Label-Free Volumetric Quantitative Imaging of the Human Somatic Cell Division by Hyperspectral Coherent Anti-Stokes Raman Scattering.
Arnica KarunaFrancesco MasiaMarie WiltshireRachel ErringtonPaola BorriWolfgang LangbeinPublished in: Analytical chemistry (2019)
Quantifying the chemical composition of unstained intact tissue and cellular samples with high spatio-temporal resolution in three dimensions would provide a step change in cell and tissue analytics critical to progress the field of cell biology. Label-free optical microscopy offers the required resolution and noninvasiveness, yet quantitative imaging with chemical specificity is a challenging endeavor. In this work, we show that hyperspectral coherent anti-Stokes Raman scattering (CARS) microscopy can be used to provide quantitative volumetric imaging of human osteosarcoma cells at various stages through cell division, a fundamental component of the cell cycle progress resulting in the segregation of cellular content to produce two progeny. We have developed and applied a quantitative data analysis method to produce volumetric three-dimensional images of the chemical composition of the dividing cell in terms of water, proteins, DNAP (a mixture of proteins and DNA, similar to chromatin), and lipids. We then used these images to determine the dry masses of the corresponding organic components. The attribution of proteins and DNAP components was validated using specific well-characterized fluorescent probes, by comparison with correlative two-photon fluorescence microscopy of DNA and mitochondria. Furthermore, we map the same chemical components under perturbed conditions, employing a drug that interferes directly with cell division (Taxol), showing its influence on cell organization and the masses of proteins, DNAP, and lipids.
Keyphrases
- high resolution
- label free
- single molecule
- single cell
- cell therapy
- cell cycle
- gene expression
- endothelial cells
- cell death
- emergency department
- dna methylation
- high throughput
- living cells
- high speed
- induced apoptosis
- stem cells
- mass spectrometry
- convolutional neural network
- big data
- circulating tumor
- genome wide
- transcription factor
- machine learning
- fluorescent probe
- electronic health record