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Home
Infrared Nanoscopy For Subcellular Chemical Imaging.

Home
Infrared Nanoscopy For Subcellular Chemical Imaging.

Related Experiment Video

Imaging Subcellular Structures in the Living Zebrafish Embryo

Imaging Subcellular Structures in the Living Zebrafish Embryo

Published on: April 2, 2016

Infrared nanoscopy for subcellular chemical imaging.

Katerina Kanevche¹, David Joll Burr^2,3, Janina Drauschke²

¹Department of Chemistry, Princeton University, Princeton, NJ, USA.

|January 23, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Infrared nanoscopy offers nanoscale chemical imaging beyond the diffraction limit. This technique reveals subcellular details and metabolic activity, with machine learning enhancing its biological applications.

Keywords:

O-PTIR machine learning organelle mapping super resolution vibrational spectroscopy

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Area of Science:

Spectroscopy and Microscopy
Nanotechnology
Chemical Imaging

Background:

Infrared (IR) nanoscopy combines vibrational spectroscopy with near-field optics for nanoscale chemical analysis.
It overcomes the classical diffraction limit, achieving nanometer-scale spatial resolution.
Atomic force microscopy probes detect light-matter interactions in the optical near field.

Purpose of the Study:

To review recent biological applications of IR nanoscopy techniques.
To highlight technical advancements and machine learning integration.
To emphasize the potential of label-free IR nanoscopy in biology.

Main Methods:

Scattering-type scanning near-field optical microscopy (s-SNOM)
Nanoscale Fourier-transform infrared spectroscopy (nano-FTIR)

Utilizing atomic force microscopy probes for near-field detection

Main Results:

Demonstrated ability to resolve subcellular ultrastructure in various cell types.
Enabled study of biological processes like single-cell metabolic activity.
Showcased technical improvements and machine learning for data analysis.

Conclusions:

Label-free IR nanoscopy provides high-resolution chemical information at the nanoscale.
It is a powerful tool for investigating complex biological systems.
Emerging machine learning approaches promise to further expand its capabilities.