AbstractTumor 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.

摘要肿瘤异质性和肿瘤进化导致癌症治疗失败。为了了解选择性压力如何驱动异质性肿瘤进化，在体内成像多个重要成分和肿瘤亚克隆将是有用的。我们提出了一种超连续谱剪裁双光子显微镜（SCT-TPM），并实现了用单光束同时观察九个荧光团，突破了活体双光子荧光成像的“颜色屏障”。

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.

它仅通过调制光纤超连续谱（SC）的相位来实现激发多路复用，从而可以在保持精确空间对齐的情况下捕获多个目标的快速事件。我们使用SCT-TPM来可视化宿主免疫监视下异质性肿瘤进化的时空动态，特别是六个肿瘤亚克隆，免疫细胞和血管网络的行为和相互作用，从而推断肿瘤进展和克隆竞争的轨迹。

SCT-TPM opens up the possibility of tumor lineage tracking and mechanism exploration in living biological systems..

SCT-TPM为活体生物系统中的肿瘤谱系追踪和机制探索开辟了可能性。。

IntroductionCancer ecosystems are complex and consist of multiple tumor subclones with distinct genetic and phenotypic characteristics. The heterogeneous tumors continuously evolve and adapt to selective pressures from the tumor microenvironment (TME), such as immune surveillance and therapeutic interventions, resulting in cells with stronger resistant or metastatic capacity winning the clonal competition and leading to therapeutic failure1,2,3.

。异质性肿瘤不断进化并适应来自肿瘤微环境（TME）的选择性压力，例如免疫监视和治疗干预，导致具有更强抗性或转移能力的细胞赢得克隆竞争并导致治疗失败1,2,3。

Recent advances in single-cell and genomic techniques have enabled the dissection of the degree and spatial distribution of tumor heterogeneity with greater precision4,5,6. However, our understanding of the temporal course of heterogeneous tumor evolution and associated biological events has lagged, mainly due to the difficulties in performing multi-target longitudinal observations in vivo.To investigate how multiple elements such as heterogeneous tumor cells, immune cells, vascular network, and stromal components interact to shape TME and regulate tumor development and cell fate7,8,9,10, new multicolor imaging techniques are needed to visualize dynamic biological processes in real time.

单细胞和基因组技术的最新进展使得能够更精确地解剖肿瘤异质性的程度和空间分布4,5,6。然而，我们对异质性肿瘤进化和相关生物事件的时间过程的理解已经滞后，主要是由于在体内进行多靶点纵向观察的困难。为了研究异质性肿瘤细胞，免疫细胞，血管网络和基质成分等多种元素如何相互作用以形成TME并调节肿瘤发展和细胞命运7,8,9,10，需要新的多色成像技术来实时可视化动态生物过程。

Techniques such as flow cytometry11, immunofluorescence imaging12, and Stimulated Raman Scattering13,14 perform well for multi-target observations of cells and molecules, but they are not suitable for imaging in live biological systems. Two-photon microscopy has long been the predominant intravital imaging technique.

流式细胞术11，免疫荧光成像12和受激拉曼散射13,14等技术在细胞和分子的多靶点观察中表现良好，但不适用于活体生物系统的成像。长期以来，双光子显微镜一直是主要的活体成像技术。

But conventional two-photon microscopy can only image 3-4 targets simultaneously, which is known as the ‘color barrier’13. Two strategies have been developed to overcome the ‘color barrier’: detection multiplexing and excitation multiplexing. Spectral imaging is a prime example of detection multiplexing, which enables multicolor ima.

但是传统的双光子显微镜只能同时成像3-4个目标，这被称为“色障”。已经开发了两种策略来克服“色障”：检测多路复用和激发多路复用。光谱成像是检测多路复用的一个主要例子，它可以实现多色ima。

(1)

(1)

where\(|E(\omega )|\) and \(|\varphi (\omega )|\) were the spectral amplitude and phase of the femtosecond laser pulse, respectively. Equation 1 reflected the fact that two-photon transitions occur for all pairs of photons between frequencies ω + Ω and ω – Ω.Phase design for SC-tailoringSince the participation of frequency components symmetric about the transition frequencies was required in the MII effect, and the transition frequencies at which the two-photon absorption process is enhanced will be expanded into bands, they could not be set so far apart as to close to the two ends of the SC.

其中，（| E（\ omega）\）和（| \ varphi（\ omega）\）分别是飞秒激光脉冲的光谱幅度和相位。等式1反映了这样一个事实，即频率ω+ Ω和ω– Ω之间的所有光子对都会发生双光子跃迁。SC尾随的相位设计由于MII效应需要参与与跃迁频率对称的频率分量，并且增强双光子吸收过程的跃迁频率将扩展到频带中，因此它们不能相距很远，以至于接近SC的两端。

To achieve a pronounced excitation multiplexing, we kept the FWHM at about 40 nm and finally set the transition frequencies at 770 nm, 810 nm, and 850 nm, respectively (Supplementary Note 1).The sinusoidal function is the most commonly chosen in phase modulation41,42, which can easily change the transition frequency by changing parameters such as amplitude, period, or horizontal shift of the function (Supplementary Figs. 24, 25 and Supplementary Movies 10, 11).

为了实现明显的激发多路复用，我们将FWHM保持在约40 nm，最后将跃迁频率分别设置为770 nm，810 nm和850 nm（补充说明1）。正弦函数是相位调制中最常选择的函数41,42，它可以通过改变函数的幅度，周期或水平偏移等参数来轻松改变过渡频率（补充图24,25和补充电影10,11）。

However, due to the periodicity and symmetry of the sinusoidal function, sub-transition frequencies are easily generated when the transition frequency is set at the two ends of SC, and thus the binarization function is used at both ends of the SC. It modulated phase by setting the spectral phase to 0 or π, with which the TL pulse could be maintained according to Eq. 1.

然而，由于正弦函数的周期性和对称性，当在SC的两端设置跃迁频率时，容易产生亚跃迁频率，因此在SC的两端使用二值化函数。它通过将光谱相位设置为0或π来调制相位，从而可以根据等式1保持TL脉冲。

The specific distribution of 0 and π is determined by the genetic algorithm (GA)43 through continuous iteration. When the transition frequency is located at the two ends of SC, it is more difficult to form pairs of frequencies, and the total number of photon pairs that can participate in two-photon absorption will be less.

0和π的具体分布由遗传算法（GA）43通过连续迭代确定。当跃迁频率位于SC的两端时，形成频率对更加困难，并且可以参与双光子吸收的光子对总数将更少。

Therefore, when deter.

因此，当威慑。

(2)

(2)

where I was the light intensity of excitation light, squared because of absorption of two photons, and \({\delta }_{i}\) denoted the two-photon absorption cross-section of i. The light source used in this system was an SC spectrum, which was a superposition of light with different frequency components, then Eq. 2 could be written:$${N}_{i,abs}\propto {\sum}_{\lambda }{\delta }_{i}(\omega )\cdot {I}^{2}(\omega )$$.

其中I是激发光的光强度，由于两个光子的吸收而平方，并且\（{\ delta}{I}\）表示I的双光子吸收截面。该系统中使用的光源是SC光谱，它是具有不同频率分量的光的叠加，那么可以写出等式2：$${N}_{i，abs}\ propto{\ sum}{\ lambda}{\ delta}{i}（\ omega）\cdot{i}^{2}（\ omega）$$。

(3)

(3)

where\({\delta }_{i}(\omega )\) was the two-photon excitation spectrum of i, and \(I(\omega )\) was the spectrum of the excitation light after objective. Assuming that there was no excited emission and self-bleaching in an excitation window, the average signal of fluorescence that could be collected by the channel be expressed as$$\langle {F}_{i}(t)\rangle \propto \frac{1}{2}\phi {\eta }_{2}{\sum}_{\lambda }{\delta }_{i}(\omega )\cdot \big\langle {I}^{2}(\omega,t)\big\rangle$$.

其中\（{\ delta}\{i}（\ omega）\）是i的双光子激发光谱，而\（i（\ omega）\）是物镜后激发光的光谱。假设在激发窗口中没有激发发射和自漂白，则通道可以收集的平均荧光信号可以表示为$$\langle{F}_{i} （t）\rangle\propto\frac{1}{2}\phi{\eta}\u2}{\sum}\uλ}{\delta}\ui}（\omega）\cdot\big\langle{i}^{2}（\omega，t）\big\rangle$$。

(4)

(4)

\({\eta }_{2}\) denoted the fluorescence quantum yield, and \(\phi\) denoted the fluorescence collection efficiency of the channel, which could be constants at the same imaging condition. \(I(\omega,t)={|{E}^{(2)}(\omega,t)|}^{2}\) substituting the formula derived in the previous section here$$\left\langle {F}_{i}(t)\right\rangle \propto \frac{1}{2}g\phi {\eta }_{2}{\sum}_{\lambda }{\delta }_{i}(\omega )\cdot \\ \left\langle {\left|{\left|\int d\varOmega \left|E(\omega+\varOmega,t)||E(\omega -\varOmega,t)\right|\exp \{i[\varphi (\omega+\varOmega,t)+\varphi (\omega -\varOmega,t)]\}\right|}^{2}\right|}^{2}\right\rangle$$.

\（{\ eta}{2}\）表示荧光量子产率，\（\ phi \）表示通道的荧光收集效率，在相同的成像条件下可以是常数\（I（\ omega，t）={{E}^{（2）}（\ omega，t）}^{2}\）替换上一节推导的公式{F}_{i} （t）\right\rangle\propto\frac{1}{2}g\phi{eta}u2}{sum}{lambda}{delta}ui}（\omega）\cdot\\ left\langle{\ left{\ left | \ int d \ varOmega \ left | E（\ omega+\ varOmega，t）| | E（\ omega-\ varOmega，t）\ right | \ exp{i[\ varphi（\ omega+\ varOmega，t）]+\varphi（\omega-\varOmega，t）]\}\右|}^{2}\右|}^{2}\右\rangle$$。

(5)

(5)

We could directly see the factors influencing fluorescent intensity in Eq. 5. Since the SC contains many frequency components when it was used as an excitation source, the fluorescence intensity produced by the excited fluorophore could be regarded as the sum of each frequency. The fluoresce intensity of excited fluorophore was closely related to both the fluorophore’s two-photon excitation spectra and the light source’s spectra (Supplementary Fig. 3).

我们可以直接在等式5中看到影响荧光强度的因素。由于SC用作激发源时包含许多频率成分，因此激发荧光团产生的荧光强度可以视为每个频率的总和。激发荧光团的荧光强度与荧光团的双光子激发光谱和光源光谱密切相关（补充图3）。

Obviously when modulating the phase of the SC, the fluorescence emitted by the fluorophore would be a function of its two-photon absorption spectrum.To simulate the nonlinear effects, we used SHG spectra mentioned in the last session as the excitation source\(I(\omega,t)\) to calculate the fluorescence intensity of different fluorophores under different phase patterns.

显然，当调节SC的相位时，荧光团发出的荧光将是其双光子吸收光谱的函数。为了模拟非线性效应，我们使用上一节中提到的SHG光谱作为激发源（I（\ω，t））来计算不同相模式下不同荧光团的荧光强度。

This was the index for us to choose fluorophores to carry out multi-labeling. The value measured by spectrometer was the average value\(\langle I(\omega,t)\rangle\), then Eq. 4 transformed to:$$\langle {F}_{i}(t)\rangle \propto \frac{1}{2}g\phi {\eta }_{2}{{\sum}_{\lambda }{\delta }_{i}(\omega )\cdot \left\langle I(\omega,t)\right\rangle }^{2}$$.

这是我们选择荧光团进行多重标记的指标。光谱仪测量的值是平均值\（\ langle I（\ omega，t \ \ rangle \），然后将等式4转换为：$$\ langle{F}_{i} （t）\rangle\propto\frac{1}{2}g\phi{\eta}\u2}{\sum}\u2{\lambda}{\delta}\ui}（\omega）\cdot\left\langle i（\omega，t）\right\rangle}^{2}$$。

(6)

(6)

where \(g=\langle {I}^{2}(t)\rangle /{\langle I(t)\rangle }^{2}\) was related to the shape of the excitation light pulse, here we considered the excitation light as a Gaussian shape, g = 0.664. When we loaded phase1-phase3 to excite samples in sequence, we could estimate the fluorescence intensity of each fluorophore under three phase patterns with their reference two-photon absorption spectra by Eq. 6.

其中\（g=\ langle{I}^{2}（t）\ langle/{\ langle I（t）\ langle}^{2}\）与激发光脉冲的形状有关，在这里我们认为激发光是高斯形状，g=0.664。当我们加载phase1-phase3以顺序激发样品时，我们可以通过方程式6用它们的参考双光子吸收光谱估计三相模式下每个荧光团的荧光强度。

We wrote this, \({F}_{i,phase1},{F}_{i,phase2},{F}_{i,phase3}\) and then define the excitation specificity of fluorophores under each phase pattern as$$specif\!icit{y}_{i,{\!.} phase}=\frac{{F}_{i,phase}}{\max ({F}_{i,phase1},{F}_{i,phase2},{F}_{i,phase3})}$$.

我们写了这个\({F}_{i，第1阶段}，{F}_，{F}_{i，phase3}\），然后将每个相模式下荧光团的激发特异性定义为$$特异性\！icit公司{y}_{i，{\！.}阶段}=\frac{{F}_{i，相}}{\max({F}_{i，第1阶段}，{F}_{i，第2阶段}，{F}_{i，第3阶段}}$$。

(7)

(7)

The maximum value of specificity was 1, under the phase pattern best suited to excite a particular fluorophore. When the emission spectra were similar, the fluorophores could be used simultaneously in SCT-TPM as long as their specificity values were maximized under different phase patterns (Supplementary Fig. 3d).

在最适合激发特定荧光团的相模式下，特异性的最大值为1。当发射光谱相似时，荧光团可以同时用于SCT-TPM，只要它们的特异性值在不同的相模式下最大化（补充图3d）。

We calculated excitation specificity of various fluorophores, and finally use it as the criteria for selecting fluorophores.NMF unmixingBecause the fluorescence energies emitted by the fluorophores during multicolor imaging were insufficient to trigger nonlinear effects, it can be assumed that the signal acquired in each channel is a linear combination of all fluorophores, and the weights correspond to the contribution of each fluorophore in the sample:$$\left[\begin{array}{c}IM{G}_{1}\\ IM{G}_{2}\\ \vdots \\ IM{G}_{m}\end{array}\right]=\left[\begin{array}{cccc}{\alpha }_{1,1} & {\alpha }_{1,2} & \cdots & {\alpha }_{1,n}\\ {\alpha }_{2,1} & {\alpha }_{2,2} & \ddots & {\alpha }_{2,n}\\ \vdots & \ddots & \ddots & \vdots \\ {\alpha }_{m,1} & {\alpha }_{m,2} & \cdots & {\alpha }_{m,n}\end{array}\right]\left[\begin{array}{c}Flu{o}_{1}\\ Flu{o}_{2}\\ \vdots \\ Flu{o}_{n}\end{array}\right]$$.

我们计算了各种荧光团的激发特异性，最后将其用作选择荧光团的标准。NMF分解由于多色成像过程中荧光团发出的荧光能量不足以触发非线性效应，因此可以假设每个通道中采集的信号是所有荧光团的线性组合，权重对应于样品中每个荧光团的贡献：$$\左[\开始{阵列}{c}IM{G}_{1} \\即时消息{G}_{2} \\\vdots\\IM{G}_{m} \end{array}\right]=\left[\begin{array}{cccc}{\alpha}{1,1}&{\alpha}}{1,2}&\cdots&{\alpha}{1,n}\\\\{\alpha}{2,1}&{\alpha}}{2,2}&\ddots&{\alpha}{2,n}\\\ vdots&\ddots D点和V点\\{\alpha}\uM，1}&{\alpha}\uM，2}&\cdots＆{\alpha}\uM，n}\end{array}\right]\left[\begin{array}{c}Flu{o}_{1} {o}_{2} \\\V点\\流感{o}_{n} \结束{数组}\右]$$。

(8)

(8)

Where, \(IM{G}_{1}\cdots IM{G}_{m}\) were images acquired from m channels, \(Flu{o}_{1}\cdots Flu{o}_{n}\) were the n fluorophores in the sample, \({\alpha }_{i,j}\) denoted the ratio of the jth fluorophore’s fluorescence collected in the ith channel to its total energy, \({\alpha }_{j}\) could be referred to the fingerprint of fluorophore j, determined by the channel settings of SCT-TPM and the fluorophores.For NMF unmixing, we performed non-negative matrix factorization to unmix bleed-through signals.

其中，\（IM{G}_{1} {G}_{m} \）是从m通道获得的图像{o}_{1} \c点流感{o}_{n} \）是样品中的n个荧光团，\（{\ alpha}ui，j}）表示在第i个通道中收集的第j个荧光团的荧光与其总能量的比率，\（{\ alpha}uj}）可以指荧光团j的指纹，由SCT-TPM的通道设置和荧光团确定。对于NMF分解，我们执行了非负矩阵分解来分解穿透信号。

The preprocessed multi-channel data cube was flattened to an image matrix \(V\in {{{\rm{R}}}}^{p\times m}\) and normalized it. \(V=W\cdot H(W\in {{{\rm{R}}}}^{p\times n},H\in {{{\rm{R}}}}^{n\times m})\) directly factorizes the image matrix22. Corresponding to Eq. 8, W was the abundance matrix, which represented the decomposed fluorophore, and H was the feature matrix, which was the fingerprint of different fluorophores.

将预处理的多通道数据立方体平坦化为图像矩阵\（V \ in{{rm{R}}}^{p \ times m}\），并对其进行归一化。\（V=W \ cdot H（W \ in{{{rm{R}}}^{p \ times n}，H \ in{{rm{R}}}}^{n \ times m}）直接分解图像矩阵22。对应于等式8，W是表示分解的荧光团的丰度矩阵，H是特征矩阵，它是不同荧光团的指纹。

The elements in W and H were restricted to be nonnegative during matrix decomposition. Using optimization theory through an iterative approach to find W and H, and using a great likelihood estimated to accurately represent the original image.The objective function of matrix decomposition was set to \(f(W,H)={\Vert V-WH\Vert }_{F}^{2}+\mu {J}_{1}(H)\), where \({\Vert V-WH\Vert }_{F}^{2}\) denoted the Euclidean distance between the image matrix V and the product of W and H obtained from the decomposition, indicating the residual of the decomposition, and\({J}_{1}(H)=|sparseness(H)-spH|\) was the penalty term in the objective function, reflecting the sparsity constraint for the abundance matrix in the decomposition process, aiming to reduce the crosstalk between the unmixed images.

在矩阵分解过程中，W和H中的元素被限制为非负。。矩阵分解的目标函数设置为\（f（W，H）={\ Vert V-WH \ Vert}{f}^{2}+\ mu{J}_{1} （H）\），其中\（{\ Vert V-WH \ Vert}{F}^{2}\）表示图像矩阵V与从分解中获得的W和H的乘积之间的欧几里得距离，表示分解的残差，以及\({J}_{1} （H）=|稀疏（H）-spH | \）是目标函数中的惩罚项，反映了分解过程中丰度矩阵的稀疏性约束，旨在减少未混合图像之间的串扰。

\(sparseness(H)=\frac{\sqrt{n}-{L}_{1}/{L}_{2}}{\sqrt{n}-1}\).

（稀疏（H）=frac{n}-{L}_1.{L}_{2} }｛\sqrt{n}-1}).

Data availability

数据可用性

Data generated in the study are included in the manuscript and its Supplementary Information. Source data are provided in this paper.

研究中产生的数据包含在手稿及其补充信息中。本文提供了源数据。

Code availability

代码可用性

The unmixing code written in MATLAB is contained in Supplementary Software along with a demo image and instruction (Supplementary Software 1).

用MATLAB编写的分解代码与演示图像和指令（补充软件1）一起包含在补充软件中。

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Download referencesAcknowledgementsThis work was supported by the National Natural Science Foundation of China No.62475063 (L.Fu), Collaborative Innovation Center Fund XTCX2022JKB12 (L.Fu), National Key Research and Development Program of China No.2022YFC2404401 (L.Fu, Q.Liu, X.Gao), and Open Project Program of Wuhan National Laboratory for Optoelectronics No.

。

2023WNLOKF013 (L.Fu, X.Gao). The authors thank the Optical Bioimaging Core Facility of WNLO-HUST for the support in data acquisition.Author informationAuthor notesThese authors contributed equally: Xiujuan Gao, Xinyuan Huang, Zhongyun Chen.Authors and AffiliationsBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaXiujuan Gao, Xinyuan Huang, Zhongyun Chen, Liu Yang, Yifu Zhou, Zhenxuan Hou, Jie Yang, Shuhong Qi, Zhihong Zhang & Ling FuMoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaXiujuan Gao, Xinyuan Huang, Zhongyun Chen, Liu Yang, Yifu Zhou, Zhenxuan Hou, Jie Yang, Shuhong Qi, Zhihong Zhang & Ling FuSchool of Biomedical Engineering, Hainan University, Sanya, Hainan, ChinaZheng Liu, Zhihong Zhang, Qian Liu, Qingming Luo & Ling FuState Key Laboratory of Digital Medical Engineering, Sanya, Hainan, ChinaZhihong Zhang, Qian Liu, Qingming Luo & Ling FuSchool of Physics and Optoelectronics Engineering, Hainan University, Haikou, Hainan, ChinaLing FuAdvanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaLing FuAuthorsXiujuan GaoView author publicationsYou can also search for this author in.

2023WNLOKF013（L.Fu，X.Gao）。作者感谢WNLO-HUST的光学生物成像核心设施在数据采集方面的支持。作者信息作者注意到这些作者做出了同样的贡献：高秀娟，黄新元，陈忠云。作者和附属机构华中科技大学武汉光电国家实验室生物医学光子学利顿机会中心，湖北武汉，中国高秀娟，黄新元，陈忠云，刘洋，周逸夫，侯振轩，杨洁红，齐树红，张志红和凌福美华中科技大学生物医学光子学重点实验室，湖北武汉，中国高秀娟，黄新元，陈忠云，刘洋，周逸夫，侯振轩，杨洁红，齐树红，张志红和凌福海南大学生物医学工程学院，海南三亚，刘正红，张志红，刘谦海南省三亚市数字医学工程国家重点实验室张志宏，刘倩，海南大学物理与光电工程学院罗庆明，海南省海口市，华中科技大学先进生物医学成像设施，湖北武汉，ChinaLing FuAuthorShuijuan GaoView作者出版物您也可以在中搜索此作者。

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PubMed Google ScholarContributionsL.Fu designed the study. X.Gao, X.Huang, and Z.Chen designed the experiments. X.Gao, X.Huang, Z.Chen, Y.Zhou, L.Yang, and Z.Hou finished the experiments and analyzed the results. S.Qi, Z.Zhang, and J.Yang helped in the experimental design and provided cell and mouse samples.

PubMed谷歌学术贡献l。傅设计了这项研究。十、 高，X。黄和Z。陈设计了实验。十、 高，黄，陈，周，杨和侯完成了实验并分析了结果。S、 Qi，Z。Zhang和J。Yang帮助了实验设计，并提供了细胞和小鼠样本。

Q.Luo, Z.Zhang, Q.Liu, S.Qi, and Z.Liu provided supervision and support review. X.Gao, X.Huang, Z.Chen, L.Fu, and Q.Luo wrote and edited the paper.Corresponding authorsCorrespondence to.

Q、 罗，Z。张，Q。刘，S。齐和Z。刘提供了监督和支持审查。十、 高，X。黄，Z。陈，L。傅和Q。罗撰写并编辑了这篇论文。通讯作者通讯。

Qingming Luo or Ling Fu.Ethics declarations

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The authors declare no competing interests.

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Nature Communications thanks Brad Amos, Wei Min, and Frank Winkler for their contribution to the peer review of this work. A peer review file is available.

Nature Communications感谢Brad Amos，Wei Min和Frank Winkler对这项工作的同行评审所做的贡献。可以获得同行评审文件。

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Reprints and permissionsAbout this articleCite this articleGao, X., Huang, X., Chen, Z. et al. Supercontinuum-tailoring multicolor imaging reveals spatiotemporal dynamics of heterogeneous tumor evolution.

转载和许可本文引用本文Gao，X.，Huang，X.，Chen，Z。等人。超连续谱裁剪多色成像揭示了异质性肿瘤进化的时空动态。

Nat Commun 15, 9313 (2024). https://doi.org/10.1038/s41467-024-53697-1Download citationReceived: 10 July 2024Accepted: 21 October 2024Published: 29 October 2024DOI: https://doi.org/10.1038/s41467-024-53697-1Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

《国家公社》159313（2024）。https://doi.org/10.1038/s41467-024-53697-1Download引文收到日期：2024年7月10日接受日期：2024年10月21日发布日期：2024年10月29日OI：https://doi.org/10.1038/s41467-024-53697-1Share本文与您共享以下链接的任何人都可以阅读此内容：获取可共享链接对不起，本文目前没有可共享的链接。复制到剪贴板。

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