A 3D image filter for parameter-free segmentation of macromolecular structures from electron tomograms

3D image reconstruction of large cellular volumes by electron tomography (ET) at high (≤ 5 nm) resolution can now routinely resolve organellar and compartmental membrane structures, protein coats, cytoskeletal filaments, and macromolecules. However, current image analysis methods for identifying in situ macromolecular structures within the crowded 3D ultrastructural landscape of a cell remain labor-intensive, time-consuming, and prone to user-bias and/or error. This paper demonstrates the development and application of a parameter-free, 3D implementation of the bilateral edge-detection (BLE) algorithm for the rapid and accurate segmentation of cellular tomograms. The performance of the 3D BLE filter has been tested on a range of synthetic and real biological data sets and validated against current leading filters-the pseudo 3D recursive and Canny filters. The performance of the 3D BLE filter was found to be comparable to or better than that of both the 3D recursive and Canny filters while offering the significant advantage that it requires no parameter input or optimisation. Edge widths as little as 2 pixels are reproducibly detected with signal intensity and grey scale values as low as 0.72% above the mean of the background noise. The 3D BLE thus provides an efficient method for the automated segmentation of complex cellular structures across multiple scales for further downstream processing, such as cellular annotation and sub-tomogram averaging, and provides a valuable tool for the accurate and high-throughput identification and annotation of 3D structural complexity at the subcellular level, as well as for mapping the spatial and temporal rearrangement of macromolecular assemblies in situ within cellular tomograms.     

3D imaging of diatoms with ion-abrasion scanning electron microscopy

Ion-abrasion scanning electron microscopy (IASEM) takes advantage of focused ion beams to abrade thin sections from the surface of bulk specimens, coupled with SEM to image the surface of each section, enabling 3D reconstructions of subcellular architecture at 30 nm resolution. Here, we report the first application of IASEM for imaging a biomineralizing organism, the marine diatom Thalassiosira pseudonana. Diatoms have highly patterned silica-based cell wall structures that are unique models for the study and application of directed nanomaterials synthesis by biological systems. Our study provides new insights into the architecture and assembly principles of both the “hard” (siliceous) and “soft” (organic) components of the cell. From 3D reconstructions of developmentally synchronized diatoms captured at different stages, we show that both micro- and nanoscale siliceous structures can be visualized at specific stages in their formation. We show that not only are structures visualized in a whole-cell context, but demonstrate that fragile, early-stage structures are visible, and that this can be combined with elemental mapping in the exposed slice. We demonstrate that the 3D architectures of silica structures, and the cellular components that mediate their creation and positioning can be visualized simultaneously, providing new opportunities to study and manipulate mineral nanostructures in a genetically tractable system.     

Fast and sensitive delineation of brain tumor with clinically compatible moxifloxacin labeling and confocal microscopy

Delineation of brain tumor margins during surgery is critical to maximize tumor removal while preserving normal brain tissue to obtain optimal clinical outcomes. Although various imaging methods have been developed, they have limitations to be used in clinical practice. We developed a high‐speed cellular imaging method by using clinically compatible moxifloxacin and confocal microscopy for sensitive brain tumor detection and delineation. Moxifloxacin is a Food and Drug Administration (FDA) approved antibiotic and was used as a cell labeling agent through topical administration. Its strong fluorescence at short visible excitation wavelengths allowed video‐rate cellular imaging. Moxifloxacin‐based confocal microscopy (MBCM) was characterized in normal mouse brain specimens and visualized their cytoarchitecture clearly. Then, MBCM was applied to both brain tumor murine models and two malignant human brain tumors of glioblastoma and metastatic cancer. MBCM detected tumors in all the specimens by visualizing dense and irregular cell distributions, and tumor margins were easily delineated based on the cytoarchitecture. An image analysis method was developed for automated detection and delineation. MBCM demonstrated sensitive delineation of brain tumors through cytoarchitecture visualization and would have potentials for human applications, such as a surgery‐guiding method for tumor removal.      

Recent advances in synthetic glycoengineering for biological applications

Carbohydrates are involved in many important biological events such as protein maturation and trafficking, pathogen invasion, immune response, cell–cell communications, and so on. Synthetic and chemoenzymatic approaches for glycoengineering have emerged and been applied in perturbing and modulating the biological processes at the protein or cellular level. In this review, we summarize the recent advances in glycoengineering, including new strategies in chemoenzymatic synthesis of glycans, glycopeptides, glycoproteins, and other glycoconjugates. And, the progresses of cell-surface glyco-editing methods for gain of functions are also discussed.

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Optimization for continuous-wave terahertz reflection imaging for biological tissues

Continuous-wave terahertz reflection imaging is a potential tool for biological tissues. Based on our home-made continuous-wave terahertz reflection imaging system, the effect of both polarization mode and reflection window on the imaging performance is studied theoretically and experimentally, showing good agreement. By taking obtaining sample information and image contrast into consideration, p-polarized terahertz waves are recommended. Moreover, considering the sample boundary identification and the image contrast, selection criteria for reflection window are proposed. This work will help to improve the performance of continuous-wave terahertz reflection imaging and accelerate the THz imaging in biological application.     

Quantitative phase microscopy monitors subcellular dynamics in single cells exposed to nanosecond pulsed electric fields

A substantial body of literature exists to study the dynamics of single cells exposed to short duration (<1 μs), high peak power (~1 MV/m) transient electric fields. Much of this research is limited to traditional fluorescence-based microscopy techniques, which introduce exogenous agents to the culture and are only sensitive to a single molecular target. Quantitative phase imaging (QPI) is a coherent imaging modality which uses optical path length as a label-free contrast mechanism, and has proven highly effective for the study of single-cell dynamics. In this work, we introduce QPI as a useful imaging tool for the study of cells undergoing cytoskeletal remodeling after nanosecond pulsed electric field (nsPEF) exposure. In particular, we use cell swelling, dry mass and disorder strength measurements derived from QPI phase images to monitor the cellular response to nsPEFs. We hope this demonstration of QPI's utility will lead to a further adoption of the technique for the study of directed energy bioeffects.     

Morphometric characterization of endometrial glands using quantitative cytology.

OBJECTIVE: To determine the usefulness of the combination of confocal laser scanning microscopy (CLSM), image cytometry and three-dimensional (3D) imaging for analyzing architectural changes indicative of endometrial hyperplasia and grade 1 adenocarcinoma. STUDY DESIGN: Papanicolaou-stained endometrial samples (n = 180) were analyzed for specific cellular characteristics and analyzed by CLSM. Confocal images were obtained and then analyzed cytometrically and used for 3D reconstruction. RESULTS: Values obtained after image cytometry and 3D imaging increased significantly (P < .01) with the degree of cellular atypia. CONCLUSION: The combination of CLSM, image cytometry and 3D imaging is a valuable method for differential diagnosis of endometrial hyperplasia and grade 1 adenocarcinoma.     

Super-Resolution Imaging Strategies for Cell Biologists Using a Spinning Disk Microscope

In this study we use a spinning disk confocal microscope (SD) to generate super-resolution images of multiple cellular features from any plane in the cell. We obtain super-resolution images by using stochastic intensity fluctuations of biological probes, combining Photoactivation Light-Microscopy (PALM)/Stochastic Optical Reconstruction Microscopy (STORM) methodologies. We compared different image analysis algorithms for processing super-resolution data to identify the most suitable for analysis of particular cell structures. SOFI was chosen for X and Y and was able to achieve a resolution of ca. 80 nm; however higher resolution was possible >30 nm, dependant on the super-resolution image analysis algorithm used. Our method uses low laser power and fluorescent probes which are available either commercially or through the scientific community, and therefore it is gentle enough for biological imaging. Through comparative studies with structured illumination microscopy (SIM) and widefield epifluorescence imaging we identified that our methodology was advantageous for imaging cellular structures which are not immediately at the cell-substrate interface, which include the nuclear architecture and mitochondria. We have shown that it was possible to obtain two coloured images, which highlights the potential this technique has for high-content screening, imaging of multiple epitopes and live cell imaging.     

Accuracy in Quantitative 3D Image Analysis

Quantitative 3D imaging is becoming an increasingly popular and powerful approach to investigate plant growth and development. With the increased use of 3D image analysis, standards to ensure the accuracy and reproducibility of these data are required. This commentary highlights how image acquisition and postprocessing can introduce artifacts into 3D image data and proposes steps to increase both the accuracy and reproducibility of these analyses. It is intended to aid researchers entering the field of 3D image processing of plant cells and tissues and to help general readers in understanding and evaluating such data.Advances in digital imaging have led to the generation of an increasing number of 3D data sets (Truernit et al., 2008; Fernandez et al., 2010; Kierzkowski et al., 2012; Roeder et al., 2012). Whole-mount and time-lapse imaging enable all cells in an organ to be analyzed in 3D over time, providing a comprehensive analysis of plant growth and development (Roeder et al., 2011).The generation of these 3D image data sets has led to the development of novel computational approaches to facilitate their analysis (Cunha et al., 2010; Kierzkowski et al., 2012; Bassel et al., 2014; Yoshida et al., 2014). With the development of these new methods comes a need for quality control and standard measures to ensure the accurate analysis of data sets. An overall objective of this approach is the accurate capture and quantification of the 3D geometry of biological objects. The inaccurate abstraction of shape data and introduction of artifacts during image acquisition and postprocessing must be kept to a minimum.Following imaging, typically using confocal microscopy, 3D objects can be identified through the process of segmentation (Roeder et al., 2012). This can be achieved using automatic seeding through a watershed approach or by inflating “balloons” with defined seeds in individual cells (Federici et al., 2012). Vertices and meshes that describe cell surfaces may then be generated using an algorithm such as marching cubes (Lorensen and Cline, 1987). Vertices at defined spacings can be placed on the surface of unique segments, and the surfaces describing these geometric shapes are represented by the triangles connecting adjacent vertices constituting a polygonal mesh.The mesh describing segment surfaces is ultimately what defines the shape of an object in question. Rough and irregular features are often represented by meshes owing to the imperfect nature of data collection from biological samples (Desbrun et al., 1999; Taubin, 2000). In the context of plant cells whose surfaces are naturally smooth, the segmentations and meshes describing them are in practice noisy and contain undesirable geometric irregularities. The lower the quality of the original image being segmented, the greater the irregularities in the mesh that describe the shape.In order to improve the quality of irregularly triangulated polygonal meshes, smoothing operations can be performed. In this way, the roughness of the surface can be reduced, improving the texture and representation of a segmented object.A straightforward and easy to implement operation to remove noise in 3D meshes is Laplacian smoothing (Field, 1988). This process repositions vertices to an average position (barycentre) along a mesh surface to create a smoothed effect. However, Laplacian smoothing has the side effect of slightly shrinking the object in question (Taubin, 2000). While this shrinkage effect is well documented among computer scientists who develop algorithms to modify polygonal meshes, it is perhaps less well understood and discussed by the end user biological community.Smoothing of meshes has the positive effect of making surfaces smoother and removing noise, while enhancing the visual aesthetic of segmented objects providing the appearance that their geometry has been captured accurately (Figures 1A and 1B). In cases where objects have been poorly segmented, the need to remove the jagged appearance of meshes is greater, and additional smoothing steps are often used. This repeated Laplacian smoothing leads to additional smoothing-induced shrinkage and greater abstraction of the object being analyzed. Given that the mesh itself is intended to represent the 3D geometry of an object in question, changing its overall size by smoothing represents a perturbation and manipulation of data that inaccurately reflects the quantitative capture of geometry. The need to remove rough edges in meshes due to noise needs to be balanced with the accuracy that the mesh represents an object in question.Open in a separate windowFigure 1.Effect of Laplacian Smoothing on the Cellular Structure of a 3D Segmented Arabidopsis Radicle.(A) Surface rendering of a mesh following generation using marching cubes with a cube size of 2 μm and no smoothing. Bar = 10 μm.(B) Same as (A) following one Laplacian smoothing pass. (C) Same as (A) following six smoothing passes. (D) Same as (A) following nine smoothing passes. (E) An original confocal stack showing cell walls in green and the multicolored segmented stack before generating the mesh using marching cubes. (F) Smoothing of the mesh in (A) using the Taubin λ/μ algorithm with λ = 0.5, μ = −0.53, and nine smoothing steps. An example of an unsmoothed mesh representing the cells of 3D segmented plant organ can in seen in Figure 1A. The rough edges and irregularities of this primary unsmoothed mesh, coming from a suboptimal noisy confocal image stack, do not accurately represent the surface of these plant cells. The mesh in Figure 1B, which has been smoothed once, appears to be a more accurate representation of cell shape than the unsmoothed mesh in Figure 1A and has not been shrunk dramatically.In the context of plant organ cellular segmentations, additional Laplacian smoothing steps create even smoother cells, but also exaggerated gaps between adjacent cells due to cell shrinkage (Figures 1C and 1D). These spaces do not reflect reality as adjacent cells are physically appressed against their cell walls, which are rarely more than several microns thick. This example demonstrates the abstraction of cell shape that can occur following multiple Laplacian smoothing steps, and the gaps between cells are a hallmark of data that have been postprocessed to the point of inaccuracy.Other factors can affect the response of 3D segmented cells to Laplacian smoothing. These include mesh triangle size, with larger triangles being more susceptible to shrinkage (Desbrun et al., 1999), and cell size, with smaller cells being more susceptible to smoothing-based shrinking than larger cells (Figure 1D).If the purpose of 3D segmentation is strictly qualitative, smoothing-based shrinkage may not present a problem. However, if quantitative analyses are applied to shrunken meshes, this will result in inaccurate data.     

Lightsheet fluorescence lifetime imaging microscopy with wide‐field time‐correlated single photon counting

We report on wide‐field time‐correlated single photon counting (TCSPC)‐based fluorescence lifetime imaging microscopy (FLIM) with lightsheet illumination. A pulsed diode laser is used for excitation, and a crossed delay line anode image intensifier, effectively a single‐photon sensitive camera, is used to record the position and arrival time of the photons with picosecond time resolution, combining low illumination intensity of microwatts with wide‐field data collection. We pair this detector with the lightsheet illumination technique, and apply it to 3D FLIM imaging of dye gradients in human cancer cell spheroids, and C. elegans.     

High‐speed X‐ray‐induced luminescence computed tomography

X‐ray‐induced luminescence computed tomography (XLCT) is an emerging molecular imaging. Challenges in improving spatial resolution and reducing the scan time in a whole‐body field of view (FOV) still remain for practical in vivo applications. In this study, we present a novel XLCT technique capable of obtaining three‐dimensional (3D) images from a single snapshot. Specifically, a customed two‐planar‐mirror component is integrated into a cone beam XLCT imaging system to obtain multiple optical views of an object simultaneously. Furthermore, a compressive sensing based algorithm is adopted to improve the efficiency of 3D XLCT image reconstruction. Numerical simulations and experiments were conducted to validate the single snapshot X‐ray‐induced luminescence computed tomography (SS‐XLCT). The results show that the 3D distribution of the nanophosphor targets can be visualized much faster than conventional cone beam XLCT imaging method that was used in our comparisons while maintaining comparable spatial resolution as in conventional XLCT imaging. SS‐XLCT has the potential to harness the power of XLCT for rapid whole‐body in vivo molecular imaging of small animals.     

Underwater photogrammetry for close‐range 3D imaging of dry‐sensitive objects: The case study of cephalopod beaks

• Technical advances in 3D imaging have contributed to quantifying and understanding biological variability and complexity. However, small, dry‐sensitive objects are not easy to reconstruct using common and easily available techniques such as photogrammetry, surface scanning, or micro‐CT scanning. Here, we use cephalopod beaks as an example as their size, thickness, transparency, and dry‐sensitive nature make them particularly challenging. We developed a new, underwater, photogrammetry protocol in order to add these types of biological structures to the panel of photogrammetric possibilities.
• We used a camera with a macrophotography mode in a waterproof housing fixed in a tank with clear water. The beak was painted and fixed on a colored rotating support. Three angles of view, two acquisitions, and around 300 pictures per specimen were taken in order to reconstruct a full 3D model. These models were compared with others obtained with micro‐CT scanning to verify their accuracy.
• The models can be obtained quickly and cheaply compared with micro‐CT scanning and have sufficient precision for quantitative interspecific morphological analyses. Our work shows that underwater photogrammetry is a fast, noninvasive, efficient, and accurate way to reconstruct 3D models of dry‐sensitive objects while conserving their shape. While the reconstruction of the shape is accurate, some internal parts cannot be reconstructed with photogrammetry as they are not visible. In contrast, these structures are visible using reconstructions based on micro‐CT scanning. The mean difference between both methods is very small (10⁻⁵ to 10⁻⁴ mm) and is significantly lower than differences between meshes of different individuals.
• This photogrammetry protocol is portable, easy‐to‐use, fast, and reproducible. Micro‐CT scanning, in contrast, is time‐consuming, expensive, and nonportable. This protocol can be applied to reconstruct the 3D shape of many other dry‐sensitive objects such as shells of shellfish, cartilage, plants, and other chitinous materials.

     

Quantification and cluster analysis of actin cytoskeletal structures in plant cells: role of actin bundling in stomatal movement during diurnal cycles in Arabidopsis guard cells

Manual evaluation of cellular structures is a popular approach in cell biological studies. However, such approaches are laborious and are prone to error, especially when large quantities of image data need to be analyzed. Here, we introduce an image analysis framework that overcomes these limitations by semi-automatic quantification and clustering of cytoskeletal structures. In our framework, cytoskeletal orientation, bundling and density are quantified by measurement of newly-developed, robust metric parameters from microscopic images. Thereafter, the microscopic images are classified without supervision by clustering based on the metric patterns. Clustering allows us to collectively investigate the large number of cytoskeletal structure images without laborious inspection. Application of this framework to images of GFP-actin binding domain 2 (GFP-ABD2)-labeled actin cytoskeletons in Arabidopsis guard cells determined that microfilaments (MFs) are radially oriented and transiently bundled in the process of diurnal stomatal opening. The framework also revealed that the expression of mouse talin GFP-ABD (GFP-mTn) continuously induced MF bundling and suppressed the diurnal patterns of stomatal opening, suggesting that changes in the level of MF bundling are crucial for promoting stomatal opening. These results clearly demonstrate the utility of our image analysis framework.     

Reducing data acquisition for light‐sheet microscopy by extrapolation between imaged planes

Light‐sheet fluorescence microscopy (LSFM) is a powerful technique that can provide high‐resolution images of biological samples. Therefore, this technique offers significant improvement for three‐dimensional (3D) imaging of living cells. However, producing high‐resolution 3D images of a single cell or biological tissues, normally requires high acquisition rate of focal planes, which means a large amount of sample sections. Consequently, it consumes a vast amount of processing time and memory, especially when studying real‐time processes inside living cells. We describe an approach to minimize data acquisition by interpolation between planes using a phase retrieval algorithm. We demonstrate this approach on LSFM data sets and show reconstruction of intermediate sections of the sparse samples. Since this method diminishes the required amount of acquisition focal planes, it also reduces acquisition time of samples as well. Our suggested method has proven to reconstruct unacquired intermediate planes from diluted data sets up to 10× fold. The reconstructed planes were found correlated to the original preacquired samples (control group) with correlation coefficient of up to 90%. Given the findings, this procedure appears to be a powerful method for inquiring and analyzing biological samples.     

Deep learning of diffraction image patterns for accurate classification of five cell types

Development of label‐free methods for accurate classification of cells with high throughput can yield powerful tools for biological research and clinical applications. We have developed a deep neural network of DINet for extracting features from cross‐polarized diffraction image (p‐DI) pairs on multiple pixel scales to accurately classify cells in five types. A total of 6185 cells were measured by a polarization diffraction imaging flow cytometry (p‐DIFC) method followed by cell classification with DINet on p‐DI data. The averaged value and SD of classification accuracy were found to be 98.9% ± 1.00% on test data sets for 5‐fold training and test. The invariance of DINet to image translation, rotation, and blurring has been verified with an expanded p‐DI data set. To study feature‐based classification by DINet, two sets of correctly and incorrectly classified cells were selected and compared for each of two prostate cell types. It has been found that the signature features of large dissimilarities between p‐DI data of correctly and incorrectly classified cell sets increase markedly from convolutional layers 1 and 2 to layers 3 and 4. These results clearly demonstrate the importance of high‐order correlations extracted at the deep layers for accurate cell classification.      

Front cover

Continuous-wave terahertz reflection imaging is a potential tool for biological tissues. Based on our home-made continuouswave terahertz reflection imaging system, the effect of both polarization mode and reflection window on the imaging performance is studied theoretically and experimentally, showing good agreement. By taking obtaining sample information and image contrast into consideration, p-polarized terahertz waves are recommended. Moreover, considering the sample boundary identification and the image contrast, selection criteria for reflection window are proposed. This work will help to improve the performance of continuous-wave terahertz reflection imaging and accelerate the THz imaging in biological application. Further details can be found in the article by Limin Wu, Yuye Wang, Haibin Li, Zelong Wang, Meilan Ge, Degang Xu, and Jianquan Yao ( e202100245 ).

     

Efficient super‐resolution volumetric imaging by radial fluctuation Bayesian analysis light‐sheet microscopy

Various computational super‐resolution methods are available based on the analysis of fluorescence fluctuation behind acquired frames. However, dilemmas often exist in the balance of fluorophore characteristics, computation cost, and achievable resolution. Here we present an approach that uses a super‐resolution radial fluctuations (SRRF) image to guide the Bayesian analysis of fluorophore blinking and bleaching (3B) events, allowing greatly accelerated localization of overlapping fluorophores with high accuracy. This radial fluctuation Bayesian analysis (RFBA) approach is also extended to three dimensions for the first time and combined with light‐sheet fluorescence microscopy, to achieve super‐resolution volumetric imaging of thick samples densely labeled with common fluorophores. For example, a 700‐nm thin Bessel plane illumination is developed to optically section the Drosophila brain, providing a high‐contrast 3D image of rhythmic neurons. RFBA analyzes 30 serial volumes to reconstruct a super‐resolved 3D image at 4‐times higher resolutions (~70 and 170 nm), and precisely resolve the axon terminals. The computation is over 2‐orders faster than conventional 3B analysis microscopy. The capability of RFBA is also verified through dual‐color imaging of cell nucleus in live Drosophila brain. The spatial co‐localization patterns of the nuclear envelope and DNA in a neuron deep inside the brain can be precisely extracted by our approach.     

Determination and correction of aberrations in full field optical coherence tomography using phase gradient autofocus by maximizing the likelihood function

A method for numerical estimation and correction of aberrations of the eye in fundus imaging with optical coherence tomography (OCT) is presented. Aberrations are determined statistically by using the estimate based on likelihood function maximization. The method can be considered as an extension of the phase gradient autofocusing algorithm in synthetic aperture radar imaging to 2D optical aberration correction. The efficacy of the proposed method has been demonstrated in OCT fundus imaging with 6λ aberrations. After correction, single photoreceptors were resolved. It is also shown that wave front distortions with high spatial frequencies can be determined and corrected.     

Interactions Between Amyloid-β (1-42) and Hydroxyapatite-Cholesterol Spherules Associated with Age-Related Macular Degeneration

Drusen deposition on sub-retinal pigment epithelium is the causal factor for age-related macular degeneration for the old-aged individuals. These deposits contain hydroxyapatite–cholesterol spherules on which several proteins and lipids accumulate to cover the retina and choroid, causing blurred vision and blindness. Amyloid-β, the known culprit in Alzheimer’s disease, is one among the few major proteins known to occur in these deposits. In the present article, we report preliminary analyses of interactions between amyloid-β and hydroxyapatite–cholesterol composites using Thioflavin-T binding kinetics, solid-state NMR and transmission electron microscopy (TEM). Thioflavin-T fluorescence kinetics shows that amyloid-β (1–42) aggregates only under certain conditions of concentration of cholesterol in the hydroxyapatite–cholesterol composites prepared by two different methods. These results were confirmed by 1D ¹³C CPMAS solid-state NMR. TEM imaging revealed that there is an exposure of the cholesterol surface in the composites prepared by sonication method. These imaging experiments explain the dependence of aggregation kinetics on the exposure and availability of cholesterol surface in the composites to a certain extent.

Graphic Abstract

     

The combination of two‐dimensional and three‐dimensional analysis methods contributes to the understanding of glioblastoma spatial heterogeneity

Heterogeneity is regarded as the major factor leading to the poor outcomes of glioblastoma (GBM) patients. However, conventional two‐dimensional (2D) analysis methods, such as immunohistochemistry and immunofluorescence, have limited capacity to reveal GBM spatial heterogeneity. Thus, we sought to develop an effective analysis strategy to increase the understanding of GBM spatial heterogeneity. Here, 2D and three‐dimensional (3D) analysis methods were compared for the examination of cell morphology, cell distribution and large intact structures, and both types of methods were employed to dissect GBM spatial heterogeneity. The results showed that 2D assays showed only cross‐sections of specimens but provided a full view. To visualize intact GBM specimens in 3D without sectioning, the optical tissue clearing methods CUBIC and iDISCO+ were used to clear opaque specimens so that they would become more transparent, after which the specimens were imaged with a two‐photon microscope. The 3D analysis methods showed specimens at a large spatial scale at cell‐level resolution and had overwhelming advantages in comparison to the 2D methods. Furthermore, in 3D, heterogeneity in terms of cell stemness, the microvasculature, and immune cell infiltration within GBM was comprehensively observed and analysed. Overall, we propose that 2D and 3D analysis methods should be combined to provide much greater detail to increase the understanding of GBM spatial heterogeneity.