Toward Single-Organelle Lipidomics in Live Cells.
Adrian LitaAndrey N KuzminArtem M PlissAlexander BaevAlexander RzhevskiiMark R GilbertMioara LarionParas N PrasadPublished in: Analytical chemistry (2019)
Detailed studies of lipids in biological systems, including their role in cellular structure, metabolism, and disease development, comprise an increasingly prominent discipline called lipidomics. However, the conventional lipidomics tools, such as mass spectrometry, cannot investigate lipidomes until they are extracted, and thus they cannot be used for probing the lipid distribution nor for studying in live cells. Furthermore, conventional techniques rely on the lipid extraction from relatively large samples, which averages the data across the cellular populations and masks essential cell-to-cell variations. Further advancement of the discipline of lipidomics critically depends on the capability of high-resolution lipid profiling in live cells and, potentially, in single organelles. Here we report a micro-Raman assay designed for single-organelle lipidomics. We demonstrate how Raman microscopy can be used to measure the local intracellular biochemical composition and lipidome hallmarks-lipid concentration and unsaturation level, cis/trans isomer ratio, sphingolipids and cholesterol levels in live cells-with a sub-micrometer resolution, which is sufficient for profiling of subcellular structures. These lipidome data were generated by a newly developed biomolecular component analysis software, which provides a shared platform for data analysis among different research groups. We outline a robust, reliable, and user-friendly protocol for quantitative analysis of lipid profiles in subcellular structures. This method expands the capabilities of Raman-based lipidomics toward the analysis of single organelles within either live or fixed cells, thus allowing an unprecedented measure of organellar lipid heterogeneity and opening new quantitative ways to study the phenotypic variability in normal and diseased cells.
Keyphrases
- induced apoptosis
- high resolution
- mass spectrometry
- single cell
- randomized controlled trial
- data analysis
- endoplasmic reticulum stress
- stem cells
- fatty acid
- oxidative stress
- high throughput
- cell therapy
- cell death
- machine learning
- signaling pathway
- bone marrow
- big data
- single molecule
- artificial intelligence
- electronic health record
- reactive oxygen species
- ms ms
- label free
- molecular dynamics simulations
- raman spectroscopy
- low density lipoprotein