Immune-monitoring of myelodysplastic neoplasms: Recommendations from the i4MDS consortium.

Advancements in comprehending myelodysplastic neoplasms (MDS) have unfolded significantly in recent years, elucidating a myriad of cellular and molecular underpinnings integral to disease progression. While molecular inclusions into prognostic models have substantively advanced risk stratification, recent revelations have emphasized the pivotal role of immune dysregulation within the bone marrow milieu during MDS evolution. Nonetheless, immunotherapy for MDS has not experienced breakthroughs seen in other malignancies, partly attributable to the absence of an immune classification that could stratify patients toward optimally targeted immunotherapeutic approaches. A pivotal obstacle to establishing "immune classes" among MDS patients is the absence of validated accepted immune panels suitable for routine application in clinical laboratories. In response, we formed International Integrative Innovative Immunology for MDS (i4MDS), a consortium of multidisciplinary experts, and created the following recommendations for standardized methodologies to monitor immune responses in MDS. A central goal of i4MDS is the development of an immune score that could be incorporated into current clinical risk stratification models. This position paper first consolidates current knowledge on MDS immunology. Subsequently, in collaboration with clinical and laboratory specialists, we introduce flow cytometry panels and cytokine assays, meticulously devised for clinical laboratories, aiming to monitor the immune status of MDS patients, evaluating both immune fitness and identifying potential immune "risk factors." By amalgamating this immunological characterization data and molecular data, we aim to enhance patient stratification, identify predictive markers for treatment responsiveness, and accelerate the development of systems immunology tools and innovative immunotherapies.

Velociraptor: Cross-Platform Quantitative Search Using Hallmark Cell Features.

A key challenge for single cell discovery analysis is to identify new cell types, describe them quantitatively, and seek these novel cells in new studies often using a different platform. Over the last decade, tools were developed to address identification and quantitative description of cells in human tissues and tumors. However, automated validation of populations at the single cell level has struggled due to the cytometry field's reliance on hierarchical, ordered use of features and on platform-specific rules for data processing and analysis. Here we present Velociraptor, a workflow that implements Marker Enrichment Modeling in three cross-platform modules: 1) identification of cells specific to disease states, 2) description of hallmark features for each cell and population, and 3) searching for cells matching one or more hallmark feature sets in a new dataset. A key advance is that Velociraptor registers cells between datasets, including between flow cytometry and quantitative imaging using different, overlapping feature sets. Four datasets were used to challenge Velociraptor and reveal new biological insights. Working at the individual sample level, Velociraptor tracked the abundance of clinically significant glioblastoma brain tumor cell subsets and characterized the cells that predominate in recurrent tumors as a close match for rare, negative prognostic cells originally observed in matched pre-treatment tumors. In patients with inborn errors of immunity, Velociraptor identified genotype-specific cells associated with GATA2 haploinsufficiency. Finally, in cross-platform analysis of immune cells in multiplex imaging of breast cancer, Velociraptor sought and correctly identified memory T cell subsets in tumors. Different phenotypic descriptions generated by algorithms or humans were shown to be effective as search inputs, indicating that cell identity need not be described in terms of per-feature cutoffs or strict hierarchical analyses. Velociraptor thus identifies cells based on hallmark feature sets, such as protein expression signatures, and works effectively with data from multiple sources, including suspension flow cytometry, imaging, and search text based on known or theoretical cell features.

Distinct Type 1 Immune Networks Underlie the Severity of Restrictive Lung Disease after COVID-19.

The variable etiology of persistent breathlessness after COVID-19 have confounded efforts to decipher the immunopathology of lung sequelae. Here, we analyzed hundreds of cellular and molecular features in the context of discrete pulmonary phenotypes to define the systemic immune landscape of post-COVID lung disease. Cluster analysis of lung physiology measures highlighted two phenotypes of restrictive lung disease that differed by their impaired diffusion and severity of fibrosis. Machine learning revealed marked CCR5+CD95+ CD8+ T-cell perturbations in mild-to-moderate lung disease, but attenuated T-cell responses hallmarked by elevated CXCL13 in more severe disease. Distinct sets of cells, mediators, and autoantibodies distinguished each restrictive phenotype, and differed from those of patients without significant lung involvement. These differences were reflected in divergent T-cell-based type 1 networks according to severity of lung disease. Our findings, which provide an immunological basis for active lung injury versus advanced disease after COVID-19, might offer new targets for treatment.

IL-8 Instructs Macrophage Identity in Lateral Ventricle Contacting Glioblastoma.

Adult IDH-wildtype glioblastoma (GBM) is a highly aggressive brain tumor with no established immunotherapy or targeted therapy. Recently, CD32+ HLA-DRhi macrophages were shown to have displaced resident microglia in GBM tumors that contact the lateral ventricle stem cell niche. Since these lateral ventricle contacting GBM tumors have especially poor outcomes, identifying the origin and role of these CD32+ macrophages is likely critical to developing successful GBM immunotherapies. Here, we identify these CD32+ cells as M_IL-8 macrophages and establish that IL-8 is sufficient and necessary for tumor cells to instruct healthy macrophages into CD32+ M_IL-8 M2 macrophages. In ex vivo experiments with conditioned medium from primary human tumor cells, inhibitory antibodies to IL-8 blocked the generation of CD32+ M_IL-8 cells. Finally, using a set of 73 GBM tumors, IL-8 protein is shown to be present in GBM tumor cells in vivo and especially common in tumors contacting the lateral ventricle. These results provide a mechanistic origin for CD32+ macrophages that predominate in the microenvironment of the most aggressive GBM tumors. IL-8 and CD32+ macrophages should now be explored as targets in combination with GBM immunotherapies, especially for patients whose tumors present with radiographic contact with the ventricular-subventricular zone stem cell niche.

Multiparametric Single-Vesicle Flow Cytometry Resolves Extracellular Vesicle Heterogeneity and Reveals Selective Regulation of Biogenesis and Cargo Distribution.

Mammalian cells release a heterogeneous array of extracellular vesicles (EVs) that contribute to intercellular communication by means of the cargo that they carry. To resolve EV heterogeneity and determine if cargo is partitioned into select EV populations, we developed a method named "EV Fingerprinting" that discerns distinct vesicle populations using dimensional reduction of multiparametric data collected by quantitative single-EV flow cytometry. EV populations were found to be discernible by a combination of membrane order and EV size, both of which were obtained through multiparametric analysis of fluorescent features from the lipophilic dye Di-8-ANEPPS incorporated into the lipid bilayer. Molecular perturbation of EV secretion and biogenesis through respective ablation of the small GTPase Rab27a and overexpression of the EV-associated tetraspanin CD63 revealed distinct and selective alterations in EV populations, as well as cargo distribution. While Rab27a disproportionately affects all small EV populations with high membrane order, the overexpression of CD63 selectively increased the production of one small EV population of intermediate membrane order. Multiplexing experiments subsequently revealed that EV cargos have a distinct, nonrandom distribution with CD63 and CD81 selectively partitioning into smaller vs larger EVs, respectively. These studies not only present a method to probe EV biogenesis but also reveal how the selective partitioning of cargo contributes to EV heterogeneity.

A multicenter phase Ib trial of the histone deacetylase inhibitor entinostat in combination with pembrolizumab in patients with myelodysplastic syndromes/neoplasms or acute myeloid leukemia refractory to hypomethylating agents.

Patients with myelodysplastic syndromes/neoplasms (MDS) or acute myeloid leukemia (AML) with hypomethylating agent failure have a poor prognosis. Myeloid-derived suppressor cells (MDSCs) can contribute to MDS progression and mediate resistance to anti-PD1 therapy. As histone deacetylase inhibitors (HDACi) decrease MDSCs in preclinical models, we conducted an investigator-initiated, NCI-Cancer Therapy Evaluation Program-sponsored, multicenter, dose escalation, and expansion phase Ib trial (NCT02936752) of the HDACi entinostat and the anti-PD1 antibody pembrolizumab. Twenty-eight patients (25 MDS and 3 AML) were enrolled. During dose escalation (n=13 patients), there was one dose-limiting toxicity (DLT) on dose level (DL) 1 (G5 pneumonia/bronchoalveolar hemorrhage) and two DLTs at DL 2 (G3 pharyngeal mucositis and G3 anorexia). Per the 3 + 3 dose escalation design, DL 1 (entinostat 8 mg PO days 1 and 15 + pembrolizumab 200 mg IV day 1 every 21 days) was expanded and another 15 patients were enrolled. Hematologic adverse events (AEs) were common. The most common non-hematologic ≥G3 AEs were infection (32%), hypoxia/respiratory failure (11%), and dyspnea (11%). There were no protocol-defined responses among the 28 patients enrolled. Two patients achieved a marrow complete remission (mCR). Using a systems immunology approach with mass cytometry and machine learning analysis, mCR patients had increased classical monocytes and macrophages but there was no significant change of MDSCs. In conclusion, combining entinostat with pembrolizumab in patients with advanced MDS and AML was associated with limited clinical efficacy and substantial toxicity. Absence of an effect on MDSCs could be a potential explanation for the limited efficacy of this combination. ClinicalTrial.gov Identifier: NCT02936752.

Integrated Multi-omics Analysis of Early Lung Adenocarcinoma Links Tumor Biological Features with Predicted Indolence or Aggressiveness.

Lung adenocarcinoma (LUAD) is a heterogeneous group of tumors associated with different survival rates, even when detected at an early stage. Here, we aim to investigate the biological determinants of early LUAD indolence or aggressiveness using radiomics as a surrogate of behavior. We present a set of 92 patients with LUAD with data collected across different methodologies. Patients were risk-stratified using the CT-based Score Indicative of Lung cancer Aggression (SILA) tool (0 = least aggressive, 1 = most aggressive). We grouped the patients as indolent (x ≤ 0.4, n = 14), intermediate (0.4 > x ≤ 0.6, n = 27), and aggressive (0.6 > x ≤ 1, n = 52). Using Cytometry by time of flight (CyTOF), we identified subpopulations with high HLA-DR expression that were associated with indolent behavior. In the RNA sequencing (RNA-seq) dataset, pathways related to immune response were associated with indolent behavior, while pathways associated with cell cycle and proliferation were associated with aggressive behavior. We extracted quantitative radiomics features from the CT scans of the patients. Integrating these datasets, we identified four feature signatures and four patient clusters that were associated with survival. Using single-cell RNA-seq, we found that indolent tumors had significantly more T cells and less B cells than aggressive tumors, and that the latter had a higher abundance of regulatory T cells and Th cells. In conclusion, we were able to uncover a correspondence between radiomics and tumor biology, which could improve the discrimination between indolent and aggressive LUAD tumors, enhance our knowledge in the biology of these tumors, and offer novel and personalized avenues for intervention. Significance: This study provides a comprehensive profiling of LUAD indolence and aggressiveness at the biological bulk and single-cell levels, as well as at the clinical and radiomics levels. This hypothesis generating study uncovers several potential future research avenues. It also highlights the importance and power of data integration to improve our systemic understanding of LUAD and to help reduce the gap between basic science research and clinical practice.

An immunosuppressed microenvironment distinguishes lateral ventricle-contacting glioblastomas.

Radiographic contact of glioblastoma (GBM) tumors with the lateral ventricle and adjacent stem cell niche correlates with poor patient prognosis, but the cellular basis of this difference is unclear. Here, we reveal and functionally characterize distinct immune microenvironments that predominate in subtypes of GBM distinguished by proximity to the lateral ventricle. Mass cytometry analysis of isocitrate dehydrogenase wild-type human tumors identified elevated T cell checkpoint receptor expression and greater abundance of a specific CD32+CD44+HLA-DRhi macrophage population in ventricle-contacting GBM. Multiple computational analysis approaches, phospho-specific cytometry, and focal resection of GBMs validated and extended these findings. Phospho-flow quantified cytokine-induced immune cell signaling in ventricle-contacting GBM, revealing differential signaling between GBM subtypes. Subregion analysis within a given tumor supported initial findings and revealed intratumor compartmentalization of T cell memory and exhaustion phenotypes within GBM subtypes. Collectively, these results characterize immunotherapeutically targetable features of macrophages and suppressed lymphocytes in GBMs defined by MRI-detectable lateral ventricle contact.

Immune Profiling Reveals Decreases in Circulating Regulatory and Exhausted T Cells in Human Hypertension.

Evidence from nonhuman animal models demonstrates an important role for immune cells in hypertension, but immune cell changes in human hypertension are less clear. Using mass cytometry, we demonstrate novel and selective reductions in CCR10+ regulatory T cells (Tregs) and PD-1+CD57-CD8+ memory T cells. RNA sequencing reveals that CCR10+ Tregs exhibit gene expression changes consistent with enhanced immunosuppressive function. In addition, CITE-Seq demonstrates that PD-1+CD57-CD8+ memory T cells exhibit features of T-cell exhaustion. Taken together, these results provide novel evidence for decreases in anti-inflammatory and/or hypofunctional T-cell populations that may contribute to enhanced inflammation in human hypertension.

Alignment, segmentation and neighborhood analysis in cyclic immunohistochemistry data using CASSATT.

Cyclic immunohistochemistry (cycIHC) uses sequential rounds of colorimetric immunostaining and imaging for quantitative mapping of location and number of cells of interest. Additionally, cycIHC benefits from the speed and simplicity of brightfield microscopy, making the collection of entire tissue sections and slides possible at a trivial cost compared to other high dimensional imaging modalities. However, large cycIHC datasets currently require an expert data scientist to concatenate separate open-source tools for each step of image pre-processing, registration, and segmentation, or the use of proprietary software. Here, we present a unified and user-friendly pipeline for processing, aligning, and analyzing cycIHC data - Cyclic Analysis of Single-Cell Subsets and Tissue Territories (CASSATT). CASSATT registers scanned slide images across all rounds of staining, segments individual nuclei, and measures marker expression on each detected cell. Beyond straightforward single cell data analysis outputs, CASSATT explores the spatial relationships between cell populations. By calculating the log odds of interaction frequencies between cell populations within tissues and tissue regions, this pipeline helps users identify populations of cells that interact-or do not interact-at frequencies that are greater than those occurring by chance. It also identifies specific neighborhoods of cells based on the assortment of neighboring cell types that surround each cell in the sample. The presence and location of these neighborhoods can be compared across slides or within distinct regions within a tissue. CASSATT is a fully open source workflow tool developed to process cycIHC data and will allow greater utilization of this powerful staining technique.

Dephosphorylation of 4EBP1/2 Induces Prenatal Neural Stem Cell Quiescence.

A limiting factor in the regenerative capacity of the adult brain is the abundance and proliferative ability of neural stem cells (NSCs). Adult NSCs are derived from a subpopulation of embryonic NSCs that temporarily enter quiescence during mid-gestation and remain quiescent until postnatal reactivation. Here we present evidence that the mechanistic/mammalian target of rapamycin (mTOR) pathway regulates quiescence entry in embryonic NSCs of the developing forebrain. Throughout embryogenesis, two downstream effectors of mTOR, p-4EBP1/2 T37/46 and p-S6 S240/244, were mutually exclusive in NSCs, rarely occurring in the same cell. While 4EBP1/2 was phosphorylated in stem cells undergoing mitosis at the ventricular surface, S6 was phosphorylated in more differentiated cells migrating away from the ventricle. Phosphorylation of 4EBP1/2, but not S6, was responsive to quiescence induction in cultured embryonic NSCs. Further, inhibition of p-4EBP1/2, but not p-S6, was sufficient to induce quiescence. Collectively, this work offers new insight into the regulation of quiescence entry in embryonic NSCs and, thereby, correct patterning of the adult brain. These data suggest unique biological functions of specific posttranslational modifications and indicate that the preferential inhibition of such modifications may be a useful therapeutic approach in neurodevelopmental diseases where NSC numbers, proliferation, and differentiation are altered.

Learning cell identity in immunology, neuroscience, and cancer.

Suspension and imaging cytometry techniques that simultaneously measure hundreds of cellular features are powering a new era of cell biology and transforming our understanding of human tissues and tumors. However, a central challenge remains in learning the identities of unexpected or novel cell types. Cell identification rubrics that could assist trainees, whether human or machine, are not always rigorously defined, vary greatly by field, and differentially rely on cell intrinsic measurements, cell extrinsic tissue measurements, or external contextual information such as clinical outcomes. This challenge is especially acute in the context of tumors, where cells aberrantly express developmental programs that are normally time, location, or cell-type restricted. Well-established fields have contrasting practices for cell identity that have emerged from convention and convenience as much as design. For example, early immunology focused on identifying minimal sets of protein features that mark individual, functionally distinct cells. In neuroscience, features including morphology, development, and anatomical location were typical starting points for defining cell types. Both immunology and neuroscience now aim to link standardized measurements of protein or RNA to informative cell functions such as electrophysiology, connectivity, lineage potential, phospho-protein signaling, cell suppression, and tumor cell killing ability. The expansion of automated, machine-driven methods for learning cell identity has further created an urgent need for a harmonized framework for distinguishing cell identity across fields and technology platforms. Here, we compare practices in the fields of immunology and neuroscience, highlight concepts from each that might work well in the other, and propose ways to implement these ideas to study neural and immune cell interactions in brain tumors and associated model systems.

Mechanism of CD79A and CD79B Support for IgM+ B Cell Fitness through B Cell Receptor Surface Expression.

The BCR consists of surface-bound Ig and a heterodimeric signaling unit comprised of CD79A and CD79B. Upon cognate Ag recognition, the receptor initiates important signals for B cell development and function. The receptor also conveys Ag-independent survival signals termed tonic signaling. Although the requirement of a CD79A/CD79B heterodimer for BCR complex assembly and surface expression is well established based on mice models, few studies have investigated this in human mature B cells. In this study, we found that human tonsillar B cells with high surface expression of IgM or IgG had potentiated BCR signaling compared with BCRlow cells, and high IgM expression in germinal center B cells was associated with reduced apoptosis. We explored the mechanism for IgM surface expression by CRISPR/Cas9-induced deletion of CD79A or CD79B in four B lymphoma cell lines. Deletion of either CD79 protein caused loss of surface IgM in all cell lines and reduced fitness in three. From two cell lines, we generated stable CD79A or CD79B knockout clones and demonstrated that loss of CD79A or CD79B caused a block in N-glycan maturation and accumulation of immature proteins, compatible with retention of BCR components in the endoplasmic reticulum. Rescue experiments with CD79B wild-type restored surface expression of CD79A and IgM with mature glycosylation, whereas a naturally occurring CD79B G137S mutant disrupting CD79A/CD79B heterodimerization did not. Our study highlights that CD79A and CD79B are required for surface IgM expression in human B cells and illuminates the importance of the IgM expression level for signaling and fitness.

An immunogenic cell injury module for the single-cell multiplexed activity metabolomics platform to identify promising anti-cancer natural products.

Natural products constitute and significantly impact many current anti-cancer medical interventions. A subset of natural products induces injury processes in malignant cells that recruit and activate host immune cells to produce an adaptive anti-cancer immune response, a process known as immunogenic cell death. However, a challenge in the field is to delineate forms of cell death and injury that best promote durable antitumor immunity. Addressing this with a single-cell chemical biology natural product discovery platform, like multiplex activity metabolomics, would be especially valuable in human leukemia, where cancer cells are heterogeneous and may react differently to the same compounds. Herein, a new ten-color, fluorescent cell barcoding-compatible module measuring six immunogenic cell injury signaling readouts are as follows: DNA damage response (γH2AX), apoptosis (cCAS3), necroptosis (p-MLKL), mitosis (p-Histone H3), autophagy (LC3), and the unfolded protein response (p-EIF2α). A proof-of-concept screen was performed to validate functional changes in single cells induced by secondary metabolites with known mechanisms within bacterial extracts. This assay was then applied in multiplexed activity metabolomics to reveal an unexpected mammalian cell injury profile induced by the natural product narbomycin. Finally, the functional consequences of injury pathways on immunogenicity were compared with three canonical assays for immunogenic hallmarks, ATP, HMGB1, and calreticulin, to correlate secondary metabolite-induced cell injury profiles with canonical markers of immunogenic cell death. In total, this work demonstrated a new phenotypic screen for discovery of natural products that modulate injury response pathways that can contribute to cancer immunogenicity.

Systems Immunology Analyses of STAT1 Gain-of-Function Immune Phenotypes Reveal Heterogeneous Response to IL-6 and Broad Immunometabolic Roles for STAT1.

Patients with STAT1 gain-of-function (GOF) pathogenic variants have enhanced or prolonged STAT1 phosphorylation following cytokine stimulation and exhibit increased yet heterogeneous susceptibility to infections, autoimmunity, and cancer. Although disease phenotypes are diverse and other genetic factors contribute, how STAT1 GOF affects cytokine sensitivity and cell biology remains poorly defined. In this study, we analyzed the immune and immunometabolic profiles of two patients with known pathogenic heterozygous STAT1 GOF mutation variants. A systems immunology approach of peripheral blood cells from these patients revealed major changes in multiple immune cell compartments relative to healthy adult and pediatric donors. Although many phenotypes of STAT1 GOF donors were shared, including increased Th1 cells but decreased class-switched B cells and plasmacytoid dendritic cell populations, others were heterogeneous. Mechanistically, hypersensitivity for cytokine-induced STAT1 phosphorylation in memory T cell populations was particularly evident in response to IL-6 in one STAT1 GOF patient. Immune cell metabolism directly influences cell function, and the STAT1 GOF patients shared an immunometabolic phenotype of heightened glucose transporter 1 (GLUT1) and carnitine palmitoyl transferase 1A (CPT1a) expression across multiple immune cell lineages. Interestingly, the metabolic phenotypes of the pediatric STAT1 GOF donors more closely resembled or exceeded those of healthy adult than healthy age-similar pediatric donors, which had low expression of these metabolic markers. These results define new features of STAT1 GOF patients, including a differential hypersensitivity for IL-6 and a shared increase in markers of metabolism in many immune cell types that suggests a role for STAT1 in metabolic regulation of immunity.

Single-cell profiling of the antigen-specific response to BNT162b2 SARS-CoV-2 RNA vaccine.

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.

High-Dimensional Analysis Reveals Distinct Endotypes in Patients With Idiopathic Inflammatory Myopathies.

The idiopathic inflammatory myopathies (IIM) are a rare clinically heterogeneous group of conditions affecting the skin, muscle, joint, and lung in various combinations. While myositis specific autoantibodies are well described, we postulate that broader immune endotypes exist in IIM spanning B cell, T cell, and monocyte compartments. This study aims to identify immune endotypes through detailed immunophenotyping of peripheral blood mononuclear cells (PBMCs) in IIM patients compared to healthy controls. We collected PBMCs from 17 patients with a clinical diagnosis of inflammatory myositis and characterized the B, T, and myeloid cell subsets using mass cytometry by time of flight (CyTOF). Data were analyzed using a combination of the dimensionality reduction algorithm t-distributed stochastic neighbor embedding (t-SNE), cluster identification, characterization, and regression (CITRUS), and marker enrichment modeling (MEM); supervised biaxial gating validated populations identified by these methods to be differentially abundant between groups. Using these approaches, we identified shared immunologic features across all IIM patients, despite different clinical features, as well as two distinct immune endotypes. All IIM patients had decreased surface expression of RP105/CD180 on B cells and a reduction in circulating CD3+CXCR3+ subsets relative to healthy controls. One IIM endotype featured CXCR4 upregulation across all cellular compartments. The second endotype was hallmarked by an increased frequency of CD19+CD21loCD11c+ and CD3+CD4+PD1+ subsets. The experimental and analytical methods we describe here are broadly applicable to studying other immune-mediated diseases (e.g., autoimmunity, immunodeficiency) or protective immune responses (e.g., infection, vaccination).

Two New Neutrophil Subsets Define a Discriminating Sepsis Signature.

Rationale: 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. Objectives: 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. Methods: 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. Measurements and Main Results: 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. Conclusions: 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.