Automated 3D cytoplasm segmentation in soft X-ray tomography.

Cells' structure is key to understanding cellular function, diagnostics, and therapy development. Soft X-ray tomography (SXT) is a unique tool to image cellular structure without fixation or labeling at high spatial resolution and throughput. Fast acquisition times increase demand for accelerated image analysis, like segmentation. Currently, segmenting cellular structures is done manually and is a major bottleneck in the SXT data analysis. This paper introduces ACSeg, an automated 3D cytoplasm segmentation model. ACSeg is generated using semi-automated labels and 3D U-Net and is trained on 43 SXT tomograms of immune T cells, rapidly converging to high-accuracy segmentation, therefore reducing time and labor. Furthermore, adding only 6 SXT tomograms of other cell types diversifies the model, showing potential for optimal experimental design. ACSeg successfully segmented unseen tomograms and is published on Biomedisa, enabling high-throughput analysis of cell volume and structure of cytoplasm in diverse cell types.

Incidences of colorectal adenomas and cancers under colonoscopy surveillance suggest an accelerated "Big Bang" pathway to CRC in three of the four Lynch syndromes.

BACKGROUND: Colorectal cancers (CRCs) in the Lynch syndromes have been assumed to emerge through an accelerated adenoma-carcinoma pathway. In this model adenomas with deficient mismatch repair have an increased probability of acquiring additional cancer driver mutation(s) resulting in more rapid progression to malignancy. If this model was accurate, the success of colonoscopy in preventing CRC would be a function of the intervals between colonoscopies and mean sojourn time of detectable adenomas. Contrary to expectations, colonoscopy did not decrease incidence of CRC in the Lynch syndromes and shorter colonoscopy intervals have not been effective in reducing CRC incidence. The prospective Lynch Syndrome Database (PLSD) was designed to examine these issues in carriers of pathogenic variants of the mis-match repair (path_MMR) genes. MATERIALS AND METHODS: We examined the CRC and colorectal adenoma incidences in 3,574 path_MLH1, path_MSH2, path_MSH6 and path_PMS2 carriers subjected to regular colonoscopy with polypectomy, and considered the results based on sojourn times and stochastic probability paradigms. RESULTS: Most of the path_MMR carriers in each genetic group had no adenomas. There was no association between incidences of CRC and the presence of adenomas. There was no CRC observed in path_PMS2 carriers. CONCLUSIONS: Colonoscopy prevented CRC in path_PMS2 carriers but not in the others. Our findings are consistent with colonoscopy surveillance blocking the adenoma-carcinoma pathway by removing identified adenomas which might otherwise become CRCs. However, in the other carriers most CRCs likely arised from dMMR cells in the crypts that have an increased mutation rate with increased stochastic chaotic probabilities for mutations. Therefore, this mechanism, that may be associated with no or only a short sojourn time of MSI tumours as adenomas, could explain the findings in our previous and current reports.

Prediction of diagnosis and diastolic filling pressure by AI-enhanced cardiac MRI: a modelling study of hospital data.

BACKGROUND: With increasing numbers of patients and novel drugs for distinct causes of systolic and diastolic heart failure, automated assessment of cardiac function is important. We aimed to provide a non-invasive method to predict diagnosis of patients undergoing cardiac MRI (cMRI) and to obtain left ventricular end-diastolic pressure (LVEDP). METHODS: For this modelling study, patients who had undergone cardiac catheterisation at University Hospital Heidelberg (Heidelberg, Germany) between July 15, 2004 and March 16, 2023, were identified, as were individual left ventricular pressure measurements. We used existing patient data from routine cardiac diagnostics. From this initial group, we extracted patients who had been diagnosed with ischaemic cardiomyopathy, dilated cardiomyopathy, hypertrophic cardiomyopathy, or amyloidosis, as well as control individuals with no structural phenotype. Data were pseudonymised and only processed within the university hospital's AI infrastructure. We used the data to build different models to predict either demographic (ie, AI-age and AI-sex), diagnostic (ie, AI-coronary artery disease and AI-cardiomyopathy [AI-CMP]), or functional parameters (ie, AI-LVEDP). We randomly divided our datasets via computer into training, validation, and test datasets. AI-CMP was not compared with other models, but was validated in a prospective setting. Benchmarking was also done. FINDINGS: 66 936 patients who had undergone cardiac catheterisation at University Hospital Heidelberg were identified, with more than 183 772 individual left ventricular pressure measurements. We extracted 4390 patients from this initial group, of whom 1131 (25·8%) had been diagnosed with ischaemic cardiomyopathy, 1064 (24·2%) had been diagnosed with dilated cardiomyopathy, 816 (18·6%) had been diagnosed with hypertrophic cardiomyopathy, 202 (4·6%) had been diagnosed with amyloidosis, and 1177 (26·7%) were control individuals with no structural phenotype. The core cohort only included patients with cardiac catherisation and cMRI within 30 days, and emergency cases were excluded. AI-sex was able to predict patient sex with areas under the receiver operating characteristic curves (AUCs) of 0·78 (95% CI 0·77-0·78) and AI-age was able to predict patient age with a mean absolute error of 7·86 years (7·77-7·95), with a Pearson correlation of 0·57 (95% CI 0·56-0·57). The AUCs for the classification tasks ranged between 0·82 (95% CI 0·79-0·84) for ischaemic cardiomyopathy and 0·92 (0·91-0·94) for hypertrophic cardiomyopathy. INTERPRETATION: Our AI models could be easily integrated into clinical practice and provide added value to the information content of cMRI, allowing for disease classification and prediction of diastolic function. FUNDING: Informatics for Life initiative of the Klaus-Tschira Foundation, German Center for Cardiovascular Research, eCardiology section of the German Cardiac Society, and AI Health Innovation Cluster Heidelberg.

Natural variability in bee brain size and symmetry revealed by micro-CT imaging and deep learning.

Analysing large numbers of brain samples can reveal minor, but statistically and biologically relevant variations in brain morphology that provide critical insights into animal behaviour, ecology and evolution. So far, however, such analyses have required extensive manual effort, which considerably limits the scope for comparative research. Here we used micro-CT imaging and deep learning to perform automated analyses of 3D image data from 187 honey bee and bumblebee brains. We revealed strong inter-individual variations in total brain size that are consistent across colonies and species, and may underpin behavioural variability central to complex social organisations. In addition, the bumblebee dataset showed a significant level of lateralization in optic and antennal lobes, providing a potential explanation for reported variations in visual and olfactory learning. Our fast, robust and user-friendly approach holds considerable promises for carrying out large-scale quantitative neuroanatomical comparisons across a wider range of animals. Ultimately, this will help address fundamental unresolved questions related to the evolution of animal brains and cognition.

Dominantly inherited micro-satellite instable cancer - the four Lynch syndromes - an EHTG, PLSD position statement.

The recognition of dominantly inherited micro-satellite instable (MSI) cancers caused by pathogenic variants in one of the four mismatch repair (MMR) genes MSH2, MLH1, MSH6 and PMS2 has modified our understanding of carcinogenesis. Inherited loss of function variants in each of these MMR genes cause four dominantly inherited cancer syndromes with different penetrance and expressivities: the four Lynch syndromes. No person has an "average sex "or a pathogenic variant in an "average Lynch syndrome gene" and results that are not stratified by gene and sex will be valid for no one. Carcinogenesis may be a linear process from increased cellular division to localized cancer to metastasis. In addition, in the Lynch syndromes (LS) we now recognize a dynamic balance between two stochastic processes: MSI producing abnormal cells, and the host's adaptive immune system's ability to remove them. The latter may explain why colonoscopy surveillance does not reduce the incidence of colorectal cancer in LS, while it may improve the prognosis. Most early onset colon, endometrial and ovarian cancers in LS are now cured and most cancer related deaths are after subsequent cancers in other organs. Aspirin reduces the incidence of colorectal and other cancers in LS. Immunotherapy increases the host immune system's capability to destroy MSI cancers. Colonoscopy surveillance, aspirin prevention and immunotherapy represent major steps forward in personalized precision medicine to prevent and cure inherited MSI cancer.

Shot noise reduction in radiographic and tomographic multi-channel imaging with self-supervised deep learning.

Shot noise is a critical issue in radiographic and tomographic imaging, especially when additional constraints lead to a significant reduction of the signal-to-noise ratio. This paper presents a method for improving the quality of noisy multi-channel imaging datasets, such as data from time or energy-resolved imaging, by exploiting structural similarities between channels. To achieve that, we broaden the application domain of the Noise2Noise self-supervised denoising approach. The method draws pairs of samples from a data distribution with identical signals but uncorrelated noise. It is applicable to multi-channel datasets if adjacent channels provide images with similar enough information but independent noise. We demonstrate the applicability and performance of the method via three case studies, namely spectroscopic X-ray tomography, energy-dispersive neutron tomography, and in vivo X-ray cine-radiography.

Machine Learning Methods Improve Specificity in Newborn Screening for Isovaleric Aciduria.

Isovaleric aciduria (IVA) is a rare disorder of leucine metabolism and part of newborn screening (NBS) programs worldwide. However, NBS for IVA is hampered by, first, the increased birth prevalence due to the identification of individuals with an attenuated disease variant (so-called "mild" IVA) and, second, an increasing number of false positive screening results due to the use of pivmecillinam contained in the medication. Recently, machine learning (ML) methods have been analyzed, analogous to new biomarkers or second-tier methods, in the context of NBS. In this study, we investigated the application of machine learning classification methods to improve IVA classification using an NBS data set containing 2,106,090 newborns screened in Heidelberg, Germany. Therefore, we propose to combine two methods, linear discriminant analysis, and ridge logistic regression as an additional step, a digital-tier, to traditional NBS. Our results show that this reduces the false positive rate by 69.9% from 103 to 31 while maintaining 100% sensitivity in cross-validation. The ML methods were able to classify mild and classic IVA from normal newborns solely based on the NBS data and revealed that besides isovalerylcarnitine (C5), the metabolite concentration of tryptophan (Trp) is important for improved classification. Overall, applying ML methods to improve the specificity of IVA could have a major impact on newborns, as it could reduce the newborns' and families' burden of false positives or over-treatment.

aRgus: Multilevel visualization of non-synonymous single nucleotide variants & advanced pathogenicity score modeling for genetic vulnerability assessment.

The widespread use of high-throughput sequencing techniques is leading to a rapidly increasing number of disease-associated variants of unknown significance and candidate genes. Integration of knowledge concerning their genetic, protein as well as functional and conservational aspects is necessary for an exhaustive assessment of their relevance and for prioritization of further clinical and functional studies investigating their role in human disease. To collect the necessary information, a multitude of different databases has to be accessed and data extraction from the original sources commonly is not user-friendly and requires advanced bioinformatics skills. This leads to a decreased data accessibility for a relevant number of potential users such as clinicians, geneticist, and clinical researchers. Here, we present aRgus (https://argus.urz.uni-heidelberg.de/), a standalone webtool for simple extraction and intuitive visualization of multi-layered gene, protein, variant, and variant effect prediction data. aRgus provides interactive exploitation of these data within seconds for any known gene of the human genome. In contrast to existing online platforms for compilation of variant data, aRgus complements visualization of chromosomal exon-intron structure and protein domain annotation with ClinVar and gnomAD variant distributions as well as position-specific variant effect prediction score modeling. aRgus thereby enables timely assessment of protein regions vulnerable to variation with single amino acid resolution and provides numerous applications in variant and protein domain interpretation as well as in the design of in vitro experiments.

Numerical and Machine Learning Analysis of the Parameters Affecting the Regionally Delivered Nasal Dose of Nano- and Micro-Sized Aerosolized Drugs.

The nasal epithelium is an important target for drug delivery to the nose and secondary organs such as the brain via the olfactory bulb. For both topical and brain delivery, the targeting of specific nasal regions such as the olfactory epithelium (brain) is essential, yet challenging. In this study, a numerical model was developed to predict the regional dose as mass per surface area (for an inhaled mass of 2.5 mg), which is the biologically most relevant dose metric for drug delivery in the respiratory system. The role of aerosol diameter (particle diameter: 1 nm to 30 µm) and inhalation flow rate (4, 15 and 30 L/min) in optimal drug delivery to the vestibule, nasal valve, olfactory and nasopharynx is assessed. To obtain the highest doses in the olfactory region, we suggest aerosols with a diameter of 20 µm and a medium inlet air flow rate of 15 L/min. High deposition on the olfactory epithelium was also observed for nanoparticles below 1 nm, as was high residence time (slow flow rate of 4 L/min), but the very low mass of 1 nm nanoparticles is prohibitive for most therapeutic applications. Moreover, high flow rates (30 L/min) and larger micro-aerosols lead to highest doses in the vestibule and nasal valve regions. On the other hand, the highest drug doses in the nasopharynx are observed for nano-aerosol (1 nm) and fine microparticles (1-20 µm) with a relatively weak dependence on flow rate. Furthermore, using the 45 different inhalation scenarios generated by numerical models, different machine learning models with five-fold cross-validation are trained to predict the delivered dose and avoid partial differential equation solvers for future predictions. Random forest and gradient boosting models resulted in R2 scores of 0.89 and 0.96, respectively. The aerosol diameter and region of interest are the most important features affecting delivered dose, with an approximate importance of 42% and 47%, respectively.

Is HLA type a possible cancer risk modifier in Lynch syndrome?

Lynch syndrome (LS) is the most common inherited cancer syndrome. It is inherited via a monoallelic germline variant in one of the DNA mismatch repair (MMR) genes. LS carriers have a broad 30% to 80% risk of developing various malignancies, and more precise, individual risk estimations would be of high clinical value, allowing tailored cancer prevention and surveillance. Due to MMR deficiency, LS cancers are characterized by the accumulation of frameshift mutations leading to highly immunogenic frameshift peptides (FSPs). Thus, immune surveillance is proposed to inhibit the outgrowth of MMR-deficient cell clones. Recent studies have shown that immunoediting during the evolution of MMR-deficient cancers leads to a counter-selection of highly immunogenic antigens. The immunogenicity of FSPs is dependent on the antigen presentation. One crucial factor determining antigen presentation is the HLA genotype. Hence, a LS carrier's HLA genotype plays an important role in the presentation of FSP antigens to the immune system, and may influence the likelihood of progression from precancerous lesions to cancer. To address the challenge of clarifying this possibility including diverse populations with different HLA types, we have established the INDICATE initiative (Individual cancer risk by HLA type, http://indicate-lynch.org/), an international network aiming at a systematic evaluation of the HLA genotype as a possible cancer risk modifier in LS. Here we summarize the current knowledge on the role of HLA type in cancer risk and outline future research directions to delineate possible association in the scenario of LS with genetically defined risk population and highly immunogenic tumors.

A simple approach for detecting HLA-A*02 alleles in archival formalin-fixed paraffin-embedded tissue samples and an application example for studying cancer immunoediting.

The HLA system represents a central component of the antigen presentation machinery. As every patient possesses a defined set of HLA molecules, only certain antigens can be presented on the cell surface. Thus, studying HLA type-dependent antigen presentation can improve the understanding of variation in susceptibility to various diseases, including infectious diseases and cancer. In archival formalin-fixed paraffin-embedded (FFPE) tissue, the HLA type is difficult to analyze because of fragmentation of DNA, hindering the application of commonly used assays that rely on long DNA stretches. Addressing these difficulties, we present a refined approach for characterizing presence or absence of HLA-A*02, the most common HLA-A allele in the Caucasian population, in archival samples. We validated our genotyping strategy in a cohort of 90 samples with HLA status obtained by an NGS-based method. 90% (n = 81) of the samples could be analyzed with the approach. For all of them, the presence or absence of HLA-A*02 alleles was correctly determined with the method, demonstrating 100% sensitivity and specificity (95% CI: 91.40%-100% and 91.19%-100%). Furthermore, we provide an example of application in an independent cohort of 73 FFPE microsatellite-unstable (MSI) colorectal cancer samples. As MSI cancer cells encompass a high number of mutations in coding microsatellites, leading to the generation of highly immunogenic frameshift peptide antigens, they are ideally suited for studying relations between the mutational landscape of tumor cells and interindividual differences in the immune system, including the HLA genotype. Overall, our method can help to promote studying HLA type-dependency during the pathogenesis of a wide range of diseases, making archival and historic tissue samples accessible for identifying HLA-A*02 alleles.

Opportunities and challenges in machine learning-based newborn screening-A systematic literature review.

The development and continuous optimization of newborn screening (NBS) programs remains an important and challenging task due to the low prevalence of screened diseases and high sensitivity requirements for screening methods. Recently, different machine learning (ML) methods have been applied to support NBS. However, most studies only focus on single diseases or specific ML techniques making it difficult to draw conclusions on which methods are best to implement. Therefore, we performed a systematic literature review of peer-reviewed publications on ML-based NBS methods. Overall, 125 related papers, published in the past two decades, were collected for the study, and 17 met the inclusion criteria. We analyzed the opportunities and challenges of ML methods for NBS including data preprocessing, classification models and pattern recognition methods based on their underlying approaches, data requirements, interpretability on a modular level, and performance. In general, ML methods have the potential to reduce the false positive rate and identify so far unknown metabolic patterns within NBS data. Our analysis revealed, that, among the presented, logistic regression analysis and support vector machines seem to be valuable candidates for NBS. However, due to the variety of diseases and methods, a general recommendation for a single method in NBS is not possible. Instead, these methods should be further investigated and compared to other approaches in comprehensive studies as they show promising results in NBS applications.

Biomechanical Influences on Mesh-Related Complications in Incisional Hernia Repair.

Aim: Hernia repair strengthens the abdominal wall with a textile mesh. Recurrence and pain indicate weak bonds between mesh and tissue. It remains a question which biomechanical factors strengthen the mesh-tissue interface, and whether surgeons can enhance the bond between mesh and tissue. Material and Methods: This study assessed the strength of the mesh-tissue interface by dynamic loads. A self-built bench test delivered dynamic impacts. The test simulated coughing. Porcine and bovine tissue were used for the bench test. Tissue quality, mesh adhesiveness, and fixation intensity influenced the retention power. The influences were condensed in a formula to assess the durability of the repair. The formula was applied to clinical work. The relative strength of reconstruction was related to the individual human abdominal wall. From computerized tomography at rest and during Valsalva's Maneuver, the tissue quality of the individual patient was determined before surgery. Results: The results showed that biomechanical parameters observed in porcine, bovine, and human tissue were in the same range. Tissues failed in distinct patterns. Sutures slackened or burst at vulnerable points. Both the load duration and the peak load increased destruction. Stress concentrations elevated failure rates. Regional areas of force contortions increased stress concentrations. Hernia repair improved strain levels. Measures for improvement included the closure of the defect, use of higher dynamic intermittent strain (DIS) class meshes, increased mesh overlap, and additional fixation. Surgeons chose the safety margin of the reconstruction as desired. Conclusion: The tissue quality has now been introduced into the concept of a critical and a gained resistance toward pressure-related impacts. A durable hernia repair could be designed from available coefficients. Using biomechanical principles, surgeons could minimize pain levels. Mesh-related complications such as hernia recurrence can potentially be avoided in incisional hernia repair.

A simple optical tissue clearing pipeline for 3D vasculature imaging of the mediastinal organs in mice.

Optical tissue clearing (OTC) methods render tissue transparent by matching the refractive index within a sample to enable three-dimensional (3D) imaging with advanced microscopes. The application of OTC method in mediastinal organs in mice remains poorly understand. Our aim was to establish a simple protocol pipeline for 3D imaging of the mediastinal organs in mice. Trachea, oesophagus, thymus and heart were harvested from mice after retrograde perfusion via the abdominal aorta. We combined and optimized antibody labelling of thick tissue samples, OTC with cheap and non-toxic solvent ethyl cinnamate (ECi), and light-sheet fluorescence microscopy (LSFM) or laser confocal fluorescence microscopy (LCFM) to visualize the vasculature of those tissues. A high degree of optical transparency of trachea, oesophagus, thymus and heart was achieved after ECi-based OTC. With anti-CD31 antibody immunofluorescence labelling before ECi-based OTC, the vasculature of these tissues with their natural morphology, location and organizational network was imaged using LSFM or LCFM. This simple protocol pipeline provides an easy-to-setup and comprehensive way to study the vasculature of mediastinal organs in 3D without any special equipment. We anticipate that it will facilitate diverse applications in biomedical research of thoracic diseases and even other organs.

Mathematical modeling of multiple pathways in colorectal carcinogenesis using dynamical systems with Kronecker structure.

Like many other types of cancer, colorectal cancer (CRC) develops through multiple pathways of carcinogenesis. This is also true for colorectal carcinogenesis in Lynch syndrome (LS), the most common inherited CRC syndrome. However, a comprehensive understanding of the distribution of these pathways of carcinogenesis, which allows for tailored clinical treatment and even prevention, is still lacking. We suggest a linear dynamical system modeling the evolution of different pathways of colorectal carcinogenesis based on the involved driver mutations. The model consists of different components accounting for independent and dependent mutational processes. We define the driver gene mutation graphs and combine them using the Cartesian graph product. This leads to matrix components built by the Kronecker sum and product of the adjacency matrices of the gene mutation graphs enabling a thorough mathematical analysis and medical interpretation. Using the Kronecker structure, we developed a mathematical model which we applied exemplarily to the three pathways of colorectal carcinogenesis in LS. Beside a pathogenic germline variant in one of the DNA mismatch repair (MMR) genes, driver mutations in APC, CTNNB1, KRAS and TP53 are considered. We exemplarily incorporate mutational dependencies, such as increased point mutation rates after MMR deficiency, and based on recent experimental data, biallelic somatic CTNNB1 mutations as common drivers of LS-associated CRCs. With the model and parameter choice, we obtained simulation results that are in concordance with clinical observations. These include the evolution of MMR-deficient crypts as early precursors in LS carcinogenesis and the influence of variants in MMR genes thereon. The proportions of MMR-deficient and MMR-proficient APC-inactivated crypts as first measure for the distribution among the pathways in LS-associated colorectal carcinogenesis are compatible with clinical observations. The approach provides a modular framework for modeling multiple pathways of carcinogenesis yielding promising results in concordance with clinical observations in LS CRCs.

Primary and Recurrent Repair of Incisional Hernia Based on Biomechanical Considerations to Avoid Mesh-Related Complications.

Aim: Mechanical principles successfully guide the construction of polymer material composites in engineering. Since the abdominal wall is a polymer composite augmented with a textile during incisional hernia repair we ask: can incisional hernia be repaired safely and durably based on biomechanical principles? Material and Methods: Repair materials were assessed on a self-built bench test using pulse loads to elude influences on the reconstruction of the abdominal wall. Tissue elasticity was analyzed preoperatively as needed with computed tomography at rest and during Valsalva's maneuver. Preoperatively, the critical retention force of the reconstruction to pulse loads was calculated and a biomechanically durable repair was designed based on the needs of the individual patient. Intraoperatively, the design was adjusted as needed. Hernia meshes with high grip factors (Progrip®, Dahlhausen® Cicat) were used for the repairs. Mesh sizes, fixation elements and reconstructive details were oriented on the biomechanical design. All patients recieved single-shot antibiosis. Patients were discharged after full ambulation was achieved. Results: A total of 163 patients (82 males and 81 females) were treated for incisional hernia in four hospitals by ten surgeons. Primary hernia was repaired in 119 patients. Recurrent hernia was operated on in 44 cases. Recurrent hernia was significantly larger (median 161 cm2 vs. 78 cm2; u-test: p = 0.00714). Re-do surgery took significantly longer (median 229 min vs. 150 min; p < 0.00001) since recurrent disease required more often transversus abdominis release (70% vs. 47%). GRIP tended to be higher in recurrent repair (p = 0.01828). Complication rates (15%) and hospital stay were the same (6 vs. 6 days; p = 0.28462). After 1 year, no recurrence was detected in either group. Pain levels were equally low in both primary and recurrent hernia repairs (median NAS = 0 in both groups at rest and under load, p = 0.88866). Conclusion: Incisional hernia can safely and durably be repaired based on biomechanical principles both in primary and recurrent disease. The GRIP concept provides a base for the application of biomechanical principles in incisional hernia repair.

Introducing Biomedisa as an open-source online platform for biomedical image segmentation.

We present Biomedisa, a free and easy-to-use open-source online platform developed for semi-automatic segmentation of large volumetric images. The segmentation is based on a smart interpolation of sparsely pre-segmented slices taking into account the complete underlying image data. Biomedisa is particularly valuable when little a priori knowledge is available, e.g. for the dense annotation of the training data for a deep neural network. The platform is accessible through a web browser and requires no complex and tedious configuration of software and model parameters, thus addressing the needs of scientists without substantial computational expertise. We demonstrate that Biomedisa can drastically reduce both the time and human effort required to segment large images. It achieves a significant improvement over the conventional approach of densely pre-segmented slices with subsequent morphological interpolation as well as compared to segmentation tools that also consider the underlying image data. Biomedisa can be used for different 3D imaging modalities and various biomedical applications.

Application of ethyl cinnamate based optical tissue clearing and expansion microscopy combined with retrograde perfusion for 3D lung imaging.

PURPOSE: 3 D imaging of the lung is not a trivial undertaking as during preparation the lung may collapse. Also serial sections and scans followed by 3 D reconstruction may lead to artifacts. The present study aims to figure out the best way to perform 3 D imaging in lung research. MATERIALS AND METHODS: We applied an optical tissue clearing (OTC) method, which uses ethyl cinnamate (ECi) as a fast, nontoxic and cheap clearing solvent, for 3 D imaging of retrograde perfused lungs by laser confocal fluorescence microscopy and light sheet fluorescence microscopy. We also introduced expansion microscopy (ExM), a cutting-edge technique, in 3 D imaging of lungs. We examined and compared the usefulness of these techniques for 3 D lung imaging. The ExM protocol was further extended to paraffin-embedded lung metastases blocks. RESULTS: The MHI148-PEI labeled lung vasculature was visualized by retrograde perfusion combined with trachea ligation and ECi based OTC. As compared with trans-cardiac perfusion, the retrograde perfusion results in a better maintenance of lung morphology. 3 D structure of alveoli, vascular branches and cilia in lung were revealed by immunofluorescence staining after ExM. 3 D distribution of microvasculature and neutrophil cells in 10 years old paraffin-embedded lung metastases were analyzed by ExM. CONCLUSIONS: The retrograde perfusion combined with trachea ligation technique could be applied in the lung research in mice. 3 D structure of lung vasculature can be visualized by MHI148-PEI perfusion and ECi based OTC in an efficient way. ExM and immunofluorescence staining protocol is highly recommended to perform 3 D imaging of fresh fixed lung as well as paraffin-embedded lung blocks.