Animals were housed and treated under an animal protocol approved by the Institutional Animal Care and Use Committee at the University of Wisconsin-Madison. The CD4 mCherry reporter mouse model was made using Cre-lox methods by crossing CD4-Cre mice (Jax 022071) with floxed H2B-mCherry mice (Jax 023139) at the University of Wisconsin Biomedical Research Model Services ( Figure S1 ) (30, 31). H2B-mCherry homozygous/CD4cre hemizygous mice were successfully bred and genotyped to confirm mCherry labeling to all CD4+ cells that expressed CD4 either during differentiation or in their mature state. The reporter mCherry was chosen to avoid spectral overlap with autofluorescence of NAD(P)H and FAD, and to confirm the identity of in vivo cells with CD4 expression presently or in their lineage (32–34).

B78-D14 (B78) melanoma is a poorly immunogenic cell line derived from B78-H1 melanoma cells, which were originally derived from B16 melanoma (35–37). These cells were obtained from Ralph Reisfeld (Scripps Research Institute) in 2002. B78 cells were transfected with functional GD2/GD3 synthase to express the disialoganglioside GD2 (36, 37), which is overexpressed on the surface of many human tumors including melanoma (38). These B78 cells were also found to lack melanin. B78 cells were grown in RPMI-1640 (Gibco) supplemented with 10% FBS and 1% penicillin/streptomycin, with periodic supplementation with 400 μg G418 and 500 μg Hygromycin B per mL. Mycoplasma testing was performed every 6 months. B78 tumors were engrafted by intradermal flank injection of 2×10⁶ tumor cells (39). We have previously developed successful immunotherapy regimens for mice bearing these B78 tumors, enabling the cure of mice with measurable tumors (~100 mm³ volume) (40–42). These cured mice have demonstrated tumor-specific T-cell mediated memory, as detected by rejection of rechallenge with the same vs. immunologically distinct tumors. Here, we continue our investigation of the B78 tumor model and expand our work into the metabolic field. Tumor size was determined using calipers and volume approximated as:

Intravital imaging of the mouse tumors (n = 2) was performed in untreated mice when tumors were well established (280-340 mm³), 5-6 weeks after inoculation. Immediately prior to tumor imaging, skin flap surgery exposed flank tumors. Mice were anesthetized with isoflurane, then the skin around the tumor was cut into a flap and separated from the body cavity so that the tumor laid flat on the imaging stage while still connected to the vasculature (43–45). Mice were placed on a specialized microscope stage for imaging and kept in a heating chamber (air maintained at 37°C) during imaging. An imaging dish insert and PBS for coupling were used with surgical tape to secure skin flap tumors ( Figure 1 ).

Intravital/in vivo imaging was performed live, as described in 2.1 above ( Figure 1 ). Ex vivo imaging of whole spleens was performed immediately following intravital imaging and euthanasia. Excised spleens were secured to an imaging dish with PBS coupling and tape. Ex vivo imaging was completed within one hour post mouse euthanasia, accurately capturing splenic metabolism based on our previous work that showed that ex vivo metabolism is statistically identical to in vivo metabolism for up to 12 hours post euthanasia (46). In vitro imaging of CD3+ splenic T cells was performed following negative selection magnetic bead separation with an EasySep™ Mouse T Cell Isolation Kit (STEMCELL Technologies) and cells were plated on a glass imaging dish.

Autofluorescence images were captured with a custom-built multi-photon microscope (Bruker) using an ultrafast femtosecond laser (InSight DSC, Spectra Physics). Fluorescence lifetime measurements were performed using time-correlated single photon counting electronics (Becker & Hickl). Fluorescence emission, in FLIM mode, was detected simultaneously in three channels using bandpass filters of 466/40 nm (NAD(P)H), 514/30 nm (FAD), and 650/45 nm (mCherry) prior to detection with three GaAsP photomultiplier tubes (Hamamatsu). All three fluorophores were simultaneously excited using a previously reported wavelength mixing approach (47, 48) with two-photon excitation at 750 nm (tunable line, λ₁) and 1041 nm (fixed line, λ₂), and two-color two-photon excitation at 872 nm (λ_2c-2p):

Briefly, the laser source tuned to 750 nm (NAD(P)H excitation) was delayed and collimated with the secondary laser line fixed at 1041 nm (mCherry excitation) for spatial and temporal overlap at each raster-scanned focal point (2-color excitation of FAD with 750 nm + 1041 nm). During wavelength mixing in FLIM mode, typical power was 0.9-1.9 mW for the 750 nm laser and 0.7-1.0 mW for the 1041 laser. Second-harmonic generation (SHG) images, in galvo mode, of collagen fibers and mCherry-expressing immune cells were detected using bandpass filters of 514/30 nm (collagen/SHG) and 650/45 nm (mCherry) at 1041 nm excitation (typical power 2.7-3.0 mW). There will be some SHG contribution to the FAD channel from our 1041 nm laser, but we can distinguish SHG signal from true FAD signal due to the clear morphology and lifetime differences between collagen fibers and FAD-containing cells. Sum frequency generation (SFG) signal (436 nm) generated via our wavelength mixing setup is excluded using these bandpass filters (47, 48). A phasor plot was used to confirm that no mCherry signal was present in the FAD channel. All images were acquired with a 40×/1.13 NA water-immersion objective (Nikon) at 512×512 pixel resolution and an optical zoom of 1.0-2.0. A daily fluorescence standard measurement was collected by imaging a YG fluorescent bead (Polysciences Inc.) and verifying the measured lifetime with reported lifetime values. NAD(P)H and FAD intensity and lifetime images were acquired to sample metabolic behavior of B78 tumor and immune cells across 3-8 fields of view and multiple depths within each tissue.

Excised tissues were formalin fixed and paraffin-embedded for antibody staining with a panel of fluorescent markers (CD4, mCherry, and CD8). Embedded sections were deparaffinized and hydrated prior to antigen retrieval and placement in blocking solution. Next, primary antibodies were sequentially applied upon removal of blocking solution at the following dilutions and incubation times: CD4 – 1:500 for 15 min, mCherry – 1:500 for 10 min, and CD8 –1:250 for 15 min. Secondary antibodies were then added following each primary antibody incubation using rat and rabbit secondary antibodies. The following staining dyes were added after secondary antibody washes at 1:100 dilution for 10 min: CD4 – Opal-dye 520, mCherry – Opal-dye 570, CD8 – Opal dye 620. Finally, stained sections were incubated in DAPI for 5 min at room temperature for nuclear labeling and mounted on coverslips for imaging. Imaging was performed at 40× using a Vectra multispectral imaging system (Akoya Biosciences) and a spectral library was generated to separate spectral curves for each of the fluorophores. Resulting images were analyzed using Nuance and inForm software (Akoya Biosciences).

Tumors and spleens were harvested from euthanized mice. Spleens were then dissociated into a single cell suspension and filtered with a 70 μm filter. Red blood cells were lysed using 1 mL ddH₂O for 10 seconds, then spleen cells were stored in PBS on ice until aliquoted into flow cytometry tubes. Tumors were cut into ~1 mm chunks using surgical scissors. The tumor chunks were collected in gentleMACS C tubes (Miltenyi Biotec) containing 2.5 mL of RPMI 1640 + 10% fetal bovine serum, 100 U/mL penicillin, 100 μg/mL streptomycin, and 2 mM L-glutamine. 100μL of DNAse I solution in RPMI 1640 (2.5 mg/mL, Sigma-Aldrich) and 100 μL collagenase IV solution in RPMI 1640 (25 mg/mL, Gibco) were then added and the samples were run on a MacsQuant Octo Dissociator (Miltenyi Biotec) using the preset dissociation protocol for mouse tumor dissociations. After dissociation, the tumors were filtered through a 70 μm cell strainer, washed with 10 ml PBS, and stored on ice until being aliquoted in flow cytometry tubes.

Flow cytometry tubes containing 2-5x10⁶ tumor or spleen cells were labeled and stained with 0.5 μL GhostRed780 (Tonbo Biosciences) in 500μL of PBS per sample and light protected at 4°C for 20-30 minutes. Samples were then washed with flow buffer (PBS +2% FBS). F_c block was not used here. In the meantime, a master mix containing all antibody markers was prepared and aliquoted at 50 μL per flow tube ( Table S2 ). Fluorescence minus one controls (FMOs) were also prepared for each marker minus live/dead. Cells were stained with surface markers for 30-45 min at 4°C in the dark. After staining, samples were washed in flow buffer and data acquired on an Attune™ NxT flow cytometer (Thermo Fisher) with manufacturer provided acquisition software. This instrument was maintained by the Flow Cytometry Core Lab through the University of Wisconsin Carbone Cancer Center, which performs daily quality control checks and instrument calibration using Attune™ Performance Tracking Beads (Thermo Fisher, cat 4449754). This cytometer was equipped with the following excitation lasers: 488nm (BL), 561nm (YL), 405m (VL), and 633nm (RL). The cytometer was equipped with the following channel/bandpass filter combinations: BL1 (530/30), BL2 (590/40), BL3 (695/40), YL1 (585/16), YL2 (620/15), YL3 (695/40), YL4 (780/60), VL1 (440/50), VL2 (512/25), VL3 (603/48), VL4 (710/50), RL1 (670/14), RL2 (720/30), and RL3 (780/60). Data were analyzed using FlowJo version 10.7.1 (FlowJo LLC, Becton Dickinson & Company (BD) 2006-2020). Gates were determined using FMOs. Gating strategies for each immune cell population are defined in Table 2 .

Z-score heatmaps were created using the Complex Heatmap package (RStudio) (53, 54). Hierarchical clustering of single cells was performed based on twelve OMI parameters (NAD(P)H τ_m, τ_1, τ_2, α₁, α₂; FAD τ_m, τ_1, τ_2, α₁, α₂; cell size; optical redox ratio) and calculated using Ward’s method. Labels for cell type, tissue type, and mouse were added afterwards and not included in cluster analysis.

Mann–Whitney statistical tests for non-parametric, unpaired comparisons were performed to assess differences in OMI parameters between cell types. This test was chosen because these data distributions were not assumed to be parametric. Results are represented as dot plots showing mean ± standard deviation where each dot represents a single cell (GraphPad Prism 9). Effect size was also calculated to assess differences in OMI parameters between cell types using Glass’s Δ because comparisons of very large sample sizes of individual cells almost always pass traditional significance tests unless the population effect size is truly zero (55). Glass’s Δ is defined as the difference between the mean of the experimental group (M₁ ) and the mean of the control group (M₂ ) divided by the standard deviation of the control group (σ_control ).

A Glass’s Δ > 0.8 was chosen to indicate significant effect size based on previous studies (22, 56, 57). When comparing immune cells from different tissues, the immune cells from the spleen were designated as the control. When comparing tumor cells and immune cells within the tumor, immune cells were designated as the control. Here, we report the absolute value of Glass’s Δ. The OMI heatmaps represent Z-score (4 standard deviations ± from the mean of all cells in the heatmap) for each OMI parameter per cell type (RStudio) (53). A Z-score of 0 indicates that data point score is identical to the mean score.

Multiphoton images of ex vivo isolated CD3+ splenic T cells illustrate successful nuclear mCherry-expression (red) by all T cell populations within the CD4 mCherry reporter mouse ( Figure 2A left) with no mCherry present in the C57BL/6 wild type mouse ( Figure 2A right). As expected, both reporter and wild type mice exhibit endogenous NAD(P)H (blue) in all T cells. Multiplex immunofluorescence images from reporter mouse spleen ( Figure 2B left) and B78 tumor ( Figure 2B right) illustrate mCherry-expression by several subsets of immune cells including CD4+mCherry+ (yellow arrow) and CD8+mCherry+ (magenta arrow) T cells as well as other immune cells. Quantitative cell population analysis via flow cytometry in reporter mouse spleens and B78 tumors ( Figure 2C ) indicates efficient mCherry-expression by all CD4+ T cells (70-100%), with mCherry labeling also occurring in other cells known to express CD4 during differentiation including CD8 T cells and NKT cells (70-100%) as well as B cells, dendritic cells, and macrophages (<20%). The data also indicates very low mCherry expression by cells that do not typically express CD4 during differentiation, including NK cells and neutrophils (<20%). During maturation, high CD4 expression is expected for CD4 and CD8 T cells as well as NKT cells while CD4 is expressed to a minor extent on macrophages, B cells, dendritic cells, and some monocytes (32–34). When comparing mCherry mean fluorescence intensity (MFI) of these more mature immune cell populations, CD8 T cells, CD4 T cells, and NKT cells exhibited the brightest mCherry signal with significantly lower MFI for all other populations ( Figure S3 ). Additionally, the mCherry MFI of NK cells, B cells, macrophages, dendritic cells, and neutrophils overlapped with our negative control – a spleen from a wild type C57BL/6 mouse ( Figure S3 ). Overall, the Cre-lox methods used to create this reporter mouse ( Figure S1 ) were successful at labeling the majority of T cell and NKT populations known to express CD4 in either their mature state or during maturation. This reporter mouse model may be a useful tool to researchers performing tumor immunotherapy work where visualization of T cells via mCherry-expression is beneficial, especially for techniques where secondary methods can be used to further identify subsets of mCherry+ cells, if desired. If a highly specific CD4 T cell model is needed, this model may not be the optimal choice.

Intravital imaging of reporter mouse tumors was performed using a skin flap surgery method ( Figure 1 ) to image B78 melanoma tumor and immune cell subsets live. Representative in vivo fluorescence intensity tumor images ( Figure 3A left, middle) highlight mCherry-expressing immune cells (red) infiltrated into the tumor tissue and within the vasculature as well as autofluorescent NAD(P)H (blue) and FAD (green) expressed and detected in all cells. Collagen fibers (endogenous SHG signal, white) and their interaction with mCherry-expressing immune cells were also visualized within the TME, where some immune cells are co-localized with collagen fibers ( Figure 3A right). Ex vivo imaging of freshly excised reporter mouse spleens was completed within one hour post mouse euthanasia, accurately capturing splenic metabolism based on our previous work that showed that ex vivo metabolism is statistically identical to in vivo metabolism for up to 12 hours post euthanasia (46). Representative ex vivo fluorescence intensity spleen images ( Figure 3B ) highlight mCherry-expressing immune cells (red) within the spleen tissue and lining the vasculature. Autofluorescent NAD(P)H (blue) and FAD (green) can be detected in all cells. Optical redox ratio images inform on tumor and immune cell redox balance in both tumor and spleen tissues ( Figure 3C ). Representative in vivo fluorescence lifetime images of the tumor and spleen show the mean lifetime of mCherry, NAD(P)H, and FAD mapped to their location with each cell ( Figure 3D ). The mCherry mean lifetime values (typical τ_m = 1,300-1,500 ps) help distinguish mCherry+ cells from nonspecific red autofluorescence in vivo (58–61). The NAD(P)H and FAD mean lifetime values inform on protein-binding and other environmental changes within the cell. Our imaging technology provides clear, concurrent images of both B78 and mCherry+ immune cells from both cancerous and healthy tissues. This imaging approach may be helpful to researchers performing immunotherapy, infectious disease, and autoimmune disorder research where the focus is on both the immune system and local tissues.

To obtain quantitative metabolic information from the images we acquired, single cell segmentation was performed. Traditionally, cell segmentation has been performed manually. However, in vivo and ex vivo images often have low signal to noise ratios, additional cell types that complicate analysis, and poorly distinguished cell boundaries – all of which makes the manual segmentation process difficult and slow. Manual segmentation also introduces variability between researchers. To promote faster and more reproducible segmentation, an automated single cell segmentation method was developed ( Figures 4A , S2 ) to segment immune cells. This custom Python algorithm provided unbiased, accurate segmentation masks and validated results against hand segmented ground truth masks ( Figures 4B, C ). The automated segmentation algorithm performed the segmentation in a fraction of the time required to segment the images manually – on the order of seconds compared to hours. Additionally, this automated method combined both fluorescence intensity and fluorescence lifetime images to create the most accurate immune cell mask – a step that helps differentiate true mCherry signal from nonspecific red autofluorescence in the tissue. The algorithm also allowed rapid iteration of cell segmentation to provide the most optimized mask. The final immune cell masks were high quality and closely matched the manually segmented masks ( Table S1 ). Dice coefficient accuracy metrics were calculated to compare images, where Dice scores range from 0 (no overlap) to 1 (perfect match). The image segmentation field typically considers 0.5 the minimum acceptable Dice coefficient, with higher regard for segmentation techniques that produce Dice coefficients in the 0.6-0.7 range (62–64). Our automated segmentation algorithm performed better on images from the spleen (Dice coefficients average 0.663) compared to images from the tumors (Dice coefficients average 0.590). Immune cell size was more heterogenous and signal to noise was worse within the tumor images, likely contributing to this difference in performance.

Using these techniques, we then quantified single cell mCherry+ immune cell OMI parameters from spleens and B78 tumors. Immune cells from B78 tumors exhibited significantly decreased FAD τ_m compared to immune cells from spleens ( Figure 5A , Glass’s Δ > 0.8). Immune cells from B78 tumors also exhibited significantly increased FAD α₁, the proportion of protein-bound FAD, compared to immune cells from spleens ( Figure 5A , Glass’s Δ > 0.8). Additionally, immune cells from B78 tumors are significantly larger in size compared to immune cells from spleens ( Figure 5A , Glass’s Δ > 0.8). Finally, immune cells from B78 tumors exhibited decreased NAD(P)H τ_m compared to immune cells from spleens; though the p value indicates substantial significance (p<0.0001), only a trend is observed using the more conservative Glass’s Δ test ( Figure 5A , Glass’s Δ = 0.5366). We also visualized single cell heterogeneity based on OMI parameters from immune cells within spleens and tumors using a heatmap of z-scores where each column is a single immune cell ( Figure 5B ). The OMI parameters cluster cells best by tissue type (spleen vs. tumor) with some clustering by mouse. When the OMI parameters are plotted without the optical redox ratio, we continue to see consistent clustering by tissue type ( Figure S4 ). Taken together, these data show that in vivo OMI can distinguish immune cells from different tissues of origin and visualize single cell heterogeneity in metabolism.

We also quantified single cell OMI parameters from B78 tumor cells versus tumor infiltrating mCherry+ immune cells within the same tumor. Tumor infiltrating immune cells exhibited significantly increased FAD τ_m compared to B78 tumor cells within the same tumor ( Figure 6A , Glass’s Δ > 0.8). Tumor infiltrating immune cells also exhibited significantly increased FAD τ₁, the lifetime of protein-bound FAD, compared to B78 tumor cells within the same tumor ( Figure 6A , Glass’s Δ > 0.8). Additionally, tumor infiltrating immune cells exhibited a decreased FAD α₁ and NAD(P)H τ_m compared to B78 tumor cells; though the p values both indicate substantial significance (p<0.0001), only a trend is observed using the more conservative Glass’s Δ test ( Figure 6A , FAD α₁ Glass’s Δ = 0.5746, NAD(P)H τ_m Glass’s Δ = 0.4448). We visualized single cell heterogeneity based on OMI parameters from tumor infiltrating immune cells and B78 tumor cells within the same tumors using a heatmap of z-scores where each column is a single cell ( Figure 6B ). The OMI parameters cluster cells best by mouse (mouse 1 vs. mouse 2) indicating variability in the different tumor microenvironments in the two mice. We also observed heterogeneity in OMI between the two tumors that were imaged on different days, which was especially prominent within the optical redox ratio parameter. Interestingly, when the OMI parameters are plotted without the optical redox ratio, cells cluster based on both the mouse and cell type (tumor infiltrating immune cell vs. tumor cell) ( Figure S5 ). Taken together, these data show that in vivo OMI can distinguish tumor infiltrating immune cells from B78 tumor cells, all within the same tumor environment, and visualize single cell heterogeneity in metabolism.