Acquisition, Processing, and Quality Control of Mass Cytometry Data

Reagents for Mass Cytometry

Mass cytometry (cytometry by time-of-flight detection [CyTOF]) is a bioanalytical technique that enables the identification and quantification of diverse features of cellular systems with single-cell resolution. In suspension mass cytometry, cells are stained with stable heavy-atom isotope-tagged reagents, and then the cells are nebulized into an inductively coupled plasma time-of-flight mass spectrometry (ICP-TOF-MS) instrument. In imaging mass cytometry, a pulsed laser is used to ablate ca. 1 μm² spots of a tissue section. The plume is then transferred to the CyTOF, generating an image of biomarker expression. Similar measurements are possible with multiplexed ion bean imaging (MIBI). The unit mass resolution of the ICP-TOF-MS detector allows for multiparametric analysis of (in principle) up to 130 different parameters. Currently available reagents, however, allow simultaneous measurement of up to 50 biomarkers. As new reagents are developed, the scope of information that can be obtained by mass cytometry continues to increase, particularly due to the development of new small molecule reagents which enable monitoring of active biochemistry at the cellular level. This review summarizes the history and current state of mass cytometry reagent development and elaborates on areas where there is a need for new reagents. Additionally, this review provides guidelines on how new reagents should be tested and how the data should be presented to make them most meaningful to the mass cytometry user community.

Prediction of Clinical Outcomes with Explainable Artificial Intelligence in Patients with Chronic Lymphocytic Leukemia

BACKGROUND

The International Prognostic Index (IPI) is applied to predict the outcome of chronic lymphocytic leukemia (CLL) with five prognostic factors, including genetic analysis. We investigated whether multiparameter flow cytometry (MPFC) data of CLL samples could predict the outcome by methods of explainable artificial intelligence (XAI). Further, XAI should explain the results based on distinctive cell populations in MPFC dot plots.

METHODS

We analyzed MPFC data from the peripheral blood of 157 patients with CLL. The ALPODS XAI algorithm was used to identify cell populations that were predictive of inferior outcomes (death, failure of first-line treatment). The diagnostic ability of each XAI population was evaluated with receiver operating characteristic (ROC) curves.

RESULTS

ALPODS defined 17 populations with higher ability than the CLL-IPI to classify clinical outcomes (ROC: area under curve (AUC) 0.95 vs. 0.78). The best single classifier was an XAI population consisting of CD4+ T cells (AUC 0.78; 95% CI 0.70-0.86; p < 0.0001). Patients with low CD4+ T cells had an inferior outcome. The addition of the CD4+ T-cell population enhanced the predictive ability of the CLL-IPI (AUC 0.83; 95% CI 0.77-0.90; p < 0.0001).

CONCLUSIONS

The ALPODS XAI algorithm detected highly predictive cell populations in CLL that may be able to refine conventional prognostic scores such as IPI.

In-Depth Immunophenotyping With Mass Cytometry During TB Treatment Reveals New T-Cell Subsets Associated With Culture Conversion

Tuberculosis (TB) is a difficult-to-treat infection because of multidrug regimen requirements based on drug susceptibility profiles and treatment observance issues. TB cure is defined by mycobacterial sterilization, technically complex to systematically assess. We hypothesized that microbiological outcome was associated with stage-specific immune changes in peripheral whole blood during TB treatment. The T-cell phenotypes of treated TB patients were prospectively characterized in a blinded fashion using mass cytometry after Mycobacterium tuberculosis (Mtb) antigen stimulation with QuantiFERON-TB Gold Plus, and then correlated to sputum culture status. At two months of treatment, cytotoxic and terminally differentiated CD8⁺ T-cells were under-represented and naïve CD4⁺ T-cells were over-represented in positive- versus negative-sputum culture patients, regardless of Mtb drug susceptibility. At treatment completion, a T-cell immune shift towards differentiated subpopulations was associated with TB cure. Overall, we identified specific T-cell profiles associated with slow sputum converters, which brings new insights in TB prognostic biomarker research designed for clinical application.

Fundamental studies on droplet throughput and the analysis of single cells using a downward-pointing ICP-time-of-flight mass spectrometer

Capabilities of the downwardly oriented inductively coupled plasma mass spectrometer (ICP-MS) recently reported (Vonderach et al. 2021) were studied using a time-of-flight mass spectrometer (TOFMS) yielding benefits for the fast detection of short transient signals containing multi-element information. The previously reported sample inlet configuration for the analysis of microdroplets was equipped with two extra gas inlets for the supply of argon and helium, which enabled a more precise optimization of the sample introduction and operating conditions of the plasma. Furthermore, the sample supply system was operated at elevated temperatures to enhance the desolvation of the droplets prior to their introduction into the plasma. Transient droplet signals with frequencies of up to 1000 Hz were recorded for 74 μm (diameter) sized droplets. The upper detectable droplet size was limited by the droplet generator used and was measured at 93 μm (diameter). The droplets served as the transporter for biological cells so that the described setup could be used to analyze single cells. Mouse lung cells embedded into droplets were detected successfully according to their Cs droplet tracer, Ir nucleus marker, surface markers and the phosphorus content. Transient signals were recorded at a time resolution of 33 μs in order to investigate the signal structure of single droplet-cell events containing multiple elements. Signals between 200-400 μs (FW base) and ≤100 μs (FWHM) in duration were measured. To ensure that the droplet formation process did not affect the sampled cells, different types of cells were localized within the droplets using optical inspection directly after droplet formation and it was possible to observe that cells remained intact with random sampling. The results indicate that a downward-pointing ICP-MS in combination with the microdroplet-based approach can be considered as an alternative to commonly used ICP-MS systems for single cell analysis, and might be suitable for online coupling to flow cytometry.

ImmunoCluster provides a computational framework for the nonspecialist to profile high-dimensional cytometry data

High-dimensional cytometry is an innovative tool for immune monitoring in health and disease, and it has provided novel insight into the underlying biology as well as biomarkers for a variety of diseases. However, the analysis of large multiparametric datasets usually requires specialist computational knowledge. Here, we describe ImmunoCluster (https://github.com/kordastilab/ImmunoCluster), an R package for immune profiling cellular heterogeneity in high-dimensional liquid and imaging mass cytometry, and flow cytometry data, designed to facilitate computational analysis by a nonspecialist. The analysis framework implemented within ImmunoCluster is readily scalable to millions of cells and provides a variety of visualization and analytical approaches, as well as a rich array of plotting tools that can be tailored to users' needs. The protocol consists of three core computational stages: (1) data import and quality control; (2) dimensionality reduction and unsupervised clustering; and (3) annotation and differential testing, all contained within an R-based open-source framework.

Method for the Analysis of the Tumor Microenvironment by Mass Cytometry: Application to Chronic Lymphocytic Leukemia

In the past 20 years, the interest for the tumor microenvironment (TME) has exponentially increased. Indeed, it is now commonly admitted that the TME plays a crucial role in cancer development, maintenance, immune escape and resistance to therapy. This stands true for hematological malignancies as well. A considerable amount of newly developed therapies are directed against the cancer-supporting TME instead of targeting tumor cells themselves. However, the TME is often not clearly defined. In addition, the unique phenotype of each tumor and the variability among patients limit the success of such therapies. Recently, our group took advantage of the mass cytometry technology to unveil the specific TME in the context of chronic lymphocytic leukemia (CLL) in mice. We found the enrichment of LAG3 and PD1, two immune checkpoints. We tested an antibody-based immunotherapy, targeting these two molecules. This combination of antibodies was successful in the treatment of murine CLL. In this methods article, we provide a detailed protocol for the staining of CLL TME cells aiming at their characterization using mass cytometry. We include panel design and validation, sample preparation and acquisition, machine set-up, quality control, and analysis. Additionally, we discuss different advantages and pitfalls of this technique.

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.