Cross-Platform Comparison of Untargeted and Targeted Lipidomics Approaches on Aging Mouse Plasma.

Lipidomics - the global assessment of lipids - can be performed using a variety of mass spectrometry (MS)-based approaches. However, choosing the optimal approach in terms of lipid coverage, robustness and throughput can be a challenging task. Here, we compare a novel targeted quantitative lipidomics platform known as the Lipidyzer to a conventional untargeted liquid chromatography (LC)-MS approach. We find that both platforms are efficient in profiling more than 300 lipids across 11 lipid classes in mouse plasma with precision and accuracy below 20% for most lipids. While the untargeted and targeted platforms detect similar numbers of lipids, the former identifies a broader range of lipid classes and can unambiguously identify all three fatty acids in triacylglycerols (TAG). Quantitative measurements from both approaches exhibit a median correlation coefficient (r) of 0.99 using a dilution series of deuterated internal standards and 0.71 using endogenous plasma lipids in the context of aging. Application of both platforms to plasma from aging mouse reveals similar changes in total lipid levels across all major lipid classes and in specific lipid species. Interestingly, TAG is the lipid class that exhibits the most changes with age, suggesting that TAG metabolism is particularly sensitive to the aging process in mice. Collectively, our data show that the Lipidyzer platform provides comprehensive profiling of the most prevalent lipids in plasma in a simple and automated manner

Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table

Determination of Creatinine in Human Urine with Flow Injection Tandem Mass Spectrometry

Background/Aims: Excretion of urinary compounds in spot urine is often estimated relative to creatinine. For the growing number of liquid chromatography-tandem mass spectrometry (LC-MS/MS) assays of urine-excreted molecules, a fast and accurate method for determination of creatinine is needed. Methods: A high-throughput flow injection tandem mass spectrometry method for exact quantitation of creatinine in urine has been developed and validated. Sample preparation used only two-step dilution for protein precipitation and matrix dilution. Flow injection analysis without chromatographic separation allowed for total run times of 1 min per sample. Creatinine concentrations were quantitated using stable isotope dilution tandem mass spectrometry. Selectivity and coelution-free quantitation were assured by qualifier ion monitoring. Results: Method validation revealed excellent injection repeatability of 1.0% coefficient of variation (CV), intraday precision of 1.2% CV and interday precision of 2.4% CV. Accuracy determined from standard addition experiments was 106.1 +/- 3.8%. The linear calibration range was adapted to physiological creatinine concentrations. Comparison of quantitation results with a routinely used method (Jaffe colorimetric assay) proved high agreement (R-2 = 0.9102). Conclusions: The new method is a valuable addition to the toolbox of LC-MS/MS laboratories where excretion of urinary compounds is studied. The `dilute and shoot' approach to isotope dilution tandem mass spectrometry makes the new method highly accurate as well as cost-and time-efficient. Copyright (C) 2012 S. Karger AG, Base

New label-free methods for protein relative quantification applied to the investigation of an animal model of Huntington Disease

Spectral Counts approaches (SpCs) are largely employed for the comparison of protein expression profiles in label-free (LF) differential proteomics applications. Similarly, to other comparative methods, also SpCs based approaches require a normalization procedure before Fold Changes (FC) calculation. Here, we propose new Complexity Based Normalization (CBN) methods that introduced a variable adjustment factor (f), related to the complexity of the sample, both in terms of total number of identified proteins (CBN(P)) and as total number of spectral counts (CBN(S)). Both these new methods were compared with the Normalized Spectral Abundance Factor (NSAF) and the Spectral Counts log Ratio (Rsc), by using standard protein mixtures. Finally, to test the robustness and the effectiveness of the CBNs methods, they were employed for the comparative analysis of cortical protein extract from zQ175 mouse brains, model of Huntington Disease (HD), and control animals (raw data available via ProteomeXchange with identifier PXD017471). LF data were also validated by western blot and MRM based experiments. On standard mixtures, both CBN methods showed an excellent behavior in terms of reproducibility and coefficients of variation (CVs) in comparison to the other SpCs approaches. Overall, the CBN(P) method was demonstrated to be the most reliable and sensitive in detecting small differences in protein amounts when applied to biological samples

An Exploratory Pilot Study with Plasma Protein Signatures Associated with Response of Patients with Depression to Antidepressant Treatment for 10 Weeks.

Major depressive disorder (MDD) is a leading cause of global disability with a chronic and recurrent course. Recognition of biological markers that could predict and monitor response to drug treatment could personalize clinical decision-making, minimize unnecessary drug exposure, and achieve better outcomes. Four longitudinal plasma samples were collected from each of ten patients with MDD treated with antidepressants for 10 weeks. Plasma proteins were analyzed qualitatively and quantitatively with a nanoflow LC-MS/MS technique. Of 1153 proteins identified in the 40 longitudinal plasma samples, 37 proteins were significantly associated with response/time and clustered into six according to time and response by the linear mixed model. Among them, three early-drug response markers (PHOX2B, SH3BGRL3, and YWHAE) detectable within one week were verified by liquid chromatography-multiple reaction monitoring/mass spectrometry (LC-MRM/MS) in the well-controlled 24 patients. In addition, 11 proteins correlated significantly with two or more psychiatric measurement indices. This pilot study might be useful in finding protein marker candidates that can monitor response to antidepressant treatment during follow-up visits within 10 weeks after the baseline visit

New label-free methods for protein relative quantification applied to the investigation of an animal model of Huntington Disease

Spectral Counts approaches (SpCs) are largely employed for the comparison of protein expression profiles in label-free (LF) differential proteomics applications. Similarly, to other comparative methods, also SpCs based approaches require a normalization procedure before Fold Changes (FC) calculation. Here, we propose new Complexity Based Normalization (CBN) methods that introduced a variable adjustment factor (f), related to the complexity of the sample, both in terms of total number of identified proteins (CBN(P)) and as total number of spectral counts (CBN(S)). Both these new methods were compared with the Normalized Spectral Abundance Factor (NSAF) and the Spectral Counts log Ratio (Rsc), by using standard protein mixtures. Finally, to test the robustness and the effectiveness of the CBNs methods, they were employed for the comparative analysis of cortical protein extract from zQ175 mouse brains, model of Huntington Disease (HD), and control animals (raw data available via ProteomeXchange with identifier PXD017471). LF data were also validated by western blot and MRM based experiments. On standard mixtures, both CBN methods showed an excellent behavior in terms of reproducibility and coefficients of variation (CVs) in comparison to the other SpCs approaches. Overall, the CBN(P) method was demonstrated to be the most reliable and sensitive in detecting small differences in protein amounts when applied to biological samples

Assessment of sample preparation bias in mass spectrometry-based proteomics

For mass spectrometry-based proteomics, the selected sample preparation strategy is a key determinant for information that will be obtained. However, the corresponding selection is often not based on a fit-for-purpose evaluation. Here we report a comparison of in-gel (IGD), in-solution (ISD), on-filter (OFD), and on-pellet digestion (OPD) workflows on the basis of targeted (QconCAT-multiple reaction monitoring (MRM) method for mitochondrial proteins) and discovery proteomics (data dependent acquisition, DDA) analyses using three different human head and neck tissues (i.e. nasal polyps, parotid gland, and palatine tonsils). Our study reveals differences between the sample preparation methods, for example with respect to protein and peptide losses, quantification variability, protocol-induced methionine oxidation and asparagine/glutamine deamidation as well as identification of cysteine containing peptides. However, none of the methods performed best for all types of tissues, which argues against the existence of a universal sample preparation method for proteome analysis

정량 단백체학 및 생물정보학을 이용한 췌관내 유두상 점액 종양 및 유방암의 바이오마커 개발

학위논문 (박사) -- 서울대학교 대학원 : 의과대학 의과학과, 2021. 2. 김영수.서론: 질량분석학 기반 단백체학적 접근법은 미량의 시료에서 수백 개의 차등적으로 발현되는 단백질을 발굴하기 위해 수천 개의 단백질을 동시에 스크리닝할 수 있는 능력을 기반으로, 특정 질병과 연관된 바이오마커를 식별하는 데 점점 더 많이 적용되고 있다. 일반적으로 임상 코호트로부터 수집된 체액과 포르말린 고정 파라핀 포매조직절편 (FFPE)과 같은 병리학적 검체를 분석한다. 단백체 분석에 있어 높은 처리량과 감도를 가진 질량분석학 기반 접근법은 바이오마커 발굴 및 임상 진단 분야에서 강력한 도구로 활용될 수 있다. 또한 단백체 연구는 질병의 생물학적 메커니즘을 이해하는데 도움을 준다. 방법: 1장과 2장에서, 고분해능 질량분석기 기반 단백체학 분석을 수행하여 췌장낭종액 시료에서 췌관내 유두상 점액 종양 (IPMN)의 악성도를 예측할 수 있는 바이오마커를 발굴하였다. 2장에서는 실제 임상 상황을 더 잘 반영하기 위해 췌관내 유두상 점액 종양 뿐만 아니라 점액성 낭성 종양 (MCN)과 장액성 낭성 종양 (SCN)을 추가한 확장된 코호트에서 시료를 수집하였다. 3장에서, 표적 단백체 기술인 다중반응검지법을 FFPE 조직에 적용하여 유방암 환자의 인간 상피 증식 인자 수용체 2 (HER2) 상태를 결정할 수 있는 새로운 분석법을 확립하였다. 결과: 1장에서, IPMN 환자의 췌장낭종액 시료에서 2,992개의 단백질을 동정하였다. IPMN의 조직학적 등급에 따라 차등적으로 발현되는 18개의 바이오마커 후보군이 발견되었으며, 그 중 일부는 독립적인 코호트를 이용하여 웨스턴 블롯으로 검증하였다. 그 결과는 단백체 데이터와 일치하였다. 2장에서, IPMN, MCN, SCN 환자의 췌장낭종액에서 5,834개의 단백을 동정하였다. IPMN 이형성증간에 차별적으로 발현되는 364개의 단백질 중, 19개의 최종 바이오마커 후보군은 IPMN의 악성도에 따라 연속적으로 증가하거나 감소하였다. 독립코호트에서 CD55 단백질을 효소면역측정법 (ELISA), 웨스턴블롯, 면역화학염색 (IHC)을 통해 검증하였으며, 단백체 데이터와 일치하는 결과를 얻을 수 있었다. 3장에서, 우리는 HER2 상태를 구별하기 위한 기존 방법을 개선하는 다중반응검지법을 확립하였다. 충분한 양의 단백질을 확보하는 데 필요한 FFPE 슬라이드 수를 산출하고, 상피 세포 특이적 단백질의 발현량을 HER2 발현량 측정을 위한 정규화 인자로 사용함으로서 시료 전처리를 단순화하였다. 이에 따라 HER2 단백질 정량의 정확성과 정밀도가 향상되었다. 결론: 1장과 2장에서, 우리는 현존하는 최대의 췌장낭종액 단백체 데이터를 생성했으며, IPMN 이형성증의 잠재적 바이오마커를 발굴하였다. 췌장낭종액 바이오마커의 발굴은 IPMN의 악성도를 정확하게 평가하는데 도움이 되며, 외과적 의사 결정을 효과적으로 도울 수 있을 것이다. 궁극적으로, 발굴한 마커가 임상에서 유용하게 사용될 수 있다면, IPMN 이형성증에 대한 정확한 평가를 통해 저위험군 IPMN 환자의 불필요한 수술적 절제를 방지하는데 기여할 수 있을 것이다. 3장에서, 모호한 HER2 그룹을 구별할 수 있는 우리의 프로토콜은 형광동소혼성화 (FISH) 테스트가 필요한 사례의 수를 줄임으로써 유방암 환자의 진단에 필요한 시간과 비용을 잠재적으로 줄일 수 있다. 또한 우리가 개발한 단순화된 분석 절차는 MRM 분석법을 임상에 적용하기 위한 진입 장벽을 낮춰준다. 우리가 확립한 MRM 분석법은 IHC에 비해 보다 정확한 HER2 발현 수준을 산출함으로서, 임상의가 유방암 환자를 위한 적절한 치료 방침을 결정하는데 도움을 줄 수 있을 것이다.Introduction: Mass spectrometry (MS)-based proteomic approaches are being increasingly applied to identify markers that are related to specific diseases, based on their ability to screen thousands of proteins simultaneously to obtain hundreds of differentially expressed proteins (DEPs) in small amounts of samples. In general, pathologic specimens collected from clinical cohorts, such as body fluids and formalin-fixed paraffin-embedded (FFPE) tissues, are analyzed. For proteomic analysis, MS-based approach is a powerful tool in biomarker discovery and clinical diagnosis with its high throughput and high sensitivity. Also, a proteomics study will help understand biological mechanisms of diseases. Methods: In chapter I and II, high-resolution mass spectrometry-based proteomics was performed using pancreatic cyst fluid samples to discover marker candidates for predicting the degree of intraductal papillary mucinous neoplasm (IPMN) malignancy. In chapter II, samples were collected from the expanded cohort that included IPMNs and other PCLs (mucinous cystic neoplasm (MCN) and serous cystic neoplasm (SCN)) to better reflect actual clinical circumstances. In chapter III, a targeted proteomic technique, multiple reaction monitoring-mass spectrometry (MRM-MS), was applied to formalin-fixed paraffin-embedded (FFPE) tissues to establish a novel assay to determine human epidermal growth factor receptor 2 (HER2) status in breast cancer patients. Results: In chapter I, a dataset of 2,992 proteins was constructed from pancreatic cyst fluid samples of IPMN patients. Eighteen biomarker candidates that were differentially expressed across histological grades of IPMN were discovered, and some of them were validated by western blot in an independent cohort, the results of which were consistent with our proteomic data. In chapter II, 5,834 proteins were identified using cyst fluid from patients with IPMN, MCN, and SCN. Among 364 proteins that differentially expressed between IPMN dysplasia, 19 final marker candidates consistently increased or decreased with greater IPMN malignancy. CD55 was validated in an independent cohort by ELISA, Western blot, and IHC, and the results were consistent with the MS data. In chapter III, we established an MRM-MS assay that improves on existing methods for differentiating HER2 status. The accuracy and precision of HER2 quantification were improved by simplifying the sample preparation through predicting the number of FFPE slides required to ensure an adequate amount of protein and using the expression levels of an epithelial cell-specific protein as a normalization factor when measuring HER2 expression levels. Conclusions: In chapter I and II, we have generated the largest proteomic dataset of pancreatic cyst fluid to date and discovered potential markers of IPMN dysplasia. The development of cyst fluid markers can facilitate an accurate assessment of the degree of IPMN malignancy and effectively guide surgical decision-making. Ultimately, if the developed marker is implemented in clinical practice, the accurate assessment of IPMN dysplasia will prevent unnecessary surgical resection for low-risk IPMN patients. In chapter III, our proposed protocol, which discriminates between equivocal HER2 subgroups, can potentially decrease the time and costs required for the diagnosis of breast cancer patients by reducing the number of cases that require ancillary fluorescence in situ hybridization (FISH) tests. In addition, the simplified assay procedure can reduce the barriers to entry for the clinical application of the MRM-MS assay. Our MRM-MS assay yields more accurate HER2 expression levels relative to immunohistochemistry and should help to guide clinicians toward the proper treatment for breast cancer patients, based on their HER2 expression.Abstract ... i Contents ... v List of Tables ... vii List of Figures ... x List of Abbreviations ... xv General Introduction ... 1 Chapter I ... 5 Introduction ... 6 Materials and Methods ... 10 Results ... 19 Discussion ... 42 Chapter II ... 48 Introduction ... 49 Materials and Methods ... 53 Results ... 64 Discussion ... 99 Chapter III ... 107 Introduction ... 108 Materials and Methods ... 111 Results ... 124 Discussion ... 148 General Conclusion ... 153 References ... 156 Abstract in Korean ... 176Docto