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JoVE Journal Cancer Research

Cancer Research

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

Published: June 7, 2024 doi: 10.3791/66729
*1, *1, 2, 1,3, 1
1Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, 2CNR Institute for Photonics and Nanotechnology, 3Fondazione Policlinico Universitario ‘A. Gemelli’ - IRCCS
* These authors contributed equally

Abstract

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.

Introduction

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 "engine" 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 naïve 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)-γ, 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'-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.

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Protocol

All the steps of the protocol requiring the use of animals are in compliance with the EU Directive 63/2010 and included in an experimental protocol approved by the Institutional Animal Experimentation Committee and the Italian Ministry of Health (approval number 858/2015/PR).

1. Preparation of cancer cells

  1. Cancer cell culture routine (day -7; duration: 2 h)
    1. Grow murine MCA205 fibrosarcoma cells in Roswell Park Memorial Institute (RPMI) 1640 growth medium supplemented with 10% (v/v) fetal bovine serum (FBS), 2 mM L-glutamine, 100 IU/mL penicillin G sodium salt, 100 µg/mL streptomycin sulfate, and 50 µg/mL hygromycin (complete growth medium), in a humidified cell culture incubator under standard culture conditions (37 °C, 5% CO2).
      NOTE: Adapt culture conditions according to the cell line of choice. In case of thawing, cells need ~1 week of recovery time to adapt to optimal culture conditions and re-establish normal cell cycles.
    2. To optimize cell culture growth, plate cells in 75 cm2 flasks at a density of 1 x 106 cells/mL in 12-15 mL of complete growth medium.
      NOTE: The number of seeded cells may vary across different cell lines usually depending on specific cell size and doubling time. Cell confluence dramatically influences the state of the cells. If cells are either little or too confluent, metabolic perturbations can occur and favor proliferative arrest, clonal selection, or cell stress. For MCA205 cells, passaging needs to be performed twice a week at a 1:10-1:20 split ratio.
    3. When cell cultures reach a 75%-80% maximum confluency, discard the complete growth medium from the cell monolayer, wash cells with pre-warmed phosphate-buffered saline (PBS) to remove FBS, and then add 1.5 mL of pre-warmed 0,25% trypsin/ethylenediaminetetraacetic acid (EDTA) for 1-2 min at 37 °C to detach cells.
      NOTE: FBS contains protease inhibitors that may inactivate trypsin enzymatic activity, therefore its complete removal is a crucial step. Trypsin incubation time differs depending on the cell type. As a general guideline, avoid long-term incubation of cells with high trypsin concentration as it may stripe cell surface proteins and trigger cell death. At this step, check the cell detachment state with a light microscope.
    4. Collect detached cells in 10 mL of pre-warmed complete growth medium to inactivate trypsin enzymatic activity and centrifuge them for 5 min at 1100 × g at room temperature (RT). Count cells in a cell counting slide using the Trypan Blue dye exclusion test and then reseed them upon dilution into appropriate supports for maintenance culture (no more than 8 passages from thawing) or for downstream experimental procedures, as detailed below.
  2. Cancer cell death - release of cancer cell Ags (day 0; duration: 30 min)
    1. For downstream functional experiments, plate 3 × 106 MCA205 cells in a 100 mm tissue culture-treated Petri dish in 10 mL of complete growth medium.
      NOTE: For specific experimental applications (e.g., analysis of DC-mediated tumor Ag uptake), before inducing cell death, cancer cells have been labeled by using a fluorescent cell linker (Table of Materials), as per the manufacturer's instructions.
    2. Irradiate cells with ultraviolet (UV) light18 (ƛ = 254 nm) at a 9-cm distance for 3 min and then incubate them at 37 °C, 5% CO2 for at least 6 h to let them die.
      NOTE: Cancer cell death can also be induced by chemotherapeutic treatment (e.g., immunogenic or non-immunogenic drugs5).

2. Co-culture of cancer and immune cells

  1. DC-mediated cancer Ag uptake and presentation (day 0; Timing: 1 h bench time, 24 h incubation time)
    1. After 4-6 h, collect DCs, in vitro differentiated from bone marrow (BM) cells of immunocompetent mice as described previously8, and wash them twice for 5 min at 1100 x g at RT in complete growth medium.
      NOTE: DCs can alternatively be isolated from mouse splenocytes by centrifugation on a Histodenz-based gradient as reported previously19. As a good practice, if cryopreserved DCs are used, ensure to pre-heat the complete growth medium and supplement it with 1:1000 benzonase to prevent cell clumping and thus ensure a good recovery upon thawing.
    2. Collect UV-irradiated cancer cells from step 1.2.2 and centrifuge them for 5 min at 1100 x g at RT. Count BM-derived "empty" DCs (DCN) as above (see step 1.1.4) and then set the co-culture with UV-irradiated apoptotic cells at a 2:1 ratio (5 × 105 apoptotic MCA205 cells for 2.5 × 105 DCs, as previously optimized8) in 12-well plates in 1 mL of fresh complete growth medium for 24 h at 37 °C, 5% CO2. As experimental controls, incubate cells at 4 °C, a condition in which phagocytosis is hampered.
      NOTE: Do not increase co-culture surface and volume as this could hinder apoptotic cancer cell-DC interactions and thus impede optimal and effective phagocytosis. Moreover, studies to optimize the cancer cell:DC ratio need to be performed if other cancer cell models are used.
  2. Cancer Ag uptake evaluation by mFC (day 1; duration: 2 h)
    1. Upon 24 h of incubation, collect cells in 15 mL tubes and wash them twice for 5 min at 1100 x g at RT in 10 mL of complete growth medium.
      NOTE: To reduce the background noise of apoptotic cells stuck to the DC membrane and not yet completely engulfed, cell pellets are incubated for 5 min at RT with 0.1 M EDTA. Cell suspensions (containing approximately 2 x 106 cells) are then centrifuged twice with 10 mL of growth medium as in step 2.2.1.
    2. For cell surface staining, resuspend BM-derived DCs in 20 µL of cold fluorescence-activated cell sorting (FACS) buffer (1% FBS, 0.1 M EDTA in PBS) containing 0.5 µg of either IgG isotype control or anti-mouse phycoerythrin (PE)-conjugated CD11c in a 96-well U-shaped bottom tissue culture-treated microplate and incubate them for 20 min at 4 °C in the dark to preserve fluorochrome signal and avoid photobleaching.
      NOTE: Once cancer antigen uptake occurs, "full" DC (DCPHAGO) activation can also be verified by checking the expression levels of costimulatory molecules (i.e., either CD80 or CD86 and MHC-II and of the immune checkpoint ligand CD274 antigen [best known as PDL1]). As a general rule, for an optimal flow cytometer performance, antibody concentration needs to be carefully titrated and cell number needs to range from 1 x 105 to 1 x 108 cells/test.
    3. Thereafter, wash cells twice in FACS buffer and add 100 µL of a PBS solution containing a vitality fixable near-infra red (IR) dye at a final concentration of 1 µM for 30 min at RT.
      NOTE: As an alternative, other vitality fixable dyes (e.g., Violet, Blue, Orange, Lime, Aqua) not overlapping with used fluorescence emissions can be used.
    4. Wash cells once in PBS and resuspend them in FACS buffer prior to flow cytometric analysis of CD11c and PKH67 levels by means of mFC. During mFC acquisition, and then in data analysis, proceed with the following gating strategy:
      1. Identify the cells of interest based on their forward scatter area (FSC-A, x-axis) and side scatter area (SSC-A, y-axis) properties (gate 1). Exclude cell doublets and clumps from the analysis by plotting FSC-A (x-axis) and forward scatter height (FSC-H, y-axis, gate 2).
      2. Remove dead cells by plotting 780/60 nm emission bandpass (bp) filter area (x-axis) and SSC-A (y-axis, gate 3). Finally, based on gate 3 detect cell positivity for CD11c marker and PKH67 fluorescent cell linker in two-parameter density plots by using a 575/26 nm and 530/30 nm emission bp filters, respectively.
    5. Perform data analysis.
      NOTE: Be sure to combine antibodies in order to guarantee easy and efficient labeling with well-separated emission wavelengths. This simplifies the compensation setting for the used fluorophores and allows for an easy, rapid, and reliable quantification of the parameters of interest.
  3. CD8 T cell priming and activation (day 1; Duration: 1 h bench time, 72-96 h incubation time)
    1. Purify murine splenic CD8 T cells (CD8N) as previously described8.
    2. Count and resuspend the isolated CD8 T cells in 500 µL of fresh complete growth medium and culture them with BM-derived DCs that had previously taken up apoptotic MCA205 cells at a 2.5:1 ratio (2.5 × 105 DCs for 1 × 105 T cells, as previously optimized8) for 72 h in 12-well plates in a final volume of 1.5 mL at 37 °C, 5% CO2.
      NOTE: To emphasize the experimental results, different cell culture ratios have been tested and the best one has been chosen.
  4. CD8 T cell proliferation analysis by EdU mFC assay (day 4; Duration: 15 min bench time, 16-20 h of incubation)
    1. Add the nucleoside analog to thymidine EdU to the co-culture medium at a final concentration of 10 µM and mix well.
      NOTE: Since culture conditions, cell type variations, and cell density may affect EdU incorporation into the DNA, test a range of EdU concentrations and incubation times in pilot experiments. Longer or shorter incubations may require lower or higher concentrations, respectively. Include cells from the same population that have not been treated with EdU as a negative staining control.
    2. After 16-20 h, recover and centrifuge CD8C-P twice for 5 min at 1100 x g at RT.
    3. Stain the cells for surface markers in 20 µL of cold FACS buffer supplemented with 0.5 µg of anti-mouse fluorescein isothiocyanate (FITC)-conjugated CD8a and 0.25 µg of anti-mouse PE-conjugated CD3 or with the respective IgG isotype controls in a 96-well U-shaped bottom TC-treated microplate and incubate them for 20 min at 4 °C in the dark.
      NOTE: To avoid any interference, it is recommended not to use Qdot antibody conjugates before performing the click reaction.
    4. Wash cells twice in FACS buffer (as in step 2.2.3) and resuspend the cell pellets in 100 µL of a PBS solution containing the vitality fixable Aqua dye at a final concentration of 1 µM for 30 min at RT.
      1. Transfer cell suspensions into flow tubes, wash them once with 3 mL of 1% bovine serum albumin (BSA) in PBS, and proceed with cell fixation in 100 µL of a fixative (i.e., 4% paraformaldehyde in PBS) for 15 min at RT, protected from light.
      2. Immediately after, wash cells once with 3 mL of 1% BSA in PBS, incubate them in 100 µL of a solution containing 1x saponin-based permeabilization and wash reagent for 15 min at RT, and then go on directly to labeling reaction.
        NOTE: 1x saponin-based permeabilization and wash reagent is prepared by diluting a volume of 10x solution 1:10 with 1% BSA in PBS. This reagent can also be used with cell suspensions containing whole blood or red blood cells, as it preserves the morphological light scatter characteristics of leukocytes while lysing red blood cells.
      3. According to the number of samples to be analyzed, prepare the reaction cocktail by gently and sequentially mixing indicated volumes of PBS, copper protectant catalyzer, Alexa Fluor (AF) 647-conjugated picolyl azide, and 1x EdU buffer additive.
        1. Use the reaction cocktail within 15 min of preparation. Add 500 µL of the reaction mixture to each tube, mix well, and incubate for 30 min at RT in the dark.
      4. Wash cells once with 3 mL of 1x saponin-based permeabilization and wash reagent, and resuspend them in 100 µL of the same solution before proceeding with standard mFC methods for determining the percentage of S-phase proliferating CD8C-P in the cell population by means of mFC. During mFC acquisition and then in data analysis, proceed with the following gating strategy:
        1. Identify the cells of interest based on their FSC-A (x-axis) and SSC-A (y-axis) properties (gate 1). Exclude cell doublets and clumps from the analysis by plotting FSC-A (x-axis) and FSC-H (y-axis, gate 2). Remove dead cells by plotting 525/50 nm emission bp filter area (x-axis) and SSC-A (y-axis, gate 3).
        2. Detect cell positivity for CD8a and CD3 in two-parameter density plots by using 530/30 nm and 575/26 nm emission bp filters, respectively (gate 4). Finally based on gate 4, further analyze cells for AF647-EdU incorporation as mean fluorescent intensity in a single parameter histogram using a 660/620 nm bp emission filter.
          NOTE: The fluorescent signal generated by EdU labeling is best detected using a low flow rate during acquisition.
      5. Perform data analysis.

3. Re-stimulation of CD8C-P with cancer cells

  1. CD8C-P recognizes live cancer cells (day 4; Duration: 1 h bench time, 48-72 h of incubation)
    1. Recover CD8C-P from co-culture (refer to step 2.3.2) and centrifuge them for 10 min at 1100 x g at RT in 10 mL of complete growth medium.
      NOTE: At this step, verify CD8 T cell differentiation by checking the expression levels of the activation marker CD95.
    2. Resuspend cell pellets, containing approximately 2.5 × 106 cells, in 100 µL of dead cell removal microbeads per 1 x 107 total cells.
      NOTE: For higher cell numbers, scale up reagent and total volumes accordingly.
    3. Mix well and incubate for 15 min at RT.
      NOTE: If necessary, the sample volume is adjusted by adding 1x binding buffer, as per the manufacturer's instructions, to achieve a minimum of 500 µL required for magnetic separation.
    4. Immediately after, proceed with subsequent magnetic cell separation. Place separation columns in the magnetic field of a suitable separator and rinse them with the appropriate amount of 1x binding buffer. Transfer cell suspensions into separation columns and collect the flow-through containing the unlabeled live cell fraction.
    5. Wash columns again with the appropriate amount of 1x binding buffer. Collect unlabeled cells that pass through and combine them with the effluent from step 3.1.4.
      NOTE: To increase the efficiency of magnetic removal of dead cells, the live cell fraction can be enriched over a second separation procedure as described in steps 3.1.1-3.1.5 by using a new column.
    6. Centrifuge enriched live CD8C-P for 5 min at 1100 x g at RT in complete growth medium before cell count and then culture them with fresh MCA205 cells at 2:1 ratio (1 x 105 CD8 T cells for 5 x 104 MCA205 cells, as previously optimized8) in 12 well-plates in a final volume of 1 mL. Following cell seeding, place co-culture plates into either a humidified cell culture incubator or a live-cell analysis system for 72 h at 37 °C, 5% CO2, before proceeding with downstream assays.
  2. CD8 T cell trafficking toward cancer cells in ToC microfluidic models (day 5; Duration: 2 h bench time, 72 h of incubation)
    1. Sterilize ad hoc fabricated ToC microfluidic devices13,20 under a UV cabinet for 20 min, wash twice with PBS, and then incubate with complete growth medium for at least 1 h.
      NOTE: MCA205 cells and CD8C-P are not stained in this protocol; however, both can be labeled with fluorescent live-compatible cell trackers.
    2. Gently resuspend 1 × 105 MCA205 cells in 15 µL of complete growth medium, being careful to obtain a homogenous cell distribution, and slowly inject them into the left-side chip chambers (tumor chamber) as described previously. Wait for 5-10 min to let cancer cells adhere to the chip basement.
      1. Check the correct and homogeneous distribution of cancer cells in the microfluidic chip under a microscope. Experimental results can be affected by the presence of bubbles.
    3. Resuspend 1 x 106 CD8C-P in 50 µL of complete growth medium. Gently pipet the immune cell suspension into the right-side chip chambers (immune chamber).
      NOTE: At this step, use a microscope to confirm that immune cells are distributed into the intermediate chamber, creating a "front", which represents the starting point of the experiment.
    4. To avoid pressure and microfluidic fluctuations, carefully fill all the chip reservoirs as indicated previously13 with up to 150 µL of complete growth medium and place assembled chips on a level surface in a humified cell culture incubator at 37 °C, 5% CO2 for at least 1 h to stabilize the system prior to time-lapse recordings.
      NOTE: Carefully check potential evaporative losses of volumes in the two reservoirs of each chip chamber and compensate them by adding fresh medium every 2-to-3 days. If necessary, up to 100 µL of supernatants can be aspirated from each cell compartment to perform cytokine profiling.
    5. Use video microscopy set up equipped with an incubation system to record bright-field image series of the microchannel array between the right and left device regions containing MCA205 and immune cells, respectively, and to visualize the dynamics of immune cell infiltration and immune cell-cancer cell interaction.
      NOTE: In the experimental setting used here, time-lapse recordings of the cells were collected in the incubator for 72 h with a microscope that acquired one microphotograph every 2 min. Optimize imaging conditions to avoid excessive photo exposure and to have a high acquisition frame rate in order to easily perform downstream immune cell tracking analyses. At the end of the time-lapse, perform semi-automatic tracking analysis13,20.
  3. CD8E activation and tumor reactivity flow cytometric assessment (day 7 and 8; Duration: 2 h)
    1. After 48 and 72 h of incubation (see step 3.1.6), recover CD8E and centrifuge twice for 5 min at 1100 x g at RT.
      NOTE: To assess the intracellular levels of cytotoxic molecules produced by CD8E (i.e.,IFN-γ and Grz-B), cancer-immune cell co-cultures have been previously incubated for at least 6 h with 1:1000 brefeldin A and 1:1500 monensin.
    2. For cell surface staining, resuspend cells in 20 µL of three different primary antibody mixes prepared in cold FACS buffer as in Supplementary Table 1.
      NOTE: To ensure the specificity of antibody binding, carry out mFC experiments in the presence of appropriate IgG isotype controls as in step 2.2.2.
    3. Incubate cells in a 96-well U-shaped bottom tissue culture-treated microplate for 20 min at 4 °C, protected from light. For cell pellets from mixes A and B (Supplementary Table 1), proceed directly to step 3.3.5.
    4. After a quick wash, add 100 µL of fixation/permeabilization solution to cell pellets from mix C (Supplementary Table 1). Upon incubation of 20 min at 4° C in the dark, wash cells twice in 200 µL of cold permeabilization/wash buffer and resuspend them in 20 µL of cold permeabilization/wash buffer containing 0.25 µg of anti-mouse FITC-conjugated IFN-γ and 0.125 µg of anti-mouse PE-conjugated Grz-B or with the respective IgG isotypes. Incubate cells for 30 min at 4° C, protected from light.
    5. Thereafter, wash cells twice in FACS buffer and add to cell pellets 100 µL of a PBS solution containing the vitality fixable Aqua dye at a final concentration of 1 µM for 30 min at RT (as in step 2.2.4).
    6. Wash cells once in PBS and resuspend them in FACS buffer prior to flow cytometric analysis of CD8 T cell activation against cancer cells by means of an mFC. During mFC acquisition and then in data analysis, proceed with the following gating strategy:
      1. Identify the cells of interest based on their FSC-A (x-axis) and SSC-A (y-axis) properties (gate 1). Exclude cell doublets and clumps from the analysis by plotting FSC-A (x-axis) and FSC-H (y-axis, gate 2). Remove dead cells by plotting 525/50 nm emission bp filter area (x-axis) and SSC-A (y-axis, gate 3).
      2. Detect cell positivity for CD8a and CD3 in two-parameter density plots by using 660/20 nm and 780/60 nm or 530/30 nm and 575/26 nm emission bp filters (gate 4). Finally, based on gate 4, further analyze cells in two-parameter density plots for CD44 (530/30 nm emission bp filter), CD25 (575/26 nm emission bp filter), and CD69 (450/50 nm emission bp filter) markers for CD137 marker (660/20 nm emission bp filter), and for IFN-γ (530/30 nm emission bp filter) and Grz-B (575/26 nm emission bp filter) for mix A, B, and C (Supplementary Table 1), respectively.
        NOTE: CD8E cell tumor reactivity can be further tested by assessing the surface expression of the CD107a lysosomal-associated protein through an ad hoc degranulation assay.
    7. Perform data analysis.
  4. CD8E cell-mediated tumor killing investigation by live-cell analysis system and mFC (day 8; Duration: 1 h bench time, 72 h of incubation)
    1. For tumor-killing assay by means of a live-cell analysis system, supplement the co-culture medium (see step 3.1.6) with a real-time cell death quantification dye at a final concentration of 250 nM.
    2. After placing the co-culture plates in the live-cell analysis system, allow the plate to warm to 37 °C for 30 min prior to scanning. Perform data scanning every 2 h up to 72 h to collect time-lapse recordings, which will be analyzed by proper softwares.
    3. For tumor-killing assay using mFC, 72 h later (see step 3.1.6), harvest and centrifuge cells twice for 5 min at 1100 x g at RT.
    4. Stain cells for surface markers in 20 µL of cold FACS buffer containing 0.125 µg of either IgG isotype control or anti-mouse pacific blue (PB)-conjugated CD45 in a 96-well U-shaped bottom TC-treated microplate and incubate them for 20 min at 4 °C in the dark. Immediately after, wash cells twice in FACS buffer and add the vitality dye propidium iodide (PI) at a final concentration of 1 µM prior to flow cytometric analysis. During mFC acquisition and then in data analysis, proceed with the following gating strategy:
      1. Identify the cells of interest based on their FSC-A (x-axis) and SSC-A (y-axis) properties (gate 1). Exclude cell doublets and clumps from the analysis by plotting FSC-A (x-axis) and FSC-H (y-axis, gate 2).
      2. Detect cancer cells for CD45 negativity using a 450/50 nm emission bp filter (gate 3). Finally analyze cells for PI incorporation as mean fluorescent intensity in a single parameter histogram using a 610/620 nm bp emission filter.
        NOTE: Cancer cell death can also be analyzed by performing Annexin V-PI apoptosis assay or by assessing the expression levels of cleaved Caspase-3 and -7.
    5. Perform data analysis.
      NOTE: Statistical analyses were performed using Prism GraphPad Software v.8.4.0.

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Representative Results

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 °C but not at 4 °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 naïve 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-γ (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).

Figure 1
Figure 1: Overview of the experimental protocol for studying the cancer-immunity cycle. Please click here to view a larger version of this figure.

Figure 2
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 °C and 4 °C (steps 1-3 of the cancer-immunity cycle). Representative biparametric plots and a histogram showing CD11c+PKH67+ cell percentage (mean ± 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 ± 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's t-test with Welch's correction compared with 4 °C. (C) Unpaired two-sided Student's t-test compared with DCN. Please click here to view a larger version of this figure.

Figure 3
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 T cells by means EdU technology (step 5 of the cancer-immunity cycle). The monoparametric histograms represent the percentage (mean ± s.e.m. with individual data point, one representative experiment out of three is shown) of proliferating EdU+ naïve 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 µm, length = 10 µm, height = 10 µm). Cell culture compartments are 1 mm wide, 8 mm long, and 100 µm 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 µm). (D) Plots representing the trajectories of individual CD8C-P (n = 100 cells per condition) towards target cancer cells (black spots) within 24-72 h are shown (scale bar, 10 µm). (A) Unpaired two-sided Student's t-test with Welch's correction compared with CD8N. Please click here to view a larger version of this figure.

Figure 4
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 ± 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 ± 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's correction compared with CD8N. Please click here to view a larger version of this figure.

Figure 5
Figure 5: CD8 T cell production of Grz-B and IFN-γ. (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 ± s.e.m. and individual data points, n = 3 independent experiments). (B) mFC analysis of intracellular IFN-γ on CD8E. Representative histogram plots showing IFN-γ+ CD8 T cell percentage in CD8N, CD8C-P, CD8E at 48 h and 72 h (mean ± s.e.m. and individual data points, n = 3 independent experiments). (A,B). Ordinary one-way ANOVA test followed by Bonferroni's correction compared with CD8N. Please click here to view a larger version of this figure.

Figure 6
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 ± s.e.m. and individual data points, n = 3 independent experiments) of dying PI+CD45− 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's t-test compared with cancer cells alone (CTR). (B) Two-tailed Mann-Whitney test compared with CTR. Please click here to view a larger version of this figure.

Supplementary Figure 1: Histogram plots represent the MFI of CD95+TCR-β+ CD8N and CD8C-P. Unpaired two-sided Student's t-test compared with CD8N. Please click here to download this File.

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 ± 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 ± 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's correction compared with CD8N. Please click here to download this File.

Supplementary Table 1: Mixes of antibodies step 3.3.2. Please click here to download this File.

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Discussion

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.

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Disclosures

The authors have no conflicts of interest to disclose.

Acknowledgments

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.

Materials

Name Company Catalog Number Comments
1.5 mL microtubes  Eppendorf 30120086
100 kV e-beam litography Vistec
100 mm Petri dishes  Greiner Bio One 664160
12-well plates Euroclone  ET3012
15 and 50 mL tubes Corning  352096; 352070
40 μm cell strainer  Corning  CLS431750
5 mL polystyrene tubes  Greiner Bio-One 120180
70 μm cell strainer  Corning  CLS431751
75 cm2 cell culture treated flask Euroclone  ET7076
Adsorbent wipes
Allumin foil
anti-mouse CD107a (LAMP-1) Antibody Miltenyi Biotec 130-111-319
anti-mouse CD25 (7D4) Antibody  Miltenyi Biotec 130-118-678
anti-mouse CD3 (17A2) Antibody BioLegend 100206
Aptes Sigma Aldrich 440140
BD Cytofix/Cytoperm Plus Fixation/Permeabilization Solution Kit with BD GolgiPlug BD Biosciences 555028
BD GolgiPlug Protein Transport Inhibitor (Containing Brefeldin A) BD Biosciences 555029
BD GolgiStop Protein Transport Inhibitor (Containing Monensin) BD Biosciences 554724
Bovine serum albumin (BSA) US Biological, Salem A1312
CD11c Monoclonal Antibody (N418) eBioscience 12-0114-81
CD137 (4-1BB) Monoclonal Antibody (17B5) eBioscience 17-1371-82
CD3 Monoclonal Antibody (17A2) eBioscience 25-0032-82
CD44 Monoclonal Antibody (IM7) eBioscience 11-0441-82
CD45 Monoclonal Antibody (30-F11) Invitrogen MCD4528
CD69 Monoclonal Antibody (H1.2F3) eBioscience 48-0691-82
CD8a Monoclonal Antibody (53-6.7) eBioscience 11-0081-82
CD8a Monoclonal Antibody (53-6.7) eBioscience 17-0081-82
CD95 (APO-1/Fas) Monoclonal Antibody (15A7) eBioscience 53-0951-82
Cell counting slides Kova International 87144E
Chromium quartz masks MB W&A, Germany
Click-iT Plus EdU Alexa Fluor 647 Flow Cytometry Assay Kit Invitrogen C10635
CytoFLEX Flow Cytometer Beckman Coulter
Dead Cell Removal Kit Miltenyi Biotec 130-090-101
Dulbecco's Phosphate-Buffered Saline (D-PBS) EuroClone ECB4053L
EDTA Invitrogen AM9260G
Fetal bovine serum (FBS) EuroClone ECS0180L
Flowjo v10.0.7 Flowjo, LLC
Granzyme B Monoclonal Antibody (NGZB) eBioscience 12-8898-82
H2O2 Sigma Aldrich
H2SO4 Sigma Aldrich
hotplate
Humified cell culture incubator (37°, 5% CO2) Thermo Scientific
Ice machine Brema Ice Makers
IFN gamma Monoclonal Antibody (XMG1.2) eBioscience 11-7311-82
Illustrator CC 2015 Adobe Systems Inc.
ImageJ National Institute of Health
Incucyte 2022A Software Sartorius
Incucyte Cytotox Dye for Counting Dead Cells Sartorius 4632
Incucyte SX5 Live-Cell Analysis System  Sartorius
JuLi Smart Fluorescent Live Cell Imaging Microscope Bulldog Bio
Laboratory bench
Laboratory refrigerator (4°C)
Laboratory Safety Cabinet (Class II) Angelantoni
L-glutamine 200 mM EuroClone ECB3004D
LIVE/DEAD Fixable Aqua Dead Cell Stain Kit Invitrogen L34957
LIVE/DEAD Fixable Near-IR Dead Cell Stain Kit Invitrogen L10119
MACS columns Miltenyi Biotec 130-042-201; 130-042-401 
MACS separators Miltenyi Biotec 130-042-10; 130-042-302
MCA205 mouse fibrosarcoma cell line Sigma-Aldrich SCC173
Microbiologically controlled animal facility equipped with Class II safety cabine
MicroCL 21R Microcentrifuge Thermo Scientific 75002552
Microsoft Excel  Microsoft, Redmond 
Mouse: C57BL/6J The Jackson Laboratory 000664
Naive CD8a+ T Cell Isolation Kit, mouse Miltenyi Biotec 130-096-543
Nikon ECLIPSE Ts2 Nikon Instruments Inc.
NIS-Elements BR 5.30.0064-BIT Nikon Instruments Inc.
Optical litography EVG
Penicillin G sodium salt and streptomycin sulfate EuroClone ECB3001D/1
Pipet aid Drummond Scientific Co., Broomall, PA  4-000-201
Pipettes Eppendorf
PKH67 Fluorescent Cell Linker Kits Sigma-Aldrich PKH67GL-1KT fluorescent cell linker  kit
plastic coverslip IBIDI 10812
Propidium Iodide Thermo Scientific P1304MP
Reactive Ion Etching system Oxford plasmalab
Roswell Park Memorial Institute 1640 (RPMI 1640) EuroClone ECB9006L
serological pipettes (2 mL, 5 mL, 10 mL, 25 mL) Corning- Millipore-Sigma; St. Louis,
MO		 CLS4486; CLS4487; CLS4488; CLS4489
SL 16 Centrifuge Series Thermo Scientific 75004031
Sterile scalpels, surgical forceps, scissors and pliers
Sterile tips (1–10 μL, 20–200 μL, 1000 μL) EuroClone Spa, Milan, Italy ECTD00010; ECTD00020; ECTD00200; ECTD01005
SU-8 3000 series MicroChem corp, Newton, (MA)
Suite of dermal biopsy punches Kai Medical, Tedpella
Sylgard 184 Dowsil, Dow Corning 101697
TCR beta Monoclonal Antibody (H57-597) eBioscience 12-5961-82
Thermostatic bath
Timer
TMCS  Sigma Aldrich 92360
Trypan Blue Stain (0.4%) Thermo Scientific 15250061
Trypsin-EDTA w/ Phenol Red EuroClone ECM0920
Vacuum dessicator

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Manduca, N., Maccafeo, E., De Ninno, … More

Manduca, N., Maccafeo, E., De Ninno, A., Sistigu, A., Musella, M. Co-Culture In Vitro Systems to Reproduce the Cancer-Immunity Cycle. J. Vis. Exp. (208), e66729, doi:10.3791/66729 (2024).

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