qMAP enabled microanatomical mapping of human skin aging.

Aging is a major driver of diseases in humans. Identifying features associated with aging is essential for designing robust intervention strategies and discovering novel biomarkers of aging. Extensive studies at both the molecular and organ/whole-body physiological scales have helped determined features associated with aging. However, the lack of meso-scale studies, particularly at the tissue level, limits the ability to translate findings made at molecular scale to impaired tissue functions associated with aging. In this work, we established a tissue image analysis workflow - quantitative micro-anatomical phenotyping (qMAP) - that leverages deep learning and machine vision to fully label tissue and cellular compartments in tissue sections. The fully mapped tissue images address the challenges of finding an interpretable feature set to quantitatively profile age-related microanatomic changes. We optimized qMAP for skin tissues and applied it to a cohort of 99 donors aged 14 to 92. We extracted 914 microanatomic features and found that a broad spectrum of these features, represented by 10 cores processes, are strongly associated with aging. Our analysis shows that microanatomical features of the skin can predict aging with a mean absolute error (MAE) of 7.7 years, comparable to state-of-the-art epigenetic clocks. Our study demonstrates that tissue-level architectural changes are strongly associated with aging and represent a novel category of aging biomarkers that complement molecular markers. Our results highlight the complex and underexplored multi-scale relationship between molecular and tissue microanatomic scales.

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.

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.

PanIN or IPMN? Redefining Lesion Size in 3 Dimensions.

Pancreatic ductal adenocarcinoma (PDAC) develops from 2 known precursor lesions: a majority (∼85%) develops from pancreatic intraepithelial neoplasia (PanIN), and a minority develops from intraductal papillary mucinous neoplasms (IPMNs). Clinical classification of PanIN and IPMN relies on a combination of low-resolution, 3-dimensional (D) imaging (computed tomography, CT), and high-resolution, 2D imaging (histology). The definitions of PanIN and IPMN currently rely heavily on size. IPMNs are defined as macroscopic: generally >1.0 cm and visible in CT, and PanINs are defined as microscopic: generally <0.5 cm and not identifiable in CT. As 2D evaluation fails to take into account 3D structures, we hypothesized that this classification would fail in evaluation of high-resolution, 3D images. To characterize the size and prevalence of PanINs in 3D, 47 thick slabs of pancreas were harvested from grossly normal areas of pancreatic resections, excluding samples from individuals with a diagnosis of an IPMN. All patients but one underwent preoperative CT scans. Through construction of cellular resolution 3D maps, we identified >1400 ductal precursor lesions that met the 2D histologic size criteria of PanINs. We show that, when 3D space is considered, 25 of these lesions can be digitally sectioned to meet the 2D histologic size criterion of IPMN. Re-evaluation of the preoperative CT images of individuals found to possess these large precursor lesions showed that nearly half are visible on imaging. These findings demonstrate that the clinical classification of PanIN and IPMN fails in evaluation of high-resolution, 3D images, emphasizing the need for re-evaluation of classification guidelines that place significant weight on 2D assessment of 3D structures.

Single-cell morphology encodes functional subtypes of senescence in aging human dermal fibroblasts.

Cellular senescence is an established driver of aging, exhibiting context-dependent phenotypes across multiple biological length-scales. Despite its mechanistic importance, profiling senescence within cell populations is challenging. This is in part due to the limitations of current biomarkers to robustly identify senescent cells across biological settings, and the heterogeneous, non-binary phenotypes exhibited by senescent cells. Using a panel of primary dermal fibroblasts, we combined live single-cell imaging, machine learning, multiple senescence induction conditions, and multiple protein-based senescence biomarkers to show the emergence of functional subtypes of senescence. Leveraging single-cell morphologies, we defined eleven distinct morphology clusters, with the abundance of cells in each cluster being dependent on the mode of senescence induction, the time post-induction, and the age of the donor. Of these eleven clusters, we identified three bona-fide senescence subtypes (C7, C10, C11), with C10 showing the strongest age-dependence across a cohort of fifty aging individuals. To determine the functional significance of these senescence subtypes, we profiled their responses to senotherapies, specifically focusing on Dasatinib + Quercetin (D+Q). Results indicated subtype-dependent responses, with senescent cells in C7 being most responsive to D+Q. Altogether, we provide a robust single-cell framework to identify and classify functional senescence subtypes with applications for next-generation senotherapy screens, and the potential to explain heterogeneous senescence phenotypes across biological settings based on the presence and abundance of distinct senescence subtypes.

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.

E-cadherin interacts with EGFR resulting in hyper-activation of ERK in multiple models of breast cancer.

The loss of intercellular adhesion molecule E-cadherin is a hallmark of the epithelial-mesenchymal transition (EMT), during which tumor cells transition into an invasive phenotype. Accordingly, E-cadherin has long been considered a tumor suppressor gene; however, E-cadherin expression is paradoxically correlated with breast cancer survival rates. Using novel multi-compartment organoids and multiple in vivo models, we show that E-cadherin promotes a hyper-proliferative phenotype in breast cancer cells via interaction with the transmembrane receptor EGFR. The E-cad and EGFR interaction results in activation of the MEK/ERK signaling pathway, leading to a significant increase in proliferation via activation of transcription factors, including c-Fos. Pharmacological inhibition of MEK activity in E-cadherin positive breast cancer significantly decreases both tumor growth and macro-metastasis in vivo. This work provides evidence for a novel role of E-cadherin in breast tumor progression and identifies a new target to treat hyper-proliferative E-cadherin-positive breast tumors, thus providing the foundation to utilize E-cadherin as a biomarker for specific therapeutic success.

A disrupted compartment boundary underlies abnormal cardiac patterning and congenital heart defects.

Failure of septation of the interventricular septum (IVS) is the most common congenital heart defect (CHD), but mechanisms for patterning the IVS are largely unknown. We show that a Tbx5+/Mef2cAHF+ progenitor lineage forms a compartment boundary bisecting the IVS. This coordinated population originates at a first- and second heart field interface, subsequently forming a morphogenetic nexus. Ablation of Tbx5+/Mef2cAHF+ progenitors cause IVS disorganization, right ventricular hypoplasia and mixing of IVS lineages. Reduced dosage of the CHD transcription factor TBX5 disrupts boundary position and integrity, resulting in ventricular septation defects (VSDs) and patterning defects, including Slit2 and Ntn1 misexpression. Reducing NTN1 dosage partly rescues cardiac defects in Tbx5 mutant embryos. Loss of Slit2 or Ntn1 causes VSDs and perturbed septal lineage distributions. Thus, we identify essential cues that direct progenitors to pattern a compartment boundary for proper cardiac septation, revealing new mechanisms for cardiac birth defects.

Substrate stiffness modulates the emergence and magnitude of senescence phenotypes in dermal fibroblasts.

Cellular senescence is a major driver of aging and disease. Here we show that substrate stiffness modulates the emergence and magnitude of senescence phenotypes after exposure to senescence inducers. Using a primary dermal fibroblast model, we show that decreased substrate stiffness accelerates senescence-associated cell-cycle arrest and regulates the expression of conventional protein-based biomarkers of senescence. We found that the expression of these senescence biomarkers, namely p21WAF1/CIP1 and p16INK4a are mechanosensitive and are in-part regulated by myosin contractility through focal adhesion kinase (FAK)-ROCK signaling. Interestingly, at the protein level senescence-induced dermal fibroblasts on soft substrates (0.5 kPa) do not express p21WAF1/CIP1 and p16INK4a at comparable levels to induced cells on stiff substrates (4GPa). However, cells express CDKN1a, CDKN2a, and IL6 at the RNA level across both stiff and soft substrates. Moreover, when cells are transferred from soft to stiff substrates, senescent cells recover an elevated expression of p21WAF1/CIP1 and p16INK4a at levels comparable to senescence cells on stiff substrates, pointing to a mechanosensitive regulation of the senescence phenotype. Together, our results indicate that the emergent senescence phenotype depends critically on the local mechanical environments of cells and that senescent cells actively respond to changing mechanical cues.

Engineering self-propelled tumor-infiltrating CAR T cells using synthetic velocity receptors.

Chimeric antigen receptor (CAR) T cells express antigen-specific synthetic receptors, which upon binding to cancer cells, elicit T cell anti-tumor responses. CAR T cell therapy has enjoyed success in the clinic for hematological cancer indications, giving rise to decade-long remissions in some cases. However, CAR T therapy for patients with solid tumors has not seen similar success. Solid tumors constitute 90% of adult human cancers, representing an enormous unmet clinical need. Current approaches do not solve the central problem of limited ability of therapeutic cells to migrate through the stromal matrix. We discover that T cells at low and high density display low- and high-migration phenotypes, respectively. The highly migratory phenotype is mediated by a paracrine pathway from a group of self-produced cytokines that include IL5, TNFα, IFNγ, and IL8. We exploit this finding to "lock-in" a highly migratory phenotype by developing and expressing receptors, which we call velocity receptors (VRs). VRs target these cytokines and signal through these cytokines' cognate receptors to increase T cell motility and infiltrate lung, ovarian, and pancreatic tumors in large numbers and at doses for which control CAR T cells remain confined to the tumor periphery. In contrast to CAR therapy alone, VR-CAR T cells significantly attenuate tumor growth and extend overall survival. This work suggests that approaches to the design of immune cell receptors that focus on migration signaling will help current and future CAR cellular therapies to infiltrate deep into solid tumors.

Cytoplasmic accumulation and plasma membrane association of anillin and Ect2 promote confined migration and invasion.

Cells migrating in confinement experience mechanical challenges whose consequences on cell migration machinery remain only partially understood. Here, we demonstrate that a pool of the cytokinesis regulatory protein anillin is retained during interphase in the cytoplasm of different cell types. Confinement induces recruitment of cytoplasmic anillin to plasma membrane at the poles of migrating cells, which is further enhanced upon nuclear envelope (NE) rupture(s). Rupture events also enable the cytoplasmic egress of predominantly nuclear RhoGEF Ect2. Anillin and Ect2 redistributions scale with microenvironmental stiffness and confinement, and are observed in confined cells in vitro and in invading tumor cells in vivo. Anillin, which binds actomyosin at the cell poles, and Ect2, which activates RhoA, cooperate additively to promote myosin II contractility, and promote efficient invasion and extravasation. Overall, our work provides a mechanistic understanding of how cytokinesis regulators mediate RhoA/ROCK/myosin II-dependent mechanoadaptation during confined migration and invasive cancer progression.

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.

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 reveals that pancreatic intraepithelial neoplasms (PanINs), the microscopic precursor lesions in the pancreatic ducts that can give rise to invasive pancreatic cancer, are significantly 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 of these lesions makes the transition to invasive cancer. Here, using artificial intelligence (AI)-based three-dimensional (3D) tissue mapping method, we measured the volumes of >1,000 PanIN and found that lesion size is distributed according to a power law with a fitted exponent of -1.7 over > 3 orders of magnitude. Our data also suggest that PanIN growth is not very sensitive to the pancreatic microenvironment or an individual's age, family history, and lifestyle, and is rather shaped by general growth behavior. We analyze several models of PanIN growth and fit the predicted size distributions to the observed data. The best fitting models suggest that both intraductal spread of PanIN lesions and fusing of multiple lesions into large, highly branched structures drive PanIN growth patterns. This work lays the groundwork for future mathematical modeling efforts integrating PanIN incidence, morphology, genomic, and transcriptomic features to understand pancreas tumorigenesis, and demonstrates the utility of combining experimental measurement of human tissues with dynamic modeling for understanding cancer tumorigenesis.

Three-dimensional analysis of ductular reactions and their correlation with liver regeneration and fibrosis.

The liver has multiple regeneration modes, including hepatocellular hypertrophy and self-renewal of hepatocytes. When hepatocyte proliferation is impaired, hepatic progenitor cells may proliferate through ductular reaction (DR), differentiate into hepatocytes, and contribute to fibrosis. However, the three-dimensional spatial relationship between DR and regenerating hepatocytes and dynamic changes in DR associated with fibrosis remain poorly understood. Here, we performed three-dimensional (3D) imaging of cleared 42 liver explants with chronic and acute liver diseases and 4 normal livers to visualize DR. In chronic hepatic liver diseases, such as viral hepatitis, steatohepatitis, autoimmune hepatitis, and cryptogenic cirrhosis, the total length and number of branches of DR showed a significant positive correlation. We studied the spatial relationship between DR and GS-expressing cells using glutamine synthetase (GS) and cytokeratin 19 (CK19) as markers of liver regeneration and DR, respectively. The percentage of CK19-positive cells that co-expressed GS was less than 10% in chronic liver diseases. In contrast, nearly one-third of CK19-positive cells co-expressed GS in acute liver diseases, and chronic cholestatic liver diseases, including primary biliary cholangitis and primary sclerosing cholangitis, showed no co-expression. We also found that DR was longer and had more branching in livers with progressive fibrosis compared to those with regressive fibrosis. Our results suggest that DR displays varying degrees of spatial complexity and contribution to liver regeneration. DR may serve as hepatobiliary junctions that maintain continuity between hepatocytes and bile ducts rather than hepatocyte regeneration in chronic liver diseases.

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior.

Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells' characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework-a cell behavior hypothesis grammar-that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.

Precision-engineered biomimetics: the human fallopian tube.

The fallopian tube has an essential role in several physiological and pathological processes from pregnancy to ovarian cancer. However, there are no biologically relevant models to study its pathophysiology. The state-of-the-art organoid model has been compared to two-dimensional tissue sections and molecularly assessed providing only cursory analyses of the model's accuracy. We developed a novel multi-compartment organoid model of the human fallopian tube that was meticulously tuned to reflect the compartmentalization and heterogeneity of the tissue's composition. We validated this organoid's molecular expression patterns, cilia-driven transport function, and structural accuracy through a highly iterative platform wherein organoids are compared to a three-dimensional, single-cell resolution reference map of a healthy, transplantation-quality human fallopian tube. This organoid model was precision-engineered to match the human microanatomy. One sentence summary: Tunable organoid modeling and CODA architectural quantification in tandem help design a tissue-validated organoid model.

Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces.

Recent advances in spatial transcriptomics (STs) enable gene expression measurements from a tissue sample while retaining its spatial context. This technology enables unprecedented in situ resolution of the regulatory pathways that underlie the heterogeneity in the tumor as well as the tumor microenvironment (TME). The direct characterization of cellular co-localization with spatial technologies facilities quantification of the molecular changes resulting from direct cell-cell interaction, as it occurs in tumor-immune interactions. We present SpaceMarkers, a bioinformatics algorithm to infer molecular changes from cell-cell interactions from latent space analysis of ST data. We apply this approach to infer the molecular changes from tumor-immune interactions in Visium spatial transcriptomics data of metastasis, invasive and precursor lesions, and immunotherapy treatment. Further transfer learning in matched scRNA-seq data enabled further quantification of the specific cell types in which SpaceMarkers are enriched. Altogether, SpaceMarkers can identify the location and context-specific molecular interactions within the TME from ST data.