Figure - available via license: Creative Commons Attribution 3.0 Unported
Content may be subject to copyright.
Schematic representation of the two-photon microscopy setup. The excitation beam is produced by a femtosecond pulsed infrared tunable (720–1020 nm) laser (Mai-Tai, Spectra Physics). The laser power is modulated by an Acousto-Optic Modulator (AOM). The beam is scanned in the xy direction by galvanometric mirrors present in the scan head of a Zeiss LMS 7 MP two-photon microscope. The beam then passes through a LP690 dichroic mirror and is focused in the brain of the anaesthetized animal by a 20X-1.0 NA water immersion objective. The emitted epifluorescence is collected and reflected by the LP690 mirror in a non-descanned mode. The fluorescence is finally splitted and filtered using a set of dichroic mirrors and filters and collected by a set of 5 non-descanned detectors mounted in cascade (NDD). The characteristics of the dichroic mirrors and filters are depicted on the scheme.
Source publication
The majority of intravital studies on brain tumor in living animal so far rely on dual color imaging. We describe here a multiphoton imaging protocol to dynamically characterize the interactions between six cellular components in a living mouse. We applied this methodology to a clinically relevant glioblastoma multiforme (GBM) model designed in rep...
Citations
... However, it is hard to provide optimal excitation of multiple fluorophores using the spectral bandwidth (∼10 nm) of pulses from a 100-fs Ti:sapphire laser, which is a standard excitation source for TPEF microscopy [6]. Conventionally, the simplest method is to sequentially tune a femtosecond laser to optimally excite each fluorophore [7]. However, the slow tuning process (seconds and minutes) restricts its application to time-insensitive events. ...
Two-photon excitation fluorescence (TPEF) microscopy has evolved into a versatile tool in biological research. However, the multiplexing capability of TPEF microscopy is limited by the narrow spectral bandwidth of the light source. In this study, we apply a photonic crystal fiber in TPEF microscopy to broaden the excitation source bandwidth. We tuned the spectral window using a spatial light modulator as a programmable diffraction grating that was placed behind a prism pair. In addition, we combined a grating pair to compensate for dispersion to improve the two-photon excitation efficiency. The combination of a broad spectrum and a programmable grating enabled fast spectral window tuning rate on a time scale of tens of milliseconds. We demonstrate the performance of our method by imaging live B16 cells labeled with four emission spectrum overlapped fluorescent proteins.
... IVM technique has been developed as an integration of methods including CLSM, multiphoton microscopy, and epifluorescence to dynamically evaluate tumor structure and physiological courses from tissue to subcellular level [149,150]. The IVM arrangements permit concurrent imaging of multiple TME constituents in combination with nanodrug circulation. ...
The innovative development of nanomedicine has promised effective treatment options compared to the standard therapeutics for cancer therapy. However, the efficiency of EPR-targeted nanodrugs is not always pleasing as it is strongly prejudiced by the heterogeneity of the enhanced permeability and retention effect (EPR). Targeting the dynamics of the EPR effect and improvement of the therapeutic effects of nanotherapeutics by using EPR enhancers is a vital approach to developing cancer therapy. Inadequate data on the efficacy of EPR in humans hampers the clinical translation of cancer drugs. Molecular targeting, physical amendment, or physiological renovation of the tumor microenvironment (TME) are crucial approaches for improving the EPR effect. Advanced imaging technologies for the visualization of EPR-induced nanomedicine distribution in tumors, and the use of better animal models, are necessary to enhance the EPR effect. This review discusses strategies to enhance EPR effect-based drug delivery approaches for cancer therapy and imaging technologies for the diagnosis of EPR effects. The effort of studying the EPR effect is beneficial, as some of the advanced nanomedicine-based EPR-enhancing approaches are currently undergoing clinical trials, which may be helpful to improve EPR-induced drug delivery and translation to clinics.
... In the bone, this has significantly advanced the scientific understanding of vascular dynamics, stem cell biology, and bone homeostasis and regeneration (Lo Celso et al., 2009;Spencer et al., 2014;Itkin et al., 2016;Wilk et al., 2017;Christodoulou et al., 2020). In the brain, intravital imaging has generated unique insight into brain circuitry and processing, brain cancer, brain trauma, and degenerative diseases (Andermann and Kerlin, 2010;Shih et al., 2012;Ricard, 2014;Yang et al., 2018;Calvo-Rodriguez et al., 2019;Chen et al., 2019;Hu et al., 2021). However, one of the most serious obstacles to imaging is the poor penetration depth of intravital optical microscopy. ...
Introduction
Mitochondria are extremely important organelles in the regulation of bone marrow and brain activity. However, live imaging of these subcellular features with high resolution in scattering tissues like brain or bone has proven challenging.
Methods
In this study, we developed a two-photon fluorescence microscope with adaptive optics (TPFM-AO) for high-resolution imaging, which uses a home-built Shack-Hartmann wavefront sensor (SHWFS) to correct system aberrations and a sensorless approach for correcting low order tissue aberrations.
Results
Using AO increases the fluorescence intensity of the point spread function (PSF) and achieves fast imaging of subcellular organelles with 400 nm resolution through 85 μm of highly scattering tissue. We achieved ~1.55×, ~3.58×, and ~1.77× intensity increases using AO, and a reduction of the PSF width by ~0.83×, ~0.74×, and ~0.9× at the depths of 0, 50 μm and 85 μm in living mouse bone marrow respectively, allowing us to characterize mitochondrial health and the survival of functioning cells with a field of view of 67.5× 67.5 μm. We also investigate the role of initial signal and background levels in sample correction quality by varying the laser power and camera exposure time and develop an intensity-based criteria for sample correction.
Discussion
This study demonstrates a promising tool for imaging of mitochondria and other organelles in optically distorting biological environments, which could facilitate the study of a variety of diseases connected to mitochondrial morphology and activity in a range of biological tissues.
... However, on different runs, NMF produces equally valid yet significantly different solutions [20]. Spectral deconvolution [21] requires users to acquire and manually select each fluorophore in regions of interest. In this age of rapidly advancing machine learning techniques, several unsupervised machine learning methods have been applied to unmix spectral data. ...
Background
Multispectral fluorescence imaging coupled with linear unmixing is a form of image data collection and analysis that allows for measuring multiple molecular signals in a single biological sample. Multiple fluorescent dyes, each measuring a unique molecule, are simultaneously measured and subsequently “unmixed” to provide a read-out for each molecular signal. This strategy allows for measuring highly multiplexed signals in a single data capture session, such as multiple proteins or RNAs in tissue slices or cultured cells, but can often result in mixed signals and bleed-through problems across dyes. Existing spectral unmixing algorithms are not optimized for challenging biological specimens such as post-mortem human brain tissue, and often require manual intervention to extract spectral signatures. We therefore developed an intuitive, automated, and flexible package called SUFI: spectral unmixing of fluorescent images.
Results
This package unmixes multispectral fluorescence images by automating the extraction of spectral signatures using vertex component analysis, and then performs one of three unmixing algorithms derived from remote sensing. We evaluate these remote sensing algorithms’ performances on four unique biological datasets and compare the results to unmixing results obtained using ZEN Black software (Zeiss). We lastly integrate our unmixing pipeline into the computational tool dotdotdot, which is used to quantify individual RNA transcripts at single cell resolution in intact tissues and perform differential expression analysis, and thereby provide an end-to-end solution for multispectral fluorescence image analysis and quantification.
Conclusions
In summary, we provide a robust, automated pipeline to assist biologists with improved spectral unmixing of multispectral fluorescence images.
... Lower energy wavelengths are used to reach deeper regions in intact tissue, e.g., approximately 500 µm in the brain cortex, a depth which is less susceptible to tissue scattering and causes less bleaching and phototoxicity [9]. In particular, multiplexing techniques [10][11][12][13] gain even deeper insights into the complex interplay of cells and structures within tissues by exciting them with multiple laser lines simultaneously, providing real-time access to more fluorophores to label structures and cell populations. ...
Two-photon excitation fluorescence laser-scanning microscopy is the preferred method for studying dynamic processes in living organ models or even in living organisms. Thanks to near-infrared and infrared excitation, it is possible to penetrate deep into the tissue, reaching areas of interest relevant to life sciences and biomedicine. In those imaging experiments, two-photon excitation spectra are needed to select the optimal laser wavelength to excite as many fluorophores as possible simultaneously in the sample under consideration. The more fluorophores that can be excited, and the more cell populations that can be studied, the better access to their arrangement and interaction can be reached in complex systems such as immunological organs. However, for many fluorophores, the two-photon excitation properties are poorly predicted from the single-photon spectra and are not yet available, in the literature or databases. Here, we present the broad excitation range (760 nm to 1300 nm) of photon-flux-normalized two-photon spectra of several fluorescent proteins in their cellular environment. This includes the following fluorescent proteins spanning from the cyan to the infrared part of the spectrum: mCerulean3, mTurquoise2, mT-Sapphire, Clover, mKusabiraOrange2, mOrange2, LSS-mOrange, mRuby2, mBeRFP, mCardinal, iRFP670, NirFP, and iRFP720.
... Measuring the mechanical properties of cells and the tumor microenvironment in live animals or patients is a formidable task, but several groups have devised techniques to understand GBM-ECM interactions in-vivo (15,(290)(291)(292)(293). We recently demonstrated the intravital multiphoton imaging to study glioma cell dynamics deep into the brain with high-resolution and low photobleaching (15). ...
Glioblastoma (GBM), an aggressive high-grade glial tumor, is resistant to therapy and has a poor prognosis due to its universal recurrence rate. GBM cells interact with the non-cellular components in the tumor microenvironment (TME), facilitating their rapid growth, evolution, and invasion into the normal brain. Herein we discuss the complexity of the interactions between the cellular and non-cellular components of the TME and advances in the field as a whole. While the stroma of non-central nervous system (CNS) tissues is abundant in fibrillary collagens, laminins, and fibronectin, the normal brain extracellular matrix (ECM) predominantly includes proteoglycans, glycoproteins, and glycosaminoglycans, with fibrillary components typically found only in association with the vasculature. However, recent studies have found that in GBMs, the microenvironment evolves into a more complex array of components, with upregulated collagen gene expression and aligned fibrillary ECM networks. The interactions of glioma cells with the ECM and the degradation of matrix barriers are crucial for both single-cell and collective invasion into neighboring brain tissue. ECM-regulated mechanisms also contribute to immune exclusion, resulting in a major challenge to immunotherapy delivery and efficacy. Glioma cells chemically and physically control the function of their environment, co-opting complex signaling networks for their own benefit, resulting in radio- and chemo-resistance, tumor recurrence, and cancer progression. Targeting these interactions is an attractive strategy for overcoming therapy resistance, and we will discuss recent advances in preclinical studies, current clinical trials, and potential future clinical applications. In this review, we also provide a comprehensive discussion of the complexities of the interconnected cellular and non-cellular components of the microenvironmental landscape of brain tumors to guide the development of safe and effective therapeutic strategies against brain cancer.
... The benefits of simultaneous 33,34 and sequential 35,36 excitation strategies for multicolor fluorescence microscopy are extensively studied using cellular structures. Simultaneous excitation of the fluorophores provides image acquisition from spectrum bands without delay. ...
Optical microscopy techniques are a popular choice for visualizing micro-agents. They generate images with relatively high spatiotemporal resolution but do not reveal encoded information for distinguishing micro-agents and surroundings. This study presents multicolor fluorescence microscopy for rendering color-coded identification of mobile micro-agents and dynamic surroundings by spectral unmixing. We report multicolor microscopy performance by visualizing the attachment of single and cluster micro-agents to cancer spheroids formed with HeLa cells as a proof-of-concept for targeted drug delivery demonstration. A microfluidic chip is developed to immobilize a single spheroid for the attachment, provide a stable environment for multicolor microscopy, and create a 3D tumor model. In order to confirm that multicolor microscopy is able to visualize micro-agents in vascularized environments, in vitro vasculature network formed with endothelial cells and ex ovo chicken chorioallantoic membrane are employed as experimental models. Full visualization of our models is achieved by sequential excitation of the fluorophores in a round-robin manner and synchronous individual image acquisition from three-different spectrum bands. We experimentally demonstrate that multicolor microscopy spectrally decomposes micro-agents, organic bodies (cancer spheroids and vasculatures), and surrounding media utilizing fluorophores with well-separated spectrum characteristics and allows image acquisition with 1280 × 1024 pixels up to 15 frames per second. Our results display that real-time multicolor microscopy provides increased understanding by color-coded visualization regarding the tracking of micro-agents, morphology of organic bodies, and clear distinction of surrounding media.
... Specifically, for the multimodal imaging platform we have developed, the location of the CIW in the skull gives visual access to the brain parenchyma, but also to the meningeal layers above it. While some IVM studies of the brain removed the dura mater during the window implantation (Alieva et al., 2019), to achieve a greater imaging depth of the brain parenchyma, others left it intact (Askoxylakis et al., 2017;Ricard and Debarbieux, 2014) and could visualize it by SHG imaging (Ricard and Debarbieux, 2014). In our IVM model, the dura mater was purposely left in place to preserve the composition of the natural brain environment. ...
... Specifically, for the multimodal imaging platform we have developed, the location of the CIW in the skull gives visual access to the brain parenchyma, but also to the meningeal layers above it. While some IVM studies of the brain removed the dura mater during the window implantation (Alieva et al., 2019), to achieve a greater imaging depth of the brain parenchyma, others left it intact (Askoxylakis et al., 2017;Ricard and Debarbieux, 2014) and could visualize it by SHG imaging (Ricard and Debarbieux, 2014). In our IVM model, the dura mater was purposely left in place to preserve the composition of the natural brain environment. ...
... IVM has been reported as a potent tool to study myeloid cell dynamics in GBM models (Chen et al., 2019;Ricard and Debarbieux, 2014;Ricard et al., 2013Ricard et al., , 2016. These elegant studies focused on specific cell features which either varied during tumor progression (Ricard et al., 2016), or at defined endpoints in different treatment conditions (Chen et al., 2019). ...
Tumors evolve in a dynamic communication with their native tissue environment and recruited immune cells. The diverse components of the tumor microenvironment (TME) can critically regulate tumor progression and therapeutic response. In turn, anti-cancer treatments may alter the composition and functions of the TME. To investigate this continuous dialog in the context of primary brain cancers, we developed a multimodal longitudinal imaging strategy. We combined macroscopical magnetic resonance imaging with subcellular resolution two-photon intravital microscopy, and leveraged the power of single cell analysis tools to gain insights into the ongoing interactions between different components of the TME and cancer cells. Our experiments revealed that the migratory behavior of tumor-associated macrophages is different in genetically-distinct glioblastomas, and in response to macrophage-targeted therapy. These results underscore the importance of studying cancer longitudinally in an in vivo setting, to reveal complex and dynamic alterations in the TME during disease progression and therapeutic intervention.
... In addition, developments in intravital endoscopy have granted visual access to deep brain regions, thereby allowing investigation of tumour and vasculature progression of previously inaccessible deep lying brain tumours (Barretto et al, 2011). Next to these advances in tissue accessibility, increasing the number of cell types that can be distinguished is a major trend in IVM in order to get a better picture of the complex and dynamic TME and its role in tumour progression (Ricard & Debarbieux, 2014;Dawson et al, 2020Dawson et al, , 2021. Application of label-free imaging approaches in IVM (You et al, 2018;preprint: Bakker et al, 2020), such as SHG and THG (Table 2) discussed above, holds promise for less perturbative imaging. ...
Our understanding of the cellular composition and architecture of cancer has primarily advanced using 2D models and thin slice samples. This has granted spatial information on fundamental cancer biology and treatment response. However, tissues contain a variety of interconnected cells with different functional states and shapes, and this complex organization is impossible to capture in a single plane. Furthermore, tumours have been shown to be highly heterogenous, requiring large-scale spatial analysis to reliably profile their cellular and structural composition. Volumetric imaging permits the visualization of intact biological samples, thereby revealing the spatio-phenotypic and dynamic traits of cancer. This review focuses on new insights into cancer biology uniquely brought to light by 3D imaging and concomitant progress in cancer modelling and quantitative analysis. 3D imaging has the potential to generate broad knowledge advance from major mechanisms of tumour progression to new strategies for cancer treatment and patient diagnosis. We discuss the expected future contributions of the newest imaging trends towards these goals and the challenges faced for reaching their full application in cancer research.
... However, these methods assume that pixels' intensities originate from linear mixtures and that reference spectra remain unchanged and independent from other fluorophores under experimental conditions, which might not always be the case (Mylle et al. 2013;Balleza et al. 2018). A solution to these limitations is to determine the spectrum of each fluorophore under experimental conditions, which is the base for methods such as spectral deconvolution (Ricard and Debarbieux 2014). Nonetheless, the experimental determination of spectra is a time-consuming task when handling large numbers of fluorophores or in time-lapse experiments (Valm et al. 2016;Zimmermann 2005). ...
Live-cell fluorescence spectral imaging is an evolving modality of microscopy that uses specific properties of fluorophores, such as excitation or emission spectra, to detect multiple molecules and structures in intact cells. The main challenge of analyzing live-cell fluorescence spectral imaging data is the precise quantification of fluorescent molecules despite the weak signals and high noise found when imaging living cells under non-phototoxic conditions. Beyond the optimization of fluorophores and microscopy setups, quantifying multiple fluorophores requires algorithms that separate or unmix the contributions of the numerous fluorescent signals recorded at the single pixel level. This review aims to provide both the experimental scientist and the data analyst with a straightforward description of the evolution of spectral unmixing algorithms for fluorescence live-cell imaging. We show how the initial systems of linear equations used to determine the concentration of fluorophores in a pixel progressively evolved into matrix factorization, clustering, and deep learning approaches. We outline potential future trends on combining fluorescence spectral imaging with label-free detection methods, fluorescence lifetime imaging, and deep learning image analysis.
