Ramanome database with machine learning helps to rapidly identify and functionally characterize microalgae.

From the soil microbiome, metabolic-function based identification, sorting and high-quality genome sequencing at the resolution of precisely one bacterial cell was demonstrated via RAGE-Seq.

Heavy water (D2O) probed Raman Microspectroscopy indicates a higher metabolic activity of live microbes to use easily degradable C after soil transplantation.

The Improved RACE-Seq technology (iRACE-Seq) was invented which elevates the success rate as well as genome coverage of cells after Raman-activated sorting (at the resolution of 5 bacterial cells per reaction).

A smartphone-based solution was introduced for rapid quantification of viable bacteria by single-cell microdroplet turbidity imaging, which reports live cell number after 6 hr-cultivation for E. coli and Bacillus subtilis.

Raman microspectroscopy probes bacterial metabolism and the intercellular interactions in situ at single-cell resolution.