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The lung is a vital organ that undergoes extensive morphological and functional changes during postnatal development. To disambiguate how different cell populations contribute to organ development, we performed proteomic and transcriptomic analyses of four sorted cell populations from the lung of human subjects aged 0 to 8 years-old with a focus on early life. The cell populations analyzed included epithelial, endothelial, mesenchymal, and immune cells. Our results revealed distinct molecular signatures for each of the sorted cell populations that enable the description of molecular shifts occurring in these populations during post-natal development. We confirmed that the proteome of the different cell populations was distinct regardless of age and identified functions specific to each population. We identified a series of cell population protein markers, including those located at the cell surface, that show differential expression and distribution on RNA in situ hybridization and immunofluorescence imaging. We validated the spatial distribution of AT1 and endothelial cell surface markers. Temporal analyses of the proteomes of the four populations revealed processes modulated during postnatal development and clarified the findings obtained from whole tissue proteome studies. Finally, the proteome was compared to a transcriptomics survey performed on the same lung samples to evaluate processes under post-transcriptional control.

An improved understanding of the human lung necessitates advanced systems models informed by an ever-increasing repertoire of molecular omics, cellular imaging, and pathological datasets. To centralize and standardize information across broad lung research efforts, we expanded the LungMAP.net website into a new gateway portal. This portal connects a broad spectrum of research networks, bulk and single-cell multiomics data, and a diverse collection of image data that span mammalian lung development and disease. The data are standardized across species and technologies using harmonized data and metadata models that leverage recent advances, including those from the Human Cell Atlas, diverse ontologies, and the LungMAP CellCards initiative. To cultivate future discoveries, we have aggregated a diverse collection of single-cell atlases for multiple species (human, rhesus, and mouse) to enable consistent queries across technologies, cohorts, age, disease, and drug treatment. These atlases are provided as independent and integrated queryable datasets, with an emphasis on dynamic visualization, figure generation, reanalysis, cell-type curation, and automated reference-based classification of user-provided single-cell genomics datasets (Azimuth). As this resource grows, we intend to increase the breadth of available interactive interfaces, supported data types, data portals and datasets from LungMAP, and external research efforts.

The Human BioMolecular Atlas Program (HuBMAP) aims to create a multi-scale spatial atlas of the healthy human body at single-cell resolution by applying advanced technologies and disseminating resources to the community. As the HuBMAP moves past its first phase, creating ontologies, protocols and pipelines, this Perspective introduces the production phase: the generation of reference spatial maps of functional tissue units across many organs from diverse populations and the creation of mapping tools and infrastructure to advance biomedical research., (© 2023. Springer Nature Limited.)

Human disease states are biomolecularly multifaceted and can span across phenotypic states, therefore it is important to understand diseases on all levels, across cell types, and within and across microanatomical tissue compartments. To obtain an accurate and representative view of the molecular landscape within human lungs, this fragile tissue must be inflated and embedded to maintain spatial fidelity of the location of molecules and minimize molecular degradation for molecular imaging experiments. Here, we evaluated agarose inflation and carboxymethyl cellulose embedding media and determined effective tissue preparation protocols for performing bulk and spatial mass spectrometry-based omics measurements. Mass spectrometry imaging methods were optimized to boost the number of annotatable molecules in agarose inflated lung samples. This optimized protocol permitted the observation of unique lipid distributions within several airway regions in the lung tissue block. Laser capture microdissection of these airway regions followed by high-resolution proteomic analysis allowed us to begin linking the lipidome with the proteome in a spatially resolved manner, where we observed proteins with high abundance specifically localized to the airway regions. We also compared our mass spectrometry results to lung tissue samples preserved using two other inflation/embedding media, but we identified several pitfalls with the sample preparation steps using this preservation method. Overall, we demonstrated the versatility of the inflation method, and we can start to reveal how the metabolome, lipidome, and proteome are connected spatially in human lungs and across disease states through a variety of different experiments., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Lukowski, Olson, Velickovic, Wang, Kyle, Kim, Williams, Zhu, Huyck, McGraw, Poole, Rogers, Misra, Alexandrov, Ansong, Pryhuber, Clair, Adkins, Carson and Anderton.)

Sm16 is a 16 KDa protein released by Schistosoma mansoni that modulates inflammatory responses in host cells. Sm16 is expressed by several life cycle stages of S. mansoni, including the egg stage. Schistosome eggs are known to provoke chronic schistosomiasis pathology, which involves the development of liver fibrosis. Hepatic stellate cells (HSCs), which are responsible for this fibrosis, are susceptible to immunomodulation by S. mansoni whole egg secretions. To define the effects of Sm16 exposure on HSCs, two synthetic peptide derivatives of Sm16, coined "KS-84″ and "KS-66″, were tested against LX-2 cells, an immortalised human HSC line, and RNA sequencing was used to assess the transcriptional changes induced by each peptide. In total, 78 and 798 genes were found to be significantly differentially expressed by KS-84 and KS-66 treatment, respectively. In silico pathway analysis of these genes revealed that KS-84 reduced LX-2 cell activation and fibrotic potential, whereas KS-66 increased both processes. Reduced transforming growth factor-β1 (TGF-β1) signalling was identified as a potential mechanism of KS-84-induced inhibition of LX-2 activation. Taken together, these findings indicate a potential role for Sm16 in combatting fibrotic liver disease., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Rationale: The current understanding of human lung development derives mostly from animal studies. Although transcript-level studies have analyzed human donor tissue to identify genes expressed during normal human lung development, protein-level analysis that would enable the generation of new hypotheses on the processes involved in pulmonary development are lacking. Objectives: To define the temporal dynamic of protein expression during human lung development. Methods: We performed proteomics analysis of human lungs at 10 distinct times from birth to 8 years to identify the molecular networks mediating postnatal lung maturation. Measurements and Main Results: We identified 8,938 proteins providing a comprehensive view of the developing human lung proteome. The analysis of the data supports the existence of distinct molecular substages of alveolar development and predicted the age of independent human lung samples, and extensive remodeling of the lung proteome occurred during postnatal development. Evidence of post-transcriptional control was identified in early postnatal development. An extensive extracellular matrix remodeling was supported by changes in the proteome during alveologenesis. The concept of maturation of the immune system as an inherent part of normal lung development was substantiated by flow cytometry and transcriptomics. Conclusions: This study provides the first in-depth characterization of the human lung proteome during development, providing a unique proteomic resource freely accessible at Lungmap.net. The data support the extensive remodeling of the lung proteome during development, the existence of molecular substages of alveologenesis, and evidence of post-transcriptional control in early postnatal development.

Background: Hepatic stellate cells (HSCs) are liver-resident myofibroblast precursors responsible for the production of collagen and maintenance of the hepatic extracellular matrix (ECM). As such, they are generally associated with fibrotic liver diseases. HSCs become "activated" in response to tissue damage or pathogen invasion, a process most commonly driven by transforming growth factor-β1 (TGF-β1). Despite this, the full extent of TGF-β1 signalling in these cells is poorly understood. Clarifying the range and diversity of this signalling will further improve our understanding of the process of HSC activation., Methods and Results: RNA sequencing was used to quantitate the transcriptomic changes induced in LX-2 cells, an activated human HSC line, following TGF-b1 treatment. In total, 5,258 genes were found to be significantly differentially expressed with a false discovery rate cut-off of < 0.1. The topmost deregulated of these genes included those with no currently characterised role in either HSC activation or fibrotic processes, including CIITA and SERPINB2. In silico analysis revealed the prominent signalling pathways downstream of TGF-β1 in LX-2 cells., Conclusions: In this study, we describe the genes and signalling pathways significantly deregulated in LX-2 cells following TGF-β1 treatment. We identified several highly deregulated genes with no currently characterised role in HSC activation, which may represent novel mediators of fibrotic responses in HSCs or the liver macroenvironment. This work may be of use in the identification of new markers of liver fibrosis and could provide insight into prospective genes or pathways that might be targeted for the amelioration of fibrotic liver disease in the future., (© 2021. The Author(s).)

During a schistosome infection, the interactions that occur between the mammalian host and the parasite change rapidly once egg laying begins. Both juvenile and adult schistosomes adapt to indefinitely avoid the host immune system. In contrast, the survival of eggs relies on quickly traversing from the host. Following the commencement of egg laying, the host immune response undergoes a shift from a type 1 helper (Th1) inflammatory response to a type 2 helper (Th2) granulomatous response. This change is driven by immunomodulatory proteins within the egg excretory/secretory products (ESPs), which interact with host cells and alter their behaviour to promote egg translocation. However, in parallel, these ESPs also provoke the development of chronic schistosomiasis pathology. Recent studies using high-throughput proteomics have begun to characterise the components of schistosome egg ESPs, particularly those of Schistosoma mansoni , S. japonicum and S. haematobium . Future application of this knowledge may lead to the identification of proteins with novel immunomodulatory activity or pathological importance. However, efforts in this area are limited by a lack of in situ or in vivo functional characterisation of these proteins. This review will highlight the current knowledge of the content and demonstrated functions of schistosome egg ESPs.

Morbidity associated with hepatic and urogenital schistosomiasis stems primarily from the host immune response directed against schistosome eggs. When eggs become entrapped in host tissues, the development of fibrotic plaques drives downstream pathology. These events occur due to the antigenic nature of egg excretory/secretory products (ESPs). Both Schistosoma mansoni and S. japonicum ESPs have been shown to interact with several cell populations in the host liver including hepatocytes, macrophages, and hepatic stellate cells, with both immunomodulatory and pathological consequences. Several protein components of the ESPs of S. mansoni and S. japonicum eggs have been characterised; however, studies into the collective contents of schistosome egg ESPs are lacking. Utilising shotgun mass spectrometry and an array of in silico analyses, we identified 266, 90 and 50 proteins within the S. mansoni, S. japonicum and S. haematobium egg secretomes respectively. We identified numerous proteins with already established immunomodulatory activities, vaccine candidates and vesicle markers. Relatively few common orthologues within the ESPs were identified by BLAST, indicating that the three egg secretomes differ in content significantly. Having a clearer understanding of these components may lead to the identification of new proteins with uncharacterised immunomodulatory potential or pathological relevance. This will enhance our understanding of host-parasite interactions, particularly those occurring during chronic schistosomiasis, and pave the way towards novel therapeutics and vaccines., (Copyright © 2020 Elsevier B.V. All rights reserved.)

This data is a curated collection of visual images of gene expression patterns from the pre- and post-natal mouse lung, accompanied by associated mRNA probe sequences and RNA-Seq expression profiles. Mammalian lungs undergo significant growth and cellular differentiation before and after the transition to breathing air. Documenting normal lung development is an important step in understanding abnormal lung development, as well as the challenges faced during a preterm birth. Images in this dataset indicate the spatial distribution of mRNA transcripts for over 500 different genes that are active during lung development, as initially determined via RNA-Seq. Images were systematically acquired using high-throughput in situ hybridization with non-radioactive digoxigenin-labeled mRNA probes across mouse lungs from developmental time points E16.5, E18.5, P7, and P28. The dataset was produced as part of The Molecular Atlas of Lung Development Program (LungMAP) and is hosted at https://lungmap.net. This manuscript describes the nature of the data and the protocols for generating the dataset.

Cell type-resolved proteome analyses of the brain, heart and liver have been reported, however a similar effort on the lipidome is currently lacking. Here we applied liquid chromatography-tandem mass spectrometry to characterize the lipidome of major lung cell types isolated from human donors, representing the first lipidome map of any organ. We coupled this with cell type-resolved proteomics of the same samples (available at Lungmap.net). Complementary proteomics analyses substantiated the functional identity of the isolated cells. Lipidomics analyses showed significant variations in the lipidome across major human lung cell types, with differences most evident at the subclass and intra-subclass (i.e. total carbon length of the fatty acid chains) level. Further, lipidomic signatures revealed an overarching posture of high cellular cooperation within the human lung to support critical functions. Our complementary cell type-resolved lipid and protein datasets serve as a rich resource for analyses of human lung function.

Lung diseases and disorders are a leading cause of death among infants. Many of these diseases and disorders are caused by premature birth and underdeveloped lungs. In addition to developmentally related disorders, the lungs are exposed to a variety of environmental contaminants and xenobiotics upon birth that can cause breathing issues and are progenitors of cancer. In order to gain a deeper understanding of the developing lung, we applied an activity-based chemoproteomics approach for the functional characterization of the xenometabolizing cytochrome P450 enzymes, active ATP and nucleotide binding enzymes, and serine hydrolases using a suite of activity-based probes (ABPs). We detected P450 activity primarily in the postnatal lung; using our ATP-ABP, we characterized a wide range of ATPases and other active nucleotide- and nucleic acid-binding enzymes involved in multiple facets of cellular metabolism throughout development. ATP-ABP targets include kinases, phosphatases, NAD- and FAD-dependent enzymes, RNA/DNA helicases, and others. The serine hydrolase-targeting probe detected changes in the activities of several proteases during the course of lung development, yielding insights into protein turnover at different stages of development. Select activity-based probe targets were then correlated with RNA in situ hybridization analyses of lung tissue sections.

Hepatic fibrosis is a common pathology in various liver diseases. Hepatic stellate cells (HSCs) are the main cell type responsible for collagen deposition and fibrosis formation in the liver. Schistosomiasis is characterised by granulomatous fibrosis around parasite eggs trapped within the liver and other host tissues. This response is facilitated by the recruitment of immune cells and the activation of HSCs. The interactions between HSCs and schistosome eggs are complex and diverse, and a better understanding of these interactions could lead to improved resolution of fibrotic liver disease, including that associated with schistosomiasis. Here, we discuss recent advances in HSC biology and the role of HSCs in hepatic schistosomiasis., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Constant mode ambient mass spectrometry imaging (MSI) of tissue sections with high lateral resolution of better than 10 μm was performed by combining shear force microscopy with nanospray desorption electrospray ionization (nano-DESI). Shear force microscopy enabled precise control of the distance between the sample and nano-DESI probe during MSI experiments and provided information on sample topography. Proof-of-concept experiments were performed using lung and brain tissue sections representing spongy and dense tissues, respectively. Topography images obtained using shear force microscopy were comparable to the results obtained using contact profilometry over the same region of the tissue section. Variations in tissue height were found to be dependent on the tissue type and were in the range of 0-5 μm for lung tissue and 0-3 μm for brain tissue sections. Ion images of phospholipids obtained in this study are in good agreement with literature data. Normalization of nano-DESI MSI images to the signal of the internal standard added to the extraction solvent allowed us to construct high-resolution ion images free of matrix effects. Graphical Abstract ᅟ.

Blood flow is critical for normal cardiac development. Hemodynamic stimuli outside of normal ranges can lead to overt cardiac defects, but how early heart tissue remodels in response to altered hemodynamics is poorly understood. This study investigated changes in tissue collagen in response to hemodynamic overload in the chicken embryonic heart outflow tract (OFT) during tubular heart stages (HH18 to HH24, ~24 h). A suture tied around the OFT at HH18 was tightened to constrict the lumen for ~24 h (constriction range at HH24: 15-60%). Expression of fibril collagens I and III and fibril organizing collagens VI and XIV were quantified at the gene and protein levels via qPCR and quantitative immunofluorescence. Collagen I was slightly elevated upstream of the band and in the cushions in banded versus control OFTs. Changes in collagen III were not observed. Collagen VI deposition was elevated downstream of the band, but not overall. Collagen XIV deposition increased throughout the OFT, and strongly correlated to lumen constriction. Interestingly, organization of collagen I fibrils was observed for the tighter banded embryos in regions that also showed increase in collagen XIV deposition, suggesting a potentially key role for collagens I and XIV in the structural adaptation of embryonic heart tissue to hemodynamic overload., Competing Interests: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

The National Heart, Lung, and Blood Institute is funding an effort to create a molecular atlas of the developing lung (LungMAP) to serve as a research resource and public education tool. The lung is a complex organ with lengthy development time driven by interactive gene networks and dynamic cross talk among multiple cell types to control and coordinate lineage specification, cell proliferation, differentiation, migration, morphogenesis, and injury repair. A better understanding of the processes that regulate lung development, particularly alveologenesis, will have a significant impact on survival rates for premature infants born with incomplete lung development and will facilitate lung injury repair and regeneration in adults. A consortium of four research centers, a data coordinating center, and a human tissue repository provides high-quality molecular data of developing human and mouse lungs. LungMAP includes mouse and human data for cross correlation of developmental processes across species. LungMAP is generating foundational data and analysis, creating a web portal for presentation of results and public sharing of data sets, establishing a repository of young human lung tissues obtained through organ donor organizations, and developing a comprehensive lung ontology that incorporates the latest findings of the consortium. The LungMAP website (www.lungmap.net) currently contains more than 6,000 high-resolution lung images and transcriptomic, proteomic, and lipidomic human and mouse data and provides scientific information to stimulate interest in research careers for young audiences. This paper presents a brief description of research conducted by the consortium, database, and portal development and upcoming features that will enhance the LungMAP experience for a community of users., (Copyright © 2017 the American Physiological Society.)

Lung immaturity is a major cause of morbidity and mortality in premature infants. Understanding the molecular mechanisms driving normal lung development could provide insights on how to ameliorate disrupted development. While transcriptomic and proteomic analyses of normal lung development have been previously reported, characterization of changes in the lipidome is lacking. Lipids play significant roles in the lung, such as dipalmitoylphosphatidylcholine in pulmonary surfactant; however, many of the roles of specific lipid species in normal lung development, as well as in disease states, are not well defined. In this study, we used liquid chromatography-mass spectrometry (LC-MS/MS) to investigate the murine lipidome during normal postnatal lung development. Lipidomics analysis of lungs from post-natal day 7, day 14 and 6-8 week mice (adult) identified 924 unique lipids across 21 lipid subclasses, with dramatic alterations in the lipidome across developmental stages. Our data confirmed previously recognized aspects of post-natal lung development and revealed several insights, including in sphingolipid-mediated apoptosis, inflammation and energy storage/usage. Complementary proteomics, metabolomics and chemical imaging corroborated these observations. This multi-omic view provides a unique resource and deeper insight into normal pulmonary development., Competing Interests: The authors declare no competing financial interests.

Laser capture microdissection (LCM)-enabled region-specific tissue analyses are critical to better understand complex multicellular processes. However, current proteomics workflows entail several manual sample preparation steps and are challenged by the microscopic mass-limited samples generated by LCM, impacting measurement robustness, quantification and throughput. Here, we coupled LCM with a proteomics workflow that provides fully automated analysis of proteomes from microdissected tissues. Benchmarking against the current state-of-the-art in ultrasensitive global proteomics (FASP workflow), our approach demonstrated significant improvements in quantification (~2-fold lower variance) and throughput (>5 times faster). Using our approach we for the first time characterized, to a depth of >3,400 proteins, the ontogeny of protein changes during normal lung development in microdissected alveolar tissue containing only 4,000 cells. Our analysis revealed seven defined modules of coordinated transcription factor-signaling molecule expression patterns, suggesting a complex network of temporal regulatory control directs normal lung development with epigenetic regulation fine-tuning pre-natal developmental processes.

Three-dimensional computational fluid dynamics and Lagrangian particle deposition models were developed to compare the deposition of aerosolized Bacillus anthracis spores in the respiratory airways of a human with that of the rabbit, a species commonly used in the study of anthrax disease. The respiratory airway geometries for each species were derived respectively from computed tomography (CT) and μCT images. Both models encompassed airways that extended from the external nose to the lung with a total of 272 outlets in the human model and 2878 outlets in the rabbit model. All simulations of spore deposition were conducted under transient, inhalation-exhalation breathing conditions using average species-specific minute volumes. Two different exposure scenarios were modeled in the rabbit based upon experimental inhalation studies. For comparison, human simulations were conducted at the highest exposure concentration used during the rabbit experimental exposures. Results demonstrated that regional spore deposition patterns were sensitive to airway geometry and ventilation profiles. Due to the complex airway geometries in the rabbit nose, higher spore deposition efficiency was predicted in the nasal sinus compared to the human at the same air concentration of anthrax spores. In contrast, higher spore deposition was predicted in the lower conducting airways of the human compared to the rabbit lung due to differences in airway branching pattern. This information can be used to refine published and ongoing biokinetic models of inhalation anthrax spore exposures, which currently estimate deposited spore concentrations based solely upon exposure concentrations and inhaled doses that do not factor in species-specific anatomy and physiology for deposition.

Subdivision-based image registration has previously been applied to co-localize digital information extracted from rigid structures in biological specimens, such as the brain. Here, we describe and demonstrate the creation and application of a two-dimensional subdivision-based atlas representing a dynamic structure: the outflow tract of the developing chicken heart. The atlas is designed to segment three different anatomical layers of the outflow tract, and is demonstrated on the characterization of collagen XIV in both control and induced abnormal flow specimens. Abnormal blood flow in the embryonic developing heart can lead to congenital heart disease. Comparing local cellular and sub-cellular changes that are caused by abnormal flow can assist in understanding the molecular pathways involved in maladaptations of the heart and congenital defects. This study demonstrates the approach and potential for more extensive applications of the subdivision-based atlas for the embryonic chicken heart.

Computational fluid dynamics (CFD) modeling is well suited for addressing species-specific anatomy and physiology in calculating respiratory tissue exposures to inhaled materials. In this study, we overcame prior CFD model limitations to demonstrate the importance of realistic, transient breathing patterns for predicting site-specific tissue dose. Specifically, extended airway CFD models of the rat and human were coupled with airway region-specific physiologically based pharmacokinetic (PBPK) tissue models to describe the kinetics of 3 reactive constituents of cigarette smoke: acrolein, acetaldehyde and formaldehyde. Simulations of aldehyde no-observed-adverse-effect levels for nasal toxicity in the rat were conducted until breath-by-breath tissue concentration profiles reached steady state. Human oral breathing simulations were conducted using representative aldehyde yields from cigarette smoke, measured puff ventilation profiles and numbers of cigarettes smoked per day. As with prior steady-state CFD/PBPK simulations, the anterior respiratory nasal epithelial tissues received the greatest initial uptake rates for each aldehyde in the rat. However, integrated time- and tissue depth-dependent area under the curve (AUC) concentrations were typically greater in the anterior dorsal olfactory epithelium using the more realistic transient breathing profiles. For human simulations, oral and laryngeal tissues received the highest local tissue dose with greater penetration to pulmonary tissues than predicted in the rat. Based upon lifetime average daily dose comparisons of tissue hot-spot AUCs (top 2.5% of surface area-normalized AUCs in each region) and numbers of cigarettes smoked/day, the order of concern for human exposures was acrolein > formaldehyde > acetaldehyde even though acetaldehyde yields were 10-fold greater than formaldehyde and acrolein., (Published by Oxford University Press on behalf of the Society of Toxicology 2015. This work is written by US Government employees and is in the public domain in the US.)

Background: Assessing heterogeneity in lung images can be an important diagnosis tool. We present a novel and objective method for assessing lung damage in a rat model of emphysema. We combined a three-dimensional (3D) computer graphics method-octree decomposition-with a geostatistics-based approach for assessing spatial relationships-the variogram-to evaluate disease in 3D computed tomography (CT) image volumes., Methods: Male, Sprague-Dawley rats were dosed intratracheally with saline (control), or with elastase dissolved in saline to either the whole lung (for mild, global disease) or a single lobe (for severe, local disease). Gated 3D micro-CT images were acquired on the lungs of all rats at end expiration. Images were masked, and octree decomposition was performed on the images to reduce the lungs to homogeneous blocks of 2 × 2 × 2, 4 × 4 × 4, and 8 × 8 × 8 voxels. To focus on lung parenchyma, small blocks were ignored because they primarily defined boundaries and vascular features, and the spatial variance between all pairs of the 8 × 8 × 8 blocks was calculated as the square of the difference of signal intensity. Variograms-graphs of distance vs. variance-were constructed, and results of a least-squares-fit were compared. The robustness of the approach was tested on images prepared with various filtering protocols. Statistical assessment of the similarity of the three control rats was made with a Kruskal-Wallis rank sum test. A Mann-Whitney-Wilcoxon rank sum test was used to measure statistical distinction between individuals. For comparison with the variogram results, the coefficient of variation and the emphysema index were also calculated for all rats., Results: Variogram analysis showed that the control rats were statistically indistinct (p = 0.12), but there were significant differences between control, mild global disease, and severe local disease groups (p < 0.0001). A heterogeneity index was calculated to describe the difference of an individual variogram from the control average. This metric also showed clear separation between dose groups. The coefficient of variation and the emphysema index, on the other hand, did not separate groups., Conclusion: These results suggest the octree decomposition and variogram analysis approach may be a rapid, non-subjective, and sensitive imaging-based biomarker for characterizing lung disease.

Understanding the structure of microbial biofilms and other complex microbial communities is now possible through x-ray microtomography imaging. Feature detection and image processing for this type of data focuses on efficiently identifying and segmenting biofilm biomass in the datasets. These datasets are very large and segmentation often requires manual interventions due to low contrast between objects and high noise levels. New software is required for the effectual interpretation and analysis of such data. This work specifies the evolution and ability to analyze and visualize high resolution x-ray microtomography datasets. Major functionalities include read/write with multiple popular file formats, down-sampling large datasets to generate quick-views on low-power computers, image processing, and generating high quality output images and videos. These capabilities have been wrapped into a new interactive software toolkit, BiofilmViewer. A major focus of our work is to facilitate data transfer and to utilize the capabilities of existing powerful visualization and analytical tools including MATLAB, ImageJ, Paraview, Chimera, Vaa3D, Cell Profiler, Icy, BioImageXD, and Drishti.

Nanospray desorption electrospray ionization (nano-DESI) combined with tandem mass spectrometry (MS/MS), high-resolution mass analysis of the fragment ions (m/Δm = 17 500 at m/z 200), and rapid spectral acquisition enabled simultaneous imaging and identification of a large number of metabolites and lipids from 92 selected m/z windows (±1 Da) with a spatial resolution of better than 150 μm. Mouse uterine sections of implantation sites on day 6 of pregnancy were analyzed in the ambient environment without any sample pretreatment. MS/MS imaging was performed by scanning the sample under the nano-DESI probe at 10 μm/s, while higher-energy collision-induced dissociation (HCD) spectra were acquired for a targeted inclusion list of 92 m/z values at a rate of ∼6.3 spectra/s. Molecular ions and their corresponding fragments, separated by high-resolution mass analysis, were assigned on the basis of accurate mass measurement. Using this approach, we were able to identify and image both abundant and low-abundance isobaric and isomeric species within each m/z window. MS/MS analysis enabled efficient separation and identification of isomeric and isobaric phospholipids that are difficult to separate in full-scan mode. Furthermore, we identified several metabolites associated with early pregnancy and obtained the first 2D images of these molecules.

Rationale and Objectives: To investigate the ability of variogram analysis of octree-decomposed computed tomography (CT) images and volume change maps to detect radiation-induced damage in rat lungs., Materials and Methods: The lungs of female Sprague-Dawley rats were exposed to one of five absorbed doses (0, 6, 9, 12, or 15 Gy) of gamma radiation from a Co-60 source. At 6 months postexposure, pulmonary function tests were performed and four-dimensional (4D) CT images were acquired using a respiratory-gated microCT scanner. Volume change maps were then calculated from the 4DCT images. Octree decomposition was performed on CT images and volume change maps, and variogram analysis was applied to the decomposed images. Correlations of measured parameters with dose were evaluated., Results: The effects of irradiation were not detectable from measured parameters, indicating only mild lung damage. Additionally, there were no significant correlations of pulmonary function results or CT densitometry with radiation dose. However, the variogram analysis did detect a significant correlation with dose in both the CT images (r = -0.57, P = .003) and the volume change maps (r = -0.53, P = .008)., Conclusion: This is the first study to use variogram analysis of lung images to assess pulmonary damage in a model of radiation injury. Results show that this approach is more sensitive to detecting radiation damage than conventional measures such as pulmonary function tests or CT densitometry., (Copyright © 2013 AUR. All rights reserved.)

The use of anatomically accurate, animal-specific airway geometries is important for understanding and modeling the physiology of the respiratory system. One approach for acquiring detailed airway architecture is to create a bronchial cast of the conducting airways. However, typical casting procedures either do not faithfully preserve the in vivo branching angles or produce rigid casts that when removed for imaging are fragile and thus easily damaged. We address these problems by creating an in situ bronchial cast of the conducting airways in rats that can be subsequently imaged in situ using three-dimensional micro-CT imaging. We also demonstrate that deformations in airway branch angles resulting from the casting procedure are small, and that these angle deformations can be reversed through an interactive adjustment of the segmented cast geometry. Animal work was approved by the Institutional Animal Care and Use Committee of Pacific Northwest National Laboratory.

In this study, we present a novel multiscale computational framework for efficiently linking multiple lower-dimensional models describing the distal lung mechanics to imaging-based 3D computational fluid dynamics (CFD) models of the upper pulmonary airways in order to incorporate physiologically appropriate outlet boundary conditions. The framework is an extension of the Modified Newton's Method with nonlinear Krylov accelerator developed by Carlson and Miller [1, 2, 3]. Our extensions include the retention of subspace information over multiple timesteps, and a special correction at the end of a timestep that allows for corrections to be accepted with verified low residual with as little as a single residual evaluation per timestep on average. In the case of a single residual evaluation per timestep, the method has zero additional computational cost compared to uncoupled or unidirectionally coupled simulations. We expect these enhancements to be generally applicable to other multiscale coupling applications where timestepping occurs. In addition we have developed a "pressure-drop" residual which allows for stable coupling of flows between a 3D incompressible CFD application and another (lower-dimensional) fluid system. We expect this residual to also be useful for coupling non-respiratory incompressible fluid applications, such as multiscale simulations involving blood flow. The lower-dimensional models that are considered in this study are sets of simple ordinary differential equations (ODEs) representing the compliant mechanics of symmetric human pulmonary airway trees. To validate the method, we compare the predictions of hybrid CFD-ODE models against an ODE-only model of pulmonary airflow in an idealized geometry. Subsequently, we couple multiple sets of ODEs describing the distal lung to an imaging-based human lung geometry. Boundary conditions in these models consist of atmospheric pressure at the mouth and intrapleural pressure applied to the multiple sets of ODEs. In both the simplified geometry and in the imaging-based geometry, the performance of the method was comparable to that of monolithic schemes, in most cases requiring only a single CFD evaluation per time step. Thus, this new accelerator allows us to begin combining pulmonary CFD models with lower-dimensional models of pulmonary mechanics with little computational overhead. Moreover, because the CFD and lower-dimensional models are totally separate, this framework affords great flexibility in terms of the type and breadth of the adopted lower-dimensional model, allowing the biomedical researcher to appropriately focus on model design. Research funded by the National Heart and Blood Institute Award 1RO1HL073598.

Changes in the shape of the lung during breathing determine the movement of airways and alveoli, and thus impact airflow dynamics. Modeling airflow dynamics in health and disease is a key goal for predictive multiscale models of respiration. Past efforts to model changes in lung shape during breathing have measured shape at multiple breath-holds. However, breath-holds do not capture hysteretic differences between inspiration and expiration resulting from the additional energy required for inspiration. Alternatively, imaging dynamically--without breath-holds--allows measurement of hysteretic differences. In this study, we acquire multiple micro-CT images per breath (4DCT) in live rats, and from these images we develop, for the first time, dynamic volume maps. These maps show changes in local volume across the entire lung throughout the breathing cycle and accurately predict the global pressure-volume (PV) hysteresis. Male Sprague-Dawley rats were given either a full- or partial-lung dose of elastase or saline as a control. After three weeks, 4DCT images of the mechanically ventilated rats under anesthesia were acquired dynamically over the breathing cycle (11 time points, ≤100 ms temporal resolution, 8 cmH2O peak pressure). Non-rigid image registration was applied to determine the deformation gradient--a numerical description of changes to lung shape--at each time point. The registration accuracy was evaluated by landmark identification. Of 67 landmarks, one was determined misregistered by all three observers, and 11 were determined misregistered by two observers. Volume change maps were calculated on a voxel-by-voxel basis at all time points using both the Jacobian of the deformation gradient and the inhaled air fraction. The calculated lung PV hysteresis agrees with pressure-volume curves measured by the ventilator. Volume maps in diseased rats show increased compliance and ventilation heterogeneity. Future predictive multiscale models of rodent respiration may leverage such volume maps as boundary conditions.

We present an atlas-based registration method for bones segmented from quantitative computed tomography (QCT) scans, with the goal of mapping their interior bone mineral densities (BMDs) volumetrically. We introduce a new type of deformable atlas, called subdivision-embedded atlas, which consists of a control grid represented as a tetrahedral subdivision mesh and a template bone surface embedded within the grid. Compared to a typical lattice-based deformation grid, the subdivision control grid possesses a relatively small degree of freedom tailored to the shape of the bone, which allows efficient fitting onto subjects. Compared with previous subdivision atlases, the novelty of our atlas lies in the addition of the embedded template surface, which further increases the accuracy of the fitting. Using this new atlas representation, we developed an efficient and fully automated pipeline for registering atlases of 12 tarsal and metatarsal bones to a segmented QCT scan of a human foot. Our evaluation shows that the mapping of BMD enabled by the registration is consistent for bones in repeated scans, and the regional BMD automatically computed from the mapping is not significantly different from expert annotations. The results suggest that our improved subdivision-based registration method is a reliable, efficient way to replace manual labor for measuring regional BMD in foot bones in QCT scans.

Imaging mass spectrometry offers simultaneous spatially resolved detection of drugs, drug metabolites, and endogenous substances in a single experiment. This is important when evaluating effects of a drug on a complex organ system such as the brain, where there is a need to understand how regional drug distribution impacts function. Nanospray desorption electrospray ionization, nano-DESI, is a new ambient technique that enables spatially resolved analysis of a variety of samples without special sample pretreatment. This study introduces an experimental approach for accurate spatial mapping of drugs and metabolites in tissue sections by nano-DESI imaging. In this approach, an isotopically labeled standard is added to the nano-DESI solvent to compensate for matrix effects and ion suppression. The analyte image is obtained by normalizing the analyte signal to the signal of the standard in each pixel. We demonstrate that the presence of internal standard enables online quantification of analyte molecules extracted from tissue sections. Ion images are subsequently mapped to the anatomical brain regions in the analyzed section by use of an atlas mesh deformed to match the optical image of the section. Atlas-based registration accounts for the physical variability between animals, which is important for data interpretation. The new approach was used for mapping the distribution of nicotine in rat brain tissue sections following in vivo drug administration. We demonstrate the utility of nano-DESI imaging for sensitive detection of the drug in tissue sections with subfemtomole sensitivity in each pixel of a 27 μm × 150 μm area. Such sensitivity is necessary for spatially resolved detection of low-abundance molecules in complex matrices.

Geometries for organ scale and multiscale simulations of organ function are now routinely derived from imaging data. However, medical images may also contain spatially heterogeneous information other than geometry that are relevant to such simulations either as initial conditions or in the form of model parameters. In this manuscript, we present an algorithm for the efficient and robust mapping of such data to imaging-based unstructured polyhedral grids in parallel. We then illustrate the application of our mapping algorithm to three different mapping problems: (i) the mapping of MRI diffusion tensor data to an unstructured ventricular grid; (ii) the mapping of serial cyrosection histology data to an unstructured mouse brain grid; and (iii) the mapping of computed tomography-derived volumetric strain data to an unstructured multiscale lung grid. Execution times and parallel performance are reported for each case., (Copyright © 2012 John Wiley & Sons, Ltd.)

An automated platform has been developed for acquisition and visualization of mass spectrometry imaging (MSI) data using nanospray desorption electrospray ionization (nano-DESI). The new system enables robust operation of the nano-DESI imaging source over many hours by precisely controlling the distance between the sample and the nano-DESI probe. This is achieved by mounting the sample holder onto an automated XYZ stage, defining the tilt of the sample plane, and recalculating the vertical position of the stage at each point. This approach is useful for imaging of relatively flat samples such as thin tissue sections. Custom software called MSI QuickView was developed for visualization of large data sets generated in imaging experiments. MSI QuickView enables fast visualization of the imaging data during data acquisition and detailed processing after the entire image is acquired. The performance of the system is demonstrated by imaging rat brain tissue sections. Low background noise enables simultaneous detection of lipids and metabolites in the tissue section. High-resolution mass analysis combined with tandem mass spectometry (MS/MS) experiments enabled identification of the observed species. In addition, the high dynamic range (>2000) of the technique allowed us to generate ion images of low-abundance isobaric lipids. A high-spatial resolution image was acquired over a small region of the tissue section revealing the distribution of an abundant brain metabolite, creatine, on the boundary between the white and gray matter. The observed distribution is consistent with the literature data obtained using magnetic resonance spectroscopy.

Phase-contrast (PC) magnetic resonance imaging (MRI) with hyperpolarized ³He is potentially useful for developing and testing patient-specific models of pulmonary airflow. One challenge, however, is that PC-MRI provides apparent values of local ³He velocity that not only depend on actual airflow but also on gas diffusion. This not only blurs laminar flow patterns in narrow airways but also introduces anomalous airflow structure that reflects gas-wall interactions. Here, both effects are predicted in a live rat using computational fluid dynamics (CFD), and for the first time, simulated patterns of apparent ³He gas velocity are compared with in vivo PC-MRI. Results show (1) that correlations (R²) between measured and simulated airflow patterns increase from 0.23 to 0.79 simply by accounting for apparent ³He transport, and (2) that remaining differences are mainly due to uncertain airway segmentation and partial volume effects stemming from relatively coarse MRI resolution. Higher-fidelity testing of pulmonary airflow predictions should therefore be possible with future imaging improvements., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Computational fluid dynamics (CFD) models are useful for predicting site-specific dosimetry of airborne materials in the respiratory tract and elucidating the importance of species differences in anatomy, physiology, and breathing patterns. We improved the imaging and model development methods to the point where CFD models for the rat, monkey, and human now encompass airways from the nose or mouth to the lung. A total of 1272, 2172, and 135 pulmonary airways representing 17±7, 19±9, or 9±2 airway generations were included in the rat, monkey and human models, respectively. A CFD/physiologically based pharmacokinetic model previously developed for acrolein was adapted for these anatomically correct extended airway models. Model parameters were obtained from the literature or measured directly. Airflow and acrolein uptake patterns were determined under steady-state inhalation conditions to provide direct comparisons with prior data and nasal-only simulations. Results confirmed that regional uptake was sensitive to airway geometry, airflow rates, acrolein concentrations, air:tissue partition coefficients, tissue thickness, and the maximum rate of metabolism. Nasal extraction efficiencies were predicted to be greatest in the rat, followed by the monkey, and then the human. For both nasal and oral breathing modes in humans, higher uptake rates were predicted for lower tracheobronchial tissues than either the rat or monkey. These extended airway models provide a unique foundation for comparing material transport and site-specific tissue uptake across a significantly greater range of conducting airways in the rat, monkey, and human than prior CFD models.

We examine a previously published branch-based approach for modeling airway diameters that is predicated on the assumption of self-consistency across all levels of the tree. We mathematically formulate this assumption, propose a method to test it and develop a more general model to be used when the assumption is violated. We discuss the effect of measurement error on the estimated models and propose methods that take account of error. The methods are illustrated on data from MRI and CT images of silicone casts of two rats, two normal monkeys, and one ozone-exposed monkey. Our results showed substantial departures from self-consistency in all five subjects. When departures from self-consistency exist, we do not recommend using the self-consistency model, even as an approximation, as we have shown that it may likely lead to an incorrect representation of the diameter geometry. The new variance model can be used instead. Measurement error has an important impact on the estimated morphometry models and needs to be addressed in the analysis., (Copyright © 2012 Wiley Periodicals, Inc.)

Mass Spectrometric Imaging (MSI) allows the generation of 2D ion density maps that help visualize molecules present in sections of tissues and cells. The combination of spatial resolution and mass resolution results in very large and complex data sets. New capabilities are necessary for efficient analysis and interpretation of this data. This work details the development and application of the capability to process, visualize, query, and analyze spatial mass spectrometry data. Applications include the generation of 2D maps for selected spectra, the manipulation of the heat maps, and the identification of spectral peaks. Heat maps are generated by projecting the sum of intensity vs. time spectra of each pixel for selected m/z value or range. These capabilities take the form of a new interactive software toolkit, MSI QuickView. This software approach is a significant advance over the previous state-of-the art methods that required the conversion of the RAW data using one software, manual assembly of the data, and visualization in another software.

One treatment increasing in use for solid tumors in the liver is radioembolization via the delivery of (90)Y microspheres to the vascular bed within or near the location of the tumor. It is desirable as part of the treatment for the microspheres to embed preferentially in or near the tumor. This work details an approach for analyzing the deposition of microspheres with respect to the location of the tumor. The approach used is based upon thin-slice serial sectioning of the tissue sample, followed by high resolution imaging, microsphere detection, and 3-D reconstruction of the tumor surface. Distance from the microspheres to the tumor was calculated using a fast deterministic point inclusion method.

Automated segmentation of multi-part anatomical objects in images is a challenging task. In this paper, we propose a similarity-based appearance-prior to fit a compartmental geometric atlas of the mouse brain in gene expression images. A subdivision mesh which is used to model the geometry is deformed using a Markov random field (MRF) framework. The proposed appearance-prior is computed as a function of the similarity between local patches at corresponding atlas locations from two images. In addition, we introduce a similarity-saliency score to select the mesh points that are relevant for the computation of the proposed prior. Our method significantly improves the accuracy of the atlas fitting, especially in the regions that are influenced by the selected similarity-salient points, and outperforms the previous subdivision mesh fitting methods for gene expression images.

An accurate labeling of a multi-part, complex anatomical structure (e.g., brain) is required in order to compare data across images for spatial analysis. It can be achieved by fitting an object-specific geometric atlas that is constructed using a partitioned, high-resolution deformable mesh and tagging each of its polygons with a region label. Subdivision meshes have been used to construct such an atlas because they can provide a compact representation of a partitioned, multi-resolution, object-specific mesh structure using only a few control points. However, automated fitting of a subdivision mesh-based geometric atlas to an anatomical structure in an image is a difficult problem and has not been sufficiently addressed. In this paper, we propose a novel Markov Random Field-based method for fitting a planar, multi-part subdivision mesh to anatomical data. The optimal fitting of the atlas is obtained by determining the optimal locations of the control points. We also tackle the problem of landmark matching in tandem with atlas fitting by constructing a single graphical model to impose pose-invariant, landmark-based geometric constraints on atlas deformation. The atlas deformation is also governed by additional constraints imposed by the mesh's geometric properties and the object boundary. We demonstrate the potential of the proposed method on the difficult problem of segmenting a mouse brain and its interior regions in gene expression images which exhibit large intensity and shape variability. We obtain promising results when compared with manual annotations and prior methods.

Analysis of gene expression patterns in brain images obtained from high-throughput in situ hybridization requires accurate and consistent annotations of anatomical regions/subregions. Such annotations are obtained by mapping an anatomical atlas onto the gene expression images through intensity- and/or landmark-based registration methods or deformable model-based segmentation methods. Due to the complex appearance of the gene expression images, these approaches require a pre-processing step to determine landmark correspondences in order to incorporate landmark-based geometric constraints. In this paper, we propose a novel method for landmark-constrained, intensity-based registration without determining landmark correspondences a priori. The proposed method performs dense image registration and identifies the landmark correspondences, simultaneously, using a single higher-order Markov Random Field model. In addition, a machine learning technique is used to improve the discriminating properties of local descriptors for landmark matching by projecting them in a Hamming space of lower dimension. We qualitatively show that our method achieves promising results and also compares well, quantitatively, with the expert's annotations, outperforming previous methods.

Thinly sliced serial tissue sections of an organ can be imaged using optical microscopy at a resolution detailing individual cells. When the tissue sections are first subjected to in situ hybridization or immunohistochemistry, these data sets can be analysed for changes in gene expression and gene products. Such spatial information is important for understanding the functional effects of experimental or environmental challenges to the organism. However, a critical step in analysing these data sets is mitigating artefacts that result from the preparation of the tissue sections. In this paper, we describe an automated method with manual validation tools that together enable detecting and addressing artefacts including dust particles and air bubbles., (© 2010 Batelle Memorial Institute Journal of Microscopy © 2010 The Royal Microscopical Society.)

As imaging, computing, and data storage technologies improve, there is an increasing opportunity for multiscale analysis of three-dimensional datasets (3-D). Such analysis enables, for example, microscale elements of multiple macroscale specimens to be compared throughout the entire macroscale specimen. Spatial comparisons require bringing datasets into co-alignment. One approach for co-alignment involves elastic deformations of data in addition to rigid alignments. The elastic deformations distort space, and if not accounted for, can distort the information at the microscale. The algorithms developed in this work address this issue by allowing multiple data points to be encoded into a single image pixel, appropriately tracking each data point to ensure lossless data mapping during elastic spatial deformation. This approach was developed and implemented for both 2-D and 3D registration of images. Lossless reconstruction and registration was applied to semi-quantitative cellular gene expression data in the mouse brain, enabling comparison of multiple spatially registered 3-D datasets without any augmentation of the cellular data. Standard reconstruction and registration without the lossless approach resulted in errors in cellular quantities of ∼ 8%.

Developing detailed lung airway models is an important step towards understanding the respiratory system. While modern imaging and airway casting approaches have dramatically improved the potential detail of such models, challenges have arisen in image processing as the demand for greater detail pushes the image processing approaches to their limits. Airway segmentations with proper topology have neither loops nor invalid voxel-to-voxel connections. Here we describe a new technique for segmenting airways with proper topology and apply the approach to an image volume generated by magnetic resonance imaging of a silicone cast created from an excised monkey lung., (Copyright (c) 2010 Elsevier Ltd. All rights reserved.)

Spatial discretization of complex imaging- derived fluid-solid geometries, such as the cardiac environment, is a critical but often overlooked challenge in biomechanical computations. This is particularly true in problems with Lagrangian interfaces, where the fluid and solid phases share a common interface geometrically. For simplicity and better accuracy, it is also highly desirable for the two phases to have a matching surface mesh at the interface between them. We outline a method for solving this problem, and illustrate the approach with a 3D fluid-solid mesh of the mouse heart. An MRI dataset of a perfusion-fixed mouse heart with 50 microm isotropic resolution was semi-automatically segmented using a customized multimaterial connected-threshold approach that divided the volume into non-overlapping regions of blood, tissue, and background. Subsequently a multimaterial marching cubes algorithm was applied to the segmented data to produce two detailed, compatible isosurfaces, one for blood and one for tissue. Both isosurfaces were simultaneously smoothed with a multimaterial smoothing algorithm that exactly conserves the volume for each phase. Using these two isosurfaces, we developed and applied novel automated meshing algorithms to generate anisotropic hybrid meshes on arbitrary biological geometries with the number of layers and the desired element anisotropy for each phase as the only input parameters. Since our meshes adapt to the local feature sizes and include boundary layer prisms, they are more efficient and accurate than non-adaptive, isotropic meshes, and the fluid-structure interaction computations will tend to have relative error equilibrated over the whole mesh.

The remodeling that occurs after a posterolateral myocardial infarction can alter mitral valve function by creating conformational abnormalities in the mitral annulus and in the posteromedial papillary muscle, leading to mitral regurgitation (MR). It is generally assumed that this remodeling is caused by a volume load and is mediated by an increase in diastolic wall stress. Thus, mitral regurgitation can be both the cause and effect of an abnormal cardiac stress environment. Computational modeling of ischemic MR and its surgical correction is attractive because it enables an examination of whether a given intervention addresses the correction of regurgitation (fluid-flow) at the cost of abnormal tissue stress. This is significant because the negative effects of an increased wall stress due to the intervention will only be evident over time. However, a meaningful fluid-structure interaction model of the left heart is not trivial; it requires a careful characterization of the in-vivo cardiac geometry, tissue parameterization though inverse analysis, a robust coupled solver that handles collapsing Lagrangian interfaces, automatic grid-generation algorithms that are capable of accurately discretizing the cardiac geometry, innovations in image analysis, competent and efficient constitutive models and an understanding of the spatial organization of tissue microstructure. In this manuscript, we profile our work toward a comprehensive fluid-structure interaction model of the left heart by reviewing our early work, presenting our current work and laying out our future work in four broad categories: data collection, geometry, fluid-structure interaction and validation.

A critical challenge in biomechanical simulations is the spatial discretization of complex fluid-solid geometries created from imaging. This is especially important when dealing with Lagrangian interfaces, as there must be at a minimum both geometric and topological compatibility between fluid and solid phases, with exact matching of the interfacial nodes being highly desirable. We have developed a solution to this problem and applied the approach to the creation of a 3D fluid-solid mesh of the mouse heart. First, a 50 micron isotropic MRI dataset of a perfusion-fixed mouse heart was segmented into blood, tissue, and background using a customized multimaterial connected fuzzy thresholding algorithm. Then, a multimaterial marching cubes algorithm was applied to produce two compatible isosurfaces, one for the blood-tissue boundary and one for the tissue-background boundary. A multimaterial smoothing algorithm that rigorously conserves volume for each phase simultaneously smoothed the isosurfaces. Next we applied novel automated meshing algorithms to generate anisotropic hybrid meshes with the number of layers and the desired element anisotropy for each material as the only input parameters. As the meshes are scale-invariant within a material and include boundary layer prisms, fluid-structure interaction computations would have a relative error equilibrated over the entire mesh. The resulting model is highly detailed mesh representation of the mouse heart, including features such as chordae and coronary vasculature, that is also maximally efficient to produce the best simulation results for the computational resources available.

Efficient and accurate reconstruction of imaging-derived geometries and subsequent quality mesh generation are enabling technologies for both clinical and research simulations. A challenging part of this process is the introduction of computable, orthogonal boundary patches, namely, the outlets, into treed structures, such as vasculature, arterial or airway trees. We present efficient and robust algorithms for automatically identifying and truncating the outlets for complex geometries. Our approach is based on a conceptual decomposition of objects into tips, segments, and branches, where the tips determine the outlets. We define the tips by introducing a novel concept called the average interior center of curvature and identify the tips that are stable and noise resistant. We compute well-defined orthogonal planes, which truncate the tips into outlets. The rims of the outlets are connected into curves, and the outlets are then closed using Delaunay triangulation. We illustrate the effectiveness and robustness of our approach with a variety of complex lung and coronary artery geometries.