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

Our plain-English summary of Selective detection and automated counting of fluorescently-labeled chrysotile asbestos using a dual-mode high-throughput microscopy (DM-HTM) method, by M.O. Cho, H.M. Chang, D. Lee, et al (Sensors, 2013).

The study

• Authors: M.O. Cho, H.M. Chang, D. Lee, Y.G. Yu, H. Han, J.K. Kim
• Journal: Sensors
• Published: 2013;13(5):5686-99
• Cited by: 8 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

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.

In plain English

This was a laboratory study developing a way to detect and count asbestos fibers. It did not study people or disease. The authors modified their earlier automated microscope so it could take both reflected-light and fluorescence images at the same slide positions, and used ImageJ software to count fibers automatically. They produced DksA, a protein that binds chrysotile, and tagged it with a fluorescent dye. In tests with chrysotile, amosite and crocidolite, the protein bound mostly to chrysotile, with slight amounts also coming off amosite and crocidolite. In diluted chrysotile samples, automatic counts from reflection images were 10% less, and counts from fluorescence images 25% greater, than manual counts by a human researcher, which the authors treated as the gold standard. In laboratory mixtures of chrysotile and amosite, fluorescence counts were directly proportional to chrysotile concentration. Background noise, which bright-field imaging often mistakes for fibers, was reduced by approximately 90% in fluorescence mode. The authors concluded that the method improved selectivity for chrysotile. They said it still needs more advanced software for overlapping and branching fibers and dust particles before airborne asbestos can be counted fully automatically.

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-development study of a microscope device and automated counting method
• Who was studied: Laboratory-prepared asbestos samples, with no human subjects. Chrysotile, amosite and crocidolite were used for protein-binding tests. Serially diluted chrysotile suspensions and chrysotile-amosite mixtures were used for counting. Fluorescent beads were used to check the fluorescence mode. 108 images were taken per slide.
• Exposure: No health exposure was studied. The asbestos fiber types tested were chrysotile, amosite and crocidolite. Chrysotile was labeled with the chrysotile-binding protein DksA tagged with Alexa Fluor 555 dye. Mixed samples had chrysotile-to-amosite ratios of 200/0, 100/100, 50/150 and 25/175 micrograms per mL.
• Main results:
  • In the binding test, DksA bound mostly to chrysotile. It was slightly eluted from amosite and crocidolite.
  • No fluorescence was seen in any asbestos type treated with dye alone, without the protein.
  • In the fluorescent bead test, measured counts against theoretical counts gave a regression slope of 0.89 (R2 = 0.99). This means measured counts were about 10% below the theoretical number.
  • In diluted chrysotile samples, automatic counts were compared with manual counts. Counts from reflection images were 10% less than manual counts, and counts from fluorescence images were 25% greater.
  • In the chrysotile-amosite mixtures, the fiber count from fluorescence images was directly proportional to chrysotile concentration.
  • In chrysotile samples, reflection-image counts fluctuated and overestimated fibers on slides where air bubbles formed.
  • Background noise that is often taken for fibers in bright-field imaging was reduced by approximately 90% in fluorescence mode.
• The authors' conclusion: The authors concluded that using the chrysotile-binding protein with the dual-mode microscope improved the selectivity of asbestos detection.
• Limitations the authors state:
  • Neither the reflection nor the fluorescence automatic counts accurately reproduced the manual counts.
  • The authors said testing procedures need standardization to reduce the bias or make it reproducible.
  • Fully automated counting would require more advanced software that can handle overlapping, cross-linking and branching fibers and tell fibers from elongated dust particles.
  • Bead counts fell about 10% short of theoretical numbers. The authors attributed this, presumably, to sample loss during pipetting and uneven particle distribution.

Topics

Medical terms in this study, explained in our Lexicon: Chrysotile, phase contrast microscopy (PCM), Amosite.

Related studies

• Automated counting of airborne asbestos fibers by a high-throughput microscopy (HTM) method — M.O. Cho, S. Yoon, et al. (2011)
• Novel selective dyeing method for chrysotile asbestos detection in concrete materials — Y. Oke, N. Yamasaki, et al. (2008)
• Development of an automated asbestos counting software based on fluorescence microscopy — M. Alexandrov, E. Ichida, et al. (2015)
• Computer-assisted diagnosis for early stage pleural mesothelioma: towards automated detection and quantitative assessment of pleural thickening from thoracic CT images — K. Chaisaowong, T. Aach, et al. (2007)
• An inter-laboratory study to determine the effectiveness of procedures for discriminating amphibole asbestos fibers from amphibole cleavage fragments in fiber counting by phase-contrast microscopy — M. Harper, E.G. Lee, et al. (2012)
• Evaluation of the dark-medium objective lens in counting asbestos fibers by phase-contrast microscopy — E.G. Lee, J.H. Nelson, et al. (2015)

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