Laboratory of Optical Bioimaging and Spectroscopy
Multimolecular Super Resolution Metabolic Imaging in Aging and Diseases
My Lab is transforming SRS microscopy into a super resolution (~59nm) multimolecular metabolic imaging platform by developing Adam optimization-based Pointillism Deconvolution (A-PoD) and penalized reference matching (PRM) algorithms.
By applying A-PoD to spatially co-register multi-photon fluorescence (MPF) imaging and deuterium oxide (D2O)-probed SRS (DO-SRS) imaging from diverse samples, we compared nanoscopic distributions of proteins and lipids in cells and subcellular organelles.
We successfully differentiated newly synthesized lipids in lipid droplets using A-PoD coupled with DO-SRS. The A-PoD-enhanced DO-SRS imaging method was also applied to reveal the metabolic change in brain samples from Drosophila on different diets during aging processes. We discovered sex dependent lipid metabolic changes in certain brain cells. See more details in our published papers and uploaded manuscripts.
This method allows us to quantitatively measure the nanoscopic co-localization of biomolecules and metabolic dynamics in cellular organelles. It shows a wide range of applications, from nano-scale measurements of biomolecules to processing astronomical images.
Further, integrating it with bioorthogonal labeling, multiphoton fluorescence (MPF), and second harmonic generation (SHG), we obtain a multimodal imaging system.
The enzymatic incorporation of deuterium (D) into newly synthesized biomolecules will generate a unique chemical bond called carbon-deuterium (C-D) bonds.
Within the broad vibrational spectra of C-D bonds, we discovered lipid-, protein-, carbohydrate-, and DNA/RNA-specific Raman shifts, and developed spectral unmixing methods to achieve macromolecular selectivity.
Figure: Schematic of workflow of bioorthogonal super resolution multimolecular imaging platform . Scale bar: 10 μ m
As Lipids play crucial roles in many biological processes under physiological and pathological conditions. Mapping spatial distribution and examining metabolic dynamics of different lipids in cells and tissues are critical for understanding aging and diseases.
Commonly used imaging methods, including mass spectrometry-based technologies or labeled imaging techniques, tend to disrupt the native environment of cells/tissues and have limited spatial or spectral resolution, while traditional optical imaging techniques still lack the capacity to distinguish chemical differences between lipid subtypes.
To overcome these limitations, we developed a new hyperspectral imaging platform that integrates a Penalized Reference Matching algorithm with Stimulated Raman Scattering (PRM-SRS) microscopy. With this new approach, we directly visualized and identified multiple lipid species in cells and tissues in situ with high chemical specificity and subcellular resolution. See more details in our published papers and uploaded manuscripts.