Evaluation of sensitivity of fluorescence-based asbestos detection by correlative microscopy

Our plain-English summary of Evaluation of sensitivity of fluorescence-based asbestos detection by correlative microscopy, by T. Ishida, M. Alexandrov, T. Nishimura, et al (Journal of Fluorescence, 2012).

The study

• Authors: T. Ishida, M. Alexandrov, T. Nishimura, K. Minakawa, R. Hirota, K. Sekiguchi, N. Kohyama, A. Kuroda
• Journal: Journal of Fluorescence
• Published: 2012;22(1):357-63
• Cited by: 9 articles, as counted by NIH iCite
• Read it: PubMed · Publisher

Abstract

Fluorescence microscopy (FM) has recently been applied to the detection of airborne asbestos fibers that can cause asbestosis, mesothelioma and lung cancer. In our previous studies, we discovered that the E. coli protein DksA specifically binds to the most commonly used type of asbestos, chrysotile. We also demonstrated that fluorescent-labeled DksA enabled far more specific and sensitive detection of airborne asbestos fibers than conventional phase contrast microscopy (PCM). However, the actual diameter of the thinnest asbestos fibers visualized under the FM platform was unclear, as their dimensions were below the resolution of optical microscopy. Here, we used correlative microscopy (scanning electron microscopy [SEM] in combination with FM) to measure the actual diameters of asbestos fibers visualized under the FM platform with fluorescent-labeled DksA as a probe. Our analysis revealed that FM offers sufficient sensitivity to detect chrysotile fibrils as thin as 30-35 nm. We therefore conclude that as an analytical method, FM has the potential to detect all countable asbestos fibers in air samples, thus approaching the sensitivity of SEM. By visualizing thin asbestos fibers at approximately tenfold lower magnifications, FM enables markedly more rapid counting of fibers than SEM. Thus, fluorescence microscopy represents an advanced analytical tool for asbestos detection and monitoring.

In plain English

This was a laboratory study of a way to detect asbestos fibers. It did not study people or disease. The authors used a fluorescent probe made from a bacterial protein, DksA, which their earlier work had found binds to chrysotile asbestos. They tested the probe on chrysotile, amosite and crocidolite reference samples and on non-asbestos fibers such as glass wool, rock wool and wollastonite. The probe bound to chrysotile but not to the amphibole asbestos or the other mineral fibers tested. They then examined one filter carrying a heavy load of chrysotile, first under a fluorescence microscope and then, at the same spot, under a scanning electron microscope (SEM). The thinnest chrysotile fibrils, measured by SEM at about 30 to 35 nanometers across, were also visible in the fluorescence image. The authors noted that about 100 SEM images were needed to cover one fluorescence field of view. They concluded that fluorescence microscopy can detect single chrysotile fibrils and give counts equivalent to SEM. They stated that the method cannot tell amphibole types apart and that not every fibrous material could be tested for false binding.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: laboratory methods study (correlative fluorescence microscopy and scanning electron microscopy of prepared filter samples)
• Who was studied: No human subjects. Reference fiber samples were dispersed in liquid and filtered onto membrane filters. The size comparison used a polycarbonate filter carrying a high load of chrysotile fibers.
• Exposure: Chrysotile (JAWE 111), amosite (JAWE 231) and crocidolite (JAWE 331) asbestos, plus non-asbestos fibers (glass wool, rock wool, micro glass fibers, refractory fibers, several whisker types, wollastonite), all stained with a Cy3-labeled DksA protein probe
• Main results:
  • The DksA-Cy3 probe bound to chrysotile but not to the amphibole asbestos or the other mineral fibers examined.
  • The thinnest chrysotile fibrils in the sample measured about 30 to 35 nm by SEM at ×28,800 magnification, and these fibers were also visible on the fluorescence micrograph.
  • SEM needed about 100 micrographs to cover one fluorescence or phase contrast field of view, because lower SEM magnifications did not reliably show the thinnest fibers.
  • EDX analysis under SEM showed magnesium, silicon and oxygen peaks, consistent with chrysotile. Fluorescent staining did not interfere with this analysis.
• The authors' conclusion: The authors concluded that fluorescence microscopy can detect single chrysotile fibrils and give counts equivalent to SEM, and that correlative microscopy can combine rapid counting by fluorescence microscopy with fiber identification by SEM.
• Limitations the authors state:
  • The method cannot distinguish between the different types of amphibole asbestos.
  • Not every existing or new fibrous material can be tested for false binding of the probe, so false positives cannot be completely ruled out.
  • The reference samples tested do not cover the full variety of natural and man-made fibers.

Topics

The National Library of Medicine indexes this study under subjects our site covers: Chrysotile.

Medical terms in this study, explained in our Lexicon: Mesothelioma, Lung Cancer, Chrysotile, phase contrast microscopy (PCM), Scanning Electron Microscopy (SEM), Fibril.

Related studies

• Differential Counting of Asbestos Using Phase Contrast and Fluorescence Microscopy — T. Nishimura, M. Alexandrov, et al. (2016)
• Development of an automated asbestos counting software based on fluorescence microscopy — M. Alexandrov, E. Ichida, et al. (2015)
• Selective detection of airborne asbestos fibers using protein-based fluorescent probes — T. Ishida, M. Alexandrov, et al. (2010)
• Rapid on-site detection of airborne asbestos fibers and potentially hazardous nanomaterials using fluorescence microscopy-based biosensing — A. Kuroda, M. Alexandrov, et al. (2016)
• Asbestos Detection with Fluorescence Microscopy Images and Deep Learning — C. Cai, T. Nishimura, et al. (2021)
• Synchrotron soft X-ray imaging and fluorescence microscopy reveal novel features of asbestos body morphology and composition in human lung tissues — L. Pascolo, A. Gianoncelli, et al. (2011)

Browse return to medical research.