A.S. is supported by AIRC (IG #28807) and by PRIN (#P2022YE2MX). M.M. is supported by the AIRC-FIRC fellowship (#25558). A.D.N. is supported by the Innovation Ecosystem Rome Technopole ECS00000024 funded by the EU - Next Generation EU, PNRR Mission 4 Component 2 Investment 1.5.

Flow Cytometry-Based Monitoring of Cancer-Immunity Cycle 

<ol>
	<li>Chen, D. S., Mellman, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Oncology+meets+immunology:+the+cancer-immunity+cycle.">Oncology meets immunology: the cancer-immunity cycle.</a> Immunity. 39 (1), 1-10 (2013).</li><li>Mellman, I., Chen, D. S., Powles, T., Turley, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+cancer-immunity+cycle:+Indication,+genotype,+and+immunotype.">The cancer-immunity cycle: Indication, genotype, and immunotype.</a> Immunity. 56 (10), 2188-2205 (2023).</li><li>Galassi, C., Musella, M., Manduca, N., Maccafeo, E., Sistigu, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+immune+privilege+of+cancer+stem+cells:+A+key+to+understanding+tumor+immune+escape+and+therapy+failure.">The immune privilege of cancer stem cells: A key to understanding tumor immune escape and therapy failure.</a> Cells. 10 (9), 2361 (2021).</li><li>Dunn, G. P., Old, L. J., Schreiber, R. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+three+Es+of+cancer+immunoediting.">The three Es of cancer immunoediting.</a> Annu Rev Immunol. 22, 329-360 (2004).</li><li>Musella, 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=Type+I+IFNs+promote+cancer+cell+stemness+by+triggering+the+epigenetic+regulator+KDM1B.">Type I IFNs promote cancer cell stemness by triggering the epigenetic regulator KDM1B.</a> Nat Immun. 23 (9), 1379-1392 (2022).</li><li>Anderson, K. G., 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=Leveraging+immune+resistance+archetypes+in+solid+cancer+to+inform+next-generation+anticancer+therapies.">Leveraging immune resistance archetypes in solid cancer to inform next-generation anticancer therapies.</a> J Immunother Cancer. 11 (6), e006533 (2023).</li><li>Sharma, P., Hu-Lieskovan, S., Wargo, J. A., Ribas, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Primary,+adaptive,+and+acquired+resistance+to+cancer+immunotherapy.">Primary, adaptive, and acquired resistance to cancer immunotherapy.</a> Cell. 168 (4), 707-723 (2017).</li><li>Musella, M., Manduca, N., Maccafeo, E., Ruggiero, E., Sistigu, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+vitro+evaluation+of+cancer+cell+immunogenicity+and+antigen-specific+T-cell+cytotoxicity+by+flow+cytometry.">In vitro evaluation of cancer cell immunogenicity and antigen-specific T-cell cytotoxicity by flow cytometry.</a> Methods Mol Biol. 2748, 13-28 (2024).</li><li>Bonilla, D. L., Reinin, G., Chua, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Full+spectrum+flow+cytometry+as+a+powerful+technology+for+cancer+immunotherapy+research.">Full spectrum flow cytometry as a powerful technology for cancer immunotherapy research.</a> Front Mol Biosci. 7, 612801 (2020).</li><li>Danova, M., Torchio, M., Comolli, G., Sbrana, A., Antonuzzo, A., Mazzini, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+role+of+automated+cytometry+in+the+new+era+of+cancer+immunotherapy.">The role of automated cytometry in the new era of cancer immunotherapy.</a> Mol Clin Oncol. 9 (4), 355-361 (2018).</li><li>Laskowski, T. J., Hazen, A. L., Collazo, R. S., Haviland, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rigor+and+reproducibility+of+cytometry+practices+for+immuno-oncology:+A+multifaceted+challenge.">Rigor and reproducibility of cytometry practices for immuno-oncology: A multifaceted challenge.</a> Cytometry A. 97 (2), 116-125 (2020).</li><li>Manduca, N., Maccafeo, E., De Maria, R., Sistigu, A., Musella, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=3D+cancer+models:+One+step+closer+to+in+vitro+human+studies.">3D cancer models: One step closer to in vitro human studies.</a> Front Immunol. 14, 1175503 (2023).</li><li>De Ninno, 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=Microfluidic+co-culture+models+for+dissecting+the+immune+response+in+in+vitro+tumor+microenvironments.">Microfluidic co-culture models for dissecting the immune response in in vitro tumor microenvironments.</a> JoVE. (170), e61895 (2021).</li><li>Mattei, F., 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=Oncoimmunology+meets+organs-on-chip.">Oncoimmunology meets organs-on-chip.</a> Front Mol Biosci. 8, 627454 (2021).</li><li>Businaro, 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=Cross+talk+between+cancer+and+immune+cells:+exploring+complex+dynamics+in+a+microfluidic+environment.">Cross talk between cancer and immune cells: exploring complex dynamics in a microfluidic environment.</a> Lab Chip. 13 (2), 229-239 (2013).</li><li>Vacchelli, E., 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=Chemotherapy-induced+antitumor+immunity+requires+formyl+peptide+receptor+1.">Chemotherapy-induced antitumor immunity requires formyl peptide receptor 1.</a> Science. 350 (6263), 972-978 (2015).</li><li>Maulana, T. I., 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=Immunocompetent+cancer-on-chip+models+to+assess+immuno-oncology+therapy.">Immunocompetent cancer-on-chip models to assess immuno-oncology therapy.</a> Adv Drug Deliv Rev. 173, 281-305 (2021).</li><li>Lorenzi, S., 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=Type+I+IFNs+control+antigen+retention+and+survival+of+CD8&#945;(+)+dendritic+cells+after+uptake+of+tumor+apoptotic+cells+leading+to+cross-priming.">Type I IFNs control antigen retention and survival of CD8&#945;(+) dendritic cells after uptake of tumor apoptotic cells leading to cross-priming.</a> J Immun. 186 (9), 5142-5150 (2011).</li><li>Musella, M., Manic, G., Galassi, C., Vitale, I., Sistigu, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cytofluorometric+assessment+of+dendritic+cell-mediated+uptake+of+cancer+cell+apoptotic+bodies.">Cytofluorometric assessment of dendritic cell-mediated uptake of cancer cell apoptotic bodies.</a> Methods Enzymol. 632, 39-54 (2020).</li><li>Lucarini, V., 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=Combining+Type+I+Interferons+and+5-Aza-2'-Deoxycitidine+to+improve+antitumor+response+against+melanoma.">Combining Type I Interferons and 5-Aza-2'-Deoxycitidine to improve antitumor response against melanoma.</a> J Invest Dermatol. 137 (1), 159-169 (2017).</li><li>Guti&#233;rrez-Mart&#237;nez, E., 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=Cross-presentation+of+cell-associated+antigens+by+MHC+Class+I+in+dendritic+cell+subsets.">Cross-presentation of cell-associated antigens by MHC Class I in dendritic cell subsets.</a> Front Immunol. 6, 363 (2015).</li><li>Joffre, O. P., Segura, E., Savina, A., Amigorena, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cross-presentation+by+dendritic+cells.">Cross-presentation by dendritic cells.</a> Nat Rev Immunol. 12 (8), 557-569 (2012).</li><li>Hegde, P. S., Karanikas, V., Evers, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+where,+the+when,+and+the+how+of+immune+monitoring+for+cancer+immunotherapies+in+the+era+of+checkpoint+inhibition.">The where, the when, and the how of immune monitoring for cancer immunotherapies in the era of checkpoint inhibition.</a> Clin Cancer Res. 22 (8), 1865-1874 (2016).</li><li>Lee, R. Y., Ng, C. W., Rajapakse, M. P., Ang, N., Yeong, J. P. S., Lau, M. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+promise+and+challenge+of+spatial+omics+in+dissecting+tumour+microenvironment+and+the+role+of+AI.">The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI.</a> Front Oncol. 13, 1172314 (2023).</li><li>Lewis, S. 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=Spatial+omics+and+multiplexed+imaging+to+explore+cancer+biology.">Spatial omics and multiplexed imaging to explore cancer biology.</a> Nat Methods. 18 (9), 997-1012 (2021).</li><li>Wu, Y., Cheng, Y., Wang, X., Fan, J., Gao, Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatial+omics:+Navigating+to+the+golden+era+of+cancer+research.">Spatial omics: Navigating to the golden era of cancer research.</a> Clin Transl Med. 12 (1), 696 (2022).</li><li>Vandereyken, K., Sifrim, A., Thienpont, B., Voet, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Methods+and+applications+for+single-cell+and+spatial+multi-omics.">Methods and applications for single-cell and spatial multi-omics.</a> Nat Rev Genet. 24 (8), 494-515 (2023).</li><li>Cheung, M., Campbell, J. J., Whitby, L., Thomas, R. J., Braybrook, J., Petzing, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Current+trends+in+flow+cytometry+automated+data+analysis+software.">Current trends in flow cytometry automated data analysis software.</a> Cytometry A. 99 (10), 1007-1021 (2021).</li><li>Zitvogel, L., Pitt, J. M., Daill&#232;re, R., Smyth, M. J., Kroemer, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mouse+models+in+oncoimmunology.">Mouse models in oncoimmunology.</a> Nat Rev Cancer. 16 (12), 759-773 (2016).</li></ol>

The authors have no conflicts of interest to disclose.

Monitoring anticancer immune response is of utmost importance to elucidate and understand the intricate molecular and cellular interplays acting in the TME and supporting a constant battle for supremacy23. Here, we detail a simple mFC- and ToC-assisted protocol for the monitoring and characterization of the steps constituting the cancer-immunity cycle. With minor variations, this protocol, based on murine cell lines and mouse-derived immune cells, can be adapted to both human immortalized and even primary cancer lines and human peripheral-blood-derived immune cells. Furthermore, combining either genetic or pharmacologic inhibition of specific pathways and their cognate-appropriate controls, this protocol allows the identification of biomarkers regulating immunosurveillance and, thus, response to immunogenic- and immune therapies.
mFC enables high-throughput analysis of diverse immune parameters and combines the ability to analyze millions of cells in a short time with the possibility to identify rare events, evaluate cell proliferation and cell death, detect multiple parameters at once in a small volume of samples and allows to perform downstream molecular and functional analyses post-fluorescence-based cell sorting9. These features together with the low cost, have favored mFC accessibility and wide use in research and clinical laboratories. Nevertheless, some important limitations remain. First, mFC analysis requires tissue dissociation to obtain single-cell suspensions during sample preparation, thus destroying the spatial information of the tumor tissue. The advent of spatial technologies in the last decade, including high-plex spatial transcriptomic, proteomic, and epigenomic, expanded our ability to profile the TME by analyzing the position of cancer, immune, and stromal cells and their mutual interactions24. Second, mFC allows the detection of up to sixteen (or so) parameters at once and thus does not cover all the cell identification, maturation, and activation status markers in a single sample. Again, the cutting-edge technologies above allow the detection of up to a hundred parameters simultaneously at single-cell and even subcellular resolution, as even before25,26,27. Third, immune monitoring of longitudinal and comparative studies requires reproducible protocols and the standardization of every step of the workflow, including data acquisition and analysis. Conventional manual gating can be subjective, and instrument maintenance and quality checks may vary, which affects data mining and interpretation. Automated acquisition and plug-ins implementing data analysis help minimize data variability28.
ToCs are gaining tremendous attention in cancer research for their ability to closely replicate several hallmarks of the TME and to replace, at least in some experimental settings, in vivo tumors12. While providing a quite realistic model of tumor-tissue architecture, multicellular complexity, and dynamic interplay between the cellular components of the TME, ToCs are still unable to fully replace animal studies in terms of reflecting the complexity of systemic immune responses, specifically the array of mechanisms responsible for the emergence of rate-limiting steps for each of the steps of the cancer-immunity cycle12,29. Beyond this aspect, the field of onco-immunology will continue to profit and benefit from the use of ToCs, as the rounds and the speed of scientific innovation are rendering them increasingly closer to in vivo models12.
In conclusion, the protocol described here serves as a powerful and simple analytical and discovery tool for unraveling the dynamic complexities of the TME. Also, the time- and cost-related features of this protocol, allow its feasibility on a large scale. The challenge ahead is to standardize it for clinical use to better inform and guide therapy decisions for cancer patients.

Over the past few decades, immunotherapy has been at the forefront of cutting-edge options for cancer treatment. Harnessing the immune system with antitumor purposes has provided powerful proof-of-concept for patient benefit across diverse hematologic and solid malignancies with historically poor prognoses. As immunotherapy is offering an opportunity for otherwise hard-to-treat cancers, it is experiencing an astoundingly rapid pace of progress. Such progress can be, at least in part, attributed to the refined understanding of the interplay between cancer cells and immune cells. This interplay resembles a feed-forward &#34;engine&#34; the immune system ignites to destroy cancer cells, the so-called cancer-immunity cycle. This anticancer immune response progresses across three main levels: recognition, processing, and reaction. Firstly, in the phase of recognition, tumor antigens (Ags) produced during tumor formation are released by dying cancer cells in the tumor microenvironment (TME, step 1) and engulfed by tumor-infiltrating dendritic cells (DCs, step 2). Next, in the phase of processing, DCs present the epitopes of the captured tumor Ags through the major histocompatibility complex (MHC) molecules, express on their surface higher levels of costimulatory molecules (step 3), and move to tumor-draining lymph nodes (dLNs) to cross-present their cargo to na&#239;ve CD8 T cells (CD8N, step 4). All these steps converge in the final process of reaction during which tumor Ag-specific cross-primed CD8 T cells (CD8C-P) are activated, mature into effector CD8 T (CD8E) cells, and undergo a clonal expansion (step 5). CD8E cells then leave the dLNs and home through the blood to the TME (step 6) where they specifically recognize and bind to cancer cells through the interaction between their T cell receptor (TCR) and their cognate tumor Ags, release cytotoxic molecules [i.e., interferon (IFN)-&#947;, perforins and granzymes (Grzs)] and kill cancer cells (step 7)1,2. Cancer cell killing leads to the release of further tumor Ags to fuel the cancer-immunity cycle. As a matter of fact, through all these steps, the immune system destroys and rejects cancer cells far more often than supposed. However, in cancer patients, at least one of these steps does not work properly. We and others showed that cancer cells seek to stall the immune response by either evolving into more aggressive and immune-privileged variants3,4,5 or hampering T-cell effectiveness6,7.
Cancer research and cancer drug development both rely on experimental models that allow the study of the relationship between cancer and immune cells, the so-called onco-immunology. Here are described fast, reliable, reproducible, and low-cost in vitro models that comprehensively reproduce each step of the onco-immunology cycle and is offered a rapid and clear view of the phenotypic and functional feature sets of immunosurveillance and eventually immunoediting.
Multiparametric flow cytometry (mFC) is one of the most successful single-cell analytical tools in fundamental cancer research, diagnosis, and translational research in cancer clinical trials. As it allows to simultaneously capture more features in each cell, mFC has earned its place as a gold-standard analysis platform in onco-immunology. It couples high sensitivity and specificity with the possibility to measure multiple protein expression patterns and functional properties quickly and reproducibly at a single cell level from heterogeneous and even heterotypic cell suspensions, as those from the TME8,9,10. As both phenotypic and functional expression patterns are time-sensitive, careful attention to experiment design, the selection of suitable panels, controls, and titred antibodies, and to appropriate sample processing and instrumentation use are critical for the reliability, comparability, and reproducibility of results and to confidently interpret experiment outcome11.
Tumor-on-a-chip microfluidic devices (ToCs) model the TME by allowing in vitro microscale biomimetics of cancer and immune cell dynamics and interplays12,13,14,15. Specifically, ToCs are multichannel microfluidic cell-culture devices able to host diverse cell types organized in either two-dimensional (2D) or three-dimensional (3D) culture settings and able to model with high fidelity and to control with high precision, key structural and functional units such as heterotypic cellular interactions and flows of chemical gradients that physiologically occur in the TME12,13,14,15. In particular, immune chemoattraction and trajectories as well as immune cell interaction with cancer cells, can be monitored in real-time and quantified by time-lapse microscopy and automated tracking analysis5,12,13,14,15,16. Furthermore, ToCs offer the possibility to both analyze and manipulate crucial processes regulating cancer onset and progression and response to therapy17.
In this article, mFC with ToCs are combined to study all the levels of the anticancer immune response going through DC-mediated phagocytosis of cancer Ags (steps 1-3), T cell cross-priming (step 4), activation and clonal expansion (the last by means of 5-ethynyl-2&#39;-deoxyuridine (EdU) and Cu(I)-catalyzed cycloaddition [click] technology, a highly sensitive and accurate methodology, step 5), CD8E cell homing to the TME (step 6) and, finally CD8E-cell-mediated cancer cell killing (step 7, Figure 1).
This work contributes to the effort toward establishing simple, fast, and reliable standard protocols to study the cancer-immunity cycle. The improvement and integration of mFC and ToC models into cancer research, TME dynamics, and response to therapy hold great potential as these models provide biological fidelity along with experimental control. Hence, this protocol helps recreate, in a stepwise manner, the cancer-immunity cycle by making it possible to characterize, monitor, and timely maneuver the roles of individual cell players and their reciprocal interactions upon natural and acquired immunosurveillance. This ultimately will help refine, reduce, and replace animal studies while providing critical insights to outsmart tumors and guide clinical care. Finally, mFC and ToC advantages and limitations are critically discussed and compared with state-of-the-art technologies (e.g., high plex spatial analyses at single cell and even sub-cellular resolution) to push onco-immunology research and therapy forward.

<table><tbody><tr><td>1.5 mL microtubes&amp;nbsp;</td><td>Eppendorf</td><td>30120086</td><td></td></tr><tr><td>100 kV e-beam litography</td><td>Vistec</td><td></td><td></td></tr><tr><td>100 mm Petri dishes&amp;nbsp;</td><td>Greiner Bio One</td><td>664160</td><td></td></tr><tr><td>12-well plates</td><td>Euroclone</td><td>&amp;nbsp;ET3012</td><td></td></tr><tr><td>15 and 50 mL tubes</td><td>Corning&amp;nbsp;</td><td>352096; 352070</td><td></td></tr><tr><td>40 &amp;mu;m cell strainer&amp;nbsp;</td><td>Corning&amp;nbsp;</td><td>CLS431750</td><td></td></tr><tr><td>5 mL polystyrene tubes&amp;nbsp;</td><td>Greiner Bio-One</td><td>120180</td><td></td></tr><tr><td>70 &amp;mu;m cell strainer&amp;nbsp;</td><td>Corning&amp;nbsp;</td><td>CLS431751</td><td></td></tr><tr><td>75 cm&lt;sup&gt;2&lt;/sup&gt; cell culture treated flask</td><td>Euroclone</td><td>&amp;nbsp;ET7076</td><td></td></tr><tr><td>Adsorbent wipes</td><td></td><td></td><td></td></tr><tr><td>Allumin foil</td><td></td><td></td><td></td></tr><tr><td>anti-mouse CD107a (LAMP-1) Antibody</td><td>Miltenyi Biotec</td><td>130-111-319</td><td></td></tr><tr><td>anti-mouse CD25 (7D4) Antibody&amp;nbsp;</td><td>Miltenyi Biotec</td><td>130-118-678</td><td></td></tr><tr><td>anti-mouse CD3 (17A2) Antibody</td><td>BioLegend</td><td>100206</td><td></td></tr><tr><td>Aptes</td><td>Sigma Aldrich</td><td>440140</td><td></td></tr><tr><td>BD Cytofix/Cytoperm Plus Fixation/Permeabilization Solution Kit with BD GolgiPlug</td><td>BD Biosciences</td><td>555028</td><td></td></tr><tr><td>BD GolgiPlug Protein Transport Inhibitor (Containing Brefeldin A)</td><td>BD Biosciences</td><td>555029</td><td></td></tr><tr><td>BD GolgiStop Protein Transport Inhibitor (Containing Monensin)</td><td>BD Biosciences</td><td>554724</td><td></td></tr><tr><td>Bovine serum albumin (BSA)</td><td>US Biological, Salem</td><td>A1312</td><td></td></tr><tr><td>CD11c Monoclonal Antibody (N418)</td><td>eBioscience</td><td>12-0114-81</td><td></td></tr><tr><td>CD137 (4-1BB) Monoclonal Antibody (17B5)</td><td>eBioscience</td><td>17-1371-82</td><td></td></tr><tr><td>CD3 Monoclonal Antibody (17A2)</td><td>eBioscience</td><td>25-0032-82</td><td></td></tr><tr><td>CD44 Monoclonal Antibody (IM7)</td><td>eBioscience</td><td>11-0441-82</td><td></td></tr><tr><td>CD45 Monoclonal Antibody (30-F11)</td><td>Invitrogen</td><td>MCD4528</td><td></td></tr><tr><td>CD69 Monoclonal Antibody (H1.2F3)</td><td>eBioscience</td><td>48-0691-82</td><td></td></tr><tr><td>CD8a Monoclonal Antibody (53-6.7)</td><td>eBioscience</td><td>11-0081-82</td><td></td></tr><tr><td>CD8a Monoclonal Antibody (53-6.7)</td><td>eBioscience</td><td>17-0081-82</td><td></td></tr><tr><td>CD95 (APO-1/Fas) Monoclonal Antibody (15A7)</td><td>eBioscience</td><td>53-0951-82</td><td></td></tr><tr><td>Cell counting slides</td><td>Kova International</td><td>87144E</td><td></td></tr><tr><td>Chromium quartz masks</td><td>MB W&amp;amp;A, Germany</td><td></td><td></td></tr><tr><td>Click-iT Plus EdU Alexa Fluor 647 Flow Cytometry Assay Kit</td><td>Invitrogen</td><td>C10635</td><td></td></tr><tr><td>CytoFLEX Flow Cytometer</td><td>Beckman Coulter</td><td></td><td></td></tr><tr><td>Dead Cell Removal Kit</td><td>Miltenyi Biotec</td><td>130-090-101</td><td></td></tr><tr><td>Dulbecco&#039;s Phosphate-Buffered Saline (D-PBS)</td><td>EuroClone</td><td>ECB4053L</td><td></td></tr><tr><td>EDTA</td><td>Invitrogen</td><td>AM9260G</td><td></td></tr><tr><td>Fetal bovine serum (FBS)</td><td>EuroClone</td><td>ECS0180L</td><td></td></tr><tr><td>Flowjo v10.0.7</td><td>Flowjo, LLC</td><td></td><td></td></tr><tr><td>Granzyme B Monoclonal Antibody (NGZB)</td><td>eBioscience</td><td>12-8898-82</td><td></td></tr><tr><td>H&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;2&lt;/sub&gt;</td><td>Sigma Aldrich</td><td></td><td></td></tr><tr><td>H&lt;sub&gt;2&lt;/sub&gt;SO&lt;sub&gt;4&lt;/sub&gt;</td><td>Sigma Aldrich</td><td></td><td></td></tr><tr><td>hotplate</td><td></td><td></td><td></td></tr><tr><td>Humified cell culture incubator (37&amp;deg;, 5% CO&lt;sub&gt;2&lt;/sub&gt;)</td><td>Thermo Scientific</td><td></td><td></td></tr><tr><td>Ice machine</td><td>Brema Ice Makers</td><td></td><td></td></tr><tr><td>IFN gamma Monoclonal Antibody (XMG1.2)</td><td>eBioscience</td><td>11-7311-82</td><td></td></tr><tr><td>Illustrator CC 2015</td><td>Adobe Systems Inc.</td><td></td><td></td></tr><tr><td>ImageJ</td><td>National Institute of Health</td><td></td><td></td></tr><tr><td>Incucyte 2022A Software</td><td>Sartorius</td><td></td><td></td></tr><tr><td>Incucyte Cytotox Dye for Counting Dead Cells</td><td>Sartorius</td><td>4632</td><td></td></tr><tr><td>Incucyte SX5 Live-Cell Analysis System&amp;nbsp;</td><td>Sartorius</td><td></td><td></td></tr><tr><td>JuLi Smart Fluorescent Live Cell Imaging Microscope</td><td>Bulldog Bio</td><td></td><td></td></tr><tr><td>Laboratory bench</td><td></td><td></td><td></td></tr><tr><td>Laboratory refrigerator (4&amp;deg;C)</td><td></td><td></td><td></td></tr><tr><td>Laboratory Safety Cabinet (Class II)</td><td>Angelantoni</td><td></td><td></td></tr><tr><td>L-glutamine 200 mM</td><td>EuroClone</td><td>ECB3004D</td><td></td></tr><tr><td>LIVE/DEAD Fixable Aqua Dead Cell Stain Kit</td><td>Invitrogen</td><td>L34957</td><td></td></tr><tr><td>LIVE/DEAD Fixable Near-IR Dead Cell Stain Kit</td><td>Invitrogen</td><td>L10119</td><td></td></tr><tr><td>MACS columns</td><td>Miltenyi Biotec</td><td>130-042-201; 130-042-401&amp;nbsp;</td><td></td></tr><tr><td>MACS separators</td><td>Miltenyi Biotec</td><td>130-042-10; 130-042-302</td><td></td></tr><tr><td>MCA205 mouse fibrosarcoma cell line</td><td>Sigma-Aldrich</td><td>SCC173</td><td></td></tr><tr><td>Microbiologically controlled animal facility equipped with Class II safety cabine</td><td></td><td></td><td></td></tr><tr><td>MicroCL 21R Microcentrifuge</td><td>Thermo Scientific</td><td>75002552</td><td></td></tr><tr><td>Microsoft Excel&amp;nbsp;</td><td>Microsoft, Redmond&amp;nbsp;</td><td></td><td></td></tr><tr><td>Mouse: C57BL/6J</td><td>The Jackson Laboratory</td><td>000664</td><td></td></tr><tr><td>Naive CD8a+ T Cell Isolation Kit, mouse</td><td>Miltenyi Biotec</td><td>130-096-543</td><td></td></tr><tr><td>Nikon ECLIPSE Ts2</td><td>Nikon Instruments Inc.</td><td></td><td></td></tr><tr><td>NIS-Elements BR 5.30.0064-BIT</td><td>Nikon Instruments Inc.</td><td></td><td></td></tr><tr><td>Optical litography</td><td>EVG</td><td></td><td></td></tr><tr><td>Penicillin G sodium salt and streptomycin sulfate</td><td>EuroClone</td><td>ECB3001D/1</td><td></td></tr><tr><td>Pipet aid</td><td>Drummond Scientific Co., Broomall, PA</td><td>&amp;nbsp;4-000-201</td><td></td></tr><tr><td>Pipettes</td><td>Eppendorf</td><td></td><td></td></tr><tr><td>PKH67 Fluorescent Cell Linker Kits</td><td>Sigma-Aldrich</td><td>PKH67GL-1KT</td><td>fluorescent cell linker&amp;nbsp; kit</td></tr><tr><td>plastic coverslip</td><td>IBIDI</td><td>10812</td><td></td></tr><tr><td>Propidium Iodide</td><td>Thermo Scientific</td><td>P1304MP</td><td></td></tr><tr><td>Reactive Ion Etching system</td><td>Oxford plasmalab</td><td></td><td></td></tr><tr><td>Roswell Park Memorial Institute 1640 (RPMI 1640)</td><td>EuroClone</td><td>ECB9006L</td><td></td></tr><tr><td>serological pipettes (2 mL, 5 mL, 10 mL, 25 mL)</td><td>Corning- Millipore-Sigma; St. Louis,&lt;br/&gt; MO</td><td>CLS4486; CLS4487; CLS4488; CLS4489</td><td></td></tr><tr><td>SL 16 Centrifuge Series</td><td>Thermo Scientific</td><td>75004031</td><td></td></tr><tr><td>Sterile scalpels, surgical forceps, scissors and pliers</td><td></td><td></td><td></td></tr><tr><td>Sterile tips (1&amp;ndash;10 &amp;mu;L, 20&amp;ndash;200 &amp;mu;L, 1000 &amp;mu;L)</td><td>EuroClone Spa, Milan, Italy</td><td>ECTD00010; ECTD00020; ECTD00200; ECTD01005</td><td></td></tr><tr><td>SU-8 3000 series</td><td>MicroChem corp, Newton, (MA)</td><td></td><td></td></tr><tr><td>Suite of dermal biopsy punches</td><td>Kai Medical, Tedpella</td><td></td><td></td></tr><tr><td>Sylgard 184</td><td>Dowsil, Dow Corning</td><td>101697</td><td></td></tr><tr><td>TCR beta Monoclonal Antibody (H57-597)</td><td>eBioscience</td><td>12-5961-82</td><td></td></tr><tr><td>Thermostatic bath</td><td></td><td></td><td></td></tr><tr><td>Timer</td><td></td><td></td><td></td></tr><tr><td>TMCS&amp;nbsp;</td><td>Sigma Aldrich</td><td>92360</td><td></td></tr><tr><td>Trypan Blue Stain (0.4%)</td><td>Thermo Scientific</td><td>15250061</td><td></td></tr><tr><td>Trypsin-EDTA w/ Phenol Red</td><td>EuroClone</td><td>ECM0920</td><td></td></tr><tr><td>Vacuum dessicator</td><td></td><td></td><td></td></tr></tbody></table>

co-culture in vitro systems to reproduce the cancer-immunity cycle

Fundamental cancer research and the development of effective counterattack therapies both rely on experimental studies detailing the interactions between cancer and immune cells, the so-called cancer-immunity cycle. In vitro co-culture systems combined with multiparametric flow cytometry (mFC) and tumor-on-a-chip microfluidic devices (ToCs) enable simple, fast, and reliable monitoring and characterization of each step of the cancer-immunity cycle and lead to the identification of the mechanisms responsible for tipping the balance between cancer immunosurveillance and immunoevasion. A thorough understanding of the dynamic interplays between cancer and immune cells provides critical insights to outsmart tumors and will accelerate the pace of therapeutic personalization and optimization in patients. Specifically, here we detail a straightforward mFC- and ToC-assisted protocol for unraveling the dynamic complexities of each step of the cancer-immunity cycle in murine cancer cell lines and mouse-derived immune cells and focus on immunosurveillance. Considering the time- and cost-related features of this protocol, it is certainly feasible on a large scale. Moreover, with minor variations, this protocol can be both adapted to human cancer cell lines and human peripheral-blood-derived immune cells and combined with genetic and/or pharmacologic inhibition of specific pathways in order to identify biomarkers of immune response.

The ability of CD11c+ DCs, the widely known phagocyte subset specialized for cross-presentation21,22 and gated as shown in Figure 2A, to engulf apoptotic bodies from UV-irradiated MCA205 cancer cells that were previously labeled with the PKH67 fluorescent cell linker was evaluated by mFC. As expected, CD11c+ DCs efficiently captured apoptotic MCA205 cells in vitro at 37 &#176;C but not at 4 &#176;C (Figure 2B, steps 1-3 of the cancer-immunity cycle). To further characterize DC maturation after tumor Ag uptake, the surface expression of costimulatory and immune checkpoint ligand molecules was checked and compared in DCs pre- and post-phagocytosis (DCN and DCPHAGO, respectively). DCPHAGO cells showed significantly increased levels of CD86 and MHC-II, along with reduced levels of PDL1 (Figure 2C, step 4 of the cancer-immunity cycle). Moreover, the clonal expansion of CD8 T cells once cross-primed by DCPHAGO (CD8C-P) was assessed by evaluating the incorporation of the EdU thymidine analog (step 5 of the cancer-immunity cycle). By properly gating live CD3+CD8+ T cells (Figure 3A), it was found that ~20% of CD8C-P, representing the clones expressing the CD95 molecule and bearing tumor Ag-specific TCRs (Supplementary Figure 1), underwent proliferation as compared to na&#239;ve CD8 T cells (CD8N, Figure 3B). By using ToC models (Figure 3C), it was evaluated the ability of CD8C-P to sense and react to chemoattraction by alarmins released by cancer cells, thus mirroring tumor-specific CD8 T cell homing to the TME (step 6 of the cancer-immunity cycle). Specifically, ToCs were employed to allow and follow the chemical and physical attraction and interaction between non-adherent immune cells, moving from the immune chamber to the tumor chamber and adherent cancer cells resting in the tumor chamber. Microphotographs taken at different time points post-co-culture showed a progressive migration of CD8C-P towards cancer cells by crossing microchannels (Figure 3C). Of interest was that approaching immune cells could stably and efficiently interact with cancer cells at 24 h, 48 h, and 72 h (Figure 3D). Finally, it was analyzed the response of CD8C-P homed to the TME and re-stimulated by cancer-presented Ags (CD8E) by assessing, through mFC, the surface expression levels of the early CD69 (Supplementary Figure 2A) and late CD44, CD25 (Figure 4A) activation markers, as well as the membrane expression of CD137 (Figure 4B) and CD107a (Supplementary Figure 2B) tumor reactivity markers as compared to CD8N and CD8C-P (step 7 of the cancer-immunity cycle). We also evaluated the intracellular production of the cytotoxic molecules Grz-B (Figure 5A) and IFN-&#947; (Figure 5B). Interestingly, all these marker levels progressively and significantly increased and reached a pick of expression at 72 h of co-culture, thus mirroring a late and effective activation. Accordingly, cognate co-cultured MCA205 cells underwent significant levels of CD8E-mediated death as revealed by a live cell analysis system and mFC analyses (Figure 6).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66729/66729fig01.jpg" /> 
Figure 1: Overview of the experimental protocol for studying the cancer-immunity cycle. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66729/66729fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/66729/66729fig02.jpg" /> 
Figure 2: DC-mediated cancer cell Ag uptake and presentation. (A) Gating strategy for DC mFC analysis. During mFC acquisition and data analysis, samples were sequentially gated (1) for morphology by plotting FSC-A and SSC-A; (2) for singlets by plotting FSC-A and FSC-H; and (3) for viability by plotting Near-IR and SSC-A. Viable cells were finally analyzed for the specific surface marker CD11c. (B) mFC analysis of the ability of CD11c+ DCs to engulf apoptotic bodies from PKH67-labeled, UV-irradiated MCA205 cancer cells at 37 &#176;C and 4 &#176;C (steps 1-3 of the cancer-immunity cycle). Representative biparametric plots and a histogram showing CD11c+PKH67+ cell percentage (mean &#177; s.e.m. with an individual data point, one representative experiment out of three is shown). (C) mFC analysis of costimulatory and immune checkpoint ligand surface molecules on DCs pre- and post-phagocytosis (DCN and DCPHAGO, respectively; step 4 of the cancer-immunity cycle). The histograms represent the percentage (mean &#177; s.e.m. and individual data points, n = 3 independent experiments) of CD11c+CD86+, CD11c+MHC-II+ and CD11c+PDL1+ cells. (B) Unpaired two-sided Student&#39;s t-test with Welch&#39;s correction compared with 4 &#176;C. (C) Unpaired two-sided Student&#39;s t-test compared with DCN. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66729/66729fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/66729/66729fig03.jpg" /> 
Figure 3: CD8 T cell proliferation and trafficking to cancer cells after cross-priming. (A) Gating strategy for CD3+CD8+ T cell analysis. During mFC acquisition and data analysis, samples were sequentially gated (1) for morphology by plotting FSC-A and SSC-A; (2) for singlets by plotting FSC-A and FSC-H; and (3) for viability by plotting Aqua and SSC-A. Viable cells were finally analyzed for the specific proliferation EdU staining. (B) mFC analysis of the clonal expansion of CD8&#160;T cells by means EdU technology (step 5 of the cancer-immunity cycle). The monoparametric histograms represent the percentage (mean &#177; s.e.m. with individual data point, one representative experiment out of three is shown) of proliferating EdU+&#160;na&#239;ve and cross-primed CD8 T cells (CD8N and CD8C-P, respectively). (C,D) ToC microfluidic model of CD8 T cell homing to the TME (step 6 of the cancer-immunity cycle). (C) Schematic planimetry of a ToC. The two lateral culture chambers, housing immune and cancer cells, are connected by micron-size channels (width = 12 &#181;m, length = 10 &#181;m, height = 10 &#181;m). Cell culture compartments are 1 mm wide, 8 mm long, and 100 &#181;m high. The microphotographs generated during time-lapse recordings represent the migration of CD8C-P towards cancer cells at different time points (from 0-72 h; scale bar, 100 &#181;m). (D) Plots representing the trajectories of individual CD8C-P (n = 100 cells per condition)&#160;towards target cancer cells (black spots) within 24-72 h are shown (scale bar, 10 &#181;m). (A) Unpaired two-sided Student&#39;s t-test with Welch&#39;s correction compared with CD8N. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66729/66729fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/66729/66729fig04.jpg" /> 
Figure 4: CD8 T cell tumor reactivity after re-stimulation with live cancer cells. (A) mFC analysis of the surface expression levels of CD44, CD25 activation markers on CD8 T cells re-stimulated by cancer-presented Ags (CD8E) (step 7 of the cancer-immunity cycle) up to 72 h. Representative biparametric plots and a histogram showing CD44+CD25+ CD8 T cell percentage in CD8N, CD8C-P, CD8E at 48 h and 72 h (mean &#177; s.e.m. and individual data points, n = 3 independent experiments). (B) mFC analysis of the membrane expression of the tumor reactivity marker CD137 on CD8E. Representative biparametric plots and a histogram showing CD137+ CD8 T cell percentage in CD8N, CD8C-P, CD8E at 48 h and 72 h (mean &#177; s.e.m. and individual data points, one representative experiment out of three is shown). (A,B) Ordinary one-way ANOVA test followed by Bonferroni&#39;s correction compared with CD8N. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66729/66729fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/66729/66729fig05.jpg" /> 
Figure 5: CD8 T cell production of Grz-B and IFN-&#947;. (A) mFC analysis of the intracellular expression levels of Grz-B on CD8E (step 7 of the cancer-immunity cycle). Representative histogram plots showing Grz-B+ CD8 T cell MFI in CD8N, CD8C-P, CD8E at 48 h and 72 h (mean &#177; s.e.m. and individual data points, n = 3 independent experiments). (B) mFC analysis of intracellular IFN-&#947; on CD8E. Representative histogram plots showing IFN-&#947;+ CD8 T cell percentage in CD8N, CD8C-P, CD8E at 48 h and 72 h (mean &#177; s.e.m. and individual data points, n = 3 independent experiments). (A,B). Ordinary one-way ANOVA test followed by Bonferroni&#39;s correction compared with CD8N. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66729/66729fig05large.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/66729/66729fig06.jpg" /> 
Figure 6: CD8 T cell-mediated tumor killing. (A) mFC analysis of CD45- cell death driven by CD8E after 72 h of co-culture (step 7 of the cancer-immunity cycle). The histograms represent the percentage (mean &#177; s.e.m. and individual data points, n = 3 independent experiments) of dying PI+CD45&#8722; cells. (B) Live-cell analysis of CD8E-mediated cancer cell death. Representative pictures and a plot showing the incorporation of Cytotox Red by dying and dead cancer cells (one representative experiment out of three is shown). (A) Unpaired two-sided Student&#39;s t-test compared with cancer cells alone (CTR). (B) Two-tailed Mann-Whitney test compared with CTR. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66729/66729fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary Figure 1: Histogram plots represent the MFI of CD95+TCR-&#946;+ CD8N and CD8C-P.&#160;Unpaired two-sided Student&#39;s t-test compared with CD8N. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66729/Suppl. Figure 1_revised.zip" target="_blank">Please click here to download this File.</a>
Supplementary Figure 2: CD8 T cell activation. (A) mFC analysis of the surface expression levels of early CD69 activation marker on CD8E (step 7 of the cancer-immunity cycle). Representative biparametric plots and a histogram showing CD69+ CD8 T cell percentage in CD8N, CD8C-P, CD8E at 48 h and 72 h (mean &#177; s.e.m. and individual data points, one experiment out of three). (B) mFC analysis of the membrane expression of the CD107a lysosomal-associated protein on CD8E. Representative biparametric plots and a histogram showing CD107a+ CD8 T cell percentage in CD8N, CD8C-P, CD8E at 48 h and 72 h (mean &#177; s.e.m. and individual data points, one representative experiment out of three is shown). (A,B). Ordinary one-way ANOVA test followed by Bonferroni&#39;s correction compared with CD8N. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66729/Suppl. Figure 2_revised .zip" target="_blank">Please click here to download this File.</a>
Supplementary Table 1: Mixes of antibodies step 3.3.2. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/66729/Supplementary Table 1.zip" target="_blank">Please click here to download this File.</a>

Monitoring the Cancer-Immunity Cycle and Exploring Tumor Microenvironment Dynamics

Here, we detail a simple, fast, and reliable multiparametric flow cytometry- and tumor-on-a-chip-assisted protocol to monitor and characterize the steps constituting the cancer-immunity cycle. Indeed, a thorough understanding of the interplays between cancer and immune cells provides critical insights to outsmart tumors and guide clinical care.

Co-Culture In Vitro Systems to Reproduce the Cancer-Immunity Cycle

Fundamental cancer research and the development of effective counterattack therapies both rely on experimental studies detailing the interactions between ...

co-culture-in-vitro-systems-to-reproduce-the-cancer-immunity-cycle

Nanyang Technological University

Research

JoVE Journal

Cancer Research

830 Views.  Università Cattolica del Sacro Cuore. Dit protocol maakt eenvoudige, snellere en betrouwbare monitoring en karakterisering van elke stap van de kankerimmuniteitscyclus mogelijk, en leidde zo tot de identificatie van de mechanismen die verantwoordelijk zijn voor het bewaren van het evenwicht tussen kanker-immuunsurveillance en immuunontwijking. De interactie en evolutie van kanker en immuuncellen in de micro-omgeving van de tumor zijn naar voren gekomen als cruciale factoren voor het detecteren va...

Video: Monitoring van de kanker-immuniteitscyclus en onderzoek naar de dynamiek van de micro-omgeving van tumoren

A Mimic of the Tumor Microenvironment: A Simple Method for Generating Enriched Cell Populations and Investigating Intercellular Communication

Understanding the early heterotypic interactions between cancer cells and the surrounding non-cancerous stroma is important in elucidating the events leading to stromal activation and establishment of the tumor microenvironment (TME). Several in vitro and in vivo models of the TME have been developed; however, in general these models do not readily permit isolation of individual cell populations, under non-perturbing conditions, for further study. To circumvent this difficulty, we have employed an in vitro TME model using a cell growth substrate consisting of a permeable microporous membrane insert that permits simple generation of highly enriched cell populations grown intimately, yet separately, on either side of the insert&#39;s membrane for extended co-culture times. Through use of this model, we are capable of generating greatly enriched cancer-associated fibroblast (CAF) populations from normal diploid human fibroblasts following co-culture (120 hr) with highly metastatic human breast carcinoma cells, without the use of fluorescent tagging and/or cell sorting. Additionally, by modulating the pore-size of the insert, we can control for the mode of intercellular communication (e.g., gap-junction communication, secreted factors) between the two heterotypic cell populations, which permits investigation of the mechanisms underlying the development of the TME, including the role of gap-junction permeability. This model serves as a valuable tool in enhancing our understanding of the initial events leading to cancer-stroma initiation, the early evolution of the TME, and the modulating effect of the stroma on the responses of cancer cells to therapeutic agents.

We adapted a permeable microporous membrane insert to mimic the tumor microenvironment (TME). The model consists of a mixed cell culture, allows simplified generation of highly enriched individual cell populations without using fluorescent tagging or cell sorting, and permits studying intercellular communication within the TME under normal or stress conditions.

Understanding the early heterotypic interactions between cancer cells and the surrounding non-cancerous stroma is important in elucidating the events ...

A Comprehensive Procedure to Evaluate the In Vitro Performance of the Putative Hemangioblastoma Neovascularization Using the Spheroid Sprouting Assay

The inactivation of the von Hippel-Lindau (VHL) tumor suppressor gene plays a crucial role in the development of hemangioblastomas (HBs) within the human central nervous system (CNS). However, both the cytological origin and the evolutionary process of HBs (including neovascularization) remain controversial, and anti-angiogenesis for VHL-HBs, based on classic HB angiogenesis, have produced disappointing results in clinical trials. One major obstacle to the successful clinical translation of anti-vascular treatment is the lack of a thorough understanding of neovascularization in this vascular tumor. In this article, we present a comprehensive procedure to evaluate in vitro whether classic tumor angiogenesis exists in HBs, as well as its role in HBs. With this procedure, researchers can accurately understand the complexity of HB neovascularization and identify the function of this common form of angiogenesis in HBs. These protocols can be used to evaluate the most promising anti-vascular therapy for tumors, which has high translational potential either for tumors treatment or for aiding in the optimization of the anti-angiogenic treatment for HBs in future translations. The results highlight the complexity of HB neovascularization and suggest that this common form angiogenesis is only a complementary mechanism in HB neovascularization.

A Comprehensive Procedure to Evaluate the In Vitro Performance of the Putative Hemangioblastoma Neovascularization Using the Spheroid Sprouting Assay

This paper presents a comprehensive procedure to evaluate in vitro whether classic tumor angiogenesis exists in hemangioblastomas (HBs) and its role in HBs. The results highlight the complexity of HB-neovascularization and suggest that this common form of angiogenesis is only a complementary mechanism in the HB-neovascularization.

The inactivation of the von Hippel-Lindau (VHL) tumor suppressor gene plays a crucial role in the development of hemangioblastomas (HBs) within the human ...

Monitoring Cancer Cell Invasion and T-Cell Cytotoxicity in 3D Culture

Significant progress has been made in treating cancer with immunotherapy, although a large number of cancers remain resistant to treatment. A limited number of assays allow for direct monitoring and mechanistic insights into the interactions between tumor and immune cells, amongst which, T-cells play a significant role in executing the cytotoxic response of the adaptive immune system to cancer cells. Most assays are based on two-dimensional (2D) co-culture of cells due to the relative ease of use but with limited representation of the invasive growth phenotype, one of the hallmarks of cancer cells. Current three-dimensional (3D) co-culture systems either require special equipment or separate monitoring for invasion of co-cultured cancer cells and interacting T-cells.
Here we describe an approach to simultaneously monitor the invasive behavior in 3D of cancer cell spheroids and T-cell cytotoxicity in co-culture. Spheroid formation is driven by enhanced cell-cell interactions in scaffold-free agarose microwell casts with U-shaped bottoms. Both T-cell co-culture and cancer cell invasion into type I collagen matrix are performed within the microwells of the agarose casts without the need to transfer the cells, thus maintaining an intact 3D co-culture system throughout the assay. The collagen matrix can be separated from the agarose cast, allowing for immunofluorescence (IF) staining and for confocal imaging of cells. Also, cells can be isolated for further growth or subjected to analyses such as for gene expression or fluorescence activated cell sorting (FACS). Finally, the 3D co-culture can be analyzed by immunohistochemistry (IHC) after embedding and sectioning. Possible modifications of the assay include altered compositions of the extracellular matrix (ECM) as well as the inclusion of different stromal or immune cells with the cancer cells.

The presented approach simultaneously evaluates cancer cell invasion in 3D spheroid assays and T-cell cytotoxicity. Spheroids are generated in a scaffold-free agarose multi-microwell cast. Co-culture and embedding in type I collagen matrix are performed within the same device which allows to monitor cancer cell invasion and T-cell mediated cytotoxicity.

Significant progress has been made in treating cancer with immunotherapy, although a large number of cancers remain resistant to treatment. A limited ...

Three-dimensional Co-culture Model for Tumor-stromal Interaction

Cancer progression (initiation, growth, invasion and metastasis) occurs through interactions between malignant cells and the surrounding tumor stromal cells. The tumor microenvironment is comprised of a variety of cell types, such as fibroblasts, immune cells, vascular endothelial cells, pericytes and bone-marrow-derived cells, embedded in the extracellular matrix (ECM). Cancer-associated fibroblasts (CAFs) have a pro-tumorigenic role through the secretion of soluble factors, angiogenesis and ECM remodeling. The experimental models for cancer cell survival, proliferation, migration, and invasion have mostly relied on two-dimensional monocellular and monolayer tissue cultures or Boyden chamber assays. However, these experiments do not precisely reflect the physiological or pathological conditions in a diseased organ. To gain a better understanding of tumor stromal or tumor matrix interactions, multicellular and three-dimensional cultures provide more powerful tools for investigating intercellular communication and ECM-dependent modulation of cancer cell behavior. As a platform for this type of study, we present an experimental model in which cancer cells are cultured on collagen gels embedded with primary cultures of CAFs.

Here we present a protocol to co-culture in three-dimensions, which is useful for investigating multicellular interactions and extracellular matrix-dependent modulation of cancer cell behavior. In this experimental model, cancer cells are cultured on collagen gels embedded with human cancer-associated fibroblasts.

Cancer progression (initiation, growth, invasion and metastasis) occurs through interactions between malignant cells and the surrounding tumor stromal ...

Medicine

Analysis of Human T Cell Activity in an Allogeneic Co-Culture Setting of Pre-Treated Tumor Cells

Cytotoxic T cells play a key role in the elimination of tumor cells and are, therefore, intensively studied in cancer immunology. The frequency and activity of cytotoxic T cells within tumors and their tumor microenvironment (TME) are now well-established prognostic and predictive biomarkers for numerous tumor types. However, it is well-known that various tumor treatment modalities, including radiotherapy, chemotherapy, immunotherapy, and targeted therapy, modulate not only the immunogenicity of the tumor but also the immune system itself. Consequently, the interaction between tumor cells and T cells requires more intensive study in different therapeutic contexts to fully understand the complex role of T cells during tumor therapy. To address this need, a protocol was developed to analyze the activity and proliferative capacity of human cytotoxic (CD8+) T cells in co-culture with pre-treated tumor cells. Specifically, CD8+ T cells from healthy donors are stained with the non-toxic proliferation marker carboxyfluorescein diacetate succinimidyl ester (CFSE) and stimulated using CD3/CD28-coated plates. Subsequently, T cells are co-cultured with pre-treated tumor cells. As a readout, T cell proliferation is quantified by measuring CFSE signal distribution and assessing the expression of surface activation markers via flow cytometry. This can be further complemented by quantifying cytokine release using enzyme-linked immunosorbent assay (ELISA). This method facilitates the evaluation of treatment-induced changes in the interaction between tumor cells and T cells, providing a foundation for more detailed analyses of tumor treatment modalities and their immunogenicity in a human ex vivo setting. Additionally, it contributes to the reduction of pre-clinical in vivo analyses.

The present protocol describes an experimental workflow that allows for the ex vivo analysis of human T cell stimulation in an allogenic co-culture system with pre-treated tumor cells.

Cytotoxic T cells play a key role in the elimination of tumor cells and are, therefore, intensively studied in cancer immunology. The frequency and ...

A Co-culture Method to Investigate the Crosstalk Between X-ray Irradiated Caco-2 Cells and PBMC

The protocol adopted in this work aims at unraveling how X-rays perturb the functioning of the intestinal barrier, focusing on the interplay between colorectal tumor cells and the immune system. Colorectal carcinoma is among the most common type of cancer, typically treated by surgery, chemotherapy, and radiotherapy. Advantages of radiotherapy in targeting the tumor are well known. However, even limited exposures of healthy tissues are of great concern, particularly regarding the effects on the intestinal barrier and the immune system. The adopted setup allows to study the interplay between two cell populations in a condition more similar to the physiological one, when compared to normal cell cultures. For this purpose, we resort to different techniques and we used an in vitro co-culture model, based on Caco-2 cells differentiated as a monolayer and PBMC, sharing the same culture medium. This protocol has been developed to focus on both macroscopic effects, i.e. cell viability and Trans-Epithelial Electrical Resistance (TEER), and, through western blot, molecular alterations, i.e. the activation of inflammatory pathway in immune cells and the tight junction protein expression in Caco-2 cells. Initial evaluation of radiation effects on Caco-2 cell viability was assessed via the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and Trypan blue assays, while TEER was measured at fixed time intervals through an ohmmeter specifically designed for co-culture systems. In this way, the effects due to radiation, the presence of Peripheral Blood Mononuclear Cells (PBMC), and eventually their synergistic effect, can be demonstrated. Through these complementary techniques, we observed a high radio-resistance of Caco-2 within the range of 2 - 10 Gy of X-rays and an increased Caco-2 monolayer permeability when PBMCs were added. In particular, PBMC presence was found to be associated with the variation in the tight junction scaffold proteins expression.

We present a protocol to investigate the crosstalk between X-ray-irradiated Caco-2 and peripheral blood mononuclear cells (PBMC). The protocol starts with Caco-2 irradiation and set-up of the co-culture with PBMC; subsequently, trans-epithelial electrical resistance is measured regularly over 48 h and western blot performed in both Caco-2 and PBMC.

The protocol adopted in this work aims at unraveling how X-rays perturb the functioning of the intestinal barrier, focusing on the interplay between ...

Immunology and Infection

Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments

Complex disease models demand cutting-edge tools able to deliver physiologically and pathologically relevant, actionable insights, and unveil otherwise invisible processes. Advanced cell assays closely mimicking in vivo scenery are establishing themselves as novel ways to visualize and measure the bidirectional tumor-host interplay influencing the progression of cancer. Here we describe two versatile protocols to recreate highly controllable 2D and 3D co-cultures in microdevices, mimicking the complexity of the tumor microenvironment (TME), under natural and therapy-induced immunosurveillance. In section 1, an experimental setting is provided to monitor crosstalk between adherent tumor cells and floating immune populations, by bright field time-lapse microscopy. As an applicative scenario, we analyze the effects of anti-cancer treatments, such as the so-called immunogenic cancer cell death inducers on the recruitment and activation of immune cells. In section 2, 3D tumor-immune microenvironments are assembled in a competitive layout. Differential immune infiltration is monitored by fluorescence snapshots up to 72 h, to evaluate combination therapeutic strategies. In both settings, image processing steps are illustrated to extract a plethora of immune cell parameters (e.g., immune cell migration and interaction, response to therapeutic agents). These simple and powerful methods can be further tailored to simulate the complexity of the TME encompassing the heterogeneity and plasticity of cancer, stromal and immune cells subtypes, as well as their reciprocal interactions as drivers of cancer evolution. The compliance of these rapidly evolving technologies with live-cell high-content imaging can lead to the generation of large informative datasets, bringing forth new challenges. Indeed, the triangle &#39;&#39;co-cultures/microscopy/advanced data analysis&#34; sets the path towards a precise problem parametrization that may assist tailor-made therapeutic protocols. We expect that future integration of cancer-immune on-a-chip with artificial intelligence for high-throughput processing will synergize a large step forward in leveraging the capabilities as predictive and preclinical tools for precision and personalized oncology.

In the age of immunotherapy and single-cell genomic profiling, cancer biology requires novel in vitro and computational tools for investigating the tumor-immune interface in a proper spatiotemporal context. We describe protocols to exploit tumor-immune microfluidic co-cultures in 2D and 3D settings, compatible with dynamic, multiparametric monitoring of cellular functions.

Complex disease models demand cutting-edge tools able to deliver physiologically and pathologically relevant, actionable insights, and unveil otherwise ...

A Preclinical Human-Derived Autologous Gastric Cancer Organoid/Immune Cell Co-Culture Model to Predict the Efficacy of Targeted Therapies

Tumors expressing programmed cell death-ligand 1 (PD-L1) interact with programmed cell death protein 1 (PD-1) on CD8+ cytotoxic T lymphocytes (CTLs) to evade immune surveillance leading to the inhibition of CTL proliferation, survival, and effector function, and subsequently cancer persistence. Approximately 40% of gastric cancers express PD-L1, yet the response rate to immunotherapy is only 30%. We present the use of human-derived autologous gastric cancer organoid/immune cell co-culture as a preclinical model that may predict the efficacy of targeted therapies to improve the outcome of cancer patients. Although cancer organoid co-cultures with immune cells have been reported, this co-culture approach uses tumor antigen to pulse the antigen-presenting dendritic cells. Dendritic cells (DCs) are then cultured with the patient's CD8+ T cells to expand the cytolytic activity and proliferation of these T lymphocytes before co-culture. In addition, the differentiation and immunosuppressive function of myeloid-derived suppressor cells (MDSCs) in culture are investigated within this co-culture system. This organoid approach may be of broad interest and appropriate to predict the efficacy of therapy and patient outcome in other cancers, including pancreatic cancer.

The goals of the protocol are to use this approach to 1) understand the role of the immunosuppressive gastric tumor microenvironment and 2) predict the efficacy of patient response, thus increasing the survival rate of patients.

Tumors expressing programmed cell death-ligand 1 (PD-L1) interact with programmed cell death protein 1 (PD-1) on CD8+ cytotoxic T lymphocytes (CTLs) to ...

3D Co-culture with Direct Interaction: Co-culturing Cancer Cells with Monocytes to Study Their Interaction

Source: Espinoza-S&#225;nchez, et al. <a href="https://www-jove-com.remotexs.ntu.edu.sg/v/56589/analyzing-communication-between-monocytes-primary-breast-cancer-cells">Analyzing the Communication Between Monocytes and Primary Breast Cancer Cells in an Extracellular Matrix Extract (ECME)-based Three-dimensional System.</a> J. Vis. Exp. (2018).
This video describes the 3D co-culture of breast cancer cells with monocytes to study their interaction in an extracellular matrix extract (ECME) based environment. This method helps in studying specific features such as collagen degradation, immune cell recruitment, and cell invasion.

Source: Espinoza-S&#225;nchez, et al. Analyzing the Communication Between Monocytes and Primary Breast Cancer Cells in an Extracellular Matrix Extract ...

JoVE Encyclopedia of Experiments

Breast Cancer

In vitro studies

A Three-Dimensional Co-culture Model of Infiltrating T Cells in Tumor Spheroids

Source: Lin, Y., et al. <a href="https://app-jove-com.remotexs.ntu.edu.sg/v/61392">Monitoring Cancer Cell Invasion and T-Cell Cytotoxicity in 3D Culture.</a> J. Vis. Exp. (2020).
This video demonstrates a method to establish a three-dimensional tumor spheroid and T cell co-culture. An agarose cast with microwells containing tumor spheroids is prepared, followed by the addition of T cells. After incubation, this setup allows studying the interaction of T cells with tumor spheroids in a controlled three-dimensional environment.

1. Generation of spheroids

	Prepare and autoclave 2% agarose in 1x phosphate-buffered saline (PBS) (e.g., 1 g agarose in 50 mL of 1x PBS) and autoclave ...

Co-Culture In Vitro Systems to Reproduce the Cancer-Immunity Cycle

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