Quantitative Measurement of Cell-Nanoparticle Interactions Using Mass Cytometry

Approaching Mass Cytometry Translational Studies by Experimental and Data Curation Settings

Clinical studies are conducted to better understand the pathological mechanism of diseases and to find biomarkers associated with disease activity, drug response, or outcome prediction. Mass cytometry (MC) is a high-throughput single-cell technology that measures hundreds of cells per second with more than 40 markers per cell. Thus, it is a suitable tool for immune monitoring and biomarker discovery studies. Working in translational and clinical settings requires a careful experimental design to minimize, monitor, and correct the variations introduced during sample collection, preparation, acquisition, and analysis. In this review, we will focus on these important aspects of MC-related experiments and data curation in the context of translational clinical research projects.

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.

Mass spectrometry for multi-dimensional characterization of natural and synthetic materials at the nanoscale

Characterization of materials at the nanoscale plays a crucial role in in-depth understanding the nature and processes of the substances. Mass spectrometry (MS) has characterization capabilities for nanomaterials (NMs) and nanostructures by offering reliable multi-dimensional information consisting of accurate mass, isotopic, and molecular structural information. In the last decade, MS has emerged as a powerful nano-characterization technique. This review comprehensively summarizes the capabilities of MS in various aspects of nano-characterization that greatly enrich the toolbox of nano research. Compared with other characterization techniques, MS has unique capabilities for real-time monitoring and tracking reaction intermediates and by-products. Moreover, MS has shown application potential in some novel aspects, such as MS imaging of the biodistribution and fate of NMs in animals and humans, stable isotopic tracing of NMs, and risk assessment of NMs, which deserve update and integration into the current knowledge framework of nano-characterization.

Key steps and methods in the experimental design and data analysis of highly multi-parametric flow and mass cytometry

High-dimensional, single-cell cell technologies revolutionized the way to study biological systems, and polychromatic flow cytometry (FC) and mass cytometry (MC) are two of the drivers of this revolution. As up to 30-50 dimensions respectively can be measured per single-cell, they allow deep phenotyping combined with cellular functions studies, like cytokine production or protein phosphorylation. In parallel, the bioinformatics field develops algorithms that are able to process incoming data and extract the most useful and meaningful biological information. However, the success of automated analysis tools depends on the generation of high-quality data. In this review we present the most recent FC and MC computational approaches that are used to prepare, process and interpret high-content cytometry data. We also underscore proper experimental design as a key step for obtaining good quality data.