Biology subjects

So, P. T. C.

Publications and source records attributed to So, P. T. C..

4 recordsLinked to original sources

bioRxiv2026

Scanless temporal focusing enables high-speed three-dimensional quantitative phase microscopy

Quantitative phase microscopy (QPM) enables label-free imaging of structure and dynamics in biological and physical systems, yet achieving high-speed three-dimensional (3D) QPM with strong optical sectioning remains a central challenge. Here, we introduce a single-shot reflection-mode temporal focusing QPM (TF-QPM) that provides sub-micron optical sectioning without needs of any mechanical scanning or multiplexed acquisitions. By extending temporal focusing beyond its conventional use in multiphoton fluorescence microscopy, TF-QPM enables diffraction-limited label-free phase-sensitive volumetric imaging with 402 nm lateral and 920 nm axial resolution, markedly reduced speckle noise, and depth-resolved imaging at 3,709 Hz frame rate --an order of magnitude faster than most existing techniques and currently only limited by the camera speed. The resulting spatiotemporal phase sensitivity enables precise 3D tracking of particle motion and quantitative characterization of fast dynamics in complex and anisotropic media. For tissue imaging applications, TF-QPM achieves histology-level resolution in intact samples and supports pixel-level virtual staining, providing a rapid, label-free alternative to conventional sectioning-based workflows. Together, these results establish TF-QPM as a scanless, high-speed platform for rapid, label-free volumetric imaging across both basic research and translational applications.

bioengineering↗

bioRxiv2025

RamanOmics Decodes Spatial Vibrational-Molecular Architecture and Rewiring in Aging and Repair

Aging and tissue repair involve multilayered and spatially heterogeneous remodeling across transcriptional, biochemical, and cellular dimensions, yet prevailing definitions rely on isolated molecular markers that obscure how biochemical and transcriptional states co-evolve in tissues. Here we present RamanOmics, a multimodal framework that integrates single-nucleus RNA sequencing (snRNA-seq), spatial transcriptomics, and label-free Raman imaging to map the spatial vibrational-biochemical and molecular architecture of aging and senescence directly in intact tissues. Applied to mouse lung and skin, RamanOmics generates spatially resolved biochemical-molecular maps revealing tissue-specific programs: lung senescent cells are enriched for extracellular matrix (ECM) remodeling and TGF-{beta} signaling (Serpine1, Dab2, Igfbp7), whereas skin senescence is dominated by keratinization and barrier homeostasis modules (Krt10, Lor, Sbsn). Across tissues, we identify a conserved branched-chain fatty-acid-linked biochemical profile and Raman signature (1131-1135 cm-{superscript 1}) that robustly marks p21 senescent cells. To unify these layers, we develop a machine learning derived "multimodal barcode" that quantitatively integrates biochemical and transcriptional features, enabling non-destructive identification of senescence in situ. In a wound-healing model, RamanOmics further reveals coordinated reactivation of barrier-repair programs in senescent cells, marked by upregulation of Krt10, Lor, Sbsn, Sfn, and Dmkn together with matching increases in lipid-associated Raman signatures, confirming biological generalizability beyond steady-state aging. By directly integrating gene programs to spatial vibrational-biochemical states, RamanOmics provides a general framework and resource for scalable, multimodal profiling of cellular states.

systems biology↗

bioRxiv2021

Raman2RNA: Live-cell label-free prediction of single-cell RNA expression profiles by Raman microscopy

Single cell RNA-Seq (scRNA-seq) and other profiling assays have opened new windows into understanding the properties, regulation, dynamics, and function of cells at unprecedented resolution and scale. However, these assays are inherently destructive, precluding us from tracking the temporal dynamics of live cells, in cell culture or whole organisms. Raman microscopy offers a unique opportunity to comprehensively report on the vibrational energy levels of molecules in a label-free and non-destructive manner at a subcellular spatial resolution, but it lacks in genetic and molecular interpretability. Here, we developed Raman2RNA (R2R), an experimental and computational framework to infer single-cell expression profiles in live cells through label-free hyperspectral Raman microscopy images and multi-modal data integration and domain translation. We used spatially resolved single-molecule RNA-FISH (smFISH) data as anchors to link scRNA-seq profiles to the paired spatial hyperspectral Raman images, and trained machine learning models to infer expression profiles from Raman spectra at the single-cell level. In reprogramming of mouse fibroblasts into induced pluripotent stem cells (iPSCs), R2R accurately (r>0.96) inferred from Raman images the expression profiles of various cell states and fates, including iPSCs, mesenchymal-epithelial transition (MET) cells, stromal cells, epithelial cells, and fibroblasts. R2R outperformed inference from brightfield images, showing the importance of spectroscopic content afforded by Raman microscopy. Raman2RNA lays a foundation for future investigations into exploring single-cell genome-wide molecular dynamics through imaging data, in vitro and in vivo.

genomics↗

bioRxiv2020

Two-photon microscopy at >500 volumes/second

We demonstrate a multi-focal multi-photon volumetric microscopy via combination of 32-beam parallel lateral-scanning, a 70-kHz axial-scanning acoustic lens, and a 32-channel photodetector, enabling unprecedented data rate (2-10 GHz) and >500-volumes/second imaging speed over ~200x200x200-m3.

biophysics↗