Broadband CARS hyperspectral classification of single leukemia cells

21 August 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Broadband CARS is a coherent Raman scattering technique that provides access to the full biological vibrational spectrum within milliseconds, facilitating the recording of widefield hyperspectral Raman images. In this work, BCARS hyperspectral images of two different unstained leukemic blood cells were recorded and analysed using multivariate statistical algorithms in order to determine the spectral differences between the species. A classifier was trained, which could distinguish the known cells with a 97 % out-of-bag accuracy. The classifier was then applied to unlabelled samples containing a mixture of the two cell types on the same coverslip. This work demonstrates the effective label-free high-throughput single-cell analysis of blood using BCARS. A key feature of this work is the use of an image-based deep-learning cell segmentation algorithm that enables the spectra recorded within a given cell boundary to be integrated producing a high quality single cell spectrum for classification.

Keywords

CARS
Raman
Coherent Raman spectroscopy

Supplementary materials

Title
Description
Actions
Title
Supplementary document
Description
Figures for the random forest classifier training accuracy, images of the studied cells, and size distribution analysis based on image segmentation
Actions

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.