The authors have nothing to disclose. AS is supported by the Fondazione Italiana per la Ricerca sul Cancro (AIRC, Start-Up 2016 #18418) and Ministero Italiano della Salute (RF_GR-2013-02357273). GS and FM are supported by the Italian Association for Cancer Research (AIRC) no. 21366 to G.S.).

The described methods try to design a general approach to recapitulate, with modulable degree of complexity, two significant aspects in the field of onco-immunology, which can benefit from the adoption of more relevant in vitro models. The first one involves the tumor cell population side, where tackling single cell characteristics may lead to a better description of heterogeneity and correlated biological and clinical significance including resistance to therapy, propension to metastasis, stem cell and differentiation grade. The other side of the story is represented by the TME, including noncancerous components (immune and stromal cells, blood vessels) and chemical/physical landscape (ECM constituents, chemokines and other soluble factors released) that can profoundly shape both the characteristics of the disease and individual&#39;s response to therapy63, particularly to immunotherapy. It is worth to note that exporting the described approach to other research fields requires a deep understanding of limitations and challenges brought by the development and adoption of new models.
Quoting a maxim applied in engineering modelling (&#34;model the problem not the system&#34;), it must be optimized which is the minimum number of (cellular, physical and chemical) components and conditions needed to make own on-chip model biologically relevant and stable along the whole experiment. Every combination of cell types/microenvironmental/experimental setting must, thus, be accurately chosen, evaluated, and continuously monitored along single experiments and from session to session. These checks include, as mentioned in the protocol sections, strict control of parameters like volumes in the microfluidic devices that can be modified by humidity conditions or heating from illumination sources, possible movements and non-planarity of stages causing uncontrolled liquid drifts, but also some endpoint verification of cell state, viability and phenotypic characterization. One critical step concerns the decision to use perfusion systems to create vascularized (like) structures, or for on-chip drug delivery33, since this may affect the experiments in terms of increasing complexity, cell culture duration, and modulation of the chemical factors in presence of floating cells like the immune ones.
In the following, we summarize the main critical and relevant issues found in the experimental settings and in the most recent literature.
ECM and chemical landscape definition 
TME is deeply affected also by the mechanical64,65 properties of the surroundings63, 66. For this reason, the choice of the culturing matrix is fundamental, especially when dealing with immune cells that, in certain conditions, could respond to signals released by the matrix itself. Relevant advances are expected in the next future also from design and development of new materials and hydrogels (featured by different stiffness, porosity, presence of soluble factors, among the other parameters) that are enabling an increasingly refined and controllable ECM, mimicking specificities of different tissues today, different patients tomorrow. On the other side is also critical to monitor the changes induced by the different cell populations in the ECM and define strategies to include this information in dynamic and phenotypic analyses.
Data management 
The path toward deployment of tumor on-chip techniques in a clinical workflow is going to benefit from a huge experimental work that is taking place along several directions. Organ-on-chip models, as many in vitro techniques, are functional, at least potentially, to perform high-throughput/high-content measurements. Parallelization of experimental conditions, including positive and negative controls, and technical/biological replicates is made possible by integrating several chips on the same plate. The increase in the quantitative throughput plays a crucial role in translating and validating these systems in fast drug or gene screening pipeline for immunotherapies. Indeed, several companies have already developed platforms in a multi-well format, and different imaging approaches are being tested to allow for monitoring a high number of devices simultaneously14. In the above described protocols, we used microscope slides allocating up to 3 chips, with the possibility to visualize up to 12 experimental conditions in parallel, using a standard microscope multi-slide tray. This setup is compatible with hand pipetting and suitable for custom oriented adjustments performed in our microfabrication facility. On the contrary, when a strong parallelization is necessary (multiple screening tests and controls), setup optimizations to set a higher level of automation (i.e., use of pipetting robots, plastic multi-wells) are required.
When designing experiments, a trade-off between spatial and temporal resolution must be taken in account.
The huge amount of data, produced by high content microscopy, constitutes a limiting factor in terms of storage, transfer and analysis. These issues are being addressed with computational approaches implementing machine learning tools and hardware/software resources, which may foster the future possibility to link on-chip/in-silico experiments67,68 in choosing therapeutic strategies, and that represents an ambitious yet non-deferrable opportunity.
Beyond Fluorescence Imaging 
Fluorescence labelling, by dyes or reporter genes, due to its high specificity, undoubtedly represents the gold standard method to identify different cell populations in co-culture conditions and to resolve molecular properties with high SNR. In OOC models this approach is commonly used as a reference and particularly in heterogeneous tumor-on-chip microenvironments, it can be valuable to characterize the phenotype of immune cells infiltrated and interacting.
Nevertheless, there is increasing evidence that effects induced by fluorophores, laborious staining procedures and illumination routines must be monitored and minimized69 because can deeply affect cell behavior and state70,71. This risk seems particularly true for fragile systems, such as immune cells72,73. Phototoxic reactions may introduce limitations in defining the temporal window acquisition of live cells when monitored at high spatial and temporal resolution. Moreover, the richness in the variety of immune sub-populations make unfeasible to discriminate among them only by fluorescent staining, considering the limited number of filters commonly available on microscopes.
To address this issue, from an instrumental point of view, novel label free74 microscopy techniques such as holographic56 or hyperspectral microscopy57 are now appearing on the stage. They promise advanced cellular process classification strategies beyond fluorescence and can be particularly valuable for studying sensitive samples. From a data analysis point of view, advanced computational approaches, based on deep learning algorithms75 (trained by fluorescence images datasets), are door-openers to perform the so called &#34;in silico labeling&#34;76,77. They have been successfully applied to predict fluorescent markers from bright-field images78, generating labeled images, without staining cells, thus increasing bright field microscopy informative power. This strategy can also be useful to save time and fluorescence channels for other markers. We believe that OOC community will benefit from these new techniques allowing a less invasive study of cell populations interaction.
Data analysis 
Mechanisms of interactions between immune and target cancer cells can be investigated through tracking of cell movements28,79. Time-lapse tracking experiments in complex and heterogeneous co-cultures are extremely valuable to extract cell migration patterns, morphological and state changes, and comprehensive lineage information. Performing manual analysis is practically only feasible for short sequences with few cells but not for high-throughput, systematic experiments. Consequently, the development of computational tools for cell tracking, either fully or partly automated, is a vital field of research in image analysis24. Typically, conventional cell tracking requires relatively high frequency of sampling and spatial resolution to correctly perform segmentation tasks, which can be challenging in many experimental conditions. To localize cells, there are available open-access segmentation and tracking tools (e.g., ImageJ software22) as presented in this protocol or dedicated proprietary software. In large-scale studies, we applied a proprietary software called Cell Hunter16,31, developed by University of Tor Vergata in Rome, to distinguish in a fully automatic way cancer and immune cells in multi-population context. Tailor-made solutions based on machine learning and Neural network approaches80,81,82 are beginning to be today implemented in microscopy software packages83, from improving SNR to managing critical acquisition parameters or segmentation steps. Machine learning can be exploited to recognize common cellular patterns (e.g., motion styles) in order to characterize the biological response with respect to microenvironmental factors.
In Comes et al.41, a pre-trained Deep Learning Convolutional Neural Network architecture was applied to classify if cancer cells are or not exposed to a drug treatment by using as a &#34;marker&#34; the motility of the immune cells, tracked from time-lapse data of co-cultures of breast cancer cells and PBMCs in collagen matrices in microdevices, as described in33.
Single-cell omics methods and ontologies development 
We point out the need for a strategic alliance between single-cell omics technologies84 (e.g., proteomics, metabolomics, genomics) and on-chip methods: the molecular detailed characterization, conjugated to functional dynamic information could enhance comprehension of basic mechanisms and clinical description. In this case new instruments for linking the two worlds are at their beginning85. The first challenge is to implement single-cell omics approaches directly on the onco-immunology chips22. Moreover, organ-on-chips could be exploited as platforms to test potential targets identified by genomics and proteomics analyses86,87. A second linking tool, which is still largely missing, is a structured, standardized way of annotating and storing measured system results88,89. But this is exactly what we need in the future to build systematic databases of cellular quantitative results and measured characteristics, to mine, infer and correlate them with the inherent biological information. In a word, the need for standardization and systematic analysis of heterogeneous experimental datasets calls for an ontology framework.
Personalizing models 
Organs-on-chip technology is suitable for personalization12, since cells and tissues from single patients (or classes of patients) can be used in the devices under controlled conditions, leading to clinically relevant readouts, useful to inform therapeutic or prevention strategies. Some examples are starting to appear in literature90. Of course, for tumor-on-chip models, this challenge poses several technical and scientific questions to be solved36 (like TME characteristics control such as oxygen concentration, cytokine gradients, etc). Importantly, the prospect of making oncology and onco-immunology therapies more effective while reducing harmful effects for each patient is attractive, from a quality of life perspective as for the optimization of healthcare resources.
In conclusion, it is apparent that this field is an authentically interdisciplinary one and as such, requires a great effort in establishing a common language and shared goals between researcher, clinicians, industry, but also from different disciplines (engineering, biology, data science, medicine, chemistry) finding good balance and new solutions91.

The evolution of different branches of medicine as experimental disciplines has depended on the ability to manipulate cell population and organ functions under controlled conditions1. Such ability has its roots in the availability of measurable models able to recapitulate processes happening in our body.
In the age of immunotherapy and single-cell genomic profiling2, cancer biology needs to take advantage of emerging in vitro and computational models for investigating the tumor-immune interface in a proper spatiotemporal context2,3.
The tumor microenvironment4 (TME) is a complex tissue where cancer cells continuously interact and dynamically co-evolve with the other cellular (immune, stromal, and endothelial cells) and non-cellular (the extracellular matrix, ECM) components. The dynamic nature of this complex landscape dictates whether immune cells play as friends or foes of malignant cells, thus strongly affecting both disease progression and response to therapy. Nowadays, great efforts from onco-immunologists, bioinformaticians, and systems biology experts are converging to address the clinical significance of cancer heterogeneity5,6, either in the space (i.e., in distinct tumoral regions) and time (i.e., at distinct tumor progression stages)5,6, and to characterize cancer and immune cell phenotype and function at a single-cell level. As an example of this synergy, advanced computer-vision techniques are now routinely used for spatial mapping of immune infiltrate in histological samples7,8.
On the front of experimental models, bridging animal studies and traditional in vitro methods, advances in microfluidics and co-culturing techniques give access to different classes of micro-engineered cellular models such as organoids, micro-physiological systems9,10,11 (MPS), and organs-on-chip12,13,14 (OOC). They share the common trait to zoom in the &#39;big picture&#39; view of the cellular ecosystems and expanding the in vitro potential to control microenvironmental factors while exploiting high-content microscopy15 and image processing approaches.
Nowadays, state-of-the-art- MPS and OOC systems have begun to include immunological aspects , incorporating different subtypes of immune cells in existing tissues- and co-cultures, so to explore and measure a variety of processes like inflammatory diseases, wound healing, mucosal immunity, and response to toxins or daily food products16. TME-on-a-chip models10,11,12,13,14,15,16,17, also integrated with perfusable microvessels18,19,20,21, have been developed to investigate cell-type-dependent interactions, physical and chemical perturbations, and the cytotoxic activity of infiltrating lymphocytes22, as well as clinically relevant immunomodulatory agents23.
Here, we provide versatile protocols, spanning from loading cells in chips to image processing tools, to exploit advanced tumor-immune microfluidic co-cultures in 2D (section 1) and 3D (section 2) settings16, compatible with dynamic, multiparametric24 monitoring and visualization of cellular functions. This is achieved maintaining easiness of use and flexibility both in sample management and data analysis, taking advantage of Fiji freeware software and its toolboxes25,26.
The microfluidic device, described in section 1, is designed to perform 2D co-cultures of adherent cancer and floating immune cells. This platform was validated for the in vitro measurement of immune cell behavior in the presence of genetic mutations27 and/or immunodeficiencies28. Here, we illustrate steps for tracking immune cells in time-lapse bright-field images, by exploiting a semi-automatic method based on Trackmate (a plugin implemented in Fiji software). This procedure enables the extraction of kinematic descriptors of immune migration 29 and response (i.e., interaction times) to target cancer cells, treated or not with immunogenic cell death inducers27.
Importantly these parameters, extracted from time-series images, can be processed with advanced mathematical machinery. As an example of the potentiality of this approach, our groups recently published an analysis based on mathematical methods from stochastic processes and statistical mechanics to model cellular network properties and provide a parametrized description of immune cell behavior (i.e., biased or uncorrelated random walk, highly or not coordinated motion30,31).
The 3D setting, provided in the second section, is based on a co-culture protocol to recreate more complex immunocompetent TMEs embedded in two gel regions with different combinations of cell types and drugs in a competitive fashion. Here, image processing steps are described to measure, at different timepoints, the infiltration of stained immune cells in human A375M melanoma cells cultivated within Matrigel, to evaluate antitumor agent combinations32. A375M line, an A375P derived cell line characterized by a highly metastatic phenotype was chosen to evaluate their metastatic capability in the presence of immune cells32.
The described models can be fully compliant with different cell sources (murine and human immortalized or primary cell lines, organoids, xenografts, among the others). In recent studies of our lab, by combining high-content video microscopy with image analysis, the competitive 3D layout was applied to investigate: i) an anti-tumoral (antibody-dependent cell-mediated cytotoxicity, ADCC) immune response and dissect the role of fibroblasts in resistance to trastuzumab therapy in HER2+ breast cancer on-chip models33; ii) the action of myeloid cells (i.e., cancer-associated macrophages) in mechanisms of tumor evasion and recruitment of T cells34; iii) the efficacy of immunotherapeutic regimes, specifically based on Interferon-&#945;-conditioned dendritic cells (IFN-DCs), cultivated with drug-treated colon cancer cells in collagen matrices, and to evaluate efficient motion and the succeeding phagocytosis events35; iv) the chemotactic migration of bone marrow-derived eosinophils towards IL-33 treated or untreated melanoma cells36.
These advanced models could serve as observation windows for understanding the role of immune contexture in cancer metastasis and resistance mechanisms, but efforts are required to translate findings into the clinics, closing the gap with basic research37.
As an emerging scenario, harnessing the power of automated high-content microscopy coupled to the use of more physiologically-relevant microsystems is opening novel potential challenges for the handling, processing, and interpretation of hundreds, and even thousands, of Gigabytes of multiparametric data, which can be generated from a single experimental campaign. This implies a direct link of OOC experiments with artificial intelligence38,39,40,41,42 (AI)-based algorithms both for advanced automated analysis, and generation of features which can feed in turn in silico models of cancer-immune interplay43, with exciting new applications at the horizon, such as the development of predictive drug screening assays44.
An ever-expanding flow of efforts is focused on the design of disease models jointly with the optimization of strategies to implement the large-scale perturbation screens with single-cell multi-omics readouts. This will undoubtedly help the development and, hopefully, the clinical implementation, accompanied by an appropriate degree of method standardization, of a systematic onco-immunology-on-a-chip approach to gain novel insights into immune disorders and cancer dissemination mechanisms.

<table><tbody><tr><td>&lt;strong&gt;Cell culture materials&amp;nbsp;&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>50 mL tubes</td><td>Corning-Sigma Aldrich, St. Louis, MO</td><td>CLS430828</td><td>centrifuge tubes</td></tr><tr><td>5-aza-2&#039;-deoxycytidine DAC</td><td>Millipore-Sigma; St. Louis, MO</td><td>A3656</td><td>DNA-hypomethylating agent</td></tr><tr><td>6-well plates</td><td>Corning-Sigma Aldrich, St. Louis, MO</td><td>CLS3506</td><td>culture dishes</td></tr><tr><td>75 cm2 cell culture treated flask</td><td>Corning, New York, NY</td><td>430641U</td><td>culture flasks</td></tr><tr><td>A365M</td><td>American Type Culture Collection (ATCC), Manassas, VA</td><td>&lt;br/&gt; CVCL_B222</td><td>human melanoma cell line</td></tr><tr><td>Doxorubicin hydrochloride</td><td>Millipore-Sigma; St. Louis, MO</td><td>D1515</td><td>anthracycline antibiotic&amp;nbsp;</td></tr><tr><td>Dulbecco&#039;s Modified Eagle Medium DMEM</td><td>EuroClone Spa, Milan, Italy</td><td>ECM0728L</td><td>Culture medium for SK-MEL-28&amp;nbsp; cells</td></tr><tr><td>Dulbecco&#039;s Phosphate Buffer Saline w/o Calcium w/o Magnesium</td><td>EuroClone Spa, Milan, Italy</td><td>ECB4004L</td><td>saline buffer solution</td></tr><tr><td>Fetal Bovine Serum</td><td>EuroClone Spa, Milan, Italy</td><td>ECS0180L</td><td>ancillary for cell culture</td></tr><tr><td>Ficoll</td><td>GE-Heathcare</td><td>17-1440-02</td><td>separation of mononuclear cells from human blood.&amp;nbsp;</td></tr><tr><td>hemocytometer</td><td>Neubauer</td><td></td><td>Cell counter</td></tr><tr><td>Heparinized vials</td><td>Thermo Fisher Scientific Inc., Waltham, MA</td><td></td><td>Vials for venous blood collection</td></tr><tr><td>interferon alpha-2b</td><td>Millipore-Sigma; St. Louis, MO</td><td>SRP4595</td><td>recombinant human cytokine</td></tr><tr><td>L-Glutamine 100X</td><td>EuroClone Spa, Milan, Italy</td><td>ECB3000D</td><td>ancillary for cell culture</td></tr><tr><td>Liquid nitrogen</td><td></td><td></td><td></td></tr><tr><td>Lympholyte cell separation media</td><td>Cedarlane Labs, Burlington, Canada</td><td></td><td>Separation of lymphocytes by density gradient centrifugation</td></tr><tr><td>Lymphoprep</td><td>Axis-Shield PoC AS, Oslo, Norway</td><td></td><td></td></tr><tr><td>Matrigel</td><td>Corning, New York, NY</td><td>354230</td><td>growth factor reduced basement membrane matrix</td></tr><tr><td>MDA-MB-231&amp;nbsp;</td><td>American Type Culture Collection (ATCC), Manassas, VA</td><td>&amp;nbsp;HTB-26</td><td>human breast cancer cell line</td></tr><tr><td>Penicillin/ Streptomycin 100X&amp;nbsp;&amp;nbsp;</td><td>EuroClone Spa, Milan, Italy</td><td>ECB3001D</td><td>ancillary for cell culture</td></tr><tr><td>Pipet aid</td><td>Drummond Scientific Co., Broomall, PA</td><td>4-000-201</td><td>Liquid handling</td></tr><tr><td>PKH26 Red Fluorescent cell linker</td><td>Millipore-Sigma; St. Louis, MO</td><td>PKH26GL</td><td>red fluorescent cell dye</td></tr><tr><td>PKH67 Green fluorescent cell linker</td><td>Millipore-Sigma; St. Louis, MO</td><td>PKH67GL</td><td>green fluorescent cell dye</td></tr><tr><td>RPMI-1640</td><td>EuroClone Spa, Milan, Italy</td><td>ECM2001L</td><td>Culture medium for MDA-MB-231 cells</td></tr><tr><td>serological pipettes (2 mL, 5 mL, 10 mL, 25 mL, 50 mL)</td><td>Corning- Millipore-Sigma; St. Louis, MO</td><td>CLS4486; CLS4487; CLS4488; CLS4489; CLS4490</td><td>Liquid handling</td></tr><tr><td>sterile tips (1-10 &amp;mu;L, 10-20 &amp;mu;L, 20-200 &amp;mu;L, 1000 &amp;mu;L)</td><td>EuroClone Spa, Milan, Italy</td><td>ECTD00010; ECTD00020; ECTD00200; ECTD01005</td><td>tips for micropipette</td></tr><tr><td>Timer</td><td></td><td></td><td></td></tr><tr><td>Trypan Blue solution</td><td>Thermo Fisher Scientific Inc., Waltham, MA</td><td>15250061</td><td>cell stain to assess cell viability</td></tr><tr><td>Trypsin</td><td>EuroClone Spa, Milan, Italy</td><td>ECM0920D</td><td>dissociation reagent for adherent cells</td></tr><tr><td></td><td></td><td></td><td></td></tr><tr><td>&lt;strong&gt;Cell culture equipment&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>EVOS-FL fluorescence microscope</td><td>Thermo Fisher Scientific Inc., Waltham, MA</td><td></td><td>Fluorescent microscope for living cells</td></tr><tr><td>Humified cell culture incubator&amp;nbsp;</td><td>Thermo Fisher Scientific Inc., Waltham, MA</td><td>311 Forma Direct Heat COIncubator; TC 230</td><td>Incubation of cell cultures at 37 &amp;deg;C, 5% CO&lt;sub&gt;2&lt;/sub&gt;</td></tr><tr><td>Juli Microscope</td><td>Nanoentek</td><td></td><td></td></tr><tr><td>Laboratory refrigerator (4 &amp;deg;C)</td><td>FDM</td><td></td><td></td></tr><tr><td>Laboratory Safety Cabinet (Class II)</td><td>Steril VBH 72 MP</td><td></td><td>Laminar flow hood</td></tr><tr><td>Optical microscope</td><td>Zeiss</td><td></td><td></td></tr><tr><td>Refrigerable centrifuge</td><td>Beckman Coulter</td><td></td><td></td></tr><tr><td>Thermostatic bath</td><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td><td></td></tr><tr><td>&lt;strong&gt;Microfabrication materials&amp;nbsp;&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>3-Aminopropyl)triethoxysilane (Aptes)</td><td>Sigma Aldrich</td><td>A3648</td><td>silanizing agent for bonding PDMS to plastic coverslip</td></tr><tr><td>Chromium quartz masks / 4&quot;x4&quot;, HRC / No AZ&amp;nbsp;</td><td>MB W&amp;amp;A,&amp;nbsp; Germany</td><td></td><td>optical masks for photolithography</td></tr><tr><td>Glass coverslip, D 263 M Schott glass,&amp;nbsp; (170 &amp;plusmn; 5 &amp;micro;m)</td><td>Ibidi, Germany</td><td>10812</td><td></td></tr><tr><td>Hydrogen Peroxide solution 30%</td><td>Carlo Erba Reagents</td><td>412081</td><td>reagents for piranha solution</td></tr><tr><td>Methyl isobutyl ketone</td><td>Carlo Erba Reagents</td><td>461945</td><td>PMMA e-beam resist developer</td></tr><tr><td>Microscope Glass Slides (Pack of 50 slides) 76.2 mm x 25.4 mm&amp;nbsp;</td><td>Sail Brand</td><td>7101</td><td>substrates for bonding chips</td></tr><tr><td>Miltex Biopsy Punch with Plunger, ID 1.0mm</td><td>Tedpella</td><td></td><td>dermal biopsy punches for chip reservoirs</td></tr><tr><td>PMMA&amp;nbsp; 950 kDa</td><td>Allresist,Germany</td><td>AR-P. 679.04</td><td>Positive electronic resists for patterning optical masks</td></tr><tr><td>Polymer untreated coverslips</td><td>Ibidi, Germany</td><td>10813</td><td>substrates for bonding chips</td></tr><tr><td>Prime CZ-Si Wafer,&amp;nbsp; 4&amp;rdquo;, (100), Boron Doped</td><td>Gambetti Xenologia Srl, Italy</td><td>30255</td><td></td></tr><tr><td>Propan-2-ol</td><td>Carlo Erba Reagents</td><td>415238</td><td></td></tr><tr><td>Propylene glycol monomethyl ether acetate (PGMEA)</td><td>Sigma Aldrich</td><td>484431-4L</td><td>SU-8 resists developer</td></tr><tr><td>SU-8 3005</td><td>Micro resist technology,Germany</td><td>C1.02.003-0001</td><td>Negative Photoresists</td></tr><tr><td>SU-8 3050</td><td>Micro resist technology,Germany</td><td>C1.02.003-0005</td><td>Negative Photoresists</td></tr><tr><td>Suite of Biopunch, ID 4.0 mm, 6.0 mm, 8.0 mm</td><td>Tedpella</td><td>15111-40, 15111-60, 15111-80</td><td>dermal biopsy punches for chip reservoirs</td></tr><tr><td>Sulfuric acid 96%</td><td>Carlo Erba Reagents</td><td>410381</td><td>reagents for piranha solution</td></tr><tr><td>SYLGARD 184 Silicone Elastomer Kit</td><td>Dowsil, Dow Corning</td><td>11-3184-01</td><td>Silicone Elastomer (PDMS)</td></tr><tr><td>Trimethylchlorosilane (TMCS)</td><td>Sigma Aldrich</td><td>92360-100ML</td><td>silanizing agent for SU-8 patterned masters</td></tr><tr><td></td><td></td><td></td><td></td></tr><tr><td>&lt;strong&gt;Microfabrication equipment&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>100 kV e-beam litography</td><td>Raith-Vistec EBPG 5HR</td><td></td><td></td></tr><tr><td>hotplate</td><td></td><td></td><td></td></tr><tr><td>Optical litography system</td><td>EV-420 double-face contact mask-aligner</td><td></td><td></td></tr><tr><td>Reactive Ion Etching system</td><td>Oxford plasmalab 80 plus system</td><td></td><td></td></tr><tr><td>Vacuum dessicator</td><td></td><td></td><td></td></tr></tbody></table>

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.

Tumor immune infiltration is a parameter of the host anti-tumor response. Tumors are heterogeneous in the composition, density, location, and functional state of infiltrating leukocytes which interactions with cancer cells can underlie clinically relevant information to predict disease course and response to therapy. In this sense, microfluidic technologies could be used as complementary and privileged in vitro tools to explore the immune contexture of tumors, as well as to monitor the response to anticancer therapies. The coupling of the microfluidic assay, live-cell imaging, and tracking software may establish reliable quantification methods to quantify how immune cells adjust their migration pattern in different contexts. In this chapter, we have reported steps for setting up versatile 2D or 3D co-cultures of immune and target cancer cells in ad hoc microfluidic devices realized by standard soft-lithographic procedures. In Section 1 microfluidic devices were employed to allow chemical and physical contacts between adherent (MDA-MB-231 cancer cells) and non-adherent (PBMCs) populations. Some chemotherapeutic agents (e.g., anthracyclines among the others) can induce &#34;immunogenic&#34; apoptosis of malignant cells, thus enhance their visibility in immunocompetent hosts. Cancer immunogenic cell death (ICD) is characterized by the release of membrane-bound and soluble signals delivered by dying cells functioning as alarmins for immune cells. To provide a quantitative validation of ICD response, we employ data collected in the microfluidic platform where leukocytes can move through suitably built microchannel bridges toward their target cells. Time-lapse recordings were performed after co-loading PBMCs, from healthy donors (WT, wild type), with human MDA-MB-231 breast cancer cells pre-treated or not with the anthracycline DOXO. Microphotographs were generated every 2 min, for two successive time intervals of 24 and 48 h. (Exemplary Movie S1 of 0-24 h interval). Tracking analysis of individual PBMCs challenged with dying (DOXO-treated) or live (PBS-treated) cancer cells was done using Trackmate plug-in, as seen in Figure 6A (left panel). Relevant chemotaxis values and migration plots were automatically generated by using the Chemotaxis and Migration Tool, as detailed in62. The cell trajectories were all extrapolated to (x, y) = 0 at time 24 h. The results indicated a different migratory profile of the immune cells when co-loaded with breast cancer cells exposed to DOXO or PBS. When PBMCs were confronted with apoptotic cancer cells, they crossed microchannels towards dying/dead cells (but not to live untreated cells). Migration X/Y spider and rose plots, presented in left panel Figure 6B-C, were mapped and compared to highlight the impact of immunogenic inducers agents on differences in immune dynamics. Rose plots demonstrate that PBMCs migrated mainly in nearly all directions in the control experiment, only a negligible fraction is guided up the gradient generated by proliferating cancer cells. Conversely, the paths of individual cells highlight a strong bias movement along the direction of apoptotic breast cancer cells (negative x-direction). To evaluate directed immune cell migration towards DOXO-treated or PBS-treated cancer cells, several chemotaxis parameters are computed, including: a) the center of mass (spatial averaged point of all endpoints); b) the directionality; c) the forward migration index (i.e., the average cell displacement in a direction of interest which means towards target tumor site, in our case). The latter values represent a measurement of the efficiency of a cell to migrate in the direction given chemotactic stimuli. After reaching the left chamber, a fraction of leukocytes with an increasing density over 24-48 h exhibited long-term (&#62;60 min) contacts with DOXO-treated MDA-MB-231. PBMCs fail to massively migrate and engage in such long-term and persistent interactions with live cancer cells (as shown by representative microphotographs of approaching PBMCs in Figure 6A, right). For quantifying differences in tumor-immune interplay, the tumor region was delineated with a fixed circle of diameter ranging 20-80 microns, named &#34;hotspot&#34;. The quantification and analysis from representative FOVs are shown in Figure 6 (Right panel, B-C).
In Section 2 a novel 3D immuno-competent tumor model was described to quantify the recruitment of immune cells in response to anti-cancer combinations of epigenetic drugs32 (DAC/IFN vs IFN alone), allowing the comparison of two different treatment conditions of A375 melanoma cells simultaneously. Thus, A375M melanoma cells, labeled with PKH67 green fluorescent dye were embedded in Matrigel matrices in the presence of DAC and/or IFN into each gel chamber, whereas PKH26 red-labeled PBMCs were distributed homogenously into the central fluidic chamber at the starting point (Figure 7A). We compared simultaneously the two tumor masses in 3D matrices for their capacity to attract PBMCs. At 48 and 72 h, PBMCs are guided massively into the right-side microchannels, as observed in Figure 7B.
A preferential homing of PBMCs toward the gel matrix containing DAC/IFN-treated, rather than IFN-treated, melanoma site was clearly observed, whereas poor migration rate was visible toward A375 cells exposed to single treatment (left chip side). The competitive setting is applicable to investigate a plethora of different cancer biology phenotypes (e.g., drug-resistant vs aggressive, primary or metastatic (e.g., A375P vs A375M melanoma cells), and responders vs non-responders). Gel chambers can be comprised of complex co-cultures of malignant cells and multiple non-cancerous tumor-associated cells, such as endothelial cells, immune cells, and fibroblasts33 to characterize TME-level drug responses. Events of mitosis and apoptotic death of cancer cells can be monitored by staining with live dyes33. Immunostaining procedures on 3D microfluidic devices could be adapted to evaluate states of activation of infiltrated immune cells in tumor sites by confocal microscopy35. In this sense, these 3D microfluidic systems may mimic complex tumor structures and multicellular interactions and therefore are valuable platforms for more reliable preclinical drug testing.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61895/61895fig01.jpg" /> 
Figure 1. Planimetry of the microfluidic device for assembling 2D tumor-immune co-cultures. (A) Real microphotograph of 3 chips assembled into a single microscope slide. The reservoirs are numbered depending on the loading sequence and are color-coded as the corresponding culture chambers listed in the legend. Scalebar, 6 mm. (B) 3D CAD of chip consisting of three main cell culture areas connected by microgrooves. C) Details of the narrow microgrooves bridge, showing the dimensions tailored to cancer cells and PBMCs size. D-E) Visible light microphotograph was acquired before the beginning of the time-lapse, showing the distribution of cancer cells and PBMCs respectively in the left and the intermediate chamber. Scalebar, 500 &#181;m.&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61895/61895fig01large.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/61895/61895fig02.jpg" /> 
Figure 2. Trackmate analysis pipeline for localization of immune spots in time-series images. A) Upper panel: Screenshot of Trackmate image calibration menu. Lower panel: Fiji &#34;Image properties menu&#34; to set time and spatial units. B) Upper panel: Screenshot of Trackmate &#34;Spots detection&#34; menu. Lower panel: Output image with applied different values of &#34;Threshold&#34;. C) Upper panel: Screenshot of Trackmate &#34;Spot filtering&#34; menu illustrating some filters. Lower panel: Exemplary time-lapse image, depicting immune spots filtered by position along X in the intermediate chamber.&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61895/61895fig02large.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/61895/61895fig03.jpg" /> 
Figure 3. Trackmate Analysis pipeline for reconstructing immune tracks in time-series image sequences. A-C) Screenshots of Trackmate menus for tracks building, tracks filtering, and for exporting final data. D) Examples of generated tracks in pre-processed time-lapse images varying the linking detector settings. E) Example of false tracks and bad links derived from the detection of tumor cells borders. F) Tracks are highlighted in green by selecting rows in the file .txt of results.&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61895/61895fig03large.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/61895/61895fig04.jpg" /> 
Figure 4. Schematic overview for 3D immunocompetent tumor-on chips. A) Example of a micro-structured silicon master. Stamps for PDMS were patterned in SU-8 negative resist in a cleanroom facility equipped with e-beam and optical lithography. B) PDMS replicas were fabricated by standard soft-lithography methods. The central unit has the chambers colored as the ones drawn in 3D rendering of the chip, depicted in the panel D. C) Scanning electron microscopy (SEM) enlarged view of the array of micropillars suited for hydrogel solution entrapment. D)3D CAD showing the chambers, connected by microgrooves and the loading wells. Dashed boxes are referred to SEM photographs of details (panel C, and E). E) SEM photograph of 10 &#181;m high connecting microgrooves. F) 2D CAD layout depicting the dimensions of microstructures.&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61895/61895fig04large.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/61895/61895fig05.jpg" /> 
Figure 5. Schematic workflow of main loading protocol steps for assembling 3D co-cultures. A) Upper panel: Schematics of injection step of Matrigel solution in each gel side region. Lower panel: Real photograph of the chip showing gel front advancement along the channel during loading step. B) Schematics of Matrigel polymerization step. Lower panel: Phase-contrast microscopic view of the gel-air interface formed after gelation step. Scalebar, 100 &#181;m. C)Upper panel: Drawing, depicting the loading of cells and media with exemplary volumes used in the protocol. Lower panel: A 4X Phase-contrast image of the assembled co-culture at 0h is shown. Scalebar, 200 &#181;m.&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61895/61895fig05large.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/61895/61895fig06.jpg" /> 
Figure 6. Analysis of migratory profiles and interaction behavior of PBMC towards dying/live tumor chamber in the time window of 24-48 h. Left panel. A) Screenshots of time-lapse pre-processed images overlaid with immune colored tracks, extracted by Trackmate. The immune paths are obtained by a single FOV in the indicated experimental conditions in the interval 24-to-48 h in the intermediate chamber. B-C) Representative migration tracks and rose plots of PBMCs cultivated in the two different conditions (n = 1550 PBMCs versus DOXO-treated cancer cells, DOXO+ or n =1434 PBMCs versus control cancer cells, DOXO-). Each line inside x-y plots depicts a single PBMC trajectory and each circle represents the final position of a single cell with respect to the initial position. Starting points are set to (0,0) using a coordinate transformation. The coordinates and displacement of the center of mass of cell migration are displayed in the indicated experimental conditions. The center of mass coordinates and displacement (computed as average Euclidean distance for the X and Y components) provide indication of the average direction in which the group of cells primarily travelled and magnitude of the overall cell movement in condition groups. Blue and green lines respectively mark individual tracks by Euclidean distance value greater/smaller than a threshold value (100 &#181;m). D) Scheme of numerical data extracted by a cell track. E-F) Box &#38; Whiskers plot depicting respectively directionality (p&#60;0,0001 Unpaired t test with Welch&#39;s correction) and FMI (p&#60;0,0001 Unpaired t test with Welch&#39;s correction). The horizontal line in boxes represents median values. The FMI values are the average for all cell-tracks in each condition group.Right panel. A) Screenshots of Trackmate extracted ROIs showing differential interactions between PBMCs and DOXO or PBS-treated cancer cells. B) Time density of PBMCs around DOXO-treated or control MDA-MB-231 cancer cells. Number of PBMCs present into selected ROI around cancer cells for each condition. Values reported are the average over 9 selected ROI from a single time-lapse FOV. Cancer hotspots are defined in a ROI (80 &#181;m&#160;in diameter). Corresponding density heatmap is displayed. Dots represent mean number of cells calculated over 9 cancer hotspots at indicated time points. C) Distribution of times (min) of contacts for each experimental group.&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61895/61895fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/61895/61895fig07.jpg" /> 
Figure 7. Preferential recruitment of PBMCs in response to DAC plus IFN drug combinations in a competitive 3D immuno-competent melanoma on chip model. A) Distribution of initially loaded PBMCs in the central chamber of microfluidic devices. Microphotographs are acquired by EVOS-FL fluorescence microscope during 0-72 h interval. Red fluorescence (PKH67-labeled cells) represents PBMCs from healthy donors. PKH67-labeled (green) human melanoma cells embedded in Matrigel containing single or double combinations treatments were plated in lateral chambers. B) A375 cells plus IFN on left versus A375 plus DAC/IFN on the right. Fluorescence images were shown at 72 h of co-culture. Discontinued yellow box depicts visibly massive recruitment in A375 plus DAC/IFN side. C) PBMC counts in four different ROIs from IFN chambers vs DAC+IFN chambers. Histograms represent cell counts +/- S.D.; heatmap enumerated values from each ROI. Scalebars, 200 &#181;m. D-G) Schematics of segmentation and quantification steps of infiltrated PBMCs in gel matrices applied to single channel fluorescence images.&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61895/61895fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary File. Microfabrication protocol&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61895/Supp_file_microfabrication_protocol.docx" target="_blank">Please click here to download this file.</a>
Supplementary Movie 1. Time-lapses sequences in a 2D tumor-immune on-chip co-culture. Microphotographs were acquired every 2 min, in a time interval of 0-24 h by means of a compact microscope placed into a standard cell culture incubator. Left panel. Movie of PBMCs from healthy donors (WT, wild type), plated with MDA-MB-231 control breast cancer cells. Right panel. Movie of a massive migration of PBMCs WT towards the chip chamber where MDA-MB-231 DOXO-treated cancer cells, are loaded. 2D chip layout is described in detail in section 1of Protocol and shown in Figure 1.&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61895/Timelapse_PBSvsDOXO_PBMCs_WT_0-24h_01.mov" target="_blank">Please click here to download this movie.</a>

Watch this Scientific Journal Video about Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments at JoVE.com

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

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 ...

microfluidic-co-culture-models-for-dissecting-immune-response-in

Nanyang Technological University

Research

JoVE Journal

Immunology and Infection

4.6K Views.  CNR Institute for Photonics and Nanotechnology. Dit protocol biedt experimentele instellingen om controleerbare 2D- en 3D-coculturen uit te voeren in micro-apparaten om tumor-immuuncelkruisingen te visualiseren en te monitoren en de effecten van antikankerbehandelingen te analyseren. Deze technologie biedt real-time en eenvoudige visualisatie van immuuncelrekrutering en interacties en kan worden gebruikt voor menselijke en patiënt-afgeleide monsters. Het is compatibel met de meeste state-of-the-art microscopieën.Francesco Noto, pr...

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

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 ...

Cancer Research

Microfluidic Device for Recreating a Tumor Microenvironment in Vitro

We have developed a microfluidic device that mimics the delivery and systemic clearance of drugs to heterogeneous three-dimensional tumor tissues in vitro. Nutrients delivered by vasculature fail to reach all parts of tumors, giving rise to heterogeneous microenvironments consisting of viable, quiescent and necrotic cell types. Many cancer drugs fail to effectively penetrate and treat all types of cells because of this heterogeneity. Monolayers of cancer cells do not mimic this heterogeneity, making it difficult to test cancer drugs with a suitable in vitro model. Our microfluidic devices were fabricated out of PDMS using soft lithography. Multicellular tumor spheroids, formed by the hanging drop method, were inserted and constrained into rectangular chambers on the device and maintained with continuous medium perfusion on one side. The rectangular shape of chambers on the device created linear gradients within tissue. Fluorescent stains were used to quantify the variability in apoptosis within tissue. Tumors on the device were treated with the fluorescent chemotherapeutic drug doxorubicin, time-lapse microscopy was used to monitor its diffusion into tissue, and the effective diffusion coefficient was estimated. The hanging drop method allowed quick formation of uniform spheroids from several cancer cell lines. The device enabled growth of spheroids for up to 3 days. Cells in proximity of flowing medium were minimally apoptotic and those far from the channel were more apoptotic, thereby accurately mimicking regions in tumors adjacent to blood vessels. The estimated value of the doxorubicin diffusion coefficient agreed with a previously reported value in human breast cancer. Because the penetration and retention of drugs in solid tumors affects their efficacy, we believe that this device is an important tool in understanding the behavior of drugs, and developing new cancer therapeutics.

Microfluidic Device for Recreating a Tumor Microenvironment in Vitro

We present the procedure for fabrication and operation of a microfluidic device that recreates heterogeneous tumor microenvironments in vitro. The variability in apoptosis within tumor tissue was quantified using fluorescent stains and the effective diffusion coefficient of the chemotherapeutic drug doxorubicin into tumor tissue was evaluated.

We have developed a microfluidic device that mimics the delivery and systemic clearance of drugs to heterogeneous three-dimensional tumor tissues in ...

Bioengineering

Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors

The tumor immune microenvironment (TIME) has recently been recognized as a critical mediator of treatment response in solid tumors, especially for immunotherapies. Recent clinical advances in immunotherapy highlight the need for reproducible methods to accurately and thoroughly characterize the tumor and its associated immune infiltrate. Tumor enzymatic digestion and flow cytometric analysis allow broad characterization of numerous immune cell subsets and phenotypes; however, depth of analysis is often limited by fluorophore restrictions on panel design and the need to acquire large tumor samples to observe rare immune populations of interest. Thus, we have developed an effective and high throughput method for separating and enriching the tumor immune infiltrate from the non-immune tumor components. The described tumor digestion and centrifugal density-based separation technique allows separate characterization of tumor and tumor immune infiltrate fractions and preserves cellular viability, and thus, provides a broad characterization of the tumor immunologic state. This method was used to characterize the extensive spatial immune heterogeneity in solid tumors, which further demonstrates the need for consistent whole tumor immunologic profiling techniques. Overall, this method provides an effective and adaptable technique for the immunologic characterization of subcutaneous solid murine tumors; as such, this tool can be used to better characterize the tumoral immunologic features and in the preclinical evaluation of novel immunotherapeutic strategies.

Here we describe a method to separate and enrich components of the tumor immune and non-immune microenvironment in established subcutaneous tumors. This technique allows for the separate analysis of tumor immune infiltrate and non-immune tumor fractions which can permit comprehensive characterization of the tumor immune microenvironment.

The tumor immune microenvironment (TIME) has recently been recognized as a critical mediator of treatment response in solid tumors, especially for ...

Studying the Effects of Tumor-Secreted Paracrine Ligands on Macrophage Activation using Co-Culture with Permeable Membrane Supports

Tumor-derived paracrine signaling is an overlooked component of local immunosuppression and can lead to a permissive environment for continued cancer growth and metastasis. Paracrine signals can involve cell-cell contact between different cell types, such as PD-L1 expressed on the surface of tumors interacting directly with PD-1 on the surface of T cells, or the secretion of ligands by a tumor cell to affect an immune cell. Here we describe a co-culture method to interrogate the effects of tumor-secreted ligands on immune cell (macrophage) activation. This straightforward procedure utilizes commercially available 0.4 &#181;m polycarbonate membrane permeable supports and standard tissue culture plates. In the process described, macrophages are cultured in the upper chamber and tumor cells in the lower chamber. The presence of the 0.4 &#181;m barrier allows for the study of intercellular signaling without the confounding variable of physical contact, because the two cell types share the same medium and exposure to paracrine ligands. This approach can be combined with others, such as genetic alterations of the macrophage (e.g., isolation from genetic knock-out mice) or tumor (e.g., CRISPR-mediated alterations) to study the role of specific secreted factors and receptors. The approach also lends itself to standard molecular biological analyses such as quantitative reverse transcription polymerase chain reaction (qRT-PCR) or Western blot analysis, without the need for flow sorting to separate the two cell populations. Enzyme-linked immunosorbent assays (ELISAs) can similarly be utilized to measure secreted ligands to better understand the dynamic interaction of cell signaling in the multiple cell type context. Duration of co-culture can also be varied for the study of temporally regulated events. This co-culture method is a robust tool that facilitates the study of tumor-secreted signals in the immune context.

Here, we present a method using permeable membrane supports to facilitate the study of non-contact paracrine signaling used by tumor cells to suppress the immune response. The system is amenable to studying the role of tumor-secreted factors in dampening macrophage activation.

Tumor-derived paracrine signaling is an overlooked component of local immunosuppression and can lead to a permissive environment for continued cancer ...

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 ...

Flow Cytometry-Based Monitoring of Cancer-Immunity Cycle 

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.

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.

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

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

Evaluation of Tumor-infiltrating Leukocyte Subsets in a Subcutaneous Tumor Model

Specialized immune cells that infiltrate the tumor microenvironment regulate the growth and survival of neoplasia.&#160; Malignant cells must elude or subvert anti-tumor immune responses in order to survive and flourish. Tumors take advantage of a number of different mechanisms of immune &#8220;escape,&#8221; including the recruitment of tolerogenic DC, immunosuppressive regulatory T cells (Tregs), and myeloid-derived suppressor cells (MDSC) that inhibit cytotoxic anti-tumor responses. Conversely, anti-tumor effector immune cells can slow the growth and expansion of malignancies: immunostimulatory dendritic cells, natural killer cells which harbor innate anti-tumor immunity, and cytotoxic T cells all can participate in tumor suppression. The balance between pro- and anti-tumor leukocytes ultimately determines the behavior and fate of transformed cells; a multitude of human clinical studies have borne this out. Thus, detailed analysis of leukocyte subsets within the tumor microenvironment has become increasingly important. Here, we describe a method for analyzing infiltrating leukocyte subsets present in the tumor microenvironment in a mouse tumor model. Mouse B16 melanoma tumor cells were inoculated subcutaneously in C57BL/6 mice. At a specified time, tumors and surrounding skin were resected en bloc and processed into single cell suspensions, which were then stained for multi-color flow cytometry. Using a variety of leukocyte subset markers, we were able to compare the relative percentages of infiltrating leukocyte subsets between control and chemerin-expressing tumors. Investigators may use such a tool to study the immune presence in the tumor microenvironment and when combined with traditional caliper size measurements of tumor growth, will potentially allow them to elucidate the impact of changes in immune composition on tumor growth. Such a technique can be applied to any tumor model in which the tumor and its microenvironment can be resected and processed.

This protocol describes a method for the detailed evaluation of leukocyte subsets within the tumor microenvironment in a mouse tumor model. Chemerin-expressing B16 melanoma cells were implanted subcutaneously into syngeneic mice. Cells from the tumor microenvironment were then stained and analyzed by flow cytometry, allowing for detailed leukocyte subset analyses.

Specialized immune cells that infiltrate the tumor microenvironment regulate the growth and survival of neoplasia.&#160; Malignant cells must elude or ...

Renal Cancer and Healthy Tissue Co-Culture Using Microphysiological Systems

Microfluidic Co-culture of Renal Healthy and Tumor Epithelium to Model Kidney Cancer Progression

Microphysiological systems (MPS) have enabled the introduction of more complex and relevant physiological elements into in vitro models, recreating intricate morphological features in three-dimensional environments with dynamic interactions lacking in conventional models. We implemented a renal cell carcinoma (RCC) co-culture model to recreate the cross-talk between healthy and malignant renal tissue. 
This model is based on the referenced multi-organ platform and consists of co-culturing a reconstructed renal proximal tubule with RCC spheroids. Custom-designed 3D-printed chambers were used to culture human renal epithelial proximal tubule cells (RPTEC) and facilitate their self-assembly into a renal tubule contained in a collagen type I matrix. Caki-1 RCC cells were embedded in an agar collagen matrix, subsequently forming cancer spheroids. Both collagen and agar/collagen gels were optimized to maintain their integrity during cyclic perfusion and withstand shear stress during a minimum culture period of 7 days. 
The gels also enable adequate nutrient supply and cell secretions. Moreover, the agar/collagen gels limit the overproliferation of RCC cells, ensuring relatively homogeneous spheroid size. The MPS chip microfluidic circuits comprise two independent culture chambers with the size of a standard 96-microplate well. The renal tubule and RCC gels populate separate chambers and share the same culture media, which is recirculated approximately twice per minute. Under these conditions, we observed upregulation of immune factor expression and secretion in the renal tubules (interleukin-8 and tumor necrosis factor-alpha). The renal tubules also shift their metabolic activity towards glycolysis under the influence of RCC. This novel approach demonstrates that a co-culture-based MPS can amplify the complexity of RCC in vitro and be employed to study the impact of cancer on non-tumor cells.

Here, we present protocols that detail instructions for implementing 3D cell cultures using collagen and collagen-agarose matrixes in a microphysiological system. These protocols support renal proximal tubule and renal cell carcinoma spheroids co-culture, simulating in vivo conditions and enabling advanced investigation of kidney cancer cell interactions.

Microphysiological systems (MPS) have enabled the introduction of more complex and relevant physiological elements into in vitro models, recreating ...

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 ...

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

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