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Culture-Free Identification and Metabolic Profiling of Microalgal Single Cells via Ensemble Learning of Ramanomes
- Source :
- Analytical Chemistry. 93:8872-8880
- Publication Year :
- 2021
- Publisher :
- American Chemical Society (ACS), 2021.
-
Abstract
- Microalgae are among the most genetically and metabolically diverse organisms on earth, yet their identification and metabolic profiling have generally been slow and tedious. Here, we established a reference ramanome database consisting of single-cell Raman spectra (SCRS) from >9000 cells of 27 phylogenetically diverse microalgal species, each under stationary and exponential states. When combined, prequenching ("pigment spectrum" (PS)) and postquenching ("whole spectrum" (WS)) signals can classify species and states with 97% accuracy via ensemble machine learning. Moreover, the biosynthetic profile of Raman-sensitive metabolites was unveiled at single cells, and their interconversion was detected via intra-ramanome correlation analysis. Furthermore, not-yet-cultured cells from the environment were functionally characterized via PS and WS and then phylogenetically identified by Raman-activated sorting and sequencing. This PS-WS combined approach for rapidly identifying and metabolically profiling single cells, either cultured or uncultured, greatly accelerates the mining of microalgae and their products.
- Subjects :
- Profiling (computer programming)
Chemistry
010401 analytical chemistry
Computational biology
Spectrum Analysis, Raman
010402 general chemistry
01 natural sciences
Ensemble learning
Combined approach
0104 chemical sciences
Analytical Chemistry
Machine Learning
Correlation analysis
Microalgae
Metabolomics
Identification (biology)
Cells, Cultured
Subjects
Details
- ISSN :
- 15206882 and 00032700
- Volume :
- 93
- Database :
- OpenAIRE
- Journal :
- Analytical Chemistry
- Accession number :
- edsair.doi.dedup.....ab8ae291ac7b8bedde201a27b4d8b1cb