Complex wavelets for extended depth-of-field: A new method for the fusion of multichannel microscopy images

Expression of the large amino acid transporter SLC7A5/LAT1 on immune cells is enhanced in primary sclerosing cholangitis-associated cholangiocarcinoma and correlates with poor prognosis in cholangiocarcinoma

Biliary tract cancers (BTC) are rare lethal malignancies arising along the biliary tree. Unfortunately, effective therapeutics are lacking and the prognosis remains dismal even for patients eligible for surgical resection. Therefore, novel therapeutic approaches along with early detection strategies and prognostic markers are urgently needed. Primary sclerosing cholangitis (PSC) is a chronic disease of the bile ducts leading to fibrosis and ultimately cirrhosis. Patients with PSC have a 5-20% lifetime risk of developing BTC; yet the molecular mechanisms that underpin the development of PSC- associated biliary tract cancer (PSC-BTC) have not been fully elucidated. SLC7A5/LAT1, a large amino acid transporter, has been shown to modulate cell growth and proliferation as well as other intracellular processes in solid tumors. In this study, we evaluated SLC7A5 expression in PSC-BTC and in sporadic BTC (sBTC) and its role as a prognostic factor. Analysis of the TGCA cohort showed a significantly higher expression of SLC7A5 in tumor tissue compared with adjacent normal tissue (p = 0.0002) in BTC. In our cohort (comprised of 69 BTC patients including 16 PSC-BTC), SLC7A5/LAT1 expression was observed in both tumor and intratumoral immune cells. A significantly higher percentage of SLC7A5/LAT1 positive intratumoral immune cells was observed in PSC-BTC compared with sBTC (p = 0.004). Multiplex immunofluorescence co-detection by indexing (CODEX) analysis identified CD4+ regulatory T lymphocytes and CD68+ macrophages as the largest immune cell populations expressing LAT1. SLC7A5/LAT1 expression as well as a higher intratumoral infiltration of SLC7A5/LAT1-positive immune cells (≥2%) were associated with a shorter overall survival in our cohort (LogRank test, p = 0.04 and p = 0.008; respectively). SLC7A5/LAT1 expressing tumors are higher staged tumors (pT3/4 versus pT1/2, p = 0.048). These results underline the potential use of SLC7A5/LAT1 as a prognostic marker in BTC. Furthermore, the higher frequency of SLC7A5/LAT1 positive immune cells in PSC-BTC compared to sBTC may hint at the potential role of SLC7A5/LAT1 in inflammation-driven carcinogenesis.

A large multi-focus dataset for white blood cell classification

The White Blood Cell (WBC) differential test ranks as the second most frequently performed diagnostic assay. It requires manual confirmation of the peripheral blood smear by experts to identify signs of abnormalities. Automated digital microscopy has emerged as a solution to reduce this labor-intensive process and improve efficiency. Several publicly available datasets provide various WBC subtypes of differing quality and resolution. These datasets have contributed to advancing WBC classification using machine learning techniques. However, digital microscopy of blood cells with high magnification often requires a wider depth of field, posing challenges for automatic digital microscopy that necessitates capturing multiple stacks of focal planes to obtain complete images of specific blood cells. Our dataset provides 25,773 image stacks from 72 patients. The image labels consist of 18 classes encompassing normal and abnormal cells, with two experts reviewing each label. Each image includes 10 z-stacks of cropped 200 by 200 pixel images, captured using a 50X microscope with 400 nm intervals. This study presents a comprehensive multi-focus dataset for WBC classification.

DL-EDOF: Novel Multi-Focus Image Data Set and Deep Learning-Based Approach for More Accurate and Specimen-Free Extended Depth of Focus

Depth of focus (DOF) is defined as the axial range in which the specimen stage moves without losing focus while the imaging apparatus remains stable. It may not be possible to capture an image that includes the entire specimen in focus due to the narrow DOF in microscopic systems. Extended depth of focus (EDOF) is used to overcome this limitation in microscopic systems. Although the researchers have developed so many EDOF microscope approaches, this research field still has some crucial shortcomings such as high computational costs, complexity and execution time, requiring additional equipment, low precise characterization of curves, and edges in images, varying performance depending on the specimen and microscope, using only gray levels of input images to acquire the pixel's focus values. In order to minimize these shortcomings and comprehensively analyze the performance of EDOF approaches, a novel multi-focus image data set is generated, and a deep learning-based EDOF microscope approach is proposed in this study. When compared with the state-of-art EDOF approaches, our study provides various crucial contributions such as the first EDOF approach based on unsupervised deep learning, providing more accurate and specimen-free EDOF, generating a novel multi-focus image data, not requiring any pre- or post-processing technique and acquiring the pixel's focus degrees using deep features. In order to evaluate the effectiveness of the suggested approach, 20 different EDOF approaches are applied to a multi-focus image data set containing 9 image collections (4 synthetic and 5 microscope image collections) in total. Performance analysis metrics with and without requiring a reference image are preferred to identify which EDOF microscope approach can extract more essential details from the multi-focus images for the synthetic and microscope image collections, which are Root Mean Square Error (RMSE), Peak Signal Noise Ratio (PSNR), Universal Quality Index (UQI), Correlation Coefficient (CC), Perception-based Image Quality Evaluator (PIQE), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), Extension of Universal Quality Index for N Images (UQIN), and Naturalness Image Quality Evaluator (NIQE). Objective and subjective analysis of this study demonstrates that unsupervised deep learning model is more efficient to transmit crucial details from multi-focus images. Moreover, the suggested EDOF microscope approach with highest PSNR, UQI, CC, UQIN and lowest RMSE, PIQE, BRISQUE, NIQE produces higher performance than the state-of-art approaches.

Leveraging deep learning for detecting red blood cell morphological changes in blood films from children with severe malaria anaemia

In sub-Saharan Africa, acute-onset severe malaria anaemia (SMA) is a critical challenge, particularly affecting children under five. The acute drop in haematocrit in SMA is thought to be driven by an increased phagocytotic pathological process in the spleen, leading to the presence of distinct red blood cells (RBCs) with altered morphological characteristics. We hypothesized that these RBCs could be detected systematically and at scale in peripheral blood films (PBFs) by harnessing the capabilities of deep learning models. Assessment of PBFs by a microscopist does not scale for this task and is subject to variability. Here we introduce a deep learning model, leveraging a weakly supervised Multiple Instance Learning framework, to Identify SMA (MILISMA) through the presence of morphologically changed RBCs. MILISMA achieved a classification accuracy of 83% (receiver operating characteristic area under the curve [AUC] of 87%; precision-recall AUC of 76%). More importantly, MILISMA's capabilities extend to identifying statistically significant morphological distinctions (p < 0.01) in RBCs descriptors. Our findings are enriched by visual analyses, which underscore the unique morphological features of SMA-affected RBCs when compared to non-SMA cells. This model aided detection and characterization of RBC alterations could enhance the understanding of SMA's pathology and refine SMA diagnostic and prognostic evaluation processes at scale.

Bone Endosteal Mimics Regulates Breast Cancer Development and Phenotype

Bone is a frequent site for metastatic development in various cancer types, including breast cancer, with a grim prognosis due to the distinct bone environment. Despite considerable advances, our understanding of the underlying processes leading to bone metastasis progression remains elusive. Here, we applied a bioactive three-dimensional (3D) model capable of mimicking the endosteal bone microenvironment. MDA-MB-231 and MCF7 breast cancer cells were cultured on the scaffolds, and their behaviors and the effects of the biomaterial on the cells were examined over time. We demonstrated that close interactions between the cells and the biomaterial affect their proliferation rates and the expression of c-Myc, cyclin D, and KI67, leading to cell cycle arrest. Moreover, invasion assays revealed increased invasiveness within this microenvironment. Our findings suggest a dual role for endosteal mimicking signals, influencing cell fate and potentially acting as a double-edged sword, shuttling between cell cycle arrest and more active, aggressive states.

JEasyTFM: an open-source software package for the analysis of large 2D TFM data within ImageJ

Motivation

Cells adhering to the extracellular matrix can sense and respond to a wide variety of chemical and physical features of the adhesive surface. Traction force microscopy (TFM) allows determining the tensile forces exerted by the cells on their substrate with high resolution.

Results

To allow broad access of this techniques to cell biology laboratories we developed JeasyTFM, an open-source ImageJ package able to process multi-color and multi-position time-lapse pictures thus suitable for the automatic analysis of large TFM data.

Availability and implementation

JEasyTFM is implemented as an ImageJ plugin and available at: http://questpharma.u-strasbg.fr/JEasyTFM.html.

Highly multiplexed spatial analysis identifies tissue-resident memory T cells as drivers of ulcerative and immune checkpoint inhibitor colitis

Colitis is a prevalent adverse event associated with immune checkpoint inhibitor (ICI) therapy with similarities to inflammatory bowel disease. Incomplete mechanistic understanding of ICI colitis curtails evidence-based treatment. Given the often-overlooked connection between tissue architecture and mucosal immune cell function, we here applied imaging mass cytometry (IMC) to gain spatial proteomic insight in ICI colitis in comparison to ulcerative colitis (UC). Using a cell segmentation pipeline that simultaneously utilizes high-resolution nuclear imaging and high-multiplexity IMC, we show that intra-epithelial CD8+ T cells are significantly more abundant (and numerically dominant) in anti-PD-1 ± anti-CTLA-4-induced colitis compared to anti-CTLA-4-induced colitis and UC. We identified activated, cycling CD8+ tissue-resident memory T(RM) cells at the lamina propria-epithelial interface as drivers of cytotoxicity in ICI colitis and UC. Moreover, we found that combined ICI-induced colitis featured highest granzyme B levels both in tissue and serum. Together, these data reinforce CD8+ TRM cells as potentially targetable drivers of ICI colitis.

Research and design of a metasurface with an extended depth of focus in the near field

A metasurface with an extended depth of focus has broad application prospects in security detection. However, in the near field, the simulation results obtained by using traditional methods to achieve an extended depth of focus have a significant deviation from the preset value. This paper discusses the relationship between the depth of focus and focusing position, and the reason why the simulation results deviate from the preset focus position in the radial modulation method.  The angle modulation method is found by a simulation. A more accurate method for an extended depth of focus was proposed by combining the radial modulation method with the quasi-optical path principle. Finally, a polarization-insensitive reflective metasurface element was designed, and elements were arranged to form a polarization-insensitive focus between 150 and 400 mm based on the focusing effect settings. The simulation results indicate that the metasurface achieves the same focusing effect between 175 and 425 mm when different linear-polarization waves are incident. This focus is greater and more accurate than the radial modulation method under the same conditions, which indicates that the method is superior to the radial modulation method in the near-field region. The simulation verifies the accuracy of the method and shows potential application prospects in fields such as microwave imaging.

The cortical microtubules of Toxoplasma gondii underlie the helicity of parasite movement

Motility is essential for apicomplexan parasites to infect their hosts. In a three-dimensional (3D) environment, the apicomplexan parasite Toxoplasma gondii moves along a helical path. The cortical microtubules, which are ultra-stable and spirally arranged, have been considered to be a structure that guides the long-distance movement of the parasite. Here, we address the role of the cortical microtubules in parasite motility, invasion and egress by utilizing a previously generated mutant (dubbed 'TKO') in which these microtubules are destabilized in mature parasites. We found that the cortical microtubules in ∼80% of the non-dividing (i.e. daughter-free) TKO parasites are much shorter than normal. The extent of depolymerization was further exacerbated upon commencement of daughter formation or cold treatment, but parasite replication was not affected. In a 3D Matrigel matrix, the TKO mutant moved directionally over long distances, but along trajectories that were significantly more linear (i.e. less helical) than those of wild-type parasites. Interestingly, this change in trajectory did not impact either movement speed in the matrix or the speed and behavior of the parasite during entry into and egress from the host cell.

Collective directional migration drives the formation of heteroclonal cancer cell clusters

Metastasisation occurs through the acquisition of invasive and survival capabilities that allow tumour cells to colonise distant sites. While the role of multicellular aggregates in cancer dissemination is acknowledged, the mechanisms that drive the formation of multiclonal cell aggregates are not fully elucidated. Here, we show that cancer cells of different tissue of origins can perform collective directional migration and can actively form heteroclonal aggregates in 3D, through a proliferation-independent mechanism. Coalescence of distant cell clusters is mediated by subcellular actin-rich protrusions and multicellular outgrowths that extend towards neighbouring aggregates. Coherently, perturbation of cytoskeletal dynamics impairs collective migration while myosin II activation is necessary for multicellular movements. We put forward the hypothesis that cluster attraction is mediated by secreted soluble factors. Such a hypothesis is consistent with the abrogation of aggregation by inhibition of PI3K/AKT/mTOR and MEK/ERK, the chemoattracting activity of conditioned culture media and with a wide screening of secreted proteins. Our results present a novel collective migration model and shed light on the mechanisms of formation of heteroclonal aggregates in cancer.

The cortical microtubules of Toxoplasma gondii underlie the helicity of parasite movement

Motility is essential for apicomplexan parasites to infect their hosts. In a three-dimensional (3-D) environment, the apicomplexan parasite Toxoplasma gondii moves along a helical path. The cortical microtubules, which are ultra-stable and spirally arranged, have been considered to be a structure that guides the long-distance movement of the parasite. Here we address the role of the cortical microtubules in parasite motility, invasion, and egress by utilizing a previously generated mutant (dubbed "TKO") in which these microtubules are destabilized in mature parasites. We found that the cortical microtubules in ~ 80% of the non-dividing (i.e. daughter-free) TKO parasites are much shorter than normal. The extent of depolymerization is further exacerbated upon commencement of daughter formation or cold treatment, but parasite replication is not affected. In a 3-D Matrigel matrix, the TKO mutant moves directionally over long distances, but along trajectories significantly more linear (i.e. less helical) than those of wild-type parasites. Interestingly, this change in trajectory does not impact either movement speed in the matrix or the speed and behavior of the parasite's entry into and egress from the host cell.

Expression and possible functions of a horizontally transferred glycosyl hydrolase gene, GH6-1, in Ciona embryogenesis

BACKGROUND

The Tunicata or Urochordata is the only animal group with the ability to synthesize cellulose directly and cellulose is a component of the tunic that covers the entire tunicate body. The genome of Ciona intestinalis type A contains a cellulose synthase gene, CesA, that it acquired via an ancient, horizontal gene transfer. CesA is expressed in embryonic epidermal cells and functions in cellulose production. Ciona CesA is composed of both a glycosyltransferase domain, GT2, and a glycosyl hydrolase domain, GH6, which shows a mutation at a key position and seems functionless. Interestingly, the Ciona genome contains a glycosyl hydrolase gene, GH6-1, in which the GH6 domain seems intact. This suggests expression and possible functions of GH6-1 during Ciona embryogenesis. Is GH6-1 expressed during embryogenesis? If so, in what tissues is the gene expressed? Does GH6-1 serve a function? If so, what is it? Answers to these questions may advance our understanding of evolution of this unique animal group.

RESULTS

Quantitative reverse transcription PCR and in situ hybridization revealed that GH6-1 is expressed in epidermis of tailbud embryos and in early swimming larvae, a pattern similar to that of CesA. Expression is downregulated at later stages and becomes undetectable in metamorphosed juveniles. The GH6-1 expression level is higher in the anterior-trunk region and caudal-tip regions of late embryos. Single-cell RNA sequencing analysis of the late tailbud stage showed that cells of three clusters with epidermal identity express GH6-1, and that some of them co-express CesA. TALEN-mediated genome editing was used to generate GH6-1 knockout Ciona larvae. Around half of TALEN-electroporated larvae showed abnormal development of adhesive papillae and altered distribution of surface cellulose. In addition, three-fourths of TALEN-electroporated animals failed to complete larval metamorphosis.

CONCLUSIONS

This study showed that tunicate GH6-1, a gene that originated by horizontal gene transfer of a prokaryote gene, is recruited into the ascidian genome, and that it is expressed and functions in epidermal cells of ascidian embryos. Although further research is required, this observation demonstrates that both CesA and GH6-1 are involved in tunicate cellulose metabolism, impacting tunicate morphology and ecology.

Large depth-of-field ultra-compact microscope by progressive optimization and deep learning

The optical microscope is customarily an instrument of substantial size and expense but limited performance. Here we report an integrated microscope that achieves optical performance beyond a commercial microscope with a 5×, NA 0.1 objective but only at 0.15 cm³ and 0.5 g, whose size is five orders of magnitude smaller than that of a conventional microscope. To achieve this, a progressive optimization pipeline is proposed which systematically optimizes both aspherical lenses and diffractive optical elements with over 30 times memory reduction compared to the end-to-end optimization. By designing a simulation-supervision deep neural network for spatially varying deconvolution during optical design, we accomplish over 10 times improvement in the depth-of-field compared to traditional microscopes with great generalization in a wide variety of samples. To show the unique advantages, the integrated microscope is equipped in a cell phone without any accessories for the application of portable diagnostics. We believe our method provides a new framework for the design of miniaturized high-performance imaging systems by integrating aspherical optics, computational optics, and deep learning.

Comprehensive Surface Histology of Fresh Resection Margins With Rapid Open-Top Light-Sheet (OTLS) Microscopy

OBJECTIVE

For tumor resections, margin status typically correlates with patient survival but positive margin rates are generally high (up to 45% for head and neck cancer). Frozen section analysis (FSA) is often used to intraoperatively assess the margins of excised tissue, but suffers from severe under-sampling of the actual margin surface, inferior image quality, slow turnaround, and tissue destructiveness.

METHODS

Here, we have developed an imaging workflow to generate en face histologic images of freshly excised surgical margin surfaces based on open-top light-sheet (OTLS) microscopy. Key innovations include (1) the ability to generate false-colored H&E-mimicking images of tissue surfaces stained for < 1 min with a single fluorophore, (2) rapid OTLS surface imaging at a rate of 15 min/cm2 followed by real-time post-processing of datasets within RAM at a rate of 5 min/cm2, and (3) rapid digital surface extraction to account for topological irregularities at the tissue surface.

RESULTS

In addition to the performance metrics listed above, we show that the image quality generated by our rapid surface-histology method approaches that of gold-standard archival histology.

CONCLUSION

OTLS microscopy has the feasibility to provide intraoperative guidance of surgical oncology procedures.

SIGNIFICANCE

The reported methods can potentially improve tumor-resection procedures, thereby improving patient outcomes and quality of life.

A Neuroligin-1 mutation associated with Alzheimer's disease produces memory and age-dependent impairments in hippocampal plasticity

Alzheimer's disease (AD) is characterized by memory impairments and age-dependent synapse loss. Experimental and clinical studies have shown decreased expression of the glutamatergic protein Neuroligin-1 (Nlgn1) in AD. However, the consequences of a sustained reduction of Nlgn1 are unknown. Here, we generated a knockin mouse that reproduces the NLGN1 Thr271fs mutation, identified in heterozygosis in a familial case of AD. We found that Nlgn1 Thr271fs mutation abolishes Nlgn1 expression in mouse brain. Importantly, heterozygous Nlgn1 Thr271fs mice showed delay-dependent amnesia for recognition memory. Electrophysiological recordings uncovered age-dependent impairments in basal synaptic transmission and long-term potentiation (LTP) in CA1 hippocampal neurons of heterozygous Nlgn1 Thr271fs mice. In contrast, homozygous Nlgn1 Thr271fs mice showed impaired fear-conditioning memory and normal basal synaptic transmission, suggesting unshared mechanisms for a partial or total loss of Nlgn1. These data suggest that decreased Nlgn1 may contribute to the synaptic and memory deficits in AD.

Invadopodia Methods: Detection of Invadopodia Formation and Activity in Cancer Cells Using Reconstituted 2D and 3D Collagen-Based Matrices

Tumor dissemination involves cancer cell migration through the extracellular matrix (ECM). ECM is mainly composed of collagen fibers that oppose cell invasion. To overcome hindrance in the matrix, cancer cells deploy a protease-dependent program in order to remodel the matrix fibers. Matrix remodeling requires the formation of actin-based matrix/plasma membrane contact sites called invadopodia, responsible for collagen cleavage through the accumulation and activity of the transmembrane type-I matrix metalloproteinase (MT1-MMP). In this article, we describe experimental procedures designed to assay for invadopodia formation and for invadopodia activity using 2D and 3D models based on gelatin (denatured collagen) and fibrillar type-I collagen matrices.

DeepProjection: specific and robust projection of curved 2D tissue sheets from 3D microscopy using deep learning

The efficient extraction of image data from curved tissue sheets embedded in volumetric imaging data remains a serious and unsolved problem in quantitative studies of embryogenesis. Here, we present DeepProjection (DP), a trainable projection algorithm based on deep learning. This algorithm is trained on user-generated training data to locally classify 3D stack content, and to rapidly and robustly predict binary masks containing the target content, e.g. tissue boundaries, while masking highly fluorescent out-of-plane artifacts. A projection of the masked 3D stack then yields background-free 2D images with undistorted fluorescence intensity values. The binary masks can further be applied to other fluorescent channels or to extract local tissue curvature. DP is designed as a first processing step than can be followed, for example, by segmentation to track cell fate. We apply DP to follow the dynamic movements of 2D-tissue sheets during dorsal closure in Drosophila embryos and of the periderm layer in the elongating Danio embryo. DeepProjection is available as a fully documented Python package.

A disector-based framework for the automatic optical fractionator

Stereology-based methods provide the current state-of-the-art approaches for accurate quantification of numbers and other morphometric parameters of biological objects in stained tissue sections. The advent of artificial intelligence (AI)-based deep learning (DL) offers the possibility of improving throughput by automating the collection of stereology data. We have recently shown that DL can effectively achieve comparable accuracy to manual stereology but with higher repeatability, improved throughput, and less variation due to human factors by quantifying the total number of immunostained cells at their maximal profile of focus in extended depth of field (EDF) images. In the first of two novel contributions in this work, we propose a semi-automatic approach using a handcrafted Adaptive Segmentation Algorithm (ASA) to automatically generate ground truth on EDF images for training our deep learning (DL) models to automatically count cells using unbiased stereology methods. This update increases the amount of training data, thereby improving the accuracy and efficiency of automatic cell counting methods, without a requirement for extra expert time. The second contribution of this work is a Multi-channel Input and Multi-channel Output (MIMO) method using a U-Net deep learning architecture for automatic cell counting in a stack of z-axis images (also known as disector stacks). This DL-based digital automation of the ordinary optical fractionator ensures accurate counts through spatial separation of stained cells in the z-plane, thereby avoiding false negatives from overlapping cells in EDF images without the shortcomings of 3D and recurrent DL models. The contribution overcomes the issue of under-counting errors with EDF images due to overlapping cells in the z-plane (masking). We demonstrate the practical applications of these advances with automatic disector-based estimates of the total number of NeuN-immunostained neurons in a mouse neocortex. In summary, this work provides the first demonstration of automatic estimation of a total cell number in tissue sections using a combination of deep learning and the disector-based optical fractionator method.

HIV Tat and cocaine interactively alter genome-wide DNA methylation and gene expression and exacerbate learning and memory impairments

Cocaine use is a major comorbidity of HIV-associated neurocognitive disorder (HAND). In this study, we show that cocaine exposure worsens the learning and memory of doxycycline-inducible and brain-specific HIV Tat transgenic mice (iTat) and results in 14,838 hypermethylated CpG-related differentially methylated regions (DMRs) and 15,800 hypomethylated CpG-related DMRs, which are linked to 52 down- and 127 upregulated genes, respectively, in the hippocampus of iTat mice. These genes are mostly enriched at the neuronal function-, cell morphology-, and synapse formation-related extracellular matrix (ECM) receptor-ligand interaction pathway and mostly impacted in microglia. The accompanying neuropathological changes include swollen dendritic spines, increased synaptophysin expression, and diminished glial activation. We also find that sex (female) and age additively worsen the behavioral and pathological changes. These findings together indicate that chronic cocaine and long-term Tat expression interactively contribute to HAND, likely involving changes of DNA methylation and ECM receptor-ligand interactions.

MATISSE: An analysis protocol for combining imaging mass cytometry with fluorescence microscopy to generate single-cell data

Exploring tissue heterogeneity on a single-cell level by imaging mass cytometry (IMC) remains challenging because of its limiting resolution. We previously demonstrated that combining higher resolution fluorescence with IMC data in the analysis pipeline resulted in high-quality single-cell segmentation. Here, we provide a step-by-step workflow of this MATISSE pipeline, including instructions regarding the staining procedure, and the analysis route to generate single-cell data.

For complete details on the use and execution of this protocol, please refer to Baars et al., 2021.

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High-plex tissue staining combining isotope-labeled antibodies with DNA intercalator

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Imaging mass cytometry (IMC) and fluorescent microscopy in a single workflow

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Combined data processing pipeline of IMC and fluorescent images

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The MATISSE pipeline generates high-quality single-cell segmentation of IMC data

Grouping of chemicals into mode of action classes by automated effect pattern analysis using the zebrafish embryo toxicity test

A central element of high throughput screens for chemical effect assessment using zebrafish is the assessment and quantification of phenotypic changes. By application of an automated and more unbiased analysis of these changes using image analysis, patterns of phenotypes may be associated with the mode of action (MoA) of the exposure chemical. The aim of our study was to explore to what extent compounds can be grouped according to their anticipated toxicological or pharmacological mode of action using an automated quantitative multi-endpoint zebrafish test. Chemical-response signatures for 30 endpoints, covering phenotypic and functional features, were generated for 25 chemicals assigned to 8 broad MoA classes. Unsupervised clustering of the profiling data demonstrated that chemicals were partially grouped by their main MoA. Analysis with a supervised clustering technique such as a partial least squares discriminant analysis (PLS-DA) allowed to identify markers with a strong potential to discriminate between MoAs such as mandibular arch malformation observed for compounds interfering with retinoic acid signaling. The capacity for discriminating MoAs was also benchmarked to an available battery of in vitro toxicity data obtained from ToxCast library indicating a partially similar performance. Further, we discussed to which extent the collected dataset indicated indeed differences for compounds with presumably similar MoA or whether other factors such as toxicokinetic differences could have an important impact on the determined response patterns.

Stain-free detection of embryo polarization using deep learning

Polarization of the mammalian embryo at the right developmental time is critical for its development to term and would be valuable in assessing the potential of human embryos. However, tracking polarization requires invasive fluorescence staining, impermissible in the in vitro fertilization clinic. Here, we report the use of artificial intelligence to detect polarization from unstained time-lapse movies of mouse embryos. We assembled a dataset of bright-field movie frames from 8-cell-stage embryos, side-by-side with corresponding images of fluorescent markers of cell polarization. We then used an ensemble learning model to detect whether any bright-field frame showed an embryo before or after onset of polarization. Our resulting model has an accuracy of 85% for detecting polarization, significantly outperforming human volunteers trained on the same data (61% accuracy). We discovered that our self-learning model focuses upon the angle between cells as one known cue for compaction, which precedes polarization, but it outperforms the use of this cue alone. By compressing three-dimensional time-lapsed image data into two-dimensions, we are able to reduce data to an easily manageable size for deep learning processing. In conclusion, we describe a method for detecting a key developmental feature of embryo development that avoids clinically impermissible fluorescence staining.

Content aware multi-focus image fusion for high-magnification blood film microscopy

Automated digital high-magnification optical microscopy is key to accelerating biology research and improving pathology clinical pathways. High magnification objectives with large numerical apertures are usually preferred to resolve the fine structural details of biological samples, but they have a very limited depth-of-field. Depending on the thickness of the sample, analysis of specimens typically requires the acquisition of multiple images at different focal planes for each field-of-view, followed by the fusion of these planes into an extended depth-of-field image. This translates into low scanning speeds, increased storage space, and processing time not suitable for high-throughput clinical use. We introduce a novel content-aware multi-focus image fusion approach based on deep learning which extends the depth-of-field of high magnification objectives effectively. We demonstrate the method with three examples, showing that highly accurate, detailed, extended depth of field images can be obtained at a lower axial sampling rate, using 2-fold fewer focal planes than normally required.

Lysyl oxidase regulation and protein aldehydes in the injured newborn lung

During newborn lung injury, excessive activity of lysyl oxidases (LOXs) disrupts extracellular matrix (ECM) formation. Previous studies indicate that TGFβ activation in the O₂-injured mouse pup lung increases lysyl oxidase (LOX) expression. But how TGFβ regulates this, and whether the LOXs generate excess pulmonary aldehydes are unknown. First, we determined that O₂-mediated lung injury increases LOX protein expression in TGFβ-stimulated pup lung interstitial fibroblasts. This regulation appeared to be direct; this is because TGFβ treatment also increased LOX protein expression in isolated pup lung fibroblasts. Then using a fibroblast cell line, we determined that TGFβ stimulates LOX expression at a transcriptional level via Smad2/3-dependent signaling. LOX is translated as a pro-protein that requires secretion and extracellular cleavage before assuming amine oxidase activity and, in some cells, reuptake with nuclear localization. We found that pro-LOX is processed in the newborn mouse pup lung. Also, O₂-mediated injury was determined to increase pro-LOX secretion and nuclear LOX immunoreactivity particularly in areas populated with interstitial fibroblasts and exhibiting malformed ECM. Then, using molecular probes, we detected increased aldehyde levels in vivo in O₂-injured pup lungs, which mapped to areas of increased pro-LOX secretion in lung sections. Increased activity of LOXs plays a critical role in the aldehyde generation; an inhibitor of LOXs prevented the elevation of aldehydes in the O₂-injured pup lung. These results reveal new mechanisms of TGFβ and LOX in newborn lung disease and suggest that aldehyde-reactive probes might have utility in sensing the activation of LOXs in vivo during lung injury.

Transmural pressure signals through retinoic acid to regulate lung branching

During development, the mammalian lung undergoes several rounds of branching, the rate of which is tuned by the relative pressure of the fluid within the lumen of the lung. We carried out bioinformatics analysis of RNA-sequencing of embryonic mouse lungs cultured under physiologic or sub-physiologic transmural pressure and identified transcription factor-binding motifs near genes whose expression changes in response to pressure. Surprisingly, we found retinoic acid (RA) receptor binding sites significantly overrepresented in the promoters and enhancers of pressure-responsive genes. Consistently, increasing transmural pressure activates RA signaling, and pharmacologically inhibiting RA signaling decreases airway epithelial branching and smooth muscle wrapping. We found that pressure activates RA signaling through the mechanosensor Yap. A computational model predicts that mechanical signaling through Yap and RA affects lung branching by altering the balance between epithelial proliferation and smooth muscle wrapping, which we test experimentally. Our results reveal that transmural pressure signals through RA to balance the relative rates of epithelial growth and smooth muscle differentiation in the developing mouse lung and identify RA as a previously unreported component in the mechanotransduction machinery of embryonic tissues.

High-Throughput, Label-Free and Slide-Free Histological Imaging by Computational Microscopy and Unsupervised Learning

Rapid and high-resolution histological imaging with minimal tissue preparation has long been a challenging and yet captivating medical pursuit. Here, the authors propose a promising and transformative histological imaging method, termed computational high-throughput autofluorescence microscopy by pattern illumination (CHAMP). With the assistance of computational microscopy, CHAMP enables high-throughput and label-free imaging of thick and unprocessed tissues with large surface irregularity at an acquisition speed of 10 mm2 /10 s with 1.1-µm lateral resolution. Moreover, the CHAMP image can be transformed into a virtually stained histological image (Deep-CHAMP) through unsupervised learning within 15 s, where significant cellular features are quantitatively extracted with high accuracy. The versatility of CHAMP is experimentally demonstrated using mouse brain/kidney and human lung tissues prepared with various clinical protocols, which enables a rapid and accurate intraoperative/postoperative pathological examination without tissue processing or staining, demonstrating its great potential as an assistive imaging platform for surgeons and pathologists to provide optimal adjuvant treatment.

Segmentation and Quantitative Analysis of Epithelial Tissues

Epithelial tissues regulate exchanges with the environment. They are highly dynamic and can acquire virtually any shape. At the cellular level, they are composed of cells tightly connected by junctions. Most often epithelia are amenable to live imaging; however, the vast number of cells composing an epithelium makes large-scale studies tedious. Here, we present Tissue Analyzer (TA), an open-source tool that can be used to segment epithelia and monitor cell and tissue dynamics.

Perspective: Emerging strategies for determining atomic-resolution structures of macromolecular complexes within cells

In principle, electron cryo-tomography (cryo-ET) of thin portions of cells provides high-resolution images of the three-dimensional spatial arrangement of all members of the proteome. In practice, however, radiation damage creates a tension between recording images at many different tilt angles, but at correspondingly reduced exposure levels, versus limiting the number of tilt angles in order to improve the signal-to-noise ratio (SNR). Either way, it is challenging to read the available information out at the level of atomic structure. Here, we first review work that explores the optimal strategy for data collection, which currently seems to favor the use of a limited angular range for tilting the sample or even the use of a single image to record the high-resolution information. Looking then to the future, we point to the alternative of so-called “deconvolution microscopy”, which may be applied to tilt-series or optically-sectioned, focal series data. Recording data as a focal series has the advantage that little or no translational alignment of frames might be needed, and a three-dimensional reconstruction might require only 2/3 the number of images as does standard tomography. We also point to the unexploited potential of phase plates to increase the contrast, and thus to reduce the electron exposure levels while retaining the ability align and merge the data. In turn, using much lower exposures per image could have the advantage that high-resolution information is retained throughout the full data-set, whether recorded as a tilt series or a focal series of images.

High-Throughput in vitro Gel-Based Plate Assay to Screen for Calcium Oxalate Stone Inhibitors

OBJECTIVE

Kidney stones are a common medical condition that is increasing in prevalence worldwide. Approximately, ∼80% of urinary calculi are composed of calcium oxalate (CaOx). There is a growing interest toward identifying therapeutic compounds that can inhibit the formation of CaOx crystals. However, some chemicals (e.g., antibiotics and bacterial metabolites) may directly promote crystallization. Current knowledge is limited regarding crystal promoters and inhibitors. Thus, we have developed an in vitro gel-based diffusion model to screen for substances that directly influence CaOx crystal formation.

MATERIALS AND METHODS

We used double diffusion of sodium oxalate and calcium chloride-loaded paper disks along an agar medium to facilitate the controlled formation of monohydrate and dihydrate CaOx crystals. A third disk was used for the perpendicular diffusion of a test substance to assess its influence on CaOx crystal formation.

RESULTS

We confirmed that citrates and magnesium are effective inhibitors of CaOx crystals. We also demonstrated that 2 strains of uropathogenic Escherichia coli are able to promote crystal formation. While the other tested uropathogens and most antibiotics did not change crystal formation, ampicillin was able to reduce crystallization.

CONCLUSION

We have developed an inexpensive and high-throughput model to evaluate substances that influence CaOx crystallization.

Stain-free identification of tissue pathology using a generative adversarial network to infer nanomechanical signatures

Intraoperative frozen section analysis can be used to improve the accuracy of tumour margin estimation during cancer resection surgery through rapid processing and pathological assessment of excised tissue. Its applicability is limited in some cases due to the additional risks associated with prolonged surgery, largely from the time-consuming staining procedure. Our work uses a measurable property of bulk tissue to bypass the staining process: as tumour cells proliferate, they influence the surrounding extra-cellular matrix, and the resulting change in elastic modulus provides a signature of the underlying pathology. In this work we accurately localise atomic force microscopy measurements of human liver tissue samples and train a generative adversarial network to infer elastic modulus from low-resolution images of unstained tissue sections. Pathology is predicted through unsupervised clustering of parameters characterizing the distributions of inferred values, achieving 89% accuracy for all samples based on the nominal assessment (n = 28), and 95% for samples that have been validated by two independent pathologists through post hoc staining (n = 20). Our results demonstrate that this technique could increase the feasibility of intraoperative frozen section analysis for use during resection surgery and improve patient outcomes.

MicroHikari3D: an automated DIY digital microscopy platform with deep learning capabilities

A microscope is an essential tool in biosciences and production quality laboratories for unveiling the secrets of microworlds. This paper describes the development of MicroHikari3D, an affordable DIY optical microscopy platform with automated sample positioning, autofocus and several illumination modalities to provide a high-quality flexible microscopy tool for labs with a short budget. This proposed optical microscope design aims to achieve high customization capabilities to allow whole 2D slide imaging and observation of 3D live specimens. The MicroHikari3D motion control system is based on the entry level 3D printer kit Tronxy X1 controlled from a server running in a Raspberry Pi 4. The server provides services to a client mobile app for video/image acquisition, processing, and a high level classification task by applying deep learning models.

Biobanked Glioblastoma Patient-Derived Organoids as a Precision Medicine Model to Study Inhibition of Invasion

Glioblastoma (GBM) is highly resistant to treatment and invasion into the surrounding brain is a cancer hallmark that leads to recurrence despite surgical resection. With the emergence of precision medicine, patient-derived 3D systems are considered potentially robust GBM preclinical models. In this study, we screened a library of 22 anti-invasive compounds (i.e., NF-kB, GSK-3-B, COX-2, and tubulin inhibitors) using glioblastoma U-251 MG cell spheroids. We evaluated toxicity and invasion inhibition using a 3D Matrigel invasion assay. We next selected three compounds that inhibited invasion and screened them in patient-derived glioblastoma organoids (GBOs). We developed a platform using available macros for FIJI/ImageJ to quantify invasion from the outer margin of organoids. Our data demonstrated that a high-throughput invasion screening can be done using both an established cell line and patient-derived 3D model systems. Tubulin inhibitor compounds had the best efficacy with U-251 MG cells, however, in ex vivo patient organoids the results were highly variable. Our results indicate that the efficacy of compounds is highly related to patient intra and inter-tumor heterogeneity. These results indicate that such models can be used to evaluate personal oncology therapeutic strategies.

High-speed multifocus phase imaging in thick tissue

Phase microscopy is widely used to image unstained biological samples. However, most phase imaging techniques require transmission geometries, making them unsuited for thick sample applications. Moreover, when applied to volumetric imaging, phase imaging generally requires large numbers of measurements, often making it too slow to capture live biological processes with fast 3D index-of-refraction variations. By combining oblique back-illumination microscopy and a z-splitter prism, we perform phase imaging that is both epi-mode and multifocus, enabling high-speed 3D phase imaging in thick, scattering tissues with a single camera. We demonstrate here 3D qualitative phase imaging of blood flow in chick embryos over a field of view of 546 × 546 × 137 µm3 at speeds up to 47 Hz.

Image-based cell subpopulation identification through automated cell tracking, principal component analysis, and partitioning around medoids clustering

In vitro cell culture model systems often employ monocultures, despite the fact that cells generally exist in a diverse, heterogeneous microenvironment in vivo. In response, heterogeneous cultures are increasingly being used to study how cell phenotypes interact. However, the ability to accurately identify and characterize distinct phenotypic subpopulations within heterogeneous systems remains a major challenge. Here, we present the use of a computational, image analysis-based approach-comprising automated contour-based cell tracking for feature identification, principal component analysis for feature reduction, and partitioning around medoids for subpopulation characterization-to non-destructively and non-invasively identify functionally distinct cell phenotypic subpopulations from live-cell microscopy image data. Using a heterogeneous model system of endothelial and smooth muscle cells, we demonstrate that this approach can be applied to both mono and co-culture nuclear morphometric and motility data to discern cell phenotypic subpopulations. Morphometric clustering identified minimal difference in mono- versus co-culture, while motility clustering revealed that a portion of endothelial cells and smooth muscle cells adopt increased motility rates in co-culture that are not observed in monoculture. We anticipate that this approach using non-destructive and non-invasive imaging can be applied broadly to heterogeneous cell culture model systems to advance understanding of how heterogeneity alters cell phenotype. This work presents a computational, image-analysis-based approach-comprising automated contour-based cell tracking for feature identification, principle component analysis for feature reduction, and partitioning around medoids for subpopulation characterization-to non-destructively and non-invasively identify functionally distinct cell phenotypic subpopulations from live-cell microscopy image data.

Multifocal imaging for precise, label-free tracking of fast biological processes in 3D

Many biological processes happen on a nano- to millimeter scale and within milliseconds. Established methods such as confocal microscopy are suitable for precise 3D recordings but lack the temporal or spatial resolution to resolve fast 3D processes and require labeled samples. Multifocal imaging (MFI) allows high-speed 3D imaging but is limited by the compromise between high spatial resolution and large field-of-view (FOV), and the requirement for bright fluorescent labels. Here, we provide an open-source 3D reconstruction algorithm for multi-focal images that allows using MFI for fast, precise, label-free tracking spherical and filamentous structures in a large FOV and across a high depth. We characterize fluid flow and flagellar beating of human and sea urchin sperm with a z-precision of 0.15 µm, in a volume of 240 × 260 × 21 µm, and at high speed (500 Hz). The sampling volume allowed to follow sperm trajectories while simultaneously recording their flagellar beat. Our MFI concept is cost-effective, can be easily implemented, and does not rely on object labeling, which renders it broadly applicable.

LocalZProjector and DeProj: a toolbox for local 2D projection and accurate morphometrics of large 3D microscopy images

Background

Quantitative imaging of epithelial tissues requires bioimage analysis tools that are widely applicable and accurate. In the case of imaging 3D tissues, a common preprocessing step consists of projecting the acquired 3D volume on a 2D plane mapping the tissue surface. While segmenting the tissue cells is amenable on 2D projections, it is still very difficult and cumbersome in 3D. However, for many specimen and models used in developmental and cell biology, the complex content of the image volume surrounding the epithelium in a tissue often reduces the visibility of the biological object in the projection, compromising its subsequent analysis. In addition, the projection may distort the geometry of the tissue and can lead to strong artifacts in the morphology measurement.

Results

Here we introduce a user-friendly toolbox built to robustly project epithelia on their 2D surface from 3D volumes and to produce accurate morphology measurement corrected for the projection distortion, even for very curved tissues. Our toolbox is built upon two components. LocalZProjector is a configurable Fiji plugin that generates 2D projections and height-maps from potentially large 3D stacks (larger than 40 GB per time-point) by only incorporating signal of the planes with local highest variance/mean intensity, despite a possibly complex image content. DeProj is a MATLAB tool that generates correct morphology measurements by combining the height-map output (such as the one offered by LocalZProjector) and the results of a cell segmentation on the 2D projection, hence effectively deprojecting the 2D segmentation in 3D. In this paper, we demonstrate their effectiveness over a wide range of different biological samples. We then compare its performance and accuracy against similar existing tools.

Conclusions

We find that LocalZProjector performs well even in situations where the volume to project also contains unwanted signal in other layers. We show that it can process large images without a pre-processing step. We study the impact of geometrical distortions on morphological measurements induced by the projection. We measured very large distortions which are then corrected by DeProj, providing accurate outputs.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12915-021-01037-w).

Optical mesoscopy, machine learning, and computational microscopy enable high information content diagnostic imaging of blood films

Automated image-based assessment of blood films has tremendous potential to support clinical haematology within overstretched healthcare systems. To achieve this, efficient and reliable digital capture of the rich diagnostic information contained within a blood film is a critical first step. However, this is often challenging, and in many cases entirely unfeasible, with the microscopes typically used in haematology due to the fundamental trade-off between magnification and spatial resolution. To address this, we investigated three state-of-the-art approaches to microscopic imaging of blood films which leverage recent advances in optical and computational imaging and analysis to increase the information capture capacity of the optical microscope: optical mesoscopy, which uses a giant microscope objective (Mesolens) to enable high-resolution imaging at low magnification; Fourier ptychographic microscopy, a computational imaging method which relies on oblique illumination with a series of LEDs to capture high-resolution information; and deep neural networks which can be trained to increase the quality of low magnification, low resolution images. We compare and contrast the performance of these techniques for blood film imaging for the exemplar case of Giemsa-stained peripheral blood smears. Using computational image analysis and shape-based object classification, we demonstrate their use for automated analysis of red blood cell morphology and visualization and detection of small blood-borne parasites such as the malarial parasite Plasmodium falciparum. Our results demonstrate that these new methods greatly increase the information capturing capacity of the light microscope, with transformative potential for haematology and more generally across digital pathology. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

The Possibilities of Using Ultrasonically Activated Streams to Reduce the Risk of Foodborne Infection from Salad

In this study, we investigated the effects of an ultrasonically activated stream (UAS) on the removal of microbial contaminants from spinach leaves. The microbial loads on samples cleaned with and without UAS were enumerated using the cell culture method and compared against unwashed samples on day 0 and day 6 after cleaning. The effects of UAS cleaning on leaf quality were also examined through both macroscopic and microscopic inspection, as well as measurement of the electrolyte leakage rate. Results showed that the microbial load on samples cleaned with UAS for 2 min was significantly lower on day 6 after cleaning than on those treated without ultrasound. Comparison between the cleaning effects of UAS for 40 s versus 2 min indicated that a cleaning duration of 2 min allowed sufficient time for UAS to disaggregate and detach the microbial contamination more effectively. In this case, the induction of bacteria into a viable but non-culturable state does not affect the shelf-life test results as much as it does with a 40 s clean. UAS cleaning for 2 min did not produce significant surface damage, which can affect overall leaf quality. These findings highlight the potential of UAS systems in the salad industry to improve the microbiological quality and shelf life of salads.

The stress responsive gene ankrd1a is dynamically regulated during skeletal muscle development and upregulated following cardiac injury in border zone cardiomyocytes in adult zebrafish

Ankyrin repeat domain 1 (ANKRD1) is a functionally pleiotropic protein found in the nuclei and sarcomeres of cardiac and skeletal muscles, with a proposed role in linking myofibrilar stress and transcriptional regulation. Rapid upregulation of its expression in response to both physiological and pathological stress supports the involvement of ANKRD1 in muscle tissue adaptation and remodeling. However, the exact role of ANKRD1 remains poorly understood. To begin to investigate its function at higher resolution, we have generated and characterized a TgBAC(ankrd1a:EGFP) zebrafish line. This reporter line displays transgene expression in slow skeletal muscle fibers during development and exercise responsiveness in adult cardiac muscle. To better understand the role of Ankrd1a in pathological conditions in adult zebrafish, we assessed ankrd1a expression after cardiac ventricle cryoinjury and observed localized upregulation in cardiomyocytes in the border zone. We show that this expression in injured hearts is recapitulated by the TgBAC(ankrd1a:EGFP) reporter. Our results identify novel expression domains of ankrd1a and suggest an important role for Ankrd1a in the early stress response and regeneration of cardiac tissue. This new reporter line will help decipher the role of Ankrd1a in striated muscle stress response, including after cardiac injury.

MATISSE: a method for improved single cell segmentation in imaging mass cytometry

Background

Visualizing and quantifying cellular heterogeneity is of central importance to study tissue complexity, development, and physiology and has a vital role in understanding pathologies. Mass spectrometry-based methods including imaging mass cytometry (IMC) have in recent years emerged as powerful approaches for assessing cellular heterogeneity in tissues. IMC is an innovative multiplex imaging method that combines imaging using up to 40 metal conjugated antibodies and provides distributions of protein markers in tissues with a resolution of 1 μm² area. However, resolving the output signals of individual cells within the tissue sample, i.e., single cell segmentation, remains challenging. To address this problem, we developed MATISSE (iMaging mAss cyTometry mIcroscopy Single cell SegmEntation), a method that combines high-resolution fluorescence microscopy with the multiplex capability of IMC into a single workflow to achieve improved segmentation over the current state-of-the-art.

Results

MATISSE results in improved quality and quantity of segmented cells when compared to IMC-only segmentation in sections of heterogeneous tissues. Additionally, MATISSE enables more complete and accurate identification of epithelial cells, fibroblasts, and infiltrating immune cells in densely packed cellular areas in tissue sections. MATISSE has been designed based on commonly used open-access tools and regular fluorescence microscopy, allowing easy implementation by labs using multiplex IMC into their analysis methods.

Conclusion

MATISSE allows segmentation of densely packed cellular areas and provides a qualitative and quantitative improvement when compared to IMC-based segmentation. We expect that implementing MATISSE into tissue section analysis pipelines will yield improved cell segmentation and enable more accurate analysis of the tissue microenvironment in epithelial tissue pathologies, such as autoimmunity and cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-021-01043-y.

Avoiding tensional equilibrium in cells migrating on a matrix with cell-scale stiffness-heterogeneity

Intracellular stresses affect various cell functions, including proliferation, differentiation and movement, which are dynamically modulated in migrating cells through continuous cell-shaping and remodeling of the cytoskeletal architecture induced by spatiotemporal interactions with extracellular matrix stiffness. When cells migrate on a matrix with cell-scale stiffness-heterogeneity, which is a common situation in living tissues, what intracellular stress dynamics (ISD) emerge? In this study, to explore this issue, finite element method-based traction force microscopy was applied to cells migrating on microelastically patterned gels. Two model systems of microelastically patterned gels (stiff/soft stripe and stiff triangular patterns) were designed to characterize the effects of a spatial constraint on cell-shaping and of the presence of different types of cues to induce competing cellular taxis (usual and reverse durotaxis) on the ISD, respectively. As the main result, the prolonged fluctuation of traction stress on a whole-cell scale was markedly enhanced on single cell-size triangular stiff patterns compared with homogeneous gels. Such ISD enhancement was found to be derived from the interplay between the nomadic migration of cells to regions with different degrees of stiffness and domain shape-dependent traction force dynamics, which should be an essential factor for keeping cells far from tensional equilibrium.

Moving Toward Standardized Toxicity Testing Procedures with Particulates by Dietary Exposure of Gammarids

Ecotoxicological effect assessment of particulate materials and sparingly soluble substances is an emerging field. Current standard toxicity tests of aquatic organisms are based on soluble substances which are added to the aqueous phase. Although soluble substances distribute homogeneously, particles can form aggregates, resulting in inhomogeneous distribution and unpredictable exposure. Therefore, test scenarios need to be adapted to overcome these uncertainties. We present a dietary particle exposure tool for the toxicity testing of sparingly soluble substances or particles in combination with a standardizable food source for gammarids based on decomposition and consumption tablets (DECOTABs). Four food supplements in the DEOCOTAB formulation were compared to test their influence on the energy reserves of gammarids. Although feeding rate was constant for most supplements, mortality and energy reserves revealed clear differences. Tabs supplemented with algae-based phyll or animal protein-based trout food best met all of the requirements. Fluorescent plastic microparticles (10-65 µm) were homogenously distributed and stable in the DECOTABs. Constant feeding was observed, and the number of ingested microparticles by Gammarus roeseli was quantified in relation to the consumed food. The developed method provides a realistic and methodologically reliable uptake from the oral pathway and allows the quantification of inner exposition via feeding rate, providing a promising tool for standardized dietary exposure scenarios with particles. Environ Toxicol Chem 2021;40:1463-1476. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

A free-form patterning method enabling endothelialization under dynamic flow

Endothelialization strategies aim at protecting the surface of cardiovascular devices upon their interaction with blood by the generation and maintenance of a mature monolayer of endothelial cells. Rational engineering of the surface micro-topography at the luminal interface provides a powerful access point to support the survival of a living endothelium under the challenging hemodynamic conditions created by the implant deployment and function. Surface structuring protocols must however be adapted to the complex, non-planar architecture of the target device precluding the use of standard lithographic approaches. Here, a novel patterning method, harnessing the condensation and evaporation of water droplets on a curing liquid elastomer, is developed to introduce arrays of microscale wells on the surface of a biocompatible silicon layer. The resulting topographies support the in vitro generation of mature human endothelia and their maintenance under dynamic changes of flow direction or magnitude, greatly outperforming identical, but flat substrates. The structuring approach is additionally demonstrated on non-planar interfaces yielding comparable topographies. The intrinsically free-form patterning is therefore compatible with a complete and stable endothelialization of complex luminal interfaces in cardiovascular implants.

Efferocytosis by Paneth cells within the intestine

Apoptotic cells are quickly and efficiently engulfed and removed via the process of efferocytosis by either professional phagocytes, such as macrophages, or non-professional phagocytes, including epithelial cells.^1,2 In addition to debris removal, a key benefit of efferocytosis is that phagocytes engulfing apoptotic cells release anti-inflammatory mediators^3,4 that help reduce local tissue inflammation;⁵ conversely, accumulation of uncleared apoptotic cells predisposes to a pro-inflammatory tissue milieu.^6-8 Due to their high proliferative capacity, intestinal epithelial cells (iECs) are sensitive to inflammation, irradiation, and chemotherapy-induced DNA damage, leading to apoptosis. Mechanisms of iEC death in the context of irradiation has been studied,^9,10 but phagocytosis of dying iECs is poorly understood. Here, we identify an unexpected efferocytic role for Paneth cells, which reside in intestinal crypts and are linked to innate immunity and maintenance of the stem cell niche in the crypt.^11,12 Through a series of studies spanning in vitro efferocytosis, ex vivo intestinal organoids ("enteroids"), and in vivo Cre-mediated deletion of Paneth cells, we show that Paneth cells mediate apoptotic cell uptake of dying neighbors. The relevance of Paneth-cell-mediated efferocytosis was revealed ex vivo and in mice after low-dose cesium-137 (¹³⁷Cs) irradiation, mimicking radiation therapies given to cancer patients often causing significant apoptosis of iECs. These data advance a new concept that Paneth cells can act as phagocytes and identify another way in which Paneth cells contribute to the overall health of the intestine. These observations also have implications for individuals undergoing chemotherapy or chronic inflammatory bowel disease.

BMP heterodimers signal via distinct type I receptor class functions

TGF-β family heterodimeric ligands show increased or exclusive signaling compared to homodimeric ligands in both vertebrate and insect development as well as in therapeutically relevant processes, like osteogenesis. However, the mechanisms that differentiate heterodimer and homodimer signaling remain uncharacterized. We show that BMP antagonists do not account for the exclusive signaling of Bmp2/7 heterodimers in zebrafish development. We found that overexpressed homodimers can signal but surprisingly require two distinct type I receptors, like heterodimers, indicating a required activity of the heteromeric type I receptor complex. We further demonstrate that a canonical type I receptor function has been delegated to only one of these receptors, Acvr1. Our findings should inform both basic and translational research in multiple TGF-β family signaling contexts.

Heterodimeric TGF-β ligands outperform homodimers in a variety of developmental, cell culture, and therapeutic contexts; however, the mechanisms underlying this increased potency remain uncharacterized. Here, we use dorsal–ventral axial patterning of the zebrafish embryo to interrogate the BMP2/7 heterodimer signaling mechanism. We demonstrate that differential interactions with BMP antagonists do not account for the reduced signaling ability of homodimers. Instead, we find that while overexpressed BMP2 homodimers can signal, they require two nonredundant type I receptors, one from the Acvr1 subfamily and one from the Bmpr1 subfamily. This implies that all BMP signaling within the zebrafish gastrula, even BMP2 homodimer signaling, requires Acvr1. This is particularly surprising as BMP2 homodimers do not bind Acvr1 in vitro. Furthermore, we find that the roles of the two type I receptors are subfunctionalized within the heterodimer signaling complex, with the kinase activity of Acvr1 being essential, while that of Bmpr1 is not. These results suggest that the potency of the Bmp2/7 heterodimer arises from the ability to recruit both Acvr1 and Bmpr1 into the same signaling complex.

Recurrent neural network-based volumetric fluorescence microscopy

Volumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical, medical and life sciences. Here we report a deep learning-based volumetric image inference framework that uses 2D images that are sparsely captured by a standard wide-field fluorescence microscope at arbitrary axial positions within the sample volume. Through a recurrent convolutional neural network, which we term as Recurrent-MZ, 2D fluorescence information from a few axial planes within the sample is explicitly incorporated to digitally reconstruct the sample volume over an extended depth-of-field. Using experiments on C. elegans and nanobead samples, Recurrent-MZ is demonstrated to significantly increase the depth-of-field of a 63×/1.4NA objective lens, also providing a 30-fold reduction in the number of axial scans required to image the same sample volume. We further illustrated the generalization of this recurrent network for 3D imaging by showing its resilience to varying imaging conditions, including e.g., different sequences of input images, covering various axial permutations and unknown axial positioning errors. We also demonstrated wide-field to confocal cross-modality image transformations using Recurrent-MZ framework and performed 3D image reconstruction of a sample using a few wide-field 2D fluorescence images as input, matching confocal microscopy images of the same sample volume. Recurrent-MZ demonstrates the first application of recurrent neural networks in microscopic image reconstruction and provides a flexible and rapid volumetric imaging framework, overcoming the limitations of current 3D scanning microscopy tools.

High-resolution optical microscopy for characterising microstructural deformation in microtensile testing

Imaging surface deformation of a coupon specimen in microtensile testing with an optical microscope presents challenges due to the narrow depth of field (DoF) of optical microscopes. Materials being heterogeneous at microscopic length scale, the sample surface deforms into a complex 3D surface texture, evolving continuously as the loading increases. Because of the narrow DoF, the region that is in focus within the field of view (FoV) decreases substantially in size with the increasing out-of-plane heterogeneous deformation. To address this challenge, a method based on image blending and stabilisation of the captured image frames is proposed. Image blending combines the partial regions that are in focus from a set of successive image frames captured at different working distances from the object surface plane to construct a single image that has a large part of the FoV in focus. The blended images are then obtained at different levels of macroscopic strains, that is the global homogeneous strain, in order to characterise the evolution of the heterogeneous deformation. The image stabilisation removes any misalignments of the blended images by spatially realigning them choosing a common feature as a reference point. The validation of the proposed method with conventionally and additively manufactured stainless steel 316L (SS 316L) specimens demonstrates excellent improvement in image quality. Almost 100% of the FoV is maintained in focus regardless of the amount of out-of-plane heterogeneous deformation caused during tensile testing, which is quite remarkable for optical microscopy imaging. Consequently, the blended and stabilised images enhanced the accuracy of digital image correlation (DIC). Time-lapse videos of the deformation generated using these images captured the evolution of the slip bands and their transmission through twinning boundaries in the stainless steel microstructure. Overall, this study demonstrates the feasibility of using image-processing techniques to advance optical microscopy to image complex 3D surfaces evolving with time.

Impacts of Biological Heating and Degradation during Bale Storage on the Surface Properties of Corn Stover

The variability of chemical, physical, and mechanical properties of lignocellulosic biomass feedstocks has a major impact on the efficiency of biomass processing and conversion to fuels and chemicals. Storage conditions represent a key source of variability that may contribute to biomass quality variations from the time of harvest until delivery to the biorefinery. In some cases, substantial microbial degradation can take place during storage. In this work, we investigate how degradation during storage affects the surface texture, surface energy, and porosity of different corn stover anatomical fractions (e.g., leaf, stalk, and cob). Understanding any potential changes in surface properties is important because interparticle interactions during bioprocessing cause aggregation and blockages that lead to at least process inefficiency and at most complete equipment failure. The surface roughness and texture parameters of corn stover with variable degrees of microbial degradation were calculated directly from stereomicroscopy and scanning electron microscopy micrographs. Surface energy and porosity were measured by inverse gas chromatography. The results show differing trends in the impact of increasing biological heating and degradation depending on the specific corn stover tissue type that was analyzed. These results also indicate that biomass surface properties are scale-dependent and that the scale, which is most industrially relevant, may depend on the specific unit operation within the biorefinery being considered.

Mouse Ataxin-2 Expansion Downregulates CamKII and Other Calcium Signaling Factors, Impairing Granule-Purkinje Neuron Synaptic Strength

Spinocerebellar ataxia type 2 (SCA2) is caused by polyglutamine expansion in Ataxin-2 (ATXN2). This factor binds RNA/proteins to modify metabolism after stress, and to control calcium (Ca2+) homeostasis after stimuli. Cerebellar ataxias and corticospinal motor neuron degeneration are determined by gain/loss in ATXN2 function, so we aimed to identify key molecules in this atrophic process, as potential disease progression markers. Our Atxn2-CAG100-Knock-In mouse faithfully models features observed in patients at pre-onset, early and terminal stages. Here, its cerebellar global RNA profiling revealed downregulation of signaling cascades to precede motor deficits. Validation work at mRNA/protein level defined alterations that were independent of constant physiological ATXN2 functions, but specific for RNA/aggregation toxicity, and progressive across the short lifespan. The earliest changes were detected at three months among Ca2+ channels/transporters (Itpr1, Ryr3, Atp2a2, Atp2a3, Trpc3), IP3 metabolism (Plcg1, Inpp5a, Itpka), and Ca2+-Calmodulin dependent kinases (Camk2a, Camk4). CaMKIV-Sam68 control over alternative splicing of Nrxn1, an adhesion component of glutamatergic synapses between granule and Purkinje neurons, was found to be affected. Systematic screening of pre/post-synapse components, with dendrite morphology assessment, suggested early impairment of CamKIIα abundance together with the weakening of parallel fiber connectivity. These data reveal molecular changes due to ATXN2 pathology, primarily impacting excitability and communication.