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1 Middle-down tandem MS of glycopeptides 1 2 3 4 5 6 Comparison of collisional and -based dissociation modes for middle-down analysis of multiply 7 8 glycosylated 9 1 2 3 1,2 #1 #1,3 10 Kshitij Khatri *, Yi Pu *, Joshua A. Klein , Catherine E. Costello , Cheng Lin , Joseph Zaia 11 12 13 14 1Dept. of Biochemistry, Center for Biomedical Spectrometry, Boston University School of 15 Medicine 16 17 2 18 Dept. of Chemistry, Boston University

19 3 20 Program in Bioinformatics, Boston University 21 22 23 24 25 26 *Contributed equally 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 #Address for correspondence 42 43 44 Joseph Zaia/Cheng Lin 45 46 Boston University Medical Campus 47 48 670 Albany St., Rm. 509 49 50 Boston, MA 02118 51 52 (v) 1-617-638-6762 53 54 (f) 1-617-638-6761 55 56 57 (e) [email protected]; [email protected] 58 59 60 61 62 63 64 65 1 Middle-down tandem MS of glycopeptides 2 2 3 4 Abstract 5 6 Analysis of singly glycosylated peptides has evolved to a point where large-scale LC-MS analyses can be 7 8 9 performed at almost the same scale as proteomics experiments. While collisionally activated dissociation 10 11 (CAD) remains the mainstay of bottom-up analyses, it performs poorly for the middle-down analysis of 12 13 multiply glycosylated peptides. With improvements in instrumentation, electron-activated dissociation 14 15 (ExD) modes are becoming increasingly prevalent for proteomics experiments and for the analysis of 16 17 18 fragile modifications such as glycosylation. While these methods have been applied for glycopeptide 19 20 analysis in isolated studies, an organized effort to compare their efficiencies, particularly for analysis of 21 22 multiply glycosylated peptides (termed here middle-down glycoproteomics), has not been made. We 23 24 therefore compared the performance of different ExD modes for middle-down glycopeptide analyses. We 25 26 identified key features among the different dissociation modes and show that increased electron energy 27 28 29 and supplemental activation provide the most useful data for middle-down glycopeptide analysis. 30 31 32 Keywords: Tandem MS, glycoproteomics, middle-down, electron-activated dissociation, hotECD, 33 EThcD, FTICR-MS 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 61 62 63 64 65 1 Middle-down tandem MS of glycopeptides 3 2 3 4 Introduction 5 6 7 Electron-based activation of glycans and glycoconjugates yields informative product that 8 9 provide information critical to confident assignment of detailed structures and site-specific post- 10 11 12 translational modifications (1–5); however, the application of these methods to biological 13 14 samples has been limited by instrument availability, sensitivity and speed. Instrument and 15 16 workflow development needs to go hand-in-hand for efficient application and dissemination of 17 18 19 these methods. Here, we developed and tested approaches for standardization of sample 20 21 preparation and ExD (electron activated dissociation) analysis of multiply glycosylated peptides 22 23 24 (referred to here as middle-down glycopeptides) that are not characterized efficiently using 25 26 collisional dissociation methods. These efforts identified key analytical features for these 27 28 29 biopolymers using electron-based versus collisional dissociation modes. 30 31 Use of beam-type CAD (collisionally activated dissociation) for glycopeptide analysis requires 32 33 34 high collision energies to allow fragmentation of the backbone. A particular problem 35 36 with such use of CAD is the low abundances of product ions from peptide backbone 37 38 fragmentation because the relatively fragile glycosidic bonds fragment easily. The glycan 39 40 41 composition can be inferred from the total mass of the precursor, once the peptide backbone has 42 43 been identified, but it may not always be possible to identify the exact site of modification if 44 45 46 peptide backbone fragments with attached saccharide units are not observed. This loss of the 47 48 fragile saccharide moiety poses a greater issue when analyzing multiply glycosylated peptides, 49 50 51 whereby the loss of saccharide from peptide backbone fragments prevents assignment of site- 52 53 specific glycan compositions. This limits the extent to which CAD-based methods can be applied 54 55 to analysis of heavily glycosylated peptides such as mucin-type O-glycosylated peptides, which 56 57 58 contain stretches of serine and threonine residues with multiple glycans attached, or peptides 59 60 61 62 63 64 65 1 Middle-down tandem MS of glycopeptides 4 2 3 4 with more than one N-glycosylation sequon. In some cases, it is possible to separate the multiple 5 6 7 sites of glycosylation using a combination of proteolytic enzymes; however, the usefulness of 8 9 CAD to analysis of multiply glycosylated peptides is limited. 10 11 12 We have previously developed analytical and bioinformatics workflows that combine 13 14 proteomics, glycomics and glycoproteomics information from bottom-up experiments to enable 15 16 efficient and comprehensive analysis of the glycoproteomes (6–8). When using a bottom-up 17 18 19 method, it is possible to determine the range of glycoforms present at each site but not to assign 20 21 the glycan combinations that exist simultaneously on different sites of the . In order to 22 23 24 determine the precise glycosylation structures of the functional proteoforms(s) of a glycoprotein, 25 26 it is necessary to develop methods for analysis of large and multiply glycosylated peptides; 27 28 29 however, this is challenging because the addition of glycans to multiplies the total 30 31 number of molecular forms many-fold due to inherent glycosylation heterogeneity. In addition, 32 33 34 the number of protein-glycan combinations multiplies with an increase in the number of 35 36 glycosylation sites. Thus, for large glycoproteins with multiple glycosylation sites, only analysis 37 38 of peptides with 2-3 glycosylation sites and masses in the range 8,000 to 12,000 Da (defined here 39 40 41 as middle-down analysis) is feasible (6–10). 42 43 The challenge to analysis of multiply glycosylated peptides increases with size and number of 44 45 46 glycosylation sites. Methods for middle-down glycoprotein analysis are not yet mature and a 47 48 detailed comparison of ExD-based methods (9, 14, 14–23) combined with high-resolution and 49 50 51 high-sensitivity MS has not appeared. Multiply O-glycosylated synthetic peptides smaller than 7 52 53 kDa have been analyzed using ExD by direct infusion electrospray (2, 24). Generally, increased 54 55 m/z and number of glycosylation sites results in inefficient dissociation using ExD. The authors 56 57 58 show that this limitation can be overcome to a degree by increasing the electron energy in ECD 59 60 61 62 63 64 65 1 Middle-down tandem MS of glycopeptides 5 2 3 4 to achieve hECD (hot Electron Capture Dissociation). While their results indicate promise for 5 6 7 use of these methods for glycopeptide analysis, a direct comparison of dissociation techniques 8 9 for multiply glycosylated peptides with molecular weights exceeding 5-7 kDa is lacking. 10 11 12 The paucity of high-resolution and high-mass-accuracy ExD data on multiply glycosylated 13 14 peptides has also limited the development of efficient bioinformatics methodologies for 15 16 automated data analysis. In this work, we evaluated the performance of different fragmentation 17 18 19 modes for analysis of bottom-up and middle-down glycopeptides from glycoprotein standards 20 21 including human transferrin and human α1-acid glycoprotein (AGP). We made comparisons of 22 23 24 different dissociation modes systematically transitioning from bottom-up to middle-down 25 26 glycopeptide analyses, with particular focus on glycan heterogeneity, dissociation efficiency, 27 28 29 charge-state dependence and compatibility with online separation. The overall effort is geared at 30 31 identifying and applying the best methods for middle-down analysis that will help develop an 32 33 34 integrated workflow combining information from bottom-up and middle-down domains for the 35 36 most comprehensive glycoproteomic analysis. 37 38 39 Materials and methods 40 41 42 43 Sample preparation 44 45 For bottom-up glycopeptide analysis, human transferrin and human AGP (Sigma-Aldrich, St. 46 47 Louis, MO) were denatured by heating at 90oC for 30 min, in the presence of 2,2,2- 48 49 50 trifluoroethanol. Samples were reduced with dithiothreitol (DTT), alkylated using iodoacetamide 51 52 (IAM) and digested with Trypsin Gold (Promega Corp., Madison, WI) in the presence of 100 53 54 55 mM ammonium bicarbonate (Sigma-Aldrich, St. Louis, MO) as buffer. The detailed digestion 56 57 protocol has been described previously (6–8). Glycopeptides were enriched using a ZIC-HILIC 58 59 60 glycopeptide enrichment kit (EMD Millipore, Billerica, MA), as per the manufacturer’s protocol. 61 62 63 64 65 1 Middle-down tandem MS of glycopeptides 6 2 3 4 Samples were desalted, where necessary, using Pierce Pepclean C18 spin columns (Thermo 5 6 7 Fisher Scientific, Pittsburgh, PA). 8 9 For middle-down glycopeptide analysis, AGP was denatured, reduced, alkylated and digested 10 11 12 using Asp N endoproteinase (Promega Corp., Madison, WI). We evaluated both endoproteinase 13 14 Asp N and endoproteinase LysC for generation of middle-down glycopeptides from AGP. We 15 16 used Asp N for these studies because it showed superior reproducibility of digestion. Middle- 17 18 19 down glycopeptides were enriched by fractionation of the Asp N digest using a Superdex 75 20 21 (3.2/300) (GE Healthcare, Pittsburgh, PA) on a Beckman Gold HPLC system (Beckman Coulter, 22 23 24 Inc., Indianapolis, IN). Ammonium formate (25 mm, pH 4.5) in 10% acetonitrile was used as 25 26 mobile phase for separation at an isocratic flow rate of 50 µL/min. Fractions were collected 27 28 29 manually based on UV absorbance at 230 nm and further desalted and fractionated using a 30 31 Vydac C18 reversed-phase HPLC column (W.R. Grace & Co., Columbia, MD) on an Agilent 32 33 34 1200 series chromatograph (Agilent, Inc., Santa Clara, CA), fitted with an automated fraction 35 36 collector. 37 38 Where indicated, for both bottom-up and middle-down analyses, the proteolytic digestion 39 40 41 product mixtures were desialylated using α2-3,6,8 neuraminidase (New England Biolabs, 42 43 Ipswich, MA), prior to LC-MS or nanoESI-MS to reduce glycoform heterogeneity. 44 45 46 47 Data acquisition 48 49 Bottom-up glycopeptide samples were either directly infused for analysis using an Advion 50 51 NanoMateTM alone or analyzed using online LC-MS at nanoliter flow-rates on a Bruker 52 53 TM 54 solariX 12T hybrid Qh-FTICR (Fourier-transform cyclotron resonance) mass 55 56 mounted with an Advion NanoMate nanoESI source (Advion Inc., Ithaca, NY). A Waters 57 58 59 NanoAcquity™ nano-flow chromatograph (Waters Corp., Milford, MA) mounted with a Waters 60 61 62 63 64 65 1 Middle-down tandem MS of glycopeptides 7 2 3 4 Xbridge™ reversed-phase column (150 μm × 100 mm) packed with 1.7 μm BEH C18 resin and 5 6 7 a Waters trap column (180 μm × 20 mm) packed with 5 μm Symmetry™ C18 stationary phase, 8 9 was used for online LC-MS. Bottom-up transferrin glycopeptides were also analyzed using CAD 10 11 12 on an Agilent 6550 Q-TOF mass spectrometer using online HILIC enrichment combined with 13 14 reversed-phase separation, as described previously (6). 15 16 Middle-down glycopeptide fractions from C18 LC separation were analyzed by nanoESI-MS 17 18 19 using the Bruker solariX 12T FTICR-MS and Advion NanoMate source described above. HCD 20 21 and EThcD (Higher-energy Collisional Dissociation and Electron Transfer Dissociation 22 23 24 supplemental collisional activation) (25–27) experiments were performed on ZIC-HILIC- 25 26 enriched and unfractionated bottom-up and middle-down glycopeptide samples by LC-MS using 27 28 29 an EASY-nLC chromatograph with an EASY-Spray C18 LC column on a Thermo 30 31 FusionTM instrument. The instrument was set to perform HCD-triggered EThcD, which allowed 32 33 34 EThcD to be performed only on precursor ions that generated saccharide oxonium ions. 35 36 For LC-MS/MS data acquisition using the solariX, precursor ions of interest were isolated using 37 38 a front-end quadrupole mass filter and accumulated in the collision cell for 100 to 1000 ms prior 39 40 41 to MS/MS analyses. Hot electron capture dissociation (hECD) was performed by irradiating 42 43 trapped ions in the ICR cell with 12-14 eV , for up to 1 s. Cathode current was set at 1.5 44 45 46 A and the cathode bias was set between -12 and -14 V. Transients were summed to improve 47 48 signal-to-noise ratio for middle-down experiments, as indicated in respective figure legends. LC- 49 50 51 MS/MS experiments were performed by data-dependent precursor selection of the most- 52 53 abundant parent ions or using a targeted inclusion list. 54 55 56 57 58 59 60 61 62 63 64 65 1 Middle-down tandem MS of glycopeptides 8 2 3 4 Data analysis 5 6 7 Glycopeptide data analysis was performed either manually or in a semi-automated manner using 8 9 Python scripts developed in-house, using the Pyteomics (28) and GlyPy libraries 10 11 12 (https://pypi.python.org/pypi/glypy/0.0.5rc2) (29). The protein sequence from Uniprot was 13 14 digested with Asp N in silico using Pyteomics to generate a theoretical list of peptidoforms and 15 16 combined with a list of human N-linked glycoforms from GlycomeDB (30, 31) using GlyPy’s 17 18 19 database module. Theoretical fragment ions were then generated for precursors that found a MS1 20 21 match. A 5ppm mass error tolerance was used for matching and assignment of fragment ions. 22 23 24 Bottom-up data were analyzed manually and ions were picked by visual inspection of the 25 26 spectra. For middle-down data analysis, the FTICR-MS spectra were deconvoluted in the Bruker 27 28 29 Compass Data analysis software (version 4.2), using the SNAP algorithm (32). Fragment ion 30 31 lists were matched against the deconvoluted/deisotoped peaklists (provided as part of 32 33 34 supplement). Middle-down glycopeptide data from the Orbitrap were manually analyzed using 35 36 the theoretical fragment lists. 37 38 39 Results and Discussion 40 41 42 43 Comparison of dissociation modes for bottom-up glycopeptide tandem-MS 44 45 Before analyzing middle-down glycopeptide samples, we compared the efficacy of different 46 47 dissociation modes, using bottom-up glycopeptides from widely used glycoprotein standards. To 48 49 50 compare the characteristics of different dissociation modes, we acquired tandem MS of the same 51 52 transferrin glycopeptide (622QQQHLFGSNVTDC*SGNFC*LFR642 – Hex5 HexNAc4 NeuAc2) 53 54 55 using CAD, ETD and ECD, respectively. Figure S-1 shows the tandem MS results using CAD. 56 57 As expected (6), extensive dissociation of the glycan leads to formation of abundant oxonium 58 59 60 ions. The intact peptide ion without any glycan attached and a glycan Y1 ion are also observed, 61 62 63 64 65 1 Middle-down tandem MS of glycopeptides 9 2 3 4 which confirm the intact peptide and glycan masses, respectively. A series of peptide backbone 5 6 7 ions are also observed at low relative abundances. In addition to the bare peptide backbone ions, 8 9 peptide fragment ions with an attached HexNAc are also observed. Together these features allow 10 11 12 unambiguous assignment of the peptide sequence and glycosylation site, as well as inference of 13 14 the glycan composition from the residual precursor mass. 15 16 An ETD for the same precursor, generated on the FTICR instrument, is shown in 17 18 19 Figure S-2. ETD generated few, if any, peptide backbone product ions for this precursor, while 20 21 some glycan fragmentation was observed, likely resulting from vibrational excitation that 22 23 24 occurred during ion transfer. The abundant charge-reduced species observed in the ETD 25 26 spectrum indicates occurrence of ETnoD (electron transfer with no dissociation), where an 27 28 29 electron is transferred to the precursor leading to charge reduction but fragments are not 30 31 generated – or do not separate from one another. (33, 34). 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 61 62 63 64 65 1 Middle-down tandem MS of glycopeptides 10 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 4+ 38 Figure 1: ECD (1.7 eV) tandem MS of a transferrin glycopeptide ([M+4H] , m/z 1180.7328) on FTICR-MS. ECD 39 irradiation time was 0.1 sec and 40 transitions were summed in this spectrum. C* = Carbamidomethyl Cysteine. 40 41 By comparison, ECD of the same precursor ion generated extensive glycopeptide fragmentation 42 43 on the FTICR-MS (Figure 1). We observed abundant c- and z-type peptide backbone ions with 44 45 the intact glycan attached in the range m/z 1600-2400. In the lower m/z range, peptide backbone 46 47 48 fragments not containing the glycosylation site were observed. A few b/y-type peptide ions and 49 50 some peaks resulting from glycan fragmentation (oxonium ions and saccharide losses from the 51 52 53 precursor) were also observed, indicating additional energy deposition. The presence of b-ions in 54 55 ECD has been previously reported and discussed by Cooper (35). 56 57 58 Although the drastic difference between the ECD and ETD spectra of this glycopeptide may 59 60 seem surprising given the common misconception that ECD and ETD are essentially the same 61 62 63 64 65 1 Middle-down tandem MS of glycopeptides 11 2 3 4 processes with the only difference being the source of electrons, sufficient differences exist 5 6 7 between these two methods, such as the lower energy deposition and collisionally cooling in 8 9 ETD. Further, McLuckey and coworkers reported that the electron transfer probability is 10 11 12 influenced by the electron affinity and the Franck-Condon factors associated with the anion 13 14 radical and its associated neutral (34). Simons and coworkers suggested that electron attachment 15 16 to a Coulomb-stabilized C=O π* orbital can compete favorably with electron transfer to a 17 18 19 charged site, and the probability of these competing electron transfer processes depends on the 20 21 location of their crossing points and the strength of coupling between electronic states (36). The 22 23 24 electron donor must get close enough to the potential attachment site before electron transfer can 25 26 take place, and the size of the ETD reagent (comparing to electrons) may prevent access to 27 28 29 certain fragmentation pathways, while favoring other dissociation channels, such as hydrogen 30 31 loss. Finally, dissimilar fragmentation behaviors have also been observed in ECD and ETD of 32 33 34 metal-cationized peptides. 35 36 While ECD of the 4+ precursor of the transferrin glycopeptide generated abundant peptide 37 38 backbone product ions (Figure 1), a 3+ precursor of the same glycopeptide did not yield useful 39 40 41 tandem MS with ETD or ECD (Figure S-3), indicating presence of electron capture with no 42 43 dissociation (ECnoD), described in Figure S-4. The poor ETD and ECD performance for the 44 45 46 lower charge-state precursor suggested charge state dependence of these processes. By contrast, 47 48 hECD yielded abundant glycopeptide fragments for the same 3+ precursor (Figure 2). As 49 50 51 expected, in addition to c- and z-type ions, hECD also produced a series of b- and y-type 52 53 fragment ions, indicating an increase in vibrational excitation during hECD. 54 55 56 57 58 59 60 61 62 63 64 65 1 Middle-down tandem MS of glycopeptides 12 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 Figure 2: The hECD (14 eV) tandem of a transferrin glycopeptide ([M+3H]3+, m/z 1573.9704) acquired on 30 FTICR-MS. ECD irradiation time was 1 sec and 16 transients were summed for this spectrum. C* = Carbamidomethyl 31 Cysteine. 32 33 While LC-coupled hECD has been reported with a quadrupole (2), we demonstrated LC- 34 35 hECD with FTICR MS could provide good sequence coverage for the peptide backbone 36 37 cleavage and included fragments containing intact glycans that allowed determination of the 38 39 40 glycoform present at each occupied site. Figure 3 shows the LC-hECD results for enriched 41 42 glycopeptides from a transferrin tryptic digest. Comprehensive glycopeptide backbone coverage 43 44 45 was generated by hECD in a single tandem-MS scan, where the amino acid sequence of the 46 47 peptide backbone was covered by both c/z- and b/y-type ions. In addition, we observed many 48 49 50 secondary fragments. While the increased electron energy caused some glycan dissociation, we 51 52 observed many peptide backbone fragments with intact glycan attached, which provides much 53 54 55 more useful tandem MS compared to a collisional dissociation spectrum where the glycan 56 57 moiety is lost from peptide backbone fragments. The higher mass accuracy and resolving power 58 59 60 61 62 63 64 65 1 Middle-down tandem MS of glycopeptides 13 2 3 4 will also be useful in assigning MS and tandem MS when analyzing complex biological samples, 5 6 7 where unexpected modifications and low abundance or overlapping ion clusters may be present. 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 Figure 3: LC-hECD (14 eV) analysis of enriched tryptic glycopeptides from human transferrin on FTICR-MS. Tandem 47 MS for m/z 1379.9067, [M+3H]3+, is shown. –Cys indicates loss of the Cys side chain. ECD irradiation time was 1 sec and a 48 single transient was acquired for this spectrum. C* = Carbamidomethyl Cysteine. 49 50 51 52 The hECD technique can be performed on FTICR-MS instruments to improve fragmentation of 53 54 glycopeptides, when ECD or ETD fragmentation is not efficient. However, FTICR-MS 55 56 57 instruments suffer from lack of sensitivity and speed, prohibiting their use in large-scale 58 59 glycoproteomics studies, with the possible exception of high-field instruments. The most 60 61 62 63 64 65 1 Middle-down tandem MS of glycopeptides 14 2 3 4 successful implementation of ExD for large scale studies has been demonstrated using hybrid 5 6 7 Orbitrap instruments capable of performing ETD (11, 26, 42–45). Many groups have 8 9 demonstrated further improvement in the efficiency of ETD fragmentation on Orbitrap 10 11 12 instruments by use of supplemental collisional activation (26, 27, 42, 43). 13 14 We evaluated higher-energy collisional dissociation (HCD) (Figure S-5) and EThcD (Figure S-6) 15 16 of an AGP glycopeptide using a Thermo Orbitrap Fusion instrument. As expected, collisional 17 18 19 dissociation of the precursor by HCD generated primarily peptide backbone fragments while the 20 21 glycan dissociated to mono-, di- and tri-saccharide oxonium ions. In addition to bare peptide 22 23 24 backbone ions, y-type ions with an attached HexNAc (yn/Y1) were observed. Also, an intact 25 26 peptide ion with a HexNAc (glycan Y1 ion) was seen in the spectrum. EThcD is performed by 27 28 29 first allowing the precursor ions to react with the electron donating radical anion, which leads to 30 31 charge reduction and formation of a c/z ion pair that may still be held together by non-covalent 32 33 34 interactions. Subsequent collisional activation of the charge reduced species results in detection 35 36 of peptide backbone product ions. The ETD reaction takes place in the LTQ , after 37 38 which these ions are transferred to the HCD cell, where they are subjected to collisional 39 40 41 activation that facilitates efficient separation of the complementary peptide backbone fragments 42 43 by disrupting non-covalent interactions, while keeping the glycan largely intact. As seen in 44 45 46 Figure S-6, EThcD of an AGP glycopeptide generated extensive coverage with c-type ions and a 47 48 few b-ions. The z7 ion was observed with the intact glycan attached. We also observed Y1 and Y2 49 50 51 ions, saccharide losses from the precursor, and oxonium ions; however, their abundances were 52 53 much lower relative to the HCD spectra. Supplemental activation using collisional and 54 55 photoactivation methods generally decreases the charge-state dependency of ETD and ECD and 56 57 58 59 60 61 62 63 64 65 1 Middle-down tandem MS of glycopeptides 15 2 3 4 improves detection of product ions for both bottom-up and top-down analyses (43–45). For 5 6 7 proteins, it also causes unfolding and makes more of the backbone accessible to ExD cleavage. 8 9 From our evaluation of the different dissociation modes for bottom-up glycopeptide 10 11 12 fragmentation, CAD-based fragmentation was the most efficient in terms of speed and 13 14 sensitivity. However, when considering the level and quality of information generated, CAD did 15 16 not produce any peptide backbone fragments retaining the intact glycan and would be incapable 17 18 19 of clearly defining the glycosylation site with multiple glycans attached to the same peptide. 20 21 EThcD and hECD generated abundant peptide fragments, while keeping the glycan moiety intact 22 23 24 during the fragmentation process, thus indicating potential for application in the analysis of 25 26 middle-down glycopeptides with multiple glycosylation sites. 27 28 29 30 Tandem MS of multiply N-glycosylated peptides 31 32 For middle-down method development, we selected the human AGP, a protein which we and 33 34 others have characterized in detail using bottom-up mass spectral methods (6, 8, 46, 47). AGP 35 36 37 has five N-glycosylation sites, each with a variety of complex-type N-glycans, and occurs as a 38 39 mixture of two protein isoforms (hAGP1 and hAGP2) (8, 48). We performed in silico digestion 40 41 42 of AGP with different enzymes and found that digestion of AGP using endoproteinase Asp N 43 44 generates multiple peptides that contain two N-linked glycosylation sequons. Therefore, we 45 46 47 selected the Asp N digests of AGP for development and evaluation of methods for middle-down 48 49 glycopeptide analysis. We reduced glycan heterogeneity by trimming the glycans from the non- 50 51 reducing end, where necessary. We used α2-3,6,8 Neuraminidase to remove NeuAc residues 52 53 54 from the AGP Asp N digests. The resulting mixtures contained unglycosylated peptides and 55 56 glycopeptides with 1, 2 or 3 glycosylation sites. To resolve multiply glycosylated peptides from 57 58 59 those with fewer glycosylation sites, we used a size exclusion fractionation step, 60 61 62 63 64 65 1 Middle-down tandem MS of glycopeptides 16 2 3 4 as shown in Figure 4. SEC provided more specificity in enrichment of glycopeptides with two 5 6 7 glycosylation sites than did HILIC SPE techniques, such as those used for bottom-up sample 8 9 preparation (23, 49, 50). MALDI-MS of SEC fractions showed presence or absence of middle- 10 11 12 down glycopeptides (Figure S-7). 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 Figure 4: SEC-MS/MS (CAD) of human AGP digested with Asp N, showing separation of glycopeptides and peptides 33 based on size and number of glycans attached 34 35 36 37 SEC fractions containing two glycosylation sites were further fractionated using an offline C18 38 39 40 analytical column and the glycopeptide fractions were analyzed by FTICR-MS, using static 41 42 nano-electrospray. We compared the different ExD modes available on FTICR-MS for middle- 43 44 45 down glycopeptide analysis. Similar to bottom-up glycopeptides, ETD of middle-down 46 47 glycopeptides only produced charge-reduced species, and while ECD produced a few fragments 48 49 50 covering the ends of the peptide, no fragmentation between the glycosylation sites was observed 51 52 (data not shown). Middle-down glycopeptides were then irradiated with higher energy electrons 53 54 55 to achieve hECD. 56 57 As shown in Figure S-8, comprehensive fragmentation of a middle-down glycopeptide from 58 59 AGP was achieved using hECD. Predominantly c-type ions were seen in the hECD tandem mass 60 61 62 63 64 65 1 Middle-down tandem MS of glycopeptides 17 2 3 4 spectra. Overall, better coverage was observed along the peptide portions not carrying 5 6 7 glycosylation, while peptide backbone coverage between the two glycosylation sites was sparse. 8 9 Nevertheless, a few c-type fragment ions carrying the intact glycan could be identified. 10 11 12 Vibrational excitation also led to generation of three peptide b-ions that still had intact glycans 13 14 attached to them. A few z-ions were also observed in the tandem mass spectra, complementing 15 16 the information from the c-ion series. The example shown in Figure S-8Error! Reference 17 18 19 source not found. had the same glycan composition present at the two glycosylation sites. 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 Figure 5: Tandem MS (hECD at 14 eV) of an AGP (Asp N) glycopeptide with two glycosylation sites on FTICR-MS (m/z: 6+ 53 1437.4571, [M+6H] ). ECD irradiation time was 0.1 sec and 100 transients were summed for this spectrum. C* = 54 Carbamidomethyl Cysteine. 55 56 For the glycopeptide whose middle-down hECD tandem mass spectrum is shown in Figure 5, the 57 58 glycan compositions identified at the two glycosylation sites were different. In this case, 59 60 61 62 63 64 65 1 Middle-down tandem MS of glycopeptides 18 2 3 4 abundant c- and z-type ion series were observed, again covering the N- and C-termini of the 5 6 7 peptide, along with y-type ions whose presence resulted from vibrational excitation. Coverage 8 9 between the two glycosylation sites was again poor, with only one fragment ion each of c- and z- 10 11 12 type identified. Interestingly, z21 ions corresponding to both a tri-antennary and a tetra-antennary 13 14 glycoform were detected in the spectrum. This indicated the presence of glycoform positional 15 16 isomers in the glycopeptide mixture. The abundance of the tri-antennary glycoform containing 17 18 19 z21 ion was much lower than that for the tetra-antennary glycoform. For this minor component, 20 21 the corresponding c15 ion with a tetra-antennary glycan, which would also be expected to have 22 23 24 low abundance, was not detected. This represents another level of complexity in the analysis of 25 26 multiply glycosylated N-glycopeptides, where the presence of positional isomers that cannot be 27 28 29 resolved by liquid-phase separation methods leads to the generation of chimeric tandem MS. 30 31 These results also demonstrate the value of high-resolution and high mass accuracy in FTICR- 32 33 34 MS for the low-abundance fragment ions which could not have been confidently assigned if the 35 36 isotopic peaks were not well-resolved or if the mass error were high. We were also able to 37 38 confirm the presence of these glycoforms at the sites indicated in Figure 5 using bottom-up 39 40 41 glycopeptide analysis, as shown in Supplemental Figures S-9 and S-10 and based on previously 42 43 reported analyses of AGP (8, 46, 47, 51). 44 45 46 Our results from hECD fragmentation of middle-down glycopeptides with two glycosylation 47 48 sites showed that, although coverage between the glycosylation sites still needed improvement, 49 50 51 presence of as few as one or two fragment ions between the glycosylation sequons is sufficient to 52 53 discriminate the exact glycan compositions simultaneously occupying the glycopeptide. Data 54 55 acquisition on the FTICR-MS for middle-down glycopeptide tandem-MS was performed using 56 57 58 static nano-ESI and has not yet been coupled to online LC-separations due to the need for signal 59 60 61 62 63 64 65 1 Middle-down tandem MS of glycopeptides 19 2 3 4 averaging to achieve sufficient S/N ratio. The speed and sensitivity of this instrument did not 5 6 7 allow for online LC-MS/MS of the middle-down samples. Middle-down glycopeptides were 8 9 analyzed using LC-MS/MS on the Orbitrap Fusion instrument that offers much higher speed and 10 11 12 sensitivity. 13 14 Since the hybrid Orbitrap instruments had shown promising results for online LC-MS/MS of 15 16 bottom-up glycopeptides using EThcD, we next evaluated the performance of this method for 17 18 19 middle-down glycopeptides. HILIC SPE-enriched glycopeptides were subjected to online C18 20 21 LC-MS on an Orbitrap Fusion instrument. Similar to bottom-up glycopeptide separation, middle- 22 23 24 down glycopeptide resolution on the reversed-phase column was peptide-centric, leading to 25 26 clustering of different glycoforms for the same glycopeptide in the LC-elution profile. An 27 28 29 example of an MS1 spectrum showing a distribution of co-eluting middle-down glycopeptide 30 31 glycoforms is shown in Figure S-11. The higher speed and sensitivity of Orbitrap-MS made data- 32 33 34 dependent LC-tandem-MS more feasible on this instrument, compared to FTICR-MS. 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 61 62 63 64 65 1 Middle-down tandem MS of glycopeptides 20 2 3 4 Figure 6: Tandem MS (EThcD) of an AGP middle-down glycopeptide on an Orbitrap Fusion instrument (precursor m/z: 5 1302.4178, [M+7H]7+). ETD reaction time was 8.75 msec and HCD normalized collision energy was set at 20 for this scan. 6 C* = Carbamidomethyl Cysteine. 7 8 While the Orbitrap-MS was able to sample many more precursors for tandem-MS during a data- 9 10 dependent acquisition experiment, only a few middle-down glycopeptides generated fragments 11 12 13 suitable for resolving glycosylation site microheterogeneity. As shown in Figure 6, the terminal 14 15 regions of the peptide backbone were well covered in the tandem MS but coverage for fragment 16 17 18 ions carrying the glycan moieties was again sparse, similar to hECD results. A z-ion series was 19 20 predominant for this glycopeptide, with only two ions covering the N-terminal region of the 21 22 23 peptide. Supplemental collisional activation also generated y-type fragment ions and glycan 24 25 oxonium ions (Figure 6). The two z-type ions with intact glycan attached, occurring in the region 26 27 between the two glycosylation sites provided adequate information for assigning the glycan 28 29 30 compositions at these two sites. A HCD tandem MS of the same precursor generated primarily 31 32 saccharide losses, with minimal peptide backbone coverage, as shown in Figure S-12. 33 34 35 36 Conclusions and future perspectives 37 38 From our evaluation of methods for middle-down glycopeptide analysis, it is clear that front-end 39 40 separation plays a crucial role in managing the extreme heterogeneity of glycoforms. Even with a 41 42 43 combination of different enrichment, fractionation and online separation methods, the 44 45 glycopeptide complexity extended far beyond what is typically seen for bottom-up 46 47 48 glycopeptides. Another level of complexity comes from the presence of glycoform combinations 49 50 that are isobaric due to the presence of glycosylation-site positional isomers. Such isobaric 51 52 53 compositions cannot be resolved using liquid-phase separation methods and generate chimeric 54 55 tandem mass spectra due to co-isolation in the mass spectrometer. Gas-phase separation methods 56 57 58 such as ion-mobility might be useful in separating such prior to fragmentation. Thus, 59 60 successful middle-down glycopeptide analysis will require a combination of analytical tools. 61 62 63 64 65 1 Middle-down tandem MS of glycopeptides 21 2 3 4 Further development of analytical methods is necessary to enable routine acquisition of high- 5 6 7 quality middle-down glycopeptide tandem MS. While hECD and EThcD have shown promising 8 9 results in the analysis of multiply glycosylated peptides, the sensitivity and speed of these 10 11 12 methods remains adequate only for abundant glycopeptides. 13 14 Several recent studies have underlined the value of combining collisional and photo-activation 15 16 modes with electron-based activation methods (42, 52–56) Zhang and Reilly have demonstrated 17 18 19 the use of for simultaneous analysis of peptide sequence and glycan structure 20 21 from glycopeptides (57). Coon and coworkers have recently reported the successful application 22 23 24 of AI-ETD for large-scale glycopeptide analysis (58). The Brodbelt group is actively exploring 25 26 the use of UVPD for glycopeptide analysis. They showed that negative mode UVPD generated 27 28 29 more extensive fragmentation for both the peptide and the glycan compared to positive mode 30 31 UVPD or CAD in positive or negative modes. More recently, they have presented the negative 32 33 34 mode UVPD results on acidic glycopeptides from kappa Casein glycoprotein and an 35 36 Acinetobacter baumannii Ompa/MotB glycoprotein (55). In both cases, UVPD produced 37 38 abundant a- and x-type peptide backbone fragments that retained the labile glycan modifications. 39 40 41 In addition, useful B/Y- and C/Z-type glycan fragments were observed, along with some cross- 42 43 ring fragments that helped sequence the glycan attached. 44 45 46 At the same time, electron-based activation modes are now being implemented on faster, more 47 48 sensitive instruments (59–61). Ion mobility- is now being used for efficient 49 50 51 online separation of glycopeptides (62, 63). Glaskin et al. have also reported the incorporation of 52 53 an ECD cell into an ion mobility-QTOF MS and its use for oligosaccharide analysis (64, 65). 54 55 Such a combination of ion-mobility and tandem MS can be extremely useful for separation and 56 57 58 identification of isomeric species. Marshall and colleagues have recently reported successful top- 59 60 61 62 63 64 65 1 Middle-down tandem MS of glycopeptides 22 2 3 4 and middle-down characterization of a monoclonal , using a 21T FTICR-MS (66). 5 6 7 This entailed ETD MS/MS analysis of large protein subunits comprising the light and 8 9 heavy chains. The antibody heavy chain contains a single N-glycosylation site, which adds to the 10 11 12 complexity of analysis. It is clear that technology development will play a key role in advancing 13 14 top- and middle-down workflows and dissemination of these methods in the glyco-analytics 15 16 community. 17 18 19 Our study provides a systematic comparison of methods for middle-down glycopeptide analysis, 20 21 whereby we first evaluated methods for bottom-up analysis and then used the knowledge from 22 23 24 bottom-up analyses to tackle middle-down method development and data analysis. The data 25 26 discussed here were analyzed using a combination of manual interpretation and prototype data 27 28 29 analysis tools written using Pyteomics (28) and GlyPy Python libraries (29). It is clear from these 30 31 analyses that middle-down glycopeptide analyses alone are not sufficient for efficient 32 33 34 bioinformatics and defining glycosylation site relationships would require a combination of 35 36 bottom-up and middle-down data sets. We have previously presented such a workflow for 37 38 bottom-up glycoproteomics and the bottom-up data would help drive analysis of the middle- 39 40 41 down data (7, 8). The ideal informatics workflow would integrate the different data streams for 42 43 efficient and comprehensive data analysis. 44 45 46 47 Acknowledgements 48 49 This work was supported by NIH grants P41 GM104603 and S10 RR025082. 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45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Manuscript for review only - changes highlighted

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