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Mining Microalgae with Ramanome Database to Reduce Carbon Emissions

A microalgal ramanome database has been developed for rapid identification and metabolic profiling of microalgae, accelerating the search of potentially advantageous species to reduce carbon emissions.

While invisible to the naked eye, microalgae are ubiquitous phytoplankton of freshwater and marine ecosystems that exist in nearly every environment on the planet, including arid climates that fail to accommodate most agricultural crops. Recently, researchers have found that these single-celled organisms hold great potential in helping to reduce carbon emissions and achieve carbon neutrality.

However, present methods of assessing microalgae require scientists to culture each species, thus slowing down the study of microalgae. Moreover, with over 100,000 different species, each with their own distinct genetic and metabolic features, data mining of microalgae has been slow and tedious, consequently obstructing the search for potential candidates to help recycle carbon emissions.

To speed up and simplify the search of potentially advantageous microalgae, scientists from the Qingdao Institute of Bioenergy and Bioprocess Technology (QIBEBT) of the Chinese Academy of Sciences (CAS) have created a microalgal database that can help to rapidly determine which species can most readily convert carbon dioxide into high-value macromolecules. These valuable compounds can potentially be used as fuels, foods, and drugs. Their work has been published in Analytical Chemistry and their database, called Microalgal Ramanome Database (MARD), is available as an open-access web platform (http://mard.single-cell.cn/).

Using Raman Microspectroscopy, Dr. Heidari Baladehi and colleagues first generated Raman spectral images of the cells’ metabolic activities, from which they compiled “ramanomes,” or single-cell Raman spectra, for the microalgae and established their database. To date, their ramanaome database has successfully compiled single-cell Raman spectra from more than 9,000 cells of 27 phylogenetically diverse microalgal species.

Their database is also equipped with a machine learning system that can help to rapidly identify and functionally characterise microalgae. Machine learning allows the system to learn to identify functional and genetic patterns between different organisms as more information about microalgae is added to the database, thus allowing the database to be constantly improved as it is updated.

The core strength of their method, however, is being able to combine two Raman-based images – the pigment spectrum for pigments in the cells and the whole spectrum for other compounds present in the cell. Their novel approach is a significant improvement to conventional techniques that usually gather only one of the two images, and rarely collect them from the same cell.

The QIBET researchers suggested combining the two portraits to create a richer and more comprehensive compilation of microalgal information. With the combined portraits and machine learning algorithm, their database has succeeded in classifying microalgal species and their metabolic functions with 97 per cent accuracy. The database can also reveal the biosynthetic profile of Raman-sensitive metabolites in each cell. However, these functions are limited to cultured microalgae.

To accommodate for microalgae that have yet to be cultured, the scientists developed an alternative strategy. Firstly, the pigment spectra and whole spectrum of the cells are generated to profile their metabolic functions. Next, scientists can sort and sequence the genomes of the cells using a tool developed at Single-Cell Center called RACS-Seq. By feeding the Raman signal of cells to the RACS-Seq, high-quality genome sequences for target cells can be produced at the resolution of precisely one cell.

“This comprehensive approach for rapidly identifying and metabolically profiling single-cells, either cultured or uncultured, greatly accelerates the mining and screening of microalgal cell factories for carbon-neutral production,” explained Xu Jian, Director of Single-Cell Center and a senior author of the study.

Combining the powers of machine learning and Raman Microspectroscopy, their database has proven to be a promising tool to rapidly identify and metabolically profile single microalgal cells in a culture-free manner, and accelerate the mining of microalgae and their metabolites. The team hopes to expand the highly versatile system to simplify and accelerate the screening and assessment of other living organisms. [APBN]

Source: Baladehi et al. (2021). Culture-Free Identification and Metabolic Profiling of Microalgal Single Cells via Ensemble Learning of Ramanomes. Analytical Chemistry, 93(25), 8872-8880.