Supervised Machine Learning with CITRUS for Single Cell Biomarker Discovery

Osteochondroprogenitor cells and neutrophils expressing p21 and senescence markers modulate fracture repair

Cells expressing features of senescence, including upregulation of p21 and p16, appear transiently following tissue injury, yet the properties of these cells or how they contrast with age-induced senescent cells remains unclear. Here, we used skeletal injury as a model and identified the rapid appearance following fracture of p21+ cells expressing senescence markers, mainly as osteochondroprogenitors (OCHs) and neutrophils. Targeted genetic clearance of p21+ cells suppressed senescence-associated signatures within the fracture callus and accelerated fracture healing. By contrast, p21+ cell clearance did not alter bone loss due to aging; conversely, p16+ cell clearance, known to alleviate skeletal aging, did not affect fracture healing. Following fracture, p21+ neutrophils were enriched in signaling pathways known to induce paracrine stromal senescence, while p21+ OCHs were highly enriched in senescence-associated secretory phenotype factors known to impair bone formation. Further analysis revealed an injury-specific stem cell-like OCH subset that was p21+ and highly inflammatory, with a similar inflammatory mesenchymal population (fibro-adipogenic progenitors) evident following muscle injury. Thus, intercommunicating senescent-like neutrophils and mesenchymal progenitor cells were key regulators of tissue repair in bone and potentially across tissues. Moreover, our findings established contextual roles of p21+ versus p16+ senescent/senescent-like cells that may be leveraged for therapeutic opportunities.

Tetracyclines enhance antitumor T-cell immunity via the Zap70 signaling pathway

BACKGROUND

Cancer immunotherapy including immune checkpoint inhibitors is only effective for a limited population of patients with cancer. Therefore, the development of novel cancer immunotherapy is anticipated. In preliminary studies, we demonstrated that tetracyclines enhanced T-cell responses. Therefore, we herein investigated the efficacy of tetracyclines on antitumor T-cell responses by human peripheral T cells, murine models, and the lung tumor tissues of patients with non-small cell lung cancer (NSCLC), with a focus on signaling pathways in T cells.

METHODS

The cytotoxicity of peripheral and lung tumor-infiltrated human T cells against tumor cells was assessed by using bispecific T-cell engager (BiTE) technology (BiTE-assay system). The effects of tetracyclines on T cells in the peripheral blood of healthy donors and the tumor tissues of patients with NSCLC were examined using the BiTE-assay system in comparison with anti-programmed cell death-1 (PD-1) antibody, nivolumab. T-cell signaling molecules were analyzed by flow cytometry, ELISA, and qRT-PCR. To investigate the in vivo antitumor effects of tetracyclines, tetracyclines were administered orally to BALB/c mice engrafted with murine tumor cell lines, either in the presence or absence of anti-mouse CD8 inhibitors.

RESULTS

The results obtained revealed that tetracyclines enhanced antitumor T-cell cytotoxicity with the upregulation of granzyme B and increased secretion of interferon-γ in human peripheral T cells and the lung tumor tissues of patients with NSCLC. The analysis of T-cell signaling showed that CD69 in both CD4+ and CD8+ T cells was upregulated by minocycline. Downstream of T-cell receptor signaling, Zap70 phosphorylation and Nur77 were also upregulated by minocycline in the early phase after T-cell activation. These changes were not observed in T cells treated with anti-PD-1 antibodies under the same conditions. The administration of tetracyclines exhibited antitumor efficacy with the upregulation of CD69 and increases in tumor antigen-specific T cells in murine tumor models. These changes were canceled by the administration of anti-mouse CD8 inhibitors.

CONCLUSIONS

In conclusion, tetracyclines enhanced antitumor T-cell immunity via Zap70 signaling. These results will contribute to the development of novel cancer immunotherapy.

Minimal Residual Disease in Acute Myeloid Leukemia: Old and New Concepts

Minimal residual disease (MRD) is of major importance in onco-hematology, particularly in acute myeloid leukemia (AML). MRD measures the amount of leukemia cells remaining in a patient after treatment, and is an essential tool for disease monitoring, relapse prognosis, and guiding treatment decisions. Patients with a negative MRD tend to have superior disease-free and overall survival rates. Considerable effort has been made to standardize MRD practices. A variety of techniques, including flow cytometry and molecular methods, are used to assess MRD, each with distinct strengths and weaknesses. MRD is recognized not only as a predictive biomarker, but also as a prognostic tool and marker of treatment efficacy. Expected advances in MRD assessment encompass molecular techniques such as NGS and digital PCR, as well as optimization strategies such as unsupervised flow cytometry analysis and leukemic stem cell monitoring. At present, there is no perfect method for measuring MRD, and significant advances are expected in the future to fully integrate MRD assessment into the management of AML patients.

Osteochondroprogenitor cells and neutrophils expressing p21 and senescence markers modulate fracture repair

Cells expressing features of senescence, including upregulation of p21 and p16, appear transiently following tissue injury, yet the properties of these cells or how they contrast with age-induced senescent cells remains unclear. Here, we used skeletal injury as a model and identified the rapid appearance following fracture of p21+ cells expressing senescence markers, mainly as osteochondroprogenitors (OCHs) and neutrophils. Targeted genetic clearance of p21+ cells suppressed senescence-associated signatures within the fracture callus and accelerated fracture healing. By contrast, p21+ cell clearance did not alter bone loss due to aging; conversely, p16+ cell clearance, known to alleviate skeletal aging, did not affect fracture healing. Following fracture, p21+ neutrophils were enriched in signaling pathways known to induce paracrine stromal senescence, while p21+ OCHs were highly enriched in senescence-associated secretory phenotype factors known to impair bone formation. Further analysis revealed an injury-specific stem cell-like OCH subset that was p21+ and highly inflammatory, with a similar inflammatory mesenchymal population (fibro-adipogenic progenitors) evident following muscle injury. Thus, intercommunicating senescent-like neutrophils and mesenchymal progenitor cells are key regulators of tissue repair in bone and potentially across tissues. Moreover, our findings establish contextual roles of p21+ vs p16+ senescent/senescent-like cells that may be leveraged for therapeutic opportunities.

Interleukin-21 promotes Type-1 activation and cytotoxicity of CD56dimCD16bright natural killer cells during kidney allograft antibody-mediated rejection showing a new link between adaptive and innate humoral allo-immunity

The role of Natural killer (NK) cells during kidney allograft antibody-mediated rejection (ABMR) is increasingly recognized, but an in-depth characterization of mechanisms that contribute to such immune response is still under investigation. Here, we characterized phenotypic, functional, and transcriptomic profiles of peripheral blood circulating and allograft infiltrating CD56dimCD16bright NK cells during anti-HLA donor-specific antibody (DSA)+ ABMR. Cross-sectional analyses performed in 71 kidney transplant recipients identified a unique phenotypic circulating CD56dimCD16bright NK cell cluster expanded in DSA+ ABMR. This cluster co-expressed high levels of the interleukin-21 Receptor (IL-21R); Type-1 transcription factors T-bet and EOMES, CD160 and natural killer group 2D cytotoxic and activating co-stimulatory receptors. CD160+ IL-21R+ NK cells correlated with elevated plasma IL-21, Ki-67+ ICOS+ (CD278) IL-21-producing circulating T follicular helper cells, enhanced Type-1 pro-inflammatory cytokines, NK cell cytotoxicity, worse microvascular inflammation and graft loss. Single-cell transcriptomic analysis of circulating NK cells delineated an expanded cluster in DSA+ ABMR characterized by elevated pro-inflammatory/cytotoxic pathways, IL-21/STAT3 signaling, and leukocyte trans-endothelial migration pathways. Infiltration of CD160+ IL-21R+ NK cells with similar transcriptomic profile was detected in DSA+ ABMR allograft biopsies, potentially contributing to allograft injury. Thus, the IL-21/IL-21R axis, linking adaptive and innate humoral allo-immunity, or NK cells may represent appealing immunotherapy targets in DSA+ ABMR.

Cyclone: an accessible pipeline to analyze, evaluate, and optimize multiparametric cytometry data

In the past decade, high-dimensional single-cell technologies have revolutionized basic and translational immunology research and are now a key element of the toolbox used by scientists to study the immune system. However, analysis of the data generated by these approaches often requires clustering algorithms and dimensionality reduction representation, which are computationally intense and difficult to evaluate and optimize. Here, we present Cytometry Clustering Optimization and Evaluation (Cyclone), an analysis pipeline integrating dimensionality reduction, clustering, evaluation, and optimization of clustering resolution, and downstream visualization tools facilitating the analysis of a wide range of cytometry data. We benchmarked and validated Cyclone on mass cytometry (CyTOF), full-spectrum fluorescence-based cytometry, and multiplexed immunofluorescence (IF) in a variety of biological contexts, including infectious diseases and cancer. In each instance, Cyclone not only recapitulates gold standard immune cell identification but also enables the unsupervised identification of lymphocytes and mononuclear phagocyte subsets that are associated with distinct biological features. Altogether, the Cyclone pipeline is a versatile and accessible pipeline for performing, optimizing, and evaluating clustering on a variety of cytometry datasets, which will further power immunology research and provide a scaffold for biological discovery.

Impact of chronic alcohol exposure on conventional and regulatory murine T cell subsets

Introduction

Chronic alcohol use poses significant negative consequences to public health and, among its many biologic effects, is associated with significant T cell dysregulation within the adaptive immune system that has yet to be fully characterized. Novel, automated strategies for high dimensional flow cytometric analysis of the immune system are rapidly improving researchers' ability to detect and characterize rare cell types.

Methods

Using a murine model of chronic alcohol ingestion in conjunction with viSNE and CITRUS analysis tools, we performed a machine-driven, exploratory analysis comparing rare splenic subpopulations within the conventional CD4+, regulatory CD4+ and CD8+ T cell compartments between alcohol- and water-fed animals.

Results

While there were no differences in the absolute numbers of bulk CD3+ T cells, bulk CD4+ T cells, bulk CD8+ T cells, Foxp3- CD4+ conventional T cells (Tconv) or Foxp3+ CD4+ regulatory T cells (Treg), we identified populations of naïve Helios+ CD4+Tconv and naïve CD103+ CD8+ splenic T cells that were decreased in chronically alcohol exposed mice versus water-fed controls. In addition, we identified increased CD69+ Treg and decreased CD103+ effector regulatory T cell (eTreg) subsets in conjunction with increased frequency of a population that may represent a transitional phenotype between central regulatory T cell (cTreg) and eTreg.

Discussion

These data provide further resolution into the character of decreased naïve T cell populations known to be present in alcohol exposed mice, as well as describe alterations in effector regulatory T cell phenotypes associated with the pathogenesis of chronic alcohol-induced immune dysfunction.

Cyclone: an accessible pipeline to analyze, evaluate and optimize multiparametric cytometry data

In the past decade, high-dimensional single cell technologies have revolutionized basic and translational immunology research and are now a key element of the toolbox used by scientists to study the immune system. However, analysis of the data generated by these approaches often requires clustering algorithms and dimensionality reduction representation which are computationally intense and difficult to evaluate and optimize. Here we present Cyclone, an analysis pipeline integrating dimensionality reduction, clustering, evaluation and optimization of clustering resolution, and downstream visualization tools facilitating the analysis of a wide range of cytometry data. We benchmarked and validated Cyclone on mass cytometry (CyTOF), full spectrum fluorescence-based cytometry, and multiplexed immunofluorescence (IF) in a variety of biological contexts, including infectious diseases and cancer. In each instance, Cyclone not only recapitulates gold standard immune cell identification, but also enables the unsupervised identification of lymphocytes and mononuclear phagocytes subsets that are associated with distinct biological features. Altogether, the Cyclone pipeline is a versatile and accessible pipeline for performing, optimizing, and evaluating clustering on variety of cytometry datasets which will further power immunology research and provide a scaffold for biological discovery.

A Novel Humanized PD-1/PD-L1 Mouse Model Permits Direct Comparison of Antitumor Immunity Generated by Food and Drug Administration-Approved PD-1 and PD-L1 Inhibitors

Seven different anti-PD-1 and PD-L1 mAbs are now widely used in the United States to treat a variety of cancer types, but no clinical trials have compared them directly. Furthermore, because many of these Abs do not cross-react between mouse and human proteins, no preclinical models exist in which to consider these types of questions. Thus, we produced humanized PD-1 and PD-L1 mice in which the extracellular domains of both mouse PD-1 and PD-L1 were replaced with the corresponding human sequences. Using this new model, we sought to compare the strength of the immune response generated by Food and Drug Administration-approved Abs. To do this, we performed an in vivo T cell priming assay in which anti-PD-1/L1 therapies were given at the time of T cell priming against surrogate tumor Ag (OVA), followed by subsequent B16-OVA tumor challenge. Surprisingly, both control and Ab-treated mice formed an equally robust OVA-specific T cell response at the time of priming. Despite this, anti-PD-1/L1-treated mice exhibited significantly better tumor rejection versus controls, with avelumab generating the best protection. To determine what could be mediating this, we identified the increased production of CX3CR1+PD-1+CD8+ cytotoxic T cells in the avelumab-treated mice, the same phenotype of effector T cells known to increase in clinical responders to PD-1/L1 therapy. Thus, our model permits the direct comparison of Food and Drug Administration-approved anti-PD-1/L1 mAbs and further correlates successful tumor rejection with the level of CX3CR1+PD-1+CD8 + T cells, making this model a critical tool for optimizing and better utilizing anti-PD-1/L1 therapeutics.

CyTOF® for the Masses

Mass cytometry has revolutionized immunophenotyping, particularly in exploratory settings where simultaneous breadth and depth of characterization of immune populations is needed with limited samples such as in preclinical and clinical tumor immunotherapy. Mass cytometry is also a powerful tool for single-cell immunological assays, especially for complex and simultaneous characterization of diverse intratumoral immune subsets or immunotherapeutic cell populations. Through the elimination of spectral overlap seen in optical flow cytometry by replacement of fluorescent labels with metal isotopes, mass cytometry allows, on average, robust analysis of 60 individual parameters simultaneously. This is, however, associated with significantly increased complexity in the design, execution, and interpretation of mass cytometry experiments. To address the key pitfalls associated with the fragmentation, complexity, and analysis of data in mass cytometry for immunologists who are novices to these techniques, we have developed a comprehensive resource guide. Included in this review are experiment and panel design, antibody conjugations, sample staining, sample acquisition, and data pre-processing and analysis. Where feasible multiple resources for the same process are compared, allowing researchers experienced in flow cytometry but with minimal mass cytometry expertise to develop a data-driven and streamlined project workflow. It is our hope that this manuscript will prove a useful resource for both beginning and advanced users of mass cytometry.

Activated but functionally impaired memory Tregs are expanded in slow progressors to type 1 diabetes

AIMS/HYPOTHESIS

Slow progressors to type 1 diabetes are individuals positive for multiple pancreatic islet autoantibodies who have remained diabetes-free for at least 10 years; regulation of the autoimmune response is understudied in this group. Here, we profile CD4⁺ regulatory T cells (Tregs) in a small but well-characterised cohort of extreme slow progressors with a median age 43 (range 31-72 years), followed up for 18-32 years.

METHODS

Peripheral blood samples were obtained from slow progressors (n = 8), age- and sex-matched to healthy donors. One participant in this study was identified with a raised HbA_1c at the time of assessment and subsequently diagnosed with diabetes; this donor was individually evaluated in the analysis of the data. Peripheral blood mononuclear cells (PBMCs) were isolated, and to assess frequency, phenotype and function of Tregs in donors, multi-parameter flow cytometry and T cell suppression assays were performed. Unsupervised clustering analysis, using FlowSOM and CITRUS (cluster identification, characterization, and regression), was used to evaluate Treg phenotypes.

RESULTS

Unsupervised clustering on memory CD4⁺ T cells from slow progressors showed an increased frequency of activated memory CD4⁺ Tregs, associated with increased expression of glucocorticoid-induced TNFR-related protein (GITR), compared with matched healthy donors. One participant with a raised HbA_1c at the time of assessment had a different Treg profile compared with both slow progressors and matched controls. Functional assays demonstrated that Treg-mediated suppression of CD4⁺ effector T cells from slow progressors was significantly impaired, compared with healthy donors. However, effector CD4⁺ T cells from slow progressors were more responsive to Treg suppression compared with healthy donors, demonstrated by increased suppression of CD25 and CD134 expression on effector CD4⁺ T cells.

CONCLUSIONS/INTERPRETATIONS

We conclude that activated memory CD4⁺ Tregs from slow progressors are expanded and enriched for GITR expression, highlighting the need for further study of Treg heterogeneity in individuals at risk of developing type 1 diabetes.

Mass Cytometry in Hematologic Malignancies: Research Highlights and Potential Clinical Applications

Recent advances in global gene sequencing technologies and the effect they have had on disease diagnosis, therapy, and research have fueled interest in technologies capable of more broadly profiling not only genes but proteins, metabolites, cells, and almost any other component of biological systems. Mass cytometry is one such technology, which enables simultaneous characterization of over 40 parameters per cell, significantly more than can be achieved by even the most state-of-the-art flow cytometers. This mini-review will focus on how mass cytometry has been utilized to help advance the field of neoplastic hematology. Common themes among published studies include better defining lineage sub-populations, improved characterization of tumor microenvironments, and profiling intracellular signaling across multiple pathways simultaneously in various cell types. Reviewed studies highlight potential applications for disease diagnosis, prognostication, response to therapy, measurable residual disease analysis, and identifying new therapies.

Advanced Flow Cytometry Analysis Algorithms for Optimizing the Detection of "Different From Normal" Immunophenotypes in Acute Myeloid Blasts

Acute myeloid leukemias (AMLs) are a group of hematologic malignancies that are heterogeneous in their molecular and immunophenotypic profiles. Identification of the immunophenotypic differences between AML blasts and normal myeloid hematopoietic precursors (myHPCs) is a prerequisite to achieving better performance in AML measurable residual disease follow-ups. In the present study, we applied high-dimensional analysis algorithms provided by the Infinicyt 2.0 and Cytobank software to evaluate the efficacy of antibody combinations of the EuroFlow AML/myelodysplastic syndrome panel to distinguish AML blasts with recurrent genetic abnormalities (n = 39 AML samples) from normal CD45low CD117+ myHPCs (n = 23 normal bone marrow samples). Two types of scores were established to evaluate the abilities of the various methods to identify the most useful parameters/markers for distinguishing between AML blasts and normal myHPCs, as well as to distinguish between different AML groups. The Infinicyt Compass database-guided analysis was found to be a more user-friendly tool than other analysis methods implemented in the Cytobank software. According to the developed scoring systems, the principal component analysis based algorithms resulted in better discrimination between AML blasts and myHPCs, as well as between blasts from different AML groups. The most informative markers for the discrimination between myHPCs and AML blasts were CD34, CD36, human leukocyte antigen-DR (HLA-DR), CD13, CD105, CD71, and SSC, which were highly rated by all evaluated analysis algorithms. The HLA-DR, CD34, CD13, CD64, CD33, CD117, CD71, CD36, CD11b, SSC, and FSC were found to be useful for the distinction between blasts from different AML groups associated with recurrent genetic abnormalities. This study identified both benefits and the drawbacks of integrating multiple high-dimensional algorithms to gain complementary insights into the flow-cytometry data.