Extracting biomolecule collision cross sections from FT-ICR mass spectral line shape

Talanta 205 (2019) 120093

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Extracting biomolecule collision cross sections from FT-ICR mass spectral line shape

T

Yang Tanga, Dayu Lib,∗∗, Dong Caoc, Wei Xua,∗ a

School of Life Science, Beijing Institute of Technology, Beijing, 100081, China School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China c State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Science, Chinese Academy of Science, Beijing 100085, China b

ABSTRACT

To extend the ion structure analysis capability of Fourier transform mass spectrometry (FT-MS), both time-domain and frequency-domain methods have been developed to extract ion collision cross sections (CCS) from high resolution mass spectra in Fourier transform ion cyclotron resonance (FT-ICR) cells. In this study, a new frequency-domain method, namely the line shape fitting method, was proposed to calculate ion CCSs from FT-ICR mass spectra line shape. Besides experimental data, simulated data with precisely controlled signal to noise levels and decay factors were also applied to characterize this method. Compared with the linewidth correction method previously proposed by our group, this line shape fitting method is more tolerant to noise, data length, and sampling rate, thus providing more consistent results. More importantly, CCS measurements of angiotensin I, bradykinin, ubiquitin and cytochrome c show that the resolving power is improved with the new method.

1. Introduction Mass spectrometry is a powerful tool for analyzing chemical and biological compounds due to its excellent performance in speed, resolution, sensitivity, and accuracy [1–5]. It can also obtain the structure information of selected compounds through performing tandem MS, in which the fragmented ions were generated and analyzed. The fragmented ions under different methods of fragmentation provide different structure information, which can be used to narrow down the possible compounds [6–11]. Benefited from high resolution and excellent mass measurement accuracy, this method usually has satisfied results for determining the structure of small molecules. For large molecule analysis, such as proteins, the structure information, especially the threedimensional structure information, is more concerned. Unfortunately, fragmented ions usually cannot provide enough information for determining the structure of large molecules, and the three-dimensional structure information is missed in a mass spectrum in most cases. Conventionally, the three-dimensional structure information is obtained by techniques such as optical spectroscopy [11,12], nuclear magnetic resonance (NMR) [13–15] and X-ray diffraction [16–19]. Besides these technologies, ion collision cross sections (CCSs) could also be used for providing ion three-dimensional structure information [20–22]. Ion CCSs are majorly determined by ion mobility spectrometry

∗

(IMS). In IMS measurements, ion CCSs are calculated by ion mobilities in electric and gas dynamic fields [22,23]. With the increasing demands of ion structure analysis, different types of IMS and IMS-MS hybrid instruments have been developed to obtain both ion CCSs and fragmentation patterns [24,25]. The measurement of ion CCSs in ion cyclotron resonance (ICR) cells has been carried out since the 1960s [26,27]. These CCS measurements in ICR cells are based on measuring ion damping motions induced by ion-neutral collisions in an ICR cell. Two conventional collision models, the Langevin collision model [26,28] and the hard-sphere collision model [29], are used to describe ion-neutral collisions. A new ionneutral collision model, the energetic hard-sphere collision model, was developed to illustrate ion-neutral collisions in stronger magnetic fields and larger radius of ICR cells [30]. Based on these models, CCS measurements of crown ether ions and amino acids were realized in Dearden's group through measuring the frequency-domain peak width at elevated pressures (above 10−6 Torr) in a 4.7T ICR cell [31–33]. In order to obtain high mass resolution simultaneously, Mao et al. extended this method to biomolecules at lower buffer gas pressures (< 10−9 Torr) using an efficient time-domain data processing method, which was based on the energetic hard-sphere collision model [34]. It is found that the windowing effect can broaden mass peaks under low buffer gas pressures (< 10−9 Torr), because the pressure-induced ion

Corresponding author. School of Life Science Beijing Institute of Technology Haidian, Beijing, 100081, China. Corresponding author. School of Computer Science & Engineering Northeastern University Shenyang, 110819, China. E-mail addresses: [email protected] (D. Li), [email protected] (W. Xu). URL: http://www.escience.cn/people/weixu (W. Xu).

∗∗

https://doi.org/10.1016/j.talanta.2019.06.093 Received 6 May 2019; Received in revised form 19 June 2019; Accepted 26 June 2019 Available online 28 June 2019 0039-9140/ © 2019 Elsevier B.V. All rights reserved.

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motion decay is reduced and the finite data length in time domain would introduce additional peak width [35]. Such effect could be eliminated by frequency-domain linewidth correction method [36]. By applying the energetic hard-sphere collision model, ion CCS measurements have also been realized in an FT electrostatic linear ion trap and an orbitrap [37,38]. However, despite these progresses, resolving power of this method still need to be improved further to accurately distinguish compounds with close CCSs. In this work, a new data processing method, the line shape fitting method, was introduced to measure ion CCSs based on data from a 9.4 T FT-ICR instrument. Conventionally, width of a mass peak in an FT-ICR mass spectrum was typically used to calculate the ion decay factor. However, the peak width calculation is sensitive to signal to noise ratio (SNR) and frequency resolving power, which in turn affects the accuracy and resolving power of ion CCS measurements. The new method utilizes the shape of a mass peak to accurately calculate ion CCSs. The CCSs of bradykinin, angiotensin, ubiquitin and cytochrome c were measured and compared with the corrected linewidth method [36]. Results show that the line shape fitting method is more consistent for shorter transient time data and has more tolerance to the background noise. More importantly, CCS resolving power and accuracy could be improved.

spectrum line shape with the theoretical line shape calculated from Equation (2), and then the ion current decay factor (c) could be obtained. Finally, CCS of the target ion can be calculated from the decay factor (c) based on the energetic hard-sphere collision model [30]. 2. Experimental section Sample preparation Peptides (bradykinin and angiotensin I) and proteins (ubiquitin and cytochrome c) used in this study were obtained from Sigma-Aldrich (St. Louis, MO, USA). High purity solvents (methanol and water) were purchased from Fisher Scientific (Waltham, MA, USA). The peptides and proteins were dissolved in water first and then diluted in 50:50 Methanol:H2O with a final concentration of 1 μM and were analyzed by direct infusion at 1 μL/min for positive ion mode electrospray ionization (ESI). Experiments Experiments were performed on a custom-built 9.4 T FT-ICR mass spectrometer, which was introduced elsewhere. Here we briefly introduce the mass spectrometer and parameters for this experiment. The main components in the 9.4 T FT-ICR mass spectrometer are Velos Pro dual linear quadrupole, external ion trap, ion transmission rods, and ICR cell, which immersed in a 9.4 T magnet. The Velos Pro with ESI ion source provides high sensitivity, efficient ion isolation, multistage MS/MS capabilities, and modulation of the ion population sent to the ICR cell via automatic gain control (AGC). The external ion trap acts as an intermediate storage device during transfer of ions from the Velos Pro to the ICR cell. The ions were transferred from the external ion trap to the ICR cell through three quadrupole ion guides with low mass cutoff of m/z at around 150 Th. The vacuum is reduced from around 3 × 10−3 Torr of nitrogen bath gas in external ion trap to ∼1 × 10−10 Torr in the ICR cell. The ICR cell utilizes external shimming to closely approximate an ideal quadrupolar trapping electric field. The potential applied to two end cap electrodes were 2 V to trap ions in Z axial and the voltages on the shim rings were optimized accordingly. Transient image current data were collected for 6.114 s with 8388608 data points and zero-filled to 25165824 data points. Data were further processed using Matlab (MathWorks Inc.).

1.1. Theory and methods In a conventional FT-ICR experiment, all ions are excited to the same cyclotron radius by a broadband excitation electric signal [39]. After excitation, ion motion induced time-domain image current (I) was recorded, and a mass spectrum could be obtained by performing Fourier Transform. The amplitude of the time-domain image current would decay due to the presence of ion-neutral collisions. Based on the energetic hard-sphere collision model [30], the ion current amplitude would follow an exponential decay [35].

N (t ) = N0 e

ct I

(1)

(t ) = N (t ) cos( t )

in which N is the ion number in a coherent ion packet, c = nvσ is the ion current decay factor, n is the density of neutral molecules, v is ion velocity and σ is the CCS of an ion. Ion CCSs could be extracted from the ion current decay rate (or decay factor, c). Both time-domain [34] and frequency-domain [31,36] methods have been proposed to calculate this decay factor. The line shape fitting method proposed in this work is a frequencydomain method, which utilizes the peak shape in a mass spectrum to calculate the corresponding ion current decay factor. In a practical FTICR experiment, the time-domain ion current signal would be truncated due to the finite signal collection duration (T). This would cause a windowing effect on the frequency-domain mass peak [35,36]. For instance, after performing analytical Fourier Transform on Equation (1), the continuous magnitude-mode spectrum (C ( 0 ) ) could be expressed as [35,40–43],

C(

0)

=

(N0/2 ) × {1 2 1/2 [c 2 + ( 0) ]

2e

cT

cos[(

0) T ]

+e

3. Results and discussion The collision model To obtain the accurate CCSs of ions, ion motion decay must be dominated by ion-neutral collisions. Other factors, including electric and magnetic field inhomogeneity, need to be eliminated from CCS measurements. Furthermore, ions in FT-ICR cells may experience different types of ion-neutral collisions, including the Langevin, hard-sphere and energetic hard-sphere collision models. In brief, the Langevin collision model and hard-sphere collision model are suitable for describing low energy ion-neutral collisions; while the energetic hard-sphere collision model is accurate for ions with higher energies. In modern high resolution FT-ICR instruments with larger physical cell radii and stronger magnetic fields, ions typically have kinetic energies on the level of 10 keV. For example, after excited to a radius of 18.8 mm, a bradykinin ion (m/z = 530) has a kinetic energy of 5.6795 keV in the 9.4 T ICR cell; while a cytochrome c ion (m/z = 884) would have a kinetic energy of 23.8493 keV at the same working condition (Table S1 in Supporting Information). Therefore, the energetic hard-sphere collision model can be used to describe ion-neutral collisions, especially for ions with lower masses. For heavier ions, such as large protein ions, it may need even higher energies to dephase or fragment an ion from the coherent packet. In experiments, nonlinear field effects on ion motion frequency can be used to differentiate energetic hard-sphere collisions from Langevin and hard-sphere collisions [30]. It was found that ion cyclotron frequency is stable, indicating that the energetic hard-sphere collision model is appropriate in this work (Fig. S1 in Supporting Information). The line shape fitting method Conventionally, mass spectral linewidth has been used to calculate ion CCSs in the frequency-domain,

2cT }1/2

(2) and in which 0 is the ion cyclotron frequency. Fig. 1 provides a schematic flowchart of the line shape fitting method. First, zero-fill the sampled transient data with three zero-fillings [42,44]. Then, the magnitude-mode spectrum was obtained by applying fast Fourier transform (FFT) [45,46] to the time-domain ion current signal. As shown in Fig. 1, the peak shape of a truncated sinusoidal wave with exponential decay would have ripples besides the main peak. To calculate the ion CCS of a specific peak, peak center (maximum of the peak amplitude) was first determined, and data points whose amplitudes were larger than 20% of this maximum value were extracted. The theoretical peak shape was calculated using Equation (2). The Least Square method was used to fit the experimental mass 2

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Fig. 1. The schematic flowchart of the line shape fitting method.

Normalization

(a) 1 0.8

1

1

0.7071

0.319 Hz

0.8

0.9966 0.7046

0.323 Hz

1

0.9867

0.8

0.6976

0.6

0.6

0.6

0.4

0.4

0.4

0.2

333332.84

333333.79

0.2

Frequency (Hz)

(b)

1.2

Decay factor (c)

Decay factor (c)

Linewidth correction method Line shape fitting method

1 0.8

10

20

30 40 SNR (dB)

0.2

333332.84

Frequency (Hz) (c)

1.4

0.6

333333.79

333332.84

333333.79

Frequency (Hz)

1.4

Linewidth correction method Line shape fitting method

1.2 1 0.8

0.6

50

0.330 Hz

10

20

30 40 SNR (dB)

50

Fig. 2. (a) Simulated signals with the same decay factor (c = 1) and slightly different sampling frequencies. Left figure: the central frequency of a peak is sampled; middle figure: the center frequency of the same peak is quarter sampling step away from actual sampling points; right figure: the center frequency of the same peak is half sampling step away from actual sampling points. Calculated decay factors of the same simulated signal (c = 1) using two methods at different SNR ratios; (b) Data point sampled at the central frequency of a mass peak, (c) mass peak central frequency lies in the middle of two sampling points.

which measures the corrected peak width at half maximum [36]. However, the accuracy of the peak width measurement depends on frequency resolving power, which is a function of sampling rate, total number of sampling points, and location of sampling points with respect to ion motion center frequency. The linewidth correction method is affected by the location of sampling points at a given frequency resolution. The frequency-domain spectra of stimulated signals with the same decay factor (c = 1) and slightly different sampling frequencies are shown in Fig. 2a. When the center frequency of a peak is sampled, the calculated peak width at half maximum would be accurate (0.7071), and the measured line width would be 0.319 Hz as shown in Fig. 2a (left figure). When the center frequency of the same peak is quarter sampling step away from actual sampling points, the calculated peak width would be lower (0.7046) than the actual value (0.7071),

and the measured line width becomes 0.323 Hz as shown in Fig. 2a (middle figure). When the center frequency of the same peak is half sampling step away from actual sampling points, the calculated peak width is 0.6976, and the measured line width becomes 0.330 Hz as shown in Fig. 2a (right figure). This error in the linewidth correction method will increase at a lower sampling frequency rate. The most common procedure before FT processing is to zero-fill the sampled transient data. Although this procedure would increase computation time for the FFT process and would not avoid line shape distortion due to less points in the frequency-domain, it is a simple and convenient technique to reduce height error [40,44]. Due to the limitation of computation time and memory, it is common to zero-fill three times of the length of transient data [44], which would still induce error in linewidth correction method to calculate the decay factor. Since only 3

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the sampling points near or at the peak half maximum were used to calculate the decay factor, measurement errors are relatively large. In the line shape fitting method, sampling points over the whole mass peak would be utilized to measure the decay factor, which would improve the accuracy and reduce the measurement error. Noise is another important factor, which would cause variation and deviation of the estimated decay factor. Simulated ion image current data with different signal noise ratio (SNR) and a decay factor of 1 were generated to test these two data processing methods. The simulated data has a central frequency of 333.3332 kHz and a sampling rate of 2.7351 MHz. In the first case, central frequency point of the mass peak was exactly sampled as shown in Fig. 2b inset. In the second case, central frequency point of the mass peak was missed, and it lied in the middle of two sampling points as shown in Fig. 2c inset. As shown in Fig. 2b and c, the shift of the central frequency point from sampling points has little or no effect on the estimated decay factor when using the shape fitting method. However, different results were achieved when using the corrected linewidth method, indicating that more accurate and consistent results could be achieved using the shape fitting method. On the other hand, the shape fitting method has more tolerance to noise. For a mass peak with a SNR of 20 dB, an estimation error of 2.8% and a standard deviation (Std) of 4.9% could be achieved. In summary, the line shape fitting method is less sensitive to sampling method, image current data length and noise level, indicating that more robust and consistent ion CCS results could be obtained using this method. Fig. 3 compares the accuracy of decay factor measurements using the linewidth correction method and the line shape fitting method. In Fig. 3a, simulated signals without noise but different decay factors (c = 0.5, 1, 2) were used to test both methods. The data sampling rate is 2.7351 MHz and central frequency is at 327.1180 kHz for the simulated data. Increased estimation errors were observed with decreased decay factors and shorter data acquisition periods, especially for the linewidth correction method. With the truncation window decreases from 6.114 s to 0.5 s, the accuracy of estimated decay factor from the linewidth correction method decreases. When the decay factor is small (c = 0.5), results of the linewidth correction method are inaccurate even with a

6.114 s data acquisition period. In contrast, the line shape fitting method can accurately measure the decay factor even when the truncation window is only 0.5 s. Fig. 3b shows the calculated decay factors of three types of ions from experimental results, which are ubiquitin (12+), ubiquitin (9+) and angiotensin I (3+). Similar results were achieved. Ions with large decay factors (such as ubiquitin (12+)) were less affected by the truncation window. On the other hand, the estimated decay factors for ions with small decay factors (ubiquitin (9+) and angiotensin I (3+)) would start to have fluctuations when the truncation window is less than 2 s. Results show that the line shape fitting method is less sensitive to the value of the decay factor, as well as the size of the truncation window. Compared with data from IMS After characterization of the line shape fitting method, this method was used to analyze the MS data collected from the 9.4 T FT-ICR instrument. Peptides (bradykinin, angiotensin I) and proteins (ubiquitin and cytochrome c) were analyzed. Fig. 4a plots the correlation between the CCSs measured by the corrected linewidth and those determined by IMS experiments [47], while Fig. 4b plots the results obtained using the line shape fitting method. A better linearity (R2 = 0.9753) was achieved using the line shape fitting method compared to that (R2 = 0.9099) using the corrected linewidth method. Since the same set of FT-ICR experimental data was used, the improved linearity suggests that the line shape fitting method could reduce the ion CCS calculation error. Furthermore, resolving power of ion CCS measurements (mean/Std) could generally be improved using this line shape fitting method. As shown in Fig. 4c, similar or better resolving powers were observed for all cases, except for ubiquitin 7 + and 9 + ions. The improved resolving power is believed to be attributed to the improved robustness of the data processing method. 4. Conclusions In this work, a new frequency-domain method, the line shape fitting method was used to determine ion CCSs under a relatively low buffer gas pressure (∼10−10 Torr) in a 9.4 T FT-ICR MS instrument. CCSs and m/z ratios of ions can be extracted simultaneously from the mass spectrum data. Instead of using the width of a mass peak, the shape of a

Fig. 3. Measured decay factor by two methods at different truncation window lengths. (a) Simulated signals with different decay factors (0.5, 1, 2); (b) Experimental signals of angiotensin I (3+), ubiquitin (9+) and ubiquitin (12+). 4

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Fig. 4. (a) Ion CCSs obtained by the corrected linewidth method versus the results from ion mobility measurements; (b) Ion CCSs obtained by the line shape fitting method versus the results from ion mobility measurements; (c) The resolving power of ion CCS measurements using the line shape fitting method and the corrected linewidth method. Note: CCSs obtained by IMS for angiotensin I, bradykinin, ubiquitin and cytochrome c were taken from ref. 47.

mass peak was utilized to calculate the ion decay factor. Compared with the corrected linewidth method developed previously, this line shape fitting method has better tolerances to noise and the frequency resolving power. As a result, the line shape fitting method shows a higher resolving power in terms of ion CCS measurements and the ability to process signals with low SNR and short durations. We believe the improvement in CCS measurement accuracy and resolving power for large molecules is an important step towards the practical application of ion CCS measurements in FT-MS. Besides improvements of data processing methods, ion transient image current data collected on a FT-MS instrument with higher ion kinetic energies, minimized space charge effect and inhomogeneous field effect would also greatly improve ion CCS measurement accuracy and reliability.

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