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Zhong B, Qin X, Xu H, Liu L, Li L, Li Z, Cao L, Lou Z, Jackman JA, Cho NJ, Wang L. Interindividual- and blood-correlated sweat phenylalanine multimodal analytical biochips for tracking exercise metabolism. Nat Commun 2024; 15:624. [PMID: 38245507 PMCID: PMC10799919 DOI: 10.1038/s41467-024-44751-z] [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: 10/18/2023] [Accepted: 01/03/2024] [Indexed: 01/22/2024] Open
Abstract
In situ monitoring of endogenous amino acid loss through sweat can provide physiological insights into health and metabolism. However, existing amino acid biosensors are unable to quantitatively assess metabolic status during exercise and are rarely used to establish blood-sweat correlations because they only detect a single concentration indicator and disregard sweat rate. Here, we present a wearable multimodal biochip integrated with advanced electrochemical electrodes and multipurpose microfluidic channels that enables simultaneous quantification of multiple sweat indicators, including phenylalanine and chloride, as well as sweat rate. This combined measurement approach reveals a negative correlation between sweat phenylalanine levels and sweat rates among individuals, which further enables identification of individuals at high metabolic risk. By tracking phenylalanine fluctuations induced by protein intake during exercise and normalizing the concentration indicator by sweat rates to reduce interindividual variability, we demonstrate a reliable method to correlate and analyze sweat-blood phenylalanine levels for personal health monitoring.
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Affiliation(s)
- Bowen Zhong
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
- Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaokun Qin
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
- Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hao Xu
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
- Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lingchen Liu
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
- Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Linlin Li
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
- Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhexin Li
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
- Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Limin Cao
- Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Zheng Lou
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China
- Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Joshua A Jackman
- School of Chemical Engineering and Translational Nanobioscience Research Center, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Nam-Joon Cho
- School of Materials Science and Engineering, Nanyang Technological University, 637553, Singapore, Singapore
| | - Lili Wang
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China.
- Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China.
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de Souza HMR, Pereira TTP, de Sá HC, Alves MA, Garrett R, Canuto GAB. Critical Factors in Sample Collection and Preparation for Clinical Metabolomics of Underexplored Biological Specimens. Metabolites 2024; 14:36. [PMID: 38248839 PMCID: PMC10819689 DOI: 10.3390/metabo14010036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 01/02/2024] [Accepted: 01/03/2024] [Indexed: 01/23/2024] Open
Abstract
This review article compiles critical pre-analytical factors for sample collection and extraction of eight uncommon or underexplored biological specimens (human breast milk, ocular fluids, sebum, seminal plasma, sweat, hair, saliva, and cerebrospinal fluid) under the perspective of clinical metabolomics. These samples are interesting for metabolomics studies as they reflect the status of living organisms and can be applied for diagnostic purposes and biomarker discovery. Pre-collection and collection procedures are critical, requiring protocols to be standardized to avoid contamination and bias. Such procedures must consider cleaning the collection area, sample stimulation, diet, and food and drug intake, among other factors that impact the lack of homogeneity of the sample group. Precipitation of proteins and removal of salts and cell debris are the most used sample preparation procedures. This review intends to provide a global view of the practical aspects that most impact results, serving as a starting point for the designing of metabolomic experiments.
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Affiliation(s)
- Hygor M. R. de Souza
- Instituto de Química, Universidade Federal do Rio de Janeiro, LabMeta—LADETEC, Rio de Janeiro 21941-598, Brazil;
| | - Tássia T. P. Pereira
- Departamento de Genética, Ecologia e Evolucao, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil;
- Departamento de Biodiversidade, Evolução e Meio Ambiente, Universidade Federal de Ouro Preto, Ouro Preto 35400-000, Brazil
| | - Hanna C. de Sá
- Departamento de Química Analítica, Instituto de Química, Universidade Federal da Bahia, Salvador 40170-115, Brazil;
| | - Marina A. Alves
- Instituto de Pesquisa de Produtos Naturais Walter Mors, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-599, Brazil;
| | - Rafael Garrett
- Instituto de Química, Universidade Federal do Rio de Janeiro, LabMeta—LADETEC, Rio de Janeiro 21941-598, Brazil;
- Department of Laboratory Medicine, Boston Children’s Hospital—Harvard Medical School, Boston, MA 02115, USA
| | - Gisele A. B. Canuto
- Departamento de Química Analítica, Instituto de Química, Universidade Federal da Bahia, Salvador 40170-115, Brazil;
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Muniz-Santos R, Magno-França A, Jurisica I, Cameron LC. From Microcosm to Macrocosm: The -Omics, Multiomics, and Sportomics Approaches in Exercise and Sports. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2023; 27:499-518. [PMID: 37943554 DOI: 10.1089/omi.2023.0169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
This article explores the progressive integration of -omics methods, including genomics, metabolomics, and proteomics, into sports research, highlighting the development of the concept of "sportomics." We discuss how sportomics can be used to comprehend the multilevel metabolism during exercise in real-life conditions faced by athletes, enabling potential personalized interventions to improve performance and recovery and reduce injuries, all with a minimally invasive approach and reduced time. Sportomics may also support highly personalized investigations, including the implementation of n-of-1 clinical trials and the curation of extensive datasets through long-term follow-up of athletes, enabling tailored interventions for athletes based on their unique physiological responses to different conditions. Beyond its immediate sport-related applications, we delve into the potential of utilizing the sportomics approach to translate Big Data regarding top-level athletes into studying different human diseases, especially with nontargeted analysis. Furthermore, we present how the amalgamation of bioinformatics, artificial intelligence, and integrative computational analysis aids in investigating biochemical pathways, and facilitates the search for various biomarkers. We also highlight how sportomics can offer relevant information about doping control analysis. Overall, sportomics offers a comprehensive approach providing novel insights into human metabolism during metabolic stress, leveraging cutting-edge systems science techniques and technologies.
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Affiliation(s)
- Renan Muniz-Santos
- Laboratory of Protein Biochemistry, The Federal University of the State of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Alexandre Magno-França
- Laboratory of Protein Biochemistry, The Federal University of the State of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Igor Jurisica
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute and Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, University Health Network, Toronto, Canada
- Departments of Medical Biophysics and Computer Science, and Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada
- Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia
| | - L C Cameron
- Laboratory of Protein Biochemistry, The Federal University of the State of Rio de Janeiro, Rio de Janeiro, Brazil
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Gotsmy M, Brunmair J, Büschl C, Gerner C, Zanghellini J. Probabilistic quotient’s work and pharmacokinetics’ contribution: countering size effect in metabolic time series measurements. BMC Bioinformatics 2022; 23:379. [PMID: 36114458 PMCID: PMC9482228 DOI: 10.1186/s12859-022-04918-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 09/02/2022] [Indexed: 11/10/2022] Open
Abstract
Metabolomic time course analyses of biofluids are highly relevant for clinical diagnostics. However, many sampling methods suffer from unknown sample sizes, commonly known as size effects. This prevents absolute quantification of biomarkers. Recently, several mathematical post acquisition normalization methods have been developed to overcome these problems either by exploiting already known pharmacokinetic information or by statistical means. Here we present an improved normalization method, MIX, that combines the advantages of both approaches. It couples two normalization terms, one based on a pharmacokinetic model (PKM) and the other representing a popular statistical approach, probabilistic quotient normalization (PQN), in a single model. To test the performance of MIX, we generated synthetic data closely resembling real finger sweat metabolome measurements. We show that MIX normalization successfully tackles key weaknesses of the individual strategies: it (i) reduces the risk of overfitting with PKM, and (ii), contrary to PQN, it allows to compute sample volumes. Finally, we validate MIX by using real finger sweat as well as blood plasma metabolome data and demonstrate that MIX allows to better and more robustly correct for size effects. In conclusion, the MIX method improves the reliability and robustness of quantitative biomarker detection in finger sweat and other biofluids, paving the way for biomarker discovery and hypothesis generation from metabolomic time course data.
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Zhang X, Wang X, Ning M, Wang P, Wang W, Zhang X, Liu Z, Zhang Y, Li S. Fast Synthesis of Au Nanoparticles on Metal-Phenolic Network for Sweat SERS Analysis. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:2977. [PMID: 36080014 PMCID: PMC9458096 DOI: 10.3390/nano12172977] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/15/2022] [Accepted: 08/26/2022] [Indexed: 06/15/2023]
Abstract
The biochemical composition of sweat is closely related to the human physiological state, which provides a favorable window for the monitoring of human health status, especially for the athlete. Herein, an ultra-simple strategy based on the surface-enhanced Raman scattering (SERS) technique for sweat analysis is established. Metal-phenolic network (MPN), an outstanding organic-inorganic hybrid material, is adopted as the reductant and platform for the in situ formation of Au-MPN, which displays excellent SERS activity with the limit of detection to 10-15 M for 4-mercaptobenzoic acid (4-MBA). As an ultrasensitive SERS sensor, Au-MPN is capable of discriminating the molecular fingerprints of sweat components acquired from a volunteer after exercise, such as urea, uric acid, lactic acid, and amino acid. For pH sensing, Au-MPN/4-MBA efficiently presents the pH values of the volunteer's sweat, which can indicate the electrolyte metabolism during exercise. This MPN-based SERS sensing strategy unlocks a new route for the real-time physiological monitoring of human health.
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Affiliation(s)
- Xiaoying Zhang
- Department of Physical Education, Guangdong Medical University, Dongguan 523808, China
| | - Xin Wang
- School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Mengling Ning
- Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, China
| | - Peng Wang
- School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Wen Wang
- School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Xiaozhou Zhang
- School of Basic Medicine, Guangdong Medical University, Dongguan 523808, China
| | - Zhiming Liu
- Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, China
| | - Yanjiao Zhang
- School of Basic Medicine, Guangdong Medical University, Dongguan 523808, China
| | - Shaoxin Li
- School of Biomedical Engineering, Guangdong Medical University, Dongguan 523808, China
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Abstract
Background Cardiorespiratory fitness (CRF) is a potent health marker, the improvement of which is associated with a reduced incidence of non-communicable diseases and all-cause mortality. Identifying metabolic signatures associated with CRF could reveal how CRF fosters human health and lead to the development of novel health-monitoring strategies. Objective This article systematically reviewed reported associations between CRF and metabolites measured in human tissues and body fluids. Methods PubMed, EMBASE, and Web of Science were searched from database inception to 3 June, 2021. Metabolomics studies reporting metabolites associated with CRF, measured by means of cardiopulmonary exercise test, were deemed eligible. Backward and forward citation tracking on eligible records were used to complement the results of database searching. Risk of bias at the study level was assessed using QUADOMICS. Results Twenty-two studies were included and 667 metabolites, measured in plasma (n = 619), serum (n = 18), skeletal muscle (n = 16), urine (n = 11), or sweat (n = 3), were identified. Lipids were the metabolites most commonly positively (n = 174) and negatively (n = 274) associated with CRF. Specific circulating glycerophospholipids (n = 85) and cholesterol esters (n = 17) were positively associated with CRF, while circulating glycerolipids (n = 152), glycerophospholipids (n = 42), acylcarnitines (n = 14), and ceramides (n = 12) were negatively associated with CRF. Interestingly, muscle acylcarnitines were positively correlated with CRF (n = 15). Conclusions Cardiorespiratory fitness was associated with circulating and muscle lipidome composition. Causality of the revealed associations at the molecular species level remains to be investigated further. Finally, included studies were heterogeneous in terms of participants’ characteristics and analytical and statistical approaches. PROSPERO Registration Number CRD42020214375. Supplementary Information The online version contains supplementary material available at 10.1007/s40279-021-01590-y.
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