TalantaMolecular BiologyPeer-reviewed journal article
Determining the stages of cellular differentiation using Deep Ultraviolet Resonance Raman Spectroscopy
Nicole M. RalbovskyiD, Paromita Dey, Bijan K. DeyiD, Igor K. LedneviD
Abstract
Cellular differentiation is a fundamental process in which one cell type changes into one or more specialized cell types. Cellular differentiation starts at the beginning of embryonic development when a simple zygote begins to transform into a complex multicellular organism composed of various cell and tissue types. This process continues into adulthood when adult stem cells differentiate into more specialized cells for normal growth, regeneration, repair, and cellular turnover. Any abnormalities associated with this fundamental process of cellular differentiation is linked to life threatening conditions including degenerative diseases and cancers. Detection of undifferentiated and different stages of differentiated cells can be used for disease diagnosis but is often challenging due to the laborious procedures, expensive tools, and specialized technical skills which are required. Here, a novel approach, called deep ultraviolet resonance Raman spectroscopy, is used to study various stages of cellular differentiation using a well-known myoblast cell line as a model system. These cells proliferate in the growth medium and spontaneously differentiate in differentiation medium into myocytes and later into myotubes and myofibers. The cellular and molecular characteristics of these cells mimic very well actual muscle tissue in vivo. We have found that undifferentiated myoblast cells and myoblast cells differentiated at three different stages are able to be easily separated using deep ultraviolet resonance Raman spectroscopy in combination with chemometric techniques. Our study has a great potential to study cellular differentiation during normal development as well as to detect abnormal cellular differentiation in human pathological conditions in future studies.
- Citations
- 9
- FWCI
- 0.90
- Field percentile
- top 31%
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