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Abstract Water plays a vital role in the life cycle of plants, participating in various critical biochemical reactions during both non‐photosynthetic and photosynthetic processes. Direct visualization of the metabolic activities of water in plants with high spatiotemporal resolution is essential to reveal the functional utilization of water. Here, stimulated Raman scattering (SRS) microscopy is applied to monitor the metabolic processes of deuterated water (D2O) in model plant Arabidopsis thaliana (A. thaliana). The work shows that in plants uptaking D2O/water solution, proton‐transfer from water to organic metabolites results in the formation of C‐D bonds in newly synthesized biomolecules (lipid, protein, and polysaccharides, etc.) that allow high‐resolution detection with SRS. Reversible metabolic pathways of oil‐starch conversion between seed germination and seed development processes are verified. Spatial heterogeneity of metabolic activities along the vertical axis of plants (root, stem, and tip meristem), as well as the radial distributions of secondary growth on the horizontal cross‐sections are quantified. Furthermore, metabolic flow of protons from plants to animals is visualized in aphids feeding on A. thaliana. Collectively, SRS microscopy has potential to trace a broad range of matter flows in plants, such as carbon storage and nutrition metabolism.

Diagnosis of gastric cancer currently requires gastroscopic biopsy, which requires time and expertize to perform. Here, the authors demonstrate a femto-SRS imaging method which showed high accuracy in diagnosing gastric cancer without the need for pathologistbased diagnosis.

Probes with reversible fluorescence are useful in super-resolution microscopy, but lack sufficient chemical specificity. Here, the authors engineer alkyne tagged diarylethene to realize photo-switchable stimulated Raman scattering probes with high chemical resolution, for applications in living cells.

Mesenchymal stem cells (MSCs) play a crucial role in tissue engineering, as their differentiation status directly affects the quality of the final cultured tissue, which is critical to the success of transplantation therapy. Furthermore, the precise control of MSC differentiation is essential for stem cell therapy in clinical settings, as low-purity stem cells can lead to tumorigenic problems. Therefore, to address the heterogeneity of MSCs during their differentiation into adipogenic or osteogenic lineages, numerous label-free microscopic images were acquired using fluorescence lifetime imaging microscopy (FLIM) and stimulated Raman scattering (SRS), and an automated evaluation model for the differentiation status of MSCs was built based on the K-means machine learning algorithm. The model is capable of highly sensitive analysis of individual cell differentiation status, so it has great potential for stem cell differentiation research.

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. [ABSTRACT FROM AUTHOR]

Intraoperative histology is essential for surgical guidance and decision-making. However, frozen-sectioned hematoxylin and eosin (H&E) staining suffers from degraded accuracy, whereas the gold-standard formalin-fixed and paraffin-embedded (FFPE) H&E is too lengthy for intraoperative use. Stimulated Raman scattering (SRS) microscopy has shown rapid histology of brain tissue with lipid/protein contrast but is challenging to yield images identical to nucleic acid-/protein-based FFPE stains interpretable to pathologists. Here, we report the development of a semi-supervised stimulated Raman CycleGAN model to convert fresh-tissue SRS images to H&E stains using unpaired training data. Within 3 minutes, stimulated Raman virtual histology (SRVH) results that matched perfectly with true H&E could be generated. A blind validation indicated that board-certified neuropathologists are able to differentiate histologic subtypes of human glioma on SRVH but hardly on conventional SRS images. SRVH may provide intraoperative diagnosis superior to frozen H&E in both speed and accuracy, extendable to other types of solid tumors. [ABSTRACT FROM AUTHOR]