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Qin S, Zhang Y, Shi M, Miao D, Lu J, Wen L, Bai Y. In-depth organic mass cytometry reveals differential contents of 3-hydroxybutanoic acid at the single-cell level. Nat Commun 2024; 15:4387. [PMID: 38782922 PMCID: PMC11116506 DOI: 10.1038/s41467-024-48865-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 05/16/2024] [Indexed: 05/25/2024] Open
Abstract
Comprehensive single-cell metabolic profiling is critical for revealing phenotypic heterogeneity and elucidating the molecular mechanisms underlying biological processes. However, single-cell metabolomics remains challenging because of the limited metabolite coverage and inability to discriminate isomers. Herein, we establish a single-cell metabolomics platform for in-depth organic mass cytometry. Extended single-cell analysis time guarantees sufficient MS/MS acquisition for metabolite identification and the isomers discrimination while online sampling ensures the high-throughput of the method. The largest number of identified metabolites (approximately 600) are achieved in single cells and fine subtyping of MCF-7 cells is first demonstrated by an investigation on the differential levels of 3-hydroxybutanoic acid among clusters. Single-cell transcriptome analysis reveals differences in the expression of 3-hydroxybutanoic acid downstream antioxidative stress genes, such as metallothionein 2 (MT2A), while a fluorescence-activated cell sorting assay confirms the positive relationship between 3-hydroxybutanoic acid and target proteins; these results suggest that the heterogeneity of 3-hydroxybutanoic acid provides cancer cells with different ability to resist surrounding oxidative stress. Our method paves the way for deep single-cell metabolome profiling and investigations on the physiological and pathological processes that occur during cancer.
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Affiliation(s)
- Shaojie Qin
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
| | - Yi Zhang
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
| | - Mingying Shi
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
| | - Daiyu Miao
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
| | - Jiansen Lu
- Biomedical Pioneering Innovative Center, School of Life Sciences, Peking University, Beijing, China
| | - Lu Wen
- Biomedical Pioneering Innovative Center, School of Life Sciences, Peking University, Beijing, China
| | - Yu Bai
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing, China.
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Nielsen RL, Monfeuga T, Kitchen RR, Egerod L, Leal LG, Schreyer ATH, Gade FS, Sun C, Helenius M, Simonsen L, Willert M, Tahrani AA, McVey Z, Gupta R. Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning. Nat Commun 2024; 15:2817. [PMID: 38561399 PMCID: PMC10985086 DOI: 10.1038/s41467-024-46663-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Osteoarthritis (OA) is increasing in prevalence and has a severe impact on patients' lives. However, our understanding of biomarkers driving OA risk remains limited. We developed a model predicting the five-year risk of OA diagnosis, integrating retrospective clinical, lifestyle and biomarker data from the UK Biobank (19,120 patients with OA, ROC-AUC: 0.72, 95%CI (0.71-0.73)). Higher age, BMI and prescription of non-steroidal anti-inflammatory drugs contributed most to increased OA risk prediction ahead of diagnosis. We identified 14 subgroups of OA risk profiles. These subgroups were validated in an independent set of patients evaluating the 11-year OA risk, with 88% of patients being uniquely assigned to one of the 14 subgroups. Individual OA risk profiles were characterised by personalised biomarkers. Omics integration demonstrated the predictive importance of key OA genes and pathways (e.g., GDF5 and TGF-β signalling) and OA-specific biomarkers (e.g., CRTAC1 and COL9A1). In summary, this work identifies opportunities for personalised OA prevention and insights into its underlying pathogenesis.
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Affiliation(s)
| | | | | | - Line Egerod
- Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Luis G Leal
- Novo Nordisk Research Centre Oxford, Oxford, UK
| | | | | | - Carol Sun
- Novo Nordisk Research Centre Oxford, Oxford, UK
| | | | | | | | | | - Zahra McVey
- Novo Nordisk Research Centre Oxford, Oxford, UK
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Zheng YZ, Chen QR, Yang HM, Zhao JA, Ren LZ, Wu YQ, Long YL, Li TM, Yu Y. Modulation of gut microbiota by crude mulberry polysaccharide attenuates knee osteoarthritis progression in rats. Int J Biol Macromol 2024; 262:129936. [PMID: 38309391 DOI: 10.1016/j.ijbiomac.2024.129936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 01/24/2024] [Accepted: 01/31/2024] [Indexed: 02/05/2024]
Abstract
Mulberry (Morus alba L.), a kind of common fruits widely cultivated worldwide, has been proven various biological activities. However, its potential role in the progression of knee osteoarthritis (KOA) remains unclear. This study aims to investigate the potential protective effects of crude polysaccharide extracted from mulberry fruit, referred to as a complex blend of polysaccharides and other unidentified extracted impurities, on KOA progression. The KOA rats were established by injection of 1 mg sodium monoiodoacetate into knee, and administrated with crude mulberry polysaccharide (Mup) by gastric gavage for 4 weeks. Furthermore, intestinal bacteria clearance assay (IBCA) and fecal microbiota transplantation were conducted for the evaluation of the effect of gut microbiota (GM) on KOA. Our findings demonstrated that Mup, particularly at a dosage of 200 mg/kg, effectively improved abnormal gait patterns, reduced the level of inflammation, mitigated subchondral bone loss, restored compromised joint surfaces, alleviated cartilage destruction, and positively modulated the dysregulated profile of GM in KOA rats. Moreover, IBCA compromised the protective effects of Mup, while transplantation of fecal bacteria from Mup-treated rats facilitated KOA recovery. Collectively, our study suggested that Mup had the potential to ameliorate the progression of KOA, potentially through its modulation of GM profile.
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Affiliation(s)
- Yi-Zhou Zheng
- School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China
| | - Qing-Rou Chen
- School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China
| | - Hong-Mei Yang
- School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China
| | - Ji-Ao Zhao
- School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China
| | - Ling-Zhi Ren
- School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China
| | - Ye-Qun Wu
- School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China
| | - Yong-Ling Long
- School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China.
| | - Tong-Ming Li
- School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China.
| | - Yang Yu
- School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China.
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