnostic cell subset has yet to be identified.
To identify an early, specific immune signature of sepsis severity that does not overlap with other inflammatory biomarkers and that distinguishes patients with sepsis from those with noninfectious inflammatory syndrome.
Mass cytometry combined with computational high-dimensional data analysis was used to measure 42 markers on whole-blood immune cells from patients with sepsis and control subjects and to automatically and comprehensively characterize circulating immune cells, which enables identification of novel, disease-specific cellular signatures.
Unsupervised analysis of high-dimensional mass cytometry data characterized previously unappreciated heterogeneity within the CD64
immature neutrophils and revealed two new subsets distinguished by CD123 and PD-L1 (programmed death ligand 1) expression. These immature neutrophils exhibited diminished activation and phagocytosis functions. The proportion of CD123-expressing neutrophils correlated with clinical severity.
This study showed that these two new neutrophil subsets were specific to sepsis and detectable through routine flow cytometry by using seven markers. The demonstration here that a simple blood test distinguishes sepsis from other inflammatory conditions represents a key biological milestone that can be immediately translated into improvements in patient care.
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