@article {70404, title = {FILM: mapping organellar metabolism by mid-infrared photothermal-modulated fluorescence.}, journal = {Nat Methods}, year = {2026}, month = {2026 May 07}, abstract = {

Metabolism unfolds within specific organelles in eukaryotic cells. Lysosomes are highly metabolically active organelles, and their metabolic states dynamically influence signal transduction, cellular homeostasis and organismal physiopathology. Despite the importance of lysosomal metabolism, a method for its in vivo measurement is currently lacking. Here we report a fluorescence-detected mid-infrared photothermal microscope (FILM) implemented with optical boxcar demodulation, artificial intelligence-assisted data denoising and spectral deconvolution, to map metabolic activity and composition of individual lysosomes in living cells and organisms. Using this method, we uncovered lipolysis and proteolysis heterogeneity across lysosomes within the same cell, as well as early-onset lysosomal dysfunction during organismal aging. In addition, we discovered organelle-level metabolic changes associated with diverse lysosomal storage diseases. This method holds the broad potential to profile metabolic fingerprints of individual organelles within their native context and quantitatively assess their dynamic changes under different physiological and pathological conditions, providing a high-resolution chemical cellular atlas.

}, issn = {1548-7105}, doi = {10.1038/s41592-026-03090-1}, url = {https://doi.org/10.1038/s41592-026-03090-1}, author = {Ao, Jianpeng and Yin, Jiaze and Lin, Haonan and Ding, Guangrui and Guan, Youchen and Savini, Marzia and Weinberg, Bethany and Dong, Dashan and Xia, Qing and Guo, Zhongyue and Liu, Bowen and Gao, Biwen and Cheng, Ji-Xin and Wang, Meng C} } @article {70405, title = {Resolving protein condensates and aggregates in vivo by boxcar-enhanced Fluorescence-detected mid-Infrared photothermaL Microscopy (FILM)}, journal = {Commun Chem}, year = {2026}, month = {2026 May 11}, abstract = {

Protein assemblies, including aggregates and condensates, are closely linked to health and diseases. We demonstrate boxcar-enhanced Fluorescence-detected mid-Infrared photothermaL Microscopy (FILM), using two model species, Caenorhabditis elegans and Saccharomyces cerevisiae, to quantitatively resolve these protein states in vivo by imaging β-sheet and α-helix secondary structures and analyzing their ratios. This method directly distinguishes polyglutamine (PolyQ) protein aggregates, α-synuclein protein condensates, and P-granule condensates implicated in neurodegenerative diseases and embryonic development in live organisms. It further enables the unraveling of protein assembly dynamics and their physio-pathological roles, such as age-related progression of PolyQ from condensates to aggregates.

}, issn = {2399-3669}, doi = {10.1038/s42004-026-01970-3}, url = {https://doi.org/10.1038/s42004-026-01970-3}, author = {Weinberg, Bethany and Guo, Zhongyue and Tang, Rong and Ao, Jianpeng and Yin, Jiaze and Ding, Guangrui and Blute, Todd A and Savini, Marzia and Chiesa, Giulio and Wang, Wentao and Good, Matt and Wang, Meng C and Cheng, Ji-Xin} } @article {46747, title = {Label-free spectroscopic detection of membrane potential using stimulated Raman scattering.}, journal = {Applied Physics Letters}, volume = {106}, year = {2015}, month = {2015 Apr 27}, pages = {173704}, abstract = {

Hyperspectral stimulated Raman scattering microscopy is deployed to measure single-membrane vibrational spectrum as a function of membrane potential. Using erythrocyte ghost as a model, quantitative correlation between transmembrane potential and Raman spectral profile was found. Specifically, the ratio between the area under Raman band at \~{}2930 cm-1 and that at \~{}2850 cm-1 increased by \~{}2.6 times when the potential across the erythrocyte ghost membrane varied from +10 mV to -10 mV. Our results show the feasibility of employing stimulated Raman scattering microscopy to probe the membrane potential without labeling.

}, doi = {10.1063/1.4919104}, author = {Liu, Bin and Lee, Hyeon Jeong and Zhang, Delong and Liao, Chien-Sheng and Ji, Na and Xia, Yuanqin and Cheng, Ji-Xin} }