Supercontinuum-tailoring multicolor imaging reveals spatiotemporal dynamics of heterogeneous tumor evolution

Abstract

Tumor heterogeneity and tumor evolution contribute to cancer treatment failure. To understand how selective pressures drive heterogeneous tumor evolution, it would be useful to image multiple important components and tumor subclones in vivo. We propose a supercontinuum-tailoring two-photon microscope (SCT-TPM) and realize simultaneous observation of nine fluorophores with a single light beam, breaking through the 'color barrier' of intravital two-photon fluorescence imaging. It achieves excitation multiplexing only by modulating the phase of fiber supercontinuum (SC), allowing to capture rapid events of multiple targets with maintaining precise spatial alignment. We employ SCT-TPM to visualize the spatiotemporal dynamics of heterogeneous tumor evolution under host immune surveillance, particularly the behaviors and interactions of six tumor subclones, immune cells and vascular network, and thus infer the trajectories of tumor progression and clonal competition. SCT-TPM opens up the possibility of tumor lineage tracking and mechanism exploration in living biological systems.

Fig. 1. Schematic illustrating how SCT-TPM achieves multicolor two-photon imaging.

a Principle of the SCT-TPM. SCT-TPM applied both excitation multiplexing and detection multiplexing. The 100-fs, 800 nm laser pulse is pumped into the PCF to generate an SC ranging from 700 to 900 nm by the dispersion and nonlinear effects of PCF. The 4 f reflective pulse shaper was programmed to compensate dispersion of the entire optical system to make the output SC beam near transform-limited, and then the designed phase patterns were applied to achieve SC tailoring. Different dichroic mirrors and optical filters were used in the detection path to realize detection multiplexing. Fluorescence signals were collected by photomultiplier tube (PMT). PCF, photonic crystal fiber; SLM, spatial light modulator; PSF, point spread function. b 100-fs, 800 nm pump pulse (left), and SC spectrum (right). c Phase modulation by SLM. d Measured PSF of identical 200 nm fluorescent bead during SC-tailoring. e Three blue fluorescent proteins, EBFP2.0, TagBFP, and mCerulean, to verify the discernibility of SCT-TPM. Two independent repeated experiments with similar results. f Two-photon absorption spectra (upper) and fluorescence emission spectra (down) of three blue fluorescent proteins. g The detailed process of supercontinuum tailoring. Two-photon absorption at different desired wavelengths occurs preferentially in different phase patterns. Phase pattern 1 corresponds to 770 nm (first row), Phase pattern 2 corresponds to 810 nm (second row), and Phase pattern 3 corresponds to 850 nm (third row). EBFP2.0, TagBFP, and mCerulean were optimally excited by phase patterns 1, 2, and 3, respectively. Individual fluorescent proteins could be discriminated against after unmixing. Purple arrows, EBFP2.0-expressing cells; blue arrows, TagBFP-expressing cells; cyan arrows, mCerulean-expressing cells (not marked all cells with arrows). Scale bars: 100 μm. Source data are provided as a Source Data file.

Fig. 2. SCT-TPM imaging of live HeLa cells labeled with nine fluorophores.

a Two-photon excitation spectra, and (b) Fluorescence emission spectra of nine fluorophores. c NMF unmixed pseudo-color images of live HeLa cells labeled with nine fluorophores. Two independent repeated experiments with similar results. d Signal intensity curves of continuous 60-frames in 240 s. The average photobleaching constant was determined to be 0.0024. Blue dots, average of nine fluorescence signals; solid red line, exponential fitting. e Intensity profiles for comparison of raw images, linear unmixing (LU), and NMF unmixing. The x-axis denoted the distance along the arrows drawn in the left images; the y-axis denoted the intensity value of three fluorophores collected by a channel. Three panels, Ch B, G, and R, denoted three channels with filters 447/60, 520/60, and 629/53, respectively. Colors in (a) and (e) match rendering in (b). Scale bars: 10 μm. Source data are provided as a Source Data file.

Fig. 3. Time-lapse imaging of live B16 cells in response to pro-proliferative signals.

a Dynamics of organelles after the treatment of 40 ng/ml EGF for 4 h: b chromosome separation, c mitochondria fusing and fissioning, d a mitochondrion moving along the actin axis, e activated Golgi apparatus and f the oscillating actin filaments. Subcellular structures of B16 cells were labeled by different fluorescent proteins, respectively. Scale bars: 5 μm. g Mitotic behavior after the treatment of EGF for 8 h: h interphase, i prophase, j prometaphase, k metaphase, l anaphase, and (m) telophase. n–q Tumor cells undergoing mitosis: n EBFP2.0-B16 cell and mKate-B16 cell, o mKate-B16 cell, p EBFP2.0-B16 cell and (q) TagBFP-B16 cell. r The relative numbers of mitotic cells after being treated with EGF or not. A two-tailed unpaired t test was performed, n = 6, p =  0.0008, 95% CI = [14.98–41.72], t =  4.724. Inset: the percentage of six subclones in all mitotic cells. s Cell growth rates of different B16 subclones detected by CCK-8. Ordinary one-way ANOVA analysis and Tukey’s multiple comparison tests were performed; n = 7, p =  0.0002, 0.0033, 95% CI = [− 0.3519 to − 0.09068], [− 0.3065 to − 0.04527], F =  6.106. Data in (r, s) were presented as mean ± SEM. t Mitotic arrest and cell apoptosis after the treatment of 40 ng/ml EGF for 8 h + 50 μM Paclitaxel for 2 h: u chromosome doubling, v abnormal binucleated tumor cell, w cell lysis, x release of extracellular vesicle, y intercellular adhesion, and (z) intercellular phagocytosis. Scale bars: 20 μm. Source data are provided as a Source Data file.

Fig. 4. Intravital 5D imaging of dynamics of eight targets in TME.

a Images of TME in different stages of tumor evolution. Seven imaging sets were recorded (Mouse #1 on Day 3 abbreviated as Day3_M1, the remaining six sets is Day4_M1, Day4_M2, Day5_M2, Day6_M2, Day7_M3 and Day8_M3). b Immune cells were besieging a mCerulean-B16 cell. c The release of extracellular vesicles. d Cell apoptosis and lysis. e Cell mitosis. f Cell migration. g–i Disorganized neovascular network appeared on Day 7 (h, i) compared to that on Day 3 (g). The magenta channel was extracted in (g–i) for clearer demonstration. Scale bars: 20 μm. j Movement trajectories of immune cells. k The time-dependent curves of the motility coefficient (M). l Chemotactic index of the three groups. One-way ANOVA, n = 23, 30, 19 cells, p <  0.0001, = 0.0190, < 0.0001, 95% CI = [0.1276 to 0.2500], [− 0.1479 to − 0.01098], [− 0.3330 to − 0.2035], F = 55.87. j–l showed results for Day 3-M1, and data for the other six sets were detailed in Supplementary Figs. 15–21. m The proportion of different groups changed during tumor progression. All the moving immune cells observed in the seven imaging sets were counted in (m). n Frequency of interaction between different tumor subclones and immune cells. One-way ANOVA, n = 7, p =  0.0333, 0.0155, 0.0072, 0.0011, 95% CI = [− 0.4124 to − 0.01131], [0.03137–0.4324], [0.05060–0.4517], [− 0.4970 to− 0.09593], F = 5.782. o The movement speed of different subclones. One-way ANOVA, n = 7, p =  0.0171, 0.0163, 0.0462, 0.0152, 95% CI = [− 0.4236 to − 0.02848], [− 0.4249 to − 0.02975], [0.002194–0.3973], [0.03152–0.4266], F = 3.71. p The movement length of different subclones. One-way ANOVA, n = 7, p =  0.0248, 0.0415, 0.0001, < 0.0001, 95% CI = [− 0.4324 to − 0.01975], [− 0.4180 to − 0.005362], [0.1525–0.5652], [0.1540–0.5666], F = 7.513. q The movement displacement of different subclones. One-way ANOVA, n = 7, p =  0.0199, 0.0466, 0.0394, 95% CI = [− 0.4616 to − 0.02713], [0.002134–0.4366], [0.007228– 0.4417], F = 3.088. All data is presented as mean ± SEM. Source data are provided as a Source Data file.

Fig. 5. Large-field multicolor imaging of tumor evolution under host immune surveillance.

a Large-field multicolor images of the TME in Mouse #A acquired from Day 3 to Day 10 after inoculation (Images acquired from the other two mice (Mouse #B and #C) were provided in Supplementary Figs. 22, 23). White dashed line: tumor boundary; green dashed line: venous vessels; red dashed line: arterial vessels; orange dashed line: region near the blood vessel. Scale bars: 100 μm. b Based on the vascular localization, the tumor boundary contours of Days 3, 5, 7, and 9 were overlaid. c The relative number of tumor cells. Ordinary one-way ANOVA analysis and Tukey’s multiple comparison tests were performed, n = 3, p =  0.0487, 0.0464, 95% CI = [− 1.482 to -0.003025], [− 1.488 to − 0.008539], F = 2.769. d The relative density of host-derived EGFP cells infiltrating into the tumor area. One-way ANOVA, n = 3, p =  0.0476, 0.0122, 95% CI = [− 1.676 to − 0.006448], [0.1775–1.847], F = 3.551. e The relative number changes of different subclones during Days 3–10. Ordinary one-way ANOVA and Tukey’s multiple comparison tests were performed to compare the relative proportions of each subclone per day, n = 3, *: p < 0.05. For Day 5, TagB-B16 vs. LSSm-B16: p = 0.0484, 95% CI = [− 29.23 to − 0.08267], F = 2.96; for Day 8, TagB-B16 vs. mAme-B16: p = 0.0396, 95% CI = [− 50.92 to − 1.026], F = 2.887; for Day 9, TagB-B16 vs. mAme-B16: p = 0.0385, 95% CI = [− 33.21 to− 0.7522], Ceru-B16 vs. mAme-B16: p = 0.0291, 95% CI = [− 34.01 to − 1.555], mAme-B16 vs. Kate-B16: p = 0.0354, 95% CI = [0.9915 to 33.45], F = 3.914. All data is presented as mean ± SEM. Source data are provided as a Source Data file.