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Issue 16, 2014
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Large scale infrared imaging of tissue micro arrays (TMAs) using a tunable Quantum Cascade Laser (QCL) based microscope

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Abstract

Chemical imaging in the field of vibrational spectroscopy is developing into a promising tool to complement digital histopathology. Applications include screening of biopsy tissue via automated recognition of tissue/cell type and disease state based on the chemical information from the spectrum. For integration into clinical practice, data acquisition needs to be speeded up to implement a rack based system where specimens are rapidly imaged to compete with current visible scanners where 100's of slides can be scanned overnight. Current Fourier transform infrared (FTIR) imaging with focal plane array (FPA) detectors are currently the state-of-the-art instrumentation for infrared absorption chemical imaging, however recent development in broadly tunable lasers in the mid-IR range is considered the most promising potential candidate for next generation microscopes. In this paper we test a prototype quantum cascade laser (QCL) based spectral imaging microscope with a focus on discrete frequency chemical imaging. We demonstrate how a protein chemical image of the amide I band (1655 cm−1) of a 2 × 2.4 cm2 breast tissue microarray (TMA) containing over 200 cores can be measured in 9 min. This result indicates that applications requiring chemical images from a few key wavelengths would be ideally served by laser-based microscopes.

Graphical abstract: Large scale infrared imaging of tissue micro arrays (TMAs) using a tunable Quantum Cascade Laser (QCL) based microscope

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Publication details

The article was received on 08 Apr 2014, accepted on 22 May 2014 and first published on 22 May 2014


Article type: Communication
DOI: 10.1039/C4AN00638K
Citation: Analyst, 2014,139, 3856-3859
  • Open access: Creative Commons BY license
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    Large scale infrared imaging of tissue micro arrays (TMAs) using a tunable Quantum Cascade Laser (QCL) based microscope

    P. Bassan, M. J. Weida, J. Rowlette and P. Gardner, Analyst, 2014, 139, 3856
    DOI: 10.1039/C4AN00638K

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