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assays/2_4-EnzymeActivityOrganellarMarkers/protocols/2_4-EnzymeActivityOrganellarMarkers.md

+# 2.4 Measurements of enzyme activity of organellar marker enzymes
+
+The distribution of peroxisomes, mitochondria, and plastids within the four organellar fractions were examined using enzymatic marker proteins. All enzyme assays were performed photospectrometrically in a plate reader (SynergyH1, BioTek) at room temperature. For each sample three technical replicates were measured. Total protein concentration of the fractions was determined using the Pierce BCA protein assay kit (ThermoFisher Scientific). Enzyme activity was expressed as units per mg total protein. The activities of the following marker enzymes were analyzed: Catalase for peroxisomes (Breidenbach et al., 1968), fumarase for mitochondria (Nishimura and Beevers, 1981), and phosphoglycerate dehydrogenase for plastids (Benstein et al., 2013).
+
+- Breidenbach, R. W., Kahn, A., and Beevers, H. (1968). Characterization of glyoxysomes from castor bean endosperm. Plant Physiol. 43, 705–713. doi: 10.1104/ pp.43.5.705
+- Nishimura, M., and Beevers, H. (1981). Isoenzymes of sugar phosphate metabolism in endosperm of germinating castor beans. Plant Physiol. 67, 1255–1258. doi: 10.1104/ pp.67.6.1255
+- Benstein, R. M., Ludewig, K., Wulfert, S., Wittek, S., Gigolashvili, T., Frerigmann, H., et al. (2013). Arabidopsis phosphoglycerate dehydrogenase1 of the phosphoserine pathway is essential for development and required for ammonium assimilation and tryptophan biosynthesis. Plant Cell 25, 5011–5029. doi: 10.1105/tpc.113.118992
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assays/2_5-OrganellarMembraneIsolation/protocols/2_5-OrganellarMembraneIsolation.md

+# 2.5 Isolation of organellar membranes
+
+Membranes were isolated from the collected organellar fractions (F1-4) as described by Fujiki et al. (1982) and Reumann et al. (1995) with modifications. The obtained fractions were slowly diluted with hypo-osmotic buffer (20 mM HEPES-KOH, pH 6.8 and 0.8 mM MgCl2) and incubated on ice for 30 minutes to osmotically disrupt the organelles. The suspension was subjected to 10 freeze/thaw cycles (freezing in liquid nitrogen, thawing at room temperature) to lyse efficiently the organelles. After each thaw cycle the sample was homogenized by mixing. Membranes were sedimented by centrifugation at 100,000 g at 4°C for 1 h and washed in 100 mM sodium carbonate (pH 11.5) to remove membrane- associated proteins. A second centrifugation step (100,000 g at 4°C for 1 h) was performed to harvest the organellar membranes. The membrane pellet was resuspended in 20 mM HEPES-KOH, pH 6.8 and 0.8 mM MgCl2 and stored at -80°C for further experiments. Concentration of the membrane proteins were determined using Pierce BCA protein assay kit (ThermoFisher Scientific).
+
+- Fujiki, Y., Hubbard, A. L., Fowler, S., and Lazarow, P. B. (1982). Isolation of intracellular membranes by means of sodium carbonate treatment: application to endoplasmic reticulum. J. Cell Biol. 93, 97–102. doi: 10.1083/jcb.93.1.97
+- Reumann, S., Maier, E., Benz, R., and Heldt, H. W. (1995). The membrane of leaf peroxisomes contains a porin-like channel. J. Biol. Chem. 270, 17559–17565. doi: 10.1074/jbc.270.29.17559
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assays/2_6-SDS-PAGE-Immunoblotting/protocols/2_6-SDS-PAGE-Immunoblotting.md

+# 2.6 SDS-PAGE and immunoblotting
+
+Membrane proteins from each organellar fraction (F1-4) were separated on an SDS-polyacrylamide gel and visualized using Colloidal Coomassie staining G-250 dye (Laemmli, 1970). To determine the purity of the organellar fractions via immunoblotting, membrane proteins separated by SDS-PAGE were transferred to a 0.2 μm polyvinylidene difluoride membrane, which was probed with primary antibodies against the following organellar membrane marker proteins: rabbit a-AOX (Agrisera, 1:1,000), rabbit a-BIP2 (Agrisera, 1:5,000), rabbit a-TIC40 (1:2,500), and rabbit a-PEX14 (Agrisera, 1:12,500). A horseradish peroxidase-conjugated goat a-rabbit IgG secondary antibody (Merck, 1:2,500) was used for chemiluminescence detection. The membrane was serially probed with antibodies to the indicated proteins. Imaging was performed with ImageQuant LAS 4000 Mini Bimolecular Imager (GE Healthcare) using exposure times between 1/8 and 15 seconds.
+
+- Laemmli, U. K. (1970). Cleavage of structural proteins during the assembly of the head of bacteriophage T4. Nature 227, 680–685. doi: 10.1038/227680a0
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assays/2_7-ProteomeAcquisition-MassSpectrometry/protocols/2_7-ProteomeAcquisition-MassSpectrometry.md

+# 2.7 Proteome acquisition via mass spectrometry analysis
+
+Total proteins and the enriched membrane proteins of the organellar fractions (F1-4) isolated from Ricinus endosperm tissue were analyzed by mass spectrometry (MS). Therefore, protein samples were loaded on an SDS-polyacrylamide gel, concentrated in the stacking gel, silver stained according to MS-compatible protocol, reduced, alkylated, and digested with trypsin. Peptides were extracted from the gel with 0.1% trifluoroacetic acid and subjected to liquid chromatography. For peptide separation an Ultimate 3000 Rapid Separation liquid chromatography system (Dionex; ThermoFisher Scientific) equipped with an Acclaim PepMap 100 C18 column (75 mm inner diameter x 50 cm length x 2 mm particle size from ThermoFisher Scientific) was used with a 140-minute LC- gradient. Mass spectrometry was carried out on an Obitrap Elite high- resolution instrument (ThermoFisher Scientific) operated in positive mode and equipped with a Nano electrospray ionization source. Capillary temperature was set to 275°C and source voltage to 1.5 kV. Survey scans were conducted in the orbitrap analyzer at a mass to charge (m/z) ranging from 350-1700 and a resolution of 60,000 (at 400 m/z). The target value for the automatic gain control was 1,000,000 and the maximum fill time 200 ms. The 20 most intense doubly and triply charged peptide ions (minimal signal intensity 500) were isolated, transferred to the linear ion trap (LTQ) part of the instrument and fragmented using collision induced dissociation (CID). Peptide fragments were analyzed using a maximal fill time of 200 ms and automatic gain control target value of 100,000. The available mass range was 200- 2000 m/z at a resolution of 5400 (at 400 m/z). Two fragment spectra were summed up and already fragmented ions excluded from fragmentation for 45 seconds.
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assays/2_8-Computational-MS-DataAnalysis/protocols/2_8-Computational-MS-DataAnalysis.md

+
+# 2.8 Computational MS data analysis
+
+For peptide and protein identification the acquired MS spectra were analyzed using the MaxQuant version 1.3.0.5 (MPI for Biochemistry, Planegg, Germany) with default parameters (Cox and Mann, 2008). Quantification was performed using the unlabeled quantification option of MaxQuant. The identified spectra were matched against the Ricinus proteome using the peptide search engine Andromeda (Cox et al., 2011). Only proteins containing at least two unique peptides and a minimum of three valid values in at least one group were quantified. A full list of all identified peptides from the proteome experiment is presented in Supplemental Table S1.
+All identified Ricinus proteins were analyzed by bidirectional BLAST against the Arabidopsis proteome (Altschul et al., 1990). Organelle distribution within the collected fractions was assayed using a set of marker proteins. Proteins were assigned as organelle markers if the experimental localization of their Arabidopsis homologues in the SUBA 5.0 database (Hooper et al., 2017; Hooper et al., 2022) corresponded with their sequence-based localization prediction in Ricinus. We predicted protein localization to peroxisomes, mitochondria, and plastids manually and using the publicly available tools PPero, PredPlantPTS1, and TargetP (Emanuelsson et al., 2000; Reumann et al., 2012; Wang et al., 2015).
+
+- Cox, J., and Mann, M. (2008). MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372. doi: 10.1038/nbt.1511
+- Cox, J., Neuhauser, N., Michalski, A., Scheltema, R. A., Olsen, J. V., and Mann, M. (2011). Andromeda: A peptide search engine integrated into the maxQuant environment. J. Proteome Res. 10, 1794–1805. doi: 10.1021/pr101065j
+- Altschul, S. F., Gish, W., Miller, W., Myers, E. W., and Lipman, D. J. (1990). Basic local alignment search tool. J. Mol. Biol. 215, 403–410. doi: 10.1016/S0022-2836(05) 80360-2
+- Hooper, C. M., Castleden, I. R., Tanz, S. K., Aryamanseh, N., and Millar, A. H. (2017). SUBA4: the interactive data analysis centre for Arabidopsis subcellular protein locations. Nucleic Acids Res. 45, D1064–D1074. doi: 10.1093/nar/gkw1041
+- Hooper, C. M., Castleden, I., Tanz, S. K., Grasso, S. V., Aryamanesh, N., and Millar, A. H. (2022). Subcellular Localisation database for Arabidopsis proteins version 5 (The University of Western Australia). doi: 10.26182/8dht-4017
+- Emanuelsson, O., Nielsen, H., Brunak, S., and von Heijne, G. (2000). Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J. Mol. Biol. 300, 1005–1016. doi: 10.1006/jmbi.2000.3903
+- Reumann, S., Buchwald, D., and Lingner, T. (2012). PredPlantPTS1: A web server for the prediction of plant peroxisomal proteins. Front. Plant Sci. 3. doi: 10.3389/ fpls.2012.00194
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assays/2_9-StatisticalAnalysis-ProteomeDeconvolution/protocols/2_9-StatisticalAnalysis-ProteomeDeconvolution.md

+# 2.9 Statistical analysis and proteome deconvolution
+
+Unless stated otherwise all data analyses were performed in R using the statistical package e1071 for support vector regression (http://www.R-project.org), ggplot2 for visualization (Wickham, 2009; Wickham, 2016; https://ggplot2.tidyverse.org) and NFM for Nonnegative Matrix factorization (Gaujoux and Seoighe, 2010; http://cran.r-project.org). To assign MS-identified proteins to a certain organellar fraction, several deconvolution approaches have been applied. Unsupervised deconvolution was performed by Nonnegative matrix factorization (NMF). We determined the rank of factorization using the Brunet- and Lee-Seung-algorithms for ranks from 2 to 10 with 50 repetitions (Gaujoux and Seoighe, 2010). Both algorithms were further constrained to sum to one in a sample, so that its value can be interpreted as the relative proportion of a fraction in a sample. We used random as well as double singular value decomposition for seeding (Boutsidis and Gallopoulos, 2008). The final factorization was performed 500 times with random seeding and a rank of 5. We determined the fractions specific for an organelle by scaling the coefficients per protein and clustered them via k-means with Euclidean distance. An enrichment of organelle-specific proteins in a cluster was determined via Fisher’s Exact Test and proteins specific for an organelle were used for classification. Proteins were assigned either to peroxisomes, plastids, mitochondria, and other organelles based on the fraction with the highest coefficient.
+For supervised deconvolution we used the distribution of marker proteins normalized to its maximum in all samples. Quadratic programming as well as Support vector regression were constrained to yield positive values. Nu-type SVR was performed with a linear kernel and a set of Nu values (0.25; 0.5; 0.75; 1.0). To test the quality of prediction, deconvolution was repeated 500 times splitting the dataset into a training set containing 70% of the dataset and a testing set using the remaining 30% of the data. Proteins were associated to the organelle with the highest coefficient and consensus classification was achieved by simple majority vote of all four algorithms.
+