A multi-tissue metabolome atlas of primate pregnancy.

Pregnancy induces dramatic metabolic changes in females; yet, the intricacies of this metabolic reprogramming remain poorly understood, especially in primates. Using cynomolgus monkeys, we constructed a comprehensive multi-tissue metabolome atlas, analyzing 273 samples from 23 maternal tissues during pregnancy. We discovered a decline in metabolic coupling between tissues as pregnancy progressed. Core metabolic pathways that were rewired during primate pregnancy included steroidogenesis, fatty acid metabolism, and arachidonic acid metabolism. Our atlas revealed 91 pregnancy-adaptive metabolites changing consistently across 23 tissues, whose roles we verified in human cell models and patient samples. Corticosterone and palmitoyl-carnitine regulated placental maturation and maternal tissue progenitors, respectively, with implications for maternal preeclampsia, diabetes, cardiac hypertrophy, and muscle and liver regeneration. Moreover, we found that corticosterone deficiency induced preeclampsia-like inflammation, indicating the atlas's potential clinical value. Overall, our multi-tissue metabolome atlas serves as a framework for elucidating the role of metabolic regulation in female health during pregnancy.

RNA m6A modification in ferroptosis: implications for advancing tumor immunotherapy.

The pursuit of innovative therapeutic strategies in oncology remains imperative, given the persistent global impact of cancer as a leading cause of mortality. Immunotherapy is regarded as one of the most promising techniques for systemic cancer therapies among the several therapeutic options available. Nevertheless, limited immune response rates and immune resistance urge us on an augmentation for therapeutic efficacy rather than sticking to conventional approaches. Ferroptosis, a novel reprogrammed cell death, is tightly correlated with the tumor immune environment and interferes with cancer progression. Highly mutant or metastasis-prone tumor cells are more susceptible to iron-dependent nonapoptotic cell death. Consequently, ferroptosis-induction therapies hold the promise of overcoming resistance to conventional treatments. The most prevalent post-transcriptional modification, RNA m6A modification, regulates the metabolic processes of targeted RNAs and is involved in numerous physiological and pathological processes. Aberrant m6A modification influences cell susceptibility to ferroptosis, as well as the expression of immune checkpoints. Clarifying the regulation of m6A modification on ferroptosis and its significance in tumor cell response will provide a distinct method for finding potential targets to enhance the effectiveness of immunotherapy. In this review, we comprehensively summarized regulatory characteristics of RNA m6A modification on ferroptosis and discussed the role of RNA m6A-mediated ferroptosis on immunotherapy, aiming to enhance the effectiveness of ferroptosis-sensitive immunotherapy as a treatment for immune-resistant malignancies.

The next "sweet" spot for pancreatic ductal adenocarcinoma: Glycoprotein for early detection.

Pancreatic ductal adenocarcinoma (PDAC) is the most common neoplastic disease of the pancreas, accounting for more than 90% of all pancreatic malignancies. As a highly lethal malignancy, PDAC is the fourth leading cause of cancer-related deaths worldwide with a 5-year overall survival of less than 8%. The efficacy and outcome of PDAC treatment largely depend on the stage of disease at the time of diagnosis. Surgical resection followed by adjuvant chemotherapy remains the only possibly curative therapy, yet 80%-90% of PDAC patients present with nonresectable PDAC stages at the time of clinical presentation. Despite our advancing knowledge of PDAC, the prognosis remains strikingly poor, which is primarily due to the difficulty of diagnosing PDAC at the early stages. Recent advances in glycoproteomics and glycomics based on mass spectrometry have shown that aberrations in protein glycosylation plays a critical role in carcinogenesis, tumor progression, metastasis, chemoresistance, and immuno-response of PDAC and other types of cancers. A growing interest has thus been placed upon protein glycosylation as a potential early detection biomarker for PDAC. We herein take stock of the advancements in the early detection of PDAC that were carried out with mass spectrometry, with special focus on protein glycosylation.

Frozen tissue coring and layered histological analysis improves cell type-specific proteogenomic characterization of pancreatic adenocarcinoma.

BACKGROUND: Omics characterization of pancreatic adenocarcinoma tissue is complicated by the highly heterogeneous and mixed populations of cells. We evaluate the feasibility and potential benefit of using a coring method to enrich specific regions from bulk tissue and then perform proteogenomic analyses. METHODS: We used the Biopsy Trifecta Extraction (BioTExt) technique to isolate cores of epithelial-enriched and stroma-enriched tissue from pancreatic tumor and adjacent tissue blocks. Histology was assessed at multiple depths throughout each core. DNA sequencing, RNA sequencing, and proteomics were performed on the cored and bulk tissue samples. Supervised and unsupervised analyses were performed based on integrated molecular and histology data. RESULTS: Tissue cores had mixed cell composition at varying depths throughout. Average cell type percentages assessed by histology throughout the core were better associated with KRAS variant allele frequencies than standard histology assessment of the cut surface. Clustering based on serial histology data separated the cores into three groups with enrichment of neoplastic epithelium, stroma, and acinar cells, respectively. Using this classification, tumor overexpressed proteins identified in bulk tissue analysis were assigned into epithelial- or stroma-specific categories, which revealed novel epithelial-specific tumor overexpressed proteins. CONCLUSIONS: Our study demonstrates the feasibility of multi-omics data generation from tissue cores, the necessity of interval H&E stains in serial histology sections, and the utility of coring to improve analysis over bulk tissue data.

Ultra-sensitive electrochemical sensor based on in situ grown ultrafine HKUST-1 nanoparticles @ graphite nanosheets and core-shell structured MoO3-polypyrrole nanowires for the detection of rutin in orange juice.

Rutin extracted from natural plants has important medical value, so developing accurate and sensitive quantitative detection methods is one of the most important tasks. In this work, HKUST-1@GN/MoO3-Ppy NWs were utilized to develop a high-performance rutin electrochemical sensor in virtue of its high conductivity and electrocatalytic activity. The morphology, crystal structure, and chemical element composition of the fabricated sensor composites were characterized by SEM, TEM, XPS, and XRD. Electrochemical techniques including EIS, CV, and DPV were used to investigate the electrocatalytic properties of the prepared materials. The electrochemical test conditions were optimized to achieve efficient detection of rutin. The 2-electron 2-proton mechanism, consisting of several rapid and sequential phases, is postulated to occur during rutin oxidation. The results show that HKUST-1@GN/MoO3-Ppy NWs have the characteristics of large specific surface area, excellent conductivity, and outstanding electrocatalytic ability. There is a significant linear relationship between rutin concentration and the oxidation peak current of DPV. The linear range is 0.50-2000 nM, and the limit of detection is 0.27 nM (S/N = 3). In addition, the prepared electrode has been confirmed to be useful for rutin analysis in orange juice.

Differentially expressed glycoproteins in pre- and post-digital rectal examination urine samples for detecting aggressive prostate cancer.

Urinary glycoproteins associated with aggressive prostate cancer (AG-PCa) were previously reported using post-digital rectal examination (DRE) urine specimens. To explore the potential of using pre-DRE urine specimens for detecting AG-PCa, we compared glycoproteins between pre- and post-DRE urine specimens, verified the previously identified post-DRE AG-PCa-associated urinary glycoproteins in pre-DRE urine specimens, and explored potential new glycoproteins for AG-PCa detection in pre-DRE urine specimens. Quantitative glycoproteomic data were acquired for 154 pre-DRE urine specimens from 41 patients with no cancer at biopsy, 48 patients with non-AG-PCa (Gleason score = 6), and 65 patients with AG-PCa (Gleason score 7 or above). Compared to glycopeptides from the post-DRE urine data, humoral immunity-related proteins were enriched in pre-DRE urine samples, whereas cell mediated immune response proteins were enriched in post-DRE urine samples. Analyses of AG-PCa-associated glycoproteins from pre-DRE urine revealed that the three urinary glycoproteins, prostate-specific antigen (PSA), prostatic acid phosphatase (ACPP), and CD97 antigen (CD97) that were previously identified in post-DRE urine samples, were also observed as AG-PCa associated glycoproteins in pre-DRE urine. In addition, we identified three new glycoproteins, fibrillin 1 (FBN1), vitronectin (VTN), and hemicentin 2 (HMCN2), to be potentially associated with AG-PCa in pre-DRE urine specimens. In summary, glycoprotein profiles differ between pre- and post-DRE urine specimens. The identified AG-PCa-associated glycoproteins may be further evaluated in large cohort of pre-DRE urine specimens for detecting clinically significant PCa.

XAI-enabled neural network analysis of metabolite spatial distributions.

We used deep neural networks to process the mass spectrometry imaging (MSI) data of mouse muscle (young vs aged) and human cancer (tumor vs normal adjacent) tissues, with the aim of using explainable artificial intelligence (XAI) methods to rapidly identify biomarkers that can distinguish different classes of tissues, from several thousands of metabolite features. We also modified classic neural network architectures to construct a deep convolutional neural network that is more suitable for processing high-dimensional MSI data directly, instead of using dimension reduction techniques, and compared it to seven other machine learning analysis methods' performance in classification accuracy. After ascertaining the superiority of Channel-ResNet10, we used a novel channel selection-based XAI method to identify the key metabolite features that were responsible for its learning accuracy. These key metabolite biomarkers were then processed using MetaboAnalyst for pathway enrichment mapping. We found that Channel-ResNet10 was superior to seven other machine learning methods for MSI analysis, reaching > 98% accuracy in muscle aging and colorectal cancer datasets. We also used a novel channel selection-based XAI method to find that in young and aged muscle tissues, the differentially distributed metabolite biomarkers were especially enriched in the propanoate metabolism pathway, suggesting it as a novel target pathway for anti-aging therapy.

Characterization of In Vivo Protein Complexes via Chemical Cross-Linking and Mass Spectrometry.

Cells perform various functions by proteins via protein complexes. Characterization of protein complexes is critical to understanding their biological and clinical significance and has been one of the major efforts of functional proteomics. To date, most protein complexes are characterized by the in vitro system from protein extracts after the cells or tissues are lysed, and it has been challenging to determine which of these protein complexes are formed in intact cells. Herein, we report an approach to preserve protein complexes using in vivo cross-linking, followed by size exclusion chromatography and data-independent acquisition mass spectrometry. This approach enables the characterization of in vivo protein complexes from cells or tissues, which allows the determination of protein complexes in clinical research. More importantly, the described approach can identify protein complexes that are not detected by the in vitro system, which provide unique protein function information.

Optimized data-independent acquisition approach for proteomic analysis at single-cell level.

BACKGROUND: Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. METHODS: We report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. RESULTS: We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. CONCLUSIONS: Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

Ultrasensitive catechin electrochemical sensor based on uniform ordered mesoporous carbon hollow spheres (MCHSs) advanced carbon-based conductive materials.

Catechin is one of the flavonoids with antioxidant activity and has attracted great interest. A rapid and accurate detection of catechin is of great significance. Herein, an ultrasensitive catechin electrochemical sensor based on uniform ordered mesoporous carbon hollow spheres (MCHSs) advanced carbon-based conductive material modified glass carbon electrode was constructed. The MCHSs were synthesized by pyrolysis using nitrogen protection and template removal methods, and they exhibited excellent electrochemical detection for catechin owing to their high conductivity and uniform and small spheres with a large specific surface area and hollow structure. Under optimal conditions for the detection of catechin, the MCHSs/GCE showed a wider linear range (10 -1400 nM) and lower detection limit (LOD, 2.82 nM, (S/N = 3)). Furthermore, the electrochemical reaction sites and redox mechanisms of catechin were revealed by electrochemical behavior and density flooding theory. Moreover, the sensor we constructed exhibited good accuracy and stability for the detection of catechin in actual sample detections.