4.7 Article

Single cell Raman spectroscopy to identify different stages of proliferating human hepatocytes for cell therapy

Journal

STEM CELL RESEARCH & THERAPY
Volume 12, Issue 1, Pages -

Publisher

BMC
DOI: 10.1186/s13287-021-02619-9

Keywords

Raman spectroscopy; Cell therapy; Identification; Proliferating human hepatocytes; Dedifferentiation

Funding

  1. 'Organ Reconstruction and Manufacturing' Strategic Priority Research Program of the Chinese Academy of Sciences [XDA16020205]
  2. National Science Foundation of China [81872927, 81771890, 91859106]
  3. International Partnership Program of Chinese Academy of Sciences [153631KYSB20160004]
  4. Independent Deployment Program of the Institute of Pharmaceutical Innovation of the Chinese Academy of Sciences [CASIMM0120184005]
  5. Shanghai Science and Technology Committee [20S11901400]
  6. Shanghai Municipal Science and Technology Major Project

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Raman spectroscopy was successfully employed to identify different stages of ProliHHs during dedifferentiation process, providing a new approach to simultaneously trace multiple changes of cellular components from somatic cells to progenitor cells.
Background Cell therapy provides hope for treatment of advanced liver failure. Proliferating human hepatocytes (ProliHHs) were derived from primary human hepatocytes (PHH) and as potential alternative for cell therapy in liver diseases. Due to the continuous decline of mature hepatic genes and increase of progenitor like genes during ProliHHs expanding, it is challenge to monitor the critical changes of the whole process. Raman microspectroscopy is a noninvasive, label free analytical technique with high sensitivity capacity. In this study, we evaluated the potential and feasibility to identify ProliHHs from PHH with Raman spectroscopy. Methods Raman spectra were collected at least 600 single spectrum for PHH and ProliHHs at different stages (Passage 1 to Passage 4). Linear discriminant analysis and a two-layer machine learning model were used to analyze the Raman spectroscopy data. Significant differences in Raman bands were validated by the associated conventional kits. Results Linear discriminant analysis successfully classified ProliHHs at different stages and PHH. A two-layer machine learning model was established and the overall accuracy was at 84.6%. Significant differences in Raman bands have been found within different ProliHHs cell groups, especially changes at 1003 cm(-1), 1206 cm(-1) and 1440 cm(-1). These changes were linked with reactive oxygen species, hydroxyproline and triglyceride levels in ProliHHs, and the hypothesis were consistent with the corresponding assay results. Conclusions In brief, Raman spectroscopy was successfully employed to identify different stages of ProliHHs during dedifferentiation process. The approach can simultaneously trace multiple changes of cellular components from somatic cells to progenitor cells.

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