Rapid methods for the evaluation of fluorescent reporters in tissue clearing and the segmentation of large vascular structures

Autofluorescence-based tissue characterization enhances clinical prospects of light-sheet-microscopy

Light sheet fluorescence microscopy (LSFM) is a transformative imaging method that enables the visualization of non-dissected specimen in real-time 3D. Optical clearing of tissues is essential for LSFM, typically employing toxic solvents. Here, we test the applicability of a non-hazardous alternative, ethyl cinnamate (ECi). We comprehensively characterized autofluorescence (AF) spectra in diverse murine tissues-ocular globe, knee, and liver-employing LSFM under various excitation wavelengths (405-785 nm) to test the feasibility of unstained samples for diagnostic purposes, in particular regarding percutaneous biopsies, as they constitute to most harvested type of tissue sample in clinical routine. Ocular globe structures were best discerned with 640 nm excitation. Knee tissue showed complex variation in AF spectra variation influenced by tissue depth and structure. Liver exhibited a unique AF pattern, likely linked to vasculature. Hepatic tissue samples were used to demonstrate the compatibility of our protocol for antibody staining. Furthermore, we employed machine learning to augment raw images and segment liver structures based on AF spectra. Radiologists rated representative samples transferred to the clinical assessment software. Learning-generated images scored highest in quality. Additionally, we investigated an actual murine biopsy. Our study pioneers the application of AF spectra for tissue characterization and diagnostic potential of optically cleared unstained percutaneous biopsies, contributing to the clinical translation of LSFM.

Tissue clearing method in visualization of cancer progression and metastasis

Since various imaging modalities have been developed, cancer metastasis can be detected from an early stage. However, limitations still exist, especially in terms of spatial resolution. Tissue-clearing technology has emerged as a new imaging modality in cancer research, which has been developed and utilized for a long time mainly in neuroscience field. This method enables us to detect cancer metastatic foci with single-cell resolution at whole mouse body/organ level. On top of that, 3D images of cancer metastasis of whole mouse organs make it easy to understand their characteristics. Recently, further applications of tissue clearing methods were reported in combination with reporter systems, labeling, and machine learning. In this review, we would like to provide an overview of this technique and current applications in cancer research and discuss their potentials and limitations.

Therapy-induced modulation of tumor vasculature and oxygenation in a murine glioblastoma model quantified by deep learning-based feature extraction

Glioblastoma presents characteristically with an exuberant, poorly functional vasculature that causes malperfusion, hypoxia and necrosis. Despite limited clinical efficacy, anti-angiogenesis resulting in vascular normalization remains a promising therapeutic approach. Yet, fundamental questions concerning anti-angiogenic therapy remain unanswered, partly due to the scale and resolution gap between microscopy and clinical imaging and a lack of quantitative data readouts. To what extend does treatment lead to vessel regression or vessel normalization and does it ameliorate or aggravate hypoxia? Clearly, a better understanding of the underlying mechanisms would greatly benefit the development of desperately needed improved treatment regimens. Here, using orthotopic transplantation of Gli36 cells, a widely used murine glioma model, we present a mesoscopic approach based on light sheet fluorescence microscopic imaging of wholemount stained tumors. Deep learning-based segmentation followed by automated feature extraction allowed quantitative analyses of the entire tumor vasculature and oxygenation statuses. Unexpectedly in this model, the response to both cytotoxic and anti-angiogenic therapy was dominated by vessel normalization with little evidence for vessel regression. Equally surprising, only cytotoxic therapy resulted in a significant alleviation of hypoxia. Taken together, we provide and evaluate a quantitative workflow that addresses some of the most urgent mechanistic questions in anti-angiogenic therapy.

Effect of methanol fixation on single-cell RNA sequencing of the murine dentate gyrus

Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to evaluate the transcriptomic landscape and heterogeneity of thousands of cells in parallel. However, complex study designs or the unavailability of in-house instruments require the temporal disconnection between sample preparation and library construction, raising the need for efficient sample preservation methods which are compatible with scRNA-seq downstream analysis. Several studies evaluated the effect of methanol fixation as preservation method, yet none of them deeply assessed its effect on adult primary dissociated brain tissue. Here, we evaluated its effect on murine dentate gyrus (DG) single cell suspensions and on subsequent scRNA-seq downstream analysis by performing SOrting and Robot-assisted Transcriptome SEQuencing (SORT-seq), a partially robotized version of the CEL-seq2 protocol. Our results show that MeOH fixation preserves RNA integrity and has no apparent effects on cDNA library construction. They also suggest that fixation protects from sorting-induced cell stress and increases the proportion of high-quality cells. Despite evidence of mRNA leakage in fixed cells, their relative gene expression levels correlate well with those of fresh cells and fixation does not significantly affect the variance of the dataset. Moreover, it allows the identification of all major DG cell populations, including neural precursors, granule neurons and different glial cell types, with a tendency to preserve more neurons that are underrepresented in fresh samples. Overall, our data show that MeOH fixation is suitable for preserving primary neural cells for subsequent single-cell RNA profiling, helping to overcome challenges arising from complex workflows, improve experimental flexibility and facilitate scientific collaboration.

Rapid and fully automated blood vasculature analysis in 3D light-sheet image volumes of different organs

Light-sheet fluorescence microscopy (LSFM) can produce high-resolution tomograms of tissue vasculature with high accuracy. However, data processing and analysis is laborious due to the size of the datasets. Here, we introduce VesselExpress, an automated software that reliably analyzes six characteristic vascular network parameters including vessel diameter in LSFM data on average computing hardware. VesselExpress is ∼100 times faster than other existing vessel analysis tools, requires no user interaction, and integrates batch processing and parallelization. Employing an innovative dual Frangi filter approach, we show that obesity induces a large-scale modulation of brain vasculature in mice and that seven other major organs differ strongly in their 3D vascular makeup. Hence, VesselExpress transforms LSFM from an observational to an analytical working tool.

An analysis modality for vascular structures combining tissue-clearing technology and topological data analysis

The blood and lymphatic vasculature networks are not yet fully understood even in mouse because of the inherent limitations of imaging systems and quantification methods. This study aims to evaluate the usefulness of the tissue-clearing technology for visualizing blood and lymphatic vessels in adult mouse. Clear, unobstructed brain/body imaging cocktails and computational analysis (CUBIC) enables us to capture the high-resolution 3D images of organ- or area-specific vascular structures. To evaluate these 3D structural images, signals are first classified from the original captured images by machine learning at pixel base. Then, these classified target signals are subjected to topological data analysis and non-homogeneous Poisson process model to extract geometric features. Consequently, the structural difference of vasculatures is successfully evaluated in mouse disease models. In conclusion, this study demonstrates the utility of CUBIC for analysis of vascular structures and presents its feasibility as an analysis modality in combination with 3D images and mathematical frameworks.

Understanding blood and lymphatic vasculature networks is currently limited by existing imaging systems and quantification methods. Here the authors use the tissue clearing method CUBIC to generate 3D images, machine learning to capture the signals, and extract geometric features by topological data analysis.

Tissue clearing may alter emission and absorption properties of common fluorophores

In recent years, 3D cell culture has been gaining a more widespread following across many fields of biology. Tissue clearing enables optical analysis of intact 3D samples and investigation of molecular and structural mechanisms by homogenizing the refractive indices of tissues to make them nearly transparent. Here, we describe and quantify that common clearing solutions including benzyl alcohol/benzyl benzoate (BABB), PEG-associated solvent system (PEGASOS), immunolabeling-enabled imaging of solvent-cleared organs (iDISCO), clear, unobstructed brain/body imaging cocktails and computational analysis (CUBIC), and ScaleS4 alter the emission spectra of Alexa Fluor fluorophores and fluorescent dyes. Clearing modifies not only the emitted light intensity but also alters the absorption and emission peaks, at times to several tens of nanometers. The resulting shifts depend on the interplay of solvent, fluorophore, and the presence of cells. For biological applications, this increases the risk for unexpected channel crosstalk, as filter sets are usually not optimized for altered fluorophore emission spectra in clearing solutions. This becomes especially problematic in high throughput/high content campaigns, which often rely on multiband excitation to increase acquisition speed. Consequently, researchers relying on clearing in quantitative multiband excitation experiments should crosscheck their fluorescent signal after clearing in order to inform the proper selection of filter sets and fluorophores for analysis.

Multimodal Imaging of Target Detection Algorithm under Artificial Intelligence in the Diagnosis of Early Breast Cancer

This study aimed to analyze the diagnostic value of multimodal images based on artificial intelligence target detection algorithms for early breast cancer, so as to provide help for clinical imaging examinations of breast cancer. This article combined residual block with inception block, constructed a new target detection algorithm to detect breast lumps, used deep convolutional neural network and ultrasound imaging in diagnosing benign and malignant breast lumps, took breast density grading with mammography, compared the convolutional neural network (CNN) algorithm with the proposed algorithm, and then applied the proposed algorithm to the diagnosis of 120 female patients with breast lumps. According to the results, accuracy rates of breast lump detection (94.76%), benign and malignant breast lumps diagnosis (98.22%), and breast grading (93.65%) with the algorithm applied in this study were significantly higher than those (75.67%, 87.23%, and 79.54%) with CNN algorithm, and the difference was statistically significant (P < 0.05); among 62 patients with malignant breast lumps of the 120 patients with breast lumps, 37 were patients with invasive ductal carcinoma, 8 with lobular carcinoma in situ, 16 with intraductal carcinoma, and 4 with mucinous carcinoma; among the remaining 58 patients with benign breast lumps, 28 were patients with fibrocystic breast disease, 17 with intraductal papilloma, 4 with breast hyperplasia, and 9 with adenopathy; the differences in shape, growth direction, edge, and internal echo of multimodal ultrasound imaging of patients with benign and malignant breast lumps had statistical significance (P < 0.05); the malignant constituent ratios of patients with breast density grades I to IV were 0%, 7.10%, 80.40%, and 100%, respectively. In short, the multimodal imaging diagnosis under the algorithm in this article was superior to CNN algorithm in all aspects; according to the judgment on benign and malignant breast lumps and breast density with multimodal imaging features, the higher the breast density, the higher the probability of breast cancer.

Drug-Induced Nephrotoxicity Assessment in 3D Cellular Models

The kidneys are often involved in adverse effects and toxicity caused by exposure to foreign compounds, chemicals, and drugs. Early predictions of these influences are essential to facilitate new, safe drugs to enter the market. However, in current drug treatments, drug-induced nephrotoxicity accounts for 1/4 of reported serious adverse reactions, and 1/3 of them are attributable to antibiotics. Drug-induced nephrotoxicity is driven by multiple mechanisms, including altered glomerular hemodynamics, renal tubular cytotoxicity, inflammation, crystal nephropathy, and thrombotic microangiopathy. Although the functional proteins expressed by renal tubules that mediate drug sensitivity are well known, current in vitro 2D cell models do not faithfully replicate the morphology and intact renal tubule function, and therefore, they do not replicate in vivo nephrotoxicity. The kidney is delicate and complex, consisting of a filter unit and a tubular part, which together contain more than 20 different cell types. The tubular epithelium is highly polarized, and maintaining cellular polarity is essential for the optimal function and response to environmental signals. Cell polarity depends on the communication between cells, including paracrine and autocrine signals, as well as biomechanical and chemotaxis processes. These processes affect kidney cell proliferation, migration, and differentiation. For drug disposal research, the microenvironment is essential for predicting toxic reactions. This article reviews the mechanism of drug-induced kidney injury, the types of nephrotoxicity models (in vivo and in vitro models), and the research progress related to drug-induced nephrotoxicity in three-dimensional (3D) cellular culture models.

Vegfr3-tdTomato, a reporter mouse for microscopic visualization of lymphatic vessel by multiple modalities

Lymphatic vessels are indispensable for tissue fluid homeostasis, transport of solutes and dietary lipids and immune cell trafficking. In contrast to blood vessels, which are easily visible by their erythrocyte cargo, lymphatic vessels are not readily detected in the tissue context. Their invisibility interferes with the analysis of the three-dimensional lymph vessel structure in large tissue volumes and hampers dynamic intravital studies on lymphatic function and pathofunction. An approach to overcome these limitations are mouse models, which express transgenic fluorescent proteins under the control of tissue-specific promotor elements. We introduce here the BAC-transgenic mouse reporter strain Vegfr3-tdTomato that expresses a membrane-tagged version of tdTomato under control of Flt4 regulatory elements. Vegfr3-tdTomato mice inherited the reporter in a mendelian fashion and showed selective and stable fluorescence in the lymphatic vessels of multiple organs tested, including lung, kidney, heart, diaphragm, intestine, mesentery, liver and dermis. In this model, tdTomato expression was sufficient for direct visualisation of lymphatic vessels by epifluorescence microscopy. Furthermore, lymph vessels were readily visualized using a number of microscopic modalities including confocal laser scanning, light sheet fluorescence and two-photon microscopy. Due to the early onset of VEGFR-3 expression in venous embryonic vessels and the short maturation time of tdTomato, this reporter offers an interesting alternative to Prox1-promoter driven lymphatic reporter mice for instance to study the developmental differentiation of venous to lymphatic endothelial cells.

Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets

BACKGROUND

Recent advances in 3D imaging technologies provide novel insights to researchers and reveal finer and more detail of examined specimen, especially in the biomedical domain, but also impose huge challenges regarding scalability for automated analysis algorithms due to rapidly increasing dataset sizes. In particular, existing research towards automated vessel network analysis does not always consider memory requirements of proposed algorithms and often generates a large number of spurious branches for structures consisting of many voxels. Additionally, very often these algorithms have further restrictions such as the limitation to tree topologies or relying on the properties of specific image modalities.

RESULTS

We propose a scalable iterative pipeline (in terms of computational cost, required main memory and robustness) that extracts an annotated abstract graph representation from the foreground segmentation of vessel networks of arbitrary topology and vessel shape. The novel iterative refinement process is controlled by a single, dimensionless, a-priori determinable parameter.

CONCLUSIONS

We are able to, for the first time, analyze the topology of volumes of roughly 1 TB on commodity hardware, using the proposed pipeline. We demonstrate improved robustness in terms of surface noise, vessel shape deviation and anisotropic resolution compared to the state of the art. An implementation of the presented pipeline is publicly available in version 5.1 of the volume rendering and processing engine Voreen.

Can Developments in Tissue Optical Clearing Aid Super-Resolution Microscopy Imaging?

The rapid development of super-resolution microscopy (SRM) techniques opens new avenues to examine cell and tissue details at a nanometer scale. Due to compatibility with specific labelling approaches, in vivo imaging and the relative ease of sample preparation, SRM appears to be a valuable alternative to laborious electron microscopy techniques. SRM, however, is not free from drawbacks, with the rapid quenching of the fluorescence signal, sensitivity to spherical aberrations and light scattering that typically limits imaging depth up to few micrometers being the most pronounced ones. Recently presented and robustly optimized sets of tissue optical clearing (TOC) techniques turn biological specimens transparent, which greatly increases the tissue thickness that is available for imaging without loss of resolution. Hence, SRM and TOC are naturally synergistic techniques, and a proper combination of these might promptly reveal the three-dimensional structure of entire organs with nanometer resolution. As such, an effort to introduce large-scale volumetric SRM has already started; in this review, we discuss TOC approaches that might be favorable during the preparation of SRM samples. Thus, special emphasis is put on TOC methods that enhance the preservation of fluorescence intensity, offer the homogenous distribution of molecular probes, and vastly decrease spherical aberrations. Finally, we review examples of studies in which both SRM and TOC were successfully applied to study biological systems.