Condensing Raman spectrum for single-cell phenotype analysis

Abstract

BackgroundIn recent years, high throughput and non-invasive Raman spectrometry technique has matured as an effective approach to identification of individual cells by species, even in complex, mixed populations. Raman profiling is an appealing optical microscopic method to achieve this. To fully utilize Raman proling for single-cell analysis, an extensive understanding of Raman spectra is necessary to answer questions such as which filtering methodologies are effective for pre-processing of Raman spectra, what strains can be distinguished by Raman spectra, and what features serve best as Raman-based biomarkers for single-cells, etc.ResultsIn this work, we have proposed an approach called rDisc to discretize the original Raman spectrum into only a few (usually less than 20) representative peaks (Raman shifts). The approach has advantages in removing noises, and condensing the original spectrum. In particular, effective signal processing procedures were designed to eliminate noise, utilising wavelet transform denoising, baseline correction, and signal normalization. In the discretizing process, representative peaks were selected to signicantly decrease the Raman data size. More importantly, the selected peaks are chosen as suitable to serve as key biological markers to differentiate species and other cellular features. Additionally, the classication performance of discretized spectra was found to be comparable to full spectrum having more than 1000 Raman shifts. Overall, the discretized spectrum needs about 5storage space of a full spectrum and the processing speed is considerably faster. This makes rDisc clearly superior to other methods for single-cell classication.

scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods

“…Recently, many clustering methods have been developed to deal with cell clustering in single-cell sequencing data ( 50 , 51 ). It proves that in downstream analysis, identification of cell subpopulations is a key application of scRNA-seq.…”

Culture‐Free Detection of Crop Pathogens at the Single‐Cell Level by Micro‐Raman Spectroscopy

“…The background noise originating from the instruments, optical elements, and surrounding environments can complicate the interpretation of Raman spectra. Thus, the initial Raman spectra of plant pathogens underwent quality control with Labspec 5 and our recently developed signal processing approach (rDisc),31 which has been demonstrated to be effective in noise eliminating, wavelet transform denoising, baseline correction, and signal normalization. These processed, high‐quality spectra were then classified using PCA analysis as described by Xie 28.…”

Spatiotemporal switching signals for cancer stem cell activation in pediatric origins of adulthood cancer: Towards a watch-and-wait lifetime strategy for cancer treatment

“…Ideal imaging would be label-free, not invasive or minimally invasive. Such technologies might include “Raman spectroscopy for spontaneous and coherent Raman scattering microscopic imaging in the context of single cells, laser tweezers, tissue sections, biopsies and condensing Raman spectrum for a single-cell phenotype analysis”[ 42 , 43 ], as currently used for defining nasopharyngeal carcinoma[ 44 ]. Raman profiling for the single-cell analysis requires establish Raman spectra of individual cells by using filtering methodologies for pre-processing of Raman spectra signature, allowing to distinguish and feature as Raman-based biomarkers for single-cells with capture of spatial and temporal changes.…”