Noise Removal with Maintained Spatial Resolution in Raman Images of Cells Exposed to Submicron Polystyrene Particles

Abstract

The biodistribution of 300 nm polystyrene particles in A549 lung epithelial cells has been studied with confocal Raman spectroscopy. This is a label-free method in which particles and cells can be imaged without using dyes or fluorescent labels. The main drawback with Raman imaging is the comparatively low spatial resolution, which is aggravated in heterogeneous systems such as biological samples, which in addition often require long measurement times because of their weak Raman signal. Long measurement times may however induce laser-induced damage. In this study we use a super-resolution algorithm with Tikhonov regularization, intended to improve the image quality without demanding an increased number of collected pixels. Images of cells exposed to polystyrene particles have been acquired with two different step lengths, i.e., the distance between pixels, and compared to each other and to corresponding images treated with the super-resolution algorithm. It is shown that the resolution after application of super-resolution algorithms is not significantly improved compared to the theoretical limit for optical microscopy. However, to reduce noise and artefacts in the hyperspectral Raman images while maintaining the spatial resolution, we show that it is advantageous to use short mapping step lengths and super-resolution algorithms with appropriate regularization. The proposed methodology should be generally applicable for Raman imaging of biological samples and other photo-sensitive samples.

Smoothing and differentiation of data by Tikhonov and fractional derivative tools, applied to surface‐enhanced Raman scattering (SERS) spectra of crystal violet dye

“…The experimental IR or Raman spectra usually contain noise that may lead to misinterpretation. In this case, analytical signal processing techniques are used to remove the noise 6–12 from the experimental data. The goal of these mathematical operations is to remove noise without losing many details in experimental data, which could be correlated to some physical or chemical property of interest.…”

Smoothing and differentiation of data by Tikhonov and fractional derivative tools, applied to surface-enhanced Raman scattering (SERS) spectra of crystal violet dye

“…The experimental IR or Raman spectra usually contain noise that can lead to misinterpretation. In this case, analytical signal processing techniques are used to remove the noise from the experimental data [3,4,5,6]. The goal of these mathematical operations is to remove noise without losing much details in experimental data, which could be correlated to some physical or chemical property of interest.…”

Nanoparticles in Bioimaging

“…In their experimental work, Ahlinder et al [ 4 ] investigated the biodistribution of polystyrene nanoparticles in A549 lung epithelial cells using confocal Raman spectroscopy imaging. Algorithms were also applied for image enhancement.…”