A Proposal for Automated Background Removal of Bio-Raman Data

Deuterium-labeled Raman tracking of glucose accumulation and protein metabolic dynamics in Aspergillus nidulans hyphal tips

Filamentous fungi grow exclusively at their tips, where many growth-related fungal processes, such as enzyme secretion and invasion into host cells, take place. Hyphal tips are also a site of active metabolism. Understanding metabolic dynamics within the tip region is therefore important for biotechnology and medicine as well as for microbiology and ecology. However, methods that can track metabolic dynamics with sufficient spatial resolution and in a nondestructive manner are highly limited. Here we present time-lapse Raman imaging using a deuterium (D) tracer to study spatiotemporally varying metabolic activity within the hyphal tip of Aspergillus nidulans. By analyzing the carbon-deuterium (C-D) stretching Raman band with spectral deconvolution, we visualize glucose accumulation along the inner edge of the hyphal tip and synthesis of new proteins from the taken-up D-labeled glucose specifically at the central part of the apical region. Our results show that deuterium-labeled Raman imaging offers a broadly applicable platform for the study of metabolic dynamics in filamentous fungi and other relevant microorganisms in vivo.

Automatic Background Removal and Correction of Systematic Error Caused by Noise Expecting Bio-Raman Big Data Analysis

Spectral pretreatments, such as background removal from Raman big data, are crucial to have a smooth link to advanced spectral analysis. Recently, we developed an automated background removal method, where we considered the shortest length of a spectrum by changing the scaling factor of the background spectrum. Here, we propose a practical way to correct the systematic error caused by noise from measurements. This correction has been realized to be more effective and accurate for automatic background removal.