MS System Using a Nonporous

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Development of a nanoLC-MS/MS system using a nonporous reverse phase column for ultrasensitive proteome analysis Yusuke Kawashima, and Osamu Ohara Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b03382 • Publication Date (Web): 12 Oct 2018 Downloaded from http://pubs.acs.org on October 13, 2018

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Analytical Chemistry

Development of a nanoLC-MS/MS system using a nonporous reverse phase column for ultrasensitive proteome analysis

Yusuke Kawashima1,2, Osamu Ohara1,2,*

1

Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama,

Kanagawa 230-0045, Japan 2

Department of Genome Research and Development, Kazusa DNA Research Institute, Kisarazu, Chiba

292-0818, Japan

* Corresponding author: Osamu Ohara, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehirocho Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. Tel: +81-45-503-9696, Fax: +81-45-503-9694, Email: [email protected]

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Abstract The design of proteome projects is often restricted by the available amounts of sample, and thus there has been much effort to date to improve the sensitivity of proteome analyses. We have developed a new ultrasensitive nanoLC-MS system for the proteome analysis of microscale samples using a nonporous reverse phase column. Although nonporous particles have low binding capacity due to low functional group density, we found that the use of C30 particles compensated for this disadvantage and nanoLC-MS proteome analyses of microscale samples using a C30-packed column exhibited superior performance compared to the use of porous C18 columns. This system enabled us to identify 3278 and 1407 proteins from 1000 and 100 HEK293F cells, respectively. The results showed that the use of nonporous columns solved problems intrinsic to the analysis of microscale protein samples.

Keywords: nonporous C30 column, nonporous particle, ultrasensitive proteomics, nanoscale sample, nanoLC-MS/MS


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Large-scale proteome analysis is currently possible due to technological advances in comprehensive proteome analysis systems, such as the evolution of MS technologies and the improvement of separation/fractionation technologies.1-4 These advanced proteome analysis systems have been applied to various biological samples and clinical specimens, and have successfully demonstrated their utility in identifying biomarkers and exploring complex biological phenomena such as disease mechanisms.5-8 However, rare cell types and small tissue slices, collected using fluorescence-activated cell sorting and laser capture microdissection techniques, respectively, are not amenable to satisfactory proteome analysis due to sensitivity issues and thus more sensitive proteome analysis methods are required. Previously, efforts have focused on enhancing sample preparation methods, and particularly minimizing sample loss using such improvements as the single-pot solid-phase-enhanced sample preparation method,9 inStageTip method,10 simple and integrated spintip-based proteomics technology (SISPROT),11 integrated proteome analysis device for 100 living cells (iPAD-100),12 nanodroplet processing in one pot for trace samples (nanoPOTS),13-16 and direct sample injection systems,17 and miniaturized 25-30 m diameter analytical columns.17-20 As a consequence of these efforts in state-of-the-art nanoscale proteomics, together with the enhanced sensitivity of mass spectrometry, more than 500 and 3000 proteins were identified from 1 and 10 HeLa cells, respectively.

13, 14

However, we considered that chromatographic

protein separation is another critical process affecting the overall sensitivity of proteome analysis. In this context, we first focused our investigation on the separation carrier used for chromatography and examined a column composed of nonporous resins previously not considered for use in high-sensitivity proteome analysis systems. Nonporous particles have only surface functional groups and thus the sample loading capacity is much lower than that of porous particles. This is a widely known disadvantage of nonporous resin columns and is why nonporous particles are not conventionally used for nanoLC, given that nanoLC systems originally suffered from low sample loading capacity. However, nonporous particles



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have the potential advantages of high separation power due to the absence of diffusion within pores and reduced sample loss within the column because the sample binds only to the particle surface (Figure 1A). The use of superficially porous particles (also called core shell, Fused-Core, or porous shell particles) containing a central nonporous particulate core reduces diffusion within the particle and enhances separation power, but its separation power is inferior to that of nonporous particles in which there is no diffusion. Therefore, despite the reduced binding capacity of nonporous particles, we anticipated that the use of nonporous particles in a nanoLC system might improve the sensitivity of proteome analysis, and particularly help address difficulties associated with small amounts of sample. The aim of the present study was to demonstrate highly sensitive proteome analyses of various amounts of HEK293F cell tryptic digests using nonporous C30 particles as opposed to typical C18 particles in the nanoLC column (see Supplementary Methods). A 150 mm × 75 m nonporous C30 (npC30) column was packed with NPS-TAS C30, a 1.5 m nonporous material (Eprogen). As a control, two porous 150 mm × 75 m C18 columns were packed with either Hypersil GOLD C18, a 1.9 m, 175 Å material (Thermo Fisher Scientific, pC18_A) or with ReproSil-Pur C18-AQ, a 1.9 m, 120 Å material (Dr. Maisch GmbH, pC18_B). Figure 1B shows the obtained nanoLC-MS/MS base peak chromatograms and Figure 1C shows the peak capacities analyzed from 100 ng of HEK293F cell tryptic digest using npC30 and two kinds of pC18 columns. In a typical proteome analysis, approximately 1–2 g of peptide digest is injected into the nanoLC-MS/MS; however, in this experiment, a small volume containing 100 ng peptide digest was injected into the nanoLC-MS/MS. The base peak chromatograms obtained using the pC18_B column appear to provide the sharpest peaks, but the peak capacity calculated using all the identified peptide peaks showed that the npC30 column had the highest peak capacity. Furthermore, when peak capacity was calculated by dividing each peak height rank, the peak capacity of the npC30 column in the top 200 peak height rankings was lower than that of


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the pC18_B column. Consequently, the peaks obtained in base peak chromatograms displaying major peaks using the pC18_B column were sharper than those observed using the npC30 column. The slightly larger loading amount for the npC30 column resulted in the peak capacity of the npC30 column being low at the top 200 peak height rankings. When the loading amount was reduced to 50 ng and 10 ng, there was no notable difference between the peak capacities at the top 200 peak height rankings and below (Figure S1). Figures 1D and 1E clearly show that the nanoLC-MS/MS analyses using the npC30 column identified more proteins and peptides than the nanoLC-MS/MS analyses using the pC18_A and pC18_B columns, respectively. Furthermore, comparison of the peak heights obtained using the npC30 and two pC18 columns showed more high peaks in the npC30 column samples than in the pC18 column samples (Figure 1F). Figures 1G and 1H show the overlap of the proteins and peptides, respectively, identified by LCMS/MS using the three columns. The many proteins and peptides identified using the pC18_A and pC18_B columns were also identified using the npC30 column. The distributions of the cellular localizations of the identified proteins were similar for the three columns (Figure 1I), and the number of proteins identified using the npC30 column increased irrespective of cell localization. There was no significant change in the distribution of grand average of hydropathy (GRAVY) values for the identified peptides (Figure 1J), but a greater number of long peptides were identified using the npC30 column compared with the two pC18 columns (Figure 1K). The sensitivity of MS decreases as the molecular weights of peptides increase, yet the improved sharpness of peaks corresponding to long peptides using the npC30 column allowed us to identify them more efficiently compared to using the pC18 columns. These results suggest that LC-MS/MS using the npC30 column resulted in the identification of more proteins and peptides because the high separation power of the npC30 column sharpened the chromatography peaks. Next, 10, 50, and 100 ng of HEK293F cell tryptic digests were analyzed by nanoLC-MS/MS using



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these three columns. Figures 2A and 2B show the total ion current (TIC) chromatograms and the number of identified proteins, respectively. The peak intensities in the TIC chromatogram obtained using the npC30 column were higher than those obtained using the two pC18 columns and this heightened intensity became more pronounced as the loading amount decreased. Furthermore, the difference in the number of proteins identified by LC-MS/MS using the npC30 column and the pC18 columns increased as the amount of tryptic digest injected into the columns decreased. Injection of the smallest test volume (containing 10 ng of HEK293F cell tryptic digest) resulted in the identification of 1917, 941, and 970 proteins on average by LC-MS/MS using the npC30, pC18_A, and pC18_B columns, respectively, indicating that use of the npC30 column enabled the detection of double the number of proteins compared to the pC18 columns. We calculated the total areas of the commonly identified peptides and found that the total area of peptides identified using the pC18_B column was smaller than that obtained using the npC30 and pC18_A columns; furthermore, sample loss in the pC18_B column was large (Figure 2C). pC18_B particles have a smaller pore size than do pC18_A particles and it may be difficult for peptides to enter and exit the particles. The total peak height of the peptides identified in common was clearly higher using the npC30 column than the pC18 columns (Figures 2D and 2E). In addition, the peak heights of 6 randomly selected peptides showed a similar trend (Figure S2）. These results suggest that the difference in the number of proteins identified using a sample loading amount of 10 ng may be due to the increased sharpness and height of the peptide peaks obtained using the npC30 column, which in turn results in high quality MS/MS patterns. Overall, our observations indicate that use of the npC30 column improves the detection sensitivity of proteome analysis for small amounts of proteins. Figure 3 shows how variations in the gradient elution length affect the peak capacity and the number of identified proteins. The peak capacity increased for all columns as the gradient length increased, and increased most for the npC30 column. As the gradient became longer, the difference in the separation


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power between the npC30 and the two pC18 columns became larger. The number of proteins identified using the npC30 and pC18 columns increased by approximately 10% and 2%, respectively, for each hour the gradient was extended. Using the pC18_B column, we identified 2966, 3166, and 3095 proteins on average using 2, 3, and 4 h gradients, respectively. The number of identified proteins increased as the gradient was extended from 2 to 3 h but decreased as the gradient was extended from 3 to 4 h, perhaps due to large sample loss in the pC18_B column and broadening of the peaks with longer gradient times. Compared with the pC18 columns, the number of proteins identified using the npC30 column increased markedly as the gradient was extended, suggesting that broadening of the peaks using longer gradients can be minimized because there is no intra-particle diffusion in npC30 particles. As shown above, we found that use of an npC30 column considerably improved the sensitivity of proteome analysis by nanoLC-MS/MS and thus we attempted to demonstrate the power of this method under more challenging, practical conditions using 100, 250, 500, and 1000 HEK293F cells as starting samples (Table 1). These cell samples were treated for shotgun proteome analysis using the phase-transfer surfactant method with reduced sample loss.17,

21

Hydrophilic-coated low-adsorption tubes and vials

(ProteoSave; AMR Inc., Tokyo, Japan) were used to minimize sample loss (see Supplementary Methods). NanoLC-MS/MS with the npC30 column was conducted using 2 and 4 h gradients for each cell sample. Table 1 shows the number of proteins identified using each experimental condition. The number of identified proteins increased for the 1000 and 500 cell samples as the duration of the gradient increased, whereas for the 100 and 250 cell samples, the number of identified proteins decreased slightly as the gradient was extended. As the number of cells decreased, the number of detectable peptide peaks decreased and the benefit of using an extended gradient disappeared. Thus, taking into consideration the throughput of LC-MS/MS analyses and the number of identified proteins, a gradient time of 2 h is suitable for the analysis of up to 250 cells. The number of proteins identified using our method was considerably



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larger compared to previous studies, which identified 1270 and 2566 proteins from 2000 and 5000 HEK293T cells, respectively, using SISPROT,11 and 635, 759, and 1060 proteins from 100, 250, and 500 DLD cells, respectively, using iPAD-100.12 However, Zhu et al. succeeded in identifying more than 3000 proteins from a sample prepared from 10 HeLa cells.14 Compared to the results in the present study, Zhu et al. identified far more proteins despite using a smaller number of cells. This indicates that the combination of nanodrop-based pretreatment, minimal sample loss, and the ultrasensitive LC-MS/MS technology developed by Zhu et al. is a powerful approach for improving detection sensitivity in nanoscale proteomics. However, since the system developed by Zhu et al. uses a pC18 column, it may be possible to further improve the detection sensitivity of their system by using an npC30 column. In conclusion, we showed that nanoLC-MS/MS using an npC30 column exhibited superior performance in the proteome analysis of microscale samples compared to pC18 columns, with this method enabling us to identify 3278 and 1407 proteins from 1000 and 100 cells, respectively. Therefore, this method may represent a technical improvement toward ultrasensitive proteome analysis in combination with advanced MS and sample preparation technologies.

Associated context Supporting Information Supporting Information Available: Experimental details about cell culture, sample preparation, LCMS/MS and data analysis, and Figures S1 and S2 (PDF). Notes The authors declare no competing financial interest. Raw data files of the LC−MS/MS analyses have been deposited in the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the jPOST partner repository (http://jpostdb.org) with the dataset identifier PXD011168.


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Acknowledgments This work was partly supported by a Grant-in-Aid for Young Scientists (B) (no. 17K18360, to Y.K.) from JSPS. References (1) Meier, F.;

Geyer, P. E.;

Virreira Winter, S.;

Cox, J.; Mann, M., BoxCar acquisition method

enables single-shot proteomics at a depth of 10,000 proteins in 100 minutes. Nat Methods 2018, 15, 440448. (2) Adachi, J.;

Hashiguchi, K.;

Nagano, M.;

Sato, M.;

Sato, A.;

Fukamizu, K.;

Ishihama, Y.;

Tomonaga, T., Improved Proteome and Phosphoproteome Analysis on a Cation Exchanger by a Combined Acid and Salt Gradient. Anal Chem 2016, 88, 7899-7903. (3) Kelstrup, C. D.;

Bekker-Jensen, D. B.;

Arrey, T. N.;

Hogrebe, A.;

Harder, A.; Olsen, J. V.,

Performance Evaluation of the Q Exactive HF-X for Shotgun Proteomics. J Proteome Res 2018, 17, 727738. (4) Iwasaki, M.;

Miwa, S.;

Ikegami, T.;

Tomita, M.;

Tanaka, N.; Ishihama, Y., One-dimensional

capillary liquid chromatographic separation coupled with tandem mass spectrometry unveils the Escherichia coli proteome on a microarray scale. Anal Chem 2010, 82, 2616-2620. (5) Aebersold, R.; Mann, M., Mass-spectrometric exploration of proteome structure and function. Nature 2016, 537 (7620), 347-355. (6) Ciuffa, R.;

Caron, E.;

Leitner, A.;

Uliana, F.;

Gstaiger, M.; Aebersold, R., Contribution of



Page 10 of 19

Mass Spectrometry-Based Proteomics to the Understanding of TNF-alpha Signaling. J Proteome Res 2017, 16, 14-33. (7) Akiya, M.; D.;

Yamazaki, M.;

Chiba, R.;

Yokoi, A.;

Matsumoto, T.;

Takahashi, H.;

Kawashima, Y.;

Oguri, Y.;

Kajita, S.;

Kijima,

Kodera, Y.; Saegusa, M., Identification of LEFTY as a

molecular marker for ovarian clear cell carcinoma. Oncotarget 2017, 8, 63646-63664. (8) Isobe, Y.;

Kawashima, Y.;

Ishihara, T.;

Watanabe, K.;

Ohara, O.; Arita, M., Identification of

Protein Targets of 12/15-Lipoxygenase-Derived Lipid Electrophiles in Mouse Peritoneal Macrophages Using Omega-Alkynyl Fatty Acid. ACS Chem Biol 2018, 13, 887-893. (9) Hughes, C. S.;

Foehr, S.;

Garfield, D. A.;

Furlong, E. E.;

Steinmetz, L. M.; Krijgsveld, J.,

Ultrasensitive proteome analysis using paramagnetic bead technology. Mol Syst Biol 2014, 10, 757. (10) Kulak, N. A.;

Pichler, G.;

Paron, I.;

Nagaraj, N.; Mann, M., Minimal, encapsulated proteomic-

sample processing applied to copy-number estimation in eukaryotic cells. Nat Methods 2014, 11, 319324. (11) Chen, W.;

Wang, S.;

Adhikari, S.;

Deng, Z.; Wang, L.; Chen, L.; Ke, M.;

Yang, P.; Tian,

R., Simple and Integrated Spintip-Based Technology Applied for Deep Proteome Profiling. Anal Chem 2016, 88, 4864-4871. (12) Chen, Q.;

Yan, G.;

Gao, M.; Zhang, X., Ultrasensitive Proteome Profiling for 100 Living Cells

by Direct Cell Injection, Online Digestion and Nano-LC-MS/MS Analysis. Anal Chem 2015, 87, 6674-


Page 11 of 19 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


6680. (13) Zhu, Y.;

Clair, G.;

Misra, R. S.;

Chrisler, W. B.;

Pryhuber, G. S.;

Smith, R. D.;

Shen, Y.;

Zhao, R.;

Shukla, A. K.;

Moore, R. J.;

Ansong, C.; Kelly, R. T., Proteomic Analysis of Single

Mammalian Cells Enabled by Microfluidic Nanodroplet Sample Preparation and Ultrasensitive NanoLCMS. Angew Chem Int Ed Engl 2018, 57 , 12370-12374. (14) Zhu, Y.;

Piehowski, P. D.;

Zhao, R.;

Petyuk, V. A.;

Campbell-Thompson, M.;

Chen, J.;

Shen, Y.;

Mathews, C. E.;

Moore, R. J.;

Shukla, A. K.;

Smith, R. D.; Qian, W. J.; Kelly, R. T.,

Nanodroplet processing platform for deep and quantitative proteome profiling of 10-100 mammalian cells. Nat Commun 2018, 9, 882. (15) Zhu, Y.; Smith, J. N.;

Dou, M.;

Piehowski, P. D.;

Schwarz, K. C.;

Shen, Y.;

Liang, Y.;

Wang, F.;

Chu, R. K.;

Shukla, A. K.; Moore, R. J.;

Chrisler, W. B.;

Smith, R. D.; Qian, W. J.;

Kelly, R. T., Spatially Resolved Proteome Mapping of Laser Capture Microdissected Tissue with Automated Sample Transfer to Nanodroplets. Mol Cell Proteomics 2018, 17, 1864-1874. (16) Liang, Y.;

Zhu, Y.; Dou, M.;

Xu, K.;

Chu, R. K.; Chrisler, W. B.;

Zhao, R.;

Hixson, K.

K.; Kelly, R. T., Spatially Resolved Proteome Profiling of

MS System Using a Nonporous

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