High-dimensional Mass Cytometry Data Research Articles

An immune signature of postoperative cognitive decline: A prospective cohort study.

Postoperative cognitive decline (POCD) is the predominant complication affecting patients over 60 years old following major surgery, yet its prediction and prevention remain challenging. Understanding the biological processes underlying the pathogenesis of POCD is essential for identifying mechanistic biomarkers to advance diagnostics and therapeutics. This study aimed to provide a comprehensive analysis of immune cell trajectories differentiating patients with and without POCD and to derive a predictive score enabling the identification of high-risk patients during the preoperative period. Twenty-six patients aged 60 years old and older undergoing elective major orthopedic surgery were enrolled in a prospective longitudinal study, and the occurrence of POCD was assessed seven days after surgery. Serial samples collected before surgery, and one, seven, and 90 days after surgery were analyzed using a combined single-cell mass cytometry and plasma proteomic approach. Unsupervised clustering of the high-dimensional mass cytometry data was employed to characterize time-dependent trajectories of all major innate and adaptive immune cell frequencies and signaling responses. Sparse machine learning coupled with data-driven feature selection was applied to the pre-surgery immunological dataset to classify patients at risk for POCD. The analysis identified cell-type and signaling-specific immune trajectories differentiating patients with and without POCD. The most prominent trajectory features revealed early exacerbation of JAK/STAT and dampening of inhibitory κB and nuclear factor-κB immune signaling responses in patients with POCD. Further analyses integrating immunological and clinical data collected before surgery identified a preoperative predictive model comprising one plasma protein and ten immune cell features that classified patients at risk for POCD with excellent accuracy (AUC=0.80, P=2.21e-02 U-test). Immune system-wide monitoring of patients over 60 years old undergoing surgery unveiled a peripheral immune signature of POCD. A predictive model built on immunological data collected before surgery demonstrated greater accuracy in predicting POCD compared to known clinical preoperative risk factors, offering a concise list of biomarker candidates to personalize perioperative management.

Immunophenotypical profiling of myeloid neoplasms with erythroid predominance using mass cytometry (CyTOF).

Acute erythroid leukemia (AEL) is a disease continuum between Myelodysplastic syndrome (MDS) and Acute myeloid leukemia (AML) with the cellular hallmark of uncontrolled proliferation and impaired differentiation of erythroid progenitor cells. First described by Giovanni di Guglielmo in 1917 AEL accounts for less than 5% of all de novo AML cases. There have been efforts to characterize AEL at a molecular level, describing recurrent alterations in TP53, NPM1 and FLT3 genes. A genomic analysis of AEL cases confirmed its complexity. Despite these advances, the biology underlying erythroid proliferations remains unclear and the prognosis is dismal with a median survival of only 3months for pure erythroid leukemia (PEL). Marker combinations suitable for the identification and characterization of leukemic stem cell (LSC) candidates, monitoring measurable residual disease (MRD) during chemotherapy treatment and the development of innovative targeted therapies are missing. Here, we developed a mass cytometry panel for an in-depth characterization of erythroid and myeloid blast cell populations from human AEL bone marrow samples in comparison to other AML subtypes and healthy counterparts. A total of 8 AEL samples were analyzed and compared to 28 AML samples from different molecular subtypes, healthy bone marrow counterparts (n=5) and umbilical cord blood (n=6) using high-dimensional mass cytometry. Identification of erythroid and myeloid blast populations in high-dimensional mass cytometry data enabled a refined view on erythroblast differentiation stages present in AEL erythroid blasts and revealed immunophenotypical profiles specific to AEL. Profiling of phenotypic LSCs revealed aberrant erythroid marker expression in the CD34+ CD38- stem cell compartment. In addition, the identification of novel candidate surface marker combinations and aberrancies might enhance clinical diagnostics of AEL. We present a high-parameter mass cytometry approach feasible for immunophenotypical analysis of blast and stem cell populations in myeloid neoplasms with erythroid predominance laying the foundation for more precise experimental approaches in the future.

Mass cytometry analysis of blood from peanut-sensitized tolerant and clinically allergic infants

IgE-mediated food allergies in infants are a significant health concern, with peanut allergy being of particular interest due to its prevalence and severity. Among individuals who produce peanut-specific IgE some experience no adverse reaction on peanut consumption. This asymptomatic phenotype is known as sensitized tolerance. To elucidate the immune environment of peanut sensitized tolerant and clinically allergic one-year-olds, high-dimensional mass cytometry was conducted as part of the HealthNuts study. The resulting data includes peripheral blood mononuclear cells from 36 participants encompassing non-allergic, peanut sensitized with tolerance, and clinically peanut allergic infants. The raw mass cytometry data is described here and freely available for reuse through the Immunology Database and Analysis Portal (ImmPort). Additional allergy information and serum vitamin D levels of the participants were measured and are also included in the data upload. These high-dimensional mass cytometry data, when combined with clinical information, offer a broad immune profile of peanut allergic and sensitized tolerant infants.

Compositional Data Analysis using Kernels in mass cytometry data.

MotivationCell-type abundance data arising from mass cytometry experiments are compositional in nature. Classical association tests do not apply to the compositional data due to their non-Euclidean nature. Existing methods for analysis of cell type abundance data suffer from several limitations for high-dimensional mass cytometry data, especially when the sample size is small.ResultsWe proposed a new multivariate statistical learning methodology, Compositional Data Analysis using Kernels (CODAK), based on the kernel distance covariance (KDC) framework to test the association of the cell type compositions with important predictors (categorical or continuous) such as disease status. CODAK scales well for high-dimensional data and provides satisfactory performance for small sample sizes (n < 25). We conducted simulation studies to compare the performance of the method with existing methods of analyzing cell type abundance data from mass cytometry studies. The method is also applied to a high-dimensional dataset containing different subgroups of populations including Systemic Lupus Erythematosus (SLE) patients and healthy control subjects.Availability and implementationCODAK is implemented using R. The codes and the data used in this manuscript are available on the web at http://github.com/GhoshLab/CODAK/.Contact prudra@okstate.edu Supplementary information Supplementary data are available at Bioinformatics Advances online.

Two New Neutrophil Subsets Define a Discriminating Sepsis Signature.

Rationale: Sepsis is the leading cause of death in adult ICUs. At present, sepsis diagnosis relies on nonspecific clinical features. It could transform clinical care to have immune-cell biomarkers that could predict sepsis diagnosis and guide treatment. For decades, neutrophil phenotypes have been studied in sepsis, but a diagnostic cell subset has yet to be identified. Objectives: To identify an early, specific immune signature of sepsis severity that does not overlap with other inflammatory biomarkers and that distinguishes patients with sepsis from those with noninfectious inflammatory syndrome. Methods: Mass cytometry combined with computational high-dimensional data analysis was used to measure 42 markers on whole-blood immune cells from patients with sepsis and control subjects and to automatically and comprehensively characterize circulating immune cells, which enables identification of novel, disease-specific cellular signatures. Measurements and Main Results: Unsupervised analysis of high-dimensional mass cytometry data characterized previously unappreciated heterogeneity within the CD64+ immature neutrophils and revealed two new subsets distinguished by CD123 and PD-L1 (programmed death ligand 1) expression. These immature neutrophils exhibited diminished activation and phagocytosis functions. The proportion of CD123-expressing neutrophils correlated with clinical severity. Conclusions: This study showed that these two new neutrophil subsets were specific to sepsis and detectable through routine flow cytometry by using seven markers. The demonstration here that a simple blood test distinguishes sepsis from other inflammatory conditions represents a key biological milestone that can be immediately translated into improvements in patient care.

Improving the t-SNE Algorithms for Cytometry and Other Technologies: Cen-Se' Mapping

SNE methods are a set of 9 to 10 interconnected algorithms that map high-dimensional data into low-dimensional space while minimizing loss of information. Each step in this process is important for producing high-quality maps. Cense′™ mapping not only enhances many of the steps in this process but also fundamentally changes the underlying mathematics to produce high-quality maps. The key mathematical enhancement is to leverage the Cauchy distribution for creating both high-dimensional and lowdimensional similarity matrices. This simple change eliminates the necessity of using perplexity and entropy and results in maps that optimally separate clusters defined in high-dimensional space. It also eliminates the loss of cluster resolution commonly seen with t-SNE with higher numbers of events. There is just one free parameter for Cen-se′ mapping, and that parameter rarely needs to change. Other enhancements include a relatively low memory footprint, highly threaded implementation, and a final classification step that can process millions of events in seconds. When the Cen-se′ mapping system is integrated with probability state modeling, the clusters of events are positioned in a reproducible manner and are colored, labeled, and enumerated automatically. We provide a step-by-step, simple example that describes how the Cen-se′ method works and differs from the t-SNE method. We present data from several experiments to compare the two mapping strategies on high-dimensional mass cytometry data. We provide a section on information theory to explain how the steepest gradient equations were formulated and how they control the movement of the low-dimensional points as the system renders the map Since existing implementations of the t-SNE algorithm can easily be modified with many of these enhancements, this work should result in more effective use of this very exciting and far-reaching new technology.

Multi-site reproducibility of a human immunophenotyping assay in whole blood and peripheral blood mononuclear cells preparations using CyTOF technology coupled with Maxpar Pathsetter, an automated data analysis system.

High-dimensional mass cytometry data potentially enable a comprehensive characterization of immune cells. In order to positively affect clinical trials and translational clinical research, this advanced technology needs to demonstrate a high reproducibility of results across multiple sites for both peripheral blood mononuclear cells (PBMC) and whole blood preparations. A dry 30-marker broad immunophenotyping panel and customized automated analysis software were recently engineered and are commercially available as the Fluidigm® Maxpar® Direct™ Immune Profiling Assay™. In this study, seven sites received whole blood and six sites received PBMC samples from single donors over a 2-week interval. Each site labeled replicate samples and acquired data on Helios™ instruments using an assay-specific acquisition template. All acquired sample files were then automatically analyzed by Maxpar Pathsetter™ software. A cleanup step eliminated debris, dead cells, aggregates, and normalization beads. The second step automatically enumerated 37 immune cell populations and performed label intensity assessments on all 30 markers. The inter-site reproducibility of the 37 quantified cell populations had consistent population frequencies, with an average %CV of 14.4% for whole blood and 17.7% for PBMC. The dry reagent coupled with automated data analysis is not only convenient but also provides a high degree of reproducibility within and among multiple test sites resulting in a comprehensive yet practical solution for deep immune phenotyping.

Computational Analysis of High-Dimensional Mass Cytometry Data from Clinical Tissue Samples.

The advent of mass cytometry has resulted in the generation of high-dimensional, single-cell expression data sets from clinical samples. These data sets cannot be effectively analyzed using traditional approaches. Instead, new approaches using dimensionality reduction and network analysis techniques have been implemented to assess these data. Here, detailed methods are described for analyzing immune cell expression from clinical samples using network analyses. Specifically, details are given for performing SCAFFoLD and CITRUS analyses. The methods described will use immune cell tumor infiltrate as an example.

Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data

The flood of high-dimensional data resulting from mass cytometry experiments that measure more than 40 features of individual cells has stimulated creation of new single cell computational biology tools. These tools draw on advances in the field of machine learning to capture multi-parametric relationships and reveal cells that are easily overlooked in traditional analysis. Here, we introduce a workflow for high dimensional mass cytometry data that emphasizes unsupervised approaches and visualizes data in both single cell and population level views. This workflow includes three central components that are common across mass cytometry analysis approaches: (1) distinguishing initial populations, (2) revealing cell subsets, and (3) characterizing subset features. In the implementation described here, viSNE, SPADE, and heatmaps were used sequentially to comprehensively characterize and compare healthy and malignant human tissue samples. The use of multiple methods helps provide a comprehensive view of results, and the largely unsupervised workflow facilitates automation and helps researchers avoid missing cell populations with unusual or unexpected phenotypes. Together, these methods develop a framework for future machine learning of cell identity.