Quantitative Comparison of Conventional and t-SNE-guided Gating Analyses

Immunophenotyping with (phospho)protein profiling and fluorescent cell barcoding for single-cell signaling analysis and biomarker discovery

The microenvironment of hematologic cancers contributes to tumor cell survival and proliferation, as well as treatment resistance. Understanding tumor- and drug-induced changes to the immune cell composition and functionality is therefore critical for implementing optimal treatment strategies and for the development of novel cancer therapies. The liquid nature of peripheral blood makes this organ uniquely suited for single-cell studies by flow cytometry. (Phospho)protein profiles detected by flow cytometry analyses have been shown to correlate with ex vivo drug sensitivity and to predict treatment outcomes in hematologic cancers, demonstrating that this method is suitable for pre-clinical studies. Here, we present a flow cytometry protocol that combines multi-parameter immunophenotyping with single-cell (phospho)protein profiling. The protocol makes use of fluorescent cell barcoding, which means that multiple cell samples, either collected from different donors or exposed to different treatment conditions, can be combined and analyzed as one experiment. This reduces variability between samples, increases the throughput of the experiment, and lowers experimental costs. This protocol may serve as a guide for the use and further development of assays to study immunophenotype and cell signaling at single-cell resolution in normal and malignant cells. The read-outs may provide biological insight into cancer pathogenesis, identify novel drug targets, and ultimately serve as a biomarker to guide clinical decision-making.

Characterization of CD34+ Cells from Patients with Acute Myeloid Leukemia (AML) and Myelodysplastic Syndromes (MDS) Using a t-Distributed Stochastic Neighbor Embedding (t-SNE) Protocol

Using multi-color flow cytometry analysis, we studied the immunophenotypical differences between leukemic cells from patients with AML/MDS and hematopoietic stem and progenitor cells (HSPCs) from patients in complete remission (CR) following their successful treatment. The panel of markers included CD34, CD38, CD45RA, CD123 as representatives for a hierarchical hematopoietic stem and progenitor cell (HSPC) classification as well as programmed death ligand 1 (PD-L1). Rather than restricting the evaluation on a 2- or 3-dimensional analysis, we applied a t-distributed stochastic neighbor embedding (t-SNE) approach to obtain deeper insight and segregation between leukemic cells and normal HPSCs. For that purpose, we created a t-SNE map, which resulted in the visualization of 27 cell clusters based on their similarity concerning the composition and intensity of antigen expression. Two of these clusters were "leukemia-related" containing a great proportion of CD34+/CD38- hematopoietic stem cells (HSCs) or CD34+ cells with a strong co-expression of CD45RA/CD123, respectively. CD34+ cells within the latter cluster were also highly positive for PD-L1 reflecting their immunosuppressive capacity. Beyond this proof of principle study, the inclusion of additional markers will be helpful to refine the differentiation between normal HSPCs and leukemic cells, particularly in the context of minimal disease detection and antigen-targeted therapeutic interventions. Furthermore, we suggest a protocol for the assignment of new cell ensembles in quantitative terms, via a numerical value, the Pearson coefficient, based on a similarity comparison of the t-SNE pattern with a reference.

Spatial distribution and source identification of potentially toxic elements in Yellow River Delta soils, China: An interpretable machine-learning approach

Identifying the driving factors and quantifying the sources of potentially toxic elements (PTEs) are essential for protecting the ecological environment of the Yellow River Delta. In this study, data from 201 surface soil samples and 16 environmental variables were collected, and the random forest (RF) and Shapley additive explanations (SHAP) methods were then combined to explore the key factors affecting soil PTEs. An innovative t-distributed random neighbor embedding-RF-SHAP model was then constructed, based on the absolute principal component score and multivariate linear regression model, to quantitatively determine PTE sources. Although average PTE concentrations did not exceed the risk control values, PTE distributions exhibited significant differences. It was found that sodium, soil organic matter, and phosphorus contents were the three most important factors affecting PTEs, and human activities and natural environmental factors both influence PTE contents by altering the soil properties. The proposed model successfully determined PTE sources in the soil, outperforming the original linear regression model with a significantly lower RMSE. Source analysis revealed that the parent material was the main contributor to soil PTEs, accounting for more than half of the total PTE content. Industrial and agricultural activities also contributed to an increase in soil PTEs, with average contributions of 19.91 % and 17.44 %, respectively. Unknown sources accounted for 10.83 % of the total PTE content. Thus, the proposed model provides innovative perspectives on source parsing. These findings provide valuable scientific insights for policymakers seeking to develop effective environmental protection measures and improve the quality of saline-alkali land in the Yellow River Delta.

Altered immune cell phenotypes within chronically ischemic human kidneys distal to occlusive renal artery disease

Renal artery stenosis (RAS) is a major cause of ischemic kidney disease, which is largely mediated by inflammation. Mapping the immune cell composition in ischemic kidneys might provide useful insight into the disease pathogenesis and uncover therapeutic targets. We used mass cytometry (CyTOF) to explore the single-cell composition in a unique data set of human kidneys nephrectomized due to chronic occlusive vascular disease (RAS, n = 3), relatively healthy donor kidneys (n = 6), and unaffected sections of kidneys with renal cell carcinoma (RCC, n = 3). Renal fibrosis and certain macrophage populations were also evaluated in renal sections. Cytobank analysis showed in RAS kidneys decreased cell populations expressing epithelial markers (CD45-/CD13+) and increased CD45+ inflammatory cells, whereas scattered tubular-progenitor-like cells (CD45-/CD133+/CD24+) increased compared with kidney donors. Macrophages switched to proinflammatory phenotypes in RAS, and the numbers of IL-10-producing dendritic cells (DC) were also lower. Compared with kidney donors, RAS kidneys had decreased overall DC populations but increased plasmacytoid DC. Furthermore, senescent active T cells (CD45+/CD28+/CD57+), aged neutrophils (CD45+/CD15+/CD24+/CD11c+), and regulatory B cells (CD45+/CD14-/CD24+/CD44+) were increased in RAS. RCC kidneys showed a distribution of cell phenotypes comparable with RAS but less pronounced, accompanied by an increase in CD34+, CD370+, CD103+, and CD11c+/CD103+ cells. Histologically, RAS kidneys showed significantly increased fibrosis and decreased CD163+/CD141+ cells. The single-cell platform CyTOF enables the detection of significant changes in renal cells, especially in subsets of immune cells in ischemic human kidneys. Endogenous pro-repair cell types in RAS warrant future study for potential immune therapy.NEW & NOTEWORTHY The single-cell platform mass cytometry (CyTOF) enables detection of significant changes in one million of renal cells, especially in subsets of immune cells in ischemic human kidneys distal to renal artery stenosis (RAS). We found that pro-repair cell types such as scattered tubular-progenitor-like cells, aged neutrophils, and regulatory B cells show a compensatory increase in RAS. Immune cell phenotype changes may reflect ongoing inflammation and impaired immune defense capability in the kidneys.

Study on the prevalence and subtypes of human papillomavirus infection among women in the Xuhui District, Shanghai City, China

Background

Human papillomavirus (HPV) can cause various gynecological diseases, create a long-term inflammatory immune microenvironment, and induce the occurrence of cervical tumors. However, the prevalence of HPV is species-specific in different eras or in different countries and regions. This paper aimed to investigate the characteristics of HPV infection in the Xuhui District, Shanghai City, China.

Methods

We collected HPV data from 6,760 female testers, focusing on the younger population for data analysis. We focused more on the HPV subtypes to which young women were susceptible, performed t-Distributed Stochastic Neighbor Embedding (TSNE) analysis to screen for characteristic subtypes, and compared the prevalent subtypes lacking effective vaccine protection.

Results

HPV infection exhibited a trend of affecting a younger population, and eight subtypes were more likely to occur in young people. HPV43, 51, 53, and 59 showed a higher incidence and lacked vaccine protection. We performed TSNE dimensionality reduction analysis to organize the HPV data. The results indicated that HPV16, 18, and 51 are characteristic subtypes in the younger population. The Thinprep cytologic test (TCT) also revealed that the infection with HPV43, 51, 53, and 59 also triggers significant pathological phenotypes.

Conclusions

HPV51 is a subtype that occurs more frequently in young women, can induce a variety of significant pathological features, and lacks effective vaccine protection. This study inspires us to take measures to deal with HPV rejuvenation and conduct research on vaccines for specific HPV subtypes.

Innate adaptive immune cell dynamics in tonsillar tissues during chronic SIV infection

HIV-infected patients are at higher risk of developing oral mucosal infection and Epstein-Barr virus (EBV)-associated B cell malignancies. However, the potential role of oral immunity in the pathogenesis of oral lesions is unknown. Tonsils are oral-pharyngeal mucosal-associated lymphoid tissues that play an important role in oral mucosal immunity. In this study, we investigated the changes of innate and adaptive immune cells in macaque tonsils during chronic SIV infection. We found significantly higher frequencies of classical monocytes, CD3+CD56+ (NKT-like) cells, CD3+CD4+CD8+ (DP), and CD161+ CD4 T cells in tonsils from chronic infected compared to naïve animals. On the contrary, intermediate monocytes and CD3+CD4-CD8- (DN) cells were lower in chronic SIV-infected macaques. We further confirmed a recently described small B-cell subset, NKB cells, were higher during chronic infection. Furthermore, both adaptive and innate cells showed significantly higher TNF-α and cytotoxic marker CD107a, while IL-22 production was significantly reduced in innate and adaptive immune cells in chronic SIV-infected animals. A dramatic reduction of IFN-γ production by innate immune cells might indicate enhanced susceptibility to EBV infection and potential transformation of B cells in the tonsils. In summary, our observation shows that the SIV-associated immune responses are distinct in the tonsils compared to other mucosal tissues. Our data extends our understanding of the oral innate immune system during SIV infection and could aid future studies in evaluating the role of tonsillar immune cells during HIV-associated oral mucosal infections.

Ribonuclease 1 Enhances Antitumor Immunity against Breast Cancer by Boosting T cell Activation

The secretory enzyme human ribonuclease 1 (RNase1) is involved in innate immunity and anti-inflammation, achieving host defense and anti-cancer effects; however, whether RNase1 contributes to adaptive immune response in the tumor microenvironment (TME) remains unclear. Here, we established a syngeneic immunocompetent mouse model in breast cancer and demonstrated that ectopic RNase1 expression significantly inhibited tumor progression. Overall changes in immunological profiles in the mouse tumors were analyzed by mass cytometry and showed that the RNase1-expressing tumor cells significantly induced CD4+ Th1 and Th17 cells and natural killer cells and reduced granulocytic myeloid-derived suppressor cells, supporting that RNase1 favors an antitumor TME. Specifically, RNase1 increased expression of T cell activation marker CD69 in a CD4+ T cell subset. Notably, analysis of cancer-killing potential revealed that T cell-mediated antitumor immunity was enhanced by RNase1, which further collaborated with an EGFR-CD3 bispecific antibody to protect against breast cancer cells across molecular subtypes. Our results uncover a tumor-suppressive role of RNase1 through adaptive immune response in breast cancer in vivo and in vitro, providing a potential treatment strategy of combining RNase1 with cancer immunotherapies for immunocompetent patients.

Age-related neuroimmune signatures in dorsal root ganglia of a Fabry disease mouse model

Pain in Fabry disease (FD) is generally accepted to result from neuronal damage in the peripheral nervous system as a consequence of excess lipid storage caused by alpha-galactosidase A (α-Gal A) deficiency. Signatures of pain arising from nerve injuries are generally associated with changes of number, location and phenotypes of immune cells within dorsal root ganglia (DRG). However, the neuroimmune processes in the DRG linked to accumulating glycosphingolipids in Fabry disease are insufficiently understood.Therefore, using indirect immune fluorescence microscopy, transmigration assays and FACS together with transcriptomic signatures associated with immune processes, we assessed age-dependent neuroimmune alterations in DRG obtained from mice with a global depletion of α-Gal A as a valid mouse model for FD. Macrophage numbers in the DRG of FD mice were unaltered, and BV-2 cells as a model for monocytic cells did not show augmented migratory reactions to glycosphingolipids exposure suggesting that these do not act as chemoattractants in FD. However, we found pronounced alterations of lysosomal signatures in sensory neurons and of macrophage morphology and phenotypes in FD DRG. Macrophages exhibited reduced morphological complexity indicated by a smaller number of ramifications and more rounded shape, which were age dependent and indicative of premature monocytic aging together with upregulated expression of markers CD68 and CD163.In our FD mouse model, the observed phenotypic changes in myeloid cell populations of the DRG suggest enhanced phagocytic and unaltered proliferative capacity of macrophages as compared to wildtype control mice. We suggest that macrophages may participate in FD pathogenesis and targeting macrophages at an early stage of FD may offer new treatment options other than enzyme replacement therapy.

CX3CR1 deficiency-induced TIL tumor restriction as a novel addition for CAR-T design in solid malignancies

Advances in the understanding of the tumor microenvironment have led to development of immunotherapeutic strategies, such as chimeric antigen receptor T cells (CAR-Ts). However, despite success in blood malignancies, CAR-T therapies in solid tumors have been hampered by their restricted infiltration. Here, we used our understanding of early cytotoxic lymphocyte infiltration of human lymphocytes in solid tumors in vivo to investigate the receptors in normal, adjacent, and tumor tissues of primary non-small-cell lung cancer specimens. We found that CX3CL1-CX3CR1 reduction restricts cytotoxic cells from the solid-tumor bed, contributing to tumor escape. Based on this, we designed a CAR-T construct using the well-established natural killer group 2, member D (NKG2D) CAR-T expression together with overexpression of CX3CR1 to promote their infiltration. These CAR-Ts infiltrate tumors at higher rates than control-activated T cells or IL-15-overexpressing NKG2D CAR-Ts. This construct also had similar functionality in a liver-cancer model, demonstrating potential efficacy in other solid malignancies.

2-DG Re-Normalized IFN-γ Production in T Cells Excluding TEMRA Cells from Patients with Aplastic Anemia

Aplastic anemia (AA) is a T cell immune mediated autoimmune disease in which cytokines, particularly IFN-γ are pathogenesis factors. Glucose metabolism is closely related to effector functions of activated T cells, such as IFN-γ production. The characteristics of glucose metabolism and whether interfering with glucose metabolism could affect T cells produce IFN-γ ability in AA patients remains unknown. In this study, we examined the characteristics of glucose metabolism in T cells from AA patients and the effects of the glucose metabolism inhibitor 2-deoxy-D-glucose (2-DG) on the ability of T cell production IFN-γ. Our data demonstrated abnormal glucose metabolism in stimulated T cells from AA patients, mainly reflected by increased glucose uptake and lactate secretion. In addition, EM and T_EMRA cells exhibit higher glucose uptake in patients with AA compared with healthy individuals. Moreover, the frequency of IFN-γ⁺ was reduced by 2-DG in T cell from AA patients. Unexpectedly, 2-DG re-normalized the frequency of IFN-γ⁺ in other T cell subsets, except for in the T_EMRA. In conclusion, our study reveals for the first time the existence of enhanced aerobic glycolysis in T cells from AA patients, and different T cell subsets exhibit different extent glucose uptake requirements. Aerobic glycolysis regulation may be a potential therapeutic strategy for aberrant T cell immunity. Moreover, T_EMRA may have specific metabolic abnormalities, which should receive more attention in future targeted immune metabolism research.

The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions

The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.

Adipose tissue is a source of regenerative cells that augment the repair of skeletal muscle after injury

Fibro-adipogenic progenitors (FAPs) play a crucial role in skeletal muscle regeneration, as they generate a favorable niche that allows satellite cells to perform efficient muscle regeneration. After muscle injury, FAP content increases rapidly within the injured muscle, the origin of which has been attributed to their proliferation within the muscle itself. However, recent single-cell RNAseq approaches have revealed phenotype and functional heterogeneity in FAPs, raising the question of how this differentiation of regenerative subtypes occurs. Here we report that FAP-like cells residing in subcutaneous adipose tissue (ScAT), the adipose stromal cells (ASCs), are rapidly released from ScAT in response to muscle injury. Additionally, we find that released ASCs infiltrate the damaged muscle, via a platelet-dependent mechanism and thus contribute to the FAP heterogeneity. Moreover, we show that either blocking ASCs infiltration or removing ASCs tissue source impair muscle regeneration. Collectively, our data reveal that ScAT is an unsuspected physiological reservoir of regenerative cells that support skeletal muscle regeneration, underlining a beneficial relationship between muscle and fat.

Unsupervised machine learning framework for discriminating major variants of concern during COVID-19

Due to the high mutation rate of the virus, the COVID-19 pandemic evolved rapidly. Certain variants of the virus, such as Delta and Omicron emerged with altered viral properties leading to severe transmission and death rates. These variants burdened the medical systems worldwide with a major impact to travel, productivity, and the world economy. Unsupervised machine learning methods have the ability to compress, characterize, and visualize unlabelled data. This paper presents a framework that utilizes unsupervised machine learning methods to discriminate and visualize the associations between major COVID-19 variants based on their genome sequences. These methods comprise a combination of selected dimensionality reduction and clustering techniques. The framework processes the RNA sequences by performing a k-mer analysis on the data and further visualises and compares the results using selected dimensionality reduction methods that include principal component analysis (PCA), t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation projection (UMAP). Our framework also employs agglomerative hierarchical clustering to visualize the mutational differences among major variants of concern and country-wise mutational differences for selected variants (Delta and Omicron) using dendrograms. We also provide country-wise mutational differences for selected variants via dendrograms. We find that the proposed framework can effectively distinguish between the major variants and has the potential to identify emerging variants in the future.

Cellular Heterogeneity and Molecular Reprogramming of the Host Response during Influenza Acute Lung Injury

An exuberant host response contributes to influenza A virus (IAV) (or influenza)-mediated lung injury. However, despite significant information on the host response to IAV, the cellular framework and molecular interactions that dictate the development of acute injury in IAV-infected lungs remain incompletely understood. We performed an unbiased single-cell RNA sequencing (scRNAseq) analysis to examine the cellular heterogeneity and regulation of host responses in the IAV model of acute lung injury. At the cellular level, IAV infection promoted the overwhelming recruitment of monocytes that exhibited the cell differentiation trajectory to monocyte-derived macrophages. Together, monocytes and monocyte-derived myeloid cells constituted over 50% of the total immune cells in IAV-infected lungs. In contrast, IAV infection resulted in a significant loss of nonhematopoietic cells. Molecularly, our data show the multidimensional cell-cell communication dynamics of interferon and chemokine signaling between immune and nonimmune cells and the cell-specific molecular pathways regulating the host responses during IAV-induced lung injury. Our data provide a foundation for further exploring the mechanistic association of the IAV host response with acute lung injury. IMPORTANCE A dysregulated host response develops acute lung injury during IAV infection. However, the pathological immune mechanism(s) associated with acute lung injury during IAV infection is yet to be elucidated. In this study, we performed scRNAseq to examine the dynamics of host responses during the peak of IAV-mediated lung injury. At the cellular level, our data reveal significant myelopoiesis predominated by monocytes and macrophages and the simultaneous disruption of the nonhematopoietic cell framework, crucial for regulating inflammation and barrier integrity in IAV-infected lungs. Molecularly, we observed a complex cellular network involving cell-cell communications and a number of unique regulons dictating the outcome of interferon and chemokine responses during peak lung injury. Our data present a unique atlas of cellular changes and the regulation of global and cell-specific host responses during IAV infection. We expect that this information will open new avenues to identify targets for therapeutic intervention against IAV lung injury.

Implementing High Dimensional Reduction Analysis on Histocytometric Data

In a previous protocol article, we demonstrated construction of a histocytometry pipeline that is capable of both segmenting highly aggregated cell populations and retaining the original intensity data range of the input microscopy images. In the protocol presented here, using the output from the aforementioned article, we demonstrate how to phenotype the data using the high dimensional reduction analysis technique optimized t-distributed stochastic neighbor embedding (opt-t-SNE) and compare it to traditional manual gating. Additionally, we present a protocol illustrating the advantage of the inclusion of cell junction/membrane markers for accurately segmenting highly aggregated cell populations in ilastik. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Phenotyping lymph node populations using manual gating Basic Protocol 2: Phenotyping lymph node populations using t-SNE dimensional reduction Support Protocol: ilastik segmentation using a pan marker.

Haisu: Hierarchically supervised nonlinear dimensionality reduction

We propose a novel strategy for incorporating hierarchical supervised label information into nonlinear dimensionality reduction techniques. Specifically, we extend t-SNE, UMAP, and PHATE to include known or predicted class labels and demonstrate the efficacy of our approach on multiple single-cell RNA sequencing datasets. Our approach, “Haisu,” is applicable across domains and methods of nonlinear dimensionality reduction. In general, the mathematical effect of Haisu can be summarized as a variable perturbation of the high dimensional space in which the original data is observed. We thereby preserve the core characteristics of the visualization method and only change the manifold to respect known or assumed class labels when provided. Our strategy is designed to aid in the discovery and understanding of underlying patterns in a dataset that is heavily influenced by parent-child relationships. We show that using our approach can also help in semi-supervised settings where labels are known for only some datapoints (for instance when only a fraction of the cells are labeled). In summary, Haisu extends existing popular visualization methods to enable a user to incorporate labels known a priori into a visualization, including their hierarchical relationships as defined by a user input graph.

A Chunking-for-Pooling Strategy for Cytometric Representation Learning for Automatic Hematologic Malignancy Classification

Differentiating types of hematologic malignancies is vital to determine therapeutic strategies for the newly diagnosed patients. Flow cytometry (FC) can be used as diagnostic indicator by measuring the multi-parameter fluorescent markers on thousands of antibody-bound cells, but the manual interpretation of large scale flow cytometry data has long been a time-consuming and complicated task for hematologists and laboratory professionals. Past studies have led to the development of representation learning algorithms to perform sample-level automatic classification. In this work, we propose a chunking-for-pooling strategy to include large-scale FC data into a supervised deep representation learning procedure for automatic hematologic malignancy classification. The use of discriminatively-trained representation learning strategy and the fixed-size chunking and pooling design are key components of this framework. It improves the discriminative power of the FC sample-level embedding and simultaneously addresses the robustness issue due to an inevitable use of down-sampling in conventional distribution based approaches for deriving FC representation. We evaluated our framework on two datasets. Our framework outperformed other baseline methods and achieved 92.3% unweighted average recall (UAR) for four-class recognition on the UPMC dataset and 85.0% UAR for five-class recognition on the hema.to dataset. We further compared the robustness of our proposed framework with that of the traditional downsampling approach. Analysis of the effects of the chunk size and the error cases revealed further insights about different hematologic malignancy characteristics in the FC data.

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.

Interferon-γ promotes monocyte-mediated lung injury during influenza infection

Influenza A virus (IAV) infection triggers an exuberant host response that promotes acute lung injury. However, the host response factors that promote the development of a pathologic inflammatory response to IAV remain incompletely understood. In this study, we identify an interferon-γ (IFN-γ)-regulated subset of monocytes, CCR2⁺ monocytes, as a driver of lung damage during IAV infection. IFN-γ regulates the recruitment and inflammatory phenotype of CCR2⁺ monocytes, and mice deficient in CCR2 (CCR2^-/-) or IFN-γ (IFN-γ^-/-) exhibit reduced lung inflammation, pathology, and disease severity. Adoptive transfer of wild-type (WT) (IFN-γR1^+/+) but not IFN-γR1^-/- CCR2⁺ monocytes restore the WT-like pathological phenotype of lung damage in IAV-infected CCR2^-/- mice. CD8⁺ T cells are the main source of IFN-γ in IAV-infected lungs. Collectively, our data highlight the requirement of IFN-γ signaling in the regulation of CCR2⁺ monocyte-mediated lung pathology during IAV infection.

Revealing new biology from multiplexed, metal-isotope-tagged, single-cell readouts

Mass cytometry (MC) is a recent technology that pairs plasma-based ionization of cells in suspension with time-of-flight (TOF) mass spectrometry to sensitively quantify the single-cell abundance of metal-isotope-tagged affinity reagents to key proteins, RNA, and peptides. Given the ability to multiplex readouts (~50 per cell) and capture millions of cells per experiment, MC offers a robust way to assay rare, transitional cell states that are pertinent to human development and disease. Here, we review MC approaches that let us probe the dynamics of cellular regulation across multiple conditions and sample types in a single experiment. Additionally, we discuss current limitations and future extensions of MC as well as computational tools commonly used to extract biological insight from single-cell proteomic datasets.

UMAP Based Anomaly Detection for Minimal Residual Disease Quantification within Acute Myeloid Leukemia

Simple Summary

Acute myeloid leukemia (AML) is the second most frequent leukemia entity in children and adolescents, and definitely the most aggressive variant. Multiparameter flow-cytometry is one of the methodologies most useful to monitor the number of remaining leukemic cells in bone marrow (minimal residual disease, MRD) in AML patients, because it is widely available and applicable to most patients. However, AML flow cytometry data show very complex patterns and identifying leukemic cells in the data is subjective, time-consuming and requires experienced operators who are not available world-wide. In this paper, we approach automatic assessment of AML flow cytometry samples with a novel semi-supervised machine learning model, leveraging implicit expert knowledge stored in a collection of manually assessed samples. Because AML data exhibit a high degree of variability in the patterns of blast cell populations that is difficult to model, the model detects anomalies starting from the appearance of normal cell populations.

Abstract

Leukemia is the most frequent malignancy in children and adolescents, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) as the most common subtypes. Minimal residual disease (MRD) measured by flow cytometry (FCM) has proven to be a strong prognostic factor in ALL as well as in AML. Machine learning techniques have been emerging in the field of automated MRD quantification with the objective of superseding subjective and time-consuming manual analysis of FCM-MRD data. In contrast to ALL, where supervised multi-class classification methods have been successfully deployed for MRD detection, AML poses new challenges: AML is rarer (with fewer available training data) than ALL and much more heterogeneous in its immunophenotypic appearance, where one-class classification (anomaly detection) methods seem more suitable. In this work, a new semi-supervised approach based on the UMAP algorithm for MRD detection utilizing only labels of blast free FCM samples is presented. The method is tested on a newly gathered set of AML FCM samples and results are compared to state-of-the-art methods. We reach a median F1-score of 0.794, while providing a transparent classification pipeline with explainable results that facilitates inter-disciplinary work between medical and technical experts. This work shows that despite several issues yet to overcome, the merits of automated MRD quantification can be fully exploited also in AML.

Contemporary Challenges in Clinical Flow Cytometry: Small Samples, Big Data, Little Time

Background

Immunophenotypic analysis of cell populations by flow cytometry has an established role in primary diagnosis and disease monitoring of many hematologic diseases. A persistent problem in evaluation of specimens is suboptimal cell counts and low cell viability, which results in an undesirable rate of analysis failure. In addition, the increased amount of data generated in flow cytometry challenges existing data analysis and reporting paradigms.

Content

We describe current and emerging technological improvements in cell analysis that allow the clinical laboratory to perform multiparameter analysis of specimens, including those with low cell counts and other quality issues. These technologies include conventional multicolor flow cytometry and new high-dimensional technologies, such as spectral flow cytometry and mass cytometry that enable detection of over 40 antigens simultaneously. The advantages and disadvantages of each approach are discussed. We also describe new innovations in flow cytometry data analysis, including artificial intelligence-aided techniques.

Summary

Improvements in analytical technology, in tandem with innovations in data analysis, data storage, and reporting mechanisms, help to optimize the quality of clinical flow cytometry. These improvements are essential because of the expanding role of flow cytometry in patient care.

A whole-process interpretable and multi-modal deep reinforcement learning for diagnosis and analysis of Alzheimer's disease∗

Objective. Alzheimer's disease (AD), a common disease of the elderly with unknown etiology, has been adversely affecting many people, especially with the aging of the population and the younger trend of this disease. Current artificial intelligence (AI) methods based on individual information or magnetic resonance imaging (MRI) can solve the problem of diagnostic sensitivity and specificity, but still face the challenges of interpretability and clinical feasibility. In this study, we propose an interpretable multimodal deep reinforcement learning model for inferring pathological features and the diagnosis of AD.Approach. First, for better clinical feasibility, the compressed-sensing MRI image is reconstructed using an interpretable deep reinforcement learning model. Then, the reconstructed MRI is input into the full convolution neural network to generate a pixel-level disease probability risk map (DPM) of the whole brain for AD. The DPM of important brain regions and individual information are then input into the attention-based fully deep neural network to obtain the diagnosis results and analyze the biomarkers. We used 1349 multi-center samples to construct and test the model.Main results.Finally, the model obtained 99.6% ± 0.2%, 97.9% ± 0.2%, and 96.1% ± 0.3% area under curve in ADNI, AIBL and NACC, respectively. The model also provides an effective analysis of multimodal pathology, predicts the imaging biomarkers in MRI and the weight of each individual item of information. In this study, a deep reinforcement learning model was designed, which can not only accurately diagnose AD, but analyze potential biomarkers.Significance. In this study, a deep reinforcement learning model was designed. The model builds a bridge between clinical practice and AI diagnosis and provides a viewpoint for the interpretability of AI technology.

CD8+ TILs in NSCLC differentiate into TEMRA via a bifurcated trajectory: deciphering immunogenicity of tumor antigens

Background

CD8⁺ tumor-infiltrating lymphocytes (TILs) comprise phenotypically and functionally heterogeneous subpopulations. Of these, effector memory CD45RA re-expressing CD8⁺ T cells (Temra) have been discovered and characterized as the most terminally differentiated subset. However, their exact ontogeny and physiological importance in association with tumor progression remain poorly understood.

Methods

We analyzed primary tumors and peripheral blood samples from 26 patients with non-small cell lung cancer and analyzed their phenotypes and functional characteristics using flow cytometry, RNA-sequencing, and bioinformatics.

Results

We found that tumor-infiltrating Temra (tilTemra) cells largely differ from peripheral blood Temra (pTemra), with distinct transcriptomes and functional properties. Notably, although majority of the pTemra was CD27⁻CD28⁻ double-negative (DN), a large fraction of tilTemra population was CD27⁺CD28⁺ double-positive (DP), a characteristic of early-stage, less differentiated effector cells. Trajectory analysis revealed that CD8⁺ TILs undergo a divergent sequence of events for differentiation into either DP or DN tilTemra. Such a differentiation toward DP tilTemra relied on persistent expression of CD27 and CD28 and was associated with weak T cell receptor engagement. Thus, a higher proportion of DP Temra was correlated with lower immunogenicity of tumor antigens and consequently lower accumulation of CD8⁺ TILs.

Conclusions

These data suggest a complex interplay between CD8⁺ T cells and tumors and define DP Temra as a unique subset of tumor-specific CD8⁺ TILs that are produced in patients with relatively low immunogenic cancer types, predicting immunogenicity of tumor antigens and CD8⁺ TIL counts, a reliable biomarker for successful cancer immunotherapy.

Use of a small molecule integrin activator as a systemically administered vaccine adjuvant in controlling Chagas disease

The development of suitable safe adjuvants to enhance appropriate antigen-driven immune responses remains a challenge. Here we describe the adjuvant properties of a small molecule activator of the integrins αLβ2 and α4β1, named 7HP349, which can be safely delivered systemically independent of antigen. 7HP349 directly activates integrin cell adhesion receptors crucial for the generation of an immune response. When delivered systemically in a model of Chagas disease following immunization with a DNA subunit vaccine encoding candidate T. cruzi antigens, TcG2 and TcG4, 7HP349 enhanced the vaccine efficacy in both prophylactic and therapeutic settings. In a prophylactic setting, mice immunized with 7HP349 adjuvanted vaccine exhibited significantly improved control of acute parasite burden in cardiac and skeletal muscle as compared to vaccination alone. When administered with vaccine therapeutically, parasite burden was again decreased, with the greatest adjuvant effect of 7HP349 being noted in skeletal muscle. In both settings, adjuvantation with 7HP349 was effective in decreasing pathological inflammatory infiltrate, improving the integrity of tissue, and controlling tissue fibrosis in the heart and skeletal muscle of acutely and chronically infected Chagas mice. The positive effects correlated with increased splenic frequencies of CD8⁺T effector cells and an increase in the production of IFN-γ and cytolytic molecules (perforin and granzyme) by the CD4⁺ and CD8⁺ effector and central memory subsets in response to challenge infection. This demonstrates that 7HP349 can serve as a systemically administered adjuvant to enhance T cell-mediated immune responses to vaccines. This approach could be applied to numerous vaccines with no reformulation of existing stockpiles.

Acquisition of High-Quality Spectral Flow Cytometry Data

Flow cytometry allows the visualization of physical, functional, and/or biological properties of cells including antigens, cytokines, size, and complexity. With increasingly large flow cytometry panels able to analyze up to 50 parameters, there is a need to standardize flow cytometry protocols to achieve high-quality data that can be input into analysis algorithms. Without this clean data, algorithms may incorrectly categorize the cell populations present in the samples. In this protocol, we outline a comprehensive methodology to prepare samples for polychromatic flow cytometry. The use of multiple washing steps and rigorous controls creates high-quality data with good separation between cell populations. Experimental data acquired using this protocol can be analyzed via computational algorithms that perform end-to-end analysis. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Preparation of single-cell suspension for flow cytometry Support Protocol 1: Lung preparation Support Protocol 2: Counting cells on a flow cytometer Basic Protocol 2: Surface and intracellular flow cytometry staining Support Protocol 3: Single-color bead controls.

A Prognostic DNA Damage Repair Genes Signature and Its Impact on Immune Cell Infiltration in Glioma

Objective

Glioma is the most frequent type of malignant cerebral tumors. DNA damage repair genes (DDRGs) play a crucial role in the development of cancer. In this study, we constructed a DDRGs signature and investigated the potential mechanisms involved in this disease.

Methods

RNA sequence data, microarray data, and corresponding clinical information of gliomas were downloaded from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO). Subsequently, we identified candidate genes by differential analysis and Cox regression analysis. The least absolute shrinkage and selection operator Cox regression model was utilized to construct a DDRGs signature using TCGA training dataset. According to this signature, patients with glioma were divided into low- and high-risk groups. The predictive ability of the signature was validated by prognostic analysis, receiver operating characteristic curves, principal component analysis, and stratification analysis in TCGA testing and CGGA verification datasets. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were used to evaluate the immune microenvironment of glioma. Moreover, we conducted GSEA to determine the functions and pathways in the low- and high-risk groups. Finally, a nomogram was constructed by combining the signature and other clinical features.

Results

A total of 1,431 samples of glioma (592 from TCGA, 686 from the CGGA, and 153 from the GEO) and 23 samples of normal brain tissue from the GEO were analyzed in this study. There were 51 prognostic differentially expressed DDRGs. Additionally, five DDRGs (CDK4、HMGB2、WEE1、SMC3 and GADD45G) were selected to construct a DDRGs signature for glioma, stratifying patients into low- and high-risk groups. The survival analysis showed that the DDRGs signature could differentiate the outcome of the low- and high-risk groups, showing that high-risk gliomas were associated with shorter overall survival. The immune microenvironment analysis revealed that more immunosuppressive cells, such as tumor associated macrophages and regulatory T cells, were recruited in the high-risk group. GSEA also showed that high-risk glioma was correlated with the immune and extracellular matrix pathways.

Conclusion

The five DDRGs signature and its impact on the infiltration of immunosuppressive cells could precisely predict the prognosis and provide guidance on the treatment of glioma.

Air pollution exposure is linked with methylation of immunoregulatory genes, altered immune cell profiles, and increased blood pressure in children

Ambient air pollution exposure is associated with cardiovascular dysregulation and immune system alterations, yet no study has investigated both simultaneously in children. Understanding the multifaceted impacts may provide early clues for clinical intervention prior to actual disease presentation. We therefore determined the associations between exposure to multiple air pollutants and both immunological outcomes (methylation and protein expression of immune cell types associated with immune regulation) and cardiovascular outcomes (blood pressure) in a cohort of school-aged children (6–8 years; n = 221) living in a city with known elevated pollution levels. Exposure to fine particular matter (PM_2.5), carbon monoxide (CO), and ozone (O₃) was linked to altered methylation of most CpG sites for genes Foxp3, IL-4, IL-10 and IFN-g, all involved in immune regulation (e.g. higher PM_2.5 exposure 1 month prior to the study visit was independently associated with methylation of the IL-4 CpG24 site (est = 0.16; P = 0.0095). Also, immune T helper cell types (Th1, Th2 and Th17) were associated with short-term exposure to PM_2.5, O₃ and CO (e.g. Th1 cells associated with PM_2.5 at 30 days: est = − 0.34, P < 0.0001). Both B cells (est = − 0.19) and CD4+ cells (est = 0.16) were associated with 1 day NO2 exposure (P ≤ 0.031), whereas CD4+ and CD8+ cells were associated with chronic exposure to PAH₄₅₆, NOx and/or NO₂ (P ≤ 0.038 for all). Finally, diastolic BP (DBP) was inversely associated with long-term exposures to both CO and PAH₄₅₆, and both systolic and pulse pressure were associated with short-term NO₂ and chronic NOx exposure. Our findings demonstrate links between air pollution exposure and methylation of immunoregulatory genes, immune cell profiles and blood pressure, suggesting that even at a young age, the immune and cardiovascular systems are negatively impacted by exposure to air pollution.

Serial Stimulation of Invariant Natural Killer T Cells with Covalently Stabilized Bispecific T-cell Engagers Generates Antitumor Immunity While Avoiding Anergy

CD1d-restricted invariant natural killer T cells (iNKT cells) mediate strong antitumor immunity when stimulated by glycolipid agonists. However, attempts to develop effective iNKT cell agonists for clinical applications have been thwarted by potential problems with dose-limiting toxicity and by activation-induced iNKT cell anergy, which limits the efficacy of repeated administration. To overcome these issues, we developed a unique bispecific T-cell engager (BiTE) based on covalent conjugates of soluble CD1d with photoreactive analogues of the glycolipid α-galactosylceramide. Here we characterize the in vivo activities of iNKT cell-specific BiTEs and assess their efficacy for cancer immunotherapy in mouse models using transplantable colorectal cancer or melanoma tumor lines engineered to express human Her2 as a tumor-associated antigen. Systemic administration of conjugated BiTEs stimulated multiple iNKT cell effector functions including cytokine release, secondary activation of NK cells, and induction of dendritic cell maturation and also initiated epitope spreading for tumor-specific CD8⁺ cytolytic T-cell responses. The antitumor effects of iNKT-cell activation with conjugated BiTEs were further enhanced by simultaneous checkpoint blockade with antibodies to CTLA-4, providing a potential approach for combination immunotherapy. Multiple injections of covalently stabilized iNKT cell-specific BiTEs activated iNKT cells without causing iNKT cell anergy or exhaustion, thus enabling repeated administration for effective and nontoxic cancer immunotherapy regimens. SIGNIFICANCE: Covalently stabilized conjugates that engage the antigen receptors of iNKT cells and target a tumor antigen activate potent antitumor immunity without induction of anergy or depletion of the responding iNKT cells.

Experimental Nanovaccine Offers Protection Against Repeat Exposures to Trypanosoma cruzi Through Activation of Polyfunctional T Cell Response

A parasitic protozoan Trypanosoma cruzi (T. cruzi) is the etiologic agent of Chagas disease. Previously, we have identified T. cruzi antigens TcG2 and TcG4 as potential vaccine candidates, cloned in eukaryotic expression vector pCDNA3.1 (referred as p2/4) and tested their ability to elicit protection from T. cruzi infection. In the present study, we subcloned the two antigens in a nanoplasmid that is optimized for delivery, antigen expression, and regulatory compliance standards, and evaluated the nanovaccine (referred as nano2/4) for prophylactic protection against repeat T. cruzi infections. For this, C57BL/6 mice were immunized with two doses of p2/4 or nano2/4 at 21 days interval, challenged with T. cruzi 21 days after 2^nd immunization, and euthanized at 10- and 21-days post-infection (pi) corresponding to parasite dissemination and replication phase, respectively. Some mice were re-challenged 21 days pi and monitored at 7 days after re-infection. Without the help of a vaccine, T. cruzi elicited delayed and sub-par T cell activation and low levels of effector molecules that failed to control tissue dissemination and replication of the parasite and provided no protection against repeat challenge infection. The nano2/4 was most effective in eliciting an early activation and production of IFN-γ by CD4⁺T effector/effector memory (T_EM) cells and cytolytic perforin (PFN) and granzyme B (GZB) molecules by CD4⁺ and CD8⁺ T_EM subsets at 10 days pi that was followed by robust expansion of CD4⁺ and CD8⁺ T_EM and T_CM cells with further increase in IFN-γ production at 21 days pi. Consequently, nano2/4-immunized mice exhibited potent control of parasite dissemination at 10 days pi, and tissue parasite burden and tissue inflammatory infiltrate and necrosis were barely detectable at 21 days pi. Furthermore, nano2/4-immunized mice responded to re-challenge infection with high levels of effector molecules production by CD4⁺ and CD8⁺ T_EM subpopulations that offered even better control of tissue parasite burden than was observed after 1^st infection. In comparison, non-vaccinated/infected mice exhibited clinical features of sickness and 59% mortality within 7 days after re-infection. In conclusion, we show that delivery of TcG2 and TcG4 in nanoplasmid offers excellent, protective T cell immunity against repeat T. cruzi infections.

Subjects at-risk for future development of rheumatoid arthritis demonstrate a PAD4-and TLR-dependent enhanced histone H3 citrullination and proinflammatory cytokine production in CD14hi monocytes

The presence of anti-citrullinated protein/peptide antibodies (ACPA) and epitope spreading across the target autoantigens is a unique feature of rheumatoid arthritis (RA). ACPA are present in the peripheral blood for several years prior to the onset of arthritis and clinical classification of RA. ACPA recognize multiple citrullinated proteins, including histone H3 (H3). Intracellular citrullination of H3 in neutrophils and T cells is known to regulate immune cell function by promoting neutrophil extracellular trap formation and citrullinated autoantigen release as well as regulating the Th2/Th17 T cell phenotypic balance. However, the roles of H3 citrullination in other immune cells are not fully elucidated. We aimed to explore H3 citrullination and cytokine/metabolomic signatures in peripheral blood immune cells from subjects prior to and after the onset of RA, at baseline and in response to ex vivo toll-like receptor (TLR) stimulation. Here, we analyzed 13 ACPA (+) subjects without arthritis but at-risk for future development of RA, 14 early RA patients, and 13 healthy controls. We found significantly elevated H3 citrullination in CD14^hi monocytes, as well as CD1c⁺ dendritic cells and CD66⁺ granulocytes. Unsupervised analysis identified two distinct subsets in CD14^hi monocytes characterized by H3 modification and unique cytokine/metabolomic signatures. CD14^hi monocytes with elevated TLR-stimulated H3 citrullination were significantly increased in ACPA (+) at-risk subjects. These cells were skewed to produce TNFα, MIP1β, IFNα, and partially IL-12. Additionally, they demonstrate peptidyl arginine deiminase 4 (PAD4) mediated upregulation of the glycolytic enzyme PFKFB3. These CD14^hi monocytes with elevated H3 citrullination morphologically formed monocyte extracellular traps (METs). Taken together, dysregulated PAD4-driven cytokine production as well as MET formation in CD14^hi monocytes in ACPA (+) at-risk subjects likely plays an important role in the development of RA via promoting and perpetuating inflammation and generation of citrullinated autoantigens.

Vitamin D/CD46 Crosstalk in Human T Cells in Multiple Sclerosis

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS), in which T-cell migration into the CNS is key for pathogenesis. Patients with MS exhibit impaired regulatory T cell populations, and both Foxp3+ Tregs and type I regulatory T cells (Tr1) are dysfunctional. MS is a multifactorial disease and vitamin D deficiency is associated with disease. Herein, we examined the impact of 1,25(OH)2D3 on CD4+ T cells coactivated by either CD28 to induce polyclonal activation or by the complement regulator CD46 to promote Tr1 differentiation. Addition of 1,25(OH)2D3 led to a differential expression of adhesion molecules on CD28- and CD46-costimulated T cells isolated from both healthy donors or from patients with MS. 1,25(OH)2D3 favored Tr1 motility though a Vitamin D-CD46 crosstalk highlighted by increased VDR expression as well as increased CYP24A1 and miR-9 in CD46-costimulated T cells. Furthermore, analysis of CD46 expression on T cells from a cohort of patients with MS supplemented by vitamin D showed a negative correlation with the levels of circulating vitamin D. Moreover, t-Distributed Stochastic Neighbor Embedding (t-SNE) analysis allowed the visualization and identification of clusters increased by vitamin D supplementation, but not by placebo, that exhibited similar adhesion phenotype to what was observed in vitro. Overall, our data show a crosstalk between vitamin D and CD46 that allows a preferential effect of Vitamin D on Tr1 cells, providing novel key insights into the role of an important modifiable environmental factor in MS.

Gene-regulatory network analysis of ankylosing spondylitis with a single-cell chromatin accessible assay

A detailed understanding of the gene-regulatory network in ankylosing spondylitis (AS) is vital for elucidating the mechanisms of AS pathogenesis. Assaying transposase-accessible chromatin in single cell sequencing (scATAC-seq) is a suitable method for revealing such networks. Thus, scATAC-seq was applied to define the landscape of active regulatory DNA in AS. As a result, there was a significant change in the percent of CD8+ T cells in PBMCs, and 37 differentially accessible transcription factor (TF) motifs were identified. T cells, monocytes-1 and dendritic cells were found to be crucial for the IL-17 signaling pathway and TNF signaling pathway, since they had 73 potential target genes regulated by 8 TF motifs with decreased accessibility in AS. Moreover, natural killer cells were involved in AS by increasing the accessibility to TF motifs TEAD1 and JUN to induce cytokine-cytokine receptor interactions. In addition, CD4+ T cells and CD8+ T cells may be vital for altering host immune functions through increasing the accessibility of TF motifs NR1H4 and OLIG (OLIGI and OLIG2), respectively. These results explain clear gene regulatory variation in PBMCs from AS patients, providing a foundational framework for the study of personal regulomes and delivering insights into epigenetic therapy.

Immune biomarkers link air pollution exposure to blood pressure in adolescents

BACKGROUND

Childhood exposure to air pollution contributes to cardiovascular disease in adulthood. Immune and oxidative stress disturbances might mediate the effects of air pollution on the cardiovascular system, but the underlying mechanisms are poorly understood in adolescents. Therefore, we aimed to identify immune biomarkers linking air pollution exposure and blood pressure levels in adolescents.

METHODS

We randomly recruited 100 adolescents (mean age, 16 years) from Fresno, California. Using central-site data, spatial-temporal modeling, and distance weighting exposures to the participant's home, we estimated average pollutant levels [particulate matter (PM), polyaromatic hydrocarbons (PAH), ozone (O₃), carbon monoxide (CO) and nitrogen oxides (NO_x)]. We collected blood samples and vital signs on health visits. Using proteomic platforms, we quantitated markers of inflammation, oxidative stress, coagulation, and endothelial function. Immune cellular characterization was performed via mass cytometry (CyTOF). We investigated associations between pollutant levels, cytokines, immune cell types, and blood pressure (BP) using partial least squares (PLS) and linear regression, while adjusting for important confounders.

RESULTS

Using PLS, biomarkers explaining most of the variance in air pollution exposure included markers of oxidative stress (GDF-15 and myeloperoxidase), acute inflammation (C-reactive protein), hemostasis (ADAMTS, D-dimer) and immune cell types such as monocytes. Most of these biomarkers were independently associated with the air pollution levels in fully adjusted regression models. In CyTOF analyses, monocytes were enriched in participants with the highest versus the lowest PM_2.5 exposure. In both PLS and linear regression, diastolic BP was independently associated with PM_2.5, NO, NO₂, CO and PAH₄₅₆ pollution levels (P ≤ 0.009). Moreover, monocyte levels were independently related to both air pollution and diastolic BP levels (P ≤ 0.010). In in vitro cell assays, plasma of participants with high PM_2.5 exposure induced endothelial dysfunction as evaluated by eNOS and ICAM-1 expression and tube formation.

CONCLUSIONS

For the first time in adolescents, we found that ambient air pollution levels were associated with oxidative stress, acute inflammation, altered hemostasis, endothelial dysfunction, monocyte enrichment and diastolic blood pressure. Our findings provide new insights on pollution-related immunological and cardiovascular disturbances and advocate preventative measures of air pollution exposure.

Multiparametric Color Tendency Analysis (MCTA): A Method to Analyze Several Flow Cytometry Labelings Simultaneously

Despite the remarkable evolution of flow cytometers, fluorescent probes, and flow cytometry analysis software, most users still follow the same ways for data analysis. Conventional flow cytometry analysis relies on the creation of dot plot sequences, based on two fluorescence parameters at a time, to evidence phenotypically distinct populations. Thus, reaching conclusions about the biological characteristics of the samples is a fragmented and challenging process. We present here the MCTA (Multiparametric Color Tendency Analysis), a method for data analysis that considers multiple labelings simultaneously, extending and complementing conventional analysis. The MCTA method executes the background fluorescence exclusion, spillover compensation, and a user-defined gating strategy for subpopulation analysis. The results are then presented in conventional FSC x SSC dot plots with statistical data. For each event, the method converts each of the multiple fluorescence colors under analysis into a vector, with longer vectors being attributed to more intense labelings. Then, the MCTA generates a resultant vector, which is therefore mostly influenced by predominant labelings. The radial position of this resultant vector corresponds to a resultant color, making it easy to visualize phenotypic modulations among cellular subpopulations. Besides, it is a deterministic method that quickly assigns a resulting color to all events that obey the gating strategy, with no polymeric regions defined by the user or downsampling. The MCTA application generates a single dot plot showing all events in the FCS file, but a resultant color is attributed only to those that obey the gating strategy. Therefore, it can also help to evidence rare events or unpredicted subpopulations naturally excluded from the regions defined by the user. We believe that the MCTA method adds a new perspective over multiparametric flow cytometry analysis while evidencing modulations of molecular labeling profiles based on multiple fluorescences. Availability and implementation: The instructions for the MCTA application is freely available at https://github.com/flowcytometry/MCTA.

Identification of stem cells from large cell populations with topological scoring

Machine learning and topological analysis methods are becoming increasingly used on various large-scale omics datasets. Modern high dimensional flow cytometry data sets share many features with other omics datasets like genomics and proteomics. For example, genomics or proteomics datasets can be sparse and have high dimensionality, and flow cytometry datasets can also share these features. This makes flow cytometry data potentially a suitable candidate for employing machine learning and topological scoring strategies, for example, to gain novel insights into patterns within the data. We have previously developed a Topological Score (TopS) and implemented it for the analysis of quantitative protein interaction network datasets. Here we show that TopS approach for large scale data analysis is applicable to the analysis of a previously described flow cytometry sorted human hematopoietic stem cell dataset. We demonstrate that TopS is capable of effectively sorting this dataset into cell populations and identify rare cell populations. We demonstrate the utility of TopS when coupled with multiple approaches including topological data analysis, X-shift clustering, and t-Distributed Stochastic Neighbor Embedding (t-SNE). Our results suggest that TopS could be effectively used to analyze large scale flow cytometry datasets to find rare cell populations.

14-Color single tube for flow cytometric characterization of CD5+ B-LPDs and high sensitivity automated minimal residual disease quantitation of CLL/SLL

INTRODUCTION

The diagnosis of CLL/SLL relies on flow cytometric immunophenotyping. Increasing emphasis is being placed on precise detection of the minimal residual disease. Following antigen recommendations of ERIC and ESCCA's Harmonization Project, we validated a 14-color assay for the characterization CD5+ lymphoproliferative neoplasms and CLL MRD with a sensitivity of at least 10^-4 .

METHODS

The assay was designed based on ERIC/ESCCA recommended antigens with the addition of CD40 for alternate gating when CD19 expression is reduced. Lower limit of quantitation/lower limit of detection, assay procedural precision, linearity, and limit of blank were established. Then, 52 CD5+ B-cell lymphoproliferative neoplasms (41 CLL/11 non-CLL) and 29 normal samples were used for parallel evaluation. Automated cluster identification and quantitation of CLL clones in MRD setting was performed using Barned-Hutt SNE. Separation analysis between CLL and non-CLL phenotypes was performed by PCA and bh-SNE.

RESULTS

Separation ratios for each antigen exceeded ERIC/ESCCA guidelines. Precision was <20% at LLOQ (0.01%). The limit of blank was <10/500,000 cells. Concordance between the 14-color and legacy assay (Deming regression y = 1.01x, r²  = .99) was seen. All 20 samples with MRD levels 0.5%-0.006% (median 0.04%) showed an abnormal cell cluster by bh-SNE, with concordant results between manual and automated quantitation (y = x, r²  = 1). CLL cases clustered together and away from mantle cell lymphoma by bh-SNE and PCA with outlier atypical phenotype CLL cases posing diagnostic challenges by both manual and automated analysis.

CONCLUSION

The 14-color CD5+ LPD assay provides a robust standardization platform for MRD and disease characterization using both manual and automated analysis.

Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data

The progress in the field of high-dimensional cytometry has greatly increased the number of markers that can be simultaneously analyzed producing datasets with large numbers of parameters. Traditional biaxial manual gating might not be optimal for such datasets. To overcome this, a large number of automated tools have been developed to aid with cellular clustering of multi-dimensional datasets. Here were review two large categories of such tools; unsupervised and supervised clustering tools. After a thorough review of the popularity and use of each of the available unsupervised clustering tools, we focus on the top six tools to discuss their advantages and limitations. Furthermore, we employ a publicly available dataset to directly compare the usability, speed, and relative effectiveness of the available unsupervised and supervised tools. Finally, we discuss the current challenges for existing methods and future direction for the new generation of cell type identification approaches.