, Volume 82, Issue 1, pp 101–110 | Cite as

High-Resolution Nano-Liquid Chromatography with Tandem Mass Spectrometric Detection for the Bottom-Up Analysis of Complex Proteomic Samples

  • Magali Dams
  • José Luís Dores-Sousa
  • Robert-Jan Lamers
  • Achim Treumann
  • Sebastiaan EeltinkEmail author
Part of the following topical collections:
  1. 50th Anniversary Commemorative Issue


Liquid chromatography coupled with mass spectrometric detection is one of the major technologies used for protein sequencing, identification, and quantification. This review provides an introduction of the current state-of-the-art technology in peptide profiling using nano-liquid chromatography–mass spectrometry applied to large-scale protein or proteome analysis. In particular, different aspects of the bottom-up proteomics workflow are covered, including aspects of sample preparation such as protein digestion, nanoflow gradient reversed-phase chromatographic separation, LC–MS interfacing via electrospray ionization, tandem mass spectrometry of digests, protein identification via database searches, and finally peptide quantitation.


Review Proteomics research Peptide mapping Peptide profiling Nano-LC 



Alternating current


Collision-induced dissociation


Direct current


Data-dependent acquisition


Particle diameter


Electrospray ionization


Formic acid


Fast atom bombardment


False discovery rate


Heptafluorobutyric acid


High-performance liquid chromatography


Internal diameter


Isobaric tag for relative and absolute quantitation


Column length


Matrix-assisted laser desorption ionization


Multiple reaction monitoring


Tandem mass spectrometry


Mass-to-charge ratio


Plate number


Peptide-spectrum match


Quadrupole time-of-flight


Radio frequency


Reversed-phase liquid chromatography


Stable isotope labeling by amino acids in cell culture


Selected reaction monitoring


Trifluoroacetic acid




Tandem mass tag


In 1997, the term ‘proteomics’ was introduced in analogy with the developments in the field of genomics [1]. Proteins are all composed of the same building blocks, i.e., 20 proteinogenic amino acids linked together via amide bonds in a linear chain. It is a convention that the sequence of these amino acids is written from the N-terminus (the amino acid with a non-linked alpha-amino group) to the C-terminus (the amino acid with a non-linked alpha carboxy-group). The large variety of proteins encoded in the genome is increased by one to two orders of magnitude through alternative splicing of messenger RNA and through the introduction of a large number of post-translational modifications that can influence structure and function of proteins [2]. Hence, the 20,360 products of the human genome [3] will yield millions of different proteoforms. Furthermore, proteoforms differ in composition, concentration, and location, and all of these factors vary with time. The protein sequence (primary structure of a protein) determines to a large degree the secondary and tertiary structures of that protein. Comparison of protein sequences can reveal functionally important regions, which can contribute to an understanding of their function.

In the 1970s, Edman degradation was introduced which allowed for relatively high-sensitivity protein identification and sequencing [4]. In the Edman degradation experiment, the N-terminus is repeatedly removed, and the amino acid sequence is determined step by step via retention time indices obtained using liquid chromatography techniques such as high-performance liquid chromatography (HPLC) or electrophoresis. The development of fast atom bombardment (FAB) was the first in a series of breakthroughs in the 80s and 90s that led to the use of mass spectrometry (MS) in proteomics [5]. FAB enabled the production of peptide ions by focusing an argon beam on a glycerol sample matrix. Around the same time, tandem mass spectrometry (MSMS) was being investigated to obtain sequence information of peptides ionized with FAB. In MSMS, peptide ions are collided with chemically inert uncharged molecules or atoms in the gas phase and the mass-to-charge ratio of thus obtained peptide fragments is determined. An important prerequisite for the identification of peptides with a high throughput rate was the development of new, robust ionization methods that allowed for the analysis of larger peptides, namely electrospray ionization (ESI) [6] and matrix-assisted laser desorption ionization–time-of-flight (MALDI-TOF) mass spectrometry [7]. The former allows for the direct coupling of HPLC to MS, contributing to the rapid development of many new LC–MS protocols [8]. A typical bottom-up proteomic workflow, schematically depicted in Fig. 1 starts with extracting proteins from cells, followed by digestion and proteome complexity reduction by chromatography techniques before MS is used to identify and quantify the proteins present in the original sample [1]. A selection of common issues encountered in bottom-up proteomics analysis by nano-RP–LC–MSMS and technological solutions are provided in Table 1.

Fig. 1

Schematic representation of the workflow of a bottom-up LC–MSMS experiment in proteomics. The proteins extracted from a cell line are converted to a protein digest after alkylation and reduction. The sample mixture is separated on a nano-LC column in gradient RP mode prior to MS and MSMS detection for identification and quantification. Protein identification is based on comparison of experimental data with theoretical mass spectra via a database search

Table 1

A selection of common issues in bottom-up proteomics by nano-RP–LC–MSMS analysis and potential technological solutions


Common issues

Potential technological solutions

Sample preparation

Lengthy protein digestion and protease autolysis

Immobilized enzymes in capillary columns to accelerate digestion [9]

Nano-RP–LC separations

Breakthrough of hydrophilic peptides

Optimized ion-pairing agent selection and concentration [10, 11], and column chemistry (e.g., C30)

Insufficient peak capacity

Long columns (> 150 mm) packed with small (sub(2)-micron) particles [12], long silica monolith columns (> 1 m) [13], and/or ultralong gradients (> 5 h) [13, 14]

Dead volumes due to faulty connections

Innovative zero dead volume connectors

ESI interfacing

Inefficient ionization

LC at ultralow flow rates (< 50 nL/min) [15]

Ion suppression

Mixtures of ion-pairing agents [16], or post-column removal of interfering ion-pairing agents after LC and before MS [17]

Extra-column peak broadening

Integrated ESI emitters in small I.D. columns [18]

Protein identification

Obtain accurate structural information

CID using a highly specific protease like trypsin

Discrimination of low-abundancy peptides

Data-dependent acquisition with dynamic exclusion, or data-independent acquisition

False-positive protein identifications

Extended False Discovery Rate estimation (both PSM and protein level) (e.g., MAYU [19])

Inflexible data exchange and analysis

Development of open community standards (e.g., mzML [20]), open source software tools (e.g., OpenMS [21])

Insufficient interlaboratory reproducibility

Standardized procedures and quality control measures [22, 23]

Sample Preparation and Protein Digestion

Proteins are obtained from biological material (this could be physiological fluids, tissue samples or cells that are kept in culture) through lysis of this biological material with a suitable buffer. An important aspect of this first sample preparation step is the solubilization of proteins from this sample, which can be achieved through the addition of detergents, through chemical cleavage of the proteins into large peptides and/or through the use of chaotropic reagents such as urea and/or thiourea [24]. The formation of inter- and intramolecular disulfide bridges is prevented by the reduction of proteins prior to enzymatic digestion into peptides. Subsequently, the reduced cysteines are blocked by alkylation to prevent them from reforming disulfide bonds through oxidation [25]. Following reduction and alkylation, the protein is digested with trypsin, a protease that cleaves the protein sequence following the alkaline amino acids lysine and arginine. After digestion and prior to LC–MS analysis, it is important to remove chemicals that would interfere either with the separation of peptides using RP–LC or with peptide ionization using ESI. Often, these chemicals were introduced to the sample to aid with protein solubilization prior to digestion [26].

To reduce the time for sample preparation, which typically involves an overnight digestion step, alternative digestion protocols have been introduced, e.g., applying immobilized enzymes where the protease is (preferably covalently) coupled to an insoluble solid-support structure [27, 28, 29]. Due to the presence of a high local concentration of immobilized enzyme and the absence of auto-digestion, the reaction rate can be significantly enhanced. Whereas autolysis of the protease during an in-solution digestion will ultimately lead to the MS detection of non-analyte peptides, the product of the enzymatic reaction utilizing immobilized enzymes is not contaminated with the free enzyme or peptide fragments thereof. A protocol incorporating the addition of enzymes immobilized on beads to the protein sample, an incubation period at high temperature to denature proteins, and a centrifugation or filtration step, allows to significantly reduce the time needed to create the digest, down to hours [30]. Applying immobilized enzyme microreactors in capillary trap column formats in a ‘preconcentration set-up’ allows for desalting and rapid digestion within a timeframe of only minutes [9].

Peptide Separations in Gradient Reversed-Phase Nano-LC Mode

LC is mainly used to decrease the number of peptides per time unit entering the MS instrument (increasing the total time of analysis), resulting in an increase of the number of detected, fragmented and analyzed peptides. At the same time, ion-suppression effects induced by co-elution are reduced. The routine high-resolution mode for biomacromolecule separations is reversed-phase liquid chromatography (RP–LC) applying a linear aqueous/acetonitrile gradient. Applying gradient elution instead of an isocratic elution mode, significantly enhances the resolving power [31]. Whereas protein separations are typically performed using columns packed with C4 silica particulate materials, silica C18 columns are commonly used for the separation of protein digest samples that typically cover a broad range of hydrophilic to hydrophobic peptides [32]. To prevent breakthrough of hydrophilic peptides, separations are typically carried out with an ion pair (0.1%) added to the mobile phase, such as formic acid (FA) [33, 34]. These acids lower the pH and protonate the applicable side groups of the peptides. Next, the anionic counter ion of the ion pair interacts with the peptide via an electrostatic bond, leading to an increase in hydrophobicity of the peptide. For reasons of flow rate compatibility, which is related to ionization efficiency (see Sect. 4), nano-LC utilizing 75–100 μm I.D. columns that are operated at a flow rate of 200–300 nL/min has become the gold standard in proteomic analysis [35]. An additional benefit of decreasing the column I.D. is that the radial dilution is significantly decreased, since column I.D.2 ~ radial dilution. When shifting from a 2.1. mm I.D. column to a 100 μm I.D. column format, radial dilution is reduced with a factor of 400. Hence, when the same mass is loaded on-column during injection, reducing the column I.D. will lead to a significant increase of the detection sensitivity. This effect is enhanced in electrospray mass spectrometry, which behaves as a concentration-sensitive (not mass sensitive) detector. Still, it is important to note that downscaling column dimensions may have negative effects on chromatographic performance. Due to the small peak volumes, the extra-column dispersion (e.g., dead volumes introduced by sub-optimal connections between the column and tubing) is much more detrimental for the separation. Also, the packing quality of capillary columns is arguably not as good as what can be achieved with conventional LC columns. In a recent study, the Jorgenson group systematically studied the effect of packing conditions on resulting bed morphology and separation efficiency [36, 37]. Generally, column packing performed with relatively high slurry concentrations yielded high-efficiency columns.

Whereas conventional peptide mapping experiments were typically conducted using 150 mm long nanocolumns packed with 3 μm particles, column technology has evolved rapidly since the introduction of ultra-high-pressure liquid chromatography in 2004 [38, 39]. The overall kinetic performance has improved significantly, since the number of plates (N) is inversely proportional to the square of the particle diameter (dp) in the C-term (diffusion) dominated region of the van Deemter curve, and N is proportional to column length (L). The current state-of-the-art column technology for bottom-up proteomics constitutes 150 mm up to 750 mm long capillary columns packed with fully porous (sub)2-micron C18 particles and the corresponding gradient duration applied varies typically between 30 min and 3 h [40, 41, 42]. Note that the peak capacity, defined as the number of peaks that elute in the gradient window and are separated with a resolution of 1, increases only with the square root of N. Moreover, peptide retention is assumed to follow a Poisson distribution, implying that a 20-fold excess in peak capacity is required to realize a baseline separation [43]. To accommodate for the large dynamic concentration range as encountered in proteomic samples, fully porous (silica-based) particulate material is preferred, instead of using emerging core–shell particle technology that has the benefit of providing better (~ 10–25%) kinetic performance [39]. A fascinating alternative format for packed columns is the monolithic column that features a macroporous interconnected structure of globules/skeletons [44, 45, 46]. The size of both the macropores and microglobules can be tailored toward separation performance and the potential of polymer-monolithic columns to achieve both high throughput (<< 1 min) and high-resolution biomolecule gradient separations yielding peak capacities in excess of 1000 have been readily demonstrated for intact proteins [47], oligonucleotides [48], and peptides [47, 49]. Figure 2 shows the separation of a tryptic digest of Escherichia coli on a monolithic capillary column of 1 m applying a 10 h gradient [49]. While the mass loadability of polymer-monolithic columns is rather low, it offers exceptionally low carry over in contrast to, for example, silica-based stationary phases (both silica particulate columns and monolithic silicas) [50].

Fig. 2

Base peak chromatogram of a reversed-phase separation of a tryptic digest of E. coli obtained on a 1-m long polymer-monolithic capillary column applying a 10 h gradient yielding a peak capacity in excess of 1000.

Adapted from reference [49] with permission

Aspects of Electrospray Ionization Interfacing

The nano-electrospray ionization (ESI) source is the most widely used interface to hyphenate LC to MS in proteomics, and its design is typically optimized to operate nanocolumns at flow rates between 200 and 600 nL/min. The sensitivity in the MS detector is defined by the efficiency in which molecules from the liquid phase are transferred into the gas phase and are subsequently ionized. The LC flow, containing peptides in an acidified aqueous mobile phase, is delivered by the ESI emitter to which a high voltage is applied. Above a threshold voltage (2–6 kV), the liquid surface forms a Taylor cone to overcome the increasing charge repulsion [51]. From the tip of the cone, a spray of small droplets is emitted and solvent is evaporated with the aid of heated gas, e.g., nitrogen gas. This process reduces the droplet size and increases the charge density at the droplet surface. At some point, the Rayleigh instability limit is reached when the Coulombic repulsive forces become equal to the surface tension. Upon further evaporation, the Rayleigh limit is exceeded, and the droplet undergoes Coulombic fission, creating droplets of approximately 1/10th of the original size [6]. After repeatedly creating smaller droplets by Coulombic fission, positively charged peptide ions are directed into the MS by application of a difference in potential. The acidic conditions required for charging the droplets also lead to protonation of the available basic sites. For peptides, these sites are located at the amine group on the N-terminus, or at the lysine (K), arginine (R), or histidine (H) side groups. Due to the specificity of trypsin, most peptides contain two basic sites, i.e., the N-terminal amine group and a C-terminal lysine or arginine side group, which leads predominantly to the formation of doubly charged peptide precursor ions. This is one reason why trypsin has become the protease of choice for most proteomic studies. Others include its very high specificity [52], the relative frequency of arginine and lysine in most proteomes leading to peptides with a length that is very amenable to mass spectrometric analysis and the localization of a positive charge on the C-terminus of tryptic peptides, contributing to more informative MSMS fragmentation patterns.

Generally, the aqueous/organic mobile phases employed in RP–LC mode are highly compatible with electrospray ionization. However, the use of ion-pairing agents utilized to enhance peptide retention typically suppresses the ionization efficiency, since the strong ion pairs are not easily broken. HFBA or TFA characterized by pKa values of 0.40 and 0.23 significantly enhance retention and are often employed to trap and desalt peptides on a trap cartridge, whereas FA with pKa value of 3.75 is preferred as agent during the RP–LC gradient run yielding adequate ionization efficiency [53, 54, 55]. It should be noted, however, that in some studies a significant decline of separation efficiency has been reported induced by the addition of FA as an ion-pairing agent [54, 55].

Tandem Mass Spectrometric Analysis of Peptides

The separation principle in the mass analyzer is based on the controlled movement of gas phase ions in an electromagnetic field under high vacuum. The movement is dependent on the mass-to-charge ratio (m/z) with the unit Thomson (Th). Ions are typically detected by applying an electron multiplier. Mass analyzers display a characteristic m/z range, resolution, and accuracy. Given the predominant occurrence of doubly-charged peptides in tryptic digests of proteins, an m/z range up to 2000 Th is considered to be appropriate for peptide mapping. Due to the presence of abundant isotopes (mainly 13C and 34S), peptides are detected as peak clusters in MS spectra, with the first peak (the monoisotopic peak) only containing 12C atoms, the second, less abundant peak containing one 13C atom, etc. In electrospray spectra, these clusters can be conveniently used to calculate the charge state of any peptide, as the distance between peaks in the cluster will be 1/z, with z being the charge. The different terms are clarified in the mass spectrum in Fig. 3.

Fig. 3

A schematic representation of a mass spectrum with isotopic resolution for doubly- and triply-charged peptides. The resolution of the monoisotopic ions is provided

Four frequently employed mass analyzers utilized in tandem mass spectrometry workflows are discussed here, i.e., the quadrupole, the quadrupole ion trap (Paul trap), the time-of-flight (TOF), and the Orbitrap mass analyzer. The quadrupole mass analyzer is an assembly of two sets of opposing rods to which a combination of direct current (DC) potential and alternating currents (AC or RF) are applied. Ions that are not affected by the alternating field, since either the charge is too low, the mass is too high, or the AC field fluctuates too quickly, are controlled by the DC field. In case of a positive DC field, ions with a high m/z will experience stable trajectories. Hence, the quadrupole can be employed as a (double) mass filter. To obtain the complete mass spectrum, narrow m/z values yielding a stable oscillation are sequentially transmitted such that a given m/z range is covered. Quadrupole analyzers typically allow for resolving peaks with a distance of 1 Th with an accuracy in the range of 100 ppm [56]. Next to its use as a mass filter, the quadrupole can also function as an ion transmitter by working with an RF potential alone. Moreover, RF-only mode can also be used as a collision cell for MSMS experiments.

The ion trap is composed of three electrodes, two opposing hyperbolic plates (the end cap electrodes to which AC current is applied) and a hyperbolic ring electrode that separates them to which DC current is applied [57]. The DC and AC and potentials are tuned such that the mass analyzer traps ions in a specific m/z range. In the next stage, the m/z range is scanned, and selected ions are ejected towards the detector which counts the ions that were accumulated in the trap during a specific accumulation time. The process of accumulating and scanning leads to high sensitivity compared to quadrupole mass analyzers. Still, overloading of the trap must be avoided because repulsive forces between the ions affect the accuracy of the measurement.

In a TOF mass analyzer, ions are accelerated in the flight tube after experiencing a high-voltage pulse. The m/z of an ion is determined by the time needed to travel a field-free drift tube. Since ions enter the flight tube with the same kinetic energy, ions with low m/z travel the flight tube faster than those with high m/z (the flight time is proportional to the square of the mass-to-charge ratio) [26]. Two types of detectors can be distinguished, i.e., linear detectors and reflectron detectors. The latter detector provides higher mass resolution, since the further ions can travel, the greater the distance between ions of slightly differing mass will be. An additional advantage is that reflectron instruments correct for the energy dispersion of ions leaving the source. However, the loss of some ions, particularly ions with a high mass-to-charge ratio, puts limits on the applicability of reflectron detectors. MALDI-TOF of linear proteins is generally performed in linear mode. The high scan rate of TOF mass analyzers (below a millisecond) translates in a very high sensitivity. Furthermore, the dynamic range is broad compared to other mass analyzers. The configuration employed for identification purposes in many proteomics laboratories combines a quadrupole mass filter via a collision cell to a reflectron instrument, i.e., a quadrupole time-of-flight (QTOF) instrument.

In 2005, the Orbitrap mass analyzer was introduced commercially, making an instrument available that provides both high sensitivity and high mass resolution [58]. This mass analyzer consists of a spindle-shaped electrode placed in between two concave electrodes. A linear electric field is realized by applying a voltage between the spindle and the outer electrodes. Ions enter the trap tangentially and follow a spiral trajectory around the spindle electrode whilst also obtaining an axial harmonic oscillation. The signal is obtained by Fourier transform of the recorded harmonic oscillation to the frequency domain, resulting in a mass spectrum. Hence, the Orbitrap detects all ions simultaneously for a certain detection time.

In tandem mass spectrometry (MSMS), the m/z value of an ion of interest is selected (precursor ion) and directed into a collision cell (usually a quadrupole operating in RF-only mode, see above). Here, the precursor is vibrationally excited via collision with a chemically inert and neutral gas (He, Ar, etc.) in a process named collision-induced dissociation (CID) [59]. The mass spectrum of the fragment ions recorded in the second mass analyzer is referred to as an MS2 spectrum. For discovery-based experiments, a high-resolution mass analyzer is most commonly used in the last stage, increasing the confidence in protein identification. For example, an Orbitrap Fusion Lumos instrument is composed of a quadrupole mass analyzer that acts as mass filter, a 2D ion trap that acts as collision cell, and, finally, the MS2 spectrum is recorded by the Orbitrap [60]. In a discovery experiment, an MS1 spectrum is acquired and instantaneously evaluated by the MS acquisition software to determine a programmable number of n precursor ions of highest intensity in the spectrum. In a process called data-dependent acquisition (DDA), these n ions are sequentially selected for the acquisition of fragment ion spectra [61]. Once an MSMS spectrum for the nth ion has been acquired, another MS spectrum will be acquired and the entire process is repeated. The sum of the time needed to acquire n MSMS spectra and one MS spectrum is referred to as the cycle time. For example, the ten most abundant ions are selected for MSMS immediately after the MS1 scan. The result is a recurring pattern of an MS1 spectrum followed by a number of MS2 spectra (Fig. 4). The analytical depth of an experiment can be increased through a process called dynamic exclusion, where ions for which a fragment spectrum has already been obtained are excluded from selection for a programmable amount of time.

Fig. 4

Illustration of an MS–MSMS experiment sequence by data-dependent acquisition in which the MSMS spectra of the two most intense monoisotopic ions was recorded

Protein Identification and Quantitation

The MSMS spectra in a proteomic LC–MSMS experiment contain information about the sequence of the peptides that were detected in the sample. Peptides will often fragment in the peptide bond, resulting in fragments that either contain the N-terminus of the peptide (a,b,c-ions) or ions that contain the C-terminus of the peptide (x,y,z-ions) [62]. A careful analysis of MSMS spectra will mostly reveal the sequence of the peptide that has given rise to this spectrum (an excellent tutorial for manual sequencing of peptide spectra was published by Bin Ma and Rich Johnson [63]). However, it is difficult to automate this process and peptides are generally identified computationally through comparison of the spectra with theoretical spectra that are derived from readily available translated genomic sequence collections (e.g., Uniprot [64]). To perform these automated protein identifications, three different items need to be available:

  • the translated genomic sequence collection, often referred to as a database,

  • the experimental data, generally in the form of a collection of peak lists representing the MS2 spectra of the peptides that were fragmented in the LC–MSMS experiment and,

  • a computer program that performs the comparison of the experimental spectra with computer-generated spectra derived from the protein database—these computer programs are referred to as search engines. There is a large and continually expanding and advancing collection of search engines that are either commercially available or open source (reviewed in Verheggen et al. [65]).

The first product of a search engine is a list of peptide spectrum matches (PSMs). In a second step (protein inference), the search engine attempts to match these PSMs to a set of proteins that gave rise to the detected peptides upon trypsin digestion. As the relationship between proteins and detected tryptic peptides is a many-to-many relationship (trypsin generates many peptides from each protein and many peptides could be derived from more than one protein), this step is often not unequivocal and produces protein groups of which one or several members could be in the sample. Peptides that can be assigned to one protein only are referred to as proteotypic peptides. The generation of PSMs is a statistical process that will also produce false-positive hits (due to experimental noise, mis-interpreted chemical or post-translational peptide modifications, low-quality MS2 spectra because of low abundance or bad fragmentation of specific peptides, missing database entries, and imperfections in the database search algorithm). Several different approaches have been developed to estimate the error rate in the identification results for a particular proteomic experiment (reviewed in [66]). An overview of the protein identification process, using the false discovery rate (FDR) approch is displayed in Fig. 5.

Fig. 5

Overview of the protein identification process. The MSMS data is fed to the database search algorithm (together with a target-decoy database for error rate estimation) and PSMs of theoretical peptides from the database with experimental MS2 spectra are returned. PSMs below a threshold algorithm-specific score are filtered out. Additional PSMs are removed to obtain the chosen false discovery error rate (FDR). The remaining peptides are inferred to proteins

While information about the identities of proteins in a biological sample can be very valuable, many proteomic questions are of a quantitative nature (reviewed in [67, 68]). The most frequent quantitative proteomic experiments allow for relative quantitation, meaning that the relative amount of a specific protein is compared in two different sample sets. For absolute quantification, it is essential to spike the sample to be analyzed with a suitable, quantified internal standard, often a stable isotope labeled peptide (AQUA) [69]. Relative quantitation with heavy isotope labeling can be carried out through biosynthetic labeling (e.g., using 15N-labeling [70] or stable isotope labeling by amino acids in cell culture (SILAC) [71]), through chemical labeling using either isobaric reagents such as tandem mass tags (TMT) [72] or isobaric tags for relative and absolute quantitation (iTRAQ) [73] or chemical derivatization of primary amino groups, such as stable isotope dimethyl labeling [74]. Concerns about experimental artifacts introduced during the labeling procedures together with advances in the accuracy, precision, and reproducibility of chromatography and mass spectrometry have resulted in the increased use of label-free quantitative proteomics. Label-free quantitation can be carried out either at the MS level (also referred to as the MS1 level), illustrated by the LFQ approach in the popular MaxQuant proteomics software environment [75] or at the MSMS level (also referred to as the MS2 level). Quantitative proteomics at the MSMS level can be carried out by operating the mass spectrometer either in the selected reaction monitoring mode (SRM, often also referred to as multiple reaction monitoring, MRM [76]) or in a data-independent acquisition mode, also referred to as SWATH [77].

Concluding Remarks

The interest in LC–MS technology for bottom-up protein sequencing as part of proteome research is still growing. Applications can be found in the molecular understanding of diseases, aiding in the development of new drugs and diagnostics. Other fields of research include food and nutrition, and plants. With careful application and optimization of nano-LC–ESI–MSMS conditions, attomole quantities of peptides can be detected and characterized. Furthermore, advanced data analysis strategies have been developed to enhance the confidence in the identification and quantitation of proteins in the sample.



This publication has been written as part of the Open Technology Programme with project number IWT.150467 (DEBOCS), which is financed by the Flemish Agency of Innovation and Entrepreneurship (VLAIO). JLDS and SE acknowledge the Research Foundation Flanders (FWO) for financial support (Grant nos. G 025916N and G033018N).

Compliance with Ethical Standards

Conflict of interest

All authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Magali Dams
    • 1
  • José Luís Dores-Sousa
    • 1
  • Robert-Jan Lamers
    • 2
  • Achim Treumann
    • 3
  • Sebastiaan Eeltink
    • 1
    Email author
  1. 1.Department of Chemical EngineeringVrije Universiteit Brussel (VUB)BrusselsBelgium
  2. 2.Abundnz B.V.WoerdenThe Netherlands
  3. 3.Newcastle University, NUPPANewcastle upon TyneUK

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