Presentation Information
[4GteX-04]Raman Spectral Decomposition Framework for Non-Destructive Single-Cell Metabolomics in Bioprocess Development
○Masahiro Ando1, Shunnosuke Suwa2, Kento Hasegawa2, Haruko Takeyama1,2,3 (1. Res. Org. Nano Life Innov., Waseda Univ. (Japan), 2. Grad. Sch. Adv. Sci. Eng., Waseda Univ. (Japan), 3. Inst. Adv. Res. Biosyst. Dynam., Waseda Res. Inst. Sci. Eng., Waseda Univ. (Japan))
Keywords:
Raman spectroscopy,single-cell metabolomics,non-destructive analysis,bioprocess monitoring,microbial heterogeneity
Understanding cellular heterogeneity is crucial for bioprocess optimization. Traditional bulk analytical methods provide only population-averaged measurements that mask the diverse metabolic states of individual cells, limiting our ability to identify and control key phenotypic variations. To address this challenge, we have developed a Raman-based non-destructive metabolomics platform that enables multi-component molecular profiling at the single-cell level.
Our approach employs confocal Raman microspectroscopy combined with an advanced spectral deconvolution framework based on Multivariate Curve Resolution (MCR). This framework resolves the highly overlapping spectral features characteristic of complex biological samples, enabling simultaneous detection and spatial mapping of multiple classes of intracellular biomolecules with subcellular resolution, without any labeling or sample destruction.
A key strength of this platform lies in its versatility across different biological systems and analytical scales. We have applied this framework to a wide range of microorganisms — from bacteria and microalgae to filamentous fungi and actinomycetes — demonstrating its broad applicability to diverse bioprocess-relevant organisms. At the single-cell level, MCR-based decomposition can extract intensity profiles of over ten molecular components from individual cells, revealing metabolic heterogeneity within populations. Integration with machine learning further enables automated classification of cell states based on their metabolic fingerprints. At the colony level, hyperspectral Raman mapping combined with MCR decomposition allows non-destructive profiling of intact microbial colonies, providing rapid identification of secondary metabolite-producing strains.
The non-destructive nature of this approach opens unique analytical possibilities. Temporal tracking of the same cells enables monitoring of metabolic dynamics during cultivation, capturing transient states that would be lost with destructive methods. Furthermore, sequential multi-omics analysis becomes feasible, where Raman metabolic phenotyping of individual cells can be followed by downstream molecular analyses such as genomics, providing integrated molecular insights across multiple levels.
This framework advances our understanding of microbial metabolism at the single-cell level, with broad applications in strain screening, bioprocess monitoring, and natural product discovery. Integration of this Raman metabolomics platform into high-throughput screening workflows further enhances its potential for the efficient exploration of useful microorganisms. By revealing cellular heterogeneity and metabolite dynamics non-destructively, this approach contributes to developing more efficient bioprocesses.
Our approach employs confocal Raman microspectroscopy combined with an advanced spectral deconvolution framework based on Multivariate Curve Resolution (MCR). This framework resolves the highly overlapping spectral features characteristic of complex biological samples, enabling simultaneous detection and spatial mapping of multiple classes of intracellular biomolecules with subcellular resolution, without any labeling or sample destruction.
A key strength of this platform lies in its versatility across different biological systems and analytical scales. We have applied this framework to a wide range of microorganisms — from bacteria and microalgae to filamentous fungi and actinomycetes — demonstrating its broad applicability to diverse bioprocess-relevant organisms. At the single-cell level, MCR-based decomposition can extract intensity profiles of over ten molecular components from individual cells, revealing metabolic heterogeneity within populations. Integration with machine learning further enables automated classification of cell states based on their metabolic fingerprints. At the colony level, hyperspectral Raman mapping combined with MCR decomposition allows non-destructive profiling of intact microbial colonies, providing rapid identification of secondary metabolite-producing strains.
The non-destructive nature of this approach opens unique analytical possibilities. Temporal tracking of the same cells enables monitoring of metabolic dynamics during cultivation, capturing transient states that would be lost with destructive methods. Furthermore, sequential multi-omics analysis becomes feasible, where Raman metabolic phenotyping of individual cells can be followed by downstream molecular analyses such as genomics, providing integrated molecular insights across multiple levels.
This framework advances our understanding of microbial metabolism at the single-cell level, with broad applications in strain screening, bioprocess monitoring, and natural product discovery. Integration of this Raman metabolomics platform into high-throughput screening workflows further enhances its potential for the efficient exploration of useful microorganisms. By revealing cellular heterogeneity and metabolite dynamics non-destructively, this approach contributes to developing more efficient bioprocesses.
Comment
To browse or post comments, you must log in.Log in