Detecting and tracking nonfluorescent nanoparticle probes in live cells

Isolation of phase edges using off-axis q-plate filters

Edge-enhanced microscopes with a q-plate have attracted more attention to enhance the edges of phase-amplitude objects in biological samples due to their capacity for all-directional edge enhancement, while differential interference-contrast microscopy enhances edges in only one-direction. However, the edge-enhanced microscopes cannot distinguish the edges of phase and amplitude objects, as both edges are equally enhanced. This study introduces a novel method for isolating the edge of a phase object from an amplitude object using an off-axis q-plate filter in a 4f system. Herein, we combined off-axis q-plates with four different displacements to isolate the phase object edge from the amplitude object. To demonstrate the proposed method, we conducted experiments using two distinct samples. The first sample comprised a phase test target surrounded by an aperture, and the second sample involved an overlap between the phase test target and a white hair with non-zero transmittance. In the samples, the isolated phase object edge is in good agreement with the theoretical expectations, and the amplitude object edge was reduced by approximately 93%. The proposed method is a novel and effective approach for isolating the edge of a phase object from an amplitude object and can be useful in various biological imaging applications.

A deep learning-based pipeline for analyzing the influences of interfacial mechanochemical microenvironments on spheroid invasion using differential interference contrast microscopic images

Metastasis is the leading cause of cancer-related deaths. During this process, cancer cells are likely to navigate discrete tissue-tissue interfaces, enabling them to infiltrate and spread throughout the body. Three-dimensional (3D) spheroid modeling is receiving more attention due to its strengths in studying the invasive behavior of metastatic cancer cells. While microscopy is a conventional approach for investigating 3D invasion, post-invasion image analysis, which is a time-consuming process, remains a significant challenge for researchers. In this study, we presented an image processing pipeline that utilized a deep learning (DL) solution, with an encoder-decoder architecture, to assess and characterize the invasion dynamics of tumor spheroids. The developed models, equipped with feature extraction and measurement capabilities, could be successfully utilized for the automated segmentation of the invasive protrusions as well as the core region of spheroids situated within interfacial microenvironments with distinct mechanochemical factors. Our findings suggest that a combination of the spheroid culture and DL-based image analysis enable identification of time-lapse migratory patterns for tumor spheroids above matrix-substrate interfaces, thus paving the foundation for delineating the mechanism of local invasion during cancer metastasis.

Edge-enhanced microscopy of complex objects using scalar and vectorial vortex filtering

Recently, a 4f system containing a q-plate has been used to perform edge detection and enhancement of amplitude or phase objects. However, only a few studies have concentrated on edge enhancement of complex phase-amplitude objects. Here we experimentally verified the functional difference between scalar and vectorial vortex filtering with the q-plate using an onion cell as a complex object and the vectorial vortex filtering successfully enhanced the edges of phase and amplitude objects in the phase-amplitude object. One problem, however, is indistinguishability of the equally-enhanced edges of the phase and amplitude objects. To address this issue, we propose a method to isolate the edge of the phase object from the edge of the amplitude object using off-axis beam illumination. We theoretically calculated the isolation of the edge of the phase object from the amplitude object, and verified via numerical simulations.

Trends in digital image processing of isolated microalgae by incorporating classification algorithm

Identification of microalgae species is of importance due to the uprising of harmful algae blooms affecting both the aquatic habitat and human health. Despite this occurence, microalgae have been identified as a green biomass and alternative source due to its promising bioactive compounds accumulation that play a significant role in many industrial applications. Recently, microalgae species identification has been conducted through DNA analysis and various microscopy techniques such as light, scanning electron, transmission electron, and atomic force -microscopy. The aforementioned procedures have encouraged researchers to consider alternate ways due to limitations such as costly validation, requiring skilled taxonomists, prolonged analysis, and low accuracy. This review highlights the potential innovations in digital microscopy with the incorporation of both hardware and software that can produce a reliable recognition, detection, enumeration, and real-time acquisition of microalgae species. Several steps such as image acquisition, processing, feature extraction, and selection are discussed, for the purpose of generating high image quality by removing unwanted artifacts and noise from the background. These steps of identification of microalgae species is performed by reliable image classification through machine learning as well as deep learning algorithms such as artificial neural networks, support vector machines, and convolutional neural networks. Overall, this review provides comprehensive insights into numerous possibilities of microalgae image identification, image pre-processing, and machine learning techniques to address the challenges in developing a robust digital classification tool for the future.

Carbonaceous Nanoparticle Air Pollution: Toxicity and Detection in Biological Samples

Among the different air pollutants, particulate matter (PM) is of great concern due to its abundant presence in the atmosphere, which results in adverse effects on the environment and human health. The different components of PM can be classified based on their physicochemical properties. Carbonaceous particles (CPs) constitute a major fraction of ultrafine PM and have the most harmful effects. Herein, we present a detailed overview of the main components of CPs, e.g., carbon black (CB), black carbon (BC), and brown carbon (BrC), from natural and anthropogenic sources. The emission sources and the adverse effects of CPs on the environment and human health are discussed. Particularly, we provide a detailed overview of the reported toxic effects of CPs in the human body, such as respiratory effects, cardiovascular effects, neurodegenerative effects, carcinogenic effects, etc. In addition, we also discuss the challenges faced by and limitations of the available analytical techniques for the qualitative and quantitative detection of CPs in atmospheric and biological samples. Considering the heterogeneous nature of CPs and biological samples, a detailed overview of different analytical techniques for the detection of CPs in (real-exposure) biological samples is also provided. This review provides useful insights into the classification, toxicity, and detection of CPs in biological samples.

CancerCellTracker: a brightfield time-lapse microscopy framework for cancer drug sensitivity estimation

MOTIVATION

Time-lapse microscopy is a powerful technique that relies on images of live cells cultured ex vivo that are captured at regular intervals of time to describe and quantify their behavior under certain experimental conditions. This imaging method has great potential in advancing the field of precision oncology by quantifying the response of cancer cells to various therapies and identifying the most efficacious treatment for a given patient. Digital image processing algorithms developed so far require high-resolution images involving very few cells originating from homogeneous cell line populations. We propose a novel framework that tracks cancer cells to capture their behavior and quantify cell viability to inform clinical decisions in a high-throughput manner.

RESULTS

The brightfield microscopy images a large number of patient-derived cells in an ex vivo reconstruction of the tumor microenvironment treated with 31 drugs for up to 6 days. We developed a robust and user-friendly pipeline CancerCellTracker that detects cells in co-culture, tracks these cells across time and identifies cell death events using changes in cell attributes. We validated our computational pipeline by comparing the timing of cell death estimates by CancerCellTracker from brightfield images and a fluorescent channel featuring ethidium homodimer. We benchmarked our results using a state-of-the-art algorithm implemented in ImageJ and previously published in the literature. We highlighted CancerCellTracker's efficiency in estimating the percentage of live cells in the presence of bone marrow stromal cells.

AVAILABILITY AND IMPLEMENTATION

https://github.com/compbiolabucf/CancerCellTracker.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ArtSeg-Artifact segmentation and removal in brightfield cell microscopy images without manual pixel-level annotations

Brightfield cell microscopy is a foundational tool in life sciences. The acquired images are prone to contain visual artifacts that hinder downstream analysis, and automatically removing them is therefore of great practical interest. Deep convolutional neural networks are state-of-the-art for image segmentation, but require pixel-level annotations, which are time-consuming to produce. Here, we propose ScoreCAM-U-Net, a pipeline to segment artifactual regions in brightfield images with limited user input. The model is trained using only image-level labels, so the process is faster by orders of magnitude compared to pixel-level annotation, but without substantially sacrificing the segmentation performance. We confirm that artifacts indeed exist with different shapes and sizes in three different brightfield microscopy image datasets, and distort downstream analyses such as nuclei segmentation, morphometry and fluorescence intensity quantification. We then demonstrate that our automated artifact removal ameliorates this problem. Such rapid cleaning of acquired images using the power of deep learning models is likely to become a standard step for all large scale microscopy experiments.

PLGA-Based Composites for Various Biomedical Applications

Polymeric materials have been extensively explored in the field of nanomedicine; within them, poly lactic-co-glycolic acid (PLGA) holds a prominent position in micro- and nanotechnology due to its biocompatibility and controllable biodegradability. In this review we focus on the combination of PLGA with different inorganic nanomaterials in the form of nanocomposites to overcome the polymer’s limitations and extend its field of applications. We discuss their physicochemical properties and a variety of well-established synthesis methods for the preparation of different PLGA-based materials. Recent progress in the design and biomedical applications of PLGA-based materials are thoroughly discussed to provide a framework for future research.

Engineering Microneedle Patches for Improved Penetration: Analysis, Skin Models and Factors Affecting Needle Insertion

Transdermal microneedle (MN) patches are a promising tool used to transport a wide variety of active compounds into the skin. To serve as a substitute for common hypodermic needles, MNs must pierce the human stratum corneum (~ 10 to 20 µm), without rupturing or bending during penetration. This ensures that the cargo is released at the predetermined place and time. Therefore, the ability of MN patches to sufficiently pierce the skin is a crucial requirement. In the current review, the pain signal and its management during application of MNs and typical hypodermic needles are presented and compared. This is followed by a discussion on mechanical analysis and skin models used for insertion tests before application to clinical practice. Factors that affect insertion (e.g., geometry, material composition and cross-linking of MNs), along with recent advancements in developed strategies (e.g., insertion responsive patches and 3D printed biomimetic MNs using two-photon lithography) to improve the skin penetration are highlighted to provide a backdrop for future research.

Label-free microscopy: A non-invasive new tool to assess gametes and embryo quality

In PubMed, it is possible to find more than 40,000 papers on embryo evaluation in various species. However, there is no consensus or gold standard method on how to assess their developmental potential. In assisted reproduction the evaluation "problem" is not only limited to embryos but involves the gametes as well. This manuscript provides an overview of some possible applications of label-free microscopy, in particular we describe the potential of the holographic microscopy in the IVF lab. We describe the positive aspects of several currently available microscopy label-free systems. In conclusion, we believe that a next generation of microscopy able to give objective markers for gamete and embryo quality is around the corner.

Subcellular Duplex DNA and G-Quadruplex Interaction Profiling of a Hexagonal PtII Metallacycle

Metal-driven self-assembly afforded a multitude of fascinating supramolecular coordination complexes (SCCs) with applications as catalysts, host-guest, and stimuli-responsive systems. However, the interest in the biological applications of SCCs is only starting to emerge and thorough characterization of their behavior in biological milieus is still lacking. Herein, we report on the synthesis and detailed in-cell tracking of a Pt₂ L₂ metallacycle. We show that our hexagonal supramolecule accumulates in cancer cell nuclei, exerting a distinctive blue fluorescence staining of chromatin resistant to UV photobleaching selectively in nucleolar G4-rich regions. SCC co-localizes with epitopes of the quadruplex-specific antibody BG4 and replaces other well-known G4 stabilizers. Moreover, the photophysical changes accompanying the metallacycle binding to G4s in solution (fluorescence quenching, absorption enhancement) also take place intracellularly, allowing its subcellular interaction tracking.

Analytical and theranostic applications of gold nanoparticles and multifunctional nanocomposites

Gold nanoparticles (GNPs) and GNP-based multifunctional nanocomposites are the subject of intensive studies and biomedical applications. This minireview summarizes our recent efforts in analytical and theranostic applications of engineered GNPs and nanocomposites by using plasmonic properties of GNPs and various optical techniques. Specifically, we consider analytical biosensing; visualization and bioimaging of bacterial, mammalian, and plant cells; photodynamic treatment of pathogenic bacteria; and photothermal therapy of xenografted tumors. In addition to recently published reports, we discuss new data on dot immunoassay diagnostics of mycobacteria, multiplexed immunoelectron microscopy analysis of Azospirillum brasilense, materno-embryonic transfer of GNPs in pregnant rats, and combined photodynamic and photothermal treatment of rat xenografted tumors with gold nanorods covered by a mesoporous silica shell doped with hematoporphyrin.