Identification of the Cellular Tipping Point in the Inflammation Model of LPS-Induced RAW264.7 Macrophages Through Raman Spectroscopy and the Dynamical Network Biomarker Theory.

Abstract

Raman spectroscopy is a non-destructive spectroscopic technique that provides complex molecular information. It is used to examine the physiological and pathological responses of living cells, such as differentiation, malignancy, and inflammation. The responses of two cellular states, initial and full-blown inflammation, have mainly been investigated using a comparative analysis with Raman spectra. However, the tipping point of the inflammatory state transition remains unclear. Therefore, the present study attempted to identify the tipping point of inflammation using a cell model. We stimulated RAW264.7 mouse macrophages with lipopolysaccharide (LPS) and continuously collected Raman spectra every 2 h for 24 h from the initial and full-blown inflammation states. A Partial Least Squares analysis and Principal Component Analysis-Linear Discriminant Analysis predicted the tipping point as 14 h after the LPS stimulation. In addition, a Dynamical Network Biomarker (DNB) analysis, identifying the tipping point of a state transition in various phenomena, indicated that the tipping point was 14 h and identified tryptophan as a biomarker. The results of a multivariate analysis and DNB analysis show the cellular tipping point.