Anal Bioanal Chem (2011) 399:229–241 DOI 10.1007/s00216-010-3946-7

REVIEW

Recent advances in micro-scale and nano-scale high-performance liquid-phase chromatography for proteome research Dingyin Tao & Lihua Zhang & Yichu Shan & Zhen Liang & Yukui Zhang

Received: 12 April 2010 / Revised: 18 June 2010 / Accepted: 20 June 2010 / Published online: 4 August 2010 # Springer-Verlag 2010

Abstract High-performance liquid chromatography–electrospray ionization tandem mass spectrometry (HPLC–ESIMS–MS) is regarded as one of the most powerful techniques for separation and identification of proteins. Recently, much effort has been made to improve the separation capacity, detection sensitivity, and analysis throughput of micro- and nano-HPLC, by increasing column length, reducing column internal diameter, and using integrated techniques. Development of HPLC columns has also been rapid, as a result of the use of submicrometer packing materials and monolithic columns. All these innovations result in clearly improved performance of micro- and nano-HPLC for proteome research. Keywords Proteome . Micro-HPLC . Nano-HPLC . Multi-dimensional separation . MS–MS

D. Tao : L. Zhang : Y. Shan : Z. Liang : Y. Zhang Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic R. & A. Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China D. Tao Graduate School of Chinese Academy of Sciences, Beijing 100039, China L. Zhang (*) National Chromatographic R. & A. Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, China e-mail: [email protected]

Introduction Proteins are the molecular-level products of gene expression, and are important in a variety of biological processes. Therefore, proteomics research, which aims to investigate proteins in a cell, tissue, or body fluid [1], is of great significance [1, 2]. However, because of the wide dynamic range in abundance, proteome analysis is extraordinarily challenging. Top-down and bottom-up are two common strategies applied in protein separation and identification. In the topdown strategy, proteins are separated by two dimensional electrophoresis (2DE), first proposed by O’Farrell and Klose [3, 4], followed by identification by tandem mass spectrometry (MS–MS). Although 2DE has overwhelming capacity for protein separation, it suffers from unavoidable limitations in the separation and detection of lowabundance proteins, membrane proteins, and proteins with extreme isoelectric points (pI) and molecular weights (MW) [5]. Another limitation is that 2DE operation is time consuming and technically demanding. Therefore, recently more attention has been focused on the bottom-up proteomic strategy in which, generally, proteins were first digested into peptides, and then separated by one dimensional (1D) or multidimensional (MD) micro- or nano-highperformance liquid chromatography (HPLC) followed by online MS–MS analysis via an electrospray ionization (ESI) interface [6–8]; this enables highly efficient, high resolution, high peak capacity, sensitive, and highthroughput proteome analysis. In on-line hyphenation of micro- or nano-HPLC with ESI-MS, detection sensitivity is closely related to the flow rate of HPLC. Lower flow rates result in smaller eluent droplets, more charges per analyte molecule, and higher ESI efficiency. Compared with micro-HPLC systems, nano-

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HPLC is more sensitive and consumes less sample. However, micro-HPLC systems are much simpler to construct than nano-HPLC systems. This review summaries recent advances on micro- and nano-HPLC techniques for proteome research based on the bottom-up strategy, with focus on the improvement of columns and techniques.

Column technology In the past decade, much attention has been focused on improvement of chromatographic columns, the “heart” of HPLC. Although compared to 2D HPLC, the peak capacity of 1D HPLC is limited, it has the advantages of easy operation and good reproducibility [9, 10]. Therefore, it has also been widely used in proteome analysis. Because of its high resolution and good compatibility with MS–MS, micro- or nano- reversed-phase chromatography (RPLC) is commonly used for peptide separation based on the bottom-up strategy. Recently, much effort has been devoted to column technology to increase the separation capacity, detection sensitivity, and analysis throughput.

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protein digest extracted from rat brain, results comparable with those obtained by 2D HPLC separation, but with obviously improved analysis speed [17]. Such a technique could also be further used to prepare nano-LC columns. However, according to Giddings’ theory, to achieve Cp over 1,500, with a 200-cm column packed with 3-μm particles, the predicted separation time should be 2,000 min, practically impossible to achieve. Therefore, to shorten analysis time without the loss of separation capacity, Shen et al. [18] performed nano-RPLC separation by using 50-μm i.d. fusedsilica capillaries, packed with submicrometer-sized (0.8– 2 μm) C18-bonded porous silica particles. When hyphenated with linear ion-trap tandem mass spectrometry, Cps of 130–420 for tryptic digests of S. oneidensis were achieved in less than 1 h, and 1,000 proteins could be identified in 50 min from 4,000 identified peptides. Micro- or nano-columns packed with submicrometersized particles (<2 μm) have advantages such as high efficiency, high resolution, high peak capacity, and high throughput. However, they simultaneously place severe demands on column packing, pumps that must ensure high back pressure, and MS with high scanning rates. Therefore, to solve these problems, the development of new columns becomes an effective solution.

Packed columns Because various packing materials with excellent properties are commercially available, packed micro- and nano-HPLC columns are often used in proteome research. The resolving power of separation systems can be described by peak capacity (Cp), the maximum number of peaks that can be resolved in a given time period. According to Giddings’ theory [11], resolution, efficiency, and throughput in 1D micro- or nano-HPLC benefit from reduced particle size. However, such changes can result in increased back pressure, which has accelerated the development of ultraperformance liquid chromatography (UPLC) [12–14]. Typical work on UPLC has been performed by Shen et al. [15]. They packed an 87 cm long capillary column (i.d. 14.9–74.5 μm) with 3-μm C18-bonded porous silica particles under a pressure of 18,000 psi, and successfully coupled it to a hybrid quadrupole time-of-flight (Q-TOF) MS via a nanoESI interface. In analysis of proteolytic polypeptide mixtures, as shown in Fig. 1, Cp up to approximately 1,000 were obtained. Later, an ultrahigh-pressure dual online SPE– capillary RPLC (75 μm i.d. × 1 m length) system was developed by Lee et al. [16], which was operated under a maximum pressure of 10,000 psi. However, it is difficult to prepare such long nano-columns with good performance and reproducibility. To solve this problem, Tao et al. connected several short microcolumns in series by use of zero-dead volume-unions, and, in 6.8 h, identified 1,692 proteins in a

Monolithic columns Monolithic materials, with the advantages of fast mass transfer, low back pressure, and easy preparation, have been successfully applied in proteome research [19–22]. Monoliths are generally classified into polymer and silica monoliths. Although polymer monoliths have advantages of good biocompatibility and high pH stability, they can undergo shrinking or swelling in organic solvents, and the microporous domains may have negative effects on peak shape and column efficiency, which might limit their applications in micro- or nano-HPLC [23, 24]. In contrast, the mesopores inside the skeleton of silica monoliths are preferable for increasing the surface area of monolithic rods, resulting in high sample loadability and enough sites to introduce function groups, and interconnecting macropores fast mass transfer with low backpressure, which enables application of long nano-HPLC column on conventional systems. By connection with a replaceable emitter, Luo et al. [25, 26] coupled an octadecylated silica-based monolithic capillary column (70 cm×20 μm i.d.) with an ion-trap mass spectrometer, and a Cp of approximately 420 was obtained under an operating pressure of 5,000 psi. In total, 2,367 different peptides from 855 distinct S. oneidensis proteins were identified in a 2.5-μg tryptic digest sample in a single 10-h analysis, as shown in Fig. 2.

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Fig. 1 Base peak chromatograms for 100 ng of a yeast soluble protein tryptic digest analyzed by nano-RPLC–ESI-MS with 74.5, 47.1, 29.7, and 14.9-μm-i.d. fused-silica packed capillaries. Conditions: all

columns were 87 cm long, and the flow rates were 393, 155, 76, and 20 nL min−1 for 74.5, 47.1, 29.7, and 14.9-μm-i.d. packed columns, respectively. Reproduced, with permission, from Ref. [15]

Although emitters are commercial available, and easy to replace if clogged, connection of a tapered ESI emitter with a monolithic column via a union could increase the post-column dead volume, resulting in reduced separation efficiency. Therefore, preparation of monolithic columns with integrated ESI emitters is preferred. Zou et al. [22] prepared C18 monolithic silica-based columns (60 cm×75 μm i.d.) with integrated tips, and identified 5,501 unique peptides, from 1,323 distinct Saccharomyces cerevisiae proteins, in over 400 min. Compared with columns connected by use of commercial emitters, the separation efficiency was improved by 20%. Later, they prepared an emitter on a 20-cm (100 μm i.d.) lauryl methacrylate–ethylene dimethacrylate (LMA–EDMA) monolithic capillary column, by means of which automated nano-LC–MS–MS without a trap column was used to enable pre-concentration and separation of peptides [27]. Compared with packed columns, because of the special structure of pores and skeletons the backpressure of monolithic columns is rather low, enabling reduction of column inner diameter, to improve the separation capacity of HPLC

and the detection sensitivity of MS–MS. Luo et al. [28] prepared a 25 cm×10 μm i.d. silica-based monolithic LC column, by means of which over 5,000 peptides were identified in 100 ng Shewanella oneidensis tryptic digests. However, with further decrease of the inner diameter, preparation of monolithic columns with good reproducibility and performance might be challenging. Therefore, for nanoHPLC with columns of inner diameter less than 10 μm, open-tubular columns with increased surface ratio might be a good solution [29, 30]. Because of the easily tailored structure of monoliths, large peptide fragments (up to 10 kDa) with or without modifications were well separated by use of narrow bore (20 and 50 μm i.d.) poly(styrene–divinylbenzene) (PS-DVB) monolithic columns. Importantly, the macroporous structure of the monolithic columns facilitated improved recovery of large peptides, compared with that obtained on packing materials with small pore size [31]. Although both packed and monolithic columns have their own advantages, as listed in Table 1, further improvement of reproducibility, efficiency, and longevity

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Fig. 2 High-efficiency on-line microSPE–nanoLC–ESI-MS–MS analysis with an extended LC gradient for analysis of a 2.5-μg S. oneidensis tryptic digest sample on a 70 cm×20 μm i.d. silica-based monolithic column. Reproduced, with permission, from Ref. [25]

of monolithic columns is indispensable if such columns are to be widely applied in proteome analysis.

Chip-based HPLC To meet the needs of trace sample analysis with high sensitivity and throughput, chip-based HPLC has undergone rapid development. The integration of injector, separation column, and stable electrospray for MS on one microfluidic device benefits various applications [32]. Therefore, significant efforts have recently been made to fabricate efficient columns for chip-based HPLC, including microfluidic open-tubular columns [33], packed-bed col-

umns [34, 35], micromachined pillar array columns [36, 37], and monolithic columns [38]. Chip-based HPLC, first introduced in 2005 [39], was constructed from multilayers of polyimide, and comprised an enrichment column, an analytical column, and a nanoelectrospray interface to a mass spectrometer. The columns were packed with conventional reversed-phase chromatography particles, and a face-seal rotary valve was used to provide switching between sample loading and separation. The HPLC chip and valve assembly were mounted within a custom electrospray source on an ion-trap mass spectrometer, and the microfluidic integration of nano-HPLC with MS enabled subfemtomole detection sensitivity, minimum carryover, and stable electrospray. Because sample injection,

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Table 1 Comparison of packed and monolithic columns

Packed columns

Monolithic columns

Column details

Sample and injection amount

Mass spectrometer

Analysis time

Data processing

Identified proteins/ peptides

Ref.

75μm i.d. × 1m long; C18 (3μm i. d. 300Å pore size)

Saccharomyces cerevisiae; 10μg

FTICR, LTQ-FT (Thermo Finnigan) 7 tesla

2.5h

BioWorks software; precursor ion 10ppm; fragment ion 0.5Da.

[16]

300μm i.d. × 30cm long; C18 (5μm i.d. 300Å pore size) 50μm i.d. × 20cm long; C18 (0.8μm i.d. 77Å pore size) 10μm i.d. × 25cm long; silica-based monolithic

Rat brain; 50μg

LTQ XL IT (Thermo Finnigan)

6.8h

BioWorks software; FDR<1%.

489 proteins/ 1,605 nonredundant peptides 1,692 proteins

Shewanella oneidensis; 2.5μg

LTQ IT (Thermo Finnigan)

50min

BioWorks software; XCorr>2.1; ΔCn>0.17

Shewanella oneidensis; 0.3μg

LTQ IT (Thermo Finnigan)

4h

BioWorks software and DTASelect; ΔCn>0.08; XCorr for peptide charge as +1>1.8; +2>2.5; +3>3.5

75μm i.d. ×60cm long; octadecylated silica monolith

Saccharomyces cerevisiae; 0.5μg

LTQ IT (Thermo Finnigan)

7.5h

BioWorks software and Buildsummary; ΔCn>0.1; XCorr for peptide charge as +1>1.9; +2> 2.2; +3>3.75

concentration, separation, and identification were integrated in one system, more attention was focused on chip-LC–MS, and more applications in proteome study were reported [40– 48], for example identification of phosphoproteins, quantification of carbonic anhydrase II in human serum, label-free quantitative analysis of bioactive lupin proteins, and detection of glycopeptides released from pmol amounts of recombinant erythropoietin. Recently, Heck et al. [45] developed an automated online RP-TiO2-RP “sandwich” chip which enabled highly sensitive identification of phosphopeptides with good reproducibility. They further coupled this chip to a high-resolution quadrupole time-offlight mass spectrometer, to explore the phosphoproteome of non-stimulated primary human leukocytes. In total, they identified 1,012 unique phosphopeptides, corresponding to 960 different phosphorylation sites, and, for the first time, provided an overview of the phosphoproteome of these important circulating white blood cells [46]. In addition, amide–silica hydrophilic interaction chromatography (HILIC) in a chip-based format for glycosaminoglycans (GAGs) glycomics profiling was also developed [47], by which stable spray could be maintained for three months, which ensured the accuracy of glycome analysis. More recently, Zaia et al. [48] demonstrated a novel amide-HILIC HPLC chip that enabled introduction of make-up flow to the effluent of the analytical column, by means of which a stable electrospray could be formed even under highly aqueous conditions.

∼1,000 proteins / ∼4,000 peptides 1,443 proteins/ 5,510 peptides 1,323 proteins/ 5,501 peptides

[17]

[18]

[26]

[22]

Besides commercial instruments, some excellent work has been done in the laboratory. As shown in Fig. 3, Liu et al. [49] prepared polymethacrylate monoliths in microfluidic chips, in the format of 5-mm-long solid-phase extraction (SPE) coupled with 15 cm-long reversed phase nano-RPLC. By use of this equipment online removal of free fluorescein, and enrichment and separation of labeled proteins were achieved simultaneously, resulting in 150-fold improvement on sensitivity by SPE, and tenfold reduction in peak width by microchip gradient LC separation. Although more attention has been paid to chip-based HPLC since commercial instruments became available, the separation capacity is still limited by column length with packed materials. To solve this problem further efforts should be made to prepare monolithic stationary phases in microchannels and to construct MD separation modes.

Multidimensional HPLC Although column technologies in 1D micro- or nano-HPLC has been seen rapid development to improve peak capacity, detection sensitivity, and analysis throughput, the extreme complexity of proteomes requires the development of MDHPLC. According to Giddings’ theory [11], with orthogonal mechanisms for each dimensional separation, Cp for the whole system should be a product of those for each dimension. Because of the high resolution and peak

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Fig. 3 (a) Microchip design and (b) experimental system for online sample cleanup and enrichment then HPLC separation with an integrated 5 mm long SPE trap column and 15 cm long separation column. Reproduced, with permission, from Ref. [49]

capacity, MD-HPLC has been widely applied in proteome analysis.

Offline MD-HPLC As shown in Fig. 4–1, for offline MD-HPLC systems, the eluents from the first-dimension separation are collected, and then re-injected for separation in the next dimension. By this means the compatibility of mobile phase and flow rates in separations in different dimensions could be easily improved. In addition, the construction of such systems is easy and flexible. To improve the identification ability of the separation in the second dimension, the sample loading amount in the first dimension should be large, to ensure a sufficient amount for injection in the following separation. In MD-HPLC, the separation in the first dimension includes strong cation-exchange chromatography (SCX) [50–52], strong anion-exchange chromatography (SAX) [53, 54], RPLC [55–60], size-exclusion chromatography (SEC) [61, 62], or HILIC [63]. To improve compatibility with MS–MS, micro- or nano-RPLC is commonly used as the separation in the final dimension.

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Among various 2D modes of HPLC, 2D SCX–RPLC is mostly preferred for bottom-up-based proteome analysis, because of high loading capacity, resolution, and peak capacity. Recently, with an offline 2D SCX–RPLC system, Gao et al. [51] developed an unbiased method for largescale depletion of high-abundance proteins. Depending on the signal intensity of RPLC, proteins were pooled into two groups—high-abundance and low-abundance proteins. After digestion, peptides from each group were further analyzed by 2D SCX–μRPLC. With this strategy, after depletion of 58 high-abundance proteins from normal human liver, a total of 1,213 proteins were identified, which was approximately 2.7 times as many as identified without high-abundance protein depletion. Although RPLC enables highly efficient separation, online coupling of two RPLC columns is difficult, because of the incompatibility of mobile phases. However, by use of an offline strategy, practice of 2D RPLC becomes possible, by removing the organic modifiers in the eluents of the first dimensional RPLC separation. Delmotte et al. [55] reported a novel separation mode for shotgun Corynebacterium glutamicum proteome analysis, with high-pH RPLC in the first dimension and low-pH ion-pair RPLC in the second (RP–IP RPLC). Compared with the classical 2D SCX– RPLC approach, approximately 13% more peptides and 7% more proteins could be identified, in 30% less analysis time. Recently, Song et al. [60] developed an offline 2D RPLC technique for analysis of phosphopeptides. A large number of fractions were collected from the first dimensional RPLC separation with a normal column (250 mm× 2.1 mm i.d., 5 μm C18) at high pH, and these fractions were pooled in pairs with equal time interval, and analyzed by nano-RPLC (120 mm×75 μm i.d.) at low pH. By this technique, 487 phosphopeptides were identified in a digest of 8 mg mouse liver proteins, enriched by use of Ti4+– IMAC microspheres. Although offline MD HPLC is highly flexible, it still suffers from the disadvantages of being time and labor consuming, the risk of sample loss or contamination, and large sample volume, which might not be suitable for highly sensitive identification of valuable proteome samples.

Online MDLC Multidimensional protein identification technology (MudPIT), first described by Yates and coworkers, and shown in Fig. 4–2a, has been widely applied in proteome analysis [64–66]. In this technique, ion exchange, for example SCX, WAX, or WCX– WAX mixture, and RPLC particles, are packed in tandem in a single capillary and enable the first and second dimensions of the separation, respectively. With this system, approximately

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Fig. 4 Various modes of multidimensional separation systems: 1, offline; 2, online with multidimensional protein identification technology (MudPIT), a, with a single binary column; b, with coupled columns; 3, online with RP-trap and valves. Reproduced and adapted, with permission, from Ref. [73]

420 μg of the soluble fraction, 440 μg of the lightly washed insoluble fraction, and 490 μg of the heavily washed insoluble fraction from the Saccharomyces cerevisiae strain BJ5460 were analyzed by fully automated 15-step chromatography and a total of 1,484 proteins were identified [65]. High

detection sensitivity for low-abundance proteins, for example transcription factors and protein kinases, was also demonstrated. To avoid the desalting procedure, Dai et al. [67] integrated an SCX column (5 cm×320 μm i.d.) with a

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μRPLC column (10 cm×150 μm i.d.) by use of a two-port union, by which peptides fractionated by SCX under a pH step were further separated by μRPLC. Because no salt was used during separation, the system could be directly coupled with linear ion-trap MS, and more than 2,000 proteins were identified from mouse liver, which showed the advantages of low overlap across pH fractions, high resolution of peptides, and good compatibility with MS. To improve the efficiency of identification of phosphopeptides, Motoyama et al. [68] reported a MudPIT method using a mixed anion and cation-exchange bed (5 cm×250 μm i.d.) for separation in the first dimension, to improve peptide recovery and orthogonality with the second-dimension RPLC separation. The number of phosphopeptides identified from phosphopeptide-enriched samples of HeLa cell nuclear extract was increased by 94% over SCX. For MudPIT strategy with packed columns, the column length is always limited by back-pressure. To solve this problem, Wang et al. [69] prepared a biphasic monolithic capillary column with a 10-cm segment of SCX and a 65-cm segment of RPLC within a single 100 μm i.d. capillary, so the backpressure of the system was only ∼900 psi. With this column, 2,953 unique peptides, corresponding to 780 distinct proteins were identified from 10 μg tryptic digest of yeast proteins within 12 h, with false-positive identification less than 1%. Although the MudPIT system has obvious advantages for shotgun proteome analysis, the multidimensional separation performed in one column still has several disadvantages, for example poor inter-column reproducibility, low column loadability, and limited choice of mobile phases. Furthermore, it usually takes a long time to inject a few microliters or hundreds of microliters of samples at the nano-flow rate, resulting in reduced separation efficiency and analysis throughput. Therefore, trap columns with large inner diameters have been applied to online automated sample loading and cleanup using splitless nano-HPLC technology in the vented column configurations [70]. The inner diameter of the trap columns was usually 2 or 3 times larger than that of the separation columns, and the column length was short, generally 2–4 cm for packed trap columns, or 5–7 cm for monolithic columns [71, 72]. As shown in Fig. 4– 2b, Wang et al. [73] prepared a 150 μm i.d. monolithic capillary SCX trap column by in situ polymerization of ethylene glycol methacrylate phosphate and bisacrylamide in a ternary porogenic solvent, and further coupled it with nano-RPLC–ESI-MS–MS. With such an improved MudPIT mode, sample could be loaded with a flow rate as high as 40 μL min−1, but the back pressure was just ∼1,300 psi. A total of 1,522 distinct proteins were identified from 5,608 unique peptides, with false positive identification of only 0.46%, from 19 μg tryptic digest of yeast proteins.

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To achieve highly sensitive detection of trace samples, the inner diameter of columns for separation in the second dimension should be further decreased, so the RP-trap column is very important for improving sample-loading capacity. Luo et al. [74] reported use of an SCX column (100 μm i.d. fused-silica capillary packed with 5 μm 300Å polysulfoethyl A), a PS-DVB monolithic trap column (50 μm i.d.), and a poly(styrene–divinylbenzene) (PSDVB) PLOT column (3.2 m×10 μm i.d.) coupled to ESIMS–MS for robust, high-performance, and ultrasensitive proteomic analysis in which 1,071 peptides associated with 536 unique proteins were identified from 75 ng protein from a gel fraction corresponding to 600 SiHa cells. They further applied an RP particle packed column before an SCX column for online sample desalting, and constructed a triphasic SCX–PLOT–MS system [75], as shown in Fig. 5. Besides MudPIT strategy, the online coupling of different separation columns by use of valves and trap columns is also commonly used in proteome analysis, as shown in Fig. 4–3 [73]. To avoid gradient delay, the optimum trapcolumn conditions for online coupling with nano-RPLC was studied, as shown in Table 2 [76]. For 2D HPLC, SCX [74], HILIC [78], and size exclusion chromatography (SEC) [79] have been reported for separation in the first dimension; among these, HILIC is a good choice for first dimension separation, with extremely good orthogonality to RPLC. However, the high organic content of the mobile phase increases the difficulty of online hyphenation with RPLC. In most cases, in the bottom-up strategy, SCX is commonly used for separation in the first dimension, because of the easy hyphenation, although not with good orthogonality. Zhou et al. [77] constructed a fully automatic online 2D SCX–RPLC–ESI-MS–MS system with two parallel RP-trap columns (20 mm×320 μm i.d.) before nano-RPLC (150 mm×75 μm i.d.). Compared with the traditional salt stepwise gradient used for SCX, a continuous pH gradient was used, and more proteins (3,391 compared with 2,981) were identified from mouse liver with improved sequence coverage and confidence level, by significant minimization of fraction overlap with SCX. In summary, online MD systems have the advantages of easy automation, good reproducibility, and high throughput. They are, however, limited by mobile phase incompatibility and analysis time, and the choice of separation modes is not as easy as that for offline systems. As shown in Table 3, both offline and online MD systems have their own advantages for proteome analysis. Researchers should therefore make their choice on the basis of the properties of their samples, the limitations of their instruments, and analytical throughput requirements.

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Fig. 5 Diagram on-line 2D SCX–PLOT–MS system with a triphasic trapping column and a 3.2 m×15 μm i.d. PLOT column. Reproduced, with permission, from Ref. [75]

Integrated systems For bottom-up strategy-based proteome study, protein digestion is indispensable. Generally, protein digestion is performed in solution, not only with time over 12 h, but also with risk of enzyme autodigestion. Therefore, immo-

Table 2 Optimum properties and conditions for trap-columns in nano-RPLC1 Flow rate (μL min−1)

Inner diameter (μm) HPLC column2

Trap column3

HPLC column2

Trap column3

15 30 50 75

50 75 150 200

∼0.02 ∼0.07 ∼0.14 ∼0.40

∼8 ∼13 ∼75 ∼120

1

Adopted from Ref. [76] with minor modification

2

Packed with 3-μm particles, 86 cm long;

3

Packed with 5-μm particles, 4 cm long

bilized enzyme reactors (IMERs), with enzyme immobilized on nanoparticles, sepharose, membranes, plates, fused-silica capillaries or monolithic materials [80–85], have been paid much attention, to improve analysis throughput by online hyphenation with micro- or nanoRPLC. Among various matrices, polymer monolith based IMERs could have high enzyme binding capacity, strong chemical stability and low non-specific adsorption. Duan et al. [86] coupled a monolithic poly(acrylamide, nacryloxysuccinimide and ethylene dimethacrylate)-based IMER with nano-RPLC, and found a residence time of 7 s was enough for online digestion and identification of cytochrome c. Feng et al. [87] coupled a similar monolithic enzyme microreactor to μRPLCMS–MS. With the digestion time shortened from 16 h to 1 min, a total of 1,578 unique peptides, corresponding to 541 proteins, were identified from 590 ng yeast proteins. Recently, Svec et al. [88] synthesized two different monolithic scaffolds based on poly(glycidyl methacrylate–co-ethylene dimethacrylate) and poly(butyl methacrylate–co-ethylene dimethacrylate), not only to reduce non-specific adsorption of proteins but also to immobi-

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Table 3 Comparison of online, offline and integrated MD-HPLC systems for proteomics research Mode

Offline

Online

Integrated

Column selected

Sample and injection amount

Mass spectrometer

Analysis time

Data processing

Identified proteins/ peptides

Ref.

First dimension

Second dimension

SCX: 2.1 mm i. d. × 250 mm (5 μm i.d. 300 Å pore size) RP: 2.1 mm i.d. × 250 mm; C18 (5 μm i.d. 300 Å pore size)

RP: 75 μm i.d. × 10 cm long

Breast cancer MCF-7 cells; 28 μg

QTOF Premier (Waters)

>59 h

MASCOT search program; FDR<0.19%

2,362 proteins

[52]

RP: 75 μm i.d. × 12 cm long; C18 (5 μm i.d. 120 Å pore size)

LTQ XL IT (Thermo Finnigan)

>41.5 h

BioWorks software and APIVASE; FDR<1%.

RP: 75 μm i.d. × 12 cm long; C18 (5 μm i.d. 120 Å pore size) RP: 100 μm i.d. × 10 cm long; C18 (3 μm i.d.)

LTQ XL IT (Thermo Finnigan)

>38 h

BioWorks software; FDR<0.46%

487 phosphopeptides from six fractions of a total of 45 fractions 1,522 proteins/ 5,608 peptides

[60]

SCX: 150 μm i. d. × 7 cm long; phosphate monolithic RP-WAX/SCX: 250 μm i.d. × (2.5+2.5) cm long; RP (5 μm)

8 mg mouse liver enriched by use of Ti4 + -IMAC microspheres Yeast; 19 μg

HeLa cell nuclear extract; ∼50 μg

LTQ XL IT (Thermo Finnigan)

∼19.5 h

Protein separation: WAX/WCX: 300 μm i.d. × 10 cm long; (5 μm, nonporous)

Peptides separation: RP: 300 μm i.d. × 10 cm long; C18 (5 μm i.d. 200 Å pore size)

Human lung cancer cell line H446; 30 μg

LCQ DUO (Thermo Finnigan)

24 h

BioWorks software, RawExtract, PARC algorithm and DTASelect 2.0; FDR<1% BioWorks software; FDR<5%

lize peptide-N-glycosidase F. By online hyphenation with a monolithic HILIC capillary column, human immunoglobulin G was deglycosylated online at room temperature in 5.5 min, and further identified by nano-RPLC–ESIMS–MS.

Fig. 6 Diagram of on-line system integrating protein denaturation, reduction, and digestion with μRPLC–ESIMS–MS. Reproduced, with permission, from Ref. [89]

[70]

2,891 proteins/ 17,262 peptides

[68]

284 proteins/ 1,042 peptides

[87]

Besides online protein digestion, attempts to integrate multiple step sample preparation with micro- or nano-HPLC have also been made to achieve high-throughput proteome analysis. Ma et al. [89] recently developed a fully integrated combination of online protein denaturation and reduction

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Fig. 7 Schematic diagram of proteome analysis by integration of online protein separation, digestion, peptide separation, and protein identification. Reproduced, with permission, from Ref. [90]

via heating at 90 °C for 2 min, proteolytic digestion by IMER within a few minutes, and separation of peptides and identification by μRPLC–ESI-MS–MS, as shown as Fig. 6. Native proteins extracted from mouse liver (∼1 mg mL−1, containing 10 mmol L−1 DTT and 1 mol L−1 urea) were analyzed within 3 h, instead of ca. 30 h by the traditional offline method. To improve the identification accuracy of proteomes, protein fractionation before digestion might be a good solution. Recently, Yuan et al. integrated 2D HPLC with protein separation on a mixed WAX/WCX microcolumn (100 mm×300 μm i.d.), online digestion by use of an IMER, peptide trapping by use of two alternate C8 trap columns (2 mm×500 μm i.d.), and further separation and identification by μRPLC (100 mm×300 μm i.d.)–ESI-MS– MS [90], as shown in Fig. 7. With the combination of protein separation and peptide separation via an IMER, improved identification accuracy and analysis throughput was obtained for proteome analysis. Compared with the traditional shotgun method, more unique proteins (284 versus 216) were identified from rat liver in less time (24 h compared with 44 h). Although IMERs have been important in integrated highthroughput analysis of proteomes, it should be pointed out that much effort should be made to prepare IMERs with negligible non-specific adsorption of proteins, good reproducibility, and

greater longevity, to make them as popular as in solution digestion.

Concluding remarks Although proteomics has been studied for over a decade, identification of low-abundance proteins, membrane proteins, protein complexes, etc., are still great challenges. Therefore, the properties of micro- or nano-HPLC columns should be further improved by synthesis of novel separation matrices, the preparation of nano-columns with extreme length and small inner diameter, and the development of trapping columns with specific selectivity for target proteins. In addition, much effort should be made to design interfaces for micro- and nano-HPLC with MS, to improve the detection sensitivity. Furthermore, to improve the analysis throughput of proteomes, fully-automatic HPLC, array-based MD HPLC, and even microchip-based MD-HPLC should be paid much attention. In summary, to meet the requirements of proteome research, further great efforts should be made to improve resolution, efficiency, peak capacity, sensitivity, and throughput of micro- and nano-HPLC systems. Acknowledgements The authors are grateful for financial support from the National Basic Research Program of China (2007CB914100), the National Natural Science Foundation (20935004), the Knowledge Innova-

240 tion Program of Chinese Academy of Sciences (KJCX2YW.H09), and DFG-NSFC cooperation project (GZ3164).

References 1. Fournier ML, Gilmore JM, Martin-Brown SA, Washburn MP (2007) Chem Rev 107:3654–3686 2. Wickware P, Smaglik P (2001) Nature 413:869–875 3. O'Farrell PH (1975) J Biol Chem 250:4007–4021 4. Klose J (1975) Humangenetik 26:231–243 5. Motoyama A, Yates JR 3rd (2008) Anal Chem 80:7187–7193 6. Yates JR, Ruse CI, Nakorchevsky A (2009) Annu Rev Biomed Eng 11:49–79 7. Sprung RW Jr, Brock JW, Tanksley JP, Li M, Washington MK, Slebos RJ, Liebler DC (2009) Mol Cell Proteomics 8:1988–1998 8. Yuan H, Zhou Y, Zhang L, Liang Z, Zhang Y (2009) J Chromatogr A 1216:7478–7482 9. Kubota K, Kosaka T, Ichikawa K (2009) Methods Mol Biol 519:483–494 10. Jiang X, Feng S, Tian R, Han G, Jiang X, Ye M, Zou H (2007) Proteomics 7:528–539 11. Giddings JC (1984) Anal Chem 56:1258A–1260A 12. MacNair JE, Lewis KC, Jorgenson JW (1997) Anal Chem 69:983–989 13. Winter D, Seidler J, Ziv Y, Shiloh Y, Lehmann WD (2009) J Proteome Res 8:418–424 14. Bordeerat NK, Georgieva NI, Klapper DG, Collins LB, Cross TJ, Borchers CH, Swenberg JA, Boysen G (2009) Proteomics 9:3939–3944 15. Shen Y, Zhao R, Berger SJ, Anderson GA, Rodriguez N, Smith RD (2002) Anal Chem 74:4235–4249 16. Min HK, Hyung SW, Shin JW, Nam HS, Ahn SH, Jung HJ, Lee SW (2007) Electrophoresis 28:1012–1021 17. Tao D, Zhu G, Sun L, Ma J, Liang Z, Zhang W, Zhang L, Zhang Y (2009) Proteomics 9:2029–2036 18. Shen Y, Smith RD, Unger KK, Juma D, Lubda F (2005) Anal Chem 77:6692–6701 19. Guryca V, Kieffer-Jaquinod S, Garin J, Masselon CD (2008) Anal Bioanal Chem 392:1291–1297 20. Dong M, Wu M, Wang F, Qin H, Han G, Dong J, Wu R, Ye M, Liu Z, Zou H (2010) Anal Chem 82:2907–2915 21. van de Meent MH, de Jong GJ (2009) J Sep Sci 32:487–493 22. Xie C, Ye M, Jiang X, Jin W, Zou H (2006) Mol Cell Proteomics 5:454–461 23. Guiochon G (2007) J Chromatogr A 1168:101–168 24. Greiderer A, Trojer L, Huck CW, Bonn GK (2009) J Chromatogr A 1216:7747–7754 25. Luo Q, Shen Y, Hixson KK, Zhao R, Yang F, Moore RJ, Mottaz HM, Smith RD (2005) Anal Chem 77:5028–5035 26. Luo Q, Page JS, Tang K, Smith RD (2007) Anal Chem 79:540–545 27. Jiang X, Dong J, Wang F, Feng S, Ye M, Zou H (2008) Electrophoresis 29:1612–1618 28. Luo Q, Tang K, Yang F, Elias A, Shen Y, Moore RJ, Zhao R, Hixson KK, Rossie SS, Smith RD (2006) J Proteome Res 5:1091–1097 29. Yue G, Luo Q, Zhang J, Wu SL, Karger BL (2007) Anal Chem 79:938–946 30. Luo Q, Rejtar T, Wu SL, Karger BL (2009) J Chromatogr A 1216:1223–1231 31. Zhang J, Wu SL, Kim J, Karger BL (2007) J Chromatogr A 1154:295–307 32. Robotti KM, Yin H, Brennen R, Trojer L, Killeen K (2009) J Sep Sci 32:3379–3387 33. Schlund M, Gilbert SE, Schnydrig S, Renaud P (2007) Sens Actuators B 123:1133–1141

D. Tao et al. 34. Ehlert S, Kraiczek K, Mora JA, Dittmann M, Rozing GP, Tallarek U (2008) Anal Chem 80:5945–5950 35. Jung S, Ehlert S, Mora JA, Kraiczek K, Dittmann M, Rozing GP, Tallarek U (2009) J Chromatogr A 1216:264–273 36. De Malsche W, Clicq D, Verdoold V, Gzil P, Desmet G, Gardeniers H (2007) Lab Chip 7:1705–1711 37. De Malsche W, Eghbali H, Clicq D, Vangelooven J, Gardeniers H, Desmet G (2007) Anal Chem 79:5915–5926 38. Levkin PA, Eeltink S, Stratton TR, Brennen R, Robotti K, Yin H, Killeen K, Svec F, Fréchet JM (2008) J Chromatogr A 1200:55–61 39. Yin H, Killeen K, Brennen R, Sobek D, Werlich M, van de Goor T (2005) Anal Chem 77:527–533 40. Chu CS, Niñonuevo MR, Clowers BH, Perkins PD, An HJ, Yin H, Killeen K, Miyamoto S, Grimm R, Lebrilla CB (2009) Proteomics 9:1939–1951 41. Ellis J, Grimm R, Clark JF, Pyne-Gaithman G, Wilbur S, Caruso JA (2008) J Proteome Res 7:4736–4742 42. Callipo L, Foglia P, Gubbiotti R, Samperi R, Laganà A (2009) Anal Bioanal Chem 394:811–820 43. Brambilla F, Resta D, Isak I, Zanotti M, Arnoldi A (2009) Proteomics 9:272–286 44. Groleau PE, Desharnais P, Coté L, Ayotte C (2008) J Mass Spectrom 43:924–935 45. Alley WR Jr, Mechref Y, Novotny MV (2009) Rapid Commun. Mass Spectrom 23:495–505 46. Raijmakers R, Kraiczek K, de Jong AP, Mohammed S, Heck AJ (2010) Anal Chem 82:824–832 47. Staples GO, Bowman MJ, Costello CE, Hitchcock AM, Lau JM, Leymarie N, Miller C, Naimy H, Shi X, Zaia J (2009) Proteomics 9:686–695 48. Staples GO, Naimy H, Yin H, Kileen K, Kraiczek K, Costello CE, Zaia J (2010) Anal Chem 82:516–522 49. Liu J, Chen CF, Tsao CW, Chang CC, Chu CC, DeVoe DL (2009) Anal Chem 81:2545–2554 50. Kulasingam V, Diamandis EP (2007) Mol Cell Proteomics 6:1997–2011 51. Gao M, Deng C, Yu W, Zhang Y, Yang P, Zhang X (2008) Proteomics 8:939–947 52. Wang N, Xie C, Young JB, Li L (2009) Anal Chem 81:1049–1060 53. Cho CK, Shan SJ, Winsor EJ, Diamandis EP (2007) Mol Cell Proteomics 6:1406–1415 54. Han G, Ye M, Zhou H, Jiang X, Feng S, Jiang X, Tian R, Wan D, Zou H, Gu J (2008) Proteomics 8:1346–1361 55. Delmotte N, Lasaosa M, Tholey A, Heinzle E, Huber CG (2007) J Proteome Res 6:4363–4373 56. Nakamura T, Kuromitsu J, Oda Y (2008) J Proteome Res 7:1007–1011 57. Lasaosa M, Delmotte N, Huber CG, Melchior K, Heinzle E, Tholey A (2009) Anal Bioanal Chem 393:1245–1256 58. Lasaosa M, Jakoby T, Delmotte N, Huber CG, Heinzle E, Tholey A (2009) Proteomics 9:4577–4581 59. Wörner M, Melchior K, Delmotte N, Hwang KH, Monostory K, Huber CG, Bernhardt R (2009) J Sep Sci 32:1165–1174 60. Song C, Ye M, Han G, Jiang X, Wang F, Yu Z, Chen R, Zou H (2009) Anal Chem 82:53–56 61. Zhu F, Kale AV, Cheryan M (2007) J Agric Food Chem 55:3843–3849 62. Zhang J, Xu X, Gao M, Yang P, Zhang X (2007) Proteomics 7:500– 512 63. Boersema PJ, Divecha N, Heck AJ, Mohammed S (2007) J Proteome Res 6:937–946 64. Link AJ, Eng J, Schieltz DM, Carmack E, Mize GJ, Morris DR, Garvik BM, Yates JR 3rd (1999) Nat Biotechnol 17:676– 682 65. Washburn MP, Wolters D, Yates JR 3rd (2001) Nat Biotechnol 19:242–247 66. Gonzalez-Begne M, Lu B, Han X, Hagen FK, Hand AR, Melvin JE, Yates JR 3rd (2009) J Proteome Res 8:1304–1314

Recent advances in micro-scale and nano-scale high-performance 67. Dai J, Shieh CH, Sheng QH, Zhou H, Zeng R (2005) Anal Chem 77:5793–5799 68. Motoyama A, Xu T, Ruse CI, Wohlschlegel JA, Yates JR 3rd (2007) Anal Chem 79:3623–3634 69. Wang F, Dong J, Ye M, Wu R, Zou H (2009) J Chromatogr A 1216:3887–3894 70. Andrews GL, Shuford CM, Burnett JC Jr, Hawkridge AM, Muddiman DC (2009) J Chromatogr B 877:948–954 71. Guzzetta AW, Chien AS (2005) J Proteome Res 4:2412–2419 72. Licklider LJ, Thoreen CC, Peng J, Gygi SP (2002) Anal Chem 74:3076–3083 73. Wang F, Dong J, Jiang X, Ye M, Zou H (2007) Anal Chem 79:6599–6606 74. Luo Q, Yue G, Valaskovic GA, Gu Y, Wu SL, Karger BL (2007) Anal Chem 79:6174–6181 75. Luo Q, Gu Y, Wu SL, Rejtar T, Karger BL (2008) Electrophoresis 29:1604–1611 76. Shen Y, Moore RJ, Zhao R, Blonder J, Auberry DL, Masselon C, Pasa-Tolić L, Hixson KK, Auberry KJ, Smith RD (2003) Anal Chem 75:3596–3605 77. Zhou H, Dai J, Sheng QH, Li RX, Shieh CH, Guttman A, Zeng R (2007) Electrophoresis 28:4311–4319 78. Mihailova A, Malerød H, Wilson SR, Karaszewski B, Hauser R, Lundanes E, Greibrokk T (2008) J Sep Sci 31:459–467

241 79. Bedani F, Kok WT, Janssen HG (2006) J Chromatogr A 1133:126–134 80. Lin S, Yun D, Qi D, Deng C, Li Y, Zhang X (2008) J Proteome Res 7:1297–1307 81. Cingöz A, Hugon-Chapuis F, Pichon V (2008) J Chromatogr A 1209:95–103 82. Liu Y, Lu H, Zhong W, Song P, Kong J, Yang P, Girault HH, Liu B (2006) Anal Chem 78:801–808 83. Li Y, Yan B, Deng C, Tang J, Liu J, Zhang X (2007) Proteomics 7:3661–3671 84. Stigter EC, de Jong GJ, van Bennekom WP (2008) Anal Chim Acta 619:231–238 85. Gao M, Zhang P, Hong G, Guan X, Yan G, Deng C, Zhang X (2009) J Chromatogr A 1216:7472–7477 86. Duan J, Liang Z, Yang C, Zhang J, Zhang L, Zhang W, Zhang Y (2006) Proteomics 6:412–419 87. Feng S, Ye M, Jiang X, Jin W, Zou H (2006) J Proteome Res 5:422–428 88. Krenkova J, Lacher NA, Svec F (2009) J Chromatogr A 1216:3252–3259 89. Ma J, Liu J, Sun L, Gao L, Liang Z, Zhang L, Zhang Y (2009) Anal Chem 81:6534–6540 90. Yuan H, Zhang L, Hou C, Zhu G, Tao D, Liang Z, Zhang Y (2009) Anal Chem 81:8708–8714