Computational Chemical Imaging Lab

Reserach

Our lab focuses on the development of computation-driven vibrational chemical imaging technologies to non-invasively measure the biochemical processes in living systems and identify novel biomarkers for diseases. Specifically, it includes (1) Developing vibrational chemical imaging platforms beyond the physical limits by computation-driven system design and image reconstruction. (2) Discovering unknown biochemical mechanisms and disease biomarkers using novel chemical imaging data and AI / ML algorithms.

Human health and disease are governed by the biochemical processes across scales and systems. Yet, tools to visualize the intracellular small biomolecules and pathways in action remain limited. Our lab is at the forefront of developing vibrational chemical imaging, including stimulated Raman scattering (SRS) and vibrational photothermal imaging (VIP), to directly measure the intrinsic chemical bond vibrations for tracking and quantifying biomolecules. We present a new paradigm to break the “no free lunch” limit in instrumentation by adding computation in the loop of system design, data collection, and image analysis, thereby surpassing the physical boundaries in speed, resolution, specificity, and penetration depth. Our research aims to provide insights into multi-scale metabolism in health and disease at unprecedented physical limits.

Pushing the resolution limit of vibrational chemical imaging

We developed high-sensitivity, high-resolution vibrational chemical imaging systems and algorithms to achieve label-free chemical nanoscopy of non-fluorescent biomolecules:

Ultrasensitive Reweighted Visible SRS (URV SRS) nanoscopy (Nature Methods, 2025)

Breaking the speed limit of vibrational chemical imaging

We developed multiple computational-driven devices and algorithms to push the speed limit of SRS microspectroscopy and break the tradeoffs between speed and spectral bandwidth, including:

Compressive spectroscopic SRS microscopy via matrix completion (Light: Science & Applications, 2018)
Single-shot femtosecond SRS mapping of cell organelles using deep learning (JPC Letters, 2021)
Spectral sparse sampling through recursive feature elimination (Theranostics, 2024)

Achieving higher sensitivity in vibrational chemical imaging

Combining hardware and algorithm innovations, we have pushed the sensitivity of SRS through deep learning, plasmon enhancement, and Raman pre-resonance:

Self-supervised non-independent noise removal in hyperspectral imaging by Self-Permutation Noise2Noise Denoiser (SPEND) (Newton, 2025)
Robust fingerprint SRS imaging by ultrafast delay tuning and spatial-spectral residual learning (Nature Communications, 2021)
Single-molecule SRS imaging via plasmonic enhancement and non-local spectroscopic denoising (Nature Communications, 2019)
Ultrasensitive chemical imaging via pre-resonance visible SRS microscopy (Advanced Science, 2021)

Revolutionizing specificity in chemical microspectroscopy

We develop novel models and algorithms to decipher multiplexed chemical information from spectroscopic chemical images:

High content SRS imaging of natural metabolites by sparsity-driven spectral unmixing (Science Advances, 2023)
Resolving fatty acid unsaturation and chain length at single-cell level by mass spec-augmented spectral analysis (Advanced Science, 2023)
Blood glucose level estimation by modeling single red blood cell transient absorption distribution (Science Advances, 2019)

Biomedical applications enabled by CCI

With next-gen capabilities of CCI, we have a broad spectrum of biomedical applications:

Synthetic biology (Nature Communications, 2021; Advanced Science, 2023; Advanced Science, 2022)
Cancer metabolism (Nature Communications, 2021; Science Advances, 2023)
Tissue histology (Nature Communications, 2021, first author; Theranostics, minor revision, first author, preprint)
Molecular virology (Nature Methods, 2025)

Page updated

Google Sites

Report abuse