Deep Profiles Research Articles

Advancements in Global Phosphoproteomics Profiling: Overcoming Challenges in Sensitivity and Quantification.

Protein phosphorylation introduces post-genomic diversity to proteins, which plays a crucial role in various cellular activities. Elucidation of system-wide signaling cascades requires high-performance tools for precise identification and quantification of dynamics of site-specific phosphorylation events. Recent advances in phosphoproteomic technologies have enabled the comprehensive mapping of the dynamic phosphoproteomic landscape, which has opened new avenues for exploring cell type-specific functional networks underlying cellular functions and clinical phenotypes. Here, we provide an overview of the basics and challenges of phosphoproteomics, as well as the technological evolution and current state-of-the-art global and quantitative phosphoproteomics methodologies. With a specific focus on highly sensitive platforms, we summarize recent trends and innovations in miniaturized sample preparation strategies for micro-to-nanoscale and single-cell profiling, data-independent acquisition mass spectrometry (DIA-MS) for enhanced coverage, and quantitative phosphoproteomic pipelines for deep mapping of cell and disease biology. Each aspect of phosphoproteomic analysis presents unique challenges and opportunities for improvement and innovation. We specifically highlight evolving phosphoproteomic technologies that enable deep profiling from low-input samples. Finally, we discuss the persistent challenges in phosphoproteomic technologies, including the feasibility of nanoscale and single-cell phosphoproteomics, as well as future outlooks for biomedical applications.

Deep profiling of gene expression across 18 human cancers.

Clinical and biological information in large datasets of gene expression across cancers could be tapped with unsupervised deep learning. However, difficulties associated with biological interpretability and methodological robustness have made this impractical. Here we describe an unsupervised deep-learning framework for the generation of low-dimensional latent spaces for gene-expression data from 50,211 transcriptomes across 18 human cancers. The framework, which we named DeepProfile, outperformed dimensionality-reduction methods with respect to biological interpretability and allowed us to unveil that genes that are universally important in defining latent spaces across cancer types control immune cell activation, whereas cancer-type-specific genes and pathways define molecular disease subtypes. By linking latent variables in DeepProfile to secondary characteristics of tumours, we discovered that mutation burden is closely associated with the expression of cell-cycle-related genes, and that the activity of biological pathways for DNA-mismatch repair and MHC class II antigen presentation are consistently associated with patient survival. We also found that tumour-associated macrophages are a source of survival-correlated MHC class II transcripts. Unsupervised learning can facilitate the discovery of biological insight from gene-expression data.

Parallel measurement of transcriptomes and proteomes from same single cells using nanodroplet splitting

Single-cell multiomics provides comprehensive insights into gene regulatory networks, cellular diversity, and temporal dynamics. Here, we introduce nanoSPLITS (nanodroplet SPlitting for Linked-multimodal Investigations of Trace Samples), an integrated platform that enables global profiling of the transcriptome and proteome from same single cells via RNA sequencing and mass spectrometry-based proteomics, respectively. Benchmarking of nanoSPLITS demonstrates high measurement precision with deep proteomic and transcriptomic profiling of single-cells. We apply nanoSPLITS to cyclin-dependent kinase 1 inhibited cells and found phospho-signaling events could be quantified alongside global protein and mRNA measurements, providing insights into cell cycle regulation. We extend nanoSPLITS to primary cells isolated from human pancreatic islets, introducing an efficient approach for facile identification of unknown cell types and their protein markers by mapping transcriptomic data to existing large-scale single-cell RNA sequencing reference databases. Accordingly, we establish nanoSPLITS as a multiomic technology incorporating global proteomics and anticipate the approach will be critical to furthering our understanding of biological systems.

Isolation and Molecular Profiling of Nuclei of Specific Neuronal Types from Human Cerebral Cortex and Striatum.

Most pathological conditions of the central nervous system do not affect all cell types to the same extent. Delineation of molecular events underlying disease symptoms, including genetic, epigenetic, and transcriptional changes, thus relies on the ability to characterize a specific cell type separately from others. We have developed a methodology for the collection of nuclear RNA and genomic DNA of specific cell types from frozen post-mortem striatum and cerebral cortex. This allows deep transcriptomic profiling of specific cell populations and characterization of their genomes and epigenetic properties. The method is based on the purification of cell nuclei, followed by fluorescence-activated sorting of nuclei (FANS) labeled with nucleic acid probes or antibodies binding to targets present in specific cell types. The protocol describes in detail the procedure for isolating and labeling neuronal and glial nuclei from human brain tissue, the steps that can be taken to extract RNA and genomic DNA, a way to combine the procedure with ATAC-seq to yield information about chromatin accessibility, as well as computational measures for assessing the quality of cell type-specific RNA-seq and ATAC-seq datasets. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Tissue homogenization, isolation of cell nuclei by ultracentrifugation and formaldehyde-fixation Basic Protocol 2: Antibody-based labeling and isolation of nuclei of specific neocortical neuron types Support Protocol 1: Generation of ATAC-seq libraries from the nuclei of specific neuron types of the cerebral cortex Basic Protocol 3: Nucleic acid hybridization-based labeling and isolation of nuclei of specific striatal projection neuron types Alternate Protocol 1: Labeling and isolation of nuclei of specific striatal interneuron types Support Protocol 2: Generation of ATAC-seq libraries from the nuclei of specific striatal neuron types Basic Protocol 4: Extraction of genomic DNA and nuclear RNA and preparation of sequencing libraries Basic Protocol 5: Processing and quality control of FANS-seq and ATAC-seq data.

Deep proteomics and network pharmacology reveal sex- and age-shared neuropathic pain signatures in mouse dorsal root ganglia.

Our understanding of how sex and age influence chronic pain at the molecular level is still limited with wide-reaching consequences for adolescent patients. Here, we leveraged deep proteome profiling of mouse dorsal root ganglia (DRG) from adolescent (4-week-old) and adult (12-week-old) male and female mice to investigate the establishment of neuropathic pain in the spared nerve injury (SNI)-model in parallel. We quantified over 12,000 proteins, including notable ion channels involved in pain, highlighting the sensitivity of our approach. Differential expression revealed sex- and age-dependent proteome changes upon nerve injury. In contrast to most previous studies, our comprehensive dataset enabled us to determine differentially expressed proteins (DEPs), which were shared between male and female mice of both age groups. Among these, the vast majority (94%) were also expressed and, in part, altered in human DRG of neuropathic pain patients, indicating evolutionary conservation. Proteome signatures represented numerous targets of FDA-approved drugs comprising both (i) known pain therapeutics (e.g. Pregabalin and opioids) and, importantly, (ii) compounds with high potential for future re-purposing, e.g. Ptprc-modulators and Epoetins. Protein network and multidimensional analysis uncovered distinct hubs of sex- and age-shared biological pathways impacted by neuropathic pain, such as neuronal activity and synaptic function, DNA-damage, and neuroimmune interactions. Taken together, our results capture the complexity of nerve injury-associated DRG alterations in mice at the network level, moving beyond single-candidate studies. Consequently, we provide an innovative resource of the molecular landscape of neuropathic pain, enabling novel opportunities for translational pain research and network-based drug discovery.

Direct imaging with multidimensional labelling and high-content analysis allows quantitative categorization and characterizations of individual small extracellular vesicles and nanoparticles (sEVPs).

Small extracellular vesicles and nanoparticles (sEVPs) are cell-secreted entities with potential as diagnostic biomarkers and therapeutic vehicles. However, significant intrinsic sEVP heterogeneity impedes analysis and understanding of their composition and functions. We employ multidimensional fluorescent labelling on sEVPs, leveraging the robustness of a newly developed membrane probe-conjugated oligoelectrolytes (COEs), and conduct total internal reflection fluorescence (TIRF) microscopy on sEVP arrays. These arrays comprise single sEVPs anchored to a soft material functionalized surface with little bias. We then develop an enhanced algorithm for colocalization analysis of the multiple labels on individual sEVPs and perform deep profiling of particle content. We categorize sEVPs derived from the same cell type into seven distinct subpopulations-some vesicular whereas others non-vesicular, and we demonstrate that sEVPs from four cell types exhibit quantitatively distinguishable subpopulation distributions. Furthermore, we gain insights into specific particle features within each subpopulation, including CD63 counts, relative particle size, relative concentration of cargoes, and correlations among different cargoes. This high-content analysis reveals common cargo sorting features in sEVP subpopulations across different cell types and suggests new statistics within the sEVP inherent heterogeneity that could differentiate sEVPs from two types of cancer cells and two types of normal cells. Collectively, our study presents a robust single-sEVP characterization platform, combining high-content imaging with comprehensive analysis. This platform is poised to advance sEVP-based theranostic assays and facilitate exploration into disease-associated sEVP biogenesis and sEVP-mediated intercellular communication.

Deep learning models for vision-based occupancy detection in high occupancy buildings

Accurate occupancy information is crucial for enhancing energy efficiency and reducing carbon emissions in buildings. However, the inherent unpredictability of occupants introduces uncertainties in energy analysis and control strategy development. To address these challenges, this study proposes a vision-based method employing state-of-the-art deep learning models to capture real-time occupancy profiles in crowded indoor spaces. Utilising a self-collected image dataset, various deep learning models, including Single Shot MultiBox Detector (SSD), Faster Region-based Convolutional Neural Networks (Faster R-CNN), and different versions of You Only Look Once (YOLO) were trained and evaluated. An experiment was conducted in a lecture room equipped with cameras and environmental sensors to evaluate the performance of each model in terms of precision, computational efficiency, and adaptability to varying occupancy levels during a lecture session. The session included varying occupancy conditions: entering (barely occupied), during the lecture (typical occupancy), and leaving the room (again barely occupied). Among the models tested, YOLOv8x exhibited the best performance in terms of accuracy, while SSD lagged notably. The impact on the detection performance of various locations of camera setups was also explored. Energy simulations revealed that deep learning-based model generated occupancy profiles significantly deviated from conventional “fixed” occupancy profiles, resulting in a 13.45 % variation in predicted heating energy demand. However, compared to the ground truth, these profiles showed minimal variation (up to 6.72 %) for the Faster R-CNN and YOLO models, highlighting their accuracy and robustness. Additionally, although the deep learning-based occupancy profiles generally overpredicted the recorded data, the CO2 concentration trends they predicted aligned closely with the recorded data, unlike the “fixed” occupancy profiles. The findings underscore the importance of realistic occupancy profiles for reliable energy predictions in buildings and demonstrate the potential of the proposed vision-based method for advancing occupancy detection and building energy management.

Research on Crack Sealing Performance of Polymer Microsphere/Hydrogel Composite System

Owing to their excellent water-absorption and swelling properties, polymer microspheres have been extensively applied as deep profile control agents in oilfields. These microspheres effectively seal large pore-throat channels in reservoirs, optimizing the water-absorption profile. In this study, a composite system was developed, comprising polymer microspheres and polyacrylamide polymers, with the inclusion of a cross-linking agent. The system leverages the synergistic effects of polymer microspheres and other plugging techniques to efficiently seal fractured reservoirs. Results indicate that the composite system exhibits strong blocking and scour resistance due to enhanced network integrity, higher viscosity, and improved elastic strength. Additionally, the composite system demonstrates a notable self-repairing capability, maintaining a high sealing efficiency even after a waterflood breakthrough.

Meta-atlas of Juvenile and Adult Enteric Neuron scRNA-seq for Dataset Comparisons and Consensus on Transcriptomic Definitions of Enteric Neuron Subtypes.

The enteric nervous system (ENS) is a complex network of interconnected ganglia within the gastrointestinal (GI) tract. Among its diverse functions, the ENS detects bowel luminal contents and coordinates the passing of stool. ENS defects predispose to GI motility disorders. Previously, distinct enteric neuron types were cataloged by dye-filling techniques, immunohistochemistry, retrograde labeling, and electrophysiology. Recent technical advances in single cell RNA-sequencing (scRNA-seq) have enabled transcriptional profiling of hundreds to millions of individual cells from the intestine. These data allow cell types to be resolved and compared to using their transcriptional profiles ("clusters") rather than relying on antibody labeling. As a result, greater diversity of enteric neuron types has been appreciated. Because each scRNA-seq study has relied on different methods for cell isolation and library generation, numbers of neuron clusters and cell types detected differs between analyses. Cell counts in each dataset are particularly important for characterization of rare cell types since small numbers of profiled cells may not sample rare cell types. Importantly, each dataset, depending on the isolation methods, may contain different proportions of cells that are not detected in other datasets. Aggregation of datasets can effectively increase the total number of cells being analyzed and can be helpful for confirming the presence of low-abundance neuron types that might be absent or observed infrequently in any single dataset. Here we briefly systematically review each Mus musculus single cell or single nucleus RNA-sequencing enteric nervous system dataset. We then reprocess and computationally integrate these select independent scRNA-seq enteric neuron datasets with the aim to identify new cell types, shared marker genes across juvenile to adult ages, dataset differences, and achieve some consensus on transcriptomic definitions of enteric neuronal subtypes. Data aggregation generates a consensus view of enteric neuron types and improves resolution of rare neuron classes. This meta-atlas offers a deeper understanding of enteric neuron diversity and may prove useful to investigators aiming to define alterations among enteric neurons in disease states. Future studies face the challenge of connecting these deep transcriptional profiles for enteric neurons with historical classification systems.

A beginner's guide to supervised analysis for mass cytometry data in cancer biology.

Mass cytometry enables deep profiling of biological samples at single-cell resolution. This technology is more than relevant in cancer research due to high cellular heterogeneity and complexity. Downstream analysis of high-dimensional datasets increasingly relies on machine learning (ML) to extract clinically relevant information, including supervised algorithms for classification and regression purposes. In cancer research, they are used to develop predictive models that will guide clinical decision making. However, the development of supervised algorithms faces major challenges, such as sufficient validation, before being translated into the clinics. In this work, we provide a framework for the analysis of mass cytometry data with a specific focus on supervised algorithms and practical examples of their applications. We also raise awareness on key issues regarding good practices for researchers curious to implement supervised ML on their mass cytometry data. Finally, we discuss the challenges of supervised ML application to cancer research.

Multiomic analysis of familial adenomatous polyposis reveals molecular pathways associated with early tumorigenesis

Familial adenomatous polyposis (FAP) is a genetic disease causing hundreds of premalignant polyps in affected persons and is an ideal model to study transitions of early precancer states to colorectal cancer (CRC). We performed deep multiomic profiling of 93 samples, including normal mucosa, benign polyps and dysplastic polyps, from six persons with FAP. Transcriptomic, proteomic, metabolomic and lipidomic analyses revealed a dynamic choreography of thousands of molecular and cellular events that occur during precancerous transitions toward cancer formation. These involve processes such as cell proliferation, immune response, metabolic alterations (including amino acids and lipids), hormones and extracellular matrix proteins. Interestingly, activation of the arachidonic acid pathway was found to occur early in hyperplasia; this pathway is targeted by aspirin and other nonsteroidal anti-inflammatory drugs, a preventative treatment under investigation in persons with FAP. Overall, our results reveal key genomic, cellular and molecular events during the earliest steps in CRC formation and potential mechanisms of pharmaceutical prophylaxis.

Single-cell landscape of innate and acquired drug resistance in acute myeloid leukemia

Deep single-cell multi-omic profiling offers a promising approach to understand and overcome drug resistance in relapsed or refractory (rr) acute myeloid leukemia (AML). Here, we combine single-cell ex vivo drug profiling (pharmacoscopy) with single-cell and bulk DNA, RNA, and protein analyses, alongside clinical data from 21 rrAML patients. Unsupervised data integration reveals reduced ex vivo response to the Bcl-2 inhibitor venetoclax (VEN) in patients treated with both a hypomethylating agent (HMA) and VEN, compared to those pre-exposed to chemotherapy or HMA alone. Integrative analysis identifies both known and unreported mechanisms of innate and treatment-related VEN resistance and suggests alternative treatments, like targeting increased proliferation with the PLK inhibitor volasertib. Additionally, high CD36 expression in VEN-resistant blasts associates with sensitivity to CD36-targeted antibody treatment ex vivo. This study demonstrates how single-cell multi-omic profiling can uncover drug resistance mechanisms and treatment vulnerabilities, providing a valuable resource for future AML research.

From bench to bedside and back to bench: investigating the role of platelet heterogeneity in coronary artery disease

Abstract Background/Introduction Platelets exhibit considerable heterogeneity in RNA content, size, and thrombogenicity. Our preliminary investigation of the transcriptome of the RNA-rich reticulated platelets(RPs) in healthy donors revealed an enrichment of prothrombotic transcripts compared to mature platelets(MPs). Recently, we validated the prognostic role of elevated RPs at clinical level in a modern cohort of ACS patients treated with potent P2Y12 inhibitors. However, the pathophysiology of RP hyperreactivity remains unclear, particularly its correlation with adverse events and cardiovascular death. Purpose To study the biology of RPs in patients with chronic coronary syndrome(CCS) and to elucidate their role in disease progression. Methods RPs were isolated from peripheral blood of CCS patients(N=20) and compared with MPs. Cell viability and reactivity of RPs were quantified by FACS. Deep transcriptomic profiling was performed using post-sorting bulk RNA sequencing of RPs and MPs and analyzed with ad-hoc bioinformatic pipelines. Proteomic profiling using mass cytometry(CyTOF) was used to validate the transcriptomic findings with single cell resolution. Results FACS analysis confirmed RPs hyperreactivity showing increased P-Selectin expression upon activation with TRAP, ADP and Collagen(Fig. 1A). Total RNA-sequencing detected 2213 differentially regulated genes with an enrichment in RPs of key functional transcript such as, the von Willebrand Factor VWF(p=3.06*10-33), the thromboxane receptor TBXA2R(p=2.07*10-29) and the collagen receptor GP6(p=1.29*10-38, Fig. 1B-C). Gene ontology and gene set enrichment analyses detected an upregulation of relevant categories as "Regulation of platelet activation" and "Platelet aggregation"(Fig. 1D-F). In addition, we detected a novel alternative splicing event on the collagen receptor transcript GP6 upregulated in RPs(Fig. 1G). We detected 33 differentially regulated miRNAs, including miR-409, miR-134 and miR-432, which have been already linked to prothrombotic phenotypes(Fig. 1H). Additionally, we detected an enrichment of circular RNAs in RPs compared to MPs with several circular RNA upregulated in RPs, that have not been described before(Fig. 1I-K). Mass cytometry detected a significant upregulation in RPs of relevant proteins including the collagen receptor GPVI(p=8.98*10-12, Fig.2) and the thrombin receptor PAR1(p=5.21*10-11). Validation assays in an independent cohort detected an upregulation of the PI3K/AKT pathway in RPs which is downstream of the receptor PAR1 and GP6. Conclusion This study represents the first comprehensive multiomic characterization of RPs in CCS patients, providing insights into their prothrombotic phenotype and into their correlation with cardiovascular events. These findings suggest potential avenues for tailored therapeutic interventions targeting the identified upregulated pathways in patients with elevated RPs, warranting further investigation at the clinical level.

A deep profile of gene expression across 18 human cancers.

Clinically and biologically valuable information may reside untapped in large cancer gene expression data sets. Deep unsupervised learning has the potential to extract this information with unprecedented efficacy but has thus far been hampered by a lack of biological interpretability and robustness. Here, we present DeepProfile, a comprehensive framework that addresses current challenges in applying unsupervised deep learning to gene expression profiles. We use DeepProfile to learn low-dimensional latent spaces for 18 human cancers from 50,211 transcriptomes. DeepProfile outperforms existing dimensionality reduction methods with respect to biological interpretability. Using DeepProfile interpretability methods, we show that genes that are universally important in defining the latent spaces across all cancer types control immune cell activation, while cancer type-specific genes and pathways define molecular disease subtypes. By linking DeepProfile latent variables to secondary tumor characteristics, we discover that tumor mutation burden is closely associated with the expression of cell cycle-related genes. DNA mismatch repair and MHC class II antigen presentation pathway expression, on the other hand, are consistently associated with patient survival. We validate these results through Kaplan-Meier analyses and nominate tumor-associated macrophages as an important source of survival-correlated MHC class II transcripts. Our results illustrate the power of unsupervised deep learning for discovery of cancer biology from existing gene expression data.

The transcriptomic landscape of spinal V1 interneurons reveals a role for En1 in specific elements of motor output.

Neural circuits in the spinal cord are composed of diverse sets of interneurons that play crucial roles in shaping motor output. Despite progress in revealing the cellular architecture of the spinal cord, the extent of cell type heterogeneity within interneuron populations remains unclear. Here, we present a single-nucleus transcriptomic atlas of spinal V1 interneurons across postnatal development. We find that the core molecular taxonomy distinguishing neonatal V1 interneurons perdures into adulthood, suggesting conservation of function across development. Moreover, we identify a key role for En1, a transcription factor that marks the V1 population, in specifying one unique subset of V1-Pou6f2 interneurons. Loss of En1 selectively disrupts the frequency of rhythmic locomotor output but does not disrupt flexion/extension limb movement. Beyond serving as a molecular resource for this neuronal population, our study highlights how deep neuronal profiling provides an entry point for functional studies of specialized cell types in motor output.

Description and functional validation of human enteroendocrine cell sensors.

Enteroendocrine cells (EECs) are gut epithelial cells that respond to intestinal contents by secreting hormones, including the incretins glucagon-like peptide 1 (GLP-1) and gastric inhibitory protein (GIP), which regulate multiple physiological processes. Hormone release is controlled through metabolite-sensing proteins. Low expression, interspecies differences, and the existence of multiple EEC subtypes have posed challenges to the study of these sensors. We describe differentiation of stomach EECs to complement existing intestinal organoid protocols. CD200 emerged as a pan-EEC surface marker, allowing deep transcriptomic profiling from primary human tissue along the stomach-intestinal tract. We generated loss-of-function mutations in 22 receptors and subjected organoids to ligand-induced secretion experiments. We delineate the role of individual human EEC sensors in the secretion of hormones, including GLP-1. These represent potential pharmacological targets to influence appetite, bowel movement, insulin sensitivity, and mucosal immunity.

PhoXplex: Combining Phospho-enrichable Cross-Linking with Isobaric Labeling for Quantitative Proteome-Wide Mapping of Protein Interfaces.

Integrating cross-linking mass spectrometry (XL-MS) into structural biology workflows provides valuable information about the spatial arrangement of amino acid stretches, which can guide elucidation of protein assembly architecture. Additionally, the combination of XL-MS with peptide quantitation techniques is a powerful approach to delineate protein interface dynamics across diverse conditions. While XL-MS is increasingly effective with isolated proteins or small complexes, its application to whole-cell samples poses technical challenges related to analysis depth and throughput. The use of enrichable cross-linkers has greatly improved the detectability of protein interfaces in a proteome-wide scale, facilitating global protein-protein interaction mapping. Therefore, bringing together enrichable cross-linking and multiplexed peptide quantification is an appealing approach to enable comparative characterization of structural attributes of proteins and protein interactions. Here, we combined phospho-enrichable cross-linking with TMT labeling to develop a streamline workflow (PhoXplex) for the detection of differential structural features across a panel of cell lines in a global scale. We achieved deep coverage with quantification of over 9000 cross-links and long loop-links in total including potentially novel interactions. Overlaying AlphaFold predictions and disorder protein annotations enables exploration of the quantitative cross-linking data set, to reveal possible associations between mutations and protein structures. Lastly, we discuss current shortcomings and perspectives for deep whole-cell profiling of protein interfaces at large-scale.

Capacity planning of wind-photovoltaic-electrolysis-battery system coupling renewable fuel synthesis

The wind-photovoltaic-electrolysis-battery (WPEB) system coupling fuel synthesis by supplying hydrogen and electricity simultaneously can reduce the carbon emission intensity of renewable fuels. However the capacity planning method is rarely reported, this work takes WPEB system coupling renewable methanol synthesis as an example. The business mode and the effect of load profile shape on the capacity configuration are first studied. Then, a two-stage capacity planning method is proposed to improve system performance: At the 1st stage, the capacity configuration is performed using 5 typical load profiles, and the case with minimum net present cost (NPC) is regarded as a pseudo-optimal solution. In the 2nd stage, the wind & solar power profile of the pseudo-optimal solution is standardized and the shape is used to build a variable renewable energy (VRE) flowing load profile as model input, the capacity planning result in the 2nd stage is regarded as the optimal case. The main conclusions are summarized below: (1) The WPEB system has five revenues from selling electricity, methanol, oxygen, CO2 trading in China certified emission reduction (CCER) market and carbon emission allowance (CEA) market. (2) Deep valley load profile obtains the lowest NPC of 2.7656 billion¥ and greatest internal rate of return (IRR) of 6.8843 % among 5 typical profiles. (3) The two-stage planning method can further decline the NPC to 2.2018 billion¥ and boost IRR to 10.5583 % by employing demand-side flexibility. (4) The sensitivity factors contributing to IRR orders from high to low are methanol price > O2 price > CO2 price of CCER > unmet H2 penalty > electricity price.

Cosmogenic isotopes in the lunar soil: solar activity and nearby Supernova outbreak

The lunar soil is an integral detector of cosmic rays (CRs) of different origin (Solar and Galactic). Analysis of the deep profiles of cosmogenic isotopes (14C, 41Ca, 36Cl, 10Be, 26Al, 53Mn) allows us to restore the intensity of CR on time scales of 3–4 half-lives of the corresponding isotope. To coordinate data on 10Be, it is required (in addition to the average flux of CRs) the presence of an additional source of accelerated particles with hard spectrum. Such a source can be, for example, an outbreak of a nearby SN 2–3 million years ago.

Selective Enrichment via TiO2 Magnetic Nanoparticles Enables Deep Profiling of Circulating Neutral Glycosphingolipids.

Circulating neutral glycosphingolipids (neutral GSLs (nGSLs)) are a unique subset of nGSLs that detach from organs or cell membranes and enter the bloodstream. Altered molecular distribution of circulating nGSL is increasingly associated with diseases. However, profiling of circulating nGSLs presents a lasting challenge due to their low abundances and structural complexity. Although TiO2 magnetic nanoparticles (TiO2 MNPs) were effective for the enrichment of nGSLs in brain tissue, the protocol showed limited selectivity for circulating nGSLs because their abundances were 100-times lower in human plasma than in brain tissue. In this work, we optimized the key parameters of selective enrichment by TiO2 MNPs and achieved 1:10,000 selectivity for nGSLs over interfering phospholipids, while maintaining ∼70% recovery for different subclasses of nGSLs. By integrating TiO2 MNP-based selective enrichment with reversed-phase liquid chromatography mass spectrometry and charge-tagging Paternò-Büchi derivatization, we achieved deep profiling of over 300 structures of nGSLs and sulfatides across 5 orders of magnitude in relative abundances, a significant leap regarding lipid coverage. We also depicted the structural atlas of nGSLs with defined headgroup, long-chain base, N-acyl chain, the location of desaturation, and 2-hydroxylation. Such information provides a valuable resource for lipidomic studies concerning the roles of circulating nGSLs in health and diseases.