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Tokareva AO, Chagovets VV, Kononikhin AS, Starodubtseva NL, Nikolaev EN, Frankevich VE. Normalization methods for reducing interbatch effect without quality control samples in liquid chromatography-mass spectrometry-based studies. Anal Bioanal Chem 2021; 413:3479-3486. [PMID: 33760933 DOI: 10.1007/s00216-021-03294-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Revised: 03/05/2021] [Accepted: 03/12/2021] [Indexed: 11/29/2022]
Abstract
Data normalization is an essential part of a large-scale untargeted mass spectrometry metabolomics analysis. Autoscaling, Pareto scaling, range scaling, and level scaling methods for liquid chromatography-mass spectrometry data processing were compared with the most common normalization methods, including quantile normalization, probabilistic quotient normalization, and variance stabilizing normalization. These methods were tested on eight datasets from various clinical studies. The efficiency of the data normalization was assessed by the distance between clusters corresponding to batches and the distance between clusters corresponding to clinical groups in the space of principal components, as well as by the number of features with a pairwise statistically significant difference between the batches and the number of features with a pairwise statistically significant difference between clinical groups. Autoscaling demonstrated the most effective reduction in interbatch variation and can be preferable to probabilistic quotient or quantile normalization in liquid chromatography-mass spectrometry data.
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Affiliation(s)
- Alisa O Tokareva
- National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov of the Ministry of Healthcare of the Russian Federation, Moscow, 117997, Russia.,V.L. Talrose Institute for Energy Problems of Chemical Physics, N.N. Semenov Federal Center of Chemical Physic, Russian Academy of Sciences, Moscow, 119334, Russia.,Moscow Institute of Physics and Technology, Moscow, 141701, Russia
| | - Vitaliy V Chagovets
- National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov of the Ministry of Healthcare of the Russian Federation, Moscow, 117997, Russia
| | - Alexey S Kononikhin
- National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov of the Ministry of Healthcare of the Russian Federation, Moscow, 117997, Russia.,Skolkovo Institute of Science and Technology, Moscow, 121205, Russia
| | - Natalia L Starodubtseva
- National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov of the Ministry of Healthcare of the Russian Federation, Moscow, 117997, Russia. .,Moscow Institute of Physics and Technology, Moscow, 141701, Russia.
| | - Eugene N Nikolaev
- Skolkovo Institute of Science and Technology, Moscow, 121205, Russia
| | - Vladimir E Frankevich
- National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov of the Ministry of Healthcare of the Russian Federation, Moscow, 117997, Russia.
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O'Rourke MB, Town SEL, Dalla PV, Bicknell F, Koh Belic N, Violi JP, Steele JR, Padula MP. What is Normalization? The Strategies Employed in Top-Down and Bottom-Up Proteome Analysis Workflows. Proteomes 2019; 7:proteomes7030029. [PMID: 31443461 PMCID: PMC6789750 DOI: 10.3390/proteomes7030029] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 08/19/2019] [Accepted: 08/20/2019] [Indexed: 12/20/2022] Open
Abstract
The accurate quantification of changes in the abundance of proteins is one of the main applications of proteomics. The maintenance of accuracy can be affected by bias and error that can occur at many points in the experimental process, and normalization strategies are crucial to attempt to overcome this bias and return the sample to its regular biological condition, or normal state. Much work has been published on performing normalization on data post-acquisition with many algorithms and statistical processes available. However, there are many other sources of bias that can occur during experimental design and sample handling that are currently unaddressed. This article aims to cast light on the potential sources of bias and where normalization could be applied to return the sample to its normal state. Throughout we suggest solutions where possible but, in some cases, solutions are not available. Thus, we see this article as a starting point for discussion of the definition of and the issues surrounding the concept of normalization as it applies to the proteomic analysis of biological samples. Specifically, we discuss a wide range of different normalization techniques that can occur at each stage of the sample preparation and analysis process.
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Affiliation(s)
- Matthew B O'Rourke
- Bowel Cancer & Biomarker Lab, Northern Clinical School, Faculty of Medicine and Health, The University of Sydney Lvl 8, Kolling Institute. Royal North Shore Hospital, St. Leonards, NSW 2065, Australia
| | - Stephanie E L Town
- School of Life Sciences and Proteomics Core Facility, Faculty of Science, The University of Technology Sydney, Ultimo 2007, Australia
| | - Penelope V Dalla
- School of Life Sciences and Proteomics Core Facility, Faculty of Science, The University of Technology Sydney, Ultimo 2007, Australia
- Respiratory Cellular and Molecular Biology, Woolcock Institute of Medical Research, The University of Sydney, Glebe 2037, Australia
| | - Fiona Bicknell
- School of Life Sciences and Proteomics Core Facility, Faculty of Science, The University of Technology Sydney, Ultimo 2007, Australia
| | - Naomi Koh Belic
- School of Life Sciences and Proteomics Core Facility, Faculty of Science, The University of Technology Sydney, Ultimo 2007, Australia
| | - Jake P Violi
- School of Life Sciences and Proteomics Core Facility, Faculty of Science, The University of Technology Sydney, Ultimo 2007, Australia
| | - Joel R Steele
- School of Life Sciences and Proteomics Core Facility, Faculty of Science, The University of Technology Sydney, Ultimo 2007, Australia
| | - Matthew P Padula
- School of Life Sciences and Proteomics Core Facility, Faculty of Science, The University of Technology Sydney, Ultimo 2007, Australia.
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Song X, He J, Pang X, Zhang J, Sun C, Huang L, Li C, Zang Q, Li X, Luo Z, Zhang R, Xie P, Liu X, Li Y, Chen X, Abliz Z. Virtual Calibration Quantitative Mass Spectrometry Imaging for Accurately Mapping Analytes across Heterogenous Biotissue. Anal Chem 2019; 91:2838-2846. [PMID: 30636407 DOI: 10.1021/acs.analchem.8b04762] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
It is highly challenging to quantitatively map multiple analytes in biotissues without specific chemical labeling. Quantitative mass spectrometry imaging (QMSI) has this potential but still poses technical issues for its variant ionization efficiency across a complicated, heterogeneous biomatrices. Herein, a self-developed air-flow-assisted desorption electrospray ionization (AFADESI) is introduced to present a proof of concept method, virtual calibration (VC) QMSI. This method screens and utilizes analyte response-related endogenous metabolite ions from each mass spectrum as native internal standards (IS). Through machine-learning-based regression and clustering, tissue-specific ionization variation can be automatically recognized, predicted, and normalized region by region or pixel by pixel. Therefore, the quantity of analytes can be accurately mapped across highly structural biosamples including whole body, kidney, brain, tumor, etc. VC-QMSI has the advantages of simple sample preparation without laborious isotopic IS synthesis, extrapolation for those unknown tissues or regions without previous investigation, and automatic spatial recognition without histological guidance. This strategy is suitable for mass spectrometry imaging using a variety of in situ ionization techniques. It is believed that VC-QMSI has wide applicability for drug candidate's discovery, molecular mechanism elucidation, biomarker validation, and clinical diagnosis.
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Affiliation(s)
- Xiaowei Song
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines , Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050 , People's Republic of China
| | - Jiuming He
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines , Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050 , People's Republic of China
| | - Xuechao Pang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines , Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050 , People's Republic of China
| | - Jin Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines , Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050 , People's Republic of China
| | - Chenglong Sun
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines , Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050 , People's Republic of China
| | - Luojiao Huang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines , Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050 , People's Republic of China
| | - Chao Li
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines , Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050 , People's Republic of China
| | - Qingce Zang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines , Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050 , People's Republic of China
| | - Xin Li
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines , Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050 , People's Republic of China
| | - Zhigang Luo
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines , Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050 , People's Republic of China
| | - Ruiping Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines , Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050 , People's Republic of China
| | - Ping Xie
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines , Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050 , People's Republic of China
| | - Xiaoyu Liu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines , Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050 , People's Republic of China
| | - Yan Li
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines , Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050 , People's Republic of China
| | - Xiaoguang Chen
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines , Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050 , People's Republic of China
| | - Zeper Abliz
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines , Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050 , People's Republic of China.,Centre for Imaging and Systems Biology, School of Pharmacy , Minzu University of China , Beijing 100081 , People's Republic of China
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Ma Q, Adua E, Boyce MC, Li X, Ji G, Wang W. IMass Time: The Future, in Future! ACTA ACUST UNITED AC 2018; 22:679-695. [DOI: 10.1089/omi.2018.0162] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Qingwei Ma
- Bioyong (Beijing) Technology Co., Ltd., Beijing, China
| | - Eric Adua
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Australia
| | - Mary C. Boyce
- School of Science, Edith Cowan University, Joondalup, Australia
| | - Xingang Li
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Australia
| | - Guang Ji
- China-Canada Centre of Research for Digestive Diseases, University of Ottawa, Ottawa, Canada
- Institute of Digestive Diseases, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wei Wang
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Australia
- School of Public Health, Taishan Medical University, Taian, China
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