Biology subjects

Fung, A. A.

Publications and source records attributed to Fung, A. A..

3 recordsLinked to original sources

bioRxiv2024

All-Optical Multimodal Mapping of Single Cell Type-Specific Metabolic Activities via REDCAT

Metabolism underlies cell growth, survival, and function, yet its activities vary widely across cell types and tissue environments. Spatially resolving these processes in situ at single-cell resolution is essential to advance our understanding of cellular function and tissue physiology in health and disease. However, existing approaches are limited by either destructive workflows, insufficient spatial resolution and biochemical specificity, or lack of direct linkage to cell identity. Here, we present Raman Enhanced Delineation of Cell Atlases in Tissues (REDCAT), a multimodal all-optical platform that integrates stimulated Raman scattering, autofluorescence redox imaging, second harmonic generation, and high-plex immunofluorescence to co-map metabolic activities and cell types within the same tissue section. REDCAT achieves subcellular resolution profiling of protein, lipid, redox, and nuclear acid metabolism, together with extracellular matrix composition, in both FFPE and fresh-frozen human tissues. Applied to normal lymph nodes, REDCAT delineated distinct redox and lipid remodeling programs across germinal center B-cell zones and immune subsets, highlighting cell-type-specific metabolic specialization. In lymphoma, it revealed profound metabolic reprogramming, including extensive lipid accumulation, nuclear metabolic heterogeneity, and a transitional metabolic state associated with transformation from chronic lymphocytic leukemia to diffuse large B-cell lymphoma, thereby illuminating tumor evolution in situ. In human liver, REDCAT resolved cell-type-specific lipid droplet diversity and zonation-dependent nuclear metabolic gradients, uncovering new principles of spatial metabolic organization. By directly linking cell identity with spatial metabolic states at single-cell or subcellular resolution, REDCAT establishes a broadly applicable framework for studying immune function, tumor progression, and tissue physiology, and offers a new path to deciphering the metabolic basis of health and disease.

cell biology↗

bioRxiv2024

Label-Free Optical Biopsy Reveals Biomolecular and Morphological Features of Diabetic Kidney Tissue in 2D and 3D

Kidney disease, the ninth leading cause of death in the United States, has one of the poorest diagnostic efficiencies of only 10%1. Conventional diagnostic methods often rely on light microscopy analysis of 2D fixed tissue sections with limited molecular insight compared to omics studies. Targeting multiple features in a biopsy using molecular or chemical reagents can enhance molecular phenotyping but are limited by overlap of their spatial and chromatic properties, variations in quality of the products, limited multimodal nature and need additional tissue processing. To overcome these limitations and increase the breadth of molecular information available from tissue without an impact on routine diagnostic workup, we implemented label-free imaging modalities including stimulated Raman scattering (SRS) microscopy, second harmonic generation (SHG), and two photon fluorescence (TPF) into a single microscopy setup. We visualized and identified morphological, structural, lipidomic, and metabolic biomarkers of control and diabetic human kidney biopsy samples in 2D and 3D at a subcellular resolution. The label-free biomarkers, including collagen fiber morphology, mesangial-glomerular fractional volume, lipid saturation, redox status, and relative lipid and protein concentrations in the form of Stimulated Raman Histology (SRH), illustrate distinct features in kidney disease tissues not previously appreciated. The same tissue section can be used for routine diagnostic work up thus enhancing the power of cliniopathological insights obtainable without compromising already limited tissue. The additional multimodal biomarkers and metrics are broadly applicable and deepen our understanding of the progression of kidney diseases by integrating lipidomic, fibrotic, and metabolic data. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/620507v1_ufig1.gif" ALT="Figure 1"> View larger version (76K): org.highwire.dtl.DTLVardef@116edb1org.highwire.dtl.DTLVardef@4f010corg.highwire.dtl.DTLVardef@cde718org.highwire.dtl.DTLVardef@1ff0841_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO Label-free indicators of diabetic nephropathies. C_FIG

bioengineering↗

bioRxiv2022

Multi-Molecular Hyperspectral PRM-SRS Imaging

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 in situ 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. High density lipoprotein (HDL) particles containing non-esterified cholesterol was observed in the kidney, indicating that these pools of cholesterol are ectopic deposits, or have yet to be enriched. We detected a higher Cholesterol to phosphatidylethanolamine (PE) ratio inside the granule cells of hippocampal samples in old mice, suggesting altered membrane lipid synthesis and metabolism in aging brains. PRM-SRS imaging also revealed subcellular distributions of sphingosine and cardiolipin in the human brain sample. Compared with other techniques, PRM-SRS demonstrates unique advantages, including faster data processing and direct user-defined visualization with enhanced chemical specificity for distinguishing clinically relevant lipid subtypes in different organs and species. Our method has broad applications in multiplexed cell and tissue imaging.

bioengineering↗