Photochromic probes with reversible fluorescence have revolutionized the fields of single molecule spectroscopy and super-resolution microscopy, but lack sufficient chemical specificity. In contrast, Raman probes with stimulated Raman scattering (SRS) microscopy provides superb chemical resolution for super-multiplexed imaging, but are relatively inert. Here we report vibrational photochromism by engineering alkyne tagged diarylethene to realize photo-switchable SRS imaging. The narrow Raman peak of the alkyne group shifts reversibly upon photoisomerization of the conjugated diarylethene when irradiated by ultraviolet (UV) or visible light, yielding “on” or “off” SRS images taken at the photoactive Raman frequency. We demonstrated photo-rewritable patterning and encryption on thin films, painting/erasing of cells with labelled alkyne-diarylethene, as well as pulse-chase experiments of mitochondria diffusion in living cells. The design principle provides potentials for super-resolution microscopy, optical memories and switches with vibrational specificity.

Gastroscopic biopsy provides the only effective method for gastric cancer diagnosis, but the gold standard histopathology is time-consuming and incompatible with gastroscopy. Conventional stimulated Raman scattering (SRS) microscopy has shown promise in label-free diagnosis on human tissues, yet it requires the tuning of picosecond lasers to achieve chemical specificity at the cost of time and complexity. Here, we demonstrate that single-shot femtosecond SRS (femto-SRS) reaches the maximum speed and sensitivity with preserved chemical resolution by integrating with U-Net. Fresh gastroscopic biopsy is imaged in <60 s, revealing essential histoarchitectural hallmarks perfectly agreed with standard histopathology. Moreover, a diagnostic neural network (CNN) is constructed based on images from 279 patients that predicts gastric cancer with accuracy >96%. We further demonstrate semantic segmentation of intratumor heterogeneity and evaluation of resection margins of endoscopic submucosal dissection (ESD) tissues to simulate rapid and automated intraoperative diagnosis. Our method holds potential for synchronizing gastroscopy and histopathological diagnosis.

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Visualizing the 3D chemical profiles of individual aerosols is crucial to understand their formation and aging processes, yet remains technically challenging. Here, the first application of stimulated Raman scattering (SRS) microscopy on 3D chemical imaging of individual aerosols in a nondestructive manner is demonstrated. SRS is capable of mapping chemical components of aerosols at a speed four orders of magnitude faster than conventional spontaneous Raman microscopy. Spatially resolved distributions of nitrates and sulfates reveal the fine structures and different mixing states of atmospheric particles. Moreover, high‐throughput quantifications of chemical compositions and particle size distributions are realized by large‐area imaging and statistical analysis. Its high‐speed and 3D chemical quantification capabilities promise SRS microscopy as a unique tool for studying the properties of single atmospheric particles, and ultimately their impacts on climate and human health.

Gout is a common metabolic disease with growing burden, caused by monosodium urate (MSU) microcrystal deposition. In situ and chemical-specific histological identification of MSU is crucial in the diagnosis and management of gout, yet it remains inaccessible for current histological methods. Methods: Stimulated Raman scattering (SRS) microscopy was utilized to image MSU based on its fingerprint Raman spectra. We first tested SRS for the diagnosis capability of gout and the differentiation power from pseudogout with rat models of acute gout arthritis, calcium pyrophosphate deposition disease (CPDD) and comorbidity. Then, human synovial fluid and surgical specimens (n=120) were were imaged with SRS to obtain the histopathology of MSU and collagen fibers. Finally, quantitative SRS analysis was performed in gout tissue of different physiological phases (n=120) to correlate with traditional histopathology including H&E and immunohistochemistry staining. Results: We demonstrated that SRS is capable of early diagnosis of gout, rapid detection of MSU in synovial fluid and fresh unprocessed surgical tissues, and accurate differentiation of gout from pseudogout in various pathophysiological conditions. Furthermore, quantitative SRS analysis revealed the optical characteristics of MSU deposition at different pathophysiological stages, which were found to matched well with corresponding immunofluorescence histochemistry features. Conclusion: Our work demonstrated the potential of SRS microscopy for rapid intraoperative diagnosis of gout and may facilitate future fundamental researches of MSU-based diseases.

Aerosol microdroplets as microreactors for many important atmospheric reactions are ubiquitous in the atmosphere. pH largely regulates the chemical processes within them; however, how pH and chemical species spatially distribute within an atmospheric microdroplet is still under intense debate. The challenge is to measure pH distribution within a tiny volume without affecting the chemical species distribution. We demonstrate a method based on stimulated Raman scattering microscopy to visualize the three-dimensional pH distribution inside single microdroplets of varying sizes. We find that the surface of all microdroplets is more acidic, and a monotonic trend of pH decreasing is observed in the 2.9-μm aerosol microdroplet from center to edge, which is well supported by molecular dynamics simulation. However, bigger cloud microdroplet differs from small aerosol for pH distribution. This size-dependent pH distribution in microdroplets can be related to the surface-to-volume ratio. This work presents noncontact measurement and chemical imaging of pH distribution in microdroplets, filling the gap in our understanding of spatial pH in atmospheric aerosol.

Fe(III)–oxalate complexes are ubiquitous in atmospheric environments, which can release reactive oxygen species (ROS) such as H2O2, O•2–, and OH• under light irradiation. Although Fe(III)–oxalate photochemistry has been investigated extensively, the understanding of its involvement in authentic atmospheric environments such as aerosol droplets is far from enough, since the current available knowledge has mainly been obtained in bulk-phase studies. Here, we find that the production of OH• by Fe(III)–oxalate in aerosol microdroplets is about 10-fold greater than that of its bulk-phase counterpart. In addition, in the presence of Fe(III)–oxalate complexes, the rate of photo-oxidation from SO2 to sulfate in microdroplets was about 19-fold faster than that in the bulk phase. The availability of efficient reactants and mass transfer due to droplet effects made dominant contributions to the accelerated OH• and SO4 2– formation. This work highlights the necessary consideration of droplet effects in atmospheric laboratory studies and model simulations.

Focal therapy (FT) has been proposed as an approach to eradicate clinically significant prostate cancer (PCa) while preserving the normal surrounding tissues to minimize treatment-related toxicity. Rapid histology of core needle biopsies is essential to ensure the precise FT for localized lesions and to determine tumor grades. However, it is difficult to achieve both high accuracy and speed with currently available histopathology methods. Here, we demonstrated that stimulated Raman scattering (SRS) microscopy could reveal the largely heterogeneous histologic features of fresh prostatic biopsy tissues in a label-free and near real-time manner. A diagnostic convolutional neural network (CNN) built based on images from 61 patients could classify Gleason patterns of PCa with an accuracy of 85.7%. An additional 22 independent cases introduced as external test dataset validated the CNN performance with 84.4% accuracy. Gleason scores of core-needle biopsies from 21 cases were calculated using the deep learning SRS system and showed a 71% diagnostic consistency with grading from three pathologists. This study demonstrates the potential of a deep learning-assisted SRS platform in evaluating the tumor grade of prostate cancer, which could help simplify the diagnostic workflow and provide timely histopathology compatible with FT treatment.