Supporting Information

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1 Supporting Information Supplemental Figure Legends Supplementary Figure 1. Experimental Design. A. To benchmark the performance of software parameter sets proteins were extracted from a pool of Arabidopsis thaliana Col-0 plants and digested with trypsin. The peptide matrix was aliquoted and each of the six standard mixtures was spiked into 1 µg of this same peptide matrix. The standard matrix mixtures were measured in random order (Block 1). The measurements were repeated three times each time in random order (Block 2, 3 and 4). The complete set of measurements (all four blocks) was analyzed with the different software parameter sets. B To assess technical and biological contributions to total variability in discovery proteomics protein abundance estimates, proteins were extracted three times each from four individual plants. The three protein extracts from each plant were digested with trypsin giving three separate peptide mixtures from each plant for a total of twelve separate peptide mixtures. These twelve peptide mixtures were measured in random order and the complete set was analyzed further. Supplementary Figure 2. SDS-PAGE of the selected standards. 0.5 mmol of each protein was loaded into each lane. Proteins were stained with Coomassie Brilliant Blue G250. Supplementary Figure 3. Quantitative relationship between measured and actual abundance of the standard proteins. The mean number of PSMs and mean Top3 PQIs (ion signal peak area of three most intense peptide ion signals) in the three measurements of each molar amount of each protein are plotted. Error bars denote standard errors (SE). PQI response is a linear function of protein molar 1

2 amount. Red symbols indicate number of PSMs, green symbols indicate Top3 PQI. For αla top R 2 refers to the number of PSMs, for CAH to Top3 PQI, for OVA to Top3 PQI. Supplementary Figure 4. Top panels: Proportionality between measured and expected standard protein log 10 fold changes for software parameter sets not shown in Figure 2. Log 10 fold changes were calculated exclusively for mean PQI values of four measurements of standard protein molar amounts. Lower panels: Percent error and variability of measured standard protein log 10 fold changes. Percent error was calculated as the difference between measured and expected log 10 fold changes normalized to the respective expected log 10 fold change multiplied by 100. When the log 10 expected fold change was zero, only the difference multiplied by 100 was used. As above, only log 10 measured fold changes calculated for mean PQI values of four measurements of standard protein molar amounts were considered. The RSD of measured log 10 fold changes was calculated from all possible pair wise fold changes of four measurements of standard protein molar amounts (4*4 = 16). Dashed horizontal lines denote -30% and 30% error respectively. Coloring is according to log 10 fold change. Supplementary Figure 5. A. Top panels. Data from phosphate replete and depleted roots study. The expected protein abundance values (PQI) of each quantified protein were estimated for the three software and parameter sets using model-based least square means and plotted as independent variables for pair wise comparisons of software and parameter sets. PQIs from three measurements each of three pools of roots each of wild type Col-0 seedlings grown on plates containing (left panel) and lacking phosphate (right panel) are plotted. Bottom left panel. Data from this study. The expected protein abundance values for the set of mutually quantified protein groups not assigned the same protein group accession by the two software, but sharing at least one common protein accession 2

3 number from the essentially isogenic Arabidopsis plants are plotted (1365 proteins). Bottom right panel. Data from phosphate replete and depleted roots study. Log 2 fold changes of PQI values for the set of mutually quantified protein groups not assigned the same protein group accession by the two software, but sharing at least one common protein accession number from the phosphate deprived and replete roots are plotted (minus/plus phosphate) (1708 proteins). B. Gene ontology enrichment analysis of biological function of the twenty five Arabidopsis thaliana proteins showing biological variance in abundance quantified with MQ with MBR settings and the LFQ PQI. GO analysis was done with the AgriGO software 1 using Plant GO slim categories and all TAIR 9 gene models as background and Yekutieli FDR corrected Fisher s exact test to test statistical significance. Corrected p-values are shown in parentheses as are the fraction of input proteins assigned to the respective GO categories. C. Mean protein abundance for three proteins (AKR2, MTHSC70-2 and PR5) involved in response to biotic stimulus in the four plants is shown. Error bars denote standard errors (SE). 3

4 Supplementary Table 1. Number of hits returned in a search of the Thomson Reuters Web of Science database on the 29 th of June, 2016 with some well known software designed expressly for label-free quantification in discovery proteomics applications. The text string used for the search was X software with X = software name given in Column 1. Software Name Number of Thomson Reuters Web of Science Hits Progenesis 50 MaxQuant 31 Scaffold MS 18 Proteome Discoverer 8 MFPaQ 5 ISOQuant 4 Synapter 2 ProtMax 1 Multi-Q 1 IRMa-hEIDI 1 4

5 Supplementary Table 2. Composition of the protein standard mixtures in fmol in 1 µl injected into the LC- MS system. The total amount in µg, and the ratio in percent of the total amount to 1 µg of A. thaliana background matrix is also given for all mixtures. The RSD of the total amount in µg on column in all mixtures is 0.3%. Standard Mixture STD (fmol in 1 µl on Column) STD (µg on Column) Matrix (µg on Column) STD/Matrix in % OVA BSA MYO ßLA ßLB αla CAH APO CYTC FET

6 Supplementary Table 3. All expected fold changes (top) and log 10 fold changes (bottom) for the proteins in the standard protein mixtures. In total there are 21 expected fold changes at different molar amounts and 12 unique expected fold changes (from low to high: 1, 1.7, 2, 3, 3.3, 6, 10, 20, 30, 33.3, 60, 100). fmol / µl fmol / µl

7 Supplementary Table 4. Arabidopsis thaliana proteins with the greatest variation in their abundance in four individual isogenic plants. Significantly enriched GO categories are indicated. Accession Number Protein Name Technical Variance Biological variance Total Variance GO Categories AT1G75040 PR5, PR-5 pathogenesis-related gene response to abiotic stimulus, response to biotic stimulus, response to stress, cellular metabolic process, multi-organism process AT4G20260 ATPCAP1, PCAP1 plasma-membrane associated cation-binding protein response to abiotic stimulus, response to stress AT3G09840 CDC48, ATCDC48, CDC48A cell division cycle 48 ATPase, AAA-type multi-organism process AT4G24280 cphsc70-1 chloroplast heat shock protein response to abiotic stimulus, response to stress, cellular metabolic process AT4G09000 GRF1, GF14 CHI general regulatory factor AT4G25050 ACP4 acyl carrier protein response to abiotic stimulus, cellular metabolic process AT5G09590 MTHSC70-2, HSC70-5 mitochondrial HSO response to abiotic stimulus, response to biotic stimulus, response to stress, cellular metabolic process, multi-organism process AT1G78850 D-mannose binding lectin protein with Apple-like carbohydrate-binding domain AT4G35450 AKR2, AFT, AKR2A ankyrin repeatcontaining protein response to biotic stimulus, response to stress, cellular metabolic process, multi-organism process AT3G09440 Heat shock protein 70 (Hsp 70) family protein response to abiotic stimulus, response to stress, cellular metabolic process AT2G27730 copper ion binding cellular metabolic process AT2G42690 alpha/beta-hydrolases superfamily protein AT3G49120 ATPERX34, PERX34, PRXCB, ATPCB, PRX34 peroxidase CB GRF6, AFT1, lambda G-box AT5G10450 regulating factor AT1G02930 GSTF6 glutathione S-transferase response to abiotic stimulus, response to biotic stimulus, response to stress, cellular metabolic process, multi-organism process response to abiotic stimulus, response to stress, cellular metabolic process AT3G29320 Glycosyl transferase, family response to abiotic stimulus, response to stress AT1G14150 PQL1, PQL2 PsbQ-like cellular metabolic process AT5G26000 TGG1, BGLU38 thioglucoside glucohydrolase cellular metabolic process AT2G39800 P5CS1 delta1-pyrroline-5-carboxylate synthase response to abiotic stimulus, response to stress, cellular metabolic process AT1G67700 unknown protein AT3G09260 PYK10, PSR3.1, BGLU23, LEB Glycosyl hydrolase superfamily protein response to biotic stimulus, multi-organism process AT3G59920 ATGDI2, GDI2 RAB GDP dissociation inhibitor AT4G30160 VLN4, ATVLN4 villin NAD-ME1 NAD-dependent malic enzyme AT2G AT3G52500 Eukaryotic aspartyl protease family protein response to abiotic stimulus, response to stress, cellular metabolic process 7

8 Supplementary Figure 1 8

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10 Supplementary Figure 3 10

11 Supplementary Figure 4 11

12 Supplementary Figure 5 References 1. Du, Z.; Zhou, X.; Ling, Y.; Zhang, Z.; Su, Z., agrigo: a GO analysis toolkit for the agricultural community. Nucleic acids research 2010, 38, (Web Server issue), W

13 List of Abbreviations PTM, post translational modification; PSM, peptide spectral match; PQI, protein quantification index; MS/MS, tandem mass spectrometry; PD, Proteome Discoverer; MQ, MaxQuant; QIP, Progenesis QIP; αla, α-lactoalbumin; APO, Apotransferin; ßLA and ßLB, ß-Lactoglobulin A and B; CAH, Carbonic Anhydrase, CYTC, Cytochrome C; FET, Fetuin; MYO, Myoglobin; OVA, Ovalbumin; IAA, iodoacetamide; RF, radio frequency; LTQ, linear quadrupole ion trap; AGC, automatic gain control; IT, injection time; ppm, parts per million; FDR, false discovery rate; XIC, extracted ion current; NSAF, normalized spectral abundance factor; ICC, intra-class correlation; Mw, molecular weight; Def, default; MBR, match between runs; Mod, modified; ibaq, intensity based absolute quantification; NoC no conflicting peptides; Ses, sensitive parameters; LOQ, limit of quantification; RSD, relative standard deviation 13

Digitizing the Proteomes From Big Tissue Biobanks

Digitizing the Proteomes From Big Tissue Biobanks Analyzing 24 Proteomes Per Day by Microflow SWATH Acquisition and Spectronaut Pulsar Analysis Jan Muntel 1, Nick Morrice 2, Roland M. Bruderer 1, Lukas