Multidimensional performance assessment of micro pillar array column chromatography combined to ion mobility-mass spectrometry for proteome research

Analytica Chimica Acta 1086 (2019) 1e13

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Multidimensional performance assessment of micro pillar array column chromatography combined to ion mobility-mass spectrometry for proteome research €l Nys, Gae €l Cobraiville, Marianne Fillet* Gwenae Laboratory for the Analysis of Medicines, Center for Interdisciplinary Research on Medicines (CIRM), ULiege, Quartier Hopital, Avenue Hippocrate 15, 4000, Liege, Belgium

h i g h l i g h t s

g r a p h i c a l a b s t r a c t


Four chromatography systems are compared for proteomics research.
Micro pillar arrays columns (mPAC) provide microfluidics-based efficient separations.
Ion mobility improves peak capacities by a factor three.
mPAC combined to ion mobility is an attractive tool for proteomics.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 3 May 2019 Received in revised form 23 August 2019 Accepted 27 August 2019 Available online 30 August 2019

Micro pillar arrays columns (mPAC) are recent nanoflow liquid chromatographic (LC) systems featuring highly ordered pillars containing an outer porous shell grafted with C18 groups. This format limits backpressure and allows the use of extremely long separation channel (up to 2 m). In this study, we evaluated the use of mPAC in combination with ion mobility mass spectrometry (IM-MS). In IM-MS, ions are separated in gas-phase based on their size and charge. mPAC was compared to two other nanoflow systems and a state-of-the-art ultra-high-pressure liquid chromatograph (UHPLC). Performances in the four dimensions of information (LC, IM, MS and intensity) were calculated to assess the multidimensional efficiency of each tested system. mPAC proved to be superior to other nanoflow systems by producing more efficient peaks regardless of the gradient time employed which resulted in higher peak capacities (386 after 240 min gradient). In combination with IM, 3 times more peaks could be separated without loss of analysis time. Although UHPLC-ESI was superior from a chromatographic point of view, its sensitivity was rather limited compared to nanoflow LCs. On average, peaks in mPAC were 45-times more intense. Finally, mPAC combined to IM prove to enhance the proteome coverage by identifying two times more peptides than nanoflow LCs and ten times more than UHPLC. As a conclusion, mPAC combined to IM seems to be a suitable platform for discovery proteomics due to its high separation capacities. © 2019 Elsevier B.V. All rights reserved.

Keywords: Micro pillar array columns Ion mobility mass spectrometry Proteomics Microfluidics Peak capacity

1. Introduction

* Corresponding author. Laboratory for the Analysis of Medicines, Department of Pharmacy, CIRM, ULiege, CHU, B36, Quartier Hopital, Avenue Hippocrate 15, 4000, Liege, Belgium. E-mail address: marianne.fi[email protected] (M. Fillet). https://doi.org/10.1016/j.aca.2019.08.068 0003-2670/© 2019 Elsevier B.V. All rights reserved.

The current era of biology aims at understanding the complex mechanisms of living organisms at the molecular level. The study of proteins provides the link between the information biologically encoded in the genome (genotype) and the dynamic expression of

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this information in living organisms (phenotype) [1]. However, the human proteome is more complex than the human genome making it a great challenge for analytical chemistry to unravel. Two main strategies coexist for the identification of proteins, namely the “topdown” and “bottom-up” approaches. In “top-down” approach, proteins are analyzed under their intact forms. In “bottom-up” approach, proteins are first digested into peptides then identified using LC-MS/MS [2e4]. The “bottom-up” approach is far more popular due to the higher ionization efficiency of peptides and their relatively straightforward identification thank to their specific fragmentations after collision-induced dissociation [5e9]. However, the analysis of thousands of peptides requires continuous improvements of the separation systems to enhance peak capacity, sensitivity and sample throughput [10]. The achievement of very high separation efficiency has been a major quest since the beginning of LC and is still crucial for proteomics since there is a direct correlation between the peak capacity and the number of annotated peptides [3,11e14]. To achieve high separation efficiency, the employment of multidimensional LC (nD-LC) separations, where peptides are separated by two or more independent mechanisms has been widely studied [4,10]. In one-dimensional LC (1D-LC), the most straightforward approaches to improve separation efficiency are either to reduce the size of the particles or to increase the length of the column. Modern columns packed with sub 2-mm superficially porous particles (SPP) and operated at ultra-high pressure (UHPLC) permit to conduct separations with a very high peak capacity thanks to the reduced eddy diffusion and the improved mass transfer kinetics [15e18]. Although peak efficiency promotes sensitivity, the relatively high flow of rates in UHPLC compared to nanoflow LC platforms hampers ionization efficiency and subsequently reduces the proteome coverage. Nanoflow LCs are defined as systems employing columns with internal diameters (ID) inferior to 100 mm operated at the nl/min flow rates [19,20]. The main advantage of nano-LC is the enhanced sensitivity due to reduced radial dilution of chromatographic bands in columns with narrow ID and the employment of reduced mobile phase flow rates [20e22]. Since radial dilution is proportional to the square of the column radius, downscaling the column ID by a factor 2 will results in a 4-times increased in band concentration. This factor represents the theoretical gain in sensitivity when using electrospray-MS (ESIMS) if it was a pure concentration-sensitive system (like ultraviolet detectors) [23]. Although it is commonly admitted that ESI exhibits a concentration-sensitive behaviour, recent researches have shown that this behavior depended on the employed flow rate: it displays a concentration-sensitivity at high flow rates (1e1000 ml/min) and mass-sensitivity at lower flow rates. This phenomenon is the reason why although a 784-fold signal is expected when using 75 mm ID columns instead of 2.1 mm columns, a signal improvement of 50e100 is generally obtained. This can partly be explained by the fact that mass spectrometers are mass-flow-sensitive detectors: the signal is proportional to the amount of sample reaching the detector per unit of time and consequently higher flow rates should produce higher signal intensities in ESI-MS by increasing the number of ions produced per unit of time [24]. On the other hand, low flow rates should produce low signal intensities for the same reasons, which is not the case in practice [25]. In reality, the signal is higher at low flow rates due to the increased band concentration, but not as high as predicted due to the reduced number of ions produced per unit of time thereby showing a superior masssensitivity behaviour [25e27]. The fact that low flow rates tend to produce smaller ESI droplets containing only few molecules of analytes (thus having a higher probability of acquiring a charge) helps in increasing the signal intensity [25e27]. Nevertheless, the benefits provided by miniaturized LC systems

are still controverted as recent researches suggest that modern ionization technology allow to use larger internal diameter columns with higher flow rates without compromising the sensitivity. Sample-limited cases would benefit from the better robustness of these systems while the improved loading capacity (compared to nano columns) would be beneficial to studies where sample volume is not a limiting factor [28,29]. Highly ordered materials (under the form of arrays of micropillar) was proposed as an alternative to packed beads columns [30e32]. The decreased flow resistance made possible by the highly ordered chromatographic bed allows to increase the length of the columns (>50 cm) while maintaining the backpressure at acceptable levels (<200 bars) [11,33]. This system has recently been commercialized by the Belgian company Pharmafluidics under the name of micro pillar array columns (mPAC) which features highly ordered pillars grafted with C18 groups inside a 315  18 mm (width x height) channel. The 2 m long channel is packed in a 10 cm chip using specifically engineered flow distributors and turns that minimize the loss in efficiency caused by turns. The unique design of this system takes advantages of the nanoflow format, which provides the needed sensitivity, and the enhanced chromatographic performances provided by the extremely long separation channel [11,34,35]. As an alternative to nD-LC combinations, the coupling of LC with ion mobility (IM) is an interesting approach to comprehensively sample the first dimensional chromatographic peak and subsequently introduce a second dimension of separation prior the measurement of ions by the MS [36]. Three types of IM-MS are commercially available: time-dispersive, spatially dispersive and scanning instruments. Among time-dispersive instruments, drifttime IM-MS (DTIMS) is conceptually the simplest form of IM-MS hyphenation, since a low uniform electric field propels ions in a tube filled with a low-pressure buffer gas [36e39]. In DTIMS, an electric field (E) is used to drag the analytes through a buffer gas. The force exerted by the electric field on the analyte is exactly balanced by the friction with the buffer gas resulting in a steadystate analyte velocity [40]. By measuring the time (td) taken by an ion to traverse the length (l) of the mobility cell, the mobility of the ion (K) can be deduced via the following equation (1) [40].

K¼

vd t ¼ d E lE

(1)

Which can be converted in the following equation (2) as described by Revercomb and Mason [41] and Gabelica et al. [40,42].

3 K¼ 16

sffiffiffiffiffiffiffiffiffiffiffiffiffi 2p ze x mkB T NU

(2)

Where m is the reduced mass of the ion-gas pair (m ¼ mM)/ (m þ M), where m and M are respectively the masses of the ion and the particle of the buffer gas, kB the Boltzmann constant and ze the analyte charge. U is the collision cross section (CCS) of the analyte and its ability to undergo collisions with the buffer gas and is hence related to the size, charge and shape of the analyte. The principle guiding separations in IM is therefore differences in mobility inside the buffer gas [40,42]. Compact ions are less susceptible to collide with buffer gas (lower CCS) hence they travel faster in the drift cell [43]. In IM-MS, each ion takes a defined amount of time to traverse the drift cell. The combination of IM with LC-MS is made possible by the differences in timescales of measurements occurring in the components (seconds for LC, ms for IM and ms for MS) [44]. In this study, the mPAC system was evaluated in combination with IM-MS to resolve a complex proteome standard sample (E. coli digest). This system was compared to two other nanofluidic

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systems and the normal-flow standard UHPLC-ESI systems operated with state-of-the-art column technology. Systems were compared in terms of chromatographic performances. Then, the improvements provided by the IM separation was evaluated as well as the improvements in sensitivity provided by the nanoflow format. Finally, the proteome coverage supplied by each system is compared. 2. Materials and methods 2.1. Chemicals and reagents Acetonitrile (ACN), water (H2O), formic acid (FA) of ULC-MS grade were obtained from Biosolve (Valkenswaard, the Netherlands). Escherichia coli (E. coli) standards were purchased from Waters (Dublin, Ireland). E. coli digests were resuspended with a mixture of H2O/ACN/FA (85/15/0.1 v/v) and diluted with the same solvent. 2.2. Ultra-high-pressure liquid chromatography conditions Ultra-high-pressure liquid chromatography separations were performed with a 1290 infinity II system (Agilent Technologies, Waldbronn, Germany) on an Aeris C18 column (1.7 mm superficially porous particle size, 150  2.1 mm ID) (Phenomenex, Torrance, CA, USA). The separation was achieved in gradient mode with mobile phase A (H2O þ 0.1% FA) and B (ACN/H2O/FA, 90:10:0.1, v/v/v) at 0.3 mL min1. The gradient was as follows: 0e5 min, 3% B; 5e8 min, from 3 to 8% B; 8e38/68/128/248, from 8 to 38% B; þ5 min, from 38 to 90% B; þ5 min, 90% B; þ5 min, from 90 to 3% B. A 5 min post-time was applied before the next injection. 8 mL of samples were injected. 2.3. mPAC liquid chromatography conditions Nanofluidic chromatographic separations were performed on a 1200 series system consisting of a capillary pump, a nanoflow pump, an autosampler and a nano-LC-MS interface (Agilent Technologies). SilicaTip srayer needles were obtained from New Objective (Woburn, MA, USA). They were 5 cm long needles with an outer diameter of 360 mm and an inner diameter of 50 mm. The diameter of the tip was 8 mm. The separation was achieved on a 200 cm long micro pillar array column (Pharmafluidics, Ghent, Belgium). The channel was 315 mm wide and contained 18 mm high pillars with a diameter of 5 mm. The interpillar distance was 2.5 mm. The 300 nm porous outer shell layer was grafted with C18 groups. The separation was achieved in gradient mode with mobile phase A (H2O þ 0.1% FA) and B (ACN/H2O/FA, 90:10:0.1, v/v) delivered by the nanoflow pump at 300 nL/min. The enrichment column was a Zorbax 300SB-C18, 5  0.3 mm (length x ID), 5 mm particle size

3

(Agilent Technologies) mounted on a 6-port commutation valve. The separation was carried out in two steps: first, analytes were trapped on the enrichment column by the capillary pump delivering 97% of A (H2O þ 0.1% FA) and 3% of B (ACN/H2O/FA, 90:10:0.1, v/v/v) at 4 mL/min. Analytes were trapped for 8 min before the valve switched from enrichment to analysis where the separation was initiated in backflush mode by a gradient delivered by the nanoflow pump through the enrichment column to the mPAC columns. The gradient was as follows: 0e10 min, 3% B; 10e13 min, from 3 to 8% B; 13e43/73/133/253, from 8 to 38% B; þ5 min, from 38 to 90% B; þ5 min, 90% B; þ5 min, from 90 to 3% B; þ60 min, 3% B. The trap column returned to enrichment position after 110, 140, 200 or 320 min, depending on the gradient length. Injection volume was set at 8 mL. 2.4. NanoLC conditions The same configuration as previously described was employed. A Zorbax 300SB-C18, 150  0.075 mm (length x ID), 3.5 mm particle size was employed for the separation. The gradient was as follows: 0e10 min, 3% B; 10e13 min, from 3 to 8% B; 13e43/73/133/253, from 8 to 38% B; þ5 min, from 38 to 90% B; þ5 min, 90% B; þ5 min, from 90 to 3% B; þ 20 min, 3% B. The trap column returned to enrichment position after 70, 100, 160 or 260 min, depending on the gradient length. 2.5. LC-chip conditions The same configuration as previously described was employed, except for the source which was a ChipCube LC-MS interface (Agilent Technologies). The separations were achieved on an ultrahigh capacity chip integrating a 500 nl enrichment column and a 150 mm  75 mm ID analytical column both packed with a Zorbax C18 phase grafted on 5 mm particles. The LC-chip was designed to integrated a 6-port commutation valve, an enrichment column, an analytical column and a nano-electrosprayer. The same gradient was employed as for nanoLC. The conditions employed for LC-chip and nano-LC systems were directly transferred from those employed for mPAC to allow comparison between systems. Only the time after which the trap column returned back to its initial position and the post-run equilibration time was adapted (Table 1, Table 2). 2.6. Source conditions MS experiments were performed on a 6560 Ion mobility Q-TOF (Agilent Technologies, Waldbronn, Germany). The source for UHPLC-ESI was the Agilent Jet Stream ESI source and the conditions used were as followed. The capillary voltage was e 3500 V. The nozzle voltage was e 500 V. The instrument was operated in

Table 1 Summary of the studied systems. System name

Column base

Dimensions

Bed type

Size

Column Interstitial volume (ml)

Operational flow rate

Nominal pressure (at 3% B) (bars)

mPAC-nanoESI

Silicon

Length: 2 m, width: 315, height: 18 mm Length: 150 mm, diameter: 2.1 mm

Pillars (outer porous shell: 300 nm) Superficially porous beads (outer porous shell: 220 nm) Fully porous beads

Diameter: 5 mm, interpillar distance: 2.5 mm 1.7 mm

10.98

0.3 ml/min

169

353.28

0.3 ml min

5 mm

0.45

0.3 ml/min

81

Fully porous beads

3.5 mm

0.45

0.3 ml/min

151

UHPLC-ESI

Silica

LC-chip-nanoESI

Silica

NanoLC-nanoESI

Silica

Length: 150 mm, diameter: 75 mm Length: 150 mm, diameter: 75 mm

1

438

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Table 2 Peak capacity, peak capacity per meter and gradient slopes measured for each system and gradient times. Data are presented as average ± SD (n ¼ 3). Gradient time (min) Peak capacity 30 60 120 240 Peak capacity per meter 30 60 120 240 Gradient slope 30 60 120 240

mPAC-nanoESI

UHPLC-ESI

LC-chip-nanoESI

NanoLC-nanoESI

108.05 ± 4.58 157.54 ± 82.4 274.75 ± 31.66 381.51 ± 27.57

336.55 ± 14.24 401.64 ± 10.88 441.27 ± 9.61 505.62 ± 19.01

43.73 ± 1.00 93.57 ± 13.81 81.51 ± 1.43 109.94 ± 2.59

84.84 ± 13.17 88.58 ± 2.89 120.76 ± 17.63 131.33 ± 1.61

76.4 ± 3.24 111.4 ± 58.26 194.28 ± 22.39 269.77 ± 19.49

868.97 ± 36.76 1037.04 ± 28.08 1139.35 ± 24.82 1305.52 ± 49.08

112.91 ± 2.57 241.6 ± 35.65 210.47 ± 3.69 283.86 ± 6.68

219.07 ± 34 228.72 ± 7.47 311.81 ± 45.51 339.1 ± 4.17

0.37 0.18 0.091 0.046

0.0041 0.0027 0.0013 0.00052

0.031 0.016 0.0078 0.0039

0.064 0.032 0.016 0.0080

positive electrospray ionization. The nebulizer pressure was set at 35 psi. Nitrogen was used as drying (325  C, 8 L min1) and sheath (380  C, 12 L min1) gas. Skimmer and octopole voltages were 400, 65 and 750 V, respectively. The instrument was operated in 100e1700 m/z mode (high sensitivity) but optics were optimized for the transmission of 50e750 m/z ions. References masses (purine and HP-921) were constantly infused using the reference sprayer at 0.1 mL min1 at a concentration of 2 mM (ACN/H2O 95:5, v/v) each using an infinity II isocratic pump (Agilent Technologies). For nanoflow LC (mPAC, nanoLC and LC-chip), the sources were respectively the Nanospray Ion source and the ChipCube interface. The capillary voltage was set at e 2200 V. The drying gas temperature was set at 300  C and delivered at 4 L min1. References masses solutions at a concentration of 2 mM (ACN/H2O 95:5, v/v) were deposed on absorptive wick located in the source. Evaporation was kept constant for 24 h by the addition of 100 mL of fluorinert FC-70 obtained from Merck (Darmstadt, Germany). 2.7. Acquisition modes Data dependent acquisition (DDA) operation parameters were as follows. MS/MS criteria for fragmentation: intensity threshold: 3000 counts, relative threshold: 0.001%; isotope model: peptides, purity stringency: 100%; purity cutoff: 30%, isolation width: 4 Da. Maximum precursor per cycles: 5. MS spectra acquired at 8 Hz. MS/ MS spectra acquired at 5 Hz. Precursor selection based on scan speed with target: 25000 counts/spectrum. Active exclusion after 1 spectrum. Release time: 0.3 min. The collision energy formula was as follows: for doubly-charged ions, slope: 3.1; offset: 1; for triplycharged ions: slope: 3.6; offset: 4.8; for more than triply-charged ions: slope: 3.6; offset: 4.8. DIA experiments were performed in IM-MS mode. Buffer gas was nitrogen (purity > 99.99%). Alternating frame (MS and MS/MS) frequency: 1 Hz. Maximum drift time: 40 ms (24 IM transients per IM frame). Trap fill time: 38 ms. Trap release time: 100 ms. Trap funnel RF: 100 V. Drift tube entrance voltage: 1574 V. Drift tube exit voltage: 224 V. Rear funnel entrance voltage: 217.5 V. Rear funnel exit voltage: 45 V. Collision energy was applied every two frames based on drift times as follows: 0 ms, 8 V; 16 ms; 8 V, 40 ms, 60 V. 2.8. Evaluation of chromatographic parameters Tracer peptides (60) were used to retrieve chromatographic parameters from DDA analysis. Datafiles were opened in MassHunter Qualitative Analysis (Agilent Technologies) and individual extracted ion chromatograms (EICs) were generated at the MS level for each m/z value of the 60 tracer peptides with a tolerance of

20 ppm. The parameters extracted from these EICs were the peak area, height, peak width at half the maximum (FWHM) and peak width (at base). Generally, peak capacities are calculated using width at 13.4% (W4s) and not with FWHM. Since this value could not be directly extracted from the software, FWHM values were mathematically transformed in their 13.4% counterparts (W4s) [45e48] via the following equation (3).

W4s ¼



  pffiffiffiffiffiffiffiffiffiffiffiffiffi 4FWHM = 2 2 ln 2

(3)

W4s was used to calculate the peak capacities (Nc) via the following equation (4):

tL  t0  Nc ¼ 1 þ P W4s n

(4)

Where tL is the time after which the last ion is eluted, t0 the time after which the first ion is eluted, W4s the peak width at 13.4% and n the number of observations. The peak capacity per meter was calculated via the following equation (5):

Ncm

rffiffiffiffiffiffiffiffiffi 100 ¼ Nc L

(5)

Where L is the length of the column (in cm). Gradient slopes (G) were calculated according the following equation (6):

G ¼ Dc*

t0 tg

(6)

Where Dc represents the change in volume fraction of organic (expressed as decimal), tg represents the gradient time and t0 the dead time. This equation is derived from Ref. [12]. Optimal gradient times were predicted using the following equation (7):

tG ¼

k* SDc Vm F

(7)

Where tG is the gradient time (min), k* the target value of 5, Vm the interstitial volume of the column (ml), S the shape selectivity factor and F the flow rate (ml/min). This equation is derived from Ref. [49]. Vm was estimated using the following equation (8):

G. Nys et al. / Analytica Chimica Acta 1086 (2019) 1e13

 2 dc Vm ¼ p L*0:68 2

(8)

Where dc is the column diameter (mm) and L the column length (mm). S was calculated with the following equation (9):

S ¼ 0:25*MW 0:5

(9)

Where MW is the molecular weight of the analyte. In this case, S values were individually calculated for each of the 60 tracer peptides and averaged. The resolution of the mass spectrometer was measured for each m/z value of the 60 tracer peptides. The FWHM of the MS peaks were derived from the following equation (10).

MSResolution ¼

m W50

(10)

Where m is the mass considered and W50 the FWHM. The peak capacity of the mass spectrometry dimension was calculated using the following equation (11).

NcMS

Range  ¼ P W50 n

(11)

The resolution of the ion mobility dimension was measured for each m/z value of the 60 tracer peptides. The FWHM of the IM peaks were derived from the following equation (12) [50].

Dt W50

5

a 3 points smoothing was applied on the LC and the IM dimensions. The datafiles were then opened in IM-MS browser software (Agilent Technologies) where a 4D-ion mobility feature extraction algorithm (IMFE) was applied to identify peptide-like ions (charge state 2e7, intensity > 50 counts). Precursor ions were aligned with their respective product ions (max 25 peaks per MS/MS spectrum) based on their retention time (±10 s window) and drift time (±0.5 ms window). Peptide sequencing was realized using Spectrum Mill software by searching into the E. coli database (SwissProt). The following parameters were employed. Fixed modifications: carbamidomethylation of cysteines. Variable modifications: oxidation of methionine. Maximum missed cleavages allowed: 2. Mass tolerance for precursor and product ions: 20 and 50 ppm, respectively. Minimum matched peak intensity: 25%. Dynamic peak thresholding was employed. Only peptides having score >5 and scored peak intensity (SPI) > 50% were exported for statistical analysis. Relative hydrophobicity (HR) and isoelectric points (pI) of peptides was calculated by the ProtPI version 2.2 online tool (www.ProtPi.ch). 2.10. Statistics Statistical interpretations were realized using GraphPad Prism (La Jolla, CA, USA). Comparisons were done using Kruskal-Wallis tests followed by Dunn's multiple comparisons. P-values inferior to 0.05 were considered as statistically significant. 3. Results and discussion 3.1. Design of the study

The improvement provided by IM was calculated using the following equation (16):

In this study, the performances reached by the mPAC system were compared to those obtained by two other nanofluidic devices: the LC-chip (a fully integrated device comprising an enrichment column, an analytical column and an ESI sprayer) and a nano-LC column, interfaced with a nanoESI needle to the MS. The systems were coupled to a drift tube ion mobility mass spectrometer through a nanoESI source (employing nanospray needles) for mPAC and nanoLC and through the ChipCube® interface (handling the commutation valve integrated on the LC-chip and the sprayer) for the LC-chip. A state-of-the-art UHPLC system was added for comparison. It was interfaced with MS through the Agilent Jet Stream ESI source (using super-heated sheath gas to help vaporization and ionization). A summary of the characteristics of the employed systems is given in Table 1. These four systems were evaluated using 4 different gradient times (30, 60, 120 and 240 min) to assess their chromatographic performances. Moreover, their performances in the other dimensions of information (IM, MS and signal intensity) were also investigated. Finally, the proteomic performances (number of annotated peptides and proteins) of each system were evaluated. The aim of this study was to quantify the benefits provided by the use of the mPAC system in combination with ion mobility mass spectrometry compared to other available systems.

IM improvement ¼ ðNcLCIMMS Þ=ðNcLCMS Þ

3.2. Evaluation of chromatographic performances

IMResolution ¼

(12)

Where Dt is the drift time (ms) and W50 the FWHM. The peak capacity of IM was calculated using the following equation (13).

Dtl  Dt0  NcIM ¼ P W50 n

(13)

Where DtL is the drift time of the last ion (ms) and Dt0 the drift time of the first ion (ms). The peak capacity of LC x MS was calculated using the following equation (14):

NcLCMS ¼ NcLC * NcMS

(14)

The peak capacity of LC x IM x MS was calculated using the following equation (15):

NcLCIMMS ¼ NcLC *NcIM * NcMS * orthogonality factor

(15)

(16)

2.9. Identification of peptides DDA files were imported into Spectrum Mill software (Agilent Technologies) for the identification of peptides. DIA files were reprocessed via IM-reprocessor and then opened in PreProcessor (Pacific Northwest National Laboratory, Richland, WA, USA) where

In order to evaluate the chromatographic performances of each tested system, 60 tracer peptides spread across a wide range of mass, isoelectric point (pI) and relative hydrophobicity (RH) were selected from the proteome of E. coli to reproduce real-life conditions of proteomic experiments (regarding the complexity of the sample and the wide range of concentration). Their sequences and main physicochemical parameters are listed in Table S1.

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Each chromatographic system was evaluated using four gradient times (ranging from 30 to 240 min) to assess their respective performances. The broadening of chromatographic peaks (measured at 13.4%) in mPAC was not significant for gradient lengths up to 120 min (peak width 0.43 ± 0.14 min, average ± SD) (Fig. 1A). The peak width significantly increased when switching to 240 min gradients (peak width 0.62 ± 0.25, average ± SD) (p < 0.05). Nevertheless, chromatographic peaks in mPAC remained significantly thinner compared to those obtained with other nanofluidic systems regardless of the gradient employed. For instance, after 240 min long gradient, peak widths of 1.87 ± 0.52 and 1.86 ± 0.57 min were obtained for LC-chip and nanoLC, respectively (average ± SD). Although slightly larger, peaks widths in mPAC were not significantly different from those obtained using conventional UHPLC (0.52 ± 0.29, average ± SD) thus showing that mPAC reached chromatographic performances comparable to state-of-the-art sub-2 mm SPP UHPLC. However, peak symmetries were better in UHPLC

than in mPAC since more tailing was experienced in the latter (symmetry factor 1.4 ± 1.0 and 2.0 ± 1.4 for UHPLC and mPAC, respectively, average ± SD) (p < 0.01) (Fig. 1B). The peak capacity (Nc) was estimated using the effective separation window and the average peak width at 13.4% (W4s) (equation (4)). Peak capacities were experimentally measured at half the maximum height (FWHM) and subsequently transformed in 4s which corresponds to a resolution of 1 between consecutive peaks [51,52]. This approach was employed as FWHM might result in overestimated peak capacities. The system producing the highest Nc was the UHPLC (505 ± 19, average ± SD) operated with a 240 min long gradient (Fig. 1C, Table 2). mPAC could separate 381 ± 28 (average ± SD) peaks in the same conditions. By comparison, nanoLC and LC-chip could respectively separate 110 ± 3 and 131 ± 2 peaks in the same conditions. Peak capacities calculated with values measured at FWHM are presented in Table S3 for comparison. Since peak capacity depends on the length of the column, the peak capacity extrapolated

Fig. 1. Chromatographic performances at different gradient times. (A) Peak width (at 13.4%) (B) Symmetry factors. (C) Peak capacity at 13.4% (Nc). (D) Productivity. (E) Repeatability of retention time measurements. (F) Repeatability of peak area measurements. A, B, C and D are presented as average ± SD (n ¼ 3). E and F are presented as median of RSDs (n ¼ 3).

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per meter unit provide a fairer assessment of the separation capabilities of each column [52]. The peak capacity per meter obtained by the tested columns were respectively 270 ± 19, 284 ± 7, 339 ± 4 and 1305 ± 49 for mPAC, LC-chip, nanoLC and UHPLC (average ± SD, 240 min gradient) (Table 2). These results should be discussed with regard to the gradient slopes. Indeed, mPAC is a long column (2 m) operated at nanoflow (300 nl/min) thus the column interstitial volume (10.98 ml, 36.6 min) affects the gradient slopes, which affects the retention factor (k*) and ultimately the peak width. As a consequence, short gradients (such as 30 or 60 min) are counterproductive with such a long column as the peptides quickly reach a state of no partitioning with the stationary phase so the last part of the column remains unemployed and only adds to peak broadening. Long columns benefit more from longer and shallower gradients which permit to maximize interaction between the analytes and the stationary phase. The gradient slopes for 240 min long gradients were 0.046, 0.0080, 0.0039 and 0.00052 for mPAC, LC-chip, nanoLC and UHPLC, respectively (Table 2). These results indicate that the gradient could even be prolonged for the mPAC system to reach higher peak capacities, especially since peak broadening occurring with long gradients is limited with this system. On the contrary, prolonging gradient times for other systems will be detrimental to separation since peaks have already significantly broaden (compared to 120 min gradient) thus longer gradients will not improve peak capacities. The predicted optimal gradient length to achieve an optimal retention factor (k*) of 5 is 642.8 min for the mPAC (keeping the flow rate and the proportion of organic solvent delivered constant) (equation (7)). For other systems, optimal retention is already reached with gradient lengths of 26.3 min (LC-chip and nanoLC, column volume of 0.45 ml) and 20.7 min (UHPLC, column volume of 353 ml). Further prolonging the gradient did not considerably improve resolution between peaks, except for the LC-chip where 60 min gradient was found to produce a higher peak capacity than with 30 min gradient (43.73 ± 1.00 and 93.57 ± 13.81 for 30 and 60 min, respectively). In the light of these results, we can conclude that peak capacity of mPAC would significantly benefit from longer gradients while other systems have already reached their limits. On the other hand, long gradients (642.8 mine10.7 h) seem rather unrealistic for routine proteome analysis but the added-value in terms of separation capacity might be of interest for projects requiring a very high separation power. The superiority of mPAC compared to other nanoflow systems comes from the pillar configuration which allows the use of 2 m long columns (favourable to the “L” term of the equation of van Deemter) and also might act on the “H” term of the same equation by reducing the probability of existence of multiple flow paths (“A” term) and on the "C" term, via the component related to the stationary phase (Cs) [53e55]. Indeed, the outer porous shell surrounding the solid-core pillars is thin (300 nm) hence molecules are not able to diffuse deeply into the stationary phase, thereby limiting peak broadening (similarly to superficially porous particles, such as those in the Aeris® column employed for UHPLC experiments where the porous shell has a thickness of 220 nm). Moreover, in packed beads columns, the linear velocity of the mobile phase is higher in the centre of the column due to a nonuniform distribution of the mobile phase at the head of the column and the side wall effect (related to the inability of packing material to form a closed structure with the rigid walls). Both phenomena contribute to the broadening of chromatographic peaks. On the contrary, the mPAC has been engineered to incorporate flow distributors to ensure a uniform delivery of the mobile phase through the chromatographic bed [56,57]. The region between the outer walls and the nearest row of pillars has been

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optimized to minimize the side wall effect [57,58]. As a consequence, differences of linear velocities between outer and inner regions are limited in the mPAC which ultimately results in sharper peaks regardless of the gradient time. The inverse of Nc (productivity) represents the number of theoretically separable peaks per unit of time. The highest productivity was obtained with a 30 min gradient on the UHPLC which was able to separate up to 11.2 ± 0.5 peaks per minute. With the same gradient, mPAC system separated 3.6 ± 0.1 peaks per minute (Fig. 1D). Finally, mPAC provided extremely repeatable retention times (RT) regardless of the gradient time employed (median 0.07% RSD, n ¼ 60) (Fig. 1E, S1). Similar results were obtained with UHPLC where variations of RT never exceeded 0.05% RSD. On the contrary, variations of RT in nanoLC and LC-chip were measured with 0.30 and 0.63% RSD for 240 min gradients, respectively. Separation efficiencies could be further improved by using higher temperatures or higher flow rates, although the latter might result in lower MS intensities [29]. As a conclusion, mPAC reached better chromatographic performances than other nanoflow systems. These improvements were obtained thanks to the length of the column but at the cost of longer analysis time since the full potential of mPAC was only reached using long and shallow gradients. 3.3. Evaluation of intensity improvements From a chromatographic point of view, UHPLC-ESI-MS achieved higher peak capacities than nanoflow devices, but it did not tell much about the advantages provided by the nanoflow format, especially concerning the sensitivity. Compared to conventional flow used in UHPLC, nanoflow systems benefit from reduced internal diameters and flow rate which both resulted in sensitivity improvements. To assess the improvements, the peak areas of the 60 tracer peptides were determined and compared among the systems. Peak areas were chosen over peak height since they are less affected by peak shape distortions and thus ensure better quantitative estimations. For instance, the median peak area measured for the 60 tracer peptides was 8.9  104 for UHPLC, whereas it was 1.6  105, 2.7  105 and 1.0  106 for nanoLCnanoESI, mPAC-nanoESI and LC-chip-nanoESI, respectively (Fig. 2 and S2). Values of intensity improvement obtained with peak height as metric are presented in Table S4. An improvement factor (representing the median increase in peak area for a given peptide compared to UHPLC-ESI) was introduced as a parameter in the following 3D equation (equation (17)).

3Defficiency ¼ NcLC * NcMS *intensity improvement

(17)

Improvements in intensity were 26.8, 45.7 and 167.4 for nanoLC,

mPAC and LC-chip, respectively compared to UHPLC-ESI (240 min gradient time, median values). The sensitivity provided by systems operating in nanoflow provides an overall 3D efficiency superior than what was obtained with UHPLC-ESI. This is crucial since the ultimate goal of proteomics is to identify proteins which concentrations may cover a wide range, with the most interesting proteins rarely being the most abundant ones. Both systems functioning with nano-ESI needles (nanoLC and mPAC) achieved similar performances in term of sensitivity improvements (around 40 times compared to UHPLC-ESI). The fully integrated system (LC-chip) provided intensity improvements four times greater. Since there was only a slight difference in terms of separation performances between LC-chip and nano-LC (peak capacity of 109.44 ± 2.59 and 131.33 ± 1.61 after 240 min gradient, respectively), we believe that the difference observed was attributable to the different design of

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the nanoESI interface. The absence of connectors in the LC-chip (due to the fully-integrated design) made it virtually free of dead volumes after the analytical columns. On the contrary, the nanoLC column was interfaced to the nano-needles with connectors. The chromatographic bands eluting from the nanoLC column might therefore suffer from dead volume and cause them to be less concentrated to than those eluting from the LC-chip, explaining the differences in terms of signal intensity. The design of the LC-chip spray tip might also promote the generation of finer droplets. The LC-chip spray was also more stable which resulted in less variations of peak areas (Fig. 1F, S3). A table summarizing the performances of each system in every dimension is presented in Table 3.

3.4. Improvements provided by ion mobility separation Drift tube ion mobility provides an additional gas phase separation dimension in which coeluting molecules can be separated based on their size-to-charge ratio. In practice, ions are driven in the tube by the force exerted by a constant electrical field. While drifting, they encounter collisions with the buffer gas. The balance between the electrical force (pushing forward) and the friction with the gas (pushing backwards) results in a velocity [40,42]. The parameters driving separations in IM are the charge of the ion and its shape, as compact ions will encounter collisions less frequently. LC and IM can therefore be considered as orthogonal techniques since

Table 3 Multidimensional efficiencies for each system and gradient time tested. Color scale encodes the greatest (red) and lowest (blue values) values for each column. Nc MS and Nc IM were respectively set at 29009 and 23.8.

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extent the measured Dt values differ from the theoretical values. An absence of difference would signify that IM did not provide any additional separation space as compared to MS alone. Only the IM x MS comprising 95% of the ions was took in account thereby rejecting outlier ions that might had led to an overestimation of the IM x MS space. The factor was calculated for each chromatographic system because they were interfaced with the MS through different source types which may create differences in charge state distributions and thus impact the way the ions drift. For mPAC-nanoESI, 95% of ion residuals was comprised between 6.96% (ions drifted slower that predicted) and þ5.70% (ions drifted faster than predicted), indicating that 12.6% of the IM x MS space was effectively used for separation. Results are shown in Fig. 3. The peak capacity of LC x IM x MS was 381  29009 x 23.8  0.126 ¼ 3.31  107 theoretically separable peaks (equation (15)). According to equation (16), the improvement in separation capacity provided by IM was 3.31  107/1.10  107 ¼ 3.03. As a consequence, thanks to the IM, three times more peaks can be separated without any loss in analysis time. This is consistent with research previously conducted by Ruotolo et al. where an improvement factor between 1 and 5 had been calculated for another IM system [59]. Finally, the performances of each systems in the four dimensions of information were evaluated using the following formula (equation (18)):

4Defficiency ¼ NcLC * NcMS *intensity improvement*NcIM * orthogonality factor Fig. 2. Peak area of each tracer peptide (consecutive replicates averaged, n ¼ 3). Data are sorted by m/z value (lowest at the top and highest at the bottom). Data are presented as log10 scale. Blanks represent missing values (peak not detected).

different physicochemical parameters are driving the separations. Previously, Causon et al. calculated that IM held the potential to improve peak capacities by a factor 36, assuming a usage of 100% of the separation space [36]. However, there is a significant overlap in the separation possibilities offered by IM and MS when operated in combination due to the presence of the charge (z) and mass (via the reduced mass term “m”) in equation (2). As a consequence, IM and MS are not orthogonal and an orthogonality factor must be considered to calculate the improvement provided by LC x IM x MS compared to LC x MS [36]. The resolution of the MS was measured as 15298 ± 2090 (average ± SD, n ¼ 60). Consequently, the MS peak widths at half the height (W50) w 0.055 ± 0.0086 via equation (10). Considering the operation range of the instrument (100e1700 m/z), the MS peak capacity (NcMS) was 1600/0.055 ¼ 29009 theoretically separable peaks in the MS dimension (equation (11)). Coupled to mPACnanoESI operated with a 240 min long gradient, a total of 381  29009 ¼ 1.10  107 theoretical peaks were separable (equation (14)). Similarly, the resolution of the IM dimension was calculated as 34.89 ± 9.89 (average ± SD, n ¼ 60) via equation (12). Consequently, the average W50 was 0.88 ± 0.27. Considering an effective drift window of 20.82 ms (Fig. 3), the peak capacity of IM (Nc IM) was calculated as 20.82/0.88 ¼ 23.8 theoretically separable peaks in the IM dimension (equation (13)). This result is consistent with previous estimations [36]. The IM x MS orthogonality factor was calculated as described by Ruotolo et al. [59,60]. First, the ions were nested by charge state and their drift times were plotted against their m/z values. A linear regression model was employed and this model was used to extrapolate the theoretical drift times (Dttheory). The differences between Dtmeasured and Dttheory were calculated to assess to which

(18)

Multidimensional efficiencies of each tested systems are shown in Fig. 4 and Table 3. Peak capacity improvements were higher when combined with UHPLC-ESI than for nanoflow systems, which could be caused by the absence of enrichment column using UHPLC therefore allowing a more heterogenous population of ions to enter the MS. 3.5. Consequences for proteomics experiments Although the previous results allowed to compare the performances of the tested system among them, the ultimate of goal of proteomics is to identify peptides and proteins. Two acquisition modes were tested (DDA and IM-DIA) in combination with the four different LC systems and gradient times in order to yield the highest number of identifications (peptides and proteins). Briefly, DDA relies on the isolation of a precursor ion (based on ion picking algorithm relying on abundance criteria) by the quadrupole for MS/MS experiments. However, DDA suffers from limitations that hamper its performances, especially caused by its incapacity to effectively select low abundant peptides for fragmentation due its selection based on intensity. On the contrary, IM-mediated DIA permits to identify peptides by alternating between low and high collision energies without isolation of precursor ions beforehand. The link between the precursor ion and its products is subsequently reconstructed based on retention time and drift time. Thanks to the absence of ion picking criteria, a wide dynamic range of proteins can be identified with only a limited bias towards highly abundant ones. As shown in Fig. S5, the increase of gradient length to 240 min was particularly profitable for mPAC operated either in DDA or DIA, with respectively 1546 (3.04 times more than with 30 min gradient) and 5379 (3.81 times more than with 30 min gradient) peptides identified after injection of 1 mg of E. coli digest, respectively corresponding to 854 and 2550 proteins. The increase in gradient length was less profitable for other systems since the highest number of identified peptides was obtained after 60 min

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Fig. 3. IM x MS orthogonality factor. Top graph: MS density plot (10 m/z bins). Right graph: IM density plot (0.25 ms bins). Maximum and minimum drift time obtained are indicated by arrows. Grey line represents the linear regression between IM and MS. Blue shading represents the space comprising 95% of ions. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

gradient for nanoLC (754) and LC-chip (971) and 30 min gradient for UHPLC (547). Increasing the gradient resulted in a significant diminution of identifications. For DIA, the optimal conditions were obtained after 240 min long gradient for both nanoLC and LC-chip, although the increase in identifications was smaller than what was obtained with mPAC (1.14 and 1.09 times more peptides, respectively). Although peak heights were much higher in LC-chip than in mPAC (Fig. 2), more proteins were identified using mPAC compared to LC-chip (2551 and 1475, respectively with a 240 min gradient in DIA mode). This might be explained by the better separation capacities offered by mPAC compared to LC-chip as illustrated by their respective peak capacities (381 ± 28 and 131 ± 2). Indeed, greater separation capacities help resolving co-eluting peptides which ultimately result in more identifications. It has previously been demonstrated by Hsieh and co-workers that a positive correlation existed between peak capacity and number of peptides annotated [14]. For UHPLC, 60 min long gradient yielded the highest number of peptides (591). Noticeably, the increase of peak capacity was more profitable to DIA than to DDA mode (for mPAC) in terms of number of annotations. For instance, 1409 and 5379 peptides were respectively identified using 30- and 240-min gradients with DIA while 508 and 1546 peptides were identified using DDA. Since the scan rate of DDA was fixed during the study, it had more opportunities to

sample unique peptides with longer gradients since they were better separated. One would therefore expect a higher dependency of DDA toward the chromatographic separation, while DIA, which does work by sequentially isolating precursors, would be less affected by chromatographic conditions. This could be partly explained by the reduction of ion suppression phenomenon due to the improved separation capacities. Perhaps precursor and product ions alignment algorithm performed better for the same reason. Interestingly, the number of identified peptides with mPAC followed by DIA acquisition was well correlated with the evolution of 4D efficiency hence showing that improving the technical performances of the LC-MS systems resulted in improvements in proteomic coverage (Fig. 5). This is well in accordance with previous studies where a direct correlation between the increase in peak capacity (in the chromatographic dimension) and the increase in identifications had been highlighted [3,13]. However, the ability to achieve high sensitivity is as important as producing high peak capacities, particularly for proteomics due to the wide concentration range. The mPAC-nanoESI system is therefore an ideal candidate for proteomics since it combines high peak capacities (as compared to other nanoflow systems) and high ionization efficiency (compared to UHPLC-ESI). The addition of IM not only permits to separate 3 times more peaks per unit of times (without increasing the analysis time) but also allows to use IM-mediated DIA, which improves the proteome coverage. The benefits of

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Fig. 5. (A) Evolution of trends of peptide identifications in DDA (black trace) and 3D efficiency (LC x MS x Intensity) (grey dashed trace, refers to right axis). (B) Evolution of trends of identifications in DIA (black trace) and 4D efficiency (LC x IM x MS x Intensity) (grey dashed trace, refers to right axis). Results shown only with mPACnanoESI. Data are presented as average ± SD (n ¼ 3).

chip (Fig. 6, Table S2). It is probable that the UHPLC needs more material to achieve such coverage due to its inherent lower sensitivity, which is not always possible in proteomics since samples are often volume-limited. However, although nanoflow chromatography has risen as standard for proteomics, recent research has proven that conventional flow chromatography may have been overlooked by the proteomics community. For instance, Len co et al. has shown that a 250  1 mm (length x ID) column operated at 68 ml/min with a 60 min gradient could outperform nanoflow chromatography in terms of proteome coverage if every parameter (column temperature, acid modifiers, charge modifiers) is carefully fine-tuned [29]. In the future, it could be interesting to compare the results obtained by microflow LC to those obtained with our nanoflow LC systems. Fig. 4. Multidimensional efficiencies of LC systems and their evolution with gradient times. (A) LC x MS peak capacities (2D efficiency), (B) LC x MS x intensity (3D efficiency), (C) LC x IM x MS x intensity (4D efficiency). Data are encoded as log10 and are presented as average of consecutive replicates (n ¼ 3).

using both acquisition modes (DIA and DDA) coupled to mPAC are illustrated in Fig. 6, where the total number of unique peptides and proteins identified by DIA and DDA after injection of increasing quantities of E. coli digest are shown. When increasing ten-fold the quantity of E. coli digest injected, the total number of identified peptides rose by a factor 1.2 for mPAC, 1.8 for LC-chip, 1.7 for nanoLC and 5.6 for UHPLC. In terms of proteins, 2950 were already identified with mPAC after injection of 1 mg while it required the injection of 10 times more to reach comparable coverage with the LC-

4. Conclusion In this study, the performances of mPAC system in combination with ion mobility-mass spectrometry were evaluated. mPAC outperformed other nanofluidic systems, with a relatively low broadening of the chromatographic peaks even when submitted to very long gradient times thus resulting in great peak capacities. However, the performances reached by mPAC in terms of peak capacity did not reach those obtained by the state-of-the-art UHPLC column, although, unlike other systems, mPAC would benefit from longer gradient times (>240 min). Besides, the nanoflow format allowed improvements in terms of signal intensity ranging from 26.8- to 167.4-fold compared to the UHPLC-ESI systems. The additional gasphase separation offered by ion mobility (LC x IM x MS) was evaluated and revealed that between 3 and 5 times more peaks can be

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Fig. 6. Number of unique peptides (A) and proteins (B) identified by each LC systems after injection of increasing quantities of E. coli digest on-column with the best gradient (see Table 2 in supplementary data). Results acquired using DIA and DDA modes were combined. Data are presented as average þ SD (n ¼ 3).

separated as compared to when LC x MS is employed alone. The four dimensions of information (LC x IM x MS x intensity) were combined to assess the 4D performances of each system. The evolution of the 4D efficiency was well correlated to the number of identified peptides at each gradient time hence indicating that when improvements were provided by the analytical systems (for instance upon increase of gradient length), improvements were observed in practice in proteome coverage. As a conclusion, mPAC is a promising candidate for discovery proteomics due to its high peak capacity for a nanoflow device. The coupling of this platform with ion mobility triples the peak capacity without losing time, which ultimately results in a better proteome coverage. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements Authors thank the National Fund for Scientific Research (FNRS) on Fre de ricq for the financial support (G.N.) and the Fondation Le provided. Research grants from Walloon Region of Belgium, EU Commission (FEDER-PHARE project) and FNRS (CHIMIC EOS project) are gratefully acknowledged. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.aca.2019.08.068. References [1] M. Gstaiger, R. Aebersold, Applying mass spectrometry-based proteomics to genetics, genomics and network biology, Nat. Rev. Genet. 10 (9) (2009) 617. [2] C.-H.W. Chen, Review of a current role of mass spectrometry for proteome research, Anal. Chim. Acta 624 (1) (2008) 16e36. [3] L. Nov akov a, et al., High-resolution peptide separations using nano-LC at ultra-high pressure, J. Sep. Sci. 36 (7) (2013) 1192e1199. [4] Q. Wu, et al., Recent advances on multidimensional liquid chromatographyemass spectrometry for proteomics: from qualitative to quantitative analysisda review, Anal. Chim. Acta 731 (2012) 1e10. [5] G. Chen, B.N. Pramanik, Application of LC/MS to proteomics studies: current

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