Biology subjects

Lin, L.-E.

Publications and source records attributed to Lin, L.-E..

4 recordsLinked to original sources

bioRxiv2025

Deep Learning-Augmented Stimulated Raman Imaging for Cell-Type-Specific Metabolic Profiling in Live Neuronal Co-Cultures

Neuronal metabolism is fundamental to brain functions and diseases, yet its spatial and temporal dynamics and interactions remain poorly understood. Here, we introduce a tandem deep-learning approach integrated with bioorthogonal chemical imaging using stimulated Raman scattering (SRS) microscopy. This method achieves high-speed and quantitative metabolic profiling in live neuronal co-cultures. Our deep-learning framework consists of a recurrent convolutional neural network (RCNN) that enables high-resolution 3D imaging with minimal photodamage and a U-Net segmentation model for cell-type-specific metabolic analysis. Using deuterium-labeled metabolites, we demonstrate the ability to trace lipid, protein, glucose, and D2O metabolism in neurons, astrocytes, and oligodendrocytes under physiological and pathological conditions, including NMDA receptor activation, proteasome inhibition, and Huntingtons disease. Our findings reveal distinct metabolic adaptations among neuronal cell types and underscore the importance of non-invasive metabolic profiling for understanding neuronal interactions and disease mechanisms. This platform significantly advances live-cell dynamic imaging with broad applications in neuroscience, disease modeling, and therapeutic screening.

neuroscience↗

bioRxiv2025

GFP-Free Live-Neuron Quantitative Imaging Reveals Compartmentalization and Growth Dynamics of PolyQ Aggregates

Huntingtons Disease (HD), the most prevalent polyglutamine (polyQ) neurodegenerative disorder, features brain aggregates induced by mutant huntingtin (mHtt) proteins harboring expanded polyQ tracts. Despite extensive efforts, molecular mechanisms of polyQ aggregates remain elusive. Here we establish quantitative stimulated Raman scattering imaging of polyQ aggregates (q-aggSRS) for non-invasive investigations in live neuronal co-cultures using deuterated glutamine labeling. Q-aggSRS allows for specific visualization by targeting the distinct Raman peak from carbon-deuterium bonds, eliminating the need for bulky GFP tagging. Coupled with analysis from aggregate-tailored expansion microscopy, newly designed two-color imaging, and pulse-chase visualization, we comprehensively quantified the mHtt and non-mHtt proteins within the same aggregates across varying sizes, cell types, mHtt constructs, and subcellular locations. Our findings demonstrate a two-phase aggregate growth model with a distinct core-shell spatial organization, reveal significant heterogeneity in nucleus/cytoplasm compartmentalization specific to neurons, and identify previously unrecognized "gel-like" aggregates specifically in neuronal nuclei. These insights should advance our understanding of native polyQ aggregates and our proposed interaction coefficients may offer new quantitative parameters for developing effective HD therapies.

biophysics↗

bioRxiv2021

Super-Resolution Label-free Volumetric Vibrational Imaging

Innovations in high-resolution optical imaging have allowed visualization of nanoscale biological structures and connections. However, super-resolution fluorescence techniques, including both optics-oriented and sample-expansion based, are limited in quantification and throughput especially in tissues from photobleaching or quenching of the fluorophores, and low-efficiency or non-uniform delivery of the probes. Here, we report a general sample-expansion vibrational imaging strategy, termed VISTA, for scalable label-free high-resolution interrogations of protein-rich biological structures with resolution down to 82 nm. VISTA achieves decent three-dimensional image quality through optimal retention of endogenous proteins, isotropic sample expansion, and deprivation of scattering lipids. Free from probe-labeling associated issues, VISTA offers unbiased and high-throughput tissue investigations. With correlative VISTA and immunofluorescence, we further validated the imaging specificity of VISTA and trained an image-segmentation model for label-free multi-component and volumetric prediction of nucleus, blood vessels, neuronal cells and dendrites in complex mouse brain tissues. VISTA could hence open new avenues for versatile biomedical studies.

neuroscience↗

bioRxiv2019

High Spatial Resolution Ambient Ionization Mass Spectrometry Imaging Using Microscopy Image Fusion

Mass spectrometry imaging (MSI) using ambient ionization technique enables a direct chemical investigation of biological samples with minimal sample pretreatment. However, detailed morphological information of the sample is often lost due to its limited spatial resolution. In this study, predictive high-resolution molecular imaging was produced by the fusion of ambient ionization MSI with optical microscopy of routine hematoxylin and eosin (H&E) staining produces. Specifically, desorption electrospray ionization (DESI) and nanospray desorption electrospray ionization (nanoDESI) mass spectrometry are employed to visualize lipid and protein species on mice tissue sections. The resulting molecular distributions obtained by ambient ionization MSI-microscopy fusion are verified with matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) MSI and immunohistochemistry (IHC) staining. Label-free molecular imaging with 5-m spatial resolution can be acquired using DESI and nanoDESI, whereas the typical spatial resolution of ambient ionization MSI is ~100 m. In this regard, sharpened molecular histology of tissue sections is achieved, providing complementary references to the pathology. Such a multimodality integration enables the discovery of potential tumor biomarkers. After image fusion, more than a dozen of potential biomarkers that could be used to determine the tumor margins on a metastatic mouse lung tissue section and Luminal B breast tumor tissue section are identified.

bioinformatics↗