Real-time, depth-resolved, in vivo multiphoton fluorescence lifetime imaging microscopy of agricultural herbicide treatments in plants.

The development of effective and safe agricultural treatments requires sub-cellular insight of the biochemical effects of treatments in living tissue in real-time. Industry-standard mass spectroscopic imaging lacks real-time in vivo capability. As an alternative, multiphoton fluorescence lifetime imaging microscopy (MPM-FLIM) allows for 3D sub-cellular quantitative metabolic imaging but is often limited to low frame rates. To resolve relatively fast effects (e.g., photosynthesis inhibiting treatments), high-frame-rate MPM-FLIM is needed. In this paper, we demonstrate and evaluate a high-speed MPM-FLIM system, "Instant FLIM", as a time-resolved 3D sub-cellular molecular imaging system in highly scattering, living plant tissues. We demonstrate simultaneous imaging of cellular autofluorescence and crystalline agrochemical crystals within plant tissues. We further quantitatively investigate the herbicidal effects of two classes of agricultural herbicide treatments, photosystem II inhibiting herbicide (Basagran) and auxin-based herbicide (Arylex), and successfully demonstrate the capability of the MPM-FLIM system to measure biological changes over a short time with enhanced imaging speed. Results indicate that high-frame-rate 3D MPM-FLIM achieves the required fluorescence lifetime resolution, temporal resolution, and spatial resolution to be a useful tool in basic plant cellular biology research and agricultural treatment development.

Nanopore-Enabled Dark-Field Digital Sensing of Nanoparticles.

In this study, we use nanopore arrays as a platform for detecting and characterizing individual nanoparticles (NPs) in real time. Dark-field imaging of nanopores with dimensions smaller than the wavelength of light occurs under conditions where trans-illumination is blocked, while the scattered light propagates to the far-field, making it possible to identify nanopores. The intensity of scattering increases dramatically during insertion of AgNPs into empty nanopores, owing to their plasmonic properties. Thus, momentary occupation of a nanopore by a AgNP produces intensity transients that can be analyzed to reveal the following characteristics: (1) NP scattering intensity, which scales with the sixth power of the AgNP radius, shows a normal distribution arising from the heterogeneity in NP size, (2) the nanopore residence time of NPs, which was observed to be stochastic with no permselective effects, and (3) the frequency of AgNP capture events on a 21 × 21 nanopore array, which varies linearly with the concentration of the NPs, agreeing with the frequency calculated from theory. The lower limit of detection (LOD) for NPs was 130 fM, indicating that the measurement can be used in applications in which ultrasensitive detection is required. The results presented here provide valuable insights into the dynamics of NP transport into and out of nanopores and highlight the potential of nanopore arrays as powerful, massively parallel tools for nanoparticle characterization and detection.

Small training dataset convolutional neural networks for application-specific super-resolution microscopy.

Significance: Machine learning (ML) models based on deep convolutional neural networks have been used to significantly increase microscopy resolution, speed [signal-to-noise ratio (SNR)], and data interpretation. The bottleneck in developing effective ML systems is often the need to acquire large datasets to train the neural network. We demonstrate how adding a "dense encoder-decoder" (DenseED) block can be used to effectively train a neural network that produces super-resolution (SR) images from conventional microscopy diffraction-limited (DL) images trained using a small dataset [15 fields of view (FOVs)]. Aim: The ML helps to retrieve SR information from a DL image when trained with a massive training dataset. The aim of this work is to demonstrate a neural network that estimates SR images from DL images using modifications that enable training with a small dataset. Approach: We employ "DenseED" blocks in existing SR ML network architectures. DenseED blocks use a dense layer that concatenates features from the previous convolutional layer to the next convolutional layer. DenseED blocks in fully convolutional networks (FCNs) estimate the SR images when trained with a small training dataset (15 FOVs) of human cells from the Widefield2SIM dataset and in fluorescent-labeled fixed bovine pulmonary artery endothelial cells samples. Results: Conventional ML models without DenseED blocks trained on small datasets fail to accurately estimate SR images while models including the DenseED blocks can. The average peak SNR (PSNR) and resolution improvements achieved by networks containing DenseED blocks are ≈ 3.2 dB and 2 × , respectively. We evaluated various configurations of target image generation methods (e.g., experimentally captured a target and computationally generated target) that are used to train FCNs with and without DenseED blocks and showed that including DenseED blocks in simple FCNs outperforms compared to simple FCNs without DenseED blocks. Conclusions: DenseED blocks in neural networks show accurate extraction of SR images even if the ML model is trained with a small training dataset of 15 FOVs. This approach shows that microscopy applications can use DenseED blocks to train on smaller datasets that are application-specific imaging platforms and there is promise for applying this to other imaging modalities, such as MRI/x-ray, etc.

Improving fluorescence lifetime imaging microscopy phasor accuracy using convolutional neural networks.

Introduction: Although a powerful biological imaging technique, fluorescence lifetime imaging microscopy (FLIM) faces challenges such as a slow acquisition rate, a low signal-to-noise ratio (SNR), and high cost and complexity. To address the fundamental problem of low SNR in FLIM images, we demonstrate how to use pre-trained convolutional neural networks (CNNs) to reduce noise in FLIM measurements. Methods: Our approach uses pre-learned models that have been previously validated on large datasets with different distributions than the training datasets, such as sample structures, noise distributions, and microscopy modalities in fluorescence microscopy, to eliminate the need to train a neural network from scratch or to acquire a large training dataset to denoise FLIM data. In addition, we are using the pre-trained networks in the inference stage, where the computation time is in milliseconds and accuracy is better than traditional denoising methods. To separate different fluorophores in lifetime images, the denoised images are then run through an unsupervised machine learning technique named "K-means clustering". Results and Discussion: The results of the experiments carried out on in vivo mouse kidney tissue, Bovine pulmonary artery endothelial (BPAE) fixed cells that have been fluorescently labeled, and mouse kidney fixed samples that have been fluorescently labeled show that our demonstrated method can effectively remove noise from FLIM images and improve segmentation accuracy. Additionally, the performance of our method on out-of-distribution highly scattering in vivo plant samples shows that it can also improve SNR in challenging imaging conditions. Our proposed method provides a fast and accurate way to segment fluorescence lifetime images captured using any FLIM system. It is especially effective for separating fluorophores in noisy FLIM images, which is common in in vivo imaging where averaging is not applicable. Our approach significantly improves the identification of vital biologically relevant structures in biomedical imaging applications.

Green tea polyphenolic antioxidants oxidize hydrogen sulfide to thiosulfate and polysulfides: A possible new mechanism underpinning their biological action.

Matcha and green tea catechins such as (-)-epicatechin (EC), (-)-epigallocatechin (EGC) and (-)-epigallocatechin gallate (EGCG) have long been studied for their antioxidant and health-promoting effects. Using specific fluorophores for H2S (AzMC) and polysulfides (SSP4) as well as IC-MS and UPLC-MS/MS-based techniques we here show that popular Japanese and Chinese green teas and select catechins all catalytically oxidize hydrogen sulfide (H2S) to polysulfides with the potency of EGC > EGCG >> EG. This reaction is accompanied by the formation of sulfite, thiosulfate and sulfate, consumes oxygen and is partially inhibited by the superoxide scavenger, tempol, and superoxide dismutase but not mannitol, trolox, DMPO, or the iron chelator, desferrioxamine. We propose that the reaction proceeds via a one-electron autoxidation process during which one of the OH-groups of the catechin B-ring is autooxidized to a semiquinone radical and oxygen is reduced to superoxide, either of which can then oxidize HS- to thiyl radicals (HS•) which react to form hydrogen persulfide (H2S2). H2S oxidation reduces the B-ring back to the hydroquinone for recycling while the superoxide is reduced to hydrogen peroxide (H2O2). Matcha and catechins also concentration-dependently and rapidly produce polysulfides in HEK293 cells with the potency order EGCG > EGC > EG, an EGCG threshold of ~300 nM, and an EC50 of ~3 µM, suggesting green tea also acts as powerful pro-oxidant in vivo. The resultant polysulfides formed are not only potent antioxidants, but elicit a cascade of secondary cytoprotective effects, and we propose that many of the health benefits of green tea are mediated through these reactions. Remarkably, all green tea leaves constitutively contain small amounts of H2S2.

Manganese Porphyrin-Based SOD Mimetics Produce Polysulfides from Hydrogen Sulfide.

Manganese-centered porphyrins (MnPs), MnTE-2-PyP5+ (MnTE), MnTnHex-2-PyP5+ (MnTnHex), and MnTnBuOE-2-PyP5+ (MnTnBuOE) have received considerable attention because of their ability to serve as superoxide dismutase (SOD) mimetics thereby producing hydrogen peroxide (H2O2), and oxidants of ascorbate and simple aminothiols or protein thiols. MnTE-2-PyP5+ and MnTnBuOE-2-PyP5+ are now in five Phase II clinical trials warranting further exploration of their rich redox-based biology. Previously, we reported that SOD is also a sulfide oxidase catalyzing the oxidation of hydrogen sulfide (H2S) to hydrogen persulfide (H2S2) and longer-chain polysulfides (H2Sn, n = 3-7). We hypothesized that MnPs may have similar actions on sulfide metabolism. H2S and polysulfides were monitored in fluorimetric assays with 7-azido-4-methylcoumarin (AzMC) and 3',6'-di(O-thiosalicyl)fluorescein (SSP4), respectively, and specific polysulfides were further identified by mass spectrometry. MnPs concentration-dependently consumed H2S and produced H2S2 and subsequently longer-chain polysulfides. This reaction appeared to be O2-dependent. MnP absorbance spectra exhibited wavelength shifts in the Soret and Q bands characteristic of sulfide-mediated reduction of Mn. Taken together, our results suggest that MnPs can become efficacious activators of a variety of cytoprotective processes by acting as sulfide oxidation catalysts generating per/polysulfides.