Impact of Temperature Dependent Sampling Procedures in Proteomics and Peptidomics - A...

Impact of Temperature Dependent SamplingProcedures in Proteomics and Peptidomics –A Characterization of the Liver and PancreasPost Mortem Degradome□S

Birger Scholz‡¶¶��, Karl Skold§��, Kim Kultima¶, Celine Fernandez�,Sofia Waldemarson�, Mikhail M. Savitski**, Marcus Soderquist§, Mats Boren§,Robert Stella‡‡, Per Andren§§, Roman Zubarev**, and Peter James�

Little is known about the nature of post mortem degrada-tion of proteins and peptides on a global level, the so-called degradome. This is especially true for nonneuraltissues. Degradome properties in relation to samplingprocedures on different tissues are of great importancefor the studies of, for instance, post translational modifi-cations and/or the establishment of clinical biobanks.Here, snap freezing of fresh (<2 min post mortem time)mouse liver and pancreas tissue is compared with rapidheat stabilization with regard to effects on the proteome(using two-dimensional differential in-gel electrophoresis)and peptidome (using label free liquid chromatography).We report several proteins and peptides that exhibitheightened degradation sensitivity, for instance superox-ide dismutase in liver, and peptidyl-prolyl cis-transisomerase and insulin C-peptides in pancreas. Tissuesampling based on snap freezing produces a greateramount of degradation products and lower levels of en-dogenous peptides than rapid heat stabilization. We alsodemonstrate that solely snap freezing related degradationcan be attenuated by subsequent heat stabilization. Weconclude that tissue sampling involving a rapid heat sta-bilization step is preferable to freezing with regard toproteomic and peptidomic sample quality. Molecular &Cellular Proteomics 10: 10.1074/mcp.M900229-MCP200,1–15, 2011.

The evolving maturation of the field of proteomics has, inthe same way as in genomics, highlighted the need of bettersampling procedures and sample preparation methodologiesto minimize the effect of post mortem alterations. The aspectof sample quality is not new in any way and is relevant in mostbiomedical fields but has only lately started to receive ade-quate attention. The main factors influencing sample quality isstorage temperature of the body until tissue removal (fore-most a problem in clinical settings and extraction of lessaccessible tissue samples from model organisms) and postmortem interval (PMI)1 (1–3). Post mortem degradation induring PMI is a well known compromising problem whenstudying endogenous peptides (2, 3) and has also beenproven to affect the results of polypeptide (here defined asproteins larger than 10 kDa) studies (3–8). PMI degradationhas mainly been studied on human or mouse brain tissue,using two-dimensional electrophoresis (2-DE), SDS-PAGE,and immunoblotting (1, 3–12). There are also a few proteomicstudies on muscle tissue degradation in livestock (13–16).

We and others have previously explored the effect of fo-cused microwave irradiation with regard to sample quality,demonstrating that this method is more reliable than snapfreezing in liquid nitrogen, especially with regard to post-translational modification (PTM) stability (2, 3, 17–20). An al-ternative method based on cryostat dissection with subse-quent heat treatment through boiling has also been reportedto improve endogenous peptide sample quality (21). Besidesfocused microwave irradiation, which is specifically used forrodent brain tissue sampling, we have also demonstrated theefficiency of rapid heat stabilization through conductivity with

From the ‡Department of Pharmaceutical Biosciences, Division ofToxicology, BMC, Box 594, SE-75124 Uppsala University, Sweden,§Denator AB, Uppsala Science Park, Dag Hammarskjolds vag 32a,SE-75183, Uppsala, Sweden, ¶Department of Medical Sciences,Clinical Pharmacology, Uppsala University Hospital, SE-751 85 Up-psala, Sweden, �Create Health, Department of Immunotechnology,BMC, Lund University, SE-221 84 Lund, Sweden, **Molecular Biom-etry Group, Institute for Cell and Molecular Biology, Box 583, BMC,Uppsala University, SE-75123, Uppsala, Sweden, ‡‡Department ofBiological Chemistry, Padova University, 3-35131 Padova, Italy,¶¶Department of Pharmaceutical Biosciences, Division of MedicalMass Spectrometry, Box 583, BMC, Uppsala University, SE-75123Uppsala, Sweden

Received May 11, 2009, and in revised form, November 4, 2009Published, MCP Papers in Press, January 28, 2010, DOI 10.1074/

mcp.M900229-MCP200

1 The abbreviations used are: PMI, post mortem interval; 2D-DIGE,two-dimensional differential in gel electrophoresis; 2-DE, two-dimen-sional electrophoresis; D, rapidly heat-stabilized samples; FD, rapidlyfrozen and subsequently heat stabilized samples; F, rapidly frozensamples; FvsD, pair-wise comparison between F and D; FDvsD,pair-wise comparison between FD and D; FvsFD, pair-wise compar-ison between F and FD; LC-MS, liquid chromatography mass spec-trometry; PCA, principal component analysis; PTM, post-translationalmodification.

Research© 2011 by The American Society for Biochemistry and Molecular Biology, Inc.This paper is available on line at http://www.mcponline.org

Molecular & Cellular Proteomics 10.3 10.1074/mcp.M900229-MCP200–1

regard to sample degradation (3, 22). Although somewhatconstrained by its dependence on how quickly the tissue isharvested from the body, the latter procedure has the addedadvantage that it can be used on any type of tissue andspecies, fresh as well as frozen. This study will compareeffects of sampling procedures on the liver and pancreasdegradome following rapid heat stabilization, the more tradi-tional snap freezing, or the combination of snap freezing withsubsequent heat stabilization.

To summarize, this study investigated the effects of postmortem degradation in pancreas and liver. Both tissues arewell studied because of their multiple functions in the bodyand their involvement in different diseases such as diabetes or

hepatocarcinoma. Pancreas is especially interesting in thiscontext as it displays endocrine secretion of peptides, andexocrine secretion of digestive enzymes, the later making it aprotease rich tissue. We used both two-dimensional differ-ence in gel electrophoresis (2D-DIGE) and label free liquidchromatography mass spectrometry (LC-MS) based differen-tial peptide display (2, 18), the later to better investigatechanges in small molecular fragment that are not easily de-tectable by gel-based methods. 2D-DIGE is an unrivaledmethodology to characterize alterations in isoform patterns,which is an important aspect considering that post-transla-tional modifications (PTMs) such as phosphorylations are es-pecially sensitive to post mortem influence within a few min-

FIG. 1. Overview of general differ-ences between sampling groupsbased on the proportion of proteinsand peptides that is detected acrossall groups. A, Hierarchical clusteringdendrograms based on significantly (F-test p � 0,05) differentially expressedspots with maximum number of obser-vations for liver (54 spots), pancreas (191spots), and peptide expression for pan-creas (26 peaks). B, Two-dimensionalprincipal component analysis (PCA)based on the same premises. C, Propor-tion of unaffected and affected (inde-pendent of level of fold change, mini-mum one missing observation pergroup, moderated t test p � 0,05 or-ange; p � 0,01 dark red) proteome andpeptidome in different pairwise compar-isons between sampling procedures forliver and pancreas.

Characterization of the Liver and Pancreas Post Mortem Degradome

10.1074/mcp.M900229-MCP200–2 Molecular & Cellular Proteomics 10.3

utes PMI (3). The peptidomics approach has been used inseveral studies to point out early post mortem changes andprotein degradation that tissue undergo following samplingand is therefore a well-suited method (3, 18, 22).

EXPERIMENTAL PROCEDURES

Animals and Tissue Sampling—A total of eight C57BL/6J micewere sacrificed by cervical dislocation. The animals were used tostudy protein degradation by following protein changes using 2D-DIGE (four animals) experiments, and peptide changes usingLC-MS (four animals). The pancreas and liver were dissected fromall animals (�2 min post mortem) and divided in three parts. Twopieces from each organ were snap frozen in liquid nitrogen. Thethird part was rapidly stabilized using the Stabilizor T1 system(Denator, Gotesberg, Sweden) (22) and thereafter frozen. Beforesample preparation one of the frozen pieces was rapidly stabilizeddirectly from the frozen state. Samples snap frozen in liquid nitro-gen were named “F,” rapidly heat inactivated “D,” and rapidlyfrozen and subsequently heat stabilized “FD.”

2D-DIGE—Frozen pancreas or liver were crushed in a liquid nitro-gen-cooled mortar and then homogenized with a glass/Teflon ho-mogenizer in lysis buffer (8 M urea, 30 mM Tris, 5 mM magnesiumacetate, and 4% (w/v) 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonic acid (CHAPS)). The homogenates were shaken atroom temperature for 30 min and then centrifuged at 40,000 � g for30 min. The protein concentration in each supernatant was deter-mined using the 2D-Quant kit (GE Healthcare). Protein supernatantswere labeled by DIGE minimal labeling with Cy3 or Cy5 according tomanufactures instruction (GE Healthcare). A total of 400 pmol of dye per50 �g proteins was used. Equal amounts of protein from each samplewere mixed to create a pool labeled with Cy2 as above. Thirty micro-grams of protein from each dye were combined and mixed with rehy-dration buffer (8 M urea, 2% (w/v) CHAPS, 0.002% bromphenol blue,18.2 mM dithiotreitol, 0.5% Pharmalyte (pH 4–7)), left at room temper-ature for 30 min and centrifuged for 10 min before being applied to a24-cm IPG 4–7 strip for overnight rehydration. Isoelectric focusing, stripequilibration, 12.5% SDS-PAGE, and gels fixation were performed asdescribed in (23). The gels were scanned with Amersham BiosciencesTyphoon 9400 variable imager as previously described (24).

FIG. 2. Pairwise comparisons of pro-tein and peptide data for differentmouse liver (841 spots) and pancreassampling (2D-DIGE: 831 spots, Pep-tidomics: 123 peaks) procedures. Theheat-inactivated (D) sample data is com-pared with rapidly frozen (F) and rapidlyfrozen with subsequent heat stabilization(FD) sample data. A, F and D compari-sons (FvD), (B) FD and D comparisons(FDvsD), and (C) F and FD comparisons(FvsFD). Proteome spots were excisedfrom separate preparative 2-DE gels forMS identification. Blue spots were iden-tified; red spots show unsuccessfulidentifications and gray spots were notmatched between 2D-DIGE and the pre-parative 2-DE gels. The Y-axis depictsthe lodsratio and the x axis shows thelog2 fold change for the pairwise com-parisons. The dotted red line designatesa p � 0.05 threshold and the green line athreshold for up or down-regulation. D,Probability density (kernel estimation)curves for log2 fold change distributionsbetween different sample preparationsand tissues.



LC-MS—The frozen pieces of pancreas (n � 4 for F and D, n � 3for FD) were homogenized in sample buffer (0.25% acetic acid) usingmicrotip sonication (Vibra cell 750, Sonics & materials, Inc., Newtown,CT). The homogenates were centrifuged (20,000 � g for 40 min at4 °C) and the supernatant were transferred to a centrifugal filterdevice with a molecular mass limit of 10,000 Da (Microcon YM-10,Millipore) and filtered through centrifugation (14,000 � g for 45 min at4 °C) (2). The filtrate was separated on a nano-LC system (EttanMDLC, GE Healthcare) coupled to a mass spectrometer Q-Tof II(Waters, Denver, CO) for profiling or LTQ-FT (Thermo Electron) foridentification. Five microliters filtrate was injected and desalted on aprecolumn (PepMapT, LC Packings, Amsterdam, The Netherlands) ata flow rate of 10 �l/min for 10 min. A 15-cm 75 �m i.d. fused silicaemitter (Proxeon Biosystems, Copenhagen, Denmark) packed in-house with Reprosil-Pur C18-AQ 3-�m resin (Dr. Maisch, GmbH,Ammerbuch-Entringen, Germany) was used as analytical column at aflow rate of about 180 nanoL/min. The pancreatic peptide samples

were separated during a 40-min gradient from 3–50% acetonitrile forthe comparative studies. A 2-h gradient was used for peptide sepa-ration in the identification study. Profiling MS data was acquired onthe Q-Tof MS instrument. The mass spectrometer was calibrated ac-cording to the manufacturer’s recommendations using a PEG solution(Fluka, Switzerland). MS data was collected in a continuous mode in them/z range 300–1000 for 40 min. The tandem MS data analysis wasperformed on the LTQ-FT instrument in a data-dependent manner andset up to continuously switch between full MS scan (m/z 300–2000),zoom scan (most intense peak in full scan) and MS/MS scan in whichthe most intense peak can be picked twice in a time window of 40 sbefore placing on an exclusion list during a 150 s period.

2D-DIGE Data Normalization and Analysis—The 2D-DIGE imageswere analyzed using the DeCyder software suite (version 5.02; GEHealthcare). Following automatic matching, the majority of spots werealso manually compared between all gels to minimize false spotmatching. The gel with the largest number of spots was designated as

TABLE ISample pretreatment sensitive differentially affected proteins and isoforms in liver. Yellow fields show proteins with log2 fold change � 1 and

F-test p � 0.05. Grey fields show proteins with t-test p � 0.05 in pair-wise comparisons



TABLE IISample pretreatment sensitive differentially affected proteins and isoforms in pancreas. Proteins are listed according to their F-statistics value.Yellow fields show proteins with log2 fold change � 1 and F-test p � 0.05. Grey fields show proteins with t-test p � 0.05 in pair-wise

comparisons



master gel. Unless otherwise stated, all proteomic and peptidomicdata normalization and analysis were performed using the statisticalsoftware R (25). The 2D-DIGE data was normalized using the “2Dloess” method which removes both intensity and spatial bias (26). Toinvestigate general differences between experimental groups, mod-erated F-statistics using the eBayes function in Lima (27) was calcu-lated for spots with no missing observations. F-statistics for spotswith a significance p � 0.05 were considered statistically different.Normalized volume intensity data for significant spots (log2Cy5/Cy2and log2Cy3/Cy2 ratios) with a maximum number of samples/groupwas analyzed by principal component analysis (PCA), using theSIMCA-P software (version 12, Umetrics AB, Umeå, Sweden) andhierarchical clustering was performed on the median spot value ineach group (F, FD, and D). The hierarchical clustering was done usingthe PermuteMatrix program (28) with complete linkage and Euclidiandistance as the distance metric. To find spots differing between D,FD, and F, we used a linear model that takes into account dye-specific differences for the Cy3 and Cy5 channels and calculatedmoderated t and F-statistics using the eBayes function in Limma (27).For pair wise comparisons, (FvsD, FDvsD, and FvsFD) we madevolcano plots displaying the statistical significance (lodsratios) versusthe estimated log2 fold change. Spots with p � 0.05 and log2 foldchange � 1 were considered statistically significantly different. Here,we included spots that were successfully matched across at leastfour out of six liver and pancreas gels. Kernel density estimationswere calculated to display the log2 fold change distributions.

Peptidomics Data Normalization and Analysis—The raw Q-Tof MSdata was converted into ASCII files using the Data Bridge moduleavailable in the MassLynx software (version 3.5, Micromass) andimported into DeCyder MS2.0 (GE Healthcare) where matching andquantification of LC-MS data were performed. The resulting data waslog2 transformed and exported to statistical software R (25). To nor-malize data between LC-MS analyses a linear regression was fitted

for each individual run to a median run that was constructed of allmedian peak values for peaks that were detected in at least twoLC-MS experiments (29). Based on the linear regression equationnew values were predicted for each LC-MS experiment.

Out of 3175 peaks detected over all samples (n � 4 for F and D, n �3 for FD), only 123 were present following normalization which suc-cessfully matched across all three treatments, with a maximum of onemissing value in each group. To find peaks that differed between D,FD, and F, we employed a linear model and calculated robust mod-erated t and F-statistics (27) using eBayes in Limma. Only peaks thatwere successfully matched across all three treatments, with a maxi-mum of one missing value in each group were retained. For each pairwise comparison (FvsD, FDvsD, and FvsFD), we made volcano plotsthat demonstrate the statistical significance (lodsratios) versus the es-timated log2 fold change. Peaks with p � 0.05 and log2 fold change � 1were considered statistically significant. Kernel density estimationswere calculated to display the log2 fold change distributions. The moregeneral relationship between experimental groups was analyzed byhierarchical clustering using complete linkage and Euclidian distance asthe distance metric and PCA. The hierarchical clustering was done onthe proportion of the data sets that had a F-statistic significance of p �0.05 and maximum samples/group (26 peaks) using the median valuefrom the log2 intensities for each peptide and group (F, FD, and D)subtracted with the total median intensity for each peptide. The PCAwas based on the same data (F-statistics p � 0.05, maximum samples/group). To compare peak detection between the D, FD, and F treat-ments, all peaks were divided into three classes in each experimentalgroup: (1) peptides detected in at least three LC-MS experiments, (2)1–2 LC-MS experiments, and (3) not detected at all. Expression differ-ences were then calculated irrespective of the number of availablevalues. When possible, robust estimates of expression differences werecalculated using Limma, otherwise arithmetic mean value differenceswere calculated.

TABLE II—continued



2D-DIGE Protein Identification

Preparative Gel—Seven hundred and fifty micrograms of proteincombining all 16 samples were loaded onto a preparative gel tomaximize the success rate of protein identification. Following the firstand second dimension, the gel was stained in ruthenium batho-phenanthroline disulfonate (RuBPS) with an improved staining anddestaining method (30). The spot picking, destaining, protein diges-tion, extraction, sample preparation, and spotting on matrix-assistedlaser desorption ionization (MALDI) target plates were carried outusing a spot handling work station (Ettan Spot Handling Work station,GE Healthcare) and a standard protocol provided by GE Healthcare.Briefly, protein spots were robotically excised using a Picker headwith Ø 2.0 mm, and digested with modified, sequence-gradeV5111porcine trypsin, 20 �g/ml (Promega Biotech AB, Stockholm,Sweden). The extract was evaporated and dissolved in matrix (2.5mg/ml of �-cyano-4-hydroxy-cinnamic acid in 50% acetonitrile con-taining 0.05% trifluoroacetic acid) 2.5 �l and 2.0 �l is spotted onMALDI target plates. Preparative 2D-PAGE, spot excision, proteindigestion, and MS were carried out at the SCIBLU proteomics re-source center in Lund (http://www.lth.se/sciblu/).

Matrix-Assisted Laser Desorption/Ionization Time-of-Flight MS—The MS analysis were performed on a MALDI-time-of-flight MS(MALDI micro MX, Waters, Manchester, UK) instrument and eachspectrum represents up to 400 laser shots. An external calibrationwas made with digested alcohol dehydrogenase from yeast. The rawdata files were converted by Pepex verision 3.1.0 (no smoothing,signal-to-noise 1.9) (31) and searched with MASCOT25 againstNCBInr: 20070109 (4396331 sequences; 1512170332 residues) witha peptide mass tolerance of 200 ppm, cysteine carbamidomethyla-

tion, as a fixed modification, methionine oxidation as variable and onemissed cleavage allowed. Protein MASCOT scores above expecta-tion values 0.05 and a minimum of three peptides were required for ahit (positively identified pancreas proteins are listed in sup-plementary file S2 and positively identified liver proteins in supple-mentary file S3).

Peptidomics Peptide Identification—Acquired RAW files were con-verted to dta files using Extract_msn through BioWorksBrowser (Ther-moElectron) and further compiled into mgf files using an in-house de-veloped script. MASCOT25 search was performed against NCBInr:20070109 (4396331 sequences; 1512170332 residues) database withthe following settings: species Mus musculus (108302 sequences); noenzyme specification; peptide mass tolerance of 1.5 Da; fragment masstolerance of 0.7 Da; potential modifications N-terminal acetylation, N-terminal pyro-glutamic acid of glutamine and glutamic acid, C-terminalamidation, oxidation of methionine. Peptides with a MASCOT score(expectation value below 0.05) above the threshold were listed insupplementary file S3. Raw data (Pancreas and Liver PHF) associatedwith the manuscript may be downloaded from ProteomCommons.org Tranche network (https://proteomecommons.org/index.jsp) us-ing the following hash: https://proteomecommons.org/tranche/datadownloader.jsp?h�Va%2bvILAGXquV3vr9MLT1gGgCGk75TXdAKImLwBr2wPLHlJUJD2yLHZSIYQFuxReB1kB11KJyKbGw7SEYfAl6dE%2f%2bxEAAAAAAAAA91Q%3d%3d.

RESULTS

The aim of this study was to evaluate how sampling pro-cedures affect post mortem proteome degradation in mouseliver and pancreatic tissue. We used three different sampling

FIG. 3. Pancreatic and Liver dataset properties. A–C shows the standard deviation (StDev) between2D-DIGE and LC-MS runs for thedifferent sample preparations. D, Average number of peptide peaks in pancreas per sample preparation group. E, Venn diagram with numberof peaks matched between groups (max samples/group). F, Venn diagram with number of peaks matched between groups (max samples/group). G, Molecular weight distribution of matched and unmatched peaks in the F, D, and FD datasets. H, Histogram with peaks that arematched between at least 50% of the samples. I, distribution of peptides identified as known endogenous peptides. J, Distribution ofdegradation peptides derived from larger proteins. The x axis’s in G–J are divided into 100-Da steps.



http://www.mcponline.org/cgi/content/full/M900229-MCP200/DC1





procedures; the most commonly used application of snapfreezing the tissue (F), rapid heat stabilization using the Sta-bilizor T1 instrument (D), and snap freezing followed by rapidheat stabilization (FD). The effects of the sampling procedureswere analyzed on a proteomic (2D-DIGE) and peptidomic(label-free LC-MS) level.

2D-DIGE of Liver and Pancreas—To better estimate globalproteome effects in the 2D-DIGE experiments we normalizedthe data as previously described (26). The liver 2D-DIGE gels(six 2D-DIGE gels, 12 samples, n � 4 for the F, D, and FDgroups) had a total of 1151 spots, whereof 841 spots werematched in at least four out of six 2D-DIGE gels and thereforeused for effect estimation. In the pancreas 2D-DIGE study (six2D-DIGE gels, 12 samples, n � 4 for the F, D, and FD groups),the corresponding numbers were 1148 spots in total, with 831matched in at least four out of the six 2D-DIGE gels. Hierar-chical clustering and PCA was performed on significantlyaffected spots (F-statistics, p � 0.05; liver n � 54, pancreasn � 191). In both tissues, the D and FD groups are moresimilar to each other than F (Fig. 1A, 1B). The first component(liver and pancreas PC1) explained �59% of the variation inthe data and separated rapid freezing sample preparations (F)from rapid heat stabilization groups (D and FD). Pair wisecomparisons (FvsD, FDvsD, and FvsFD) were performed onspots matched in at least four gels (Fig. 1C). Irrespective offold change differences, the liver tissue was not as affected bythe post mortem sampling procedure as the protease richpancreas, 86–87% of the spots being unaffected between

snap freezing (F) and preparations involving rapid heat stabi-lization (D, FD), compared with 66–72% in pancreas (Fig. 1C).That the pancreas is more subjected to degradation than liveris also seen by the fact that there were virtually no differencesbetween liver FD and D (FD versus D) samples, whereas 11%is differed in pancreas. No particular bias regarding increaseor decrease of protein intensities between sampling proce-dures was detected. The fold-change distributions for all 2D-DIGE pair wise comparisons for liver and pancreas weremainly contained within log2 � 1 (Fig. 2D). Statistically sig-nificant different proteins (p � 0.05, log2 fold change � 1 ) arelisted in Table I and II. Although there was a somewhat greatervariation in the liver 2D-DIGE data compared with the pan-creas data, the levels were comparable between all samplepreparation groups (Fig. 3A, 3B).

General proteome degradation that affects molecularweight can be hypothesized to increase the expression ofsmall proteins spots in the lower part of the gels as largerproteins are degraded into smaller fragments. A spatial anal-ysis of increase and decrease in expression levels in liver andpancreas gels did not discover any such effect in any pairwise comparison (supplemental file S1).

Sampling Procedure Sensitive Proteins—Following 2D-DIGE image and data analysis, several proteins were pickedfrom preparative liver and pancreas 2-DE gels for proteinidentification. A total of 32 liver spots were identified (sixsignificantly affected fold change log2 � 1 , F-test p � 0.05),the main candidates being Superoxide dismutase [Cu-Zn]

TABLE III“2-DEgradome” proteins also found in brain and muscle. Notice that the different cited studies use different sampling procedures and PMI,whereof many substantially longer than 2 min, and should therefore not be taken to be identical to the experimental design in this study. L is

liver, P is pancreas, Mm is mouse musculus and HS is human

** Not significantly affected in liver (p � 0.15).




(P08228), Sorbitol dehydrogenase (Q64442), and Regucalcin(Q64374) (Table I). Sample preparation effects on superoxidedismutase demonstrated by 2D-DIGE (three out of four iden-tified 2D-DIGE isospots were more highly expressed in D andFD compared with F liver tissue) were confirmed by immuno-blotting (supplemental file S4).

All three top candidate proteins were detected as differen-tially regulated isoforms, Sorbitol dehydrogenase and Regu-calcin being found as both affected and unaffected isoforms.In pancreas, a total of 88 spots were identified (20 significantlyaffected, fold change log2 � 1 , F-test p � 0.05). The toppancreas candidates include Peptidyl-prolyl cis-trans isomer-ase (Q9DCY1), �-enolase (P17182), type II cytoskeletal kera-tin 8 (P11679) and type I cytoskeletal keratin 18 (P05784),and several protein translation regulation-related proteins

(P63242, P57776, Q8BGD9 and O70251) (Table II). In bothliver and pancreas, the top candidates exhibit the greatestdifferences in the F and D/FD group comparisons. Severalproteins affected both in pancreas and liver are involved inchaperone and stress functions. These include 78 kDa glu-cose-regulated protein precursor (GRP78; P20029), 60 kDaheat shock protein (P63038), heat shock cognate 71 kDaprotein (P63017), and the mitochondrial Stress-70 proteinprecursor (P38647). Only three out of 13 identified GRP78isoforms were significantly affected (F-test p � 0.05) in pan-creas and one out of two detected GRP78 isoforms in liver,indicating degradation modulations of post-translationalmodifications rather then a breakdown of total protein levels.

Differential Peptide Display of Pancreas—Considering thelevel of pancreatic peptide hormone production and its im-

FIG. 4. Presence of endogenous pancreatic peptides following different sampling procedures. A, Endogenous peptides derived fromknown protein precursors. Green letters show amino acids surrounding the identified peptides. B, Comparison of oxidized peptides. Peptidespresent in at least three samples/group are marked green, between one and two samples/group are blue, and not detected at all are black.Log2 fold change differences of F or FD compared with D (centered at log2 fold change � 0) are also shown. F-statistics significance: * p �0.05, *** p � 0.001, NS is nonsignificant and NE is not estimable. Green letters in the sequences designate surrounding amino acids.




portance for metabolic syndrome diseases such as diabetes,it is especially interesting to study the effect of samplingprocedures on the pancreatic peptidome (enabling moremechanistic analysis). Out of 3175 peaks detected over allsamples (n � 4 for F and D, n � 3 for FD), only 123 werepresent following normalization in at least eight of the 11samples analyzed and of those 26 peaks were differentiallyexpressed (F-test p � 0.05, maximum number of samples/group). A hierarchical clustering and PCA based on these 26peaks shows the same profile as in the 2D-DIGE data (Fig. 1A,1B), i.e. D and DF groups being similar to each other andseparate from the F group. The general variation in the LC-MSdata set is comparable between the different sample prepa-ration groups (Fig. 3C).

The peptide data demonstrates that F samples containsignificantly (p � 0.05, pair wise t test) more degradationpeptides compared with D or FD (Fig. 3D, supplemen-tal file S5). The 176 peaks in common for all groups representonly a small fraction of the number of peaks found in therespective experimental groups (Fig. 3D, 3E, 3F). The numberof peptides shared by D and FD (demonstrated for �2 sam-ples/group and maximum samples/group) is greater than be-

tween F and FD or F and D, again indicating that D and FD aremore similar to each other than F. F on the other hand exhibitsmany more unique degradation peaks than the other twogroups. If the sums of the mean in each group in steps of 100Da are compared between the groups there is no differencebetween the D and the FD samples but the intensity of Fsample peaks are higher especially at the low molecular area(Fig. 3G). If peaks that could be matched between samples(�2 samples/group) are analyzed in the same way it displaysan increased intensity among the D and FD sample peakscompared with F-sample peaks (Fig. 3H).

Interestingly, if only identified peaks that originate fromprecursors known to contain endogenous peptides are com-pared between groups the intensity is higher in the D and FDcompared with the F group 15 (Fig. 3I). The relationship isreversed if only the identities of the peptides originate fromproteins not known to contain any endogenous peptides arecompared (Fig. 3J).

Sampling Procedure for Sensitive Peptides—The differen-tial peptide display analysis identified 218 peptides, 36 ofthem being present in at least eight of the 11 samples ana-lyzed (Table IV, supplemental file S5, S6, S7, S8). Forty-six

FIG. 5. Overview of Insulin 1 C-peptide derived peptides. Peptides present in at least three samples/group are marked green, betweenone and two samples/group are blue, and not detected at all are black. Log2 fold change differences of F or FD compared with D (centeredat log2 fold change � 0) are also shown. F-statistics significance: * p � 0.05, ** p � 0.01, *** p � 0.001, NS is nonsignificant and NE is notestimable. Green letters in the sequences designate the cleavage sites of the C-peptide.






percent of the peptides were derivates from the insulin-I andII C-peptides. No other insulin precursor-derived peptideswere detected. Besides the C-peptides, we also detectedendogenous peptides derived from chromogranin I, II, and III,glucagon, islet amyloid polypeptide (IAPP), neuroendocrineconvertase II, somatostatin, and thymosin �-4 (Fig. 4, TableIII). The majority of these endogenous peptides were notestimable for statistical significance because of low coveragebetween samples. Among the C-peptides, most estimablepeptides, in particular the longest, were significantly less ex-pressed (t and F-statistics p � 0.05) following the F-proce-dure (Figs. 5 and 6). The full length insulin 1 and 2 C-peptidesare four times more prevalent in D and FD compared with F.There were several peptides that were only found in certainsample procedures. The D procedure resulted in only a few

D-specific peptides, with five degradation fragments fromlarger proteins (four nonredundant identities). The F-group onthe other hand, had up to 33 degradation fragments fromlarger proteins (21 nonredundant identities) (see supple-mental file S5). There is furthermore a pattern of oxidizedmethionine (M) among the F-peptides that is not present orless expressed among D and FD samples (Fig. 4). The oxi-dized form of a somatostatin-28 derived peptide (SANSN-PAMAPRE) shows both an F and FD sensitive pattern as it isleast four more times more expressed in FD than D whereasthe unoxidized form is six times less expressed in F than D.

DISCUSSION

This is the first study to analyze how sampling proceduresaffect the proteome in liver and pancreas tissues; liver being

FIG. 6. Overview of Insulin 2 C-peptide derived peptides. Peptides present in at least three samples/group are marked green, betweenone and two samples/group are blue, and not detected at all are black. Log2 fold change differences of F or FD compared with D (centeredat log2 fold change � 0) are also shown. F-statistics significance: * p � 0.05, ** p � 0.01, *** p � 0.001, NS is non significant and NE is notestimable. Green letters in the sequences designate the cleavage sites of the C-peptide.





one of the most common tissues analyzed in many types ofproteomics experiments whereas the pancreas is a morespecialized tissue with small islands of hormone producingendocrine cells (Islets of Langerhans) embedded within thelarger structure of digestive enzyme producing exocrine cells.The overall results demonstrate that rapid heat stabilization(D) is very similar to snap freezing followed by rapid heatstabilization (FD) whereas single snap freezing (F) differs. Asstated in the introduction, the methodology of measuring theproteins/peptides that are in common between all the sam-ples following a very short PMI (�2 min) and using total lysate2D-DIGE is potentially more likely to reflect regulated proteasedriven activity and/or PTM alterations than stochastic post mor-tem degradation. The more sensitive LC-MS analysis of endog-enous �10 kDa peptides is on the other hand based on anindividual sample approach and demonstrates (see Venn dia-grams in Fig. 3) that the F procedure displays two to three timesmore peptides/peak, indicating increased degradation.

The liver proteome is less affected by the different sampleprocedures than the proteome in the protease rich pancreas(Fig. 1). In 2D-DIGE, based on total lysate sample prepara-tions, the spots analyzed are mainly of multicopy housekeep-

ing and tissue-specific nature and therefore relatively easy tomatch between different individuals. It is unlikely that specificprotein spots are completely absent at this level of resolution,although this of course is very much correlated with theproperties of the image analysis software used. On the plusside, 2-DE in general and 2D-DIGE in particular is unrivaled asmethodology to characterize alterations in isoform patterns,which is an important aspect when one considers that PTMs,such as phosphorylations are especially sensitive to postmortem influence already within minutes post mortem (3, 17).The properties of the usual 2-DE based approach thereforeclearly influence the degradome data, making it tempting todefine these results as “2-DEgradomes.” It should also benoticed that the PMI PTM knowledge is mainly based onprotein phosphorylation, with different proteins displayingvarying levels of PMI sensitivity within the first 10h (1, 3, 9, 17)but there are also reports on PMI sensitivity among acetylatedlysines (9). The more proteolytic nature of the pancreas isprobably responsible for the fact that �99% of the liver pro-teome is similar between D and FD whereas this is only thecase for �89% in pancreas. Our analysis to see if the poten-tial degradation of large proteins would affect the occurrence

TABLE IVIdentified peptides with effect estimations. Yellow fields show peptides with log2 fold change � 1 and p value �0.001. Grey fields indicate

nonsignificance (p value �0.05)



of small proteins in the lower part of the gels did not detectany such trend (supplemental file S1). Instead, whole spotfamilies were affected in the pancreas with no similar pat-tern in liver except possibly among the more acidic lowmolecular weight proteins. The sensitivity of total lysate2D-DIGE is too low and too few proteins were identified inorder to be certain that these spot families encompass allisoforms of any given protein, which seems unlikely anyway.It seems more reasonable that the spot families representan altered PTM status such as phosphorylations, as de-scribed for dynamin-1 in post mortem mouse brain tissue(12). The general level of sample procedure induced differ-ential intensity between the samples, with the exception ofsome few proteins, lies within � 100% for the proteomes(Fig. 2). The pancreatic peptidome on the other hand showsa larger impact, differences in ranging to at least � 400%when comparing snap freezing with rapid heat stabilizationprocedures (F versus D, F versus FD). This is especially

noticeable for the full length insulin 1 and 2 C-peptides,which were four times more prevalent in D or FD groupsthan F (Figs. 5 and 6). Muscle and brain are the mostcharacterized tissues with regard to post mortem degrada-tion, with most proteomics studies focused on brain tissue(1, 3–5, 11–16). Several proteins found in the brain degra-dome were also detected in the liver or pancreas degra-dome (Table III, IV). The absence of cytoskeletal associatedproteins, with the exception of cytoplasmic actin in liver andpancreas is noticeable considering their general presence inthe brain degradome (3–5, 8, 11). These data emphasize theimpact of tissue specificity on degradation properties andthe relation to the chosen sampling procedure.

Among the pancreatic peptides, there was a clear over-representation (46%) of insulin C-peptide derived fragments.The insulin C-peptides have graduated from being consideredas merely insulin byproducts to becoming bioactive peptidesin their own right (32). In contrast to insulin, the C-peptide

TABLE IV—continued




does not seem to posses any secondary structure in aqueoussolutions (33), possibly making it more accessible to degra-dation and explaining the large number of C-peptide derivatesdetected whereas no other insulin peptides were detected. Inrat, the mid-third region of the insulin-I C-peptide and theC-terminal penta-peptide (Fig. 5) are biological functionalfragments with regard to stimulating Na�, K� -ATPase activ-ity, at least when de novo synthesized and tested in vitro (34).It is not clear if the C-peptide derivates posses any biologicalfunction of their own, but it is intriguing that the majority retainthe mid third region previously mentioned.

Overall, more peaks/degradation peptide were found inF-samples (Fig. 3D, 3E, 3F). This together with a greaterpresence of oxidized peptides clearly marks solely snap freez-ing as a poor sampling procedure with regard to samplequality. We also detected a general trend among estimablepancreatic peptides (i.e. peptides that are present in the ma-jority of samples from all groups) to have a slightly higher ionintensity when derived from D or FD samples compared withF-samples and vice versa (Fig. 3H). Peptides derived frompeptide precursors have a somewhat higher intensity in D andFD samples than F whereas the patterns was the reverse forpeptides derived from probable degradation of larger proteins(Figs. 3I and 3J). It remains to be seen if these patterns alsooccur when using other LC-MS systems and in other tissues.The purpose of the sampling (for instance searching for clin-ical biomarkers, peptides, proteins, and PTMs) together withthe sensitivity of the technique (such as gel-based versusLC-based proteomics) need to be taken into account, but it isstill striking that such a large extent (around 40% in thepancreas) of the measured 2-DEgradome is affected by thesample preparation procedure. Some of us previously dem-onstrated the use of the instrument Stabilizor T1 (Denator AB),for rapid heat stabilization of fresh or frozen samples (22). Theclose similarity between D and FD compared with F pro-teomes together with the greater amount of degradationproducts in F samples in this study further proves that rapidheat stabilization is superior to snap freezing and that frozentissues practically can become “rescued” by subsequentrapid heat stabilization.

Finally, on a more general level, it should be noted that anyexperiment that studies biology on an omics level needs touse adequate data normalization tools. This is especially truefor 2-DE based methods (26) and should be even more rele-vant when studying a system in degradation. A more compre-hensive approach to the field of degradomics is thereforeneeded. There are without doubt numerous proteins, whosedegradation (or cleavage) products may have biological prop-erties of their own, the C-peptide being just one case in point.A better understanding of the properties (biochemical, prote-ase cleavage sites and otherwise) that promote or inhibitdegradation in different systems, post-translational status andthe differences between more regulated and random degra-dation in vivo and ex vivo, has therefore the potential to lead

to the discovery of several new bioactive protein isoforms andpeptides.

CONCLUSIONS

This is the first in-depth study of sampling procedure-re-lated proteomic degradation in liver and pancreatic tissue. Weshow that both mouse liver and pancreas are very sensitive tothe choice of sampling procedure, the protease rich pancreasespecially so. We present several liver and pancreatic pro-teins and peptides that are especially sensitive to post mor-tem degradation and choice of sampling procedure. A com-parison between rapid heat stabilization and snap freezing offresh tissue samples demonstrates that rapid heat stabiliza-tion is better for sample quality, with snap frozen samplesdisplaying a greater amount of degradation products. We alsodemonstrate on a systemic proteomic and peptidomic levelthat snap frozen samples can be substantially improved bysubsequent heat stabilization, making them very similar todirectly heat stabilized samples.

□S This article contains supplemental Files S1 to S8.�� To whom correspondence should be addressed: Department of

Pharmaceutical Biosciences, Division of Toxicology, BMC, Box 594,SE-75124 Uppsala University, Sweden.

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