Insights from multi-omics integration into seed germination of Taxus chinensis var mairei.

The transition from deep dormancy to seed germination is essential for the life cycle of plants, but how this process occurs in the gymnosperm Chinese yew (Taxus chinensis var mairei), the natural source of the anticancer drug paclitaxel, remains unclear. Herein, we analyse the transcriptome, proteome, spatial metabolome, and spatial lipidome of the Chinese yew and present the multi-omics profiles of dormant and germinating seeds. Our results show that abscisic acid and gibberellic acid 12 homoeostasis is closely associated with gene transcription and protein translation, and the balance between these phytohormones thereby determines if seeds remain dormant or germinate. We find that an energy supply of carbohydrates from glycolysis and the TCA cycle feed into the pentose phosphate pathway during seed germination, and energy supplied from lipids are mainly derived from the lipolysis of triacylglycerols. Using mass spectrometry imaging, we demonstrate that the spatial distribution of plant hormones and phospholipids has a remarkable influence on embryo development. We also provide an atlas of the spatial distribution of paclitaxel C in Chinese yew seeds for the first time. The data from this study enable exploration of the germination mechanism of Chinese yew seeds across several omics levels.

High-throughput MALDI-MSI metabolite analysis of plant tissue microarrays.

A novel metabolomics analysis technique, termed matrix-assisted laser desorption/ionization mass spectrometry imaging-based plant tissue microarray (MALDI-MSI-PTMA), was successfully developed for high-throughput metabolite detection and imaging from plant tissues. This technique completely overcomes the disadvantage that metabolites cannot be accessible on an intact plant tissue due to the limitations of the special structures of plant cells (e.g. epicuticular wax, cuticle and cell wall) through homogenization of plant tissues, preparation of PTMA moulds and matrix spraying of PTMA sections. Our study shows several properties of MALDI-MSI-PTMA, including no need of sample separation and enrichment, high-throughput metabolite detection and imaging (>1000 samples per day), high-stability mass spectrometry data acquisition and imaging reconstruction and high reproducibility of data. This novel technique was successfully used to quickly evaluate the effects of two plant growth regulator treatments (i.e. 6-benzylaminopurine and N-phenyl-N'-1,2,3-thiadiazol-5-ylurea) on endogenous metabolite expression in plant tissue culture specimens of Dracocephalum rupestre Hance (D. rupestre). Intra-day and inter-day evaluations indicated that the metabolite data detected on PTMA sections had good reproducibility and stability. A total of 312 metabolite ion signals in leaves tissues of D. rupestre were detected, of which 228 metabolite ion signals were identified, they were composed of 122 primary metabolites, 90 secondary metabolites and 16 identified metabolites of unknown classification. The results demonstrated the advantages of MALDI-MSI-PTMA technique for enhancing the overall detection ability of metabolites in plant tissues, indicating that MALDI-MSI-PTMA has the potential to become a powerful routine practice for high-throughput metabolite study in plant science.

Stable tissue-mimicking phantoms for longitudinal multimodality imaging studies that incorporate optical, CT, and MRI contrast.

Significance: Tissue phantoms that mimic the optical and radiologic properties of human or animal tissue play an important role in the development, characterization, and evaluation of imaging systems. Phantoms that are easily produced and stable for longitudinal studies are highly desirable. Aim: A new type of long-lasting phantom was developed with commercially available materials and was assessed for fabrication ease, stability, and optical property control. Magnetic resonance imaging (MRI) and x-ray computed tomography (CT) contrast properties were also evaluated. Approach: A systematic investigation of relationships between concentrations of skin-like pigments and composite optical properties was conducted to realize optical property phantoms in the red and near-infrared (NIR) wavelength range that also offered contrast for CT and MRI. Results: Phantom fabrication time was < 1 h and did not involve any heating or cooling processes. Changes in optical properties were < 2 % over a 12-month period. Phantom optical and spectral features were similar to human soft tissue over the red to NIR wavelength ranges. Pigments used in the study also had CT and MRI contrasts for multimodality imaging studies. Conclusions: The phantoms described here mimic optical properties of soft tissue and are suitable for multimodality imaging studies involving CT or MRI without adding secondary contrast agents.

Selfrec-Net: self-supervised deep learning approach for the reconstruction of Cherenkov-excited luminescence scanned tomography.

As an emerging imaging technique, Cherenkov-excited luminescence scanned tomography (CELST) can recover a high-resolution 3D distribution of quantum emission fields within tissue using X-ray excitation for deep penetrance. However, its reconstruction is an ill-posed and under-conditioned inverse problem because of the diffuse optical emission signal. Deep learning based image reconstruction has shown very good potential for solving these types of problems, however they suffer from a lack of ground-truth image data to confirm when used with experimental data. To overcome this, a self-supervised network cascaded by a 3D reconstruction network and the forward model, termed Selfrec-Net, was proposed to perform CELST reconstruction. Under this framework, the boundary measurements are input to the network to reconstruct the distribution of the quantum field and the predicted measurements are subsequently obtained by feeding the reconstructed result to the forward model. The network was trained by minimizing the loss between the input measurements and the predicted measurements rather than the reconstructed distributions and the corresponding ground truths. Comparative experiments were carried out on both numerical simulations and physical phantoms. For singular luminescent targets, the results demonstrate the effectiveness and robustness of the proposed network, and comparable performance can be attained to a state-of-the-art deep supervised learning algorithm, where the accuracy of the emission yield and localization of the objects was far superior to iterative reconstruction methods. Reconstruction of multiple objects is still reasonable with high localization accuracy, although with limits to the emission yield accuracy as the distribution becomes more complex. Overall though the reconstruction of Selfrec-Net provides a self-supervised way to recover the location and emission yield of molecular distributions in murine model tissues.

Polyacrylamide gel as a new embedding medium for the enhancement of metabolite MALDI imaging.

In this study, polyacrylamide gel (PAAG) was successfully used as a new embedding medium to provide the more effective maintenance of biological tissues during the sectioning process, enhancing the tissue imaging of metabolites via matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Herein, PAAG, agarose, gelatin, optimal cutting temperature compound (OCT), and ice media were used to embed rat liver and Atlantic salmon (Salmo salar) eyeball samples. These embedded tissues were then sectioned into thin slices and thaw-mounted on conductive microscope glass slides for MALDI-MSI detection to evaluate the embedding effects. The results showed that PAAG embedding has characteristics superior to those of commonly-used embedding media (e.g., agarose, gelatin, OCT, and ice) with the advantages of one-step operation without heating, a better performance of morphology maintenance, the absence of PAAG polymer-ion-related interference below m/z 2000, and the more efficient in situ ionization of metabolites, providing a significant enhancement of both the numbers and intensities of the metabolite ion signals. Our study demonstrates the potential of PAAG embedding as a standard practice for metabolite MALDI tissue imaging, which will lead to an expanded application scope of MALDI-MSI.

X-ray Cherenkov-luminescence tomography reconstruction with a three-component deep learning algorithm: Swin transformer, convolutional neural network, and locality module.

Significance: X-ray Cherenkov-luminescence tomography (XCLT) produces fast emission data from megavoltage (MV) x-ray scanning, in which the excitation location of molecules within tissue is reconstructed. However standard filtered backprojection (FBP) algorithms for XCLT sinogram reconstruction can suffer from insufficient data due to dose limitations, so there are limits in the reconstruction quality with some artifacts. We report a deep learning algorithm for XCLT with high image quality and improved quantitative accuracy. Aim: To directly reconstruct the distribution of emission quantum yield for x-ray Cherenkov-luminescence tomography, we proposed a three-component deep learning algorithm that includes a Swin transformer, convolution neural network, and locality module model. Approach: A data-to-image model x-ray Cherenkov-luminescence tomography is developed based on a Swin transformer, which is used to extract pixel-level prior information from the sinogram domain. Meanwhile, a convolutional neural network structure is deployed to transform the extracted pixel information from the sinogram domain to the image domain. Finally, a locality module is designed between the encoder and decoder connection structures for delivering features. Its performance was validated with simulation, physical phantom, and in vivo experiments. Results: This approach can better deal with the limits to data than conventional FBP methods. The method was validated with numerical and physical phantom experiments, with results showing that it improved the reconstruction performance mean square error ( > 94.1 % ), peak signal-to-noise ratio ( > 41.7 % ), and Pearson correlation ( > 19 % ) compared with the FBP algorithm. The Swin-CNN also achieved a 32.1% improvement in PSNR over the deep learning method AUTOMAP. Conclusions: This study shows that the three-component deep learning algorithm provides an effective reconstruction method for x-ray Cherenkov-luminescence tomography.

A concentration-descending washing strategy with methanol for the enhancement of protein imaging in biological tissues by MALDI-MS.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a powerful approach that has been widely used for in situ detection of various endogenous compounds in tissues. However, there are still challenges with in situ analysis of proteins using MALDI-MSI due to the ion suppression effects of small molecules in tissue sections. Therefore, tissue-washing steps are crucial for protein MALDI tissue imaging to remove these interfering molecules. Here, we successfully developed a new method named the concentration-descending washing strategy (CDWS) with methanol (MeOH), i.e., washing of biological tissue with 100%, 95%, and 70% MeOH solutions, for the enhancement of endogenous in situ protein detection and imaging in tissues using MALDI-MS. The method of MeOH-based CDWS (MeOH-CDWS) led to the successful in situ detection of 272 ± 3, 185 ± 4, and 134 ± 2 protein ion signals from rat liver, rat brain, and germinating Chinese-yew seed tissue sections, respectively. By comparison, 161 ± 2, 121 ± 1, and 114 ± 2 protein ions were detected by three commonly used methods, i.e., Carnoy's wash, ethanol (EtOH)-based CAWS (i.e., concentration-ascending washing strategy, 70% EtOH followed by 90% EtOH/9% AcOH), and isopropanol (iPrOH)-based CAWS (70% iPrOH followed by 95% iPrOH), respectively, in rat liver tissue sections, indicating that 68.9 ± 3.1%, 124.8 ± 3.3%, and 138.6 ± 4.4% more protein ion signals could be detected by the use of MeOH-CDWS than the three abovementioned washing strategies. Our results show that the use of MeOH-CDWS improves the performance of MALDI-MSI for in situ protein detection such as the number and intensity of proteins. The use of MeOH-CDWS improves the fixation of proteins and thus reduces the loss of proteins, which significantly reduces protein delocalization in tissue and enhances the performance of MALDI tissue imaging of protein. Thus, the use of MeOH-CDWS improves the quality of protein images in tissue sections through MALDI-MSI and has the potential to be used as standard practice for MALDI tissue imaging of proteins.

Physiological and transcriptome analyses reveal the response of Ammopiptanthus mongolicus to extreme seasonal temperatures in a cold plateau desert ecosystem.

Ammopiptanthus mongolicus is the only evergreen broad-leaved shrub present in arid areas of Northwest China and plays an important role in maintaining the stability of the local desert ecosystem. It can survive under extreme temperatures (e.g., extreme low temperature: - 24.8 °C and extreme high temperature: 37.7 °C). To understand the gene expression-physiological regulation network of A. mongolicus in extreme temperature environments, we monitored the changes in gene expression and photosynthetic traits of the leaves. The results showed that at low temperatures, the net photosynthetic rates (A), Fv'/Fm' and electron transport rate (ETR) decreased, the Fv/Fm ratio was only 0.32, and the proportion of nonregulatory heat dissipation Y(NO) increased. Based on a KEGG analysis of the differentially expressed genes, 15 significantly enriched KEGG pathways were identified, which were mainly related to circadian rhythm, photosynthesis, lipid metabolism, carbohydrate metabolism, plant hormones and other life activities. At high temperatures, the A value increased, and the proportion of regulatory energy dissipation Y(NPQ) increased. The KEGG analysis identified 24 significantly enriched KEGG pathways, which are mainly related to circadian rhythm, carbon sequestration of photosynthesis, carotenoid biosynthesis, secondary metabolites, cofactors and vitamin metabolism. In general, at the expense of photosynthesis, A. mongolicus can ensure the survival of leaves by increasing Y(NO) levels, regulating the circadian rhythm, increasing the synthesis of unsaturated fatty acids and changing the role of plant hormones. Under high-temperature stress, a high photosynthetic capacity was maintained by adjusting the stomatal conductance (gsw), increasing Y(NPQ), consuming excess light energy, continuously assembling and maintaining PSII, and changing the production of antioxidants.

Unlocking Drought-Induced Tree Mortality: Physiological Mechanisms to Modeling.

Drought-related tree mortality has become a major concern worldwide due to its pronounced negative impacts on the functioning and sustainability of forest ecosystems. However, our ability to identify the species that are most vulnerable to drought, and to pinpoint the spatial and temporal patterns of mortality events, is still limited. Model is useful tools to capture the dynamics of vegetation at spatiotemporal scales, yet contemporary land surface models (LSMs) are often incapable of predicting the response of vegetation to environmental perturbations with sufficient accuracy, especially under stressful conditions such as drought. Significant progress has been made regarding the physiological mechanisms underpinning plant drought response in the past decade, and plant hydraulic dysfunction has emerged as a key determinant for tree death due to water shortage. The identification of pivotal physiological events and relevant plant traits may facilitate forecasting tree mortality through a mechanistic approach, with improved precision. In this review, we (1) summarize current understanding of physiological mechanisms leading to tree death, (2) describe the functionality of key hydraulic traits that are involved in the process of hydraulic dysfunction, and (3) outline their roles in improving the representation of hydraulic function in LSMs. We urge potential future research on detailed hydraulic processes under drought, pinpointing corresponding functional traits, as well as understanding traits variation across and within species, for a better representation of drought-induced tree mortality in models.

Deep-learning based image reconstruction for MRI-guided near-infrared spectral tomography.

Non-invasive near-infrared spectral tomography (NIRST) can incorporate the structural information provided by simultaneous magnetic resonance imaging (MRI), and this has significantly improved the images obtained of tissue function. However, the process of MRI guidance in NIRST has been time consuming because of the needs for tissue-type segmentation and forward diffuse modeling of light propagation. To overcome these problems, a reconstruction algorithm for MRI-guided NIRST based on deep learning is proposed and validated by simulation and real patient imaging data for breast cancer characterization. In this approach, diffused optical signals and MRI images were both used as the input to the neural network, and simultaneously recovered the concentrations of oxy-hemoglobin, deoxy-hemoglobin, and water via end-to-end training by using 20,000 sets of computer-generated simulation phantoms. The simulation phantom studies showed that the quality of the reconstructed images was improved, compared to that obtained by other existing reconstruction methods. Reconstructed patient images show that the well-trained neural network with only simulation data sets can be directly used for differentiating malignant from benign breast tumors.

Different Phenylalanine Pathway Responses to Cold Stress Based on Metabolomics and Transcriptomics in Tartary Buckwheat Landraces.

Tartary buckwheat (Fagopyrum tataricum) is strongly adapted to growth in adverse environments. In Liangshan, the Yi people cultivate different Tartary buckwheat landraces in different habitats. In this study, we aimed to understand the molecular differences in transcriptomic and metabolomic responses underlying cold tolerance between two Tartary buckwheat landraces (TM and RG) cultivated at different altitudes. After cold treatment, TM showed normal growth in the seedling stage and had significantly higher total flavonoids (16.53 mg/g, 1.47 times), rutin (5.73 mg/g, 1.32 times), and quercetin (0.08 mg/g, 2.67 times), which were higher than those in RG. In addition, TM showed higher-level changes in carbon and nitrogen metabolism than RG. Combined transcriptome and metabolomic analyses showed that phenylpropanoid biosynthesis was upregulated after cold treatment, and in TM, rutin synthesis was upregulated with a higher-level response to cold stress. RG showed higher expression in anthocyanins in response to cold stress. In addition, 24 structural genes involved in flavonoid synthesis, including 6 PAL, 3 C4H, 2 4CL, 2 CHS, 1 CHI, 3 F3H, 3 DFR, 1 FLS, 1 F3'H, and 4 GTR genes, were identified. These results will provide sufficient information for breeding Tartary buckwheat with high cold tolerance and constructing rutin high-yield varieties based on genetic engineering.

Lack of phenotypic plasticity in leaf hydraulics for 10 woody species common to urban forests of North China.

The survival and performance of urban forests are increasingly challenged by urban drought, consequently compromising the sustainability and functionality of urban vegetation. Plant-water relations largely determine species drought tolerance, yet little is known about the hydraulics of urban forest species. Here, we report the leaf hydraulic and carbon traits that govern plant growth and drought resistance, including vulnerability to embolism, hydraulic conductivity and leaf gas exchange characteristics, as well as morphological traits that are potentially linked with these physiological attributes, with the aim of guiding species selection and management in urban forests. Plant materials were collected from mature shrubs and trees on our university campus in Beijing, representing 10 woody species common to urban forests in north China. We found that the leaf embolism resistance, represented by the water potential inducing 50% loss of hydraulic conductivity (P50), as well as the hydraulic safety margin (HSM) defined by P50 and the water potential threshold at the inception of embolism (P12), varied remarkably across species, but was unrelated to growth form. Likewise, stem and leaf-specific hydraulic conductivity (Kstem and kl) was also highly species-specific. Leaf P50 was positively correlated with hydraulic conductivity. However, neither P50 nor hydraulic conductivity was correlated with leaf gas exchange traits, including maximum photosynthetic rate (Amax) and stomatal conductance (gs). Plant morphological and physiological traits were not related, except for specific leaf area, which showed a negative relationship with HSM. Traits influencing plant-water transport were primarily correlated with the mean annual precipitation of species climatic niche. Overall, current common woody species in urban forest environments differed widely in their drought resistance and did not have the capacity to modify these characteristics in response to a changing climate. Species morphology provides limited information regarding physiological drought resistance. Thus, screening urban forest species based on plant physiology is essential to sustain the ecological services of urban forests.

Michler's ethylketone as a novel negative-ion matrix for the enhancement of lipid MALDI tissue imaging.

Michler's ethylketone (MEK, 4,4'-bis(diethylamino)benzophenone), commonly-known as an intermediate in the synthesis of dyes and pigments, was successfully screened and optimized as a novel matrix for the enhancement of lipid in situ detection and imaging in tissues by MALDI-MSI. The results show several properties of MEK as a powerful MALDI matrix, including strong UV absorption, µm-sized crystals and uniform matrix-coating, super high vacuum chemical stability, low matrix-related ion interference, super soft ionization, and high lipid ionization efficiency.

The long-term effects of microplastics on soil organomineral complexes and bacterial communities from controlled-release fertilizer residual coating.

Controlled-release fertilizer (CRF) was applied widely in China as an efficient utilization strategy for improving grain yield and reducing the nitrogen contamination. However, it was indeterminate to know the impacts of inevitably imported plastic into the soil on sustainable development. After ten-year fixed-site experiment, the visible residual coating microplastics were separated from the soil to measure their changes, then the long-term effects of CRF application (theoretical microplastic content 0.018-0.151 g kg-1 soil) on soil architecture and bacterial communities were evaluated. Based on soil organomineral complexes (OMC) distribution experiments and soil 16S rRNA sequence analysis, residual coating microplastics had no significant impact on soil architecture and limited effects on soil bacteria, but became the specific microbial habitat. The nitrogen rate and nitrogen release mode affected sand- and silt-grade OMC, and nitrogen rate impacted soil bacteria communities. The residual coating, small inert particles, is safe for soil OMC and bacterial communities in agricultural soil. Due to the effectiveness of CRF on reducing environmental pollution, CRF is considered as a favorable measure to the sustainable agricultural development in Shandong Province, China.

Corrigendum: Unlocking drought-induced tree mortality: Physiological mechanisms to modelling.

[This corrects the article DOI: 10.3389/fpls.2022.835921.].

Exercise Training Increases Resting Calf Muscle Oxygen Metabolism in Patients with Peripheral Artery Disease.

Exercise training can mitigate symptoms of claudication (walking-induced muscle pain) in patients with peripheral artery disease (PAD). One adaptive response enabling this improvement is enhanced muscle oxygen metabolism. To explore this issue, we used arterial-occlusion diffuse optical spectroscopy (AO-DOS) to measure the effects of exercise training on the metabolic rate of oxygen (MRO2) in resting calf muscle. Additionally, venous-occlusion DOS (VO-DOS) and frequency-domain DOS (FD-DOS) were used to measure muscle blood flow (F) and tissue oxygen saturation (StO2), and resting calf muscle oxygen extraction fraction (OEF) was calculated from MRO2, F, and blood hemoglobin. Lastly, the venous/arterial ratio (γ) of blood monitored by FD-DOS was calculated from OEF and StO2. PAD patients who experience claudication (n = 28) were randomly assigned to exercise and control groups. Patients in the exercise group received 3 months of supervised exercise training. Optical measurements were obtained at baseline and at 3 months in both groups. Resting MRO2, OEF, and F, respectively, increased by 30% (12%, 44%) (p < 0.001), 17% (6%, 45%) (p = 0.003), and 7% (0%, 16%) (p = 0.11), after exercise training (median (interquartile range)). The pre-exercise γ was 0.76 (0.61, 0.89); it decreased by 12% (35%, 6%) after exercise training (p = 0.011). Improvement in exercise performance was associated with a correlative increase in resting OEF (R = 0.45, p = 0.02).

Quantification of blood flow index in diffuse correlation spectroscopy using long short-term memory architecture.

Diffuse correlation spectroscopy (DCS) is a noninvasive technique that derives blood flow information from measurements of the temporal intensity fluctuations of multiply scattered light. Blood flow index (BFI) and especially its variation was demonstrated to be approximately proportional to absolute blood flow. We investigated and assessed the utility of a long short-term memory (LSTM) architecture for quantification of BFI in DCS. Phantom and in vivo experiments were established to measure normalized intensity autocorrelation function data. Improved accuracy and faster computational time were gained by the proposed LSTM architecture. The results support the notion of using proposed LSTM architecture for quantification of BFI in DCS. This approach would be especially useful for continuous real-time monitoring of blood flow.

A Caffeic Acid Matrix Improves In Situ Detection and Imaging of Proteins with High Molecular Weight Close to 200,000 Da in Tissues by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging.

To our knowledge, this was the first study in which caffeic acid (CA) was successfully evaluated as a matrix to enhance the in situ detection and imaging of endogenous proteins in three biological tissue sections (i.e., a rat brain and Capparis masaikai and germinating soybean seeds) by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Our results show several properties of CA, including strong ultraviolet absorption, a super-wide MS detection mass range close to 200,000 Da, micrometer-sized matrix crystals, uniform matrix deposition, and high ionization efficiency. More high-molecular-weight (HMW) protein ion signals (m/z > 30,000) could be clearly detected in biological tissues with the use of CA, compared to two commonly used MALDI matrices, i.e., sinapinic acid (SA) and ferulic acid (FA). Notably, CA shows excellent performance for HMW protein in situ detection from biological tissues in the mass range m/z > 80,000, compared to the use of SA and FA. Furthermore, the use of a CA matrix also significantly enhanced the imaging of proteins on the surface of selected biological tissue sections. Three HMW protein ion signals (m/z 50,419, m/z 65,874, and m/z 191,872) from a rat brain, two sweet proteins (mabinlin-2 and mabinlin-4) from a Capparis masaikai seed, and three HMW protein ion signals (m/z 94,838, m/z 134,204, and m/z 198,738) from a germinating soybean seed were successfully imaged for the first time. Our study proves that CA has the potential to become a standard organic acid matrix for enhanced tissue imaging of HMW proteins by MALDI-MSI in both animal and plant tissues.