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The respective roles of aligned collagen fiber morphology found in the extracellular matrix (ECM) of pancreatic cancer patients and cellular migration dynamics have been gaining attention because of their connection with increased aggressive phenotypes and poor prognosis. To better understand how collagen fiber morphology influences cell-matrix interactions associated with metastasis, we used Second Harmonic Generation (SHG) images from patient biopsies with Pancreatic ductal adenocarcinoma (PDAC) as models to fabricate collagen scaffolds to investigate processes associated with motility. Using the PDAC BxPC-3 metastatic cell line, we investigated single and collective cell dynamics on scaffolds of varying collagen alignment. Collective or clustered cells grown on the scaffolds with the highest collagen fiber alignment had increased E-cadherin expression and larger focal adhesion sites compared to single cells, consistent with metastatic behavior. Analysis of single cell motility revealed that the dynamics were characterized by random walk on all substrates. However, examining collective motility over different time points showed that the migration was super-diffusive and enhanced on highly aligned fibers, whereas it was hindered and sub-diffusive on un-patterned substrates. This was further supported by the more elongated morphology observed in collectively migrating cells on aligned collagen fibers. Overall, this approach allows the decoupling of single and collective cell behavior as a function of collagen alignment and shows the relative importance of collective cell behavior as well as fiber morphology in PDAC metastasis. We suggest these scaffolds can be used for further investigations of PDAC cell biology. STATEMENT OF SIGNIFICANCE: Pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate, where aligned collagen has been associated with poor prognosis. Biomimetic models representing this architecture are needed to understand complex cellular interactions. The SHG image-based models based on stromal collagen from human biopsies afford the measurements of cell morphology, cadherin and focal adhesion expression as well as detailed motility dynamics. Using a metastatic cell line, we decoupled the roles of single cell and collective cell behavior as well as that arising from aligned collagen. Our data suggests that metastatic characteristics are enhanced by increased collagen alignment and that collective cell behavior is more relevant to metastatic processes. These scaffolds provide new insight in this disease and can be a platform for further experiments such as testing drug efficacy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.)

Post-TB lung disease (PTLD) causes a significant burden of global disease. Fibrosis is a central component of many clinical features of PTLD. To date, we have a limited understanding of the mechanisms of TB-associated fibrosis and how these mechanisms are similar to or dissimilar from other fibrotic lung pathologies. We have adapted a mouse model of TB infection to facilitate the mechanistic study of TB-associated lung fibrosis. We find that the morphologies of fibrosis that develop in the mouse model are similar to the morphologies of fibrosis observed in human tissue samples. Using Second Harmonic Generation (SHG) microscopy, we are able to quantify a major component of fibrosis, fibrillar collagen, over time and with treatment. Inflammatory macrophage subpopulations persist during treatment; matrix remodeling enzymes and inflammatory gene signatures remain elevated. Our mouse model suggests that there is a therapeutic window during which adjunctive therapies could change matrix remodeling or inflammatory drivers of tissue pathology to improve functional outcomes after treatment for TB infection.

Deep learning has greatly accelerated research in biological image analysis yet it often requires programming skills and specialized tool installation. Here we present Piximi, a modern, no-programming image analysis tool leveraging deep learning. Implemented as a web application at Piximi.app, Piximi requires no installation and can be accessed by any modern web browser. Its client-only architecture preserves the security of researcher data by running all computation locally. Piximi offers four core modules: a deep learning classifier, an image annotator, measurement modules, and pre-trained deep learning segmentation modules. Piximi is interoperable with existing tools and workflows by supporting import and export of common data and model formats. The intuitive researcher interface and easy access to Piximi allows biological researchers to obtain insights into images within just a few minutes. Piximi aims to bring deep learning-powered image analysis to a broader community by eliminating barriers to entry.

Significance: To enable non-destructive longitudinal assessment of drug agents in intact tumor tissue without the use of disruptive probes, we have designed a label-free method to quantify the health of individual tumor cells in excised tumor tissue using multiphoton fluorescence lifetime imaging microscopy (MP-FLIM)., Aim: Using murine tumor fragments which preserve the native tumor microenvironment, we seek to demonstrate signals generated by the intrinsically fluorescent metabolic co-factors nicotinamide adenine dinucleotide phosphate [NAD(P)H] and flavin adenine dinucleotide (FAD) correlate with irreversible cascades leading to cell death., Approach: We use MP-FLIM of NAD(P)H and FAD on tissues and confirm viability using standard apoptosis and live/dead (Caspase 3/7 and propidium iodide, respectively) assays., Results: Through a statistical approach, reproducible shifts in FLIM data, determined through phasor analysis, are shown to correlate with loss of cell viability. With this, we demonstrate that cell death achieved through either apoptosis/necrosis or necroptosis can be discriminated. In addition, specific responses to common chemotherapeutic treatment inducing cell death were detected., Conclusions: These data demonstrate that MP-FLIM can detect and quantify cell viability without the use of potentially toxic dyes, thus enabling longitudinal multi-day studies assessing the effects of therapeutic agents on tumor fragments., (© 2024 The Authors.)

Significance: Label-free multimodal imaging methods that can provide complementary structural and chemical information from the same sample are critical for comprehensive tissue analyses. These methods are specifically needed to study the complex tumor-microenvironment where fibrillar collagen's architectural changes are associated with cancer progression. To address this need, we present a multimodal computational imaging method where mid-infrared spectral imaging (MIRSI) is employed with second harmonic generation (SHG) microscopy to identify fibrillar collagen in biological tissues., Aim: To demonstrate a multimodal approach where a morphology-specific contrast mechanism guides a mid-infrared spectral imaging method to detect fibrillar collagen based on its chemical signatures., Approach: We trained a supervised machine learning (ML) model using SHG images as ground truth collagen labels to classify fibrillar collagen in biological tissues based on their mid-infrared hyperspectral images. Five human pancreatic tissue samples (sizes are in the order of millimeters) were imaged by both MIRSI and SHG microscopes. In total, 2.8 million MIRSI spectra were used to train a random forest (RF) model. The remaining 68 million spectra were used to validate the collagen images generated by the RF-MIRSI model in terms of collagen segmentation, orientation, and alignment., Results: Compared to the SHG ground truth, the generated MIRSI collagen images achieved a high average boundary F-score (0.8 at 4 pixels threshold) in the collagen distribution, high correlation (Pearson's R 0.82) in the collagen orientation, and similarly high correlation (Pearson's R 0.66) in the collagen alignment., Conclusions: We showed the potential of ML-aided label-free mid-infrared hyperspectral imaging for collagen fiber and tumor microenvironment analysis in tumor pathology samples.

Förster resonance energy transfer (FRET) spectrometry is a method for determining the quaternary structure of protein oligomers from distributions of FRET efficiencies that are drawn from pixels of fluorescence images of cells expressing the proteins of interest. FRET spectrometry protocols currently rely on obtaining spectrally resolved fluorescence data from intensity-based experiments. Another imaging method, fluorescence lifetime imaging microscopy (FLIM), is a widely used alternative to compute FRET efficiencies for each pixel in an image from the reduction of the fluorescence lifetime of the donors caused by FRET. In FLIM studies of oligomers with different proportions of donors and acceptors, the donor lifetimes may be obtained by fitting the temporally resolved fluorescence decay data with a predetermined number of exponential decay curves. However, this requires knowledge of the number and the relative arrangement of the fluorescent proteins in the sample, which is precisely the goal of FRET spectrometry, thus creating a conundrum that has prevented users of FLIM instruments from performing FRET spectrometry. Here, we describe an attempt to implement FRET spectrometry on temporally resolved fluorescence microscopes by using an integration-based method of computing the FRET efficiency from fluorescence decay curves. This method, which we dubbed time-integrated FRET (or tiFRET), was tested on oligomeric fluorescent protein constructs expressed in the cytoplasm of living cells. The present results show that tiFRET is a promising way of implementing FRET spectrometry and suggest potential instrument adjustments for increasing accuracy and resolution in this kind of study.

Assessing cell viability is important in many fields of research. Current optical methods to assess cell viability typically involve fluorescent dyes, which are often less reliable and have poor permeability in primary tissues. Dynamic optical coherence microscopy (dOCM) is an emerging tool that provides label-free contrast reflecting changes in cellular metabolism. In this work, we compare the live contrast obtained from dOCM to viability dyes, and for the first time to our knowledge, demonstrate that dOCM can distinguish live cells from dead cells in murine syngeneic tumors. We further demonstrate a strong correlation between dOCM live contrast and optical redox ratio by metabolic imaging in primary mouse liver tissue. The dOCM technique opens a new avenue to apply label-free imaging to assess the effects of immuno-oncology agents, targeted therapies, chemotherapy, and cell therapies using live tumor tissues., Competing Interests: Elephas Biosciences Corporation has filed a provisional patent application 63/459,804 that encompasses aspects of the data described in this paper., (© 2024 Optica Publishing Group.)

The role of fibrillar collagen in the tissue microenvironment is critical in disease contexts ranging from cancers to chronic inflammations, as evidenced by many studies. Quantifying fibrillar collagen organization has become a powerful approach for characterizing the topology of collagen fibers and studying the role of collagen fibers in disease progression. We present a deep learning-based pipeline to quantify collagen fibers' topological properties in microscopy-based collagen images from pathological tissue samples. Our method leverages deep neural networks to extract collagen fiber centerlines and deep generative models to create synthetic training data, addressing the current shortage of large-scale annotations. As a part of this effort, we have created and annotated a collagen fiber centerline dataset, with the hope of facilitating further research in this field. Quantitative measurements such as fiber orientation, alignment, density, and length can be derived based on the centerline extraction results. Our pipeline comprises three stages. Initially, a variational autoencoder is trained to generate synthetic centerlines possessing controllable topological properties. Subsequently, a conditional generative adversarial network synthesizes realistic collagen fiber images from the synthetic centerlines, yielding a synthetic training set of image-centerline pairs. Finally, we train a collagen fiber centerline extraction network using both the original and synthetic data. Evaluation using collagen fiber images from pancreas, liver, and breast cancer samples collected via second-harmonic generation microscopy demonstrates our pipeline's superiority over several popular fiber centerline extraction tools. Incorporating synthetic data into training further enhances the network's generalizability. Our code is available at https://github.com/uw-loci/collagen-fiber-metrics., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Kevin W. Eliceiri reports financial support was provided by National Institutes of Health. Kevin W. Eliceiri reports financial support was provided by Semiconductor Research Corp. Kevin W. Eliceiri reports financial support was provided by Morgridge Institute for Research., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Fluorescence lifetime imaging microscopy (FLIM) provides valuable quantitative insights into fluorophores' chemical microenvironment. Due to long computation times and the lack of accessible, open-source real-time analysis toolkits, traditional analysis of FLIM data, particularly with the widely used time-correlated single-photon counting (TCSPC) approach, typically occurs after acquisition. As a result, uncertainties about the quality of FLIM data persist even after collection, frequently necessitating the extension of imaging sessions. Unfortunately, prolonged sessions not only risk missing important biological events but also cause photobleaching and photodamage. We present the first open-source program designed for real-time FLIM analysis during specimen scanning to address these challenges. Our approach combines acquisition with real-time computational and visualization capabilities, allowing us to assess FLIM data quality on the fly. Our open-source real-time FLIM viewer, integrated as a Napari plugin, displays phasor analysis and rapid lifetime determination (RLD) results computed from real-time data transmitted by acquisition software such as the open-source Micro-Manager-based OpenScan package. Our method facilitates early identification of FLIM signatures and data quality assessment by providing preliminary analysis during acquisition. This not only speeds up the imaging process, but it is especially useful when imaging sensitive live biological samples., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision, (Copyright © 2023 Tan, Tsuchida, Chacko, Gahm and Eliceiri.)

The 'Bridging Imaging Users to Imaging Analysis' survey was conducted in 2022 by the Center for Open Bioimage Analysis (COBA), BioImaging North America (BINA) and the Royal Microscopical Society Data Analysis in Imaging Section (RMS DAIM) to understand the needs of the imaging community. Through multichoice and open-ended questions, the survey inquired about demographics, image analysis experiences, future needs and suggestions on the role of tool developers and users. Participants of the survey were from diverse roles and domains of the life and physical sciences. To our knowledge, this is the first attempt to survey cross-community to bridge knowledge gaps between physical and life sciences imaging. Survey results indicate that respondents' overarching needs are documentation, detailed tutorials on the usage of image analysis tools, user-friendly intuitive software, and better solutions for segmentation, ideally in a format tailored to their specific use cases. The tool creators suggested the users familiarise themselves with the fundamentals of image analysis, provide constant feedback and report the issues faced during image analysis while the users would like more documentation and an emphasis on tool friendliness. Regardless of the computational experience, there is a strong preference for 'written tutorials' to acquire knowledge on image analysis. We also observed that the interest in having 'office hours' to get an expert opinion on their image analysis methods has increased over the years. The results also showed less-than-expected usage of online discussion forums in the imaging community for solving image analysis problems. Surprisingly, we also observed a decreased interest among the survey respondents in deep/machine learning despite the increasing adoption of artificial intelligence in biology. In addition, the community suggests the need for a common repository for the available image analysis tools and their applications. The opinions and suggestions of the community, released here in full, will help the image analysis tool creation and education communities to design and deliver the resources accordingly., (© 2023 Royal Microscopical Society.)

CellProfiler is a widely used software for creating reproducible, reusable image analysis workflows without needing to code. In addition to the >90 modules that make up the main CellProfiler program, CellProfiler has a plugins system that allows for the creation of new modules which integrate with other Python tools or tools that are packaged in software containers. The CellProfiler-plugins repository contains a number of these CellProfiler modules, especially modules that are experimental and/or dependency-heavy. Here, we present an upgraded CellProfiler-plugins repository, an example of accessing containerised tools, improved documentation and added citation/reference tools to facilitate the use and contribution of the community., (© 2023 Royal Microscopical Society.)

Objectives: Non-invasive methods for monitoring arterial health and identifying early injury to optimize treatment for patients are desirable. The objective of this study was to demonstrate the use of an adaptive Bayesian regularized Lagrangian carotid strain imaging (ABR-LCSI) algorithm for monitoring of atherogenesis in a murine model and examine associations between the ultrasound strain measures and histology., Methods: Ultrasound radiofrequency (RF) data were acquired from both the right and left common carotid artery (CCA) of 10 (5 male and 5 female) ApoE tm1Unc/J mice at 6, 16 and 24 wk. Lagrangian accumulated axial, lateral and shear strain images and three strain indices-maximum accumulated strain index (MASI), peak mean strain of full region of interest (ROI) index (PMSRI) and strain at peak axial displacement index (SPADI)-were estimated using the ABR-LCSI algorithm. Mice were euthanized (n = 2 at 6 and 16 wk, n = 6 at 24 wk) for histology examination., Results: Sex-specific differences in strain indices of mice at 6, 16 and 24 wk were observed. For male mice, axial PMSRI and SPADI changed significantly from 6 to 24 wk (mean axial PMSRI at 6 wk = 14.10 ± 5.33% and that at 24 wk = -3.03 ± 5.61%, p < 0.001). For female mice, lateral MASI increased significantly from 6 to 24 wk (mean lateral MASI at 6 wk = 10.26 ± 3.13% and that at 24 wk = 16.42 ± 7.15%, p = 0.048). Both cohorts exhibited strong associations with ex vivo histological findings (male mice: correlation between number of elastin fibers and axial PMSRI: r s  = 0.83, p = 0.01; female mice: correlation between shear MASI and plaque score: r s  = 0.77, p = 0.009)., Conclusion: The results indicate that ABR-LCSI can be used to measure arterial wall strain in a murine model and that changes in strain are associated with changes in arterial wall structure and plaque formation., (Copyright © 2023 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

As biological imaging continues to rapidly advance, it results in increasingly complex image data, necessitating a reevaluation of conventional bioimage analysis methods and their accessibility. This perspective underscores our belief that a transition from desktop-based tools to web-based bioimage analysis could unlock immense opportunities for improved accessibility, enhanced collaboration, and streamlined workflows. We outline the potential benefits, such as reduced local computational demands and solutions to common challenges, including software installation issues and limited reproducibility. Furthermore, we explore the present state of web-based tools, hurdles in implementation, and the significance of collective involvement from the scientific community in driving this transition. In acknowledging the potential roadblocks and complexity of data management, we suggest a combined approach of selective prototyping and large-scale workflow application for optimal usage. Embracing web-based bioimage analysis could pave the way for the life sciences community to accelerate biological research, offering a robust platform for a more collaborative, efficient, and democratized science., Competing Interests: WO is a co-founder of Amun AI AB, a commercial company that builds, delivers, supports and integrates AI-powered data management systems for academic, biotech and pharmaceutical industries. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Ouyang, Eliceiri and Cimini.)

Objective: In humans, arterial grayscale ultrasound texture features independently predict adverse cardiovascular disease (CVD) events and change with medical interventions. We performed this study to examine how grayscale ultrasound texture features and elastin fibers change in plaque-free segments of the arterial wall in a murine model prone to atherosclerosis., Methods: A total of 10 Apoe tm1Unc/J mice ( n  = 5 male, n  = 5 female) were imaged at 6, 16, and 24 weeks of age. Two mice were euthanized at 6 and 16 weeks and the remaining mice at 24 weeks. Texture features were extracted from the ultrasound images of the distal 1.0 mm of the common carotid artery wall, and elastin measures were extracted from histology images. Two-way analysis of variance was used to evaluate associations between week, sex, and grayscale texture features. Texture feature and elastin number comparisons between weeks were conducted using the sex-by-week two-way interaction contrasts. Sex-specific correlations between the number of elastin fibers and grayscale texture features were analyzed by conducting non-parametric Spearman's rank correlation analyses., Results: Arterial wall homogeneity changed significantly in male mice from 6 to 24 weeks, with a mean (SD) of 0.14 (0.03) units at 6 weeks and 0.18 (0.03) units at 24 weeks ( p  = 0.026). Spatial gray level dependence matrices-homogeneity (SGLD-HOM) also correlated with carotid artery plaque score ( r s  = 0.707, p  = 0.033). Elastin fibers in the region of interest decreased from 6 to 24 weeks for both male and female mice, although only significantly in male mice. The mean (SD) number of elastin fibers for male mice was 5.32 (1.50) at 6 weeks and 3.59 (0.38) at 24 weeks ( p  = 0.023). For female mice, the mean (SD) number of elastin fibers was 3.98 (0.38) at 6 weeks and 3.46 (0.19) at 24 weeks ( p  = 0.051)., Conclusion: Grayscale ultrasound texture features that are associated with increased risk for CVD events in humans were used in a murine model, and the grayscale texture feature SGLD-HOM was shown to change in male mice from 6 weeks to 24 weeks. Structural alterations of the arterial wall (change in elastin fiber number) were observed during this time and may differ by sex., Competing Interests: CM: W. L. Gore & Associates contracted research grants to the University of Wisconsin-Madison, consulting Acoustic Range Estimates. TV: Research agreement with Fujifilm VisualSonics (Toronto, CA) to acquire photoacoustic radiofrequency data not used in this paper. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Mitchell, Al Mukaddim, Liu, Graham, Eickhoff, Weichmann, Tattersall, Korcarz, Stein, Varghese and Eliceiri.)

Label-free and nondestructive mid-infrared vibrational hyperspectral imaging is an essential tissue analysis tool, providing spatially resolved biochemical information critical to understanding physiological and pathological processes. However, the chemically complex and spatially heterogeneous composition of tissue specimens and the inherently weak interaction of infrared light with biomolecules limit the analytical performance of infrared absorption spectroscopy. Here, an advanced mid-infrared spectrochemical tissue imaging modality is introduced using metasurfaces that support strong surface-localized electromagnetic fields to capture quantitative molecular maps of large-area murine brain tissue sections. The approach leverages polarization-multiplexed multi-resonance plasmonic metasurfaces to simultaneously detect various functional biomolecules. The surface-enhanced mid-infrared spectral imaging method eliminates the non-specific effects of bulk tissue morphology on quantitative spectral analysis and improves chemical selectivity. This study shows that metasurface enhancement increases the retrieval of amide I and II bands associated with protein secondary structures. Moreover, it is demonstrated that plasmonic metasurfaces enhance the chemical contrast in infrared images and enable detection of ultrathin tissue regions that are not otherwise visible to conventional mid-infrared spectral imaging. While this work uses murine brain tissue sections, the chemical imaging method is well-suited for other tissue types, which broadens its potential impact for translational research and clinical histopathology., (© 2023 The Authors. Advanced Materials published by Wiley-VCH GmbH.)

Technological advancements in biology and microscopy have empowered a transition from bioimaging as an observational method to a quantitative one. However, as biologists are adopting quantitative bioimaging and these experiments become more complex, researchers need additional expertise to carry out this work in a rigorous and reproducible manner. This Essay provides a navigational guide for experimental biologists to aid understanding of quantitative bioimaging from sample preparation through to image acquisition, image analysis, and data interpretation. We discuss the interconnectedness of these steps, and for each, we provide general recommendations, key questions to consider, and links to high-quality open-access resources for further learning. This synthesis of information will empower biologists to plan and execute rigorous quantitative bioimaging experiments efficiently., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Senft et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Significance: Fluorescence lifetime imaging microscopy (FLIM) of the metabolic co-enzyme nicotinamide adenine dinucleotide (phosphate) [NAD(P)H] is a popular method to monitor single-cell metabolism within unperturbed, living 3D systems. However, FLIM of NAD(P)H has not been performed in a light-sheet geometry, which is advantageous for rapid imaging of cells within live 3D samples., Aim: We aim to design, validate, and demonstrate a proof-of-concept light-sheet system for NAD(P)H FLIM., Approach: A single-photon avalanche diode camera was integrated into a light-sheet microscope to achieve optical sectioning and limit out-of-focus contributions for NAD(P)H FLIM of single cells., Results: An NAD(P)H light-sheet FLIM system was built and validated with fluorescence lifetime standards and with time-course imaging of metabolic perturbations in pancreas cancer cells with 10 s integration times. NAD(P)H light-sheet FLIM in vivo was demonstrated with live neutrophil imaging in a larval zebrafish tail wound also with 10 s integration times. Finally, the theoretical and practical imaging speeds for NAD(P)H FLIM were compared across laser scanning and light-sheet geometries, indicating a 30 × to 6 × acquisition speed advantage for the light sheet compared to the laser scanning geometry., Conclusions: FLIM of NAD(P)H is feasible in a light-sheet geometry and is attractive for 3D live cell imaging applications, such as monitoring immune cell metabolism and migration within an organism., (© 2023 The Authors.)

Image sensors capable of capturing individual photons have made tremendous progress in recent years. However, this technology faces a major limitation. Because they capture scene information at the individual photon level, the raw data is sparse and noisy. Here we propose CASPI: Collaborative Photon Processing for Active Single-Photon Imaging, a technology-agnostic, application-agnostic, and training-free photon processing pipeline for emerging high-resolution single-photon cameras. By collaboratively exploiting both local and non-local correlations in the spatio-temporal photon data cubes, CASPI estimates scene properties reliably even under very challenging lighting conditions. We demonstrate the versatility of CASPI with two applications: LiDAR imaging over a wide range of photon flux levels, from a sub-photon to high ambient regimes, and live-cell autofluorescence FLIM in low photon count regimes. We envision CASPI as a basic building block of general-purpose photon processing units that will be implemented on-chip in future single-photon cameras., (© 2023. The Author(s).)

Hepatitis B virus (HBV) capsid assembly is traditionally thought to occur predominantly in the cytoplasm, where the virus gains access to the virion egress pathway. To better define sites of HBV capsid assembly, we carried out single cell imaging of HBV Core protein (Cp) subcellular trafficking over time under conditions supporting genome packaging and reverse transcription in Huh7 hepatocellular carcinoma cells. Time-course analyses including live cell imaging of fluorescently tagged Cp derivatives showed Cp to accumulate in the nucleus at early time points (~24 h), followed by a marked re-distribution to the cytoplasm at 48 to 72 h. Nucleus-associated Cp was confirmed to be capsid and/or high-order assemblages using a novel dual label immunofluorescence strategy. Nuclear-to-cytoplasmic re-localization of Cp occurred predominantly during nuclear envelope breakdown in conjunction with cell division, followed by strong cytoplasmic retention of Cp. Blocking cell division resulted in strong nuclear entrapment of high-order assemblages. A Cp mutant, Cp-V124W, predicted to exhibit enhanced assembly kinetics, also first trafficked to the nucleus to accumulate at nucleoli, consistent with the hypothesis that Cp's transit to the nucleus is a strong and constitutive process. Taken together, these results provide support for the nucleus as an early-stage site of HBV capsid assembly, and provide the first dynamic evidence of cytoplasmic retention after cell division as a mechanism underpinning capsid nucleus-to-cytoplasm relocalization. IMPORTANCE Hepatitis B virus (HBV) is an enveloped, reverse-transcribing DNA virus that is a major cause of liver disease and hepatocellular carcinoma. Subcellular trafficking events underpinning HBV capsid assembly and virion egress remain poorly characterized. Here, we developed a combination of fixed and long-term (>24 h) live cell imaging technologies to study the single cell trafficking dynamics of the HBV Core Protein (Cp). We demonstrate that Cp first accumulates in the nucleus, and forms high-order structures consistent with capsids, with the predominant route of nuclear egress being relocalization to the cytoplasm during cell division in conjunction with nuclear membrane breakdown. Single cell video microscopy demonstrated unequivocally that Cp's localization to the nucleus is constitutive. This study represents a pioneering application of live cell imaging to study HBV subcellular transport, and demonstrates links between HBV Cp and the cell cycle.

The ubiquitin-binding NBR1 autophagy receptor plays a prominent role in recognizing ubiquitylated protein aggregates for vacuolar degradation by macroautophagy. Here, we show that upon exposing Arabidopsis plants to intense light, NBR1 associates with photodamaged chloroplasts independently of ATG7, a core component of the canonical autophagy machinery. NBR1 coats both the surface and interior of chloroplasts, which is then followed by direct engulfment of the organelles into the central vacuole via a microautophagy-type process. The relocalization of NBR1 into chloroplasts does not require the chloroplast translocon complexes embedded in the envelope but is instead greatly enhanced by removing the self-oligomerization mPB1 domain of NBR1. The delivery of NBR1-decorated chloroplasts into vacuoles depends on the ubiquitin-binding UBA2 domain of NBR1 but is independent of the ubiquitin E3 ligases SP1 and PUB4, known to direct the ubiquitylation of chloroplast surface proteins. Compared to wild-type plants, nbr1 mutants have altered levels of a subset of chloroplast proteins and display abnormal chloroplast density and sizes upon high light exposure. We postulate that, as photodamaged chloroplasts lose envelope integrity, cytosolic ligases reach the chloroplast interior to ubiquitylate thylakoid and stroma proteins which are then recognized by NBR1 for autophagic clearance. This study uncovers a new function of NBR1 in the degradation of damaged chloroplasts by microautophagy., Competing Interests: HL, JC, AG, KC, JB, SS, AM, RV, KE, MO No competing interests declared, (© 2023, Lee et al.)

Significance: Advanced digital control of microscopes and programmable data acquisition workflows have become increasingly important for improving the throughput and reproducibility of optical imaging experiments. Combinations of imaging modalities have enabled a more comprehensive understanding of tissue biology and tumor microenvironments in histopathological studies. However, insufficient imaging throughput and complicated workflows still limit the scalability of multimodal histopathology imaging., Aim: We present a hardware-software co-design of a whole slide scanning system for high-throughput multimodal tissue imaging, including brightfield (BF) and laser scanning microscopy., Approach: The system can automatically detect regions of interest using deep neural networks in a low-magnification rapid BF scan of the tissue slide and then conduct high-resolution BF scanning and laser scanning imaging on targeted regions with deep learning-based run-time denoising and resolution enhancement. The acquisition workflow is built using Pycro-Manager, a Python package that bridges hardware control libraries of the Java-based open-source microscopy software Micro-Manager in a Python environment., Results: The system can achieve optimized imaging settings for both modalities with minimized human intervention and speed up the laser scanning by an order of magnitude with run-time image processing., Conclusions: The system integrates the acquisition pipeline and data analysis pipeline into a single workflow that improves the throughput and reproducibility of multimodal histopathological imaging., (© 2023 The Authors.)

Whole slide image (WSI) analysis is increasingly being adopted as an important tool in modern pathology. Recent deep learning-based methods have achieved state-of-the-art performance on WSI analysis tasks such as WSI classification, segmentation, and retrieval. However, WSI analysis requires a significant amount of computation resources and computation time due to the large dimensions of WSIs. Most of the existing analysis approaches require the complete decompression of the whole image exhaustively, which limits the practical usage of these methods, especially for deep learning-based workflows. In this paper, we present compression domain processing-based computation efficient analysis workflows for WSIs classification that can be applied to state-of-the-art WSI classification models. The approaches leverage the pyramidal magnification structure of WSI files and compression domain features that are available from the raw code stream. The methods assign different decompression depths to the patches of WSIs based on the features directly retained from compressed patches or partially decompressed patches. Patches from the low-magnification level are screened by attention-based clustering, resulting in different decompression depths assigned to the high-magnification level patches at different locations. A finer-grained selection based on compression domain features from the file code stream is applied to select further a subset of the high-magnification patches that undergo a full decompression. The resulting patches are fed to the downstream attention network for final classification. Computation efficiency is achieved by reducing unnecessary access to the high zoom level and expensive full decompression. With the number of decompressed patches reduced, the time and memory costs of downstream training and inference procedures are also significantly reduced. Our approach achieves a 7.2× overall speedup, and the memory cost is reduced by 1.1 orders of magnitudes, while the resulting model accuracy is comparable to the original workflow., Competing Interests: The authors declare no conflicts of interest., (© 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.)

With recent advances in cancer therapeutics, there is a great need for improved imaging methods for characterizing cancer onset and progression in a quantitative and actionable way. Collagen, the most abundant extracellular matrix protein in the tumor microenvironment (and the body in general), plays a multifaceted role, both hindering and promoting cancer invasion and progression. Collagen deposition can defend the tumor with immunosuppressive effects, while aligned collagen fiber structures can enable tumor cell migration, aiding invasion and metastasis. Given the complex role of collagen fiber organization and topology, imaging has been a tool of choice to characterize these changes on multiple spatial scales, from the organ and tumor scale to cellular and subcellular level. Macroscale density already aids in the detection and diagnosis of solid cancers, but progress is being made to integrate finer microscale features into the process. Here we review imaging modalities ranging from optical methods of second harmonic generation (SHG), polarized light microscopy (PLM), and optical coherence tomography (OCT) to the medical imaging approaches of ultrasound and magnetic resonance imaging (MRI). These methods have enabled scientists and clinicians to better understand the impact collagen structure has on the tumor environment, at both the bulk scale (density) and microscale (fibrillar structure) levels. We focus on imaging methods with the potential to both examine the collagen structure in as natural a state as possible and still be clinically amenable, with an emphasis on label-free strategies, exploiting intrinsic optical properties of collagen fibers., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Microglia are the immune cell in the central nervous system (CNS) and exist in a surveillant state characterized by a ramified form in the healthy brain. In response to brain injury or disease including neurodegenerative diseases, they become activated and change their morphology. Due to known correlation between this activation and neuroinflammation, there is great interest in improved approaches for studying microglial activation in the context of CNS disease mechanisms. One classic approach has utilized Microglia's morphology as one of the key indicators of its activation and correlated with its functional state. More recently microglial activation has been shown to have intrinsic NADH metabolic signatures that are detectable via fluorescence lifetime imaging (FLIM). Despite the promise of morphology and metabolism as key fingerprints of microglial function, they has not been analyzed together due to lack of an appropriate computational framework. Here we present a deep neural network to study the effect of both morphology and FLIM metabolic signatures toward identifying its activation status. Our model is tested on 1, 000+ cells (ground truth generated using LPS treatment) and provides a state-of-the-art framework to identify microglial activation and its role in neurodegenerative diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Mukherjee, Sagar, Ouellette, Watters and Eliceiri.)

Introduction: Hypospadias repair is an index pediatric urology procedure that requires trainee familiarity with surgical loupes. A previous low-fidelity, 6-step curriculum was proposed that deconstructed the most important steps of loupe surgery. We expanded on this curriculum with an intermediate-fidelity silicone hypospadias model and designed an abbreviated version of the 6-step curriculum to precede the hypospadias repair simulation., Objective: To assess the validity of our prior, low-fidelity conceptual model using the metric of improved performance on the intermediate-fidelity silicone hypospadias model., Study Design: A silicone model was first prototyped with the design software Solidworks™, and then fabricated using a cast made of a mixture of silicone rubbers designed to function like skin and soft tissue (Mold Star 20T, Dragon skin FX-pro and Slacker). Casts were used to create the penile shaft model and the dorsal hooded foreskin model. The urethral plate was cast separately on a flat surface. The model was then assembled by hand. The model used for simulation included the penile shaft and urethral plate, while the dorsal-hooded foreskin was prepared to simulate the penile anatomy separately. Trainees were then divided into two groups. Group 1 practiced the low-fidelity curriculum (3 tasks) and then performed dissection of the urethral plate and suturing using the intermediate-fidelity hypospadias model. Group 2 practiced hypospadias repair prior to the low-fidelity curriculum. Both groups' models were scored by 3 blinded urologists. Trainees were then asked to complete a post simulation satisfaction survey. Data analysis was performed in IBM SPSS Statistics for Macintosh (Version 28.0 Armonk, NY: IBM Corp)., Results: Twenty-two candidates across Wisconsin, USA, and Dublin, Ireland participated in the study. This included 7 s-year residents, 9 third-year residents, 2 fourth-year residents, and 3 fifth-year residents. Both Groups 1 and 2 had a similar distribution of trainees (p = 0.60). Group 1 outperformed group 2 in all tasks (p < 0.05, Table 1). Trainees reported that the platform was very useful (91%)., Discussion: Our curriculum showed improvement in trainee ability and comfort to perform hypospadias repair. Advantages of such a simulated curriculum include improving current resident training in microsurgery, improving surgical ergonomics for trainees prior to real-time experience, and decreasing the learning curve for trainees pursuing pediatric urology., Conclusion: An intermediate-fidelity hypospadias platform externally validates the conceptual model implemented in the low-fidelity loupes curriculum. This appears to lead to improvement in loupe surgical skills regardless of trainee level., Competing Interests: Conflicts of interest The authors have no relevant conflicts of interest to disclose. Dr. Farhat serves as Chief Financial Officer of Neocirc.org., (Copyright © 2022 Journal of Pediatric Urology Company. Published by Elsevier Ltd. All rights reserved.)

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal human cancers. However, the symptoms and radiographic appearance of chronic pancreatitis (CP) mimics that of PDAC, and sometimes the 2 entities can also be difficult to differentiate microscopically. The need for accurate differentiation of PDAC and CP has become a major topic in pancreatic pathology. These 2 diseases can present similar histomorphological features, such as excessive deposition of fibrotic stroma in the tissue microenvironment and inflammatory cell infiltration. In this paper, we present a quantitative analysis pipeline empowered by graph neural networks (GNN) capable of automatic detection and differentiation of PDAC and CP in human histological specimens. Modeling histological images as graphs and deploying graph convolutions can enable the capture of histomorphological features at different scales, ranging from nuclear size to the organization of ducts. The analysis pipeline combines image features computed from co-registered hematoxylin and eosin (H&E) images and Second-Harmonic Generation (SHG) microscopy images, with the SHG images enabling the extraction of collagen fiber morphological features. Evaluating the analysis pipeline on a human tissue micro-array dataset consisting of 786 cores and a tissue region dataset consisting of 268 images, it attained 86.4% accuracy with an average area under the curve (AUC) of 0.954 and 88.9% accuracy with an average AUC of 0.957, respectively. Moreover, incorporating topological features of collagen fibers computed from SHG images into the model further increases the classification accuracy on the tissue region dataset to 91.3% with an average AUC of 0.962, suggesting that collagen characteristics are diagnostic features in PDAC and CP detection and differentiation., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s).)

Development of elaborate and polarized neuronal morphology requires precisely regulated transport of cellular cargos by motor proteins such as kinesin-1. Kinesin-1 has numerous cellular cargos which must be delivered to unique neuronal compartments. The process by which this motor selectively transports and delivers cargo to regulate neuronal morphogenesis is poorly understood, although the cargo-binding kinesin light chain (KLC) subunits contribute to specificity. Our work implicates one such subunit, KLC4, as an essential regulator of axon branching and arborization pattern of sensory neurons during development. Using live imaging approaches in klc4 mutant zebrafish, we show that KLC4 is required for stabilization of nascent axon branches, proper microtubule (MT) dynamics, and endosomal transport. Furthermore, KLC4 is required for proper tiling of peripheral axon arbors: in klc4 mutants, peripheral axons showed abnormal fasciculation, a behavior characteristic of central axons. This result suggests that KLC4 patterns axonal compartments and helps establish molecular differences between central and peripheral axons. Finally, we find that klc4 mutant larva are hypersensitive to touch and adults show anxiety-like behavior in a novel tank test, implicating klc4 as a new gene involved in stress response circuits., Competing Interests: EH, KB, JH, MJ, MJ, JH, MH No competing interests declared, KE is a consultant for Bruker, the manufacturer of the Opterra swept field confocal used in this work, (© 2022, Haynes et al.)

Single-cell imaging has emerged as a powerful means to study viral replication dynamics and identify sites of virus−host interactions. Multivariate aspects of viral replication cycles yield challenges inherent to handling large, complex imaging datasets. Herein, we describe the design and implementation of an automated, imaging-based strategy, “Human Immunodeficiency Virus Red-Green-Blue” (HIV RGB), for deriving comprehensive single-cell measurements of HIV-1 unspliced (US) RNA nuclear export, translation, and bulk changes to viral RNA and protein (HIV-1 Rev and Gag) subcellular distribution over time. Differentially tagged fluorescent viral RNA and protein species are recorded using multicolor long-term (>24 h) time-lapse video microscopy, followed by image processing using a new open-source computational imaging workflow dubbed “Nuclear Ring Segmentation Analysis and Tracking” (NR-SAT) based on ImageJ plugins that have been integrated into the Konstanz Information Miner (KNIME) analytics platform. We describe a typical HIV RGB experimental setup, detail the image acquisition and NR-SAT workflow accompanied by a step-by-step tutorial, and demonstrate a use case wherein we test the effects of perturbing subcellular localization of the Rev protein, which is essential for viral US RNA nuclear export, on the kinetics of HIV-1 late-stage gene regulation. Collectively, HIV RGB represents a powerful platform for single-cell studies of HIV-1 post-transcriptional RNA regulation. Moreover, we discuss how similar NR-SAT-based design principles and open-source tools might be readily adapted to study a broad range of dynamic viral or cellular processes.

Rodents have been the dominant animal models in neurobiology and neurological disease research over the past 60 years. The prevalent use of rats and mice in neuroscience research has been driven by several key attributes including their organ physiology being more similar to humans, the availability of a broad variety of behavioral tests and genetic tools, and widely accessible reagents. However, despite the many advances in understanding neurobiology that have been achieved using rodent models, there remain key limitations in the questions that can be addressed in these and other mammalian models. In particular, in vivo imaging in mammals at the cell-resolution level remains technically difficult and demands large investments in time and cost. The simpler nervous systems of many non-mammalian models allow for precise mapping of circuits and even the whole brain with impressive subcellular resolution. The types of non-mammalian neuroscience models available spans vertebrates and non-vertebrates, so that an appropriate model for most cell biological questions in neurodegenerative disease likely exists. A push to diversify the models used in neuroscience research could help address current gaps in knowledge, complement existing rodent-based bodies of work, and bring new insight into our understanding of human disease. Moreover, there are inherent aspects of many non-mammalian models such as lifespan and tissue transparency that can make them specifically advantageous for neuroscience studies. Crispr/Cas9 gene editing and decreased cost of genome sequencing combined with advances in optical microscopy enhances the utility of new animal models to address specific questions. This review seeks to synthesize current knowledge of established and emerging non-mammalian model organisms with advances in cellular-resolution in vivo imaging techniques to suggest new approaches to understand neurodegeneration and neurobiological processes. We will summarize current tools and in vivo imaging approaches at the single cell scale that could help lead to increased consideration of non-mammalian models in neuroscience research., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Haynes, Ulland and Eliceiri.)

With an increase in subject knowledge expertise required to solve specific biological questions, experts from different fields need to collaborate to address increasingly complex issues. To successfully collaborate, everyone involved in the collaboration must take steps to " meet in the middle ". We thus present a guide on truly cross-disciplinary work using bioimage analysis as a showcase, where it is required that the expertise of biologists, microscopists, data analysts, clinicians, engineers, and physicists meet. We discuss considerations and best practices from the perspective of both users and technology developers, while offering suggestions for working together productively and how this can be supported by institutes and funders. Although this guide uses bioimage analysis as an example, the guiding principles of these perspectives are widely applicable to other cross-disciplinary work., Competing Interests: 7Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

In this paper, we summarize a global survey of 484 participants of the imaging community, conducted in 2020 through the NIH Center for Open BioImage Analysis (COBA). This 23-question survey covered experience with image analysis, scientific background and demographics, and views and requests from different members of the imaging community. Through open-ended questions we asked the community to provide feedback for the open-source tool developers and tool user groups. The community's requests for tool developers include general improvement of tool documentation and easy-to-follow tutorials. Respondents encourage tool users to follow the best practices guidelines for imaging and ask their image analysis questions on the Scientific Community Image forum (forum.image.sc). We analyzed the community's preferred method of learning, based on level of computational proficiency and work description. In general, written step-by-step and video tutorials are preferred methods of learning by the community, followed by interactive webinars and office hours with an expert. There is also enthusiasm for a centralized location online for existing educational resources. The survey results will help the community, especially developers, trainers, and organizations like COBA, decide how to structure and prioritize their efforts., Competing Interests: Competing interests The authors declare that there are no competing interests associated with the manuscript.

Background: Elevated mammographic breast density is a strong breast cancer risk factor with poorly understood etiology. Increased deposition of collagen, one of the main fibrous proteins present in breast stroma, has been associated with increased mammographic density. Collagen fiber architecture has been linked to poor outcomes in breast cancer. However, relationships of quantitative collagen fiber features assessed in diagnostic biopsies with mammographic density and lesion severity are not well-established., Methods: Clinically indicated breast biopsies from 65 in situ or invasive breast cancer cases and 73 frequency matched-controls with a benign biopsy result were used to measure collagen fiber features (length, straightness, width, alignment, orientation and density (fibers/µm 2 )) using second harmonic generation microscopy in up to three regions of interest (ROIs) per biopsy: normal, benign breast disease, and cancer. Local and global mammographic density volumes were quantified in the ipsilateral breast in pre-biopsy full-field digital mammograms. Associations of fibrillar collagen features with mammographic density and severity of biopsy diagnosis were evaluated using generalized estimating equation models with an independent correlation structure to account for multiple ROIs within each biopsy section., Results: Collagen fiber density was positively associated with the proportion of stroma on the biopsy slide (p < 0.001) and with local percent mammographic density volume at both the biopsy target (p = 0.035) and within a 2 mm perilesional ring (p = 0.02), but not with global mammographic density measures. As severity of the breast biopsy diagnosis increased at the ROI level, collagen fibers tended to be less dense, shorter, straighter, thinner, and more aligned with one another (p < 0.05)., Conclusions: Collagen fiber density was positively associated with local, but not global, mammographic density, suggesting that collagen microarchitecture may not translate into macroscopic mammographic features. However, collagen fiber features may be markers of cancer risk and/or progression among women referred for biopsy based on abnormal breast imaging., (© 2021. The Author(s).)

An open source remote monitoring system is designed and built to address the needs of researchers to provide basic illuminated visual indication of laser operation for university research laboratories that are equipped with multiple types of high-powered lasers and have limited financial resources. The 3D printed remote monitoring system selectively monitors either the total current running through a laser or a TTL shutter signal to wirelessly indicate at the laboratory entrances that a laser is in use. Several lasers can be monitored in a single room and each room entrance can have its own wireless laser activity indicator. The wireless feature eliminates the expense of in-wall wiring for the system. An emergency shut off switch is included as an optional attachment. This article describes the design of the readily deployed open source laser monitoring system, including how it was built and tested for integration into a microscopy research laboratory., Competing Interests: Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Over the past two decades, fibrillar collagen reorganization parameters such as the amount of collagen deposition, fiber angle and alignment have been widely explored in numerous studies. These parameters are now widely accepted as stromal biomarkers and linked to disease progression and survival time in several cancer types. Despite all these advances, there has not been a significant effort to make it possible for clinicians to explore these biomarkers without adding steps to the clinical workflow or by requiring high-cost imaging systems. In this paper, we evaluate previously described polychromatic polarization microscope (PPM) to visualize collagen fibers with an optically generated color representation of fiber orientation and alignment when inspecting the sample by a regular microscope with minor modifications. This system does not require stained slides, but is compatible with histological stains such as H&E. Consequently, it can be easily accommodated as part of regular pathology review of tissue slides, while providing clinically useful insight into stromal composition., (© 2021. The Author(s).)

ImageJ provides a framework for image processing across scientific domains while being fully open source. Over the years ImageJ has been substantially extended to support novel applications in scientific imaging as they emerge, particularly in the area of biological microscopy, with functionality made more accessible via the Fiji distribution of ImageJ. Within this software ecosystem, work has been done to extend the accessibility of ImageJ to utilize scripting, macros, and plugins in a variety of programming scenarios, e.g., from Groovy and Python and in Jupyter notebooks and cloud computing. We provide five protocols that demonstrate the extensibility of ImageJ for various workflows in image processing. We focus first on Fluorescence Lifetime Imaging Microscopy (FLIM) data, since this requires significant processing to provide quantitative insights into the microenvironments of cells. Second, we show how ImageJ can now be utilized for common image processing techniques, specifically image deconvolution and inversion, while highlighting the new, built-in features of ImageJ-particularly its capacity to run completely headless and the Ops matching feature that selects the optimal algorithm for a given function and data input, thereby enabling processing speedup. Collectively, these protocols can be used as a basis for automating biological image processing workflows. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Using PyImageJ for FLIM data processing Alternate Protocol: Groovy FLIMJ in Jupyter Notebooks Basic Protocol 2: Using ImageJ Ops for image deconvolution Support Protocol 1: Using ImageJ Ops matching feature for image inversion Support Protocol 2: Headless ImageJ deconvolution., (© 2021 Wiley Periodicals LLC.)

Background:  The skills required for supermicrosurgery are hard-earned and difficult to master. The University of Wisconsin "blue-blood" chicken thigh model incorporates perfusion of the thigh vessels with a blue liquid solution, allowing users to visualize flow across their anastomoses. This model has proven to be an excellent source of small vessels (down to 0.3 mm) but assessing the quality of anastomoses at this spatial scale has proven difficult. We evaluated whether fluorescent imaging with indocyanine green (ICG) in this realistic training model would enhance the assessment of supermicrosurgical anastomoses, and therefore improve real-time feedback to trainees., Methods:  Anastomoses of vessels ranging from 0.35 to 0.55mm in diameter were performed followed by the capture of white light with and without fluorescence imaging overlay during infusion of "blue-blood" and ICG. Videos were randomized and shown to seven fellowship-trained microsurgeons at the University of Wisconsin-Madison who rated each anastomosis as "patent," "not patent," or "unsure." Surgeon accuracy, uncertainty, and inter-rater agreement were measured for each imaging modality., Results:  Use of fluorescence significantly increased surgeon accuracy to 91% compared with 47% with white light alone ( p  = 0.015), decreased surgeon uncertainty to 4% compared with 41% with white light alone ( p  = 0.011), and improved inter-rater agreement from 53.1% with white light alone to 91.8% ( p  = 0.016)., Conclusion:  Augmentation of the University of Wisconsin "blue-blood" chicken thigh model with ICG fluorescence improves accuracy, decreases uncertainty, and improves inter-rater agreement when assessing supermicrosurgical anastomoses in a training setting. This improved, real-time feedback enhances this model's value as a supermicrosurgical training tool., Competing Interests: C.L. is employed by OnLume, Inc.; A.J.U. is a co-founder and employee of OnLume, Inc.; K.W.E. is a co-founder of OnLume, Inc. The other authors report no conflict of interest., (Thieme. All rights reserved.)

We address the challenging problem of whole slide image (WSI) classification. WSIs have very high resolutions and usually lack localized annotations. WSI classification can be cast as a multiple instance learning (MIL) problem when only slide-level labels are available. We propose a MIL-based method for WSI classification and tumor detection that does not require localized annotations. Our method has three major components. First, we introduce a novel MIL aggregator that models the relations of the instances in a dual-stream architecture with trainable distance measurement. Second, since WSIs can produce large or unbalanced bags that hinder the training of MIL models, we propose to use self-supervised contrastive learning to extract good representations for MIL and alleviate the issue of prohibitive memory cost for large bags. Third, we adopt a pyramidal fusion mechanism for multiscale WSI features, and further improve the accuracy of classification and localization. Our model is evaluated on two representative WSI datasets. The classification accuracy of our model compares favorably to fully-supervised methods, with less than 2% accuracy gap across datasets. Our results also outperform all previous MIL-based methods. Additional benchmark results on standard MIL datasets further demonstrate the superior performance of our MIL aggregator on general MIL problems.

Age is a major risk factor for late-onset Alzheimer's disease (AD) but seldom features in laboratory models of the disease. Furthermore, heterogeneity in size and density of AD plaques observed in individuals are not recapitulated in transgenic mouse models, presenting an incomplete picture. We show that the amyloid plaque microenvironment is not equivalent between rodent and primate species, and that differences in the impact of AD pathology on local metabolism and inflammation might explain established differences in neurodegeneration and functional decline. Using brain tissue from transgenic APP/PSEN1 mice, rhesus monkeys with age-related amyloid plaques, and human subjects with confirmed AD, we report altered energetics in the plaque microenvironment. Metabolic features included changes in mitochondrial distribution and enzymatic activity, and changes in redox cofactors NAD(P)H that were shared among species. A greater burden of lipofuscin was detected in the brains from monkeys and humans of advanced age compared to transgenic mice. Local inflammatory signatures indexed by astrogliosis and microglial activation were detected in each species; however, the inflamed zone was considerably larger for monkeys and humans. These data demonstrate the advantage of nonhuman primates in modeling the plaque microenvironment, and provide a new framework to investigate how AD pathology might contribute to functional loss., (© 2021 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.)

Background: Early mechanisms underlying the progressive tissue death and the regenerative capability of burn wounds are understudied in human skin. A clinically relevant, reproducible model for human burn wound healing is needed to elucidate the early changes in the human burn wound environment. This study reports a reproducible contact burn model on human skin that explores the extent of tissue injury and healing over time, and defines the inter-individual variability in human skin to enable use in mechanistic studies on burn wound progression and healing., Methods: Using a customized burn device, contact burns of various depths were created on human skin by two operators and were evaluated for histologic depth by three raters to determine reproducibility. Early burn wound progression and wound healing were also evaluated histologically after the thermally injured human skin was cultured ex vivo for up to 14 days., Results: Burn depths were reproducibly generated on human skin in a temperature- or time-dependent manner. No significant difference in operator-created or rater-determined depth was observed within each patient sample. However, significant inter-individual variation was identified in burn depth in ten patient samples. Burn-injured ex vivo human skin placed into culture demonstrated differential progression of cell death and collagen denaturation for high and low temperature contact burns, while re-epithelialization was observed in superficial burn wounds over a period of 14 days., Conclusion: This model represents an invaluable tool to evaluate the inter-individual variability in early burn wound progression and wound healing to complement current animal models and enhance the translation of preclinical research to improvements in patient care., (Copyright © 2020 Elsevier Ltd and ISBI. All rights reserved.)

ImageJ and CellProfiler have long been leading open-source platforms in the field of bioimage analysis. ImageJ's traditional strength is in single-image processing and investigation, while CellProfiler is designed for building large-scale, modular analysis pipelines. Although many image analysis problems can be well solved with one or the other, using these two platforms together in a single workflow can be powerful. Here, we share two pipelines demonstrating mechanisms for productively and conveniently integrating ImageJ and CellProfiler for (1) studying cell morphology and migration via tracking, and (2) advanced stitching techniques for handling large, tiled image sets to improve segmentation. No single platform can provide all the key and most efficient functionality needed for all studies. While both programs can be and are often used separately, these pipelines demonstrate the benefits of using them together for image analysis workflows. ImageJ and CellProfiler are both committed to interoperability between their platforms, with ongoing development to improve how both are leveraged from the other. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Studying cell morphology and cell migration in time-lapse datasets using TrackMate (Fiji) and CellProfiler Basic Protocol 2: Creating whole plate montages to easily assess adaptability of segmentation parameters., (© 2021 Wiley Periodicals LLC.)