The FluPRINT dataset, a multidimensional analysis of the influenza vaccine imprint on the immune system

Identification of Pathogenic Immune Cell Subsets Associated With Checkpoint Inhibitor-Induced Myocarditis

BACKGROUND

Immune checkpoint inhibitors (ICIs) are monoclonal antibodies used to activate the immune system against tumor cells. Despite therapeutic benefits, ICIs have the potential to cause immune-related adverse events such as myocarditis, a rare but serious side effect with up to 50% mortality in affected patients. Histologically, patients with ICI myocarditis have lymphocytic infiltrates in the heart, implicating T cell-mediated mechanisms. However, the precise pathological immune subsets and molecular changes in ICI myocarditis are unknown.

METHODS

To identify immune subset(s) associated with ICI myocarditis, we performed time-of-flight mass cytometry on peripheral blood mononuclear cells from 52 individuals: 29 patients with autoimmune adverse events (immune-related adverse events) on ICI, including 8 patients with ICI myocarditis, and 23 healthy control subjects. We also used multiomics single-cell technology to immunophenotype 30 patients/control subjects using single-cell RNA sequencing, single-cell T-cell receptor sequencing, and cellular indexing of transcriptomes and epitopes by sequencing with feature barcoding for surface marker expression confirmation. To correlate between the blood and the heart, we performed single-cell RNA sequencing/T-cell receptor sequencing/cellular indexing of transcriptomes and epitopes by sequencing on MRL/Pdcd1^-/- (Murphy Roths large/programmed death-1-deficient) mice with spontaneous myocarditis.

RESULTS

Using these complementary approaches, we found an expansion of cytotoxic CD8⁺ T effector cells re-expressing CD45RA (Temra CD8⁺ cells) in patients with ICI myocarditis compared with control subjects. T-cell receptor sequencing demonstrated that these CD8⁺ Temra cells were clonally expanded in patients with myocarditis compared with control subjects. Transcriptomic analysis of these Temra CD8⁺ clones confirmed a highly activated and cytotoxic phenotype. Longitudinal study demonstrated progression of these Temra CD8⁺ cells into an exhausted phenotype 2 months after treatment with glucocorticoids. Differential expression analysis demonstrated elevated expression levels of proinflammatory chemokines (CCL5/CCL4/CCL4L2) in the clonally expanded Temra CD8⁺ cells, and ligand receptor analysis demonstrated their interactions with innate immune cells, including monocytes/macrophages, dendritic cells, and neutrophils, as well as the absence of key anti-inflammatory signals. To complement the human study, we performed single-cell RNA sequencing/T-cell receptor sequencing/cellular indexing of transcriptomes and epitopes by sequencing in Pdcd1^-/- mice with spontaneous myocarditis and found analogous expansions of cytotoxic clonal effector CD8⁺ cells in both blood and hearts of such mice compared with controls.

CONCLUSIONS

Clonal cytotoxic Temra CD8⁺ cells are significantly increased in the blood of patients with ICI myocarditis, corresponding to an analogous increase in effector cytotoxic CD8⁺ cells in the blood/hearts of Pdcd1^-/- mice with myocarditis. These expanded effector CD8⁺ cells have unique transcriptional changes, including upregulation of chemokines CCL5/CCL4/CCL4L2, which may serve as attractive diagnostic/therapeutic targets for reducing life-threatening cardiac immune-related adverse events in ICI-treated patients with cancer.

Divergent trajectories of antiviral memory after SARS-CoV-2 infection

The trajectories of acquired immunity to severe acute respiratory syndrome coronavirus 2 infection are not fully understood. We present a detailed longitudinal cohort study of UK healthcare workers prior to vaccination, presenting April-June 2020 with asymptomatic or symptomatic infection. Here we show a highly variable range of responses, some of which (T cell interferon-gamma ELISpot, N-specific antibody) wane over time, while others (spike-specific antibody, B cell memory ELISpot) are stable. We use integrative analysis and a machine-learning approach (SIMON - Sequential Iterative Modeling OverNight) to explore this heterogeneity. We identify a subgroup of participants with higher antibody responses and interferon-gamma ELISpot T cell responses, and a robust trajectory for longer term immunity associates with higher levels of neutralising antibodies against the infecting (Victoria) strain and also against variants B.1.1.7 (alpha) and B.1.351 (beta). These variable trajectories following early priming may define subsequent protection from severe disease from novel variants.

treekoR: identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data

High-throughput single-cell technologies hold the promise of discovering novel cellular relationships with disease. However, analytical workflows constructed for these technologies to associate cell proportions with disease often employ unsupervised clustering techniques that overlook the valuable hierarchical structures that have been used to define cell types. We present treekoR, a framework that empirically recapitulates these structures, facilitating multiple quantifications and comparisons of cell type proportions. Our results from twelve case studies reinforce the importance of quantifying proportions relative to parent populations in the analyses of cytometry data — as failing to do so can lead to missing important biological insights.

Activated natural killer cells predict poor clinical prognosis in high-risk B- and T-cell acute lymphoblastic leukemia

B- and T-cell acute lymphoblastic leukemia (B/T-ALL) may be refractory or recur after therapy by suppressing host anticancer immune surveillance mediated specifically by natural killer (NK) cells. We delineated the phenotypic and functional defects in NK cells from high-risk patients with B/T-ALL using mass cytometry, flow cytometry, and in silico cytometry, with the goal of further elucidating the role of NK cells in sustaining acute lymphoblastic leukemia (ALL) regression. We found that, compared with their normal counterparts, NK cells from patients with B/T-ALL are less cytotoxic but exhibit an activated signature that is characterized by high CD56, high CD69, production of activated NK cell-origin cytokines, and calcium (Ca2+) signaling. We demonstrated that defective maturation of NK cells into cytotoxic effectors prevents NK cells from ALL from lysing NK cell-sensitive targets as efficiently as do normal NK cells. Additionally, we showed that NK cells in ALL are exhausted, which is likely caused by their chronic activation. We found that increased frequencies of activated cytokine-producing NK cells are associated with increased disease severity and independently predict poor clinical outcome in patients with ALL. Our studies highlight the benefits of developing NK cell profiling as a diagnostic tool to predict clinical outcome in patients with ALL and underscore the clinical potential of allogeneic NK cell infusions to prevent ALL recurrence.

Mucosal vaccines - fortifying the frontiers

Mucosal vaccines offer the potential to trigger robust protective immune responses at the predominant sites of pathogen infection. In principle, the induction of adaptive immunity at mucosal sites, involving secretory antibody responses and tissue-resident T cells, has the capacity to prevent an infection from becoming established in the first place, rather than only curtailing infection and protecting against the development of disease symptoms. Although numerous effective mucosal vaccines are in use, the major advances seen with injectable vaccines (including adjuvanted subunit antigens, RNA and DNA vaccines) have not yet been translated into licensed mucosal vaccines, which currently comprise solely live attenuated and inactivated whole-cell preparations. The identification of safe and effective mucosal adjuvants allied to innovative antigen discovery and delivery strategies is key to advancing mucosal vaccines. Significant progress has been made in resolving the mechanisms that regulate innate and adaptive mucosal immunity and in understanding the crosstalk between mucosal sites, and this provides valuable pointers to inform mucosal adjuvant design. In particular, increased knowledge on mucosal antigen-presenting cells, innate lymphoid cell populations and resident memory cells at mucosal sites highlights attractive targets for vaccine design. Exploiting these insights will allow new vaccine technologies to be leveraged to facilitate rational mucosal vaccine design for pathogens including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and for cancer.

Acute Chelation Therapy-Associated Changes in Urine Gadolinium, Self-reported Flare Severity, and Serum Cytokines in Gadolinium Deposition Disease

OBJECTIVES

The aim of this study was to determine the following in patients who have undergone magnetic resonance imaging with gadolinium-based contrast agents (GBCAs) and meet the proposed diagnostic criteria for gadolinium deposition disease (GDD): (1) the effectiveness of chelation therapy (CT) with intravenous Ca-diethylenetriaminepentaacetic acid in removing retained gadolinium (Gd) and factors affecting the amount removed; (2) the frequency of CT-induced Flare, that is, GDD diagnostic symptom worsening, and factors affecting Flare intensity; (3) whether, as reported in a separate cohort, GDD patients' serum cytokine levels differ significantly from those in healthy normal controls and change significantly in response to CT; and (4) whether urine Gd, Flare reaction, and serum cytokine findings in GDD patients are mimicked in non-ill patients described as having gadolinium storage condition (GSC).

MATERIALS AND METHODS

Twenty-one GDD subjects and 3 GSC subjects underwent CT. Patients provided pre-CT and post-CT 24-hour urine samples for Gd content determination along with pre-CT and 24-hour post-CT serum samples for cytokine analysis. Patients rated potential Flare 24 hours after CT. Pre-CT and post-CT 24-hour urine Gd analyses and Luminex serum cytokine assays were performed blind to patients' GDD and GSC status and all other data except age and sex. Serum cytokine levels in a healthy normal control group of age- and sex-matched subjects drawn from Stanford influenza vaccination studies were measured once, contemporaneously with those of GDD and GSC patients, using the same Luminex assay.

RESULTS

Urine Gd amounts increased post-CT by 4 times or more after 87% of the 30 CT sessions. The most important factors appeared to be the time since the last GBCA dose and the cumulative dose received. Urine Gd amounts for GDD and GSC patients fell in the same ranges. All GDD patients, and no GSC patient, reported a Flare 24 hours post-CT. Linear regression found that Flare intensity was significantly predicted by a model including pre- and post-CT Gd amounts and the number of GBCA-enhanced magnetic resonance imaging. Post-CT, multiple cytokines showed strong positive relationships with GDD patients' Flare intensity in multivariable models. The pre-CT serum levels of 12 cytokines were significantly different in GDD patients compared with healthy flu vaccine controls. The small number of GSC patients precluded analogous statistical testing. Post-CT, GDD patients' serum levels of 20 cytokines were significantly decreased, and 2 cytokines significantly increased. These cytokines did not exhibit the same change pattern in the 3 GSC patients. The small number of GSC patients precluded statistical comparisons of GSC to GDD patients' results.

CONCLUSIONS

In this preliminary study, 24-hour urine Gd content increased markedly and similarly in GDD and GSC patients after Ca-diethylenetriaminepentaacetic acid CT. Post-CT Flare reaction developed only in GDD patients. The current study is the second finding significantly different serum cytokine levels in GDD patients compared with healthy normal controls. These differences and the difference between GDD and GSC patients' Flare and cytokine responses to CT suggest some inflammatory, immunologic, or other physiological differences in patients with GDD. Further research into the treatment and physiological underpinnings of GDD is warranted.

Systems Immunology: Revealing Influenza Immunological Imprint

Understanding protective influenza immunity and identifying immune correlates of protection poses a major challenge and requires an appreciation of the immune system in all of its complexity. While adaptive immune responses such as neutralizing antibodies and influenza-specific T lymphocytes are contributing to the control of influenza virus, key factors of long-term protection are not well defined. Using systems immunology, an approach that combines experimental and computational methods, we can capture the systems-level state of protective immunity and reveal the essential pathways that are involved. New approaches and technological developments in systems immunology offer an opportunity to examine roles and interrelationships of clinical, biological, and genetic factors in the control of influenza infection and have the potential to lead to novel discoveries about influenza immunity that are essential for the development of more effective vaccines to prevent future pandemics. Here, we review recent developments in systems immunology that help to reveal key factors mediating protective immunity.

A robust and interpretable end-to-end deep learning model for cytometry data

Cytometry technologies are able to profile immune cells at single-cell resolution. They are widely used for both clinical diagnosis and biological research. We developed a deep learning model for analyzing cytometry data. We demonstrated that the deep learning model accurately diagnoses the latent cytomegalovirus (CMV) in healthy individuals. In addition, we developed a method for interpreting the deep learning model, allowing us to identify biomarkers associated with latent CMV infection. The deep learning model is widely applicable to other cytometry data related to human diseases.

Cytometry technologies are essential tools for immunology research, providing high-throughput measurements of the immune cells at the single-cell level. Existing approaches in interpreting and using cytometry measurements include manual or automated gating to identify cell subsets from the cytometry data, providing highly intuitive results but may lead to significant information loss, in that additional details in measured or correlated cell signals might be missed. In this study, we propose and test a deep convolutional neural network for analyzing cytometry data in an end-to-end fashion, allowing a direct association between raw cytometry data and the clinical outcome of interest. Using nine large cytometry by time-of-flight mass spectrometry or mass cytometry (CyTOF) studies from the open-access ImmPort database, we demonstrated that the deep convolutional neural network model can accurately diagnose the latent cytomegalovirus (CMV) in healthy individuals, even when using highly heterogeneous data from different studies. In addition, we developed a permutation-based method for interpreting the deep convolutional neural network model. We were able to identify a CD27- CD94+ CD8+ T cell population significantly associated with latent CMV infection, confirming the findings in previous studies. Finally, we provide a tutorial for creating, training, and interpreting the tailored deep learning model for cytometry data using Keras and TensorFlow (https://github.com/hzc363/DeepLearningCyTOF).