Rapid Intracellular Detection and Analysis of Lipid Droplets' Morpho-Chemical Composition by Phase-Guided Raman Sampling

Real-Time Analysis of Lipid Droplet Morpho-Chemical Dynamics in Living Human Hepatocytes via Phase-Guided Raman Sampling

Lipid droplets (LDs) are highly dynamic organelles, undertaking many important functions such as maintaining lipid metabolism and cellular homeostasis. Traditional methods to analyze LD dynamics focus on morphological changes, while chemical dynamics cannot be easily probed with traditional analytical chemistry techniques. To overcome this challenge, we show here how our phase-guided Raman sampling method, where high-resolution phase microscopy images direct a Raman sampling beam, can perform label-free, multimodal characterization of LD dynamics in living cells at both the single-cell and single-LD levels with submicron accuracy and high temporal resolution. We demonstrate the study of the morphological-compositional dynamics of human hepatocellular carcinoma cells (PLC cells) under different environmental conditions and with and without fatty acid supplementation, providing insight into LD heterogeneity and heterogeneity of response. Finally, we introduce a measurement method for the dynamics of cell-average LD composition, which can quickly and accurately characterize the lipid dynamics at the single-cell level with <30 s temporal resolution. The results here show the promise of the phase-guided Raman sampling method for dynamic morpho-chemical profiling of organelle populations.

Chemical Landscape of Adipocytes Derived from 3T3-L1 Cells Investigated by Fourier Transform Infrared and Raman Spectroscopies

Adipocytes derived from 3T3-L1 cells are a gold standard for analyses of adipogenesis processes and the metabolism of fat cells. A widely used histological and immunohistochemical staining and mass spectrometry lipidomics are mainly aimed for examining lipid droplets (LDs). Visualizing other cellular compartments contributing to the cellular machinery requires additional cell culturing for multiple labeling. Here, we present the localization of the intracellular structure of the 3T3-L1-derived adipocytes utilizing vibrational spectromicroscopy, which simultaneously illustrates the cellular compartments and provides chemical composition without extensive sample preparation and in the naïve state. Both vibrational spectra (FTIR-Fourier transform infrared and RS-Raman scattering spectroscopy) extended the gathered chemical information. We proved that both IR and RS spectra provide distinct chemical information about lipid content and their structure. Despite the expected presence of triacylglycerols and cholesteryl esters in lipid droplets, we also estimated the length and unsaturation degree of the fatty acid acyl chains that were congruent with known MS lipidomics of these cells. In addition, the clustering of spectral images revealed that the direct surroundings around LDs attributed to lipid-associated proteins and a high abundance of mitochondria. Finally, by using quantified markers of biomolecules, we showed that the fixative agents, paraformaldehyde and glutaraldehyde, affected the cellular compartment differently. We concluded that PFA preserves LDs better, while GA fixation is better for cytochromes and unsaturated lipid analysis. The proposed analysis of the spectral images constitutes a complementary tool for investigations into the structural and molecular features of fat cells.

Organelle-specific phase contrast microscopy (OS-PCM) enables facile correlation study of organelles and proteins

Current methods for studying organelle and protein interactions and correlations depend on multiplex fluorescent labeling, which is experimentally complex and harmful to cells. Here we propose to solve this challenge via OS-PCM, where organelles are imaged and segmented without labels, and combined with standard fluorescence microscopy of protein distributions. In this work, we develop new neural networks to obtain unlabeled organelle, nucleus and membrane predictions from a single 2D image. Automated analysis is also implemented to obtain quantitative information regarding the spatial distribution and co-localization of both protein and organelle, as well as their relationship to the landmark structures of nucleus and membrane. Using mitochondria and DRP1 protein as a proof-of-concept, we conducted a correlation study where only DRP1 is labeled, with results consistent with prior reports utilizing multiplex labeling. Thus our work demonstrates that OS-PCM simplifies the correlation study of organelles and proteins.