Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI Era

Biosensors (Basel). 2023 Jan 26;13(2):187. doi: 10.3390/bios13020187.

Abstract

Nowadays, morphology and molecular analyses at the single-cell level have a fundamental role in understanding biology better. These methods are utilized for cell phenotyping and in-depth studies of cellular processes, such as mitosis. Fluorescence microscopy and optical spectroscopy techniques, including Raman micro-spectroscopy, allow researchers to examine biological samples at the single-cell level in a non-destructive manner. Fluorescence microscopy can give detailed morphological information about the localization of stained molecules, while Raman microscopy can produce label-free images at the subcellular level; thus, it can reveal the spatial distribution of molecular fingerprints, even in live samples. Accordingly, the combination of correlative fluorescence and Raman microscopy (CFRM) offers a unique approach for studying cellular stages at the single-cell level. However, subcellular spectral maps are complex and challenging to interpret. Artificial intelligence (AI) may serve as a valuable solution to characterize the molecular backgrounds of phenotypes and biological processes by finding the characteristic patterns in spectral maps. The major contributions of the manuscript are: (I) it gives a comprehensive review of the literature focusing on AI techniques in Raman-based cellular phenotyping; (II) via the presentation of a case study, a new neural network-based approach is described, and the opportunities and limitations of AI, specifically deep learning, are discussed regarding the analysis of Raman spectroscopy data to classify mitotic cellular stages based on their spectral maps.

Keywords: Raman spectroscopy; machine learning; microscopy; mitosis; phenotypic discovery; single-cell analysis.

Publication types

  • Review

MeSH terms

  • Artificial Intelligence*
  • Microscopy, Fluorescence / methods
  • Spectrum Analysis, Raman* / methods

Grants and funding

C.V., D.B., E.M., I.G., T.D., K.K., and P.H. acknowledge support from the LENDULET BIOMAG Grant (2018–342), from TKP2021-EGA09, from H2020-COMPASS-ERAPerMed, from CZI Deep Visual Proteomics, from H2020-DiscovAir, H2020-Fair-CHARM, from the ELKH-Excellence grant, from the FIMM High Content Imaging and Analysis Unit (FIMM-HCA; HiLIFE-HELMI), and Biocenter Finland, Finnish Cancer Society, Juselius Foundation. P.H., C.V., and S.D. acknowledge support from OTKA-SNN 139455/ARRS N2-0136. G.C. and F.P. acknowledge support from the MAECI Science and Technology Cooperation Italy-South Korea Grant Years 2023–2025 (ID project: KR23GR04) by the Italian Ministry of Foreign Affairs and International Cooperation. F.P. acknowledges support from the Italian Ministry of Health, contribution “Ricerca Corrente” within the research line: “Appropriateness, outcomes, drug value and organizational models for the continuity of diagnostic therapeutic pathways in oncology”.