Detection of chrysotile asbestos by using a chrysotile-binding protein

Recent progress and perspectives on the mechanisms underlying Asbestos toxicity

Most cases of mesothelioma are known to result from exposure to asbestos fibers in the environment or occupational ambient air. The following questions regarding asbestos toxicity remain partially unanswered: (i) why asbestos entering the alveoli during respiration exerts toxicity in the pleura; and (ii) how asbestos causes mesothelioma, even though human mesothelial cells are easily killed upon exposure to asbestos. As for the latter question, it is now thought that the frustrated phagocytosis of asbestos fibers by macrophages prolongs inflammatory responses and gives rise to a “mutagenic microenvironment” around mesothelial cells, resulting in their malignant transformation. Based on epidemiological and genetic studies, a carcinogenic model has been proposed in which BRCA1-associated protein 1 mutations are able to suppress cell death in mesothelial cells and increase genomic instability in the mutagenic microenvironment. This leads to additional mutations, such as CDKN2A [p16], NF2, TP53, LATS2, and SETD2, which are associated with mesothelioma carcinogenesis. Regarding the former question, the receptors involved in the intracellular uptake of asbestos and the mechanism of transfer of inhaled asbestos from the alveoli to the pleura are yet to be elucidated. Further studies using live-cell imaging techniques will be critical to fully understanding the mechanisms underlying asbestos toxicity.

Asbestos Detection with Fluorescence Microscopy Images and Deep Learning

Fluorescent probes can be used to detect various types of asbestos (serpentine and amphibole groups); however, the fiber counting using our previously developed software was not accurate for samples with low fiber concentration. Machine learning-based techniques (e.g., deep learning) for image analysis, particularly Convolutional Neural Networks (CNN), have been widely applied to many areas. The objectives of this study were to (1) create a database of a wide-range asbestos concentration (0-50 fibers/liter) fluorescence microscopy (FM) images in the laboratory; and (2) determine the applicability of the state-of-the-art object detection CNN model, YOLOv4, to accurately detect asbestos. We captured the fluorescence microscopy images containing asbestos and labeled the individual asbestos in the images. We trained the YOLOv4 model with the labeled images using one GTX 1660 Ti Graphics Processing Unit (GPU). Our results demonstrated the exceptional capacity of the YOLOv4 model to learn the fluorescent asbestos morphologies. The mean average precision at a threshold of 0.5 (mAP@0.5) was 96.1% ± 0.4%, using the National Institute for Occupational Safety and Health (NIOSH) fiber counting Method 7400 as a reference method. Compared to our previous counting software (Intec/HU), the YOLOv4 achieved higher accuracy (0.997 vs. 0.979), particularly much higher precision (0.898 vs. 0.418), recall (0.898 vs. 0.780) and F-1 score (0.898 vs. 0.544). In addition, the YOLOv4 performed much better for low fiber concentration samples (<15 fibers/liter) compared to Intec/HU. Therefore, the FM method coupled with YOLOv4 is remarkable in detecting asbestos fibers and differentiating them from other non-asbestos particles.

Differential Counting of Asbestos Using Phase Contrast and Fluorescence Microscopy

Considering the increasing use of various asbestos substitutes, asbestos risk management in many industries may require accurate techniques for detecting and distinguishing asbestos from non-asbestos fibers. Using fluorescently labeled asbestos-binding proteins, we previously developed a novel method for detection and counting of asbestos fibers under fluorescence microscopy (FM). This method can provide speedy, on-site detection and identification of the asbestos fibers and has higher sensitivity than phase contrast microscopy (PCM). However, current asbestos exposure limits are derived from risk assessments based on epidemiological studies that were conducted using PCM fiber counts. Therefore, the sensitivity of asbestos testing should be maintained at PCM level to properly assess compliance with these limit values. Here, we developed and tested a novel application of FM as a differential counting method that complements PCM analysis and is fully compatible with the PCM-based epidemiological data. In the combined PCM-FM method, the fluorescent asbestos-binding probe is applied prior to filter clearing. The method makes it possible to easily switch between two microscopic techniques while analyzing the same fields of view: PCM is used for counting fibers, and FM for differentiating asbestos from non-asbestos fibers. Using airborne dust samples from demolition sites in Japan, we compared PCM-FM with scanning electron microscopy (SEM)-based differential counting method. Statistical analysis indicated a slight conservative bias of PCM-FM method, combined with relatively high variability across the full range of fiber concentrations in our sample set. Using correlative microscopy, we also evaluated the specificity of FM staining, which is a potential cause of variability between the two methods. The energy-dispersive X-ray analysis indicated that ~95% of fluorescently stained fibers in the demolition site samples were correctly identified as asbestos. While further research is needed to fully clarify the causes of variability between FM- and SEM-based differential counting, PCM-FM could be used for rapid and selective detection of asbestos fibers in field samples.

In Situ Liquid Cell Observations of Asbestos Fiber Diffusion in Water

We present real-time observations of the diffusion of individual asbestos fibers in water. We first scaled up a technique for fluorescent tagging and imaging of chrysotile asbestos fibers and prepared samples with a distribution of fiber lengths ranging from 1 to 20 μm. Experiments were then conducted by placing a 20, 100, or 150 ppm solution of these fibers in a liquid cell mounted on a spinning-disk confocal microscope. Using automated elliptical-particle detection methods, we determined the translation and rotation and two-dimensional (2D) trajectories of thousands of diffusing chrysotile fibers. We find that fiber diffusion is size-dependent and in reasonable agreement with theoretical predictions for the Brownian motion of rods. This agreement is remarkable given that experiments involved non-idealized particles at environmentally relevant concentrations in a confined cell, in which particle-particle and particle-wall interactions might be expected to cause deviations from theory. Experiments also confirmed that highly elongated chrysotile fibers exhibit anisotropic diffusion at short time scales, a predicted effect that may have consequences for aggregate formation and transport of asbestos in confined spaces. The examined fibers vary greatly in their lengths and were prepared from natural chrysotile. Our findings thus indicate that the diffusion rates of a wide range of natural colloidal particles can be predicted from theory, so long as the particle aspect ratio is properly taken into account. This is an important first step for understanding aggregate formation and transport of non-spherical contaminant particles, in the environment and in vivo.

Development of an automated asbestos counting software based on fluorescence microscopy

An emerging alternative to the commonly used analytical methods for asbestos analysis is fluorescence microscopy (FM), which relies on highly specific asbestos-binding probes to distinguish asbestos from interfering non-asbestos fibers. However, all types of microscopic asbestos analysis require laborious examination of large number of fields of view and are prone to subjective errors and large variability between asbestos counts by different analysts and laboratories. A possible solution to these problems is automated counting of asbestos fibers by image analysis software, which would lower the cost and increase the reliability of asbestos testing. This study seeks to develop a fiber recognition and counting software for FM-based asbestos analysis. We discuss the main features of the developed software and the results of its testing. Software testing showed good correlation between automated and manual counts for the samples with medium and high fiber concentrations. At low fiber concentrations, the automated counts were less accurate, leading us to implement correction mode for automated counts. While the full automation of asbestos analysis would require further improvements in accuracy of fiber identification, the developed software could already assist professional asbestos analysts and record detailed fiber dimensions for the use in epidemiological research.

Molecular engineering of a fluorescent bioprobe for sensitive and selective detection of amphibole asbestos

Fluorescence microscopy-based affinity assay could enable highly sensitive and selective detection of airborne asbestos, an inorganic environmental pollutant that can cause mesothelioma and lung cancer. We have selected an Escherichia coli histone-like nucleoid structuring protein, H-NS, as a promising candidate for an amphibole asbestos bioprobe. H-NS has high affinity to amphibole asbestos, but also binds to an increasingly common asbestos substitute, wollastonite. To develop a highly specific Bioprobe for amphibole asbestos, we first identified a specific but low-affinity amosite-binding sequence by slicing H-NS into several fragments. Second, we constructed a streptavidin tetramer complex displaying four amosite-binding fragments, resulting in the 250-fold increase in the probe affinity as compared to the single fragment. The tetramer probe had sufficient affinity and specificity for detecting all the five types of asbestos in the amphibole group, and could be used to distinguish them from wollastonite. In order to clarify the binding mechanism and identify the amino acid residues contributing to the probe’s affinity to amosite fibers, we constructed a number of shorter and substituted peptides. We found that the probable binding mechanism is electrostatic interaction, with positively charged side chains of lysine residues being primarily responsible for the probe’s affinity to asbestos.

Selective detection and automated counting of fluorescently-labeled chrysotile asbestos using a dual-mode high-throughput microscopy (DM-HTM) method

Phase contrast microscopy (PCM) is a widely used analytical method for airborne asbestos, but it is unable to distinguish asbestos from non-asbestos fibers and requires time-consuming and laborious manual counting of fibers. Previously, we developed a high-throughput microscopy (HTM) method that could greatly reduce human intervention and analysis time through automated image acquisition and counting of fibers. In this study, we designed a dual-mode HTM (DM-HTM) device for the combined reflection and fluorescence imaging of asbestos, and automated a series of built-in image processing commands of ImageJ software to test its capabilities. We used DksA, a chrysotile-adhesive protein, for selective detection of chrysotile fibers in the mixed dust-free suspension of crysotile and amosite prepared in the laboratory. We demonstrate that fluorescently-stained chrysotile and total fibers can be identified and enumerated automatically in a high-throughput manner by the DM-HTM system. Combined with more advanced software that can correctly identify overlapping and branching fibers and distinguish between fibers and elongated dust particles, the DM-HTM method should enable fully automated counting of airborne asbestos.

Liquid-phase sample preparation method for real-time monitoring of airborne asbestos fibers by dual-mode high-throughput microscopy

Asbestos that had been used widely as a construction material is a first-level carcinogen recognized by the World Health Organization. It can be accumulated in body by inhalation causing virulent respiratory diseases including lung cancer. In our previous study, we developed a high-throughput microscopy (HTM) system that can minimize human intervention accompanied by the conventional phase contrast microscopy (PCM) through automated counting of fibrous materials and thus significantly reduce analysis time and labor. Also, we attempted selective detection of chrysotile using DksA protein extracted from Escherichia coli through a recombinant protein production technique, and developed a dual-mode HTM (DM-HTM) by upgrading the HTM device. We demonstrated that fluorescently-labeled chrysotile asbestos fibers can be identified and enumerated automatically among other types of asbestos fibers or non-asbestos particles in a high-throughput manner through a newly modified HTM system for both reflection and fluorescence imaging. However there is a limitation to apply DM-HTM to airborne sample with current air collecting method due to the difficulty of applying the protein to dried asbestos sample. Here, we developed a technique for preparing liquid-phase asbestos sample using an impinger normally used to collect odor molecules in the air. It would be possible to improve the feasibility of the dual-mode HTM by integrating a sample preparation unit for making collected asbestos sample dispersed in a solution. The new technique developed for highly sensitive and automated asbestos detection can be a potential alternative to the conventional manual counting method, and it may be applied on site as a fast and reliable environmental monitoring tool.

Automated counting of airborne asbestos fibers by a high-throughput microscopy (HTM) method

Inhalation of airborne asbestos causes serious health problems such as lung cancer and malignant mesothelioma. The phase-contrast microscopy (PCM) method has been widely used for estimating airborne asbestos concentrations because it does not require complicated processes or high-priced equipment. However, the PCM method is time-consuming and laborious as it is manually performed off-site by an expert. We have developed a high-throughput microscopy (HTM) method that can detect fibers distinguishable from other spherical particles in a sample slide by image processing both automatically and quantitatively. A set of parameters for processing and analysis of asbestos fiber images was adjusted for standard asbestos samples with known concentrations. We analyzed sample slides containing airborne asbestos fibers collected at 11 different workplaces following PCM and HTM methods, and found a reasonably good agreement in the asbestos concentration. Image acquisition synchronized with the movement of the robotic sample stages followed by an automated batch processing of a stack of sample images enabled us to count asbestos fibers with greatly reduced time and labors. HTM should be a potential alternative to conventional PCM, moving a step closer to realization of on-site monitoring of asbestos fibers in air.

Selective detection of airborne asbestos fibers using protein-based fluorescent probes

Fluorescence microscopy (FM) is one of the most important analytical tools in modern life sciences, sufficiently sensitive to allow observation of single molecules. Here we describe the first application of the FM technique for the detection of inorganic environmental pollutants-airborne asbestos fibers that can cause asbestosis, mesothelioma, and lung cancer. In order to assess FM capabilities for detecting and counting asbestos fibers, we screened E. coli lysate for proteins that bind to amphibole asbestos. In combination with the previously discovered E. coli protein DksA (Kuroda et al., Biotechnol. Bioeng. 2008, 99, 285-289) that can specifically bind to chrysotile, the newly identified GatZ protein was used for selective and highly sensitive detection of two different asbestos types. Our novel FM-based method overcomes a number of limitations of the commonly used phase-contrast microscopy (PCM) method, offering a convenient alternative to PCM for airborne asbestos monitoring.

Development of an Automated Microscope for Supporting Qualitative Asbestos Analysis by Dispersion Staining

This paper introduces automated microscopic observation supporting qualitative asbestos analysis. Visual qualitative asbestos evaluation generally involves dispersion staining. Operators conventionally check and count asbestos fibers visually by microscope. We are developing automated microscopic observation to support qualitative asbestos analysis. The system images fibers by microscope and saves them automatically to a database. We introduce system concepts and performance using the prototype we developed.