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Marić I, Stevenson DK, Aghaeepour N, Gaudillière B, Wong RJ, Angst MS. Predicting Preterm Birth Using Proteomics. Clin Perinatol 2024; 51:391-409. [PMID: 38705648 PMCID: PMC11186213 DOI: 10.1016/j.clp.2024.02.011] [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] [Indexed: 05/07/2024]
Abstract
The complexity of preterm birth (PTB), both spontaneous and medically indicated, and its various etiologies and associated risk factors pose a significant challenge for developing tools to accurately predict risk. This review focuses on the discovery of proteomics signatures that might be useful for predicting spontaneous PTB or preeclampsia, which often results in PTB. We describe methods for proteomics analyses, proteomics biomarker candidates that have so far been identified, obstacles for discovering biomarkers that are sufficiently accurate for clinical use, and the derivation of composite signatures including clinical parameters to increase predictive power.
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Affiliation(s)
- Ivana Marić
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, 453 Quarry Road, Palo Alto, CA 94304, USA.
| | - David K Stevenson
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, 453 Quarry Road, Palo Alto, CA 94304, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Grant Building, Office 276A, 300 Pasteur Drive, Stanford, CA 94305-5117, USA; Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305, USA
| | - Brice Gaudillière
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Grant Building, Office 276A, 300 Pasteur Drive, Stanford, CA 94305-5117, USA; Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, 300 Pasteur Drive, Grant S280, Stanford, CA 94305, USA
| | - Ronald J Wong
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, 453 Quarry Road, Palo Alto, CA 94304, USA
| | - Martin S Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Grant Building, Office 276A, 300 Pasteur Drive, Stanford, CA 94305-5117, USA
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Shannon CP, Lee AH, Tebbutt SJ, Singh A. A Commentary on Multi-omics Data Integration in Systems Vaccinology. J Mol Biol 2024; 436:168522. [PMID: 38458605 DOI: 10.1016/j.jmb.2024.168522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 03/04/2024] [Accepted: 03/04/2024] [Indexed: 03/10/2024]
Affiliation(s)
| | - Amy Hy Lee
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, Canada
| | - Scott J Tebbutt
- PROOF Centre of Excellence, Vancouver, Canada; Department of Medicine, The University of British Columbia, Vancouver, Canada; Centre for Heart Lung Innovation, Vancouver, Canada
| | - Amrit Singh
- Centre for Heart Lung Innovation, Vancouver, Canada; Department of Anesthesiology, Pharmacology and Therapeutics, The University of British Columbia, Vancouver, Canada.
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Liu J, Zeng W, Kan S, Li M, Zheng R. CAKE: a flexible self-supervised framework for enhancing cell visualization, clustering and rare cell identification. Brief Bioinform 2023; 25:bbad475. [PMID: 38145950 PMCID: PMC10749894 DOI: 10.1093/bib/bbad475] [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: 10/07/2023] [Revised: 11/13/2023] [Accepted: 11/30/2023] [Indexed: 12/27/2023] Open
Abstract
Single cell sequencing technology has provided unprecedented opportunities for comprehensively deciphering cell heterogeneity. Nevertheless, the high dimensionality and intricate nature of cell heterogeneity have presented substantial challenges to computational methods. Numerous novel clustering methods have been proposed to address this issue. However, none of these methods achieve the consistently better performance under different biological scenarios. In this study, we developed CAKE, a novel and scalable self-supervised clustering method, which consists of a contrastive learning model with a mixture neighborhood augmentation for cell representation learning, and a self-Knowledge Distiller model for the refinement of clustering results. These designs provide more condensed and cluster-friendly cell representations and improve the clustering performance in term of accuracy and robustness. Furthermore, in addition to accurately identifying the major type cells, CAKE could also find more biologically meaningful cell subgroups and rare cell types. The comprehensive experiments on real single-cell RNA sequencing datasets demonstrated the superiority of CAKE in visualization and clustering over other comparison methods, and indicated its extensive application in the field of cell heterogeneity analysis. Contact: Ruiqing Zheng. (rqzheng@csu.edu.cn).
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Affiliation(s)
- Jin Liu
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China
| | - Weixing Zeng
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China
| | - Shichao Kan
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China
| | - Ruiqing Zheng
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China
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