This project was supported by grant #2021-349 of the Strategic Focus Area Personalized Health and Related Technologies (PHRT) of the ETH Domain (Swiss Federal Institutes of Technology), the European Research Council starting grant (grant #803,336), and the Swiss National Science Foundation (grant #310030_197619). Additionally, we thank Guilhem Chenon and Jean Baudry for their work and development of the initial DropMap analyzer.

Microfluidic Droplet Assay for Single-Cell Cytokine Secretion Analysis

<ol>
	<li>Chen, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inflammatory+responses+and+inflammation-associated+diseases+in+organs.">Inflammatory responses and inflammation-associated diseases in organs.</a> Oncotarget. 9 (6), 7204-7218 (2017).</li><li>Cicchese, J. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamic+balance+of+pro-+and+anti-inflammatory+signals+controls+disease+and+limits+pathology.">Dynamic balance of pro- and anti-inflammatory signals controls disease and limits pathology.</a> Immunol Rev. 285 (1), 147-167 (2018).</li><li>Liu, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cytokines:+From+clinical+significance+to+quantification.">Cytokines: From clinical significance to quantification.</a> Adv Sci. 8 (15), e2004433 (2021).</li><li>Rojas, J. M., Avia, M., Mart&#237;n, V., Sevilla, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=IL-10:+A+multifunctional+cytokine+in+viral+infections.">IL-10: A multifunctional cytokine in viral infections.</a> J Immunol Res. 2017, 6104054 (2017).</li><li>Kohanawa, Y. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+regulatory+effect+of+the+balance+between+TNF-&#945;+and+IL-6+in+the+granulomatous+and+inflammatory+response+to+Rhodococcus+aurantiacus+infection+in+mice.">A regulatory effect of the balance between TNF-&#945; and IL-6 in the granulomatous and inflammatory response to Rhodococcus aurantiacus infection in mice.</a> J Immunol. 177 (1), 642-650 (2006).</li><li>Geginat, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Plasticity+of+human+CD4+T+cell+subsets.">Plasticity of human CD4 T cell subsets.</a> Front Immunol. 5, 630 (2014).</li><li>Sallusto, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Heterogeneity+of+human+CD4++T+cells+against+microbes.">Heterogeneity of human CD4+ T cells against microbes.</a> Ann Rev Immunol. 34 (1), 317-334 (2016).</li><li>Chetaille N&#233;zondet, A. L., Poubelle, P. E., Pelletier, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+evaluation+of+cytokines+to+help+establish+diagnosis+and+guide+treatment+of+autoinflammatory+and+autoimmune+diseases.">The evaluation of cytokines to help establish diagnosis and guide treatment of autoinflammatory and autoimmune diseases.</a> J Leukocyte Biol. 108 (2), 647-657 (2020).</li><li>Sims, J. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Characterization+of+the+cytokine+storm+reflects+hyperinflammatory+endothelial+dysfunction+in+COVID-19.">Characterization of the cytokine storm reflects hyperinflammatory endothelial dysfunction in COVID-19.</a> J Allergy Clin Immunol. 147 (1), 107-111 (2021).</li><li>Yasen, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+RNA+sequencing+reveals+the+heterogeneity+of+infiltrating+immune+cell+profiles+in+the+hepatic+cystic+echinococcosis+microenvironment.">Single-cell RNA sequencing reveals the heterogeneity of infiltrating immune cell profiles in the hepatic cystic echinococcosis microenvironment.</a> Infection and Immunity. 89 (12), (2021).</li><li>Jiang, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+RNA+sequencing+highlights+intratumor+heterogeneity+and+intercellular+network+featured+in+adamantinomatous+craniopharyngioma.">Single-cell RNA sequencing highlights intratumor heterogeneity and intercellular network featured in adamantinomatous craniopharyngioma.</a> Sci Adv. 9 (15), (2023).</li><li>Tanguay, S., Killion, J. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Direct+comparison+of+ELISPOT+and+ELISA-based+assays+for+detection+of+individual+cytokine-secreting+cells.">Direct comparison of ELISPOT and ELISA-based assays for detection of individual cytokine-secreting cells.</a> Lymphokine Cytokine Res. 13 (4), 259-263 (1994).</li><li>Bucheli, O. T. M., Sigvaldad&#243;ttir, I., Eyer, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measuring+single-cell+protein+secretion+in+immunology:+Technologies,+advances,+and+applications.">Measuring single-cell protein secretion in immunology: Technologies, advances, and applications.</a> Eur J Immunol. 51 (6), 1334-1347 (2021).</li><li>Brower, K. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Double+emulsion+flow+cytometry+with+high-throughput+single+droplet+isolation+and+nucleic+acid+recovery.">Double emulsion flow cytometry with high-throughput single droplet isolation and nucleic acid recovery.</a> Lab Chip. 20 (12), 2062-2074 (2020).</li><li>Brower, K. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Double+emulsion+picoreactors+for+high-throughput+single-cell+encapsulation+and+phenotyping+via+FACS.">Double emulsion picoreactors for high-throughput single-cell encapsulation and phenotyping via FACS.</a> Anal Chem. 92 (19), 13262-13270 (2020).</li><li>Luo, X., Chen, J. Y., Ataei, M., Lee, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microfluidic+compartmentalization+platforms+for+single+cell+analysis.">Microfluidic compartmentalization platforms for single cell analysis.</a> Biosensors. 12 (2), 58 (2022).</li><li>Bounab, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamic+single-cell+phenotyping+of+immune+cells+using+the+microfluidic+platform+DropMap.">Dynamic single-cell phenotyping of immune cells using the microfluidic platform DropMap.</a> Nat Protoc. 15 (9), 2920-2955 (2020).</li><li>Eyer, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+deep+phenotyping+of+IgG-secreting+cells+for+high-resolution+immune+monitoring.">Single-cell deep phenotyping of IgG-secreting cells for high-resolution immune monitoring.</a> Nat Biotechnol. 35 (10), 977-982 (2017).</li><li>Gaa, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Versatile+and+rapid+microfluidics-assisted+antibody+discovery.">Versatile and rapid microfluidics-assisted antibody discovery.</a> mAbs. 13 (1), 198130 (2021).</li><li>Gerard, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+single-cell+activity-based+screening+and+sequencing+of+antibodies+using+droplet+microfluidics.">High-throughput single-cell activity-based screening and sequencing of antibodies using droplet microfluidics.</a> Nat Biotechnol. 38 (6), 715-721 (2020).</li><li>De Jonghe, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=spinDrop:+a+droplet+microfluidic+platform+to+maximise+single-cell+sequencing+information+content.">spinDrop: a droplet microfluidic platform to maximise single-cell sequencing information content.</a> Nat Comm. 14 (1), 4788 (2023).</li><li>Wheeler, M. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Droplet-based+forward+genetic+screening+of+astrocyte-microglia+cross-talk.">Droplet-based forward genetic screening of astrocyte-microglia cross-talk.</a> Science. 379 (6636), 1023-1030 (2023).</li><li>Portmann, K., Linder, A., Oelgarth, N., Eyer, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Single-cell+deep+phenotyping+of+cytokine+release+unmasks+stimulation-specific+biological+signatures+and+distinct+secretion+dynamics.">Single-cell deep phenotyping of cytokine release unmasks stimulation-specific biological signatures and distinct secretion dynamics.</a> Cell Rep Meth. 3 (7), 100502 (2023).</li><li>Portmann, K., Linder, A., Eyer, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stimulation-induced+cytokine+polyfunctionality+as+a+dynamic+concept.">Stimulation-induced cytokine polyfunctionality as a dynamic concept.</a> eLife. 12, 89781 (2023).</li><li>Armbruster, D. A., Pry, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Limit+of+blank,+limit+of+detection+and+limit+of+quantitation.">Limit of blank, limit of detection and limit of quantitation.</a> Clin Biochem Rev. 29, S49-S52 (2008).</li><li>Yang, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=New+insight+into+neurological+degeneration:+Inflammatory+cytokines+and+blood-brain+barrier.">New insight into neurological degeneration: Inflammatory cytokines and blood-brain barrier.</a> Front Mol Neurosci. 15, 1013933 (2022).</li><li>Kim, P. S., Ahmed, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Features+of+responding+T+cells+in+cancer+and+chronic+infection.">Features of responding T cells in cancer and chronic infection.</a> Curr Opin Immunol. 22 (2), 223-230 (2010).</li><li>Becher, B., Spath, S., Goverman, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cytokine+networks+in+neuroinflammation.">Cytokine networks in neuroinflammation.</a> Nat Rev Immunol. 17 (1), 49-59 (2017).</li><li>Cosenza, M., Sacchi, S., Pozzi, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cytokine+release+syndrome+associated+with+T-cell-based+therapies+for+hematological+malignancies:+Pathophysiology,+clinical+presentation,+and+treatment.">Cytokine release syndrome associated with T-cell-based therapies for hematological malignancies: Pathophysiology, clinical presentation, and treatment.</a> Int J Mol Sci. 22 (14), 7652 (2021).</li><li>Hu, B., Huang, S., Yin, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+cytokine+storm+and+COVID-19.">The cytokine storm and COVID-19.</a> J Med Virol. 93 (1), 250-256 (2021).</li><li>Marcuzzi, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Autoinflammatory+diseases+and+cytokine+storms-Imbalances+of+innate+and+adaptative+immunity.">Autoinflammatory diseases and cytokine storms-Imbalances of innate and adaptative immunity.</a> Int J Mol Sci. 22 (20), 11241 (2021).</li></ol>

Specific aspects such as the beadline measurements of cells have been patented.

Cytokine release and secretion are frequently investigated in immunology and clinical medicine3. Unbalanced cytokine secretion can lead to detrimental effects for patients suffering from infections but also in neurological disease, inflammation, or cancer26,27,28. Even though their importance in health and disease is well established, studying cytokines and their secreting cells remains challenging as the current methodologies are not capable of accurately detecting and quantifying cytokines originating from a single cell in a time-resolved manner. For the workflow presented here, an established stimulation protocol with PBMC was used and their secretion of IL-6, TNF-&#945; and IL-1&#946; was measured. The choice of using PBMCs instead of individual, purified subpopulations stemmed from the previous application to investigate cytokine release syndromes (CRS)23, a condition characterized by highly elevated plasma concentrations of pro-inflammatory cytokines, including IL-6, TNF-&#945; and IL-1&#946;29. As CRS is usually not only linked to one population, we used PBMCs as they would be present in vivo. However, cellular subpopulations can be purified and assessed individually, if the scientific question demands this step. The incubation time, stimulation conditions and dynamic assay ranges were optimized to measure secretion for the three cytokines of interest. The workflow and data presented here demonstrate how to set up, calibrate, quantify, measure, and analyze time-resolved single-cell secretion of multiple cytokines. This protocol provides a blueprint on how multi-functional analysis of cytokine secretion could enable the large functional and dynamic diversity of the cytokine secreted in patients.
Several crucial aspects of the described assay protocol enable a unique biological readout. First, single-cell encapsulation in microfluidic droplets allowed the extraction of data for each individual cell. Events of multiple cell encapsulations can be detected and sorted in or out by image analysis, depending on the research question. Second, the inclusion of several independent in-droplet fluorescent immunoassays and the alignment of the functionalized nanoparticles allowed for the quantitative measurement of up to three cytokine concentrations in parallel. This multiplexing enabled the analysis of cytokine co-secretion patterns on a single-cell level. Third, immobilization of the droplets permitted the time-wise measurement and correlation of cytokine secretion for each secreting cell and allowed to distinguish co-occurrent from sequential secretion. The time resolution uniquely provided data on secretion patterns and subpopulations of different secretor types. Finally, parallelized image analysis enabled the efficient extraction and tracking of large amounts of data from measurements with over 20,000 individual cells. The extraction from single secretion curves further permitted the discovery of phenotypic subpopulations and functionalities.
Next to its unique readout, the assay also has technical advantages over standard cytokine analysis. Thanks to the small size of the encapsulation compartments of around 60 pL, absolute amounts of secreted cytokines can be detected directly from the biological source with detection limits fitting cell secretion. The assay miniaturization also uses smaller quantities of expensive bio-reagents. Furthermore, the setup requires very little specialized equipment, which is often already available in biology and bioengineering laboratories. Fluorescence microscopes are broadly available, and syringe pumps are frequently used in bioengineering laboratories or can be purchased at a relatively low cost. If a cell culture is present, the cost for the full equipment needed to run the experiments is around 148,000 Euros, with the majority contributed by the automated epifluorescent microscope (130,000 Euros). However, such an instrument can be often found in biological laboratories, and the rest of the cost is distributed to the syringe pump (13,000 Euros, but cheaper alternatives are available) and smaller equipment. The fabrication of the droplet chip and observation chamber is very well described17 and can be performed outside of a cleanroom environment with necessary infrastructure, such as ovens and plasma cleaners present in most bioengineering labs. Alternatively, different suppliers are available to supply interested laboratories with droplet generator chips. Due to the small volumes needed, the assay is cost-effective and simple to set up.
The ensure the highest degree of reproducibility, we identified some critical steps for the success of the protocol. A common problem for first-time users is droplet movement during the measurement. While the analysis software can track individual droplets to some degree, excessive movement leads to loss of single-cell resolution and inaccurate results. Movement can be avoided by using properly airtight measurement chambers, correct droplet size and chamber sizes, a short equilibration period before starting the measurement, and proper surfactant concentration. Another critical step is accurate focusing before starting the measurement. Improper focusing leads to significantly lowered fluorescence relocation values and the underestimation of the amount of secreted cytokine. Finally, depending on the question and protocol at hand, correct timing between the different steps is of utmost importance for reproducibility. Especially the waiting time between filling the chamber and starting the measurement should be consistent, otherwise the measurement window of the secreted cytokines might be missed.
Limitations of the presented technology include the restricted ability to further manipulate the cells after encapsulation. It is therefore currently not possible to add or remove stimulants, antibodies, or additional reagents. Additionally, since the cells are encapsulated in their isolated bioreactor, no interactions between cells (contact-based or paracrine signaling) can take place during the measurement. This limitation can be partially overcome with bulk incubations beforehand. Besides, enhanced autocrine effects from secreted cytokines are also possible and these effects cannot be quantified or excluded with certainty, as only antibody-detected secreted cytokines are measured. So, the isolated view on cytokine secretion must always be described in the context of the corresponding question and application. However, this limitation could also be used for the detailed study of encapsulated multiples, doublets, and triplets if of interest. This would provide an interesting setup useful to investigate cell-cell contact or paracrine-based questions. Lastly, also the dynamic range of the assay is limited and needs adaptation to the specific application. Here, we have adapted the dynamic range of the assay to the expected secreted amount of the measured cytokines.
To further advance the capabilities and applicability of the assay, several developments could be addressed in the future, in biological, technical and data analysis aspects. On the biological side, the measurement of additional cytokines, other secreted proteins, metabolic or cell surface markers could be integrated by adapting the assay. Furthermore, this assay could be integrated into a workflow alongside other cell-based assays to broaden the readouts (e.g., flow cytometry staining or sequencing). Additionally, the usability of the assay could be simplified, e.g., by creating an integrated microfluidic chip for droplet creation and observation, thereby potentially enabling a wider application outside bioengineering laboratories in a clinical setting. Regarding the data analysis, extraction and tracking of information from images could be extended by enhancing automation and using machine learning approaches, e.g., to detect the presence and position of the cell(s) and beadline in each droplet without fluorescent labeling. Doing so would open additional fluorescent channels that could be used for immunoassays, resulting in the measurement of even more cytokines in parallel.
The presented assay and the associated protocols and analysis can be applied for diverse potential use cases related to cytokine secretion dynamics. More specifically, the assay could potentially address fundamental immunological questions such as identifying cell-type and activation-specific cytokine-secretion profiles, polyfunctionality of cytokine-secreting cells, or the temporality and maintenance mechanisms of cytokine balances. Furthermore, in clinical applications, the platform might enable the unraveling of the role of cytokines during active or chronic inflammatory responses, as observed in COVID-1930, or provide a tool for stratifying patients and personalizing treatments based on unique signatures such as in autoinflammation31. In conclusion, quantitative time-resolved assessment of cytokine secretion from single cells is a much-needed method as it elucidates how a particular drug, infection, genetic alteration, or ex vivo stimulation induces a particular response.

Infections often cause complex host reactions involving the innate and adaptive immune systems1,2. Upon infection or recognition of infectious agents, host cells can produce a diverse range of chemo- and cytokines, which are small proteins known as critical communicators and modulatory of the immune system3. Pro-inflammatory cytokines are released early upon infection to initiate the immune response, followed later on by anti-inflammatory cytokines, which are critical to prevent tissue damage and subsequent chronic or autoinflammatory diseases. This balance between threat elimination and tissue protection manifests as a wide repertoire of cytokines exerting different functions during the infection, allowing for a fine-tuning of the response4,5. Within this mixture, unique signatures can be observed depending on the pathogen and the signals they induce, the tissue location, and the immune cells from which they originate. However, cytokine release also appears to constitute a multi-functional biological process unique to each cell population, diverse in secretion dynamics and individual response. This heterogeneity has been described in the literature for many years, for instance, among T-cell subpopulations6,7, where investigations into autoinflammatory diseases and severe COVID-19 infections exhibited a large functional diversity of inflammatory markers within and in between patients8,9. Lately, the advent of single-cell sequencing highlighted the high plasticity and crosstalk between subpopulations within immune microenvironments that were not previously apparent, indicating that single-cell methods are necessary to capture this heterogeneity10,11. While novel methods are being developed to analyze the transcriptome, phenotypic analysis remains challenging, as this requires simultaneous, quantitative, and time-resolved measurements of protein secretion on a single-cell level. Such measurements allow us to investigate secreting cell identities, dynamics, and secretion patterns (slow/fast, early/late, simultaneous/sequential) for a repertoire or a panel of cytokines. By enabling the study of the dynamics of cytokine release during an immune response quantitatively and with temporal resolution, the resulting insights might allow for an understanding of the cellular ensemble and the induced response.
In standard protocols, cytokines are usually detected in the supernatant of cell suspensions and serum using enzyme-linked immunosorbent assay (ELISA), yielding bulk-secreted amounts. Bulk measurements do not allow for quantification of the cytokine amounts produced by each cell, a problem especially highlighted in heterogeneous cell samples. Alternative methods such as intracellular cytokine staining, enzyme-linked immunospot (ELISpot) assay or micro-engraved assays (e.g., Isoplexis) detect cytokines expressed by individual cells but provide endpoint measurements only12,13. This means that secretion dynamics and changes that might happen in the cellular secretion pattern over the incubation time are ignored. Additionally, endpoint measurements cannot differentiate between simultaneous and sequential cytokine secretion, so the true extent of simultaneous polyfunctionality of immune cells in cytokine secretion remains unclear using these methods.
Single-cell resolution can be achieved using droplet microfluidics to generate and process picoliter-sized physical compartments in order to study immune cells on their unique cytokine secretion phenotypes on the single-cell level14,15. These compartments consist of water-in-oil emulsions and can be generated using microfluidic chips16,17. Indeed, droplet-based microfluidic assays have demonstrated extreme versatility in enabling the analysis of different biological samples and repertoires on the single-cell level and their integration with upstream (cell and reagent processing) and downstream processes (single-cell sorting, proteomics or sequencing)18,19,20,21,22. In particular, setups that permit droplet immobilization allow for the measurement of a single-cell functionality over time, which is valuable for the analysis of protein secretion18. Furthermore, integrating multiplexed, quantitative assays facilitates additional investigations in previously inaccessible dimensions, into processes such as co-secretion and the identification of polyfunctional immune cells23,24.
In this protocol, we describe an immobilized droplet-based single-cell workflow to detect, quantify and temporally measure the secretion of up to three cytokines in parallel from individual cells17,23. The technology offers the ability to monitor cytokine responses from over 20,000 cells in parallel.
The presented workflow consists of the microfluidic encapsulation of single immune cells and functionalized nanoparticles into 60 pL water-in-oil droplets. The immobilization of &#62;100,000 droplets in an observation chamber and time-resolved fluorescence microscopy allow for the measurement of cytokine secretion dynamics within each droplet and each cytokine (Figure 1A). For each individual cell within a droplet, the cytokine secretion is measured by a sandwich immunoassay, where magnetic nanoparticles functionalized with a specific capture antibody bind the secreted cytokine, leading to the subsequent relocation and binding of fluorescently labeled detection antibodies (Figure 1B,C). A beadline is formed by aligning the magnetic nanoparticles, to which fluorescence relocation can be quantified in the presence of cytokine. Here, fluorescence relocation is defined as the average fluorescence intensity found on the beadline divided by the average fluorescence intensity of the remaining droplet. This assay can be multiplexed for several cytokines by mixing differently functionalized nanoparticle batches and respective detection antibodies labeled in different fluorescence channels23, resulting in specific fluorescence relocations in the different channels. With the help of a customized analysis script, fluorescence relocation values can be extracted, and the images can be converted into secretion dynamic profiles for every individual cell and cytokine. Therefore, the resulting datasets yield numerous readouts, such as the quantitative secretion measurement over time, the identification of co-secreting subpopulations, and the distributions of the cells according to secreted amounts, rates, and combinations of cytokines.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66492/66492fig01.jpg" /> 
Figure 1: Workflow and assay principle. (A) Overview of the workflow for analyzing&#160;cytokine-secreting cells after stimulation. Single-cell suspensions and magnetic nanoparticles are prepared and encapsulated into 60 pL in volume oil/water emulsions (droplets). Droplets are immobilized and nanoparticles aligned inside a magnetic field before measurement for up to 4 h every 30 min. Finally, images are analyzed, and the parameters for every droplet, timepoint and fluorescent channel are extracted. This figure has been modified from17. (B) Principle of the droplet sandwich bioassay. Functionalized nanoparticles bind the secreted cytokines, which leads to the subsequent relocation of fluorescently labeled detection antibodies to the nanoparticles. This relocation of fluorescence is quantified and validated with calibration experiments performed with recombinant cytokines. Mixing different functionalized nanoparticles allows the multiplexed measurements of up to three cytokines simultaneously. (C) In cell-based experiments, droplets are followed over the measurement time and secreting cells are identified by an overtime increase of fluorescence relocation onto the nanoparticles. Schematics are not up to scale. Figure created with BioRender.com. <a href="https://www.jove.com/files/ftp_upload/66492/66492fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>008-FluoroSurfactant</td>
			<td>RAN Biotechnologies</td>
			<td>008-FluoroSurfactant-10G</td>
			<td></td>
		</tr>
		<tr>
			<td>2-Stream flow-focusing droplet maker, 30 &#181;m nozzle, PFOS hydrophobic surface treatment</td>
			<td>Wunderli chips</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Alexa Fluor 647 NHS Ester&#160;</td>
			<td>ThermoFisher</td>
			<td>A20006</td>
			<td>https://www.thermofisher.com/ch/en/home/references/protocols/cell-and-tissue-analysis/labeling-chemistry-protocols/fluorescent-amine-reactive-alexa-fluor-dye-labeling-of-igm-antibodies.html</td>
		</tr>
		<tr>
			<td>Anti-Human IL-1&#946; (Monoclonal Mouse), AF647 labelled in-house</td>
			<td>PeproTech</td>
			<td>500-M01B</td>
			<td></td>
		</tr>
		<tr>
			<td>ARcare92524 double-sided adhesive tape</td>
			<td>Adhesvies Reasearch</td>
			<td>ARcare92524</td>
			<td></td>
		</tr>
		<tr>
			<td>Bio-Adembeads Streptavidin plus 300nm</td>
			<td>Ademtech</td>
			<td>Cat#03233</td>
			<td></td>
		</tr>
		<tr>
			<td>Biotinylated Goat Anti-Human IL-1&#946;</td>
			<td>PeproTech</td>
			<td>500-P21BGBT</td>
			<td></td>
		</tr>
		<tr>
			<td>Bovine Serum Albumin (BSA)</td>
			<td>Sigma-Aldrich</td>
			<td>A3059</td>
			<td></td>
		</tr>
		<tr>
			<td>Cell Scraper</td>
			<td>TPP</td>
			<td>99002</td>
			<td></td>
		</tr>
		<tr>
			<td>CellTrace Violet Cell Proliferation Kit</td>
			<td>Invitrogen</td>
			<td>C34557</td>
			<td>Cell staining solution</td>
		</tr>
		<tr>
			<td>Chromafil Xtra PTFE-45/25 syringe filters</td>
			<td>Macherey-Nagel</td>
			<td>729205</td>
			<td></td>
		</tr>
		<tr>
			<td>Costar 6-well Clear Flat Bottom Ultra-Low Attachment</td>
			<td>Corning</td>
			<td>3471</td>
			<td></td>
		</tr>
		<tr>
			<td>Countess Cell Counting Chamber Slides</td>
			<td>Invitrogen</td>
			<td>C10283</td>
			<td></td>
		</tr>
		<tr>
			<td>D-Biotin</td>
			<td>Fluorochem</td>
			<td>M02926</td>
			<td></td>
		</tr>
		<tr>
			<td>DPBS, no calcium, no magnesium</td>
			<td>Gibco&#160;</td>
			<td>14196-094</td>
			<td></td>
		</tr>
		<tr>
			<td>epT.I.P.S.&#160;Standard 2-200 &#181;l</td>
			<td>Eppendorf</td>
			<td>30000889</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethylenediaminetetraacetic acid disodium salt solution</td>
			<td>Sigma-Aldrich</td>
			<td>3690</td>
			<td></td>
		</tr>
		<tr>
			<td>EZ-LINK-NHS-PEG4-Biotin</td>
			<td>ThermoFisher</td>
			<td>A39259</td>
			<td>https://www.thermofisher.com/order/catalog/product/20217</td>
		</tr>
		<tr>
			<td>FcR Blocking Reagent, human</td>
			<td>Miltenyi Biotec</td>
			<td>130-059-901</td>
			<td></td>
		</tr>
		<tr>
			<td>Fetal Bovine Serum</td>
			<td>Gibco&#160;</td>
			<td>10270-106</td>
			<td></td>
		</tr>
		<tr>
			<td>Handy dish soap</td>
			<td>Migros</td>
			<td>5.01002E+11</td>
			<td></td>
		</tr>
		<tr>
			<td>HEPES (1 M)</td>
			<td>Gibco&#160;</td>
			<td>15630-080</td>
			<td></td>
		</tr>
		<tr>
			<td>HFE-7500 Oil 3M TM Novec</td>
			<td>Fluorochem</td>
			<td>B40045191</td>
			<td></td>
		</tr>
		<tr>
			<td>Idex F-120 Fingertight One-Piece Fitting, Standard Knurl, Natural PEEK, 1/16&#34; OD Tubing, 10-32 Coned</td>
			<td>Cole-Parmer</td>
			<td>GZ-02014-15</td>
			<td></td>
		</tr>
		<tr>
			<td>IL-6 Monoclonal Antibody (MQ2-13A5 - Rat), FITC</td>
			<td>ThermoFisher</td>
			<td>11-7069-81</td>
			<td></td>
		</tr>
		<tr>
			<td>IL-6 Monoclonal Antibody (MQ2-39C3), Biotin</td>
			<td>ThermoFisher</td>
			<td>13-7068-85</td>
			<td></td>
		</tr>
		<tr>
			<td>KnockOut Serum Replacement</td>
			<td>ThermoFisher</td>
			<td>10828-010</td>
			<td></td>
		</tr>
		<tr>
			<td>Loctite AA 3491 curable UV glue</td>
			<td>Henkel AG &#38; Co</td>
			<td>3491</td>
			<td></td>
		</tr>
		<tr>
			<td>Microscope slides (76x26x1mm, clear white)</td>
			<td>Menzel Gl&#228;ser</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Mineral oil light</td>
			<td>Sigma-Aldrich</td>
			<td>330779</td>
			<td></td>
		</tr>
		<tr>
			<td>NanoPort Assembly Headless, 10-32 Coned, for 1/16&#34; OD</td>
			<td>Idex</td>
			<td>N-333</td>
			<td></td>
		</tr>
		<tr>
			<td>Neodymium block magnet</td>
			<td>K&#38;J Magnetics</td>
			<td>BZX082</td>
			<td></td>
		</tr>
		<tr>
			<td>Omnifix-F Spritze, 1 ml, LS</td>
			<td>Braun</td>
			<td>9161406V</td>
			<td></td>
		</tr>
		<tr>
			<td>Penicillin-Streptomycin (10,000 U/mL)</td>
			<td>Gibco&#160;</td>
			<td>15140-122&#160;</td>
			<td></td>
		</tr>
		<tr>
			<td>Phosphate buffered saline</td>
			<td>Sigma-Aldrich</td>
			<td>P4417</td>
			<td></td>
		</tr>
		<tr>
			<td>Pluronic F-127, 0.2 &#181;m filtered (10% Solution in Water)</td>
			<td>ThermoFisher</td>
			<td>P6866</td>
			<td></td>
		</tr>
		<tr>
			<td>Precision wipes</td>
			<td>Kimtech Science</td>
			<td>5511</td>
			<td></td>
		</tr>
		<tr>
			<td>PTFE microtubing 0.30 &#215; 0.76 mm</td>
			<td>FisherScientific</td>
			<td>1191-9445</td>
			<td></td>
		</tr>
		<tr>
			<td>PTFE microtubing 0.56 &#215; 1.07 mm</td>
			<td>FisherScientific</td>
			<td>1192-9445</td>
			<td></td>
		</tr>
		<tr>
			<td>Recombinant Human IL-1&#946;</td>
			<td>Peprotech</td>
			<td>Cat#200-01B</td>
			<td></td>
		</tr>
		<tr>
			<td>Recombinant Human IL-6</td>
			<td>Peprotech</td>
			<td>Cat#200-06</td>
			<td></td>
		</tr>
		<tr>
			<td>Recombinant human serum albumine (HSA)</td>
			<td>Sigma-Aldrich</td>
			<td>A9731</td>
			<td></td>
		</tr>
		<tr>
			<td>Recombinant Human TNF-&#945;</td>
			<td>Peprotech</td>
			<td>Cat#300-01A</td>
			<td></td>
		</tr>
		<tr>
			<td>Reusable biopsy punch diameter 0.75 mm and 6 mm</td>
			<td>Stiefel</td>
			<td>&#160;504529 and 504532</td>
			<td></td>
		</tr>
		<tr>
			<td>RPMI 1640 Medium, no phenol red</td>
			<td>Gibco</td>
			<td>11835-030</td>
			<td></td>
		</tr>
		<tr>
			<td>Standard LPS, E. coli K12</td>
			<td>InvivoGen</td>
			<td>tlrl-eklps</td>
			<td></td>
		</tr>
		<tr>
			<td>Sterican needles 23 G for 0.56 mm diameter microtubing</td>
			<td>FisherScientific</td>
			<td>15351547</td>
			<td></td>
		</tr>
		<tr>
			<td>Sterican needles 27 G for 0.30mm diameter microtubing</td>
			<td>FisherScientific</td>
			<td>15341557</td>
			<td></td>
		</tr>
		<tr>
			<td>TNF alpha Monoclonal Antibody (MAb11), PE</td>
			<td>ThermoFisher</td>
			<td>12-7349-81</td>
			<td></td>
		</tr>
		<tr>
			<td>TNF-alpha Monoclonal Antibody (MAb1), biotinylated in-house</td>
			<td>ThermoFisher</td>
			<td>14-7348-85</td>
			<td></td>
		</tr>
		<tr>
			<td>Trypan Blue Stain (0.4%) for use with the Countess Automated Cell Counter</td>
			<td>Invitrogen</td>
			<td>T10282</td>
			<td></td>
		</tr>
		<tr>
			<td>Vacuum Filtration &#34;rapid&#34;-Filtermax</td>
			<td>TPP</td>
			<td>99500</td>
			<td></td>
		</tr>
		<tr>
			<td>Devices</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Cameo 4 automatic cutting machine</td>
			<td>Silhouette</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Cetoni Base 120 + 3x NEMESYS Low Pressure Syringe Pumps</td>
			<td>Cetoni</td>
			<td>NEM-B101-03 A</td>
			<td></td>
		</tr>
		<tr>
			<td>Countess II Automated Cell Counter</td>
			<td>ThermoFisher</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Inverted Epi-fluorescence microscope Ti2</td>
			<td>Nikon</td>
			<td>ECLIPSE Ti2-E, Ti2-E/B*1</td>
			<td></td>
		</tr>
		<tr>
			<td>OKO Lab Cage Incubator, dark panels</td>
			<td>OKO Lab</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>ORCA-Fusion Digital CMOS camera</td>
			<td>Hammatsu</td>
			<td>C14440</td>
			<td></td>
		</tr>
		<tr>
			<td>SOLA Light Engine</td>
			<td>Lumencor</td>
			<td>sola 80-10247</td>
			<td></td>
		</tr>
	</tbody>
</table>

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.

Author Spotlight: Unveiling the Polyfunctionality and Heterogeneity in Immune Responses

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

The presented functional single-cell platform allowed the measurement of several parameters. First, and similar to standard techniques, the frequency of secreting cells is depicted at the end of the measurement (Figure 4A). Following the stimulation with 1 &#181;g/mL of lipopolysaccharide (LPS) for 6 h of peripheral blood mononuclear cells (PBMC), 5.81% of the cells secreted IL-6 (n= 1270), 4.55% TNF&#945; (n= 995) and 6.06% IL-1&#946; (n= 1326).
To quantify the cytokine secretion, calibration curves were generated with known concentrations of recombinant cytokines (Figure 4B). These calibration curves allow the quantification of the in-droplet cytokine concentrations over time. Exemplarily, the average in-droplet IL-6 concentration reached a plateau after 90 min for LPS-stimulated PBMC, whereas the average in-droplet IL-1&#946; increased more rapidly from 90 min, displaying the dynamic resolution of the platform and the possibility to extract cell subpopulations secreting specific cytokines (Figure 4C). As the concentration changes between measurement points, calculating dynamic secretion rates per cytokine is possible. Considering the average secretion rate for each cytokine (Figure 4D), IL-6 secreting cells exhibited a constant decrease in average secretion rate, while TNF&#945; and IL-1&#946; secreting cells both showed an increase in secretion rate after 90 min measurement time and a second decrease after 150 min.
Furthermore, it is possible to cluster cells into subpopulations depending on the secreted and co-secreted cytokines (Figure 4E). Here, IL-6 and TNF&#945; are single-secreted by 30.2% and 26.4% of the cells secreting IL-6 or TNF&#945;, respectively, whereas single-secreting IL-1&#946; cells made up 68.8% of all IL-1&#946; secreting cells. Additionally, the effects of co-secretion on secreted concentrations and secretion rates can be resolved (Figure 4F). By looking at IL-6-secreting cells, different amounts of IL-6 were secreted if the cells additionally produced TNF&#945; or IL-1&#946;. Similarly, the distribution of averaged secretion rates over the measurement statistically differed between the cells secreting only IL-6 or IL-6 alongside TNF&#945; (higher secretion rates) and IL-1&#946; (lower IL-6 secretion rates).
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/66492/66492fig04.jpg" /> 
Figure 4: Representative results of IL-6, TNF&#945; and IL-1&#946; secreting PBMC after 6 h stimulation with 1 &#181;g/mL LPS. (A) Percentage of PBMC secreting IL-6, TNF&#945; and IL-1&#946; at the end of the 4 h measurement. (B) Multiplexed cytokine calibration curves are generated with known concentrations of recombinant cytokines. This allows the quantification of cell experiments by computing from the relocation value the cytokine concentration within the droplet. Points were fitted using a non-linear one-phase association curve fit, r2=0.9926 (IL-6), 0.9901 (TNF&#945;), 0.9990 (IL-1&#946;). (C) Average secreted concentrations of IL-6, TNF&#945; and IL-1&#946; released by secreting PBMC over the 4 h measurement time. (D) Average secretion rates of IL-6, TNF&#945; and IL-1&#946; over the 4 h measurement time. (E) Relative percentage of co-secreting cells secreting IL-6, TNF&#945; or IL-1&#946; and combinations thereof. Normalized to all of the secreting cells detected for each cytokine. (F) Averaged IL-6 concentrations over the measurement time and average secretion rate (log) distributions for IL-6 secreting cells with co-secretion resolution (n=383 for IL-6 only, n=531 for IL-6 + TNF&#945;, n= 213 for IL-6 + IL-1&#946; and n=143 for IL-6+TNF&#945;+IL-1&#946;). Statistical differences in secretion rate distributions were assessed using two-sided, unpaired, nonparametric Kolmogorov-Smirnov tests with 95% confidence, the p-value are represented. ** (p &#60;0.002) and **** (p &#60;0.0001). The full line represents the median and the dotted line the quartiles. ntotal cells = 21 866. <a href="https://www.jove.com/files/ftp_upload/66492/66492fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
To extract additional information on the single-cell level, a sigmoid function can be fitted to the concentration-time points of each cell and cytokine (Figure 5). An exemplary concentration over time dataset for one cell and the corresponding sigmoidal fit is depicted in Figure 5A. Here, the least squares fitting procedure yields the following parameters: C, corresponding to the upper plateau value of the curve, t50 quantifying the time-wise shift of the curve from zero, and the Hill slope m, describing the steepness of the rising part of the sigmoid curve with 10% and 90% concentration values reached throughout the measurement. From these fit parameters, some curve descriptors can be extracted as explained in step 7.12. yielding the Cmax, the highest concentration value of the data, tstart, the start time of secretion, defined as reaching 10% of the upper plateau concentration value, and SRlin, the secretion rate during the rising part of the curve.
To classify cell subpopulations, the curve descriptors obtained from all single-cell fits were classified into three categories each: Cmax values were grouped into low, medium, and high for tstart into early, medium, and late an SRlin into slow, medium and fast secretors. To illustrate this classification, four exemplary single-cell secretion curves and their corresponding curve descriptors are shown (Figure 5A-D), where curve A exhibits the characteristics of an early low secretor of medium rate, curve B is an early, slow, and high secretor, curve C an early fast high secretor, and curve D shows late low secretion. It is important to note that the cutoffs for these criteria are cell-, cytokine-, and assay parameter-specific, and need to be adapted for each research question. Furthermore, only IL-6 secretion of PBMC after 1 &#181;g/mL LPS stimulation for 6 h was considered here, meaning that most cells were early and high secretors with 80% and 79%, respectively (Figure 5E-F). Regarding the secretion rate, a bipolar response was observed with 55% of IL-6 secreting cells are slow secretors and 39% as fast secretors (Figure 5G).
To further characterize secretion behavior, the curve descriptors for each cell were plotted against each other and different clusters were extracted (Figure 5H-J). No clear correlation is given between tstart and Cmax (Figure 5H): the two largest populations were early low secretors and high secretors independent of secretion start. Considering the relation between tstart and SRlin (Figure 5I), most cells were early slow secretors with a clear population of early high secretors and few slow/medium to late secretors. Regarding SRlin and Cmax (Figure 5J) correlations, almost no fast low to medium secretors were present, with only a bigger population of fast low secretors. Furthermore, there was a large population of fast secretors that did not depend on the maximal measured concentration, and two populations of high secretors secreted either slow or fast. In summary, it can be concluded that investigating the relationship between the curve descriptors for individual cells yields a much more detailed analysis and can potentially extract new biological findings from single-cell secretion measurements.
With the analysis introduced above, we extracted the secretion dynamics of co-secreting cells (Figure 6). Two example curves show different dynamics of co-secretion for IL-6 and TNF&#945; from two single cells with a simultaneous start of both cytokines (Figure 6A), or a sequential secretion start, with IL-6 being secreted first (Figure 6B). To classify all co-secreting cells, a secretion delay of 60 min was defined, where all cells starting secretion within this range are considered simultaneous secretors and all cells with longer delays are considered sequential secretors. This analysis also allowed the possibility to observe which cytokine was secreted first. For IL-6 and TNF&#945;, mainly simultaneous co-secretion was observed in 76% of the cells (Figure 6C), while for IL-6 and IL-1&#946;, sequential co-secretion was observed in 86% of the cells with IL-6 being the first cytokine to be secreted in most cases (Figure 6D).
Looking at the starting time of secretion for the different cytokines for all individual co-secreting cells, no clear correlation between secretion starting times was observed in the performed experiments. For IL-6 and TNF&#945; co-secretion (Figure 6E), a larger vertical cluster around 0 min was present, corresponding to the co-secreting cells more prevalently starting with IL-6. For IL-6 and IL-1&#946; co-secretion (Figure 6F), most cells started secreting IL-6 around the start of the measurement, while IL-1&#946; was mainly secreted later. In summary, the analysis presented here enabled the identification of different secretor sub-populations and complex cytokine co-secretion dynamics.
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/66492/66492fig05.jpg" /> 
Figure 5: Detailed analysis of different secretion dynamic patterns for single IL-6 secreting cells curves. (A) Representative single-cell cytokine concentration data over measurement time with the fitted sigmoid curve and the extracted parameters. (B-D) Three exemplary single-cell cytokine concentration curves for the different cytokine secretor types found for IL-6 secretion after LPS stimulation. (E-G) Percentages of IL-6 secreting cells that are classified into the different secretor types with the following criteria (n=633): E. Cmax: low &#60;5 nM, high &#62;19.5 nM, F. tstart: early &#60;30 min, late &#62;120min, G. SRlin: slow &#60;250 molecules/s, fast &#62;750 molecules/s. (H-J) Relation between the three secretion curve descriptors Cmax, tstart and SRlin for each individual cell (n=633). The large population at Cmax=20nM results from reaching the upper detection limit of the assay. <a href="https://www.jove.com/files/ftp_upload/66492/66492fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/66492/66492fig06v2.jpg" /> 
Figure 6: Extraction of co-secretion patterns from single-cell concentration curves. (A-B) Representative concentration curves for single cells co-secreting IL-6 and TNF&#945; (A) simultaneously and (B) sequentially, respectively. (C-D) Percentage of cells exhibiting simultaneous and sequential co-secretion of IL-6 and TNF&#945; (n=249), or IL-6 and IL-1&#946; (n=72), respectively. Sequential secretion is defined through the delay between cytokine secretion starts of more than 60 min. Colors indicate which of the cytokines started secretion first. (E-F) Relation between the secretion start times for the different cytokines for each secreting cell (nIL6-TNF&#945;=249, nIL6-IL1&#946;=72). <a href="https://www.jove.com/files/ftp_upload/66492/66492fig06largev2.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Read this Scientific Article Protocol about Author Spotlight: Unveiling the Polyfunctionality and Heterogeneity in Immune Responses at JoVE.com

The protocol describes an advanced microfluidic platform to quantitatively measure cytokine secretion dynamics of individual human peripheral blood mononuclear cells. The platform measures up to three cytokines in parallel (IL-6, TNF&#945;, and IL-1&#946;) for each individual cell stimulated with lipopolysaccharide as an example.

Infections, autoimmune diseases, desired and adverse immunological responses to treatment can lead to a complex and dynamic cytokine response in vivo. ...

microfluidic-approach-to-resolve-simultaneous-sequential-cytokine

Microsoft Bing

Research

JoVE Journal

Immunology and Infection

Observation Chamber Fabrication for Imaging Single-Cell Suspensions and Magnetic Nanoparticles

Preparation of Functionalized Magnetic Nanoparticles for Time-Resolved Fluorescent Imaging

Encapsulation of the Cells and Nanoparticles into Droplets for Fluorescent Imaging

802 Views.  ETH Zürich.  The protocol describes an advanced microfluidic platform to quantitatively measure cytokine secretion dynamics of individual human peripheral blood mononuclear cells. The platform measures up to three cytokines in parallel (IL-6, TNFα, and IL-1β) for each individual cell stimulated with lipopolysaccharide as an example.

Microfluidic Droplet Assay for Single-Cell Cytokine Secretion Analysis

Summary