3D imaging for driving cancer discovery
2022; Springer Nature; Volume: 41; Issue: 10 Linguagem: Inglês
10.15252/embj.2021109675
ISSN1460-2075
AutoresRavian L. van Ineveld, Esmée J. van Vliet, Ellen J. Wehrens, María Alieva, Anne C. Rios,
Tópico(s)Congenital heart defects research
ResumoReview11 April 2022free access 3D imaging for driving cancer discovery Ravian L van Ineveld Ravian L van Ineveld Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands Oncode Institute, Utrecht, The Netherlands Search for more papers by this author Esmée J van Vliet Esmée J van Vliet orcid.org/0000-0002-9301-1598 Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands Oncode Institute, Utrecht, The Netherlands Search for more papers by this author Ellen J Wehrens Ellen J Wehrens Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands Oncode Institute, Utrecht, The Netherlands Search for more papers by this author Maria Alieva Maria Alieva Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands Oncode Institute, Utrecht, The Netherlands Search for more papers by this author Anne C Rios Corresponding Author Anne C Rios [email protected] orcid.org/0000-0002-9082-8068 Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands Oncode Institute, Utrecht, The Netherlands Search for more papers by this author Ravian L van Ineveld Ravian L van Ineveld Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands Oncode Institute, Utrecht, The Netherlands Search for more papers by this author Esmée J van Vliet Esmée J van Vliet orcid.org/0000-0002-9301-1598 Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands Oncode Institute, Utrecht, The Netherlands Search for more papers by this author Ellen J Wehrens Ellen J Wehrens Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands Oncode Institute, Utrecht, The Netherlands Search for more papers by this author Maria Alieva Maria Alieva Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands Oncode Institute, Utrecht, The Netherlands Search for more papers by this author Anne C Rios Corresponding Author Anne C Rios [email protected] orcid.org/0000-0002-9082-8068 Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands Oncode Institute, Utrecht, The Netherlands Search for more papers by this author Author Information Ravian L Ineveld1,2,†, Esmée J Vliet1,2,†, Ellen J Wehrens1,2,†, Maria Alieva1,2 and Anne C Rios *,1,2 1Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands 2Oncode Institute, Utrecht, The Netherlands † These authors contributed equally to this work as first authors *Corresponding author. Tel: +31 (0)8 89 72 50 29; E-mail: [email protected] The EMBO Journal (2022)41:e109675https://doi.org/10.15252/embj.2021109675 This article is part of the Cancer Reviews series. PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract 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. Introduction Visualization of tissues in three dimensions (3D) has increased our understanding of their structural organization and how this relates to organ development and function. Advances in tissue clearing (Richardson & Lichtman, 2015; Almagro et al, 2021) coupled with light sheet (Reynaud et al, 2015), confocal (Jonkman et al, 2020) and multiphoton (Andresen et al, 2009) microscopy technologies allow for the observation of complex structures within large intact tissue specimens and led to discoveries in many fields of interest, including embryonic development (Belle et al, 2017), brain connectivity and function (Kim et al, 2013; Renier et al, 2016; Murakami et al, 2018; Ueda et al, 2020), organ morphology (Hannezo et al, 2017; Li et al, 2017, 2019) and stem cell biology (Snippert et al, 2010; Rios et al, 2011, 2014; Scheele et al, 2017). In addition, super-resolution imaging (Bates et al, 2007; Huang et al, 2008a, 2009) has expanded our ability to differentiate ultrastructures beyond the diffraction limit. A concomitant evolution in quantitative analysis and computational modelling has furthermore transformed fluorescent microscopy from a descriptive methodology into a powerful spatial and phenotypic (single-cell) technology (Messal et al, 2019; Albanese et al, 2020; Stoltzfus et al, 2020; Zhao et al, 2020; van Ineveld et al, 2021). By providing 3D context of tissue architecture and cellular composition, and even subcellular organization of healthy and diseased tissue, these techniques can also uniquely unveil spatio-dynamic traits of cancer. Moreover, an increased number of sample preparation methods (Box 1) designed for preserving endogenous fluorescence (Li et al, 2019; Rios et al, 2019; Messal et al, 2021), performing large sample immunolabelling (Renier et al, 2014; Ku et al, 2020) and moving towards multiplex imaging (Murray et al, 2015; Goltsev et al, 2018; Stoltzfus et al, 2020; preprint: Seo et al, 2021; van Ineveld et al, 2021; van Ineveld et al, in press), allowed adaptation of 3D imaging from application in fluorescently-engineered animal models to studying biological processes in human samples. Together with advances in molecular and human tissue modelling techniques, this now provides important new arsenal for imaging modalities to explore human cancer biology (Rios & Clevers, 2018), covering key tumour features, such as competition and plasticity, also discussed elsewhere in this review series (Brabletz et al, 2021; Parker et al, 2021). 3D imaging-driven knowledge of cancer biology includes insights into tumour architecture and its evolving heterogeneity (Oshimori et al, 2015; Tanaka et al, 2017; Messal et al, 2019; Rios et al, 2019), metastatic seeding (Kubota et al, 2017; Pan et al, 2019), interplay with the tumour microenvironment (TME) (Garofalo et al, 2015; Brown et al, 2018) and response to neoadjuvant treatments (Kubota et al, 2017; Kingston et al, 2019). Here, we review recent advances in 3D (live) imaging technology and novel understanding of tumour biology generated by it (Table 1; Figure 1). Furthermore, we discuss expected future contributions and the technological requirements that need to be met and challenges overcome to reach the full application of 3D imaging in the field of cancer research. Table 1. Milestone discoveries in cancer research using 3D imaging. Cancer discovery Impact Microscopy Sample preparation Study Macroscale Tumour cell dissemination via lymph node blood vessels New active process for metastasis at distant site Light sheet Multiphoton Fixed; CUBIC IVM Brown et al (2018); Pereira et al (2018) Previously unrecognized micrometastases and degree of overlap with therapeutic antibody distribution Metastatic burden assessment and treatment evaluation Light sheet Fixed, whole animal; CUBIC, DISCO Kubota et al (2017); Pan et al (2019) Epithelial mechanical forces that lead to aggressive forms of cancer Biophysical prediction and potential targeting of tumour aggressiveness Confocal Fixed; FLASH Messal et al (2019); Fiore et al (2020) Archival Heterogeneity in lymphatic microvasculature and identification of vascular versus lymphatic invasion Tumour staging and patient stratification Light sheet FFPE; DIPCO Tanaka et al (2017); Tanaka et al (2018) Her2-enriched membrane protrusions in breast cancer biopsies Biomarker ultrastructure that might relate to tumour aggressiveness Super-resolution; STORM FFPE Creech et al (2017) High dimensionality Relation between tumour cells and fibroblasts in hypoxic tumour environment Cancer cell functional profiling in relation to the TME Spinning disc Confocal Fixed; RNA multiplexing ExSeq Alon et al (2021) Developmental trajectory and heterogeneity mapping of Wilms tumour Identification of new tumour cell subset Multispectral confocal Fixed; mLSR-3D/FUnGI van Ineveld et al (2021) Time resolved Tumour plasticity and clonal evolution restriction in breast tumours Clonal evolution of cancer Multispectral confocal Fixed; LSR-3D/FUnGI Rios et al (2019) Stemness represents a reversible tumour cell state that can be gained or lost over time Cancer stem cell plasticity in tumour progression Multiphoton IVM; mammary imaging windows Zomer et al (2013) Colorectal cancer metastasis linked to mutations that allow growth independence of niche signals Driver mechanism of late-stage cancer progression Multiphoton IVM; abdominal imaging windows Fumagalli et al (2017) Glioma cell functional interaction endows stemness and therapy resistance. Connections with neurons potentiates invasive growth Importance of functional interconnections between tumour cells and environmental cells in tumour progression Multiphoton; scanning electron microscopy IVM; cranial imaging windows; optogenetics Osswald et al (2015); Venkataramani et al (2019); Xie et al (2021) Interplay with extracellular matrix regulates breast tumour dissemination and cooperation with macrophages supports intravasation Role of the TME in tumour metastasis Multiphoton IVM; mammary imaging windows; skin-fold windows Harney et al (2015); Ilina et al (2020) In vivo functional heterogeneity of CAR T cells and action radius of anti-PD1 Cancer immunotherapy mode of action and targets to improve efficacy Confocal; multiphoton IVM; skin-fold windows Arlauckas et al (2017); Cazaux et al (2019) Organoids Highly heterogeneous and multi-phasic Wilms tumour-derived organoid cultures Resemblance of human cancer organoid biobank to tumour tissue Multispectral; confocal Fixed; mLSR-3D/FUnGI Calandrini et al (2020) Widespread chromosomal instability in human cancer, yet with heterogenous outcome for cell death Functional heterogeneity shaping human tumour evolution Confocal; spinning disc Live Bolhaqueiro et al (2019) Tumour cells actively eliminate healthy cells by JNK-dependent growth promotion Mechanism of tumour cell competition in human cancer Confocal; spinning disc Live and fixed Krotenberg Garcia et al (2021) Widespread behavioural heterogeneity in engineered T cells targeting human cancer organoids Opportunities to improve treatment efficacy by skewing towards T cell functional behaviour Multispectral; confocal Live preprint: Dekkers et al (2021) Figure 1. Key 3D imaging technologies driving insight into cancer biologyTechnology advance in various areas of 3D imaging and key insight in cancer biology obtained by it. Download figure Download PowerPoint Box 1. Sample preparation and acquisition methods for 3D imaging of cancer biology. To interrogate cancer biology using 3D imaging, optical clearing to reach tissue transparency and fluorescent labelling of markers of interest form critical components of the workflow. To achieve this, various sample preparation pipelines have been developed, each with their own advantages related to the kind of tumour tissue that needs to be imaged and the type of fluorescence detected. Furthermore, the chosen microscope technology for acquisition will depend on these factors and needs to be balanced between the required resolution and imaging volume. Optical clearing. A large body of clearing methods to homogenize the refractive index of tissues and thereby render them transparent have been developed and extensively reviewed elsewhere (Richardson & Lichtman, 2015; Ueda et al, 2020; Almagro et al, 2021). 3D imaging methods, like CUBIC (Susaki et al, 2014; Kubota et al, 2017) and various DISCO approaches (Erturk et al, 2012; Renier et al, 2014; Pan et al, 2016, 2019; Molbay et al, 2021) (Table 2), can achieve entire organ and even mouse whole-body transparency and have been instrumental in cancer research for visualization and quantification of bodywide metastases (Kubota et al, 2017; Pan et al, 2019). Other clearing agents have been developed to offer more rapid and routine sample preparation pipelines that are furthermore better at conserving tissue morphology and reporter fluorescence, while being compatible with a wide range of antibody labels. FLASH (Table 2) implements an antigen retrieval step tailored to the tissue of interest that, importantly, includes preservation of epithelial markers (Messal et al, 2021). It has been applied to reveal the epithelial physical properties dictating the morphology of neoplasms in the pancreas (Messal et al, 2019), of which the epithelium could not effectively be stained with existing 3D imaging protocols that had been mostly developed for the brain (Messal et al, 2021) and the same was true for other abdominal organs. Ce3D (Table 2) was similarly developed to tackle issues of antibody-based immunolabelling observed with alternative clearing methods, resulting in a protocol robustly compatible with extensive multiplexing (Li et al, 2017, 2019). It has subsequently been applied to interrogate the subset composition and spatial distribution of immune cells in the TME (Stoltzfus et al, 2020). Finally, the clearing agent FUnGI (Table 2) was developed to preserve mammary gland tissue integrity and allow for concurrent immunolabelling and detection of endogenous fluorescence to reveal the clonal evolution of breast cancer through lineage tracing combined with phenotypic profiling (Rios et al, 2019). Altogether, a wide range of clearing agents are available to interrogate cancer biology through 3D imaging, and the method of choice will depend on the scale and type of tissue to be imaged, as well as the fluorescent labelling strategy. Fluorescent labelling. 3D visualization of markers of interest will depend on their robust fluorescence which can be achieved through specific labelling with fluorescent antibodies or dyes. This often requires a permeabilization step to enable penetration of antibodies and dyes deep and uniformly into the tissue (Almagro et al, 2021; Messal et al, 2021). In addition, markers of interest can be visualized through endogenous, reporter-driven fluorescence. This requires clearing agents that optimally preserve such endogenous fluorescent expression (Li et al, 2019; Rios et al, 2019), or dedicated sample preparation techniques to boost the endogenous signal deep inside the tissue. vDISCO (Table 2), for instance, implements high-pressure perfusion of nanobodies against fluorescent proteins to enhance their signal intensity by two orders of a magnitude (Cai et al, 2019; Pan et al, 2019). In the future, label-free imaging (Rust et al, 2006; Gavgiotaki et al, 2020), for instance exploiting differential autofluorescence of distinct cell types (Rivenson et al, 2019), might become a valuable addition to increase the number of labels in 3D imaging approaches, or circumvent the need for fluorescent labelling all together. Microscope technology. Light sheet technology captures large fields of view using low NA objectives. It thereby allows for rapid imaging of very large, even centimetre-scale, specimens, such as entire mice and full human organs, yet at the expense of some resolution. Light sheet imaging has been widely applied to capture the macroscopic properties of cancer, including blood and lymph vasculature (Brown et al, 2018; Kastelein et al, 2020) and whole-body tumour dissemination (Kubota et al, 2017; Pan et al, 2019). In contrast, confocal or two-photon microscopes use high NA objectives to achieve higher resolution and magnification, yet at the sacrifice of some working depth and require lengthier imaging times, thereby more susceptible to photobleaching. When equipped with a multispectral detector, confocal/multiphoton imaging can be especially useful for multi-marker imaging of cancer biology at high resolution, exploiting contemporary advances in unmixing of spectrally overlapping fluorophores (Seo et al, 2021; van Ineveld et al, 2021) to achieve high-dimensional and high-definition profiling of cancer. 3D anatomical mapping of cancer Large-scale 3D imaging has been instrumental for unravelling the structural landmarks of cancer in intact tumours and even organisms. In this section, we review the insights into tumour architecture and systemic cancer biology generated by 3D imaging in the context of tumour progression and treatment. We also highlight examples of how such technology can be exploited towards prediction of cancer aggressiveness and treatment targeting efficacy, when combined with advanced computational modelling. Light sheet technology for studying blood and lymphatic vasculature The blood and lymphatic vascular systems not only represent structural landmarks of the microenvironment within malignant tissues, but also a gateway for whole-body tumour dissemination. They thereby hold important implications for cancer targeting and late metastatic progression. Volumetric imaging allows to fully appreciate the complex organization of these networks in 3D and has provided key information on how these structures in the direct tumour microenvironment (Lin et al, 2016) are actively remodelled by the tumour (Liu et al, 2013; Shen et al, 2019). 3D imaging of blood vasculature has also helped to assess the efficacy of therapy delivery (Lee et al, 2019a), as well as micro-vascular damage as a side effect of cancer treatment (Craver et al, 2016). Light sheet technology, which can capture large field of views using low numerical aperture (NA) objectives, yet in detriment to some resolution, has been demonstrated especially suited to study such macroscopic structures within tissue (Box 1). Combined with advances in optical clearing, it has been instrumental in revealing the full vasculature network of organs, such as the murine brain (Todorov et al, 2020). Moreover, it has been applied to entire tumours, uniquely revealing their chaotic and immature angioarchitecture and identifying tumour areas with poorly-perfused microvasculature (Dobosz et al, 2014; Kastelein et al, 2020). Next to providing a cause to hypoxia, a well-recognized tumour feature that often leads to an invasive phenotype of cancer, these findings thereby also explain varying responses to systemic treatment that have been linked to perfusion constraints (Mendler et al, 2016; Viallard et al, 2020). As such, modulation of key signalling molecules involved in vascular remodelling, such as VEGF and BMP9, is of therapeutic interest to normalize the tumour vasculature, and 3D imaging has been used to visualize the outcome of such approaches. This indeed revealed a decrease in hypoxia and increased perfusion (Viallard et al, 2020), beneficial for drug delivery. An additional layer of complexity can be reached by also taking into account the lymphatic system. Here, light sheet 3D imaging uniquely revealed that metastasizing cells can exit the lymph node by invading local lymph node blood vessels, instead of using efferent lymphatic vessels (Brown et al, 2018), a finding also confirmed through intravital imaging (Pereira et al, 2018) (Table 1), discussed later on in this review. This key observation received a lot of attention, as it shed new light on an ongoing debate whether lymph node metastasis could be an active route for tumour cells to disperse to distant sites. It might also have implications for treatment decisions, as few tumour cells located adjacent to or within blood vessels are expected to be more predictive of poor prognosis, compared to potentially larger tumour cell deposits distant from blood vessels (2018; Dart, 2018; Tjan-Heijnen & Viale, 2018). For large-scale 3D imaging, deep and homogeneous penetration of antibodies even in difficult to access anatomical structures, such as bone tissue, is essential (Box 1) (Tainaka et al, 2018). As an example, decalcified vertebral segments immunolabelled using the iDISCO+ method (see Table 2 for glossary of 3D imaging and data analysis tools) provided a major step forward in mapping the 3D architecture and function of vertebral lymphatic vessels (Jacob et al, 2019), of interest for cancer research, as the vertebral column represents a common site for metastasis. Thus, 3D imaging of blood and lymph vasculature has generated critical insights into large-scale cancer architecture of therapeutic relevance for improving drug delivery and predicting metastatic routes. Table 2. Glossary of 3D imaging methodologies and data tools. Abbreviation Name Reference BEHAV3D Behavioral-phenotypic characterization of dynamic immune-organoid 3D imaging preprint: Dekkers et al (2021) Ce3D Clearing enhanced 3D Li et al (2017, 2019) CODEX CO-Detection by indEXing Goltsev et al (2018) CUBIC Clear, Unobstructed Brain Imaging Cocktails and Computational analysis Susaki et al (2014) CytoMAP histo-Cytometric Multidimensional Analysis Pipeline Stoltzfus et al (2020) DeepMACT Deep learning-enabled Metastasis Analysis in Cleared Tissues Pan et al (2019) DIPCO Diagnosing Immunolabeled Paraffin-embedded Cleared Organs Tanaka et al (2017, 2018) 3DISCO 3D Imaging of Solvent-Cleared Organs Erturk et al (2012) iDISCO+ immunolabeling-enabled three-Dimensional Imaging of Solvent-Cleared Organs Renier et al (2016) uDISCO Ultimate DISCO Pan et al (2016) vDISCO nanobody VhH boosted DISCO Pan et al (2019) DVEX Drosophila Virtual Expression eXplorer Karaiskos et al (2017) ExSeq Expansion Sequencing Alon et al (2021) FLASH Fast Lightmicroscopic analysis of Antibody-Stained wHole organs Messal et al (2019) FTIR Fourier-Transform InfraRed spectroscopy Rivenson et al (2019) FUnGI Fructose, Urea and Glycerol for Imaging Rios et al (2019) Geo-seq Geographical position sequencing Chen et al (2017) LSR-3D Large-scale Single-cell Resolution 3D Rios et al (2019) LvSEM Low-vacuum Scanning Electron Microscopy Jones (2012) mLSR-3D multispectral Large-scale Single-cell Resolution 3D van Ineveld et al (2021) Opal multiplexed IHC protocol Opal Nolan et al (2017); Viratham Pulsawatdi et al (2020) PICASSO blind unmixing technique Seo et al (2021) SHG Second Harmonic Generation Ren et al (2017) SMART 3D Spatial filtering-based background removal and Multi-chAnnel forest classifiers-based 3D ReconsTruction Guldner et al (2016) STAPL-3D SegmenTation Analysis by ParaLlelization of 3D datasets van Ineveld et al (2021) STARmap Spatially-resolved Transcript Amplicon Readout mapping Wang et al (2018) STORM Stochastic Optical Reconstruction Microscopy Rust et al (2006) SWITCH System-Wide control of Interaction Time and kinetics of CHemicals Murray et al (2015) THG Third Harmonic Generation Gavgiotaki et al (2020) Thick PS-LvSEM Thick Paraffin Sections - Low-vacuum Scanning Electron Microscopy Sawaguchi et al (2018) Tomo-seq Tomos (slice) sequencing Junker et al (2014) Whole-body 3D imaging to assess metastatic burden 3D imaging plays an essential role in studying late manifestation of cancer, by enabling quantification of metastasis in cleared secondary organs. Moreover, as further discussed below, the highly potent clearing techniques CUBIC (Susaki et al, 2014) and derivates of 3DISCO (Erturk et al, 2012) (Table 2) have allowed for pan-scale imaging to study systemic cancer biology, including distant dissemination, and explore therapeutic avenues (Table 1). Together with upgraded light sheet microscope systems and advanced fluorescent labelling pipelines, they allow for the detection of cells in microscopic quantities, even single metastasized tumour cells (Pan et al, 2019), while scanning massive volumes to image entire mice and full human organs (Box 1) (Kubota et al, 2017; Cai et al, 2019; Zhao et al, 2020). Kubota et al (2017) used CUBIC to quantify metastasis at the whole mouse body level and assess their response to chemotherapy (Kubota et al, 2017). Panoramic imaging further improved with the development of uDISCO (Pan et al, 2016), followed by next-generation DISCO, vDISCO (Pan et al, 2019) (Table 2). The unprecedented scale of whole-body imaging poses significant challenges for data handling and analysis, thereby deep learning-based quantification forms an indispensable part of this technology advance. The DeepMACT analysis pipeline (Table 2; Box 2) (Pan et al, 2019) allows mapping of individual metastasis in whole-animal cancer models and quantifying spatial differences in their response to immunotherapy. This uniquely revealed a previously unrecognized micro-metastasis pattern at numerous sites across the body, as well as their degree of overlap with the distribution of a monoclonal antibody therapy, indicative of targeting (Pan et al, 2019). These examples also highlight how 3D imaging methods initially developed for other areas of interest, for example, brain function, are subsequently applied in cancer research, leading to new biological insight in cancer (Figure 2). Building from this seminal work, whole-body distribution mapping of novel targeted or cellular therapies is a promising avenue that could uniquely be addressed by these 3D panoramic imaging technologies. Figure 2. Timeline illustrating the developmental cycle between (imaging) technology advance and new knowledgeTimeline of selected advances in 3D imaging (T, orange box) or related technologies (T, blue arrows) and biological insights (I, lightbulb) for the main areas of 3D imaging, to illustrate the developmental cycle between technology and knowledge advance. Download figure Download PowerPoint Box 2. Artificial intelligence to analyse complex 3D imaging data. With both scale and dimensionality of 3D imaging data rapidly increasing, automated analysis pipelines become imperative for extracting the large amount of spatial and phenotypic single-cell information. Artificial intelligence (AI)-based data analysis depends on high-quality imaging data to train algorithms to automatically detect single objects, such as cells and regions, in 3D imaging data and perform quantitative analysis. Several recent 3D image analysis pipelines that have been applied for cancer research implement either deep learning or machine learning to achieve rapid, accurate, automated and unbiased analysis. DeepMACT (Table 2) is a deep learning-based pipeline that employs a U-Net-like convolutional neural network exploiting 2D maximum-intensity projections with high signal-to-noise ratio to rapidly and unbiasedly segment and quantify tumour metastases in whole mouse 3D imaging data (Pan et al, 2019). As such, DeepMACT has been instrumental in mapping the metastatic landscape of tumours, including thousands of micrometastases and their targeting by therapeutic antibodies. CytoMAP (Table 2) employs machine learning-based data clustering to group cells into local neighbourhoods based on their cell type and position to explore the architectural organization of cells within tissues (Stoltzfus et al, 2020). It can thereby resolve the spatial relationships of cells, for instance the interrelations between immune and tumour cells in the TME. STAPL-3D (Table 2) implements deep learning algorithms: 3D-UNET for membrane prediction and Stardist for nuclear prediction, to accurately and timely segment millions of cells in large 3D imaging datasets (van Ineveld et al, 2021). The resulting high-dimensional dataset allows for omics-like analysis, such as unbiased cell population clustering, revealing novel tumour cell subsets. STAPL-3D, furthermore, offers a co-affine registration method to obtain large volumes of high-quality 3D imaging data for training the deep learning algorithms. Confocal and two-photon microscopy resolves refined tumour growth patterns As opposed to light sheet technology, confocal or two-photon microscopes offer more mainstream systems for large-scale 3D imaging with higher resolution, yet at the sacrifice of some working depth (Box 1). Therefore, recent efforts have been directed at obtaining easy-to-use protocols for routine 3D imaging of intact tumours with clearing methodologies that preserve epitope integrity, limit distortion of tissue and allow for rapid sample preparation (Box 1) (Li et al, 2017, 2019; Rios et al, 2019; Stoltzfus et al, 2020; Messal et al, 2021). Typically combined with confocal and multiphoton imaging using high NA objectives, such methodologies paved the way for studying tissue disruption during cancer morphogenesis at high resolution. Indeed, growth patterns of lesions in murine cancer models have been suggested to be a shared consequence of both genetic alterations and the geometry of normal epithelium. Using the FLASH protocol (Table 2) with subsequent morphometrics analysis and mathematical modelling, Messal et al (2019) showed that physical constraints such as epithelial curvature can not only dictate morphology, but also predict tumour cell infiltration in the surrounding parenchyma in cleared pancreatic neoplastic tissues (Messal et al, 2019). Similarly, in skin cancer, different forms of the disease
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