Microfluidic Approach to Resolve Simultaneous and Sequential Cytokine Secretion of Individual Polyfunctional Cells.

Infections, autoimmune diseases, desired and adverse immunological responses to treatment can lead to a complex and dynamic cytokine response in vivo. This response involves numerous immune cells secreting various cytokines to orchestrate the immune reaction. However, the secretion dynamics, amounts, and co-occurrence of the different cytokines by various cell subtypes remain poorly understood due to a lack of appropriate tools to study them. Here, we describe a protocol using a microfluidic droplet platform that allows the time-resolved quantitative measurement of secretion dynamics for several cytokines in parallel on the single-cell level. This is enabled by the encapsulation of individual cells into microfluidic droplets together with a multiplexed immunoassay for parallel quantification of cytokine concentrations, their immobilization for dynamic fluorescent imaging, and the analysis of the respective images to derive secreted quantities and dynamics. The protocol describes the preparation of functionalized magnetic nanoparticles, calibration experiments, cell preparation, and the encapsulation of the cells and nanoparticles into droplets for fluorescent imaging and subsequent image and data analysis using the example of lipopolysaccharide-stimulated human peripheral blood mononuclear cells. The presented platform identified distinct cytokine secretion behavior for single and co-secreting cells, characterizing the expected phenotypic heterogeneity in the measured cell sample. Furthermore, the modular nature of the assay allows its adaptation and application to study a variety of proteins, cytokines, and cell samples, potentially leading to a deeper understanding of the interplay between different immune cell types and the role of the different cytokines secreted dynamically to shape the tightly regulated immune response. These new insights could be particularly interesting in the studies of immune dysregulations or in identifying target populations in therapy and drug development.

ePlatypus: an ecosystem for computational analysis of immunogenomics data.

MOTIVATION: The maturation of systems immunology methodologies requires novel and transparent computational frameworks capable of integrating diverse data modalities in a reproducible manner. RESULTS: Here, we present the ePlatypus computational immunology ecosystem for immunogenomics data analysis, with a focus on adaptive immune repertoires and single-cell sequencing. ePlatypus is an open-source web-based platform and provides programming tutorials and an integrative database that helps elucidate signatures of B and T cell clonal selection. Furthermore, the ecosystem links novel and established bioinformatics pipelines relevant for single-cell immune repertoires and other aspects of computational immunology such as predicting ligand-receptor interactions, structural modeling, simulations, machine learning, graph theory, pseudotime, spatial transcriptomics, and phylogenetics. The ePlatypus ecosystem helps extract deeper insight in computational immunology and immunogenomics and promote open science. AVAILABILITY AND IMPLEMENTATION: Platypus code used in this manuscript can be found at github.com/alexyermanos/Platypus.