Ultra-high-throughput mapping of the chemical space of asymmetric catalysis enables accelerated reaction discovery.

The discovery of highly enantioselective catalysts and elucidating their generality face great challenges due to the complex multidimensional chemical space of asymmetric catalysis and inefficient screening methods. Here, we develop a general strategy for ultra-high-throughput mapping of the chemical space of asymmetric catalysis by escaping the time-consuming chiral chromatography separation. The ultrafast ( ~ 1000 reactions/day) and accurate (median error < ±1%) analysis of enantiomeric excess are achieved through the ion mobility-mass spectrometry combines with the diastereoisomerization strategy. A workflow for accelerated asymmetric reaction screening is established and verified by mapping the large-scale chemical space of more than 1600 reactions of α-asymmetric alkylation of aldehyde with organocatalysis and photocatalysis. Importantly, a class of high-enantioselectivity primary amine organocatalysts of 1,2-diphenylethane-1,2-diamine-based sulfonamides is discovered by the accelerated screening, and the mechanism for high-selectivity is demonstrated by computational chemistry. This study provides a practical and robust solution for large-scale screening and discovery of asymmetric reactions.

4D-diaXLMS: Proteome-wide Four-Dimensional Data-Independent Acquisition Workflow for Cross-Linking Mass Spectrometry.

Cross-linking mass spectrometry (XL-MS) is a powerful tool for examining protein structures and interactions. Nevertheless, analysis of low-abundance cross-linked peptides is often limited in the data-dependent acquisition (DDA) mode due to its semistochastic nature. To address this issue, we introduced a workflow called 4D-diaXLMS, representing the first-ever application of four-dimensional data-independent acquisition for proteome-wide cross-linking analysis. Cross-linking studies of the HeLa cell proteome were evaluated using the classical cross-linker disuccinimidyl suberate as an example. Compared with the DDA analysis, 4D-diaXLMS exhibited marked improvement in the identification coverage of cross-linked peptides, with a total increase of 36% in single-shot analysis across all 16 SCX fractions. This advantage was further amplified when reducing the fraction number to 8 and 4, resulting in 125 and 149% improvements, respectively. Using 4D-diaXLMS, up to 83% of the cross-linked peptides were repeatedly identified in three replicates, more than twice the 38% in the DDA mode. Furthermore, 4D-diaXLMS showed good performance in the quantitative analysis of yeast cross-linked peptides even in a 15-fold excess amount of HeLa cell matrix, with a low coefficient of variation and high quantitative accuracies in all concentrations. Overall, 4D-diaXLMS was proven to have high coverage, good reproducibility, and accurate quantification for in-depth XL-MS analysis in complex samples, demonstrating its immense potential for advances in the field.

High-Coverage Four-Dimensional Data-Independent Acquisition Proteomics and Phosphoproteomics Enabled by Deep Learning-Driven Multidimensional Predictions.

Four-dimensional (4D) data-independent acquisition (DIA)-based proteomics is a promising technology. However, its full performance is restricted by the time-consuming building and limited coverage of a project-specific experimental library. Herein, we developed a versatile multifunctional deep learning model Deep4D based on self-attention that could predict the collisional cross section, retention time, fragment ion intensity, and charge state with high accuracies for both the unmodified and phosphorylated peptides and thus established the complete workflows for high-coverage 4D DIA proteomics and phosphoproteomics based on multidimensional predictions. A 4D predicted library containing ∼2 million peptides was established that could realize experimental library-free DIA analysis, and 33% more proteins were identified than using an experimental library of single-shot measurement in the example of HeLa cells. These results show the great values of the convenient high-coverage 4D DIA proteomics methods.

A protocol for investigating lipidomic dysregulation and discovering lipid biomarkers from human serums.

Lipids play important roles in various human diseases. Disease-associated lipid dysregulation and biomarkers could provide molecular clues for diagnosis, pathogenesis, and therapy. This protocol provides a step-by-step workflow to investigate lipid dysregulation and discover biomarkers in human serum samples by liquid chromatography-mass spectrometry (LC-MS)-based lipidomics and machine learning analysis. The workflow includes project design, serum collection, sample preparation, data acquisition, data processing, and machine learning analysis. For complete details on the use and execution of this profile, please refer to Hao et al. (2021).

High-Throughput Nano-Electrostatic-Spray Ionization/Photoreaction Mass Spectrometric Platform for the Discovery of Visible-Light-Activated Photocatalytic Reactions in the Picomole Scale.

Visible-light-activated photocatalysis has emerged as a green and powerful tool for the synthesis of various organic compounds under mild conditions. However, the expeditious discovery of novel photocatalysts and synthetic pathways remains challenging. Here, we developed a bifunctional platform that enabled the high-throughput discovery and optimization of new photochemical reactions down to the picomole scale. This platform was designed based on a contactless nano-electrostatic-spray ionization technique, which allows synchronized photoreactions and high-throughput in situ mass spectrometric analysis with a near-100% duty cycle. Using this platform, we realized the rapid screening of photocatalytic reactions in ambient conditions with a high speed of less than 1.5 min/reaction using picomolar materials. The versatility was validated by multiple visible-light-induced photocatalytic reactions, especially the discovery of aerobic C-H thiolation with low-cost organic photocatalysts without any other additives. This study provided a new paradigm for the integration of ambient ionization techniques and new insights into photocatalytic reaction screening, which will have broad applications in the development of new visible-light-promoted reactions.

Machine Learning of Serum Metabolic Patterns Encodes Asymptomatic SARS-CoV-2 Infection.

Asymptomatic COVID-19 has become one of the biggest challenges for controlling the spread of the SARS-CoV-2. Diagnosis of asymptomatic COVID-19 mainly depends on quantitative reverse transcription PCR (qRT-PCR), which is typically time-consuming and requires expensive reagents. The application is limited in countries that lack sufficient resources to handle large-scale assay during the COVID-19 outbreak. Here, we demonstrated a new approach to detect the asymptomatic SARS-CoV-2 infection using serum metabolic patterns combined with ensemble learning. The direct patterns of metabolites and lipids were extracted by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) within 1 s with simple sample preparation. A new ensemble learning model was developed using stacking strategy with a new voting algorithm. This approach was validated in a large cohort of 274 samples (92 asymptomatic COVID-19 and 182 healthy control), and provided the high accuracy of 93.4%, with only 5% false negative and 7% false positive rates. We also identified a biomarker panel of ten metabolites and lipids, as well as the altered metabolic pathways during asymptomatic SARS-CoV-2 Infection. The proposed rapid and low-cost approach holds promise to apply in the large-scale asymptomatic COVID-19 screening.

Distinct lipid metabolic dysregulation in asymptomatic COVID-19.

Asymptomatic infection is a big challenge in curbing the spread of COVID-19. However, its identification and pathogenesis elucidation remain issues. Here, by performing comprehensive lipidomic characterization of serum samples from 89 asymptomatic COVID-19 patients and 178 healthy controls, we screened out a panel of 15 key lipids that could accurately identify asymptomatic patients using a new ensemble learning model based on stacking strategy with a voting algorithm. This strategy provided a high accuracy of 96.0% with only 3.6% false positive rate and 4.8% false negative rate. More importantly, the unique lipid metabolic dysregulation was revealed, especially the enhanced synthesis of membrane phospholipids, altered sphingolipids homeostasis, and differential fatty acids metabolic pattern, implicating the specific host immune, inflammatory, and antiviral responses in asymptomatic COVID-19. This study provides a potential prediagnostic method for asymptomatic COVID-19 and molecular clues for the pathogenesis and therapy of this disease.

Lutein protects against severe traumatic brain injury through anti­inflammation and antioxidative effects via ICAM­1/Nrf­2.

Many studies have reported that lutein may exert its biological activities, including anti­inflammation, anti­oxidase and anti­apoptosis, through effects on reactive oxygen species (ROS). Thus, lutein may prevent the damaging activities of ROS in cells. The current study investigated the effect of lutein against severe traumatic brain injury (STBI) and examined the mechanism of this protective effect. Sprague­Dawley rats were randomly divided into 5 groups: Control group, STBI model group, 40 mg/kg lutein­treated group, 80 mg/kg lutein­treated group and 160 mg/kg lutein­treated group. In this study, lutein protects against STBI, suppressed, interleukin (IL)­1ß, IL­6 and monocyte chemoattractant protein­1 expression, reduced serum ROS levels, and reduced superoxide dismutase and glutathione peroxidase activities in STBI rats. Treatment with lutein effectively downregulated the expression of NF­κB p65 and cyclooxygenase­2, intercellular adhesion molecule (ICAM)­1 protein, and upregulated nuclear factor erythroid 2 like 2 (Nrf­2) and endothelin­1 protein levels in STBI rats. These findings demonstrated that lutein protects against STBI, has anti­inflammation and antioxidative effects and alters ICAM­1/Nrf­2 expression, which may be a novel therapeutic for STBI the clinic.

[Juglone inhibits proliferation and induces apoptosis of human cervical squamous cancer SiHa cells].

OBJECTIVE: To explore the effect of juglone on proliferation and apoptosis of human cervical squamous cancer SiHa cells. METHODS: Cultured SiHa cells in the exponential growth phase were grouped into blank control group and 10, 20, 50, 80 and 100 µmol/L juglone treatment groups. Methyl thiazolyl tetrazolium (MTT) assay was adopted to observe the inhibitory effect of juglone on the proliferation of SiHa cells, and then 50% inhibitory concentration (IC50) was calculated through formula. Annexin V-FITC/PI double staining and flow cytometry were used to detect the effect of 20 µmol/L juglone on SiHa cell apoptosis. Western blot was applied to determine the expressions of Bcl-2 and Bax. RESULTS: MTT assay showed that, compared with the control group, treatment groups all showed significant inhibitory effects on SiHa cell growth, and IC50 was 20.4 µmol/L. Flow cytometry demonstrated that early apoptosis rate of SiHa cells in the control group was (2.46 ± 0.37)%, and after treatment with 20 µmol/L Juglone for 12 hours, the apoptosis rate was raised to (18.47 ± 2.26)%; Western blot analysis showed that the expression of Bcl-2 decreased while the expression of Bax increased significantly in SiHa cells treated with 20 µmol/L juglone. CONCLUSION: Juglone could significantly inhibit the proliferation and induce the apoptosis of SiHa cells.

Experimental realization of multipartite entanglement of 60 modes of a quantum optical frequency comb.

We report the experimental realization and characterization of one 60-mode copy and of two 30-mode copies of a dual-rail quantum-wire cluster state in the quantum optical frequency comb of a bimodally pumped optical parametric oscillator. This is the largest entangled system ever created whose subsystems are all available simultaneously. The entanglement proceeds from the coherent concatenation of a multitude of Einstein, Podolsky, and Rosen pairs by a single beam splitter, a procedure which is also a building block for the realization of hypercubic-lattice cluster states for universal quantum computing.

Real-time study of graphene's phase transition in polymer matrices.

We present real-time study of pristine graphene sandwiched in a homogeneous polymer matrix and its phase transition where the graphene membrane irreversibly scrolls and folds above the polymer's glass temperature. Tubular structures tend to form by curling up from edge defects of graphene and roll along its surface. A single-layer can also fold into two- or three-layer stacks and the overlapping between layers extends along the membrane surface to enlarge up to micrometer sizes. Further, oxidized graphene does not show such reactivity at even higher temperatures, indicating that the intrinsic thermal instability of pristine graphene in the polymer matrix is the origin of the transition.

[Measurement of cerebral blood oxygen saturation with near infrared spectroscopy and its application in hypergravity].

OBJECTIVE: To measure the oxygen saturation of blood in rat's brain non-invasively after acceleration with near infrared spectroscopy technique. METHOD: A serious of model about different ischemia and hypoxia of head, measured oxygen saturation by phlebotomize were made, and the difference with normal group was obtained. Its absorption intensity was measured and a curve showing the difference between the normal value was drawn. According to the curve, cerebral blood oxygen saturation after acceleration was calculated with the measured absorption intensity after high or push-pull maneuver by near infrared spectroscopy. RESULT: Compared high +Gz and push pull effect group with 0 Gz group, the content of ox hemoglobin reduced, in opposition the deox hemoglobin increased, the greater the difference, the lower the content of oxygen saturation of blood. CONCLUSION: Oxygen saturation of blood in the rat's brain can be obtained non-invasively with near infrared spectroscopy.