Infrared spectroscopic laser scanning confocal microscopy for whole-slide chemical imaging

Kevin Yeh, Ishaan Sharma, Kianoush Falahkheirkhah, Matthew P. Confer, Andres C. Orr, Yen-Ting Liu, Yamuna Phal, Ruo-Jing Ho, Manu Mehta, Ankita Bhargava, Wenyan Mei, Georgina Cheng, John C. Cheville and Rohit Bhargava ()
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Kevin Yeh: University of Illinois at Urbana-Champaign
Ishaan Sharma: University of Illinois at Urbana-Champaign
Kianoush Falahkheirkhah: University of Illinois at Urbana-Champaign
Matthew P. Confer: University of Illinois at Urbana-Champaign
Andres C. Orr: University of Illinois at Urbana-Champaign
Yen-Ting Liu: University of Illinois at Urbana-Champaign
Yamuna Phal: University of Illinois at Urbana-Champaign
Ruo-Jing Ho: University of Illinois at Urbana-Champaign
Manu Mehta: University of Illinois at Urbana-Champaign
Ankita Bhargava: University of Illinois Laboratory High School
Wenyan Mei: University of Illinois at Urbana-Champaign
Georgina Cheng: University of Illinois at Urbana-Champaign
John C. Cheville: Mayo Clinic
Rohit Bhargava: University of Illinois at Urbana-Champaign

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Chemical imaging, especially mid-infrared spectroscopic microscopy, enables label-free biomedical analyses while achieving expansive molecular sensitivity. However, its slow speed and poor image quality impede widespread adoption. We present a microscope that provides high-throughput recording, low noise, and high spatial resolution where the bottom-up design of its optical train facilitates dual-axis galvo laser scanning of a diffraction-limited focal point over large areas using custom, compound, infinity-corrected refractive objectives. We demonstrate whole-slide, speckle-free imaging in ~3 min per discrete wavelength at 10× magnification (2 μm/pixel) and high-resolution capability with its 20× counterpart (1 μm/pixel), both offering spatial quality at theoretical limits while maintaining high signal-to-noise ratios (>100:1). The data quality enables applications of modern machine learning and capabilities not previously feasible – 3D reconstructions using serial sections, comprehensive assessments of whole model organisms, and histological assessments of disease in time comparable to clinical workflows. Distinct from conventional approaches that focus on morphological investigations or immunostaining techniques, this development makes label-free imaging of minimally processed tissue practical.

Date: 2023
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DOI: 10.1038/s41467-023-40740-w

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