3D genomic mapping reveals multifocality of human pancreatic precancers.

Pancreatic intraepithelial neoplasias (PanINs) are the most common precursors of pancreatic cancer, but their small size and inaccessibility in humans make them challenging to study1. Critically, the number, dimensions and connectivity of human PanINs remain largely unknown, precluding important insights into early cancer development. Here, we provide a microanatomical survey of human PanINs by analysing 46 large samples of grossly normal human pancreas with a machine-learning pipeline for quantitative 3D histological reconstruction at single-cell resolution. To elucidate genetic relationships between and within PanINs, we developed a workflow in which 3D modelling guides multi-region microdissection and targeted and whole-exome sequencing. From these samples, we calculated a mean burden of 13 PanINs per cm3 and extrapolated that the normal intact adult pancreas harbours hundreds of PanINs, almost all with oncogenic KRAS hotspot mutations. We found that most PanINs originate as independent clones with distinct somatic mutation profiles. Some spatially continuous PanINs were found to contain multiple KRAS mutations; computational and in situ analyses demonstrated that different KRAS mutations localize to distinct cell subpopulations within these neoplasms, indicating their polyclonal origins. The extensive multifocality and genetic heterogeneity of PanINs raises important questions about mechanisms that drive precancer initiation and confer differential progression risk in the human pancreas. This detailed 3D genomic mapping of molecular alterations in human PanINs provides an empirical foundation for early detection and rational interception of pancreatic cancer.

CODA: quantitative 3D reconstruction of large tissues at cellular resolution.

A central challenge in biology is obtaining high-content, high-resolution information while analyzing tissue samples at volumes relevant to disease progression. We address this here with CODA, a method to reconstruct exceptionally large (up to multicentimeter cubed) tissues at subcellular resolution using serially sectioned hematoxylin and eosin-stained tissue sections. Here we demonstrate CODA's ability to reconstruct three-dimensional (3D) distinct microanatomical structures in pancreas, skin, lung and liver tissues. CODA allows creation of readily quantifiable tissue volumes amenable to biological research. As a testbed, we assess the microanatomy of the human pancreas during tumorigenesis within the branching pancreatic ductal system, labeling ten distinct structures to examine heterogeneity and structural transformation during neoplastic progression. We show that pancreatic precancerous lesions develop into distinct 3D morphological phenotypes and that pancreatic cancer tends to spread far from the bulk tumor along collagen fibers that are highly aligned to the 3D curves of ductal, lobular, vascular and neural structures. Thus, CODA establishes a means to transform broadly the structural study of human diseases through exploration of exhaustively labeled 3D microarchitecture.

Cell Trafficking at the Intersection of the Tumor-Immune Compartments.

Migration is an essential cellular process that regulates human organ development and homeostasis as well as disease initiation and progression. In cancer, immune and tumor cell migration is strongly associated with immune cell infiltration, immune escape, and tumor cell metastasis, which ultimately account for more than 90% of cancer deaths. The biophysics and molecular regulation of the migration of cancer and immune cells have been extensively studied separately. However, accumulating evidence indicates that, in the tumor microenvironment, the motilities of immune and cancer cells are highly interdependent via secreted factors such as cytokines and chemokines. Tumor and immune cells constantly express these soluble factors, which produce a tightly intertwined regulatory network for these cells' respective migration. A mechanistic understanding of the reciprocal regulation of soluble factor-mediated cell migration can provide critical information for the development of new biomarkers of tumor progression and of tumor response to immuno-oncological treatments. We review the biophysical andbiomolecular basis for the migration of immune and tumor cells and their associated reciprocal regulatory network. We also describe ongoing attempts to translate this knowledge into the clinic.

Dissecting cellular mechanics: Implications for aging, cancer, and immunity.

Cells are dynamic structures that must respond to complex physical and chemical signals from their surrounding environment. The cytoskeleton is a key mediator of a cell's response to the signals of both the extracellular matrix and other cells present in the local microenvironment and allows it to tune its own mechanical properties in response to these cues. A growing body of evidence suggests that altered cellular viscoelasticity is a strong indicator of disease state; including cancer, laminopathy (genetic disorders of the nuclear lamina), infection, and aging. Here, we review recent work on the characterization of cell mechanics in disease and discuss the implications of altered viscoelasticity in regulation of immune responses. Finally, we provide an overview of techniques for measuring the mechanical properties of cells deeply embedded within tissues.

A comparison of methods to assess cell mechanical properties.

The mechanical properties of cells influence their cellular and subcellular functions, including cell adhesion, migration, polarization, and differentiation, as well as organelle organization and trafficking inside the cytoplasm. Yet reported values of cell stiffness and viscosity vary substantially, which suggests differences in how the results of different methods are obtained or analyzed by different groups. To address this issue and illustrate the complementarity of certain approaches, here we present, analyze, and critically compare measurements obtained by means of some of the most widely used methods for cell mechanics: atomic force microscopy, magnetic twisting cytometry, particle-tracking microrheology, parallel-plate rheometry, cell monolayer rheology, and optical stretching. These measurements highlight how elastic and viscous moduli of MCF-7 breast cancer cells can vary 1,000-fold and 100-fold, respectively. We discuss the sources of these variations, including the level of applied mechanical stress, the rate of deformation, the geometry of the probe, the location probed in the cell, and the extracellular microenvironment.

Three-dimensional analysis of extrahepatic cholangiocarcinoma and tumor budding.

Advances in tissue clearing and microscopy make it possible to study human diseases in three dimensions (3D). High-grade tumor budding is known to be associated with poor prognosis in various cancers; however, little is known about the 3D architecture of tumor budding. Using tissue clearing, we analyzed the 3D structure of tumor budding and E-cadherin expression in 31 extrahepatic cholangiocarcinomas. A total of 31 thick slabs (up to 5 mm) were harvested from surgically resected tumor tissue, including 27 hilar and 4 distal cholangiocarcinomas. Twenty-eight cases were adenocarcinoma, and three were undifferentiated carcinoma. After clearing, the tissues were immunolabeled with antibodies to cytokeratin 19 and to E-cadherin, and then visualized using light-sheet and confocal laser scanning microscopy. Tumor budding was evaluated in hematoxylin and eosin-stained sections (2D) using standard pathological criteria. Of the 31 cancers, 13 showed low-grade tumor budding and 18 showed high-grade tumor budding. First, 3D analysis revealed that the neoplastic cells in tumor buds of adenocarcinoma were typically not individual islands of cells, but rather tips of attenuated protrusions connected to the main tumor. Second, adenocarcinomas with low-grade tumor budding were composed predominantly of tubules that only focally form cords at the periphery. By contrast, adenocarcinomas with high-grade tumor budding predominantly formed cords in both centers and peripheries of the tumors. Third, adenocarcinoma with low-grade tumor budding was characterized by a few short protrusions with few branches, whereas adenocarcinoma with high-grade tumor budding was characterized by longer protrusions with more branching. Finally, immunolabeling of E-cadherin was stronger in the center of the adenocarcinoma but decreased at the tips of protrusions. E-cadherin loss was more extensive in the protrusions of high-grade tumor budding than in the protrusions of low-grade tumor budding. Our findings suggest that tumor buds as seen in 2D are, in fact, cross-sections of attenuated but contiguous protrusions extending from the main tumor. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Algal extracellular organic matter mediated photocatalytic degradation of estrogens.

Estrogens are among the most concerned emerging contaminants in the wastewater treatment effluent due to their sexual disruption in aquatic wildlife. The use of microalgae for secondary wastewater effluent polishing is a promising approach due to the economic benefit and value-added products. In this study, three microalgae species, including Selenastrum capricornutum, Scenedesmus quadricauda and Chlorella vulgaris were selected to conduct batch experiments to examine important mechanisms, especially the role of algal extracellular organic matter (AEOM) on two selected estrogens (17ß-estradiol, E2 and 17α-ethynylestradiol, EE2) removal. Results showed that estrogens could not be significantly degraded under visible light irradiation and adsorption of estrogens by microalgae was negligible. All three living microalgae cultures have ability to remove E2 and EE2, and Selenastrum capricornutum showed the highest E2 and EE2 removal efficiency of 91% and 83%, respectively, corresponding to the reduction of predicted estrogenic activity of 86%. AEOM from three microalgae cultures could induce photodegradation of estrogens, and AEOM from Selenastrum capricornutum and Chlorella vulgaris achieved 100% of E2 and EE2 removal under visible light irradiation. Fluorescence excitation-emission matrix spectroscopy identified humic/fulvic-like substances in AEOM from three microalgae cultures, which might be responsible for inducing the indirect photolysis of E2 and EE2. Therefore, in the living microalgae cultures, the major estrogens removal mechanisms should include biotransformation as well as AEOM meditated photocatalytic degradation. Since removal rates through photodegradation could be faster than biotransformation, the AEOM mediated photocatalytic degradation can play a potential role to remove emerging contaminants when using microalgae technology for wastewater effluent treatment.

Three-dimensional visualization of cleared human pancreas cancer reveals that sustained epithelial-to-mesenchymal transition is not required for venous invasion.

Venous invasion is three times more common in pancreatic cancer than it is in other major cancers of the gastrointestinal tract, and venous invasion may explain why pancreatic cancer is so deadly. To characterize the patterns of venous invasion in pancreatic cancer, 52 thick slabs (up to 5 mm) of tissue were harvested from 52 surgically resected human ductal adenocarcinomas, cleared with a modified iDISCO method, and labeled with fluorescent-conjugated antibodies to cytokeratin 19, desmin, CD31, p53 and/or e-cadherin. Labeled three-dimensional (3D) pancreas cancer tissues were visualized with confocal laser scanning or light sheet microscopy. Multiple foci of venous and even arterial invasion were visualized. Venous invasion was detected more often in 3D (88%, 30/34 cases) than in conventional 2D slide evaluation (75%, 25/34 cases, P < 0.001). 3D visualization revealed pancreatic cancer cells crossing the walls of veins at multiple points, often at points where preexisting capillary structures bridge the blood vessels. The neoplastic cells often retained a ductal morphology (cohesive cells forming tubes) as they progressed from a stromal to intravenous location. Although immunolabeling with antibodies to e-cadherin revealed focal loss of expression at the leading edges of the cancers, the neoplastic cells within veins expressed e-cadherin and formed well-oriented glands. We conclude that venous invasion is almost universal in pancreatic cancer, suggesting that even surgically resectable PDAC has access to the venous spaces and thus the ability to disseminate widely. Furthermore, we observe that sustained epithelial-mesenchymal transition is not required for venous invasion in pancreatic cancer.

Supramolecular Design of Unsymmetric Reverse Bolaamphiphiles for Cell-Sensitive Hydrogel Degradation and Drug Release.

Self-assembly of peptide-based building units into supramolecular nanostructures creates an important class of biomaterials with robust mechanical properties and improved resistance to premature degradation. Yet, upon aggregation, substrate-enzyme interactions are often compromised because of the limited access of macromolecular proteins to the peptide substrate, leading to either a reduction or loss of responsiveness to biomolecular cues. Reported here is the supramolecular design of unsymmetric reverse bolaamphiphiles (RBA) capable of exposing a matrix metalloproteinase (MMP) substrate on the surface of their filamentous assemblies. Upon addition of MMP-2, these filaments rapidly break into fragments prior to reassembling into spherical micelles. Using 3D cell culture, it is shown that drug release is commensurate with cell density, revealing more effective cell killing when more cancer cells are present. This design platform could serve as a cell-responsive therapeutic depot for local chemotherapy.

Immunolabeling of Cleared Human Pancreata Provides Insights into Three-Dimensional Pancreatic Anatomy and Pathology.

Visualizing pathologies in three dimensions can provide unique insights into the biology of human diseases. A rapid and easy-to-implement dibenzyl ether-based technique was used to clear thick sections of surgically resected human pancreatic parenchyma. Protocols were applicable to both fresh and formalin-fixed, paraffin-embedded tissue. The penetration of antibodies into dense pancreatic parenchyma was optimized using both gradually increasing antibody concentrations and centrifugal flow. Immunolabeling with antibodies against cytokeratin 19 was visualized using both light sheet and confocal laser scanning microscopy. The technique was applied successfully to 26 sections of pancreas, providing three-dimensional (3D) images of normal pancreatic tissue, pancreatic intraepithelial neoplasia, intraductal papillary mucinous neoplasms, and infiltrating pancreatic ductal adenocarcinomas. 3D visualization highlighted processes that are hard to conceptualize in two dimensions, such as invasive carcinoma growing into what appeared to be pre-existing pancreatic ducts and within venules, and the tracking of long cords of neoplastic cells parallel to blood vessels. Expanding this technique to formalin-fixed, paraffin-embedded tissue opens pathology archives to 3D visualization of unique biosamples and rare diseases. The application of immunolabeling and clearing to human pancreatic parenchyma provides detailed visualization of normal pancreatic anatomy, and can be used to characterize the 3D architecture of diseases including pancreatic intraepithelial neoplasia, intraductal papillary mucinous neoplasm, and pancreatic ductal adenocarcinomas.

Differential vesicular sorting of AMPA and GABAA receptors.

In mature neurons AMPA receptors cluster at excitatory synapses primarily on dendritic spines, whereas GABAA receptors cluster at inhibitory synapses mainly on the soma and dendritic shafts. The molecular mechanisms underlying the precise sorting of these receptors remain unclear. By directly studying the constitutive exocytic vesicles of AMPA and GABAA receptors in vitro and in vivo, we demonstrate that they are initially sorted into different vesicles in the Golgi apparatus and inserted into distinct domains of the plasma membrane. These insertions are dependent on distinct Rab GTPases and SNARE complexes. The insertion of AMPA receptors requires SNAP25-syntaxin1A/B-VAMP2 complexes, whereas insertion of GABAA receptors relies on SNAP23-syntaxin1A/B-VAMP2 complexes. These SNARE complexes affect surface targeting of AMPA or GABAA receptors and synaptic transmission. Our studies reveal vesicular sorting mechanisms controlling the constitutive exocytosis of AMPA and GABAA receptors, which are critical for the regulation of excitatory and inhibitory responses in neurons.

Three-dimensional cell migration does not follow a random walk.

Cell migration through 3D extracellular matrices is critical to the normal development of tissues and organs and in disease processes, yet adequate analytical tools to characterize 3D migration are lacking. Here, we quantified the migration patterns of individual fibrosarcoma cells on 2D substrates and in 3D collagen matrices and found that 3D migration does not follow a random walk. Both 2D and 3D migration features a non-Gaussian, exponential mean cell velocity distribution, which we show is primarily a result of cell-to-cell variations. Unlike in the 2D case, 3D cell migration is anisotropic: velocity profiles display different speed and self-correlation processes in different directions, rendering the classical persistent random walk (PRW) model of cell migration inadequate. By incorporating cell heterogeneity and local anisotropy to the PRW model, we predict 3D cell motility over a wide range of matrix densities, which identifies density-independent emerging migratory properties. This analysis also reveals the unexpected robust relation between cell speed and persistence of migration over a wide range of matrix densities.

A robust nonlinear tissue-component discrimination method for computational pathology.

Advances in digital pathology, specifically imaging instrumentation and data management, have allowed for the development of computational pathology tools with the potential for better, faster, and cheaper diagnosis, prognosis, and prediction of disease. Images of tissue sections frequently vary in color appearance across research laboratories and medical facilities because of differences in tissue fixation, staining protocols, and imaging instrumentation, leading to difficulty in the development of robust computational tools. To address this challenge, we propose a novel nonlinear tissue-component discrimination (NLTD) method to register automatically the color space of histopathology images and visualize individual tissue components, independent of color differences between images. Our results show that the NLTD method could effectively discriminate different tissue components from different types of tissues prepared at different institutions. Further, we demonstrate that NLTD can improve the accuracy of nuclear detection and segmentation algorithms, compared with using conventional color deconvolution methods, and can quantitatively analyze immunohistochemistry images. Together, the NLTD method is objective, robust, and effective, and can be easily implemented in the emerging field of computational pathology.

Morphological effects on expression of growth differentiation factor 15 (GDF15), a marker of metastasis.

Cancer cells typically demonstrate altered morphology during the various stages of disease progression as well as metastasis. While much is known about how altered cell morphology in cancer is a result of genetic regulation, less is known about how changes in cell morphology affect cell function by influencing gene expression. In this study, we altered cell morphology in different types of cancer cells by disrupting the actin cytoskeleton or by modulating attachment and observed a rapid up-regulation of growth differentiation factor 15 (GDF15), a member of the transforming growth factor-beta (TGF-ß) super-family. Strikingly, this up-regulation was sustained as long as the cell morphology remained altered but was reversed upon allowing cell morphology to return to its typical configuration. The potential significance of these findings was examined in vivo using a mouse model: a small number of cancer cells grown in diffusion chambers that altered morphology increased mouse serum GDF15. Taken together, we propose that during the process of metastasis, cancer cells experience changes in cell morphology, resulting in the increased production and secretion of GDF15 into the surrounding environment. This indicates a possible relationship between serum GDF15 levels and circulating tumor cells may exist. Further investigation into the exact nature of this relationship is warranted.

Simultaneously defining cell phenotypes, cell cycle, and chromatin modifications at single-cell resolution.

Heterogeneity of cellular phenotypes in asynchronous cell populations placed in the same biochemical and biophysical environment may depend on cell cycle and chromatin modifications; however, no current method can measure these properties at single-cell resolution simultaneously and in situ. Here, we develop, test, and validate a new microscopy assay that rapidly quantifies global acetylation on histone H3 and measures a wide range of cell and nuclear properties, including cell and nuclear morphology descriptors, cell-cycle phase, and F-actin content of thousands of cells simultaneously, without cell detachment from their substrate, at single-cell resolution. These measurements show that isogenic, isotypic cells of identical DNA content and the same cell-cycle phase can still display large variations in H3 acetylation and that these variations predict specific phenotypic variations, in particular, nuclear size and actin cytoskeleton content, but not cell shape. The dependence of cell and nuclear properties on cell-cycle phase is assessed without artifact-prone cell synchronization. To further demonstrate its versatility, this assay is used to quantify the complex interplay among cell cycle, epigenetic modifications, and phenotypic variations following pharmacological treatments affecting DNA integrity, cell cycle, and inhibiting chromatin-modifying enzymes.

Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic findings.

Pancreatic ductal adenocarcinoma is a rare but lethal cancer. Recent evidence suggests that pancreatic intraepithelial neoplasia (PanIN), a microscopic precursor lesion that gives rise to pancreatic cancer, is larger and more prevalent than previously believed. Better understanding of the growth-law dynamics of PanINs may improve our ability to understand how a miniscule fraction makes the transition to invasive cancer. Here, using three-dimensional tissue mapping, we analyzed >1000 PanINs and found that lesion size is distributed according to a power law. Our data suggest that in bulk, PanIN size can be predicted by general growth behavior without consideration for the heterogeneity of the pancreatic microenvironment or an individual's age, history, or lifestyle. Our models suggest that intraductal spread and fusing of lesions drive our observed size distribution. This analysis lays the groundwork for future mathematical modeling efforts integrating PanIN incidence, morphology, and molecular features to understand tumorigenesis and demonstrates the utility of combining experimental measurement with dynamic modeling in understanding tumorigenesis.

Senescent cell heterogeneity and responses to senolytic treatment are related to cell cycle status during cell growth arrest.

Cellular senescence has been strongly linked to aging and age-related diseases. It is well established that the phenotype of senescent cells is highly heterogeneous and influenced by their cell type and senescence-inducing stimulus. Recent single-cell RNA-sequencing studies identified heterogeneity within senescent cell populations. However, proof of functional differences between such subpopulations is lacking. To identify functionally distinct senescent cell subpopulations, we employed high-content image analysis to measure senescence marker expression in primary human endothelial cells and fibroblasts. We found that G2-arrested senescent cells feature higher senescence marker expression than G1-arrested senescent cells. To investigate functional differences, we compared IL-6 secretion and response to ABT263 senolytic treatment in G1 and G2 senescent cells. We determined that G2-arrested senescent cells secrete more IL-6 and are more sensitive to ABT263 than G1-arrested cells. We hypothesize that cell cycle dependent DNA content is a key contributor to the heterogeneity within senescent cell populations. This study demonstrates the existence of functionally distinct senescent subpopulations even in culture. This data provides the first evidence of selective cell response to senolytic treatment among senescent cell subpopulations. Overall, this study emphasizes the importance of considering the senescent cell heterogeneity in the development of future senolytic therapies.

Three-dimensional reconstruction of fetal rhesus macaque kidneys at single-cell resolution reveals complex inter-relation of structures.

Kidneys are among the most structurally complex organs in the body. Their architecture is critical to ensure proper function and is often impacted by diseases such as diabetes and hypertension. Understanding the spatial interplay between the different structures of the nephron and renal vasculature is crucial. Recent efforts have demonstrated the value of three-dimensional (3D) imaging in revealing new insights into the various components of the kidney; however, these studies used antibodies or autofluorescence to detect structures and so were limited in their ability to compare the many subtle structures of the kidney at once. Here, through 3D reconstruction of fetal rhesus macaque kidneys at cellular resolution, we demonstrate the power of deep learning in exhaustively labelling seventeen microstructures of the kidney. Using these tissue maps, we interrogate the spatial distribution and spatial correlation of the glomeruli, renal arteries, and the nephron. This work demonstrates the power of deep learning applied to 3D tissue images to improve our ability to compare many microanatomical structures at once, paving the way for further works investigating renal pathologies.

Three-dimensional assessments are necessary to determine the true, spatially-resolved composition of tissues.

Methods for spatially resolved cellular profiling using thinly cut sections have enabled in-depth quantitative tissue mapping to study inter-sample and intra-sample differences in normal human anatomy and disease onset and progression. These methods often profile extremely limited regions, which may impact the evaluation of heterogeneity due to tissue sub-sampling. Here, we applied CODA, a deep learning-based tissue mapping platform, to reconstruct the three-dimensional (3D) microanatomy of grossly normal and cancer-containing human pancreas biospecimens obtained from individuals who underwent pancreatic resection. To compare inter- and intra-sample heterogeneity, we assessed bulk and spatially resolved tissue composition in a cohort of two-dimensional (2D) whole slide images (WSIs) and a cohort of thick slabs of pancreas tissue that were digitally reconstructed in 3D from serial sections. To demonstrate the marked under sampling of 2D assessments, we simulated the number of WSIs and tissue microarrays (TMAs) necessary to represent the compositional heterogeneity of 3D data within 10% error to reveal that tens of WSIs and hundreds of TMA cores are sometimes needed. We show that spatial correlation of different pancreatic structures decay significantly within a span of microns, demonstrating that 2D histological sections may not be representative of their neighboring tissues. In sum, we demonstrate that 3D assessments are necessary to accurately assess tissue composition in normal and abnormal specimens and in order to accurately determine neoplastic content. These results emphasize the importance of intra-sample heterogeneity in tissue mapping efforts.

Generative interpolation and restoration of images using deep learning for improved 3D tissue mapping.

The development of novel imaging platforms has improved our ability to collect and analyze large three-dimensional (3D) biological imaging datasets. Advances in computing have led to an ability to extract complex spatial information from these data, such as the composition, morphology, and interactions of multi-cellular structures, rare events, and integration of multi-modal features combining anatomical, molecular, and transcriptomic (among other) information. Yet, the accuracy of these quantitative results is intrinsically limited by the quality of the input images, which can contain missing or damaged regions, or can be of poor resolution due to mechanical, temporal, or financial constraints. In applications ranging from intact imaging (e.g. light-sheet microscopy and magnetic resonance imaging) to sectioning based platforms (e.g. serial histology and serial section transmission electron microscopy), the quality and resolution of imaging data has become paramount. Here, we address these challenges by leveraging frame interpolation for large image motion (FILM), a generative AI model originally developed for temporal interpolation, for spatial interpolation of a range of 3D image types. Comparative analysis demonstrates the superiority of FILM over traditional linear interpolation to produce functional synthetic images, due to its ability to better preserve biological information including microanatomical features and cell counts, as well as image quality, such as contrast, variance, and luminance. FILM repairs tissue damages in images and reduces stitching artifacts. We show that FILM can decrease imaging time by synthesizing skipped images. We demonstrate the versatility of our method with a wide range of imaging modalities (histology, tissue-clearing/light-sheet microscopy, magnetic resonance imaging, serial section transmission electron microscopy), species (human, mouse), healthy and diseased tissues (pancreas, lung, brain), staining techniques (IHC, H&E), and pixel resolutions (8 nm, 2 µm, 1mm). Overall, we demonstrate the potential of generative AI in improving the resolution, throughput, and quality of biological image datasets, enabling improved 3D imaging.