Bright-field Microscopy Images Research Articles

Multiphoton Microscopy Assessment of Healing From Tendon Laceration and Microthermal Coagula in a Rat Model.

To study the healing response of rat Achilles tendon when lacerated or treated with intense therapeutic ultrasound (ITU) via utilization of multiphoton microscopy (MPM) imaging and histology. The right Achilles tendon of each Sprague Dawley rat within a cohort was partially lacerated. 1 to 2 days post-surgery, each rat received ITU treatment of the Achilles tendon on either the right or left leg. Rats were euthanized in groups at 1, 3, 7, 14, or 28 days posttreatment and their tendons were explanted, formalin fixed, paraffin embedded, sectioned, and placed on slides for imaging. Slides from each time point were imaged using a laboratory built MPM with a 780 nm Ti:Sapphire laser. The resulting second harmonic generation (SHG) and two-photon excited fluorescence (2PEF) signals were captured, assessed, and compared to brightfield microscopy images of the same section subsequently stained with hematoxylin and eosin. At early timepoints, 2PEF images show the presence of red blood cells, infiltration of inflammatory cells and formation of a fibrin clot at laceration sites, and attraction of fibroblasts to ITU coagula. SHG images indicate an absence of organized collagen in both types of lesions. At later timepoints, new organized collagen can be seen at the laceration sites, and the concentration of inflammatory cells has noticeably decreased. Automated detection of red blood cells and infiltrative cells, as well as analysis of SHG signal intensity and homogeneity was performed at laceration locations. Results show that all quantities except SHG signal intensity approach normal values by day 28. Thus, combined analysis of 2PEF and SHG images elucidates tendon healing processes that align with and complement histological findings. These results indicate that multiphoton imaging can effectively visualize the healing response to mechanical (laceration) and thermal (ITU) injury, including the organization of new collagen which is more difficult to visualize with histology.

Synthesis of Porous Metal Oxides Utilizing Nanosheet Colloid with Liquid Crystallinity

Activated carbon, a high specific surface area microporous carbon(>2000 m2 g–1), has been used as the electrode material for electrochemical double layer capacitors.1 The structure of activated carbon is characterized by a three-dimensional network consisting of graphene-like layers. Preparation of metal oxides with a similar porous structure is anticipated to behave as pseudocapacitive electrode materials with high energy density. However, the hierarchical porous structure of activated carbon is obtained by gas-phase or chemical activation treatment and is applicable only to carbonaceous materials. Thus, an alternative strategy is necessary to prepare metal oxides with porous architectures similar to activated carbon.Assembly of inorganic oxide nanosheets is one of the promising methods to prepare hierarchical porous metal oxides. Here, we focus on the phenomenon where inorganic oxide nanosheets form liquid crystal structures in colloids and align macroscopically under an electric field. A highly concentrated hexaniobate nanosheet ([Nb6O17]4 –(ns)) colloid is known to exhibit liquid crystalline behavior.2 Additionally, the orientation of nanosheet with micro-mesoscale sheet spacing (< tens of nm) forms a hierarchical macroscopic structure (< several hundred µm) under an electric field.3 Therefore, the liquid crystalline structure and its responsiveness to electric fields in nanosheet colloids are effective for controlling the hierarchically nanosheet-assembled structure. In this study, we applied the freeze-drying method of nanosheet liquid crystals (NLC) to transfer the structure within the fluid nanosheet colloid to the dried aggregates (Figure 1).[Nb6O17]4 –(ns) colloids with developed liquid crystal domains form stripe-shaped macrostructures by applying an alternating current (AC) electric field (10 Vp–p, 50 kHz) perpendicular to the direction of gravity.3 Figure 1(a) shows the polarized optical microscopy (POM) image of the stripe-shaped macrostructures with a width of 100 µm and intervals of 200 µm composed of [Nb6O17]4 –(ns). In the birefringent regions, NLC oriented perpendicular to the direction of the AC electric field and parallel to the direction of the gravity. After applying an electric field to the cell, the cell was immersed in liquid nitrogen with applying the electric field and then dried in vacuum, resulting in the formation of a sponge-like nanosheet aggregate between the electrodes. The bright-field optical microscope image of the freeze-dried aggregates shows the similar stripe-shaped macrostructures to that of NLC under the electric field (Figure 1(b)). Therefore, the macrostructure of NLC oriented in an electric field can be transferred into the porous nanosheet aggregates by freeze-drying method. These results indicate that the liquid crystalline and electric field responsive properties of nanosheet colloids are useful for structural design of nanosheet-assembled materials. References P. Simon and Y. Gogotsi, Nat. Mater., 7, 845 (2008).N. Miyamoto and T. Nakato, Adv. Mater., 14, 1267 (2002).T. Nakato, Y. Nono, E. Mouri and M. Nakata, Phys. Chem. Chem. Phys., 16, 955 (2014). Figure 1

Fluorescence microscopic image enhancement method based on multi-saliency guided filtering fusion

Abstract Aiming at addressing the issues of colour deviation and clarity reduction in digital pathological imaging systems caused by complex environmental interferences such as uneven slice thickness, sample jitter, and background noise, this paper proposes a multi-saliency-guided filtering fusion enhancement method that combines energy saliency and Gaussian saliency. Firstly, a series of Gamma correction and spatial linear saturation adjustments are applied to enhance the contrast and colour saturation of blurred images, resulting in a sequence of pseudo-exposure images with improved contrast and saturation. Then, the pseudo-exposure images are decomposed into base and detail layers, and the fusion rules of energy saliency and Gaussian saliency-guided filtering are applied to each layer separately. Finally, clear microscopic images are obtained through two-scale reconstruction. This method overcomes the limitations of conventional deblurring algorithms that rely on prior estimation of scene depth and complex depth mapping processes. Experimental results on a dataset of cytological bright-field microscopic imaging samples show that the clarity evaluation metrics, including peak signal-to-noise ratio, root mean square error, and structural similarity index, have significantly improved, indicating that the proposed method can effectively improve the colour fidelity of the samples while eliminating noise interference and enhancing the clarity of cellular texture structures.

Machine Learning Accelerates Screening of Osteoclast Differentiation Inhibitors from Natural Products.

Natural products that inhibit osteoclast differentiation are promising therapeutic and preventive agents for osteoporosis. Conventionally, identifying osteoclast differentiation involves visual inspection of the microscope images of stained osteoclasts. In this study, a supervised machine learning model was developed to classify bright-field microscope images of osteoclasts without staining. The model was used to screen a compound library, and osteoclast differentiation inhibitors were identified, demonstrating the validity of our method. Next, an in-house library of fungal extracts was screened, and pinolidoxin was revealed as an inhibitor of osteoclast differentiation. Our machine learning method enabled accurate, objective, and high-throughput evaluation of osteoclast differentiation and efficient screening of the inhibitors from natural product extracts. This study represents the first machine learning classification developed to evaluate the inhibitory activity of natural products in osteoclast differentiation.

A Simple Method for Visualization and Quantitative Evaluation of Single and Collective Cell Migration in Bright Field Microscopy Images

Cell migration is observed in various cases such as embryonic and lesion developments. The migration directly influences the phenomena around the migration path. Bright field microscopy, generally used for cell observation, is effective in tracking cell movement, but the detection of the cell outline via image processing methods partially fails. In this study, a simple method, utilizing the intensity fluctuation of the image caused by the passage of a cell as a parameter for evaluation of the cell movement, has been proposed to visualize the region where the cell passed and quantitatively evaluate its frequency by the fluctuation pattern. The map that depicts the activity of cell movement is created, and the geometrical characteristics of each region and fluctuation degree are quantitatively shown. Because detection of the cell outline is not necessary, this method is applicable to collective cells as well as single cells. When this method was applied to the images of hemocytes inHalocynthia roretzi(Ascidiacea), which were composed of single and collective cells and showed complex patterns in movement, the map and quantitative parameters for the fluctuation were successfully obtained. The method can be improved by the calibration of intensity distribution and applied to cells in various species.

Cross-modality sub-image retrieval using contrastive multimodal image representations

In tissue characterization and cancer diagnostics, multimodal imaging has emerged as a powerful technique. Thanks to computational advances, large datasets can be exploited to discover patterns in pathologies and improve diagnosis. However, this requires efficient and scalable image retrieval methods. Cross-modality image retrieval is particularly challenging, since images of similar (or even the same) content captured by different modalities might share few common structures. We propose a new application-independent content-based image retrieval (CBIR) system for reverse (sub-)image search across modalities, which combines deep learning to generate representations (embedding the different modalities in a common space) with robust feature extraction and bag-of-words models for efficient and reliable retrieval. We illustrate its advantages through a replacement study, exploring a number of feature extractors and learned representations, as well as through comparison to recent (cross-modality) CBIR methods. For the task of (sub-)image retrieval on a (publicly available) dataset of brightfield and second harmonic generation microscopy images, the results show that our approach is superior to all tested alternatives. We discuss the shortcomings of the compared methods and observe the importance of equivariance and invariance properties of the learned representations and feature extractors in the CBIR pipeline. Code is available at: https://github.com/MIDA-group/CrossModal_ImgRetrieval.

Generative AI for Cell Type-Specific Fluorescence Image Generation of hPSC-derived Cardiac Organoid.

Human pluripotent stem cell (hPSC)-derived cardiac organoid is the most recent three-dimensional tissue structure that mimics the structure and functionality of the human heart and plays a pivotal role in modeling heart development and disease. The hPSC-derived cardiac organoids are commonly characterized by bright-field microscopic imaging for tracking daily organoid differentiation and morphology formation. Although the brightfield microscope provides essential information about hPSC-derived cardiac organoids, such as morphology, size, and general structure, it does not extend our understanding of cardiac organoids on cell type-specific distribution and structure. Then, fluorescence microscopic imaging is required to identify the specific cardiovascular cell types in the hPSC-derived cardiac organoids by fluorescence immunostaining fixed organoid samples or fluorescence reporter imaging of live organoids. Both approaches require extra steps of experiments and techniques and do not provide general information on hPSC-derived cardiac organoids from different batches of differentiation and characterization, which limits the biomedical applications of hPSC-derived cardiac organoids. This research addresses this limitation by proposing a comprehensive workflow for colorizing phase contrast images of cardiac organoids from brightfield microscopic imaging using conditional Generative Adversarial Networks (GANs) to provide cardiovascular cell type-specific information in hPSC-derived cardiac organoids. By infusing these phase contrast images with accurate fluorescence colorization, our approach aims to unlock the hidden wealth of cell type, structure, and further quantifications of fluorescence intensity and area, for better characterizing hPSC-derived cardiac organoids.

Abstract We016: Generative Artificial Intelligence for hPSC-derived Cardiac Organoid Florescence Generation

Background: Human pluripotent stem cell (hPSC)-derived cardiac organoids (COs) are extensively applied in modeling heart development and disease. COs are daily monitored by bright-field microscopic imaging for tracking organoid differentiation, morphology, and size, however, it does not provide important information on cell distribution and network in COs. Then, fluorescence microscopic imaging is required with extra steps of experiments when multiple hPSC lines are required. Recently, generative artificial intelligence (AI) has been increasingly applied for image colorization, which extends the possibility of colorizing COs with fluorescence from phase/contrast images. While the phase/contrast image is routinely applied and conveniently used in every biomedical lab daily, is it possible to generate accurate and useful fluorescence information or image colorization from the phase/contrast images of cardiac organoids under the same differentiation protocol is crucial for a broader characterization and analysis of hPSC-derived cardiac organoids and further applications? Hypothesis: We hypothesize that generative AI will generate accurate fluorescence images from phase/contrast images of COs. Approach: 1300 images from COs (differentiated from one reporter hPSC line) were used as the training dataset for the Pix2Pix Conditional Generative Adversarial Networks (GANs) model ( Fig.1a ) with the Convolution Block Attention Module (CBAM) in the U-Net generator ( Fig.1b ). Three models were designed: U-Net (Model 1), U-Net generator with CBAM (Model 2), and U-Net with CBAM and Generator Iteration (Model 3). Three different metrics were included: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Weighted Patch Histogram to evaluate the prediction outcomes. Results: The predicted fluorescence images from phase/contrast images of different COs are very similar to the ground truths ( Fig.1c ). The similarities evaluated by all three metrics ( Fig.1d ) have the scores of PSNR over 30, SSIM over 0.92, and a Weighted Patch Histogram score over 0.75, which are the most accurate and similar to the ground truth. Conclusions: A generative AI model was established to colorize phase images of COs using cGANs and CBAM, which capture intricate fluorescence details to support the biomedical research of COs with enhanced interpretability and analysis of cardiac organoid morphology and structure in biomedical research and applications.

Deep-learning assisted detection and quantification of (oo)cysts of Giardia and Cryptosporidium on smartphone microscopy images

The consumption of microbial-contaminated food and water is responsible for the deaths of millions of people annually. Smartphone-based microscopy systems are portable, low-cost, and more accessible alternatives for the detection of Giardia and Cryptosporidium than traditional brightfield microscopes. However, the images from smartphone microscopes are noisier and require manual cyst identification by trained technicians, usually unavailable in resource-limited settings. Automatic detection of (oo)cysts using deep-learning-based object detection could offer a solution for this limitation. We evaluate the performance of four state-of-the-art object detectors to detect (oo)cysts of Giardia and Cryptosporidium on a custom dataset that includes both smartphone and brightfield microscopic images from vegetable samples. Faster RCNN, RetinaNet, You Only Look Once (YOLOv8s), and Deformable Detection Transformer (Deformable DETR) deep-learning models were employed to explore their efficacy and limitations. Our results show that while the deep-learning models perform better with the brightfield microscopy image dataset than the smartphone microscopy image dataset, the smartphone microscopy predictions are still comparable to the prediction performance of non-experts. Also, we publicly release brightfield and smartphone microscopy datasets with the benchmark results for the detection of Giardia and Cryptosporidium, independently captured on reference (or standard lab setting) and vegetable samples. Our code and dataset are available at &lt;a href='https://github.com/naamiinepal/smartphone_microscopy'&gt;https://github.com/naamiinepal/smartphone_microscopy&lt;/a&gt; and &lt;a href='https://doi.org/10.5281/zenodo.7813183'&gt;https://doi.org/10.5281/zenodo.7813183&lt;/a&gt;, respectively

Multimodal Image Fusion Workflow Incorporating MALDI Imaging Mass Spectrometry and Microscopy for the Study of Small Pharmaceutical Compounds.

Multimodal imaging analyses of dosed tissue samples can provide more comprehensive insights into the effects of a therapeutically active compound on a target tissue compared to single-modal imaging. For example, simultaneous spatial mapping of pharmaceutical compounds and endogenous macromolecule receptors is difficult to achieve in a single imaging experiment. Herein, we present a multimodal workflow combining imaging mass spectrometry with immunohistochemistry (IHC) fluorescence imaging and brightfield microscopy imaging. Imaging mass spectrometry enables direct mapping of pharmaceutical compounds and metabolites, IHC fluorescence imaging can visualize large proteins, and brightfield microscopy imaging provides tissue morphology information. Single-cell resolution images are generally difficult to acquire using imaging mass spectrometry but are readily acquired with IHC fluorescence and brightfield microscopy imaging. Spatial sharpening of mass spectrometry images would thus allow for higher fidelity coregistration with other higher-resolution microscopy images. Imaging mass spectrometry spatial resolution can be predicted to a finer value via a computational image fusion workflow, which models the relationship between the intensity values in the mass spectrometry image and the features of a high-spatial resolution microscopy image. As a proof of concept, our multimodal workflow was applied to brain tissue extracted from a Sprague-Dawley rat dosed with a kratom alkaloid, corynantheidine. Four candidate mathematical models, including linear regression, partial least-squares regression, random forest regression, and two-dimensional convolutional neural network (2-D CNN), were tested. The random forest and 2-D CNN models most accurately predicted the intensity values at each pixel as well as the overall patterns of the mass spectrometry images, while also providing the best spatial resolution enhancements. Herein, image fusion enabled predicted mass spectrometry images of corynantheidine, GABA, and glutamine to approximately 2.5 μm spatial resolutions, a significant improvement compared to the original images acquired at 25 μm spatial resolution. The predicted mass spectrometry images were then coregistered with an H&E image and IHC fluorescence image of the μ-opioid receptor to assess colocalization of corynantheidine with brain cells. Our study also provides insights into the different evaluation parameters to consider when utilizing image fusion for biological applications.

Antibacterial Interactions of Ethanol-Dispersed Multiwalled Carbon Nanotubes with Staphylococcus aureus and Pseudomonas aeruginosa.

Infectious diseases are acknowledged as one of the leading causes of death worldwide. Statistics show that the annual death toll caused by bacterial infections has reached 14 million, most of which are caused by drug-resistant strains. Bacterial antibiotic resistance is currently regarded as a compelling problem with dire consequences, which motivates the urgent identification of alternative ways of fighting bacteria. Various types of nanomaterials have been reported to date as efficient antibacterial solutions. Among these, carbon-based nanomaterials, such as carbon nanodots, carbon graphene oxide, and carbon nanotubes (CNTs), have been shown to be effective in killing a wide panel of pathogenic bacteria. With this study, we aim to provide additional insights into this topic of research by investigating the antibacterial activity of a specific type of multiwalled CNTs, with diameters from 50 to 150 nm, against two representative opportunistic pathogens, i.e., the Gram-positive bacterium Staphylococcus aureus and the Gram-negative bacterium Pseudomonas aeruginosa, both included among the top antibiotic-resistant pathogens. We also test the synergistic effect of CNTs with different antibiotics commonly used in the treatment of infections caused by S. aureus and/or P. aeruginosa. Additionally, a novel approach for quantitatively analyzing bacterial aggregation in brightfield microscopy images was implemented. This method was utilized to assess the effectiveness of CNTs, either alone or in combination with antibiotics, in dispersing bacterial aggregates. Finally, atomic force microscopy coupled with a newly devised image analysis pipeline was used to examine any potential morphological changes in bacterial cells following exposure to CNTs and antibiotics.

A Dual-Modal, Label-Free Raman Imaging Method for Rapid Virtual Staining of Large-Area Breast Cancer Tissue Sections.

As one of the most common cancers, accurate, rapid, and simple histopathological diagnosis is very important for breast cancer. Raman imaging is a powerful technique for label-free analysis of tissue composition and histopathology, but it suffers from slow speed when applied to large-area tissue sections. In this study, we propose a dual-modal Raman imaging method that combines Raman mapping data with microscopy bright-field images to achieve virtual staining of breast cancer tissue sections. We validate our method on various breast tissue sections with different morphologies and biomarker expressions and compare it with the golden standard of histopathological methods. The results demonstrate that our method can effectively distinguish various types and components of tissues, and provide staining images comparable to stained tissue sections. Moreover, our method can improve imaging speed by up to 65 times compared to general spontaneous Raman imaging methods. It is simple, fast, and suitable for clinical applications.

In Situ Lung Dust Analysis by Automated Field Emission Scanning Electron Microscopy With Energy Dispersive X-ray Spectroscopy: A Method for Assessing Inorganic Particles in Lung Tissue From Coal Miners.

Overexposure to respirable coal mine dust can cause severe lung disease including progressive massive fibrosis (PMF). Field emission scanning electron microscopy with energy dispersive x-ray spectroscopy (FESEM-EDS) has been used for in situ lung dust particle analysis for evaluation of disease etiology. Automating such work can reduce time, costs, and user bias. To develop and test an automated FESEM-EDS method for in situ analysis of inorganic particles in coal miner lung tissue. We programmed an automated FESEM-EDS procedure to collect particle size and elemental data, using lung tissue from 10 underground coal miners with PMF and 4 control cases. A statistical clustering approach was used to establish classification criteria based on particle chemistry. Data were correlated to PMF/non-PMF areas of the tissue, using corresponding brightfield microscopy images. Results for each miner case were compared with a separate corresponding analysis of particles recovered following tissue digestion. In situ analysis of miner tissues showed higher particle number densities than controls and densities were generally higher in PMF than non-PMF areas. Particle counts were typically dominated by aluminum silicates with varying percentages of silica. Compared to digestion results for the miner tissues, in situ results indicated lower density of particles (number per tissue volume), larger size, and a lower ratio of silica to total silicates-probably due to frequent particle clustering in situ. Automated FESEM-EDS analysis of lung dust is feasible in situ and could be applied to a larger set of mineral dust-exposed lung tissues to investigate specific histologic features of PMF and other dust-related occupational diseases.

A large and diverse brain organoid dataset of 1,400 cross-laboratory images of 64 trackable brain organoids

Brain organoids represent a useful tool for modeling of neurodevelopmental disorders and can recapitulate brain volume alterations such as microcephaly. To monitor organoid growth, brightfield microscopy images are frequently used and evaluated manually which is time-consuming and prone to observer-bias. Recent software applications for organoid evaluation address this issue using classical or AI-based methods. These pipelines have distinct strengths and weaknesses that are not evident to external observers. We provide a dataset of more than 1,400 images of 64 trackable brain organoids from four clones differentiated from healthy and diseased patients. This dataset is especially powerful to test and compare organoid analysis pipelines because of (1) trackable organoids (2) frequent imaging during development (3) clone diversity (4) distinct clone development (5) cross sample imaging by two different labs (6) common imaging distractors, and (6) pixel-level ground truth organoid annotations. Therefore, this dataset allows to perform differentiated analyses to delineate strengths, weaknesses, and generalizability of automated organoid analysis pipelines as well as analysis of clone diversity and similarity.

Human Enteroid Monolayers: A Novel, Functionally-Stable Model for Investigating Oral Drug Disposition.

To further the development of an in vitro model which faithfully recapitulates drug disposition of orally administered drugs, we investigated the utility of human enteroid monolayers to simultaneously assess intestinal drug absorption and first-pass metabolism processes. We cultured human enteroid monolayers from three donors, derived via biopsies containing duodenal stem cells that were propagated and then differentiated atop permeable Transwell® inserts, and confirmed transformation into a largely enterocyte population via RNA-seq analysis and immunocytochemical (ICC) assays. Proper cell morphology was assessed and confirmed via bright field microscopy and ICC imaging of tight junction proteins and other apically and basolaterally localized proteins. Enteroid monolayer barrier integrity was demonstrated by elevated transepithelial electrical resistance (TEER) that stabilized after 10 days in culture and persisted for 42 days. These results were corroborated by low paracellular transport probe permeability at 7 and 21 days in culture. The activity of a prominent drug metabolizing enzyme, CYP3A, was confirmed at 7, 21, and 42 days culture under basal, 1α,25(OH)2 vitamin D3-induced, and 6',7'-dihydroxybergamottin-inhibited conditions. The duration of these experiments is particularly noteworthy, as this is the first study assessing drug metabolizing enzymes and transporters (DMET) expression/function for enteroids cultured for greater than 12 days. The sum of these results suggests enteroid monolayers are a promising ex vivo model to investigate and quantitatively predict an orally administered drug's intestinal absorption and/or metabolism. Significance Statement This study presents a novel ex vivo model of the human intestine, human intestinal organoid (enteroid) monolayers, that maintain barrier function and metabolic functionality for up to 42-days in culture. The incorporation of both barrier integrity and metabolic function over an extended period within the same model is an advancement over historically used in vitro systems, which either lack one or both of these attributes or have limited viability.

Abstract PO3-07-01: Multiscale Computational Analysis of Patient-Matched Multi-Modal Imaging of Breast Tissue Microenvironment

Abstract Introduction: Mammographic breast density is a strong quantitative cancer risk factor, but its link to underlying tissue and cellular level changes are weakly understood. Understanding the complex restructuring changes in the breast tumor microenvironment and its impact on breast density is integral to improving risk assessment and treatment strategies. A novel approach was developed to stratify mammographic dense breast tissue into areas of active dense tissue and passive dense tissue. Active dense tissue is structurally reorganizing and links to cancer dynamics, whereas passive dense mammographic tissue remains organized.1,2 A complementary wavelet-based analysis technique to measure the multiscale anisotropy of collagen fibers from Second-Harmonic Generation (SHG) imaging demonstrated clinical cancer diagnostic potential in mouse carcinoma and human pancreatic cancer.3,4 This tool is now leveraged for a multi-modal study in which patient-matched H&amp;E tissue biopsies are imaged using both bright-field microscopy and SHG imaging to compare with the patient’s corresponding mammograms. Stratifying the mammographic density into active and passive dense areas, a correlation between biopsy tissue anisotropy and mammographic density subtypes can be calculated to help understand how tumor microenvironment changes affect a patient’s mammographic breast tissue composition. Methods: SHG and brightfield-imaged biopsy slides from 10 patients (five malignant, five benign) collected from Maine Medical Center (Maine, US) were analyzed using the 2D Wavelet Transform Modulus Maxima (WTMM) Anisotropy Method generating a large-scale and a small-scale anisotropy factor as well as a scale-combined anisotropy calculated from their large- and small-scale anisotropy separation. A multi-modal anisotropy score was calculated by adding the imaged area’s H&amp;E combined anisotropy score with its respective SHG combined anisotropy score. This analysis was done on eight randomly selected ductal areas for each imaging modality on a slide and correlated with the measured areas of density subtypes from the whole mammographic view. Linear regression was used for determining correlation between H&amp;E and SHG anisotropy factors as well as with active and passive mammographic density subtype measurements. Results: The malignant patients’ small-scale H&amp;E anisotropy factor positively correlated with their small scale SHG anisotropy factor (R2=0.571). No correlation was found for the benign patients (R2=0.011). The strongest correlations were found between benign patients’ mammographic passive (positive correlation) and active (negative correlation) dense tissues vs the H&amp;E anisotropy scale-combined factor (R2=0.858 and R2=0.961, respectively). The largest malignant patient correlation was a negative relationship between SHG anisotropy scale-combined factor and overall mammographic density (inclusive of both active and passive dense tissue) (R2=0.891). The multi-modal anisotropy score correlated with overall mammographic density for both benign (positive correlation, R2=0.698) and malignant patients (negative correlation, R2=0.773). Combining the multi-modal anisotropy factor from SHG and H&amp;E with the active dense tissue measurement using K-Means clustering, 5/5 malignant and 4/5 benign patients were correctly classified. Conclusion: Density in mammograms correlates with underlying quantitative changes in a patient’s tissue and collagen anisotropy. Combining both H&amp;E and SHG analysis on tissue biopsy with active and passive density metrics from computational mammography leads to the best predictive potential than any one method on their own.

Classification and counting of cells in brightfield microscopy images: an application of convolutional neural networks

Microscopy is integral to medical research, facilitating the exploration of various biological questions, notably cell quantification. However, this process's time-consuming and error-prone nature, attributed to human intervention or automated methods usually applied to fluorescent images, presents challenges. In response, machine learning algorithms have been integrated into microscopy, automating tasks and constructing predictive models from vast datasets. These models adeptly learn representations for object detection, image segmentation, and target classification. An advantageous strategy involves utilizing unstained images, preserving cell integrity and enabling morphology-based classification—something hindered when fluorescent markers are used. The aim is to introduce a model proficient in classifying distinct cell lineages in digital contrast microscopy images. Additionally, the goal is to create a predictive model identifying lineage and determining optimal quantification of cell numbers. Employing a CNN machine learning algorithm, a classification model predicting cellular lineage achieved a remarkable accuracy of 93%, with ROC curve results nearing 1.0, showcasing robust performance. However, some lineages, namely SH-SY5Y (78%), HUH7_mayv (85%), and A549 (88%), exhibited slightly lower accuracies. These outcomes not only underscore the model's quality but also emphasize CNNs' potential in addressing the inherent complexities of microscopic images.

Glutamic Acid Modified Gold Nanorod Sensor for the Detection of Calcium ions in Neuronal Cells.

Calcium (Ca2+) ions play a crucial role in the functioning of neurons, governing various aspects of neuronal activity such as rapid modulation and alterations in gene expression. Ca2+ signaling has a significant impact on the development of diseases and the impairment of neuronal functions. Herein, the study reports a Ca2+ ion sensor in neuronal cells using a gold nanorod. The gold nanorod (GA-GNR) conjugated glutamic acid developed in the study was used as a nano-bio probe for the experimental and in vitro detection of calcium. The nanosensor is colloidally stable, preserves plasmonic properties, and shows good viability in neuronal cells, as well as promoting neuron cell line growth. The cytotoxicity and cell penetration of the nanosensor are studied using Raman spectroscopy, brightfield and darkfield microscopy imaging, and MTT assays. The quantification of Ca2+ ions in neuronal cells is determined by monitoring the surface plasmon resonance (SPR) of the GA-GNR. The change in the intensity profile in the presence of Ca2+ incubated neurons was effectively used to develop a portable prototype of an optical Ca2+ sensor, proposing it as a tool for neurodegenerative disease diagnosis and neuromodulation evaluation.

Abstract 2073: Human breast patient derived organoids: Development and characterization of a new tissue mimetic model

Abstract Worldwide, breast cancer (BC), is the most frequently diagnosed cancer among women. In the US, 1 out of 8 women may expect to be diagnosed with BC In their lifetime. Current models for studying BC tumor biology and BC drug discovery include 2D cultured cell lines and patient derived xenografts (PDX). As 2D cultured cells do not represent the 3D complexity of human tissue, and PDX models are time consuming and cost prohibitive, better tissue mimetic models are needed. Organoids are self-assembled 3D cultured cellular units that may mimic the corresponding human tissue structurally and functionally (1). Patient derived organoids (PDO) are developed from tissue of specific patients and may resemble the source tissue in molecular features, including recapitulation of patient specific responses to therapies (2). Here, we report development and characterization of novel breast PDO lines from fresh (≤24h post-surgery) and cryopreserved (≤7 days post-surgery) tissue. Breast tumor and normal, tumor adjacent (NTA) tissue were sourced from consented patients. Breast PDO were generated following modifications to a published protocol (3). Briefly, tissue samples were minced, digested with collagenase, and filtered. The filtrate was resuspended in Matrigel (Corning) and small drops were added on cell culture plates. Both tumor and NTA PDO were cultured using the same media and organoids were monitored using brightfield microscopy (Olympus CK40) and live cell imaging (Muvicyte, Perkin Elmer). PDO lines were analyzed for expression and distribution of important breast-specific biomarkers using confocal microscopy (ImagExpress, Molecular Devices). PDO lines displayed hormone receptors (ER, PR), cell surface (EpCAM, CD49f), cytoskeletal (cytokeratin 14, 19, E-cadherin, vimentin) and cell proliferation (Ki67) markers. Transcriptome profiles were analyzed using vendor services (Azenta). PDO were also tested for their susceptibility to breast cancer drugs, e.g., Tamoxifen, chlorambucil and Docetaxel. We have successfully generated one NTA ER+/PR+/HER2− PDO line and one tumor ER+/PR+/HER2− PDO line from different donors. Our breast PDO lines would be highly useful in understanding tumor biology, BC progression, signal transduction, and drug discovery/testing. Future efforts would include process scaling up and development of BC specific assays.

Abstract 2072: Human liver patient derived organoids: Development and characterization of a new tissue mimetic model

Abstract Liver is the largest solid organ and performs hundreds of vital functions including drug metabolism. Liver diseases, including viral infections, cancer and metabolic disorders account for about 2 million deaths annually, worldwide (1). Current models for studying liver biology and drug metabolism include 2D cultured primary cells/cell lines, which do not represent the metabolic and structural complexity of human tissue. In vivo models for liver diseases are time consuming, cost prohibitive and often ineffective. Therefore, better tissue mimetic liver models are needed. Organoids are self-assembled 3D cultured cellular units that may mimic the corresponding human tissue structurally and functionally (2). Patient derived organoids (PDO) are developed from tissue of specific patients and may resemble the source tissue in molecular features, including recapitulation of patient specific responses to therapies. Here, we report development and characterization of novel liver PDO lines from vendor-sourced cryopreserved tissue. We have developed biobanks of undifferentiated as well as differentiated liver PDO models.Human cadaver-sourced liver tissue were obtained with explicit donor consent. Liver PDO were generated following modification of a previously established protocol (3). Briefly, samples were minced, digested to single cells, and filtered. Specific number of cells were mixed with diluted Matrigel (Corning) and added as small drops on cell culture plates and grown in liver PDO specific growth media. Whenever appropriate, PDO were differentiated by culturing in differentiation-specific media in multi-well plates. In all cases, PDO were monitored using brightfield microscopy (Olympus CK40) and live cell imaging (Muvicyte, Perkin Elmer); and passaged every 6-8 days. Undifferentiated PDO were cryopreserved/thawed following in-house SOP to confirm viability. Liver-specific biomarker expression in the PDO lines were confirmed by confocal microscopy (ImageExpress, Molecular Devices). PDO lines were also assayed for ALT activity, albumin production and urea. PDO were cleared for microbial/viral contamination and confirmed as unique lines using single tandem repeat analyses. Transcriptome profiles were analyzed using vendor services. So far, we have successfully generated 3 liver PDO lines, both undifferentiated as well as differentiated. Our liver PDO lines would be highly useful in understanding liver biology and disease models; and will have significant usage in drug testing, including DMPK and ADME/Tox studies. Future efforts would include scaling up, development of liver specific assays and developing PDO from specific liver diseases, including hepatocellular carcinoma, MASLD and NASH.