研究目的
To study the biomolecular composition of quiescent dermal fibroblast cells and compare them with those of proliferating and senescent cells using label-free in vitro vibrational spectroscopy.
研究成果
The study demonstrates that Raman and infrared spectroscopy can distinguish between proliferating, quiescent, and senescent fibroblast cells based on their biomolecular composition. Quiescent and proliferating cells age by similar but biochemically distinct processes. Long-term quiescent cells can return to the cell cycle but eventually enter an irreversible senescent state.
研究不足
The study is limited by the technical constraints of vibrational spectroscopy, such as the need for careful sample preparation and the potential for spectral overlap from different biomolecules. Additionally, the study focuses on in vitro conditions, which may not fully replicate in vivo cellular environments.
1:Experimental Design and Method Selection:
The study employed Raman spectroscopy (RS) and Fourier-transform infrared spectroscopy (FT-IRS) to analyze the biomolecular composition of fibroblast cells in different states (proliferating, quiescent, and senescent). Multivariate statistical analysis using a PLS-LDA classification model was used to examine the spectra.
2:Sample Selection and Data Sources:
Primary human dermal fibroblasts from fetal foreskin (BJ; CRL-2522) were obtained from ATCC. Cells were cultivated under various conditions to induce different states (proliferation, quiescence by contact inhibition or serum starvation, and senescence).
3:List of Experimental Equipment and Materials:
Confocal Raman microscope (alpha300 R; WITec), Fourier-transform infrared spectrometer (Varian 670-IR; Agilent), calcium fluoride (CaF2) slides, Dulbecco’s modified Eagles medium (DMEM), fetal bovine serum (FBS), and other standard cell culture reagents.
4:Experimental Procedures and Operational Workflow:
Cells were cultivated, fixed, and analyzed using RS and FT-IRS. Spectra were pre-processed and analyzed using statistical algorithms in the software "R".
5:Data Analysis Methods:
Pre-processing included noise reduction, baseline correction, and cosmic ray removal. PLS-LDA was applied for classification, validated with a 10-fold cross-validation with 100 iterations.
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