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

Abstract

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.

Label-Free, Noninvasive Bone Cell Classification by Hyperspectral Confocal Raman Microscopy

“…Figure shows the average spectra for each of the cell types. Identifiable peak positions are labeled and the accompanying assignments are given in Table . − In cases where there is a peak label but no assignment is given, an unambiguous assignment from the existing literature could not be made. In these cases, we refrain from attempting to make an assignment.…”

Quantitative chemometric phenotyping of three-dimensional liver organoids by Raman spectral imaging

“…The role of localized concentrations and biomolecular density at subcellular levels is gaining attention as a relevant phenotypic parameter for cell-type-specific functions 49 , 50 and can be indicative, for example, of the maturation state of cells and their response to xenobiotic exposures. 49 …”

Applying machine learning for multi-individual Raman spectroscopic data to identify different stages of proliferating human hepatocytes

“…Proliferating human hepatocytes (ProliHHs) were induced according to previously reported methods. 17 , 34 Briefly, to generate ProliHHs at passage 1 (P1), PHHs were treated with HM for 7 days under hypoxic cell culture conditions (2% O 2 ).…”

“…In our previous investigation, we successfully performed Raman profiling of PHHs and different passages of ProliHHs and found that Raman spectroscopy could track the biochemical changes occurring during the dedifferentiation process from PHHs to ProliHHs. 34 Considering the heterogeneity of individuals and cells, we expanded the number of human donors from one to six, expanded more passages of ProliHHs, collected Raman spectra of a larger number of cells, and conducted this research to construct a more accurate and robust machine learning model for classifying PHHs and ProliHHs.…”