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A set of novel automated quantitative microproteomics protocols for small sample amounts and its application to kidney tissue substructures Erik Leonardus de Graaf, Davide Pelligrini, and Liam A. McDonnell J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00889 • Publication Date (Web): 03 Nov 2016 Downloaded from http://pubs.acs.org on November 8, 2016

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Journal of Proteome Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

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Journal of Proteome Research

A set of novel automated quantitative microproteomics protocols for small sample amounts and its application to kidney tissue substructures.

Erik Leonardus de Graaf1, Davide Pellegrini2,1, Liam A McDonnell1,3*

1 Fondazione Pisana per la Scienza ONLUS, Pisa, Italy. 2 NEST, Scuola Normale Superiore, Pisa, Italy. 3 Leiden University Medical Center, Leiden, The Netherlands * Corresponding author: [email protected]

Keywords: microgram, sample amount, preconditioning, automated, high ph, fractionation, label free, TMT, kidney proteome, cortex, medulla, protein assay, modified BCA assay.

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Abstract Here we assessed the ability of an automated sample preparation device equipped with disposable microcolumns to prepare mass-limited samples for high-sensitivity quantitative proteomics, using both label-free and isobaric labeling approaches. Firstly, we compared peptide label-free quantification reproducibility for 1.5-150 µg of cell lysates and found that labware preconditioning was essential for reproducible quantification of 98%. Thirdly, compared to a single long gradient experiment, a simple robotized high pH fractionation protocol using only 6 µg of starting material, doubled the number of unique peptides and increased proteome coverage 1.43-fold. To facilitate the analysis of heterogeneous tissue samples, such as those obtained from laser capture microdissection, a modified BCA protein assay was developed that consumes and detects down to 15 ng of protein. As a proof-of-principle the modular automated workflow was applied to 0.5 and 1 mm2 mouse kidney cortex and medulla microdissections to show the method's potential for real-life small sample sources and to create kidney substructure-specific proteomes.


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Introduction Over the past decade mass spectrometry-based proteomics has matured into the method of choice for the large scale identification and characterization of proteins associated to biological processes and disease.1,2 However a remaining challenge is the analysis of proteins and polypeptides from sample limited sources such as single cells3, purified cell populations4, subcellular organelles, exosomes or small histologically defined regions of tissue isolated by laser capture microdissection5–7 . Recent advances in liquid chromatography (LC) and mass spectrometry (MS) equipment have greatly improved the analysis of low sample amounts. The development of mass spectrometers has increased sequencing speed8–11 and ion transmission12,13 resulting in an increase in dynamic range and sensitivity. The advancements in (ultra) high pressure liquid chromatography (HPLC) has enabled the routine use of long columns (≥50 cm) with small internal diameter and smaller particle sizes (0.9, yellow squares Figure 1B). The rest of the heat map and the scatter plots in Figure 1B show the comparisons between different sample amounts, e.g. 15 vs. 1.5 µg. Good intensity correlations (p>0.9) with symmetrical peptide intensity distributions were obtained for sample amounts of 7.5 µg and greater. However, poor correlations (Pearson correlation 0.71-0.85) were observed when the lowest sample amount, 1.5 µg, was included in the comparison. Close examination of the scatter plots of the 1.5µg sample group revealed an asymmetry due to systematically lower intensities for many peptides in this sample, most likely due to adsorbance of these peptides on to the labware during sample preparation because when the labware was preconditioned using a concentrated BSA solution the systematic bias observed with the 1.5µg sample group was negated. Preconditioning of labware reestablished high intensity correlations between different amounts as well as symmetrical peptide intensity distributions (“PreCon” columns and rows in figure 1B). Thus, when handling low sample amounts and performing label-free quantification it is advised to first precondition labware to mitigate any quantitative bias that may be introduced by handling different sample amounts. As a side note, we found a similar behavior albeit a slightly greater intensity correlation with reversed phase-S (RPS) material compared to C18 (Figure S-1).


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TMT labeling of low sample amounts To explore the potential gains of automated offline on-column labeling of 1 µg, we have investigated chemical tandem mass tag (TMT) labeling on miniaturized columns using a robotized system and compared it to normal and optimized in-solution labeling protocols for 1 µg of sample. To compare on-column with in-solution labeling, first an in-solution reference protocol was established for low sample amounts. When performing in-solution TMT labeling of 50 µg of digest using the factory recommended protocol a good labeling efficiency is achieved (>98%) (Figure 2A). However, for low sample amounts such as 1 µg of digest, a simple downscaling of the TEABbased protocol resulted in poor labeling efficiency of N-termini, even at high levels of excess TMT (1:20) (Figure 2A). Therefore, we compared and optimized labeling conditions for 1 µg of sample by varying peptide-to-TMT mass ratios and using different TMT labeling buffers. Basic TEAB, basic HEPES31 and phosphate27 buffers were tested. Though HEPES and TEAB have the exact same pH (pH8.5), the labeling efficiency using HEPES was significantly greater. Even a 5-fold TMT excess in HEPES outperformed a 10-fold TMT excess in TEAB with 95% vs 65% N-terminal labeling, respectively, indicating TMT labeling in HEPES is more efficient but also more economical. The labeling of 1 µg digest in HEPES using a 10-fold TMT excess achieved similar labeling efficiencies (>98%) as obtained using the standard protocol with 50 µg digest, and with a significant reduction in undesired side reactions such as serine, threonine, histidine and tyrosine labeling (Figure 2B). A 10- or even 20-fold excess of TMT in HEPES resulted in less than 1.5% over labeling compared to more than 10% observed for the 50 µg sample using the standard protocol. Even though labeling 1 µg sample in TEAB (1:20 TMT) did not result in full labeling of primary amines, the rate of over labeling was greater than that observed with complete labeling in HEPES. The phosphate based buffer was also tested but was found to be far from optimal for insolution labeling of peptides with poor lysine labeling efficiencies of 5 to 40% for 10- to 20-fold TMT excess. As expected, the absolute number of identified peptide spectral matches was the highest for the larger scale labeling at 50 µg (Figure 2C). Although, 10- and 20-fold TMT excess in 11 Environment ACS Paragon Plus


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HEPES showed more complete labeling, 5-fold excess TMT in HEPES identified slightly more PSMs. All parameters considered, for in-solution labeling of 1 µg sample, 50 mM HEPES (pH8.5) gave the best and most consistent labeling results using a 1:10 or 1:20 TMT excess ratio. Next the performance of on-column labeling was assessed using the AssayMap Bravo platform and microcolumns/cartridges. In all labeling experiments cartridges with RPS chemistry were used because pilot studies showed that although C18 cartridges show slightly less peptide breakthrough during the labeling process their labeling efficiency is significantly reduced compared to RPS cartridges (Figure S-2). On-column labeling was performed using 1 µg in HEPES, the optimal insolution buffer, or phosphate buffer, previously reported to be suitable for on-column TMT labeling27. The optimal conditions for in-solution labeling, 20-fold TMT excess in HEPES buffer, showed a poor labeling efficiency on-column and also led to a greater degree of over labeling when compared to the in-solution protocol (Figure 2A,B). In contrast to the very poor in-solution labeling obtained using the phosphate buffer, it provided the greatest efficiencies for on-column labeling. When comparing the complete on-column to complete in-solution labeling experiments, a higher amount of over labeling was observed on-column (Figure 2B). Even for HEPES that displayed very low over labeling in solution a 9-fold higher over labeling was observed on column. However, when quenching the reaction by eluting the peptide / TMT mixture into hydroxylamine the high degree of over labeling could be negated to less than 0.5% whilst still maintaining a high label efficiency (>95%) and providing the highest amount of identified fully labeled peptides. Interestingly, on-column labeling increased the number of identified spectra (Figure 2C). A 146% increase in N-terminal and lysine labeled PSMs was observed when comparing the maximum number of PSMs in in-solution (1:5 in HEPES) to on-column (1:20 in phosphate buffer). All in all, the best on-column labeling protocol (phosphate buffer, TMT 1:40 with quenching) performed the best for 1 µg of sample and even performed similar to labeling 50 µg using the conventional protocol in terms of labeling efficiency (>96%). 12 Environment ACS Paragon Plus

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Fractionation of low sample amounts Next we investigated if the proteome coverage could be increased by extending the above described workflow with automated peptide prefractionation using the same robot and microcolumns. We have chosen RP high pH (HpH) over strong cation exchange (SCX) chromatography as we found that on 100 µg of sample HpH identified more unique peptides because with SCX a larger number of peptides were identified in multiple fractions (Figure S-4). Initially, 6 µg of HeLa digest was fractionated to mimic the analysis of 1 µg samples in a TMT 6-plex experiment. For the low sample amount comparison a single 4 hour run was compared to seven HpH-RP fractions, each of which was analyzed with a 2 hour gradient. To prevent overloading of the LC column in the single run analysis, only 5 µg of peptides was injected. As observed in figure 3, the prefractionation has increased the number of acquired spectra ~2.6 fold, resulting in a doubling of unique peptide identifications (23946 vs. 47220). As a result the proteome coverage was increased by 143% from 4237 to 6052 protein groups (grouped from 10277 and 14645 identified protein accessions, respectively).

Protein determination of minute sample amounts Many direct (tyrosine and tryptophan absorption method), colorimetric (Lowry32, Bradford33 and BCA34) and fluorescence based assays (as Qubit35, FluoroProfile36 and NanoOrange37) have been developed to achieve fast and/or reliable protein quantification. However, not all of them are compatible with sample lysis buffers commonly used for proteomics sample preparation. Among these methods, the BCA assay provides a good tolerance to detergents and a range of different buffers (Table S-1). Although the micro BCA protein determination assay is highly sensitive for medium sample volumes (2-40ng/µl for 150 µl sample), this does not suffice if only a few microliters of sample can be spared for the protein determination assay. Therefore we modified the 13 Environment ACS Paragon Plus


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standard BCA assay (see experimental section) to consume less sample (only 1 µl) whilst increasing the detection range. We obtained a reproducible BSA standard calibration curve between 5 and 120 ng/µl by using only 1 µl of standard (Figure S-5). Using this optimized method we could reproducibly measure down to 15 ng of protein from only 1 µl of sample with an average CV of 3% (average from nine BSA standards). For our tissue lysis in 20 µl of lysis buffer, this translates into measuring tissue protein contents ranging from 300ng to 7.2 µg whilst consuming only 5% of sample.

Mouse kidney tissue substructure proteome analysis To study the in-vivo interplay of the different layers in the kidney, laser capture microdissection (LCM) was used to excise and isolate the cortex and medula regions of fresh frozen mouse kidney followed by the herein developed protein assay and automated microproteomics workflow. Five small tissue samples of 0.5 mm2, and five of 1 mm2, were microdissected from both the cortex and medulla. The resulting 20 tissue samples were lysed using strong chemical and mechanical conditions resulting in an average of 1.5 µg of protein per sample. After protein cleanup and digestion using the SP3 method, samples were transferred to the robotized platform for automated peptide desalting, TMT-labeling and HpH fractionation. Each of the ten 0.5 mm2 and ten 1 mm2 samples were labeled with a different 10-plex TMT label. This resulted in two datasets, one from 0.5 mm2 samples and the other from 1 mm2 samples, each containing 5 replicates from the cortex and medulla. As seen in figure 4A a deep proteome coverage was achieved for a sample area of only 1 mm2 (~34 547 peptides, 5002 protein groups), even when using TMT labeling that reduces sequencing speed. A lower proteome depth was observed for the 0.5 mm2 area samples, reflecting the lower amount of protein available for the analysis (3440 protein groups). Still, when comparing our approach using only ~1.5 µg per sample and analyzing only 7 HpH-RP fractions in 7 LC-MS runs, an equal or greater proteome coverage was achieved then a recent dataset19 in which ~450 µg


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of human kidney homogenate was separated into 24 HpH-RP fractions and analyzed in 24 LC-MS runs (Figure 4A). When comparing the protein expression levels from the two different kidney regions we can distill a cortex and medulla specific proteome signature (Figure 4B). Indeed, many marker proteins known to be localized to the different kidney layers (Human Protein Atlas) were detected in the layerspecific proteomes (see Table S-2 for all proteins). Interestingly, the 0.5 mm2 proteome shows more variation (lower –log p-values) and larger ratios. This could be a result of a higher sample heterogeneity for smaller tissue sections due to random inclusion or exclusion of smaller tissue substructures / cell populations, leading to a lower degree of protein averaging and more sample variation. Despite a slightly higher variation, the proteomes obtained from 0.5 and 1 mm2 tissues are very similar in terms of relative expression levels and the set of significantly regulated proteins, as can be seen from the consistently regulated kidney markers (Figure 4B) and the high correlation between the two proteomes (Figure 4C). Having defined the in-depth cortex and medulla enriched proteome, a functional analysis was performed. Gene ontology over- and underrepresentation analysis of proteins significantly upregulated in each part of the kidney (Figure S-6A) supported the well documented function of each layer. It is known that the main function of the cortex is to resorb salt ions, glucose, amino acids and organic acids whereas in the medulla mainly ions and water are secreted and resorbed. Indeed, many molecular functions and biological processes enriched in the cortex proteome confirm the transporting activity of the previously mentioned molecules. Interestingly, in addition to transporting proteins many metabolic enzymes are enriched in the cortex as well. This could indicate other important process in the cortex, including the modification, transformation and/or clearance of the just resorbed amino acids, carbohydrates and other organic compounds. Again in correspondence with its function, in the medulla water transport/homeostasis and salt transport is strongly enriched due to the relatively high levels of aquaporins and specific ion transporters.



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The dataset also allowed for the large scale monitoring of kidney substructure specific protein paralogue expression (Figure S-6B). Whereas most of the detoxifying glutathione S-transferase proteins are expressed at similar levels in each substructure, some are specifically expressed in the cortex (Gsta2, Gstm5, Mgst1) or medulla (Gstm2 & Gstm6). Similar analysis was also performed for membrane transporter proteins from the solute carrier and ATPase families. Specific bicarbonate transporters (SLC family 4) are expressed in the medulla (Slc4a2 and Slc4a7) or cortex (Slc4a4). Whereas, only organic ion transporters (SLC family 22) where found significantly elevated in the cortex (5 out of 10 quantified). Regarding ATPases, alpha, beta and gamma subunits (Atp1a1, Atp1a4, Atp1b1 and Fxyd2) of the Na+/K+ transporting ATPase assembly were significantly upregulated in the medula, whereas many different H+ transporting subunits showed elevated but insignificant upregulation in the Cortex (see Figure S-7 for more details).

Conclusions We have demonstrated that using an automated sample preparation station, a good qualitative and quantitative peptide intensity reproducibility is achieved for sample amounts from 150 to 7.5 µg. However, when using low sample amounts (1.5 µg) peptide loss is observed resulting in a bias in quantification only. This peptide loss is often overlooked because generally higher sample amounts (>100 µg) are used during sample preparation workflows. However, when analyzing small sample sources containing ~1 µg of protein/peptide, sample loss should be taken into consideration. Especially, when considering a label-free quantitative comparison a lower signal for a specific peptide might be a result of more adsorbance due to small differences in sample amounts processed instead of biological regulation, potentially leading to wrong conclusions. Indeed, Cunningham et al.38 has investigated different protocols to decrease sample loss for molecular weight cut-off membrane-based centrifugal filters. To overcome the peptide intensity bias for low sample amounts in peptide cartridge desalting, we have shown that preconditioning of labware with BSA protein 16 Environment ACS Paragon Plus

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recovered good peptide intensity reproducibility in quantitative comparisons. Therefore, for labelfree quantification of sample amounts under 7.5ug, we would recommended labware preconditioning. Qualitatively we have not seen large differences. However, this could be expected because in this experiment we have used short gradients (to keep the measurement time for many repeats manageable) resulting in substantial under sampling of the complex HeLa peptide mixture and thus the identification of mostly higher abundant peptides.

We also investigated TMT labeling workflows for low sample amounts. We found that traditional in-solution labeling workflows show a decrease in labeling efficiency as samples are more diluted. Online on-column chemical labeling has been successfully used before to concentrate the sample and allow for a sensitive and fast workflow39. However, the use of online systems requires skilled personnel for quality control and maintenance. Therefore, we have chosen for a labeling strategy based on offline on-column peptide labeling by using disposable cartridges as described before by Boersema et al. and Bohm et al.

26,27

. To complement these previous studies we have focused on

low sample amounts and the incorporation of an automated offline liquid handling system for a strategy that is less error-prone, more reproducible and may be adopted without extensive training. Due to the 96-well parallel format of the AssayMAP robot the same amount of time will be spent to label 1 sample as for 96 samples, which is not the case for the current implementation of TMTSPAL27 or manual in-solution labeling which both consume considerable more time when the number of samples increase. Indeed, Dayon et al.40 have previously developed an automated protocol with integrated in-solution TMT labeling for sample amounts down to 25 µg. Disappointingly, when scaling down TMT labeling to 1 µg of sample using current protocols for insolution or on-column labeling, we observed a poor labeling efficiency (200-fold less starting material (450 vs ~1.5 µg per sample) and ~20-fold less LC-MS run time was used (36h per sample vs 17h for 10 samples). The differences between the two studies are mainly due to the use of a more sensitive protocol and multiplexing but in part also due to different LC-MS setups. The selection and microdissection of cortex and medulla kidney substructures was used to create a detailed list of region specific protein expression. Many well documented functions of the different regions were molecularly confirmed and in addition further specified in detail, because of the obtained protein isoform specific expression patterns. The herein developed set of microproteomics protocols can also be used to study the proteomes of other heterogeneous low sample amount sources such as ex-vivo tumours.

Supporting Information. The following files are available free of charge. SI-de Graaf.pdf

Extended description of methods and supporting results (Table S-1, and Figures S-1 to S-7).

Supporting Table 2.xls

Kidney Protein Quantification for 1 mm2 and 0.5 mm2 microdissected sample sizes.

Acknowledgments We would like to thank Professor Mateo Caleo for supplying a mouse kidney and Rima AitBelkacem for cutting mouse kidney sections.



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References (1)

Aebersold, R.; Mann, M. Mass spectrometry-based proteomics. Nature 2003, 422 (6928), 198–207.

(2)

Altelaar, A. F. M.; Munoz, J.; Heck, A. J. R. Next-generation proteomics: towards an integrative view of proteome dynamics. Nat. Rev. Genet. 2013, 14 (1), 35–48.

(3)

Ong, T.-H.; Kissick, D. J.; Jansson, E. T.; Comi, T. J.; Romanova, E. V.; Rubakhin, S. S.; Sweedler, J. V. Classification of Large Cellular Populations and Discovery of Rare Cells Using Single Cell Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry. Anal. Chem. 2015, 87 (14), 7036–7042.

(4)

Di Palma, S.; Stange, D.; van de Wetering, M.; Clevers, H.; Heck, A. J. R.; Mohammed, S. Highly Sensitive Proteome Analysis of FACS-Sorted Adult Colon Stem Cells. J. Proteome Res. 2011, 10 (8), 3814–3819.

(5)

Han, M. H.; Hwang, S.-I.; Roy, D. B.; Lundgren, D. H.; Price, J. V.; Ousman, S. S.; Fernald, G. H.; Gerlitz, B.; Robinson, W. H.; Baranzini, S. E.; et al. Proteomic analysis of active multiple sclerosis lesions reveals therapeutic targets. Nature 2008, 451 (7182), 1076–1081.

(6)

Umar, A.; Kang, H.; Timmermans, A. M.; Look, M. P.; Meijer-van Gelder, M. E.; den Bakker, M. a; Jaitly, N.; Martens, J. W. M.; Luider, T. M.; Foekens, J. a; et al. Identification of a putative protein profile associated with tamoxifen therapy resistance in breast cancer. Mol. Cell. Proteomics 2009, 8 (6), 1278–1294.

(7)

Wiśniewski, J. R.; Ostasiewicz, P.; Mann, M. High Recovery FASP Applied to the Proteomic Analysis of Microdissected Formalin Fixed Paraffin Embedded Cancer Tissues Retrieves Known Colon Cancer Markers. J. Proteome Res. 2011, 10 (7), 3040–3049.

(8)

Andrews, G. L.; Simons, B. L.; Young, J. B.; Hawkridge, A. M.; Muddiman, D. C. Performance characteristics of a new hybrid quadrupole time-of-flight tandem mass spectrometer (TripleTOF 5600). Anal. Chem. 2011, 83 (13), 5442–5446. 20 Environment ACS Paragon Plus

Page 20 of 32

Page 21 of 32

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


(9)

Hebert, A. S.; Richards, A. L.; Bailey, D. J.; Ulbrich, A.; Coughlin, E. E.; Westphall, M. S.; Coon, J. J. The One Hour Yeast Proteome. Mol. Cell. Proteomics 2013, 13 (1), 339–347.

(10)

Kelstrup, C. D.; Jersie-Christensen, R. R.; Batth, T. S.; Arrey, T. N.; Kuehn, A.; Kellmann, M.; Olsen, J. V. Rapid and deep proteomes by faster sequencing on a benchtop quadrupole ultra-high-field Orbitrap mass spectrometer. J. Proteome Res. 2014, 13 (12), 6187–6195.

(11)

Beck, S.; Michalski, A.; Raether, O.; Lubeck, M.; Kaspar, S.; Goedecke, N.; Baessmann, C.; Hornburg, D.; Meier, F.; Paron, I.; et al. The impact II, a very high resolution quadrupole time-of-flight instrument for deep shotgun proteomics. Mol. Cell. Proteomics 2015, 14 (7) 2014–2029.

(12)

Olsen, J. V.; Schwartz, J. C.; Griep-Raming, J.; Nielsen, M. L.; Damoc, E.; Denisov, E.; Lange, O.; Remes, P.; Taylor, D.; Splendore, M.; et al. A Dual Pressure Linear Ion Trap Orbitrap Instrument with Very High Sequencing Speed. Mol. Cell. Proteomics 2009, 8 (12), 2759–2769.

(13)

Tang, K.; Shvartsburg, A. A.; Lee, H.-N.; Prior, D. C.; Buschbach, M. A.; Li, F.; Tolmachev, A. V; Anderson, G. A.; Smith, R. D. High-sensitivity ion mobility spectrometry/mass spectrometry using electrodynamic ion funnel interfaces. Anal. Chem. 2005, 77 (10), 3330– 3339.

(14)

Köcher, T.; Swart, R.; Mechtler, K. Ultra-high-pressure RPLC hyphenated to an LTQOrbitrap Velos reveals a linear relation between peak capacity and number of identified peptides. Anal. Chem. 2011, 83 (7), 2699–2704.

(15)

Pirmoradian, M.; Budamgunta, H.; Chingin, K.; Zhang, B.; Astorga-Wells, J.; Zubarev, R. a. Rapid and deep human proteome analysis by single-dimension shotgun proteomics. Mol. Cell. Proteomics 2013, 12 (11), 3330–3338.

(16)

Yamana, R.; Iwasaki, M.; Wakabayashi, M.; Nakagawa, M.; Yamanaka, S.; Ishihama, Y. Rapid and deep profiling of human induced pluripotent stem cell proteome by one-shot NanoLC-MS/MS analysis with meter-scale monolithic silica columns. J. Proteome Res. 21 Environment ACS Paragon Plus


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

2013, 12 (1), 214–221. (17)

Ji, A. J.; Jiang, Z.; Livson, Y.; Davis, J. A.; Chu, J. X.; Weng, N. Challenges in urine bioanalytical assays: overcoming nonspecific binding. Bioanalysis 2010, 2 (9), 1573–1586.

(18)

Boyacı, E.; Pawliszyn, J. Micelle assisted thin-film solid phase microextraction: a new approach for determination of quaternary ammonium compounds in environmental samples. Anal. Chem. 2014, 86 (18), 8916–8921.

(19)

Kim, M.-S.; Pinto, S. M.; Getnet, D.; Nirujogi, R. S.; Manda, S. S.; Chaerkady, R.; Madugundu, A. K.; Kelkar, D. S.; Isserlin, R.; Jain, S.; et al. A draft map of the human proteome. Nature 2014, 509 (7502), 575–581.

(20)

Wilhelm, M.; Schlegl, J.; Hahne, H.; Moghaddas Gholami, A.; Lieberenz, M.; Savitski, M. M.; Ziegler, E.; Butzmann, L.; Gessulat, S.; Marx, H.; et al. Mass-spectrometry-based draft of the human proteome. Nature 2014, 509 (7502), 582–587.

(21)

Peng, J.; Elias, J. E.; Thoreen, C. C.; Licklider, L. J.; Gygi, S. P. Evaluation of Multidimensional Chromatography Coupled with Tandem Mass Spectrometry (LC/LC−MS/MS) for Large-Scale Protein Analysis: The Yeast Proteome. J. Proteome Res. 2003, 2 (1), 43–50.

(22)

Wang, Y.; Yang, F.; Gritsenko, M. A.; Wang, Y.; Clauss, T.; Liu, T.; Shen, Y.; Monroe, M. E.; Lopez-Ferrer, D.; Reno, T.; et al. Reversed-phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells. Proteomics 2011, 11 (10), 2019–2026.

(23)

Washburn, M. P.; Wolters, D.; Yates, J. R. Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol. 2001, 19 (3), 242–247.

(24)

Altelaar, A. F. M.; Frese, C. K.; Preisinger, C.; Hennrich, M. L.; Schram, A. W.; Timmers, H. T. M.; Heck, A. J. R.; Mohammed, S. Benchmarking stable isotope labeling based quantitative proteomics. J. Proteomics 2013, 88, 14–26.

(25)

Thompson, A.; Schäfer, J.; Kuhn, K.; Kienle, S.; Schwarz, J.; Schmidt, G.; Neumann, T.; 22 Environment ACS Paragon Plus

Page 22 of 32

Page 23 of 32

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


Hamon, C. Tandem Mass Tags: A Novel Quantification Strategy for Comparative Analysis of Complex Protein Mixtures by MS/MS. Anal. Chem. 2003, 75 (8), 1895-1904. (26)

Boersema, P. J.; Raijmakers, R.; Lemeer, S.; Mohammed, S.; Heck, A. J. R. Multiplex peptide stable isotope dimethyl labeling for quantitative proteomics. Nat. Protoc. 2009, 4 (4), 484–494.

(27)

Böhm, G.; Prefot, P.; Jung, S.; Selzer, S.; Mitra, V.; Britton, D.; Kuhn, K.; Pike, I.; Thompson, A. H. Low-pH Solid-Phase Amino Labeling of Complex Peptide Digests with TMTs Improves Peptide Identification Rates for Multiplexed Global Phosphopeptide Analysis. J. Proteome Res. 2015, 14 (6), 2500–2510.

(28)

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.

(29)

Eng, J. K.; McCormack, A. L.; Yates, J. R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 1994, 5 (11), 976–989.

(30)

Mi, H.; Poudel, S.; Muruganujan, A.; Casagrande, J. T.; Thomas, P. D. PANTHER version 10: expanded protein families and functions, and analysis tools. Nucleic Acids Res. 2016, 44 (D1), D336–D342.

(31)

Isasa, M.; Rose, C. M.; Elsasser, S.; Navarrete-Perea, J.; Paulo, J. A.; Finley, D. J.; Gygi, S. P. Multiplexed, Proteome-wide Protein Expression Profiling: Yeast Deubiquitylating Enzyme Knockout Strains. J. Proteome Res. 2015, 14 (12), 5306–5317.

(32)

Lowry, O. H.; Rosebrough, N. J.; Lewis Farr, A.; Randall, R. J. Protein Measurement with the Folin phenol reagent. J. Biol. Chem. 1951, 193 (1), 265–275.

(33)

Bradford, M. M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 1976, 72 (1), 248–254.

(34)

Smith, P. K.; Krohn, R. I.; Hermanson, G. T.; Mallia, A. K.; Gartner, F. H.; Provenzano, M. 23 Environment ACS Paragon Plus


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

D.; Fujimoto, E. K.; Goeke, N. M.; Olson, B. J.; Klenk, D. C. Measurement of protein using bicinchoninic acid. Anal. Biochem. 1985, 150 (1), 76–85. (35)

Haughland, R. P. Protein detection and proteomics technology. In A Guide to fluorescent probes and labeling technologies; Spence, M. T. Z., Ed.; 2005; pp 413–472.

(36)

Bell, P. J. L.; Karuso, P. Epicocconone, A Novel Fluorescent Compound from the Fungus Epicoccum nigrum. J. Am. Chem. Soc. 2003, 125 (31), 9304–9305.

(37)

Jones, L. J.; Haugland, R. P.; Singer, V. L. Development and characterization of the NanoOrange protein quantitation assay: a fluorescence-based assay of proteins in solution. Biotechniques 2003, 34 (4), 856–858.

(38)

Cunningham, R.; Wang, J.; Wellner, D.; Li, L. Investigation and reduction of sub-microgram peptide loss using molecular weight cut-off fractionation prior to mass spectrometric analysis. J. Mass Spectrom. 2012, 47 (10), 1327–1332.

(39)

Raijmakers, R.; Berkers, C. R.; de Jong, A.; Ovaa, H.; Heck, A. J. R.; Mohammed, S. Automated online sequential isotope labeling for protein quantitation applied to proteasome tissue-specific diversity. Mol. Cell. Proteomics 2008, 7 (9), 1755–1762.

(40)

Dayon, L.; Núñez Galindo, A.; Corthésy, J.; Cominetti, O.; Kussmann, M. Comprehensive and scalable highly automated MS-based proteomic workflow for clinical biomarker discovery in human plasma. J. Proteome Res. 2014, 13 (8), 3837–3845.

(41)

Rappsilber, J.; Mann, M.; Ishihama, Y. Protocol for micro-purification, enrichment, prefractionation and storage of peptides for proteomics using StageTips. Nat. Protoc. 2007, 2 (8), 1896–1906.

(42)

Han, D.; Moon, S.; Kim, Y.; Kim, J.; Jin, J.; Kim, Y. In-depth proteomic analysis of mouse microglia using a combination of FASP and StageTip-based, high pH, reversed-phase fractionation. Proteomics 2013, 13 (20), 2984–2988.

(43)

Lawrence, R. T.; Perez, E. M.; Hernández, D.; Miller, C. P.; Haas, K. M.; Irie, H. Y.; Lee, S. I.; Blau, A. C.; Villén, J. The Proteomic Landscape of Triple-Negative Breast Cancer. Cell 24 Environment ACS Paragon Plus

Page 24 of 32

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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


Rep. 2015, 11 (4), 630–644. (44)

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 (9), 4864-4871.



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Figure Legends Figure 1: Automated quadruplicate HeLa digest cleanup using standard (Control) or preconditioned (PreCon) labware injected in equal amounts on the LC-MS. A) Qualitative analysis of all runs. B) Peptide intensity correlation between replicates (A,B,C,D) and different sample amounts (150, 15, 7.5, 1.5 µg) (Pearson heatmap p=0.71-0.96, black-red). Lower left part shows replicate averaged log2 intensity plots of different sample amount comparisons and its representative Pearson correlation score. Figure 2. Optimization of TMT labeling for 1 µg peptides. In-solution and on-column labeling was performed on 50 ug and 1µg HeLa digests in triplicate, using different buffers and different peptide to TMT mass ratios. Buffers used: 100 mM TEAB pH8.5 (T), 50 mM HEPES pH8.5 (H) and NaH2PO4 pH4.5 (P). Triplicate averages and s.e.m error bars. Figure 3. Low sample amount proteome coverage optimization for 6 µg of sample: single 4h LC-MS run vs. LC-MS analysis of offline HpH RP prefractionated sample (7 fractions x 2 hour gradients). Figure 4: Quantitative mouse kidney cortex and medulla proteomes from 0.5 and 1 mm2 tissue. A) Number of identified and quantified peptides and proteins. Kim et al. refers to the human kidney dataset from the recent human proteome draft. Asterisks indicate peptides/protein groups quantified in at least 3 samples from each tissue type. B) Protein expression comparisons between kidney cortex and medula. Cortex markers: Lrp2, Ggt1, Hpd, Hrsp12, Pklr, Slc22a8, Dpys, Slc22a13. Medula markers: Aqp2, Cryab, Ut1, Ut2. C) Protein group expression correlation. Significantly different protein groups between cortex and medulla in one (orange) or both (red) datasets are indicated with orange and red, respectively.


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Figure 1 Figure 1 166x90mm (300 x 300 DPI)

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Graphic abstract - TOC Graphic abstract - TOC 84x47mm (300 x 300 DPI)


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Set of Novel Automated Quantitative ... - ACS Publications

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