Automated 20 kpsi RPLC-MS and MS/MS with Chromatographic Peak Capacities of 1000−1500 and Capabilities in Proteomics and Metabolomics

Shen, Yufeng; Zhang, Rui; Moore, Ronald J.; Kim, Jeongkwon; Metz, Thomas O.; Hixson, Kim; Zhao, Rui; Livesay, Eric A.; Udseth, Harold R.; Smith, Richard D.

doi:10.1021/ac0483062

Cited by 232 publications

(228 citation statements)

References 34 publications

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“…Using LSST theory, the retention time (t R ) in linear gradient elution is given as: 24 (13) where t 0 is the column dead time, t D is the system dwell time, k' 0 is the isocratic retention factor in the initial eluent of the gradient. The gradient steepness (b) is defined as: (14) where F is the flow rate (mL/min), and t G is the gradient time, V m is the column dead volume (mL) and ϕinitial and ϕfinal are the initial and final eluent compositions of the gradient, respectively.…”

Section: Prediction Of Retention Timesmentioning

confidence: 99%

Peak Capacity Optimization of Peptide Separations in Reversed-Phase Gradient Elution Chromatography: Fixed Column Format

et al. 2006

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The optimization of peak capacity in gradient elution RPLC is essential for the separation of multicomponent samples such as those encountered in proteomic research. In this work we study the effect of gradient time (t G ), flow rate (F), temperature (T) and final eluent strength (ϕ final ) on the peak capacity of separations of peptides that are representative of the range in peptides found in a tryptic digest. We find that there are very strong interactions between the individual variables (e.g. flow rate and gradient time) which make the optimization quite complicated. On a given column, one should first set the gradient time to the longest tolerable and then set the temperature to the highest achievable with the instrument. Next, the flow rate should be optimized using a reasonable but arbitrary value of ϕ final . Last, the final eluent strength should be adjusted so that the last solute elutes as close as possible to the gradient time. We also develop an easily implemented, highly efficient and effective Monte Carlo search strategy to simultaneously optimize all the variables. We find that gradient steepness is an important parameter that influences peak capacity and an optimum range of gradient steepness exists in which the peak capacity is maximized.

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Section: Prediction Of Retention Timesmentioning

confidence: 99%

Peak Capacity Optimization of Peptide Separations in Reversed-Phase Gradient Elution Chromatography: Fixed Column Format

et al. 2006

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“…Among the various LC platforms, ultra-performance LC (UPLC) is considered suitable for metabolite profiling and metabolomics [17 -28], especially, for large-scale untargeted metabolic profiling due to its enhanced reproducibility in retention time [24]. The ability to generate high peak capacities in short time by UPLC has facilitated the simultaneous analysis of the complex samples (e. g., herbal extracts and biological samples) with diverse chemical characteristics [22,24,25]. Several profound reports have covered the application of UPLC to pharmaceutical analyses [26 -31] particularly in the field of metabolism or metabolomic studies [17,22,23,26,27,32].…”

Section: Introductionmentioning

confidence: 99%

Ultra‐performance LC/TOF MS analysis of medicinal Panax herbs for metabolomic research

Xie

Plumb

et al. 2008

J of Separation Science

159

108

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Original Paper Ultra-performance LC/TOF MS analysis of medicinal Panax herbs for metabolomic researchIn this study, metabolite profiling of five medicinal Panax herbs including Panax ginseng (Chinese ginseng), Panax notoginseng (Sanchi), Panax japonicus (Rhizoma Panacis Majoris), Panax quinquefolium L. (American ginseng), and P. ginseng (Korean ginseng) were performed using ultra-performance LC-quadrupole TOF MS (UPLC-QTOFMS) and multivariate statistical analysis technique. Principal component analysis (PCA) of the analytical data showed that the five Panax herbs could be separated into five different groups of phytochemicals. The chemical markers such as ginsenoside Rf, 20(S)-pseudoginsenoside F11, malonyl gisenoside Rb1, and gisenoside Rb2 accountable for such variations were identified through the loadings plot of PCA, and were identified tentatively by the accurate mass of TOFMS and partially verified by the available reference standards. Results from this study indicate that the proposed method is reliable for the rapid analysis of a group of metabolites present in herbal medicines and other natural products and applicable in the differentiation of complex samples that share similar chemical ingredients. IntroductionMetabolomics aims to identify and quantify the full complement of low-molecular-weight, soluble metabolites in actively metabolizing tissues [1 -4], which has aroused extensive awareness and interests, especially in the botanical studies such as quality control of plants [5 -9], plant engineering [10, 11] using a number of techniques including NMR or different combinations of LC, GC, and MS. Also, based on the multivariate analysis of complex biological profiles, metabolomics has been successfully applied to plant genotype discrimination and differentiation [1, 12 -14]. Plants are rich in chemically diverse metabolites, which cannot be completely detected by a single analytical method currently available in phytochemical laboratories. Over the past 20 years, HPLC/MS (LC/MS) has become one of the main analytical techniques used in controlling the quality and consistency of both the chemical markers and active substances of traditional Chinese medicines (TCMs). Despite early claims that MS, and particularly MS/MS, is able to remove the need for good chromatographic separation in the analysis of low abundance samples, a poor LC run prior to MS analysis will lead to issues of ion suppression and isobaric interferences that cannot be overcome by a spectrometer alone. More recently, the desire for significantly reduced analysis time with increased sample throughput, resolution, and sensitivity has resulted in the development of ultrafast separation and identification using LC-MS techniques [15,16]. Among the various LC platforms, ultra-performance LC (UPLC) is considered suitable for metabolite profiling and metabolomics [17 -28], especially, for large-scale untargeted metabolic profiling due to its enhanced reproducibility in retention time [24]. The ability to generate high peak capacities in short...

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“…34 In a 20 kpsi system, they achieved a peak capacity of >1000 by using a 2 m capillary column packed with 3 µm RP packing material. 35 Although reported peak capacities are not absolute indices and thus are hard to compare, this is undoubtedly an important achievement because improved RP separation has a direct impact on the total system performance of multidimensional methods. 36,37 Commercially available UPLC systems and mass spectrometers capable of taking advantage of the narrower peak widths have engendered increased interest in this technology for proteomics applications.…”

Section: Emerging Technologiesmentioning

confidence: 99%

Multidimensional LC Separations in Shotgun Proteomics

2008

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Two modes of separation coupled with MS enable researchers to study complicated biological structures.Proteins are the molecular products of genes and play a central role in many biological processes. Protein expression occurs as a function of cellular and environmental conditions, and consequently proteins are expressed at different times and under different conditions. Insight is thus garnered by determining how protein expression has changed in a biological context.Over the past two decades, MS has become an important tool for the analysis of proteins. 1,2 One current method for the analysis of protein mixtures is proteolytic digestion followed by LC/MS/ MS. Once hard-to-handle proteins are converted into chemically well-behaved peptides, the approach overcomes many difficulties associated with protein mixture analysis. 3 Tandem MS has been particularly effective because the data can be directly used to identify peptides and subsequently infer which proteins (digested or undigested) are in the mixture. 4 This type of approach for the analysis of protein mixtures is often referred to as "shotgun" proteomics ( Figure 1).Because increasingly complicated biological structures are studied by tandem MS, the need for more powerful and highly resolving separation methods has grown. Consequently, the development and use of multidimensional LC (MDLC) in proteomics has thrived over the past few years. MDLC combines two or more forms of LC to increase the peak capacity, and thus the resolving power, of separations to better fractionate peptides prior to entering the mass spectrometer.High-resolution separation serves two functions in combination with MS. It better resolves peptides differing in charge and hydrophobicity to minimize ion suppression and improve ionization efficiency, and it simplifies the complexity of peptide ions entering the mass spectrometer to minimize undersampling. This last aspect is important because the tandem MS process is driven by data-dependent data acquisition and has a finite cycle time. A higher peak capacity and better resolving power improve the acquisition of data and can lead to a better representation of the proteins in the mixture. Improved resolution also results in a larger dynamic range.Although MDLC is by no means a new concept and has a long history, it has been enjoying a renaissance in proteomics. 5 In this article, we will provide an overview of the background, important applications, and future prospects for MDLC; in particular, we will focus on applications involving peptides. (To listen to a podcast about this feature, please go to the Analytical Chemistry website at pubs.acs.org/ac.)

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Automated 20 kpsi RPLC-MS and MS/MS with Chromatographic Peak Capacities of 1000−1500 and Capabilities in Proteomics and Metabolomics

Cited by 232 publications

References 34 publications

Peak Capacity Optimization of Peptide Separations in Reversed-Phase Gradient Elution Chromatography: Fixed Column Format

Peak Capacity Optimization of Peptide Separations in Reversed-Phase Gradient Elution Chromatography: Fixed Column Format

Ultra‐performance LC/TOF MS analysis of medicinal Panax herbs for metabolomic research

Multidimensional LC Separations in Shotgun Proteomics

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