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Proteomic Landscape of the Human Choroid–Retinal Pigment Epithelial Complex

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Proteomic Landscape of the Human Choroid–Retinal Pigment Epithelial Complex Supplementary Online Content Skeie JM, Mahajan VB. Proteomic landscape of the human choroid–retinal pigment epithelial complex. JAMA Ophthalmol. Published online July 24, 2014. doi:10.1001/jamaophthalmol.2014.2065. eFigure 1. Choroid–retinal pigment epithelial (RPE) proteomic analysis pipeline. eFigure 2. Gene ontology (GO) distributions and pathway analysis of human choroid– retinal pigment epithelial (RPE) protein show tissue similarity. eMethods. Tissue collection, mass spectrometry, and analysis. eTable 1. Complete table of proteins identified in the human choroid‐RPE using LC‐ MS/MS. eTable 2. Top 50 signaling pathways in the human choroid‐RPE using MetaCore. eTable 3. Top 50 differentially expressed signaling pathways in the human choroid‐RPE using MetaCore. eTable 4. Differentially expressed proteins in the fovea, macula, and periphery of the human choroid‐RPE. eTable 5. Differentially expressed transcription proteins were identified in foveal, macular, and peripheral choroid‐RPE (p<0.05). eTable 6. Complement proteins identified in the human choroid‐RPE. eTable 7. Proteins associated with age related macular degeneration (AMD). This supplementary material has been provided by the authors to give readers additional information about their work. © 2014 American Medical Association. All rights reserved. 1 Downloaded From: https://jamanetwork.com/ on 09/25/2021 eFigure 1. Choroid–retinal pigment epithelial (RPE) proteomic analysis pipeline. A. The human choroid‐RPE was dissected into fovea, macula, and periphery samples. B. Fractions of proteins were isolated and digested. C. The peptide fragments were analyzed using multi‐dimensional LC‐MS/MS. D. X!Hunter, X!!Tandem, and OMSSA were used for peptide fragment identification. E. Proteins were further analyzed using bioinformatics. © 2014 American Medical Association. All rights reserved. 2 Downloaded From: https://jamanetwork.com/ on 09/25/2021 eFigure 2. Gene ontology (GO) distributions and pathway analysis of human choroid– retinal pigment epithelial (RPE) protein show tissue similarity. A. Expressed proteins of the peripheral, macular, and foveal regions of the human choroid‐RPE. Proteins were grouped into subcategories of biological processes, molecular functions, and cellular component for each region of choroid‐RPE. B. Top‐ten pathways represenpathways represented in the choroid‐RPE. © 2014 American Medical Association. All rights reserved. 3 Downloaded From: https://jamanetwork.com/ on 09/25/2021 eMethods. Tissue collection, mass spectrometry, and analysis An outline of the methods can be visualized in eFigure 1. Human choroid–retinal pigment epithelial (RPE) tissue collection This study was approved by the University of Iowa’s institutional review board and adheres to the Declaration of Helsinki. Informed consent was obtained from participants. Human donor tissue was obtained from the Iowa Lions Eye Bank, Iowa City, within 5 hours following death. Three eyes were used in this study. They were obtained from an 84‐year‐old male, an 83‐year‐old female, and a 71‐year‐old female. None of the eyes showed signs of retinal disease. Eyes were flowered into four quadrants as previously described.1 The vitreous core, cortex and retina were removed. Using a 4‐mm biopsy punch, the foveal and peripheral choroid‐RPE were collected and separated from the sclera. Using an 8‐mm biopsy punch, the macula was collected circumferentially around the foveal punch and separated from the sclera (eFigure 1A). Choroid‐RPE tissues were flash frozen in liquid nitrogen and stored in our biorepository until processed for mass spectrometry.2 Multidimensional protein identification technology mass spectrometry Mass spectrometry was performed as previously described.3 Briefly, proteins were prepared for digestion using the filter‐assisted sample preparation (FASP) method (eFigure 1B‐C).4 Concentrations were measured using a Qubit fluorometer (Invitrogen). 5 Digested peptides were desalted using C18 stop‐and‐go extraction (STAGE) tips. Peptides were fractionated by strong anion exchange STAGE tip chromatography. Fractions were analyzed by liquid chromatography tandem mass spectrometry (LC‐ MS/MS) (eFigure 1C). LC was performed on an Agilent 1100 Nano‐flow system where mobile phase A was 94.5% MilliQ water, 5% acetonitrile, 0.5% acetic acid and mobile phase B was 80% acetonitrile, 19.5% MilliQ water, 0.5% acetic acid. Trap and analytical © 2014 American Medical Association. All rights reserved. 4 Downloaded From: https://jamanetwork.com/ on 09/25/2021 columns were packed with 3.5 um C18 resin (Zorbax SB, Agilent). The LC was interfaced with a dual pressure linear ion trap mass spectrometer (LTQ Velos, Thermo Fisher) using nano‐electrospray ionization. An electrospray voltage of 1.8 kV was applied to a pre‐ column tee. The mass spectrometer acquired tandem mass spectra from the top 15 ions in the full scan from 400 ‐ 1400 m/z. Dynamic exclusion was set to 30 seconds. Mass spectrometer RAW data files were converted to MGF format using msconvert and all searches required strict tryptic cleavage, 0 or 1 missed cleavages, fixed modification of cysteine alkylation, variable modification of methionine oxidation and expectation value scores of 0.01 or lower. The MGF files were searched with X!Hunter6 against the latest library available in 2010 on the GPM7 and X!!Tandem8,9 using both the native and k‐ score10 algorithms and by OMSSA.11 All searches were performed on Amazon Web Services‐based cluster compute instances using the Proteome Cluster interface. XML output files were parsed and non‐redundant protein sets determined (eFigure 1D). Proteins were required to have 2 or more unique peptides with E‐value scores of 0.01 or less. Relative quantitation was performed by spectral counting. Data were normalized based on total spectral counts (hits) per sample. 63 decoy proteins were identified out of a total of 5570 proteins, giving a 2% false discovery rate at the protein level. Bioinformatic and Statistical analysis Proteins were considered identified if they had an expectation value < 0.01 (less then 1% chance of being a random assignment). Bioinformatic analyses were used to determine significant protein expression (Partek Geonomics Suite 6.6), gene ontology (GO terminology, Panther 7.2), and pathway representation (MetaCore) (eFigure 1E). Using Partek Genomics Suite 6.6, the protein lists for all three mass spectrometry runs for all regions of choroid‐RPE tissue were analyzed. All data values were set to a minimum of 0.001, normalized to log base 2, and compared using analysis of variance. Statistically significant proteins (P < .05) were visualized using an un‐discriminated clustered heatmap with a normalized clustering function. Some proteins that showed a © 2014 American Medical Association. All rights reserved. 5 Downloaded From: https://jamanetwork.com/ on 09/25/2021 trend but did not meet statistical significance were analyzed further because they were an important component of a specific pathway or classification. Pie charts were created for the visualization of Gene Ontology GO distributions within the list of proteins using Panther 7.2 Classification system under the Batch ID search menu. Pie charts were created for each GO term category including biological process, molecular function, and cellular component. MetaCore (GeneGO Inc., St. Joseph, MI, USA) OMICs data analysis was used to determine the most significant protein signaling/interaction pathways. MetaCore generates protein pathway maps using curated literature databases.12 The names of pathways are representative of single proteins in that pathway, known signaling cascades, or even an associated disease, and therefore may not coincide directly with our dataset even though our dataset contained several proteins within the pathway. Information regarding MetaCore software can be obtained at www.genego.com. Lists of choroid‐RPE proteins were curated using Excel and uploaded into the MetaCore website. Pathway lists were created comparing regional protein lists simultaneously, i.e. differentially expressed fovea, macula, periphery protein lists were analyzed simultaneously. Lists of the 50 most significant pathways were exported to excel files. References 1. Skeie JM, Mahajan VB. Dissection of human vitreous body elements for proteomic analysis. J Vis Exp. 2011;(47):pii 2455. doi:10.3791/2455. 21304469 2. Skeie JM, Tsang SH, Zande RV, et al. A biorepository for ophthalmic surgical specimens. Proteomics Clin Appl. 2014;8(3‐4):209‐217. 24115637 3. Skeie JM, Mahajan VB. Proteomic interactions in the mouse vitreous‐retina complex. PLoS One. 2013;8(11):e82140. doi:10.1371/journal.pone.0082140. 4. Wiśniewski JR, Zougman A, Nagaraj N, Mann M. Universal sample preparation method for proteome analysis. Nat Methods. 2009;6(5):359‐362. 5. Rappsilber J, Ishihama Y, Mann M. Stop and go extraction tips for matrix‐assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. Anal Chem. 2003;75(3):663‐670. © 2014 American Medical Association. All rights reserved. 6 Downloaded From: https://jamanetwork.com/ on 09/25/2021 6. Craig R, Cortens JC, Fenyo D, Beavis RC. Using annotated peptide mass spectrum libraries for protein identification. J Proteome Res. 2006;5(8):1843‐1849. 7. Beavis RC. Using the global proteome machine for protein identification. Methods Mol Biol. 2006;328:217‐228. 8. Bjornson RD, Carriero NJ, Colangelo C, et al. X!!Tandem, an improved method for running X!tandem in parallel on collections of commodity
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