Minimalistic encapsulated proteomic sample processing applied to copy number

Size: px

Start display at page:

Download "Minimalistic encapsulated proteomic sample processing applied to copy number"

Camilla Rogers
5 years ago
Views:

1 Nature Methods Minimalistic encapsulated proteomic sample processing applied to copy number estimation in eukaryotic cells Nils A. Kulak, Garwin Pichler, Igor Paron, Nagarjuna Nagaraj and Matthias Mann Supplementary Figure 1 Comparison of SILAC-based ratios using different sample processing conditions, Supplementary Figure 2 Comparison of SILAC-based ratios of HeLa proteins using various StageTip materials. Supplementary Figure 3 Comparison of SILAC-based ratios of HeLa proteins using non-activated SDB-RPS vs. activated SDB-RPS. Supplementary Figure 4 Loading capacity test of StageTip materials. Supplementary Figure 5 Comparison of StageTip based SAX, SCX, and SDB-RPS fractionation techniques. Supplementary Figure 6 Distribution of estimated copy numbers for S. pombe. Supplementary Figure 7 S. pombe correlation of estimated copy numbers using 6-fraction ist-scx analysis to copy numbers reported in Marguerat et al. using 34 synthetic peptide standards (R 2 = 0.89). Supplementary Figure 8 Distribution of median estimated copy numbers per cell for HeLa (21,057), S. pombe (5,137) and S. cerevisiae (822). Supplementary Figure 9 2D annotation enrichment score for GO terms based on estimated copy numbers between orthologs. Supplementary Figure 10 Multi-scatter plot of label-free quantification intensities depicting the reproducibility across four technical replicates of 6-fraction ist-scx analysis of HeLa proteome. Supplementary Figure 11 Protein copy number distribution with respect to individual copy number bins. Supplementary Table 1 Protein identifications and protein copy number estimations of S.cerevisiae, S.pombe and HeLa cells. Supplementary Table 2 Orthologs of S.cerevisiae, S.pombe and HeLa cells and their protein copy number estimations. Supplementary Table 3 Buffer compositions and materials for the ist method. Supplementary Note Supplementary Video 1 ist sample preparation video tutorial. The video tutorial shows all steps of the ist sample preparation workflow. It further shows how to troubleshoot the method in case of need.

2 Supplementary Figure 1 Comparison of SILAC-based ratios using different sample processing conditions. (a) SILAC-based protein ratios of clarified vs. non-clarified HeLa cell lysates prior to proteolytic digestion. (b) SILAC-based protein ratios of FASP vs. ist HeLa cell lysates. (c) SILAC-based protein ratios of StageTip vs. tube-processed HeLa cell lysates. (d) Comparison of the ist-sdc method to a published SDC based in-solution protocol. Shown are median unique peptide identifications of technical triplicates ± s.e.m.

Supplementary Figure 2 Comparison of SILAC-based ratios of HeLa proteins using various StageTip materials. (a) SILAC-based ratios of SDB-XC vs. C 18. (b) SILAC-based ratios of SDB-RPS vs. C 18. (c) SILACbased ratios of SCX vs.

3 Supplementary Figure 2 Comparison of SILAC-based ratios of HeLa proteins using various StageTip materials. (a) SILAC-based ratios of SDB-XC vs. C 18. (b) SILAC-based ratios of SDB-RPS vs. C 18. (c) SILACbased ratios of SCX vs. C 18. Single-shot measurements were performed. Supplementary Figure 3 Comparison of SILAC-based ratios of HeLa proteins using non-activated SDB- RPS vs. activated SDB-RPS. Single-shot measurements were performed.

Supplementary Figure 4 Loading capacity test of StageTip materials. Respective quantities of peptides were loaded onto 16-gauge double-plug StageTips.

4 Supplementary Figure 4 Loading capacity test of StageTip materials. Respective quantities of peptides were loaded onto 16-gauge double-plug StageTips. The eluates were dried and re-suspended in corresponding volumes (10 µl, 15 µl, 20 µl, 25 µl Buffer A*: 2% (v/v) ACN, 0.1% TFA (v/v)). 1 µl of the peptides were measured on a 2h gradient. Shown are technical duplicates ± s.d. The data indicate that loadings of up to 20 µg (up to 10 µg per plug) have little effect on peptide identifications using SDB-RPS material.

$Supplementary Figure 5 Comparison of StageTip based SAX, SCX, and SDB-RPS fractionation techniques.$

5 Supplementary Figure 5 Comparison of StageTip based SAX, SCX, and SDB-RPS fractionation techniques. (a) Rounded percent of unique peptides in each fraction. (b) Rounded percent of exclusive peptides in each fraction compared to other fractions. (c) Rounded percent of fractionation efficiencies.

$Blue bins represent proteins that are identified uniquely in the 6-fraction ist-scx analysis in$ $pombe correlation of estimated copy numbers using 6-fraction ist-scx analysis to copy numbers$

6 Supplementary Figure 6 Distribution of estimated copy numbers for S. pombe. Blue bins represent proteins that are identified uniquely in the 6-fraction ist-scx analysis in comparison to Gunaratne et al 32. Supplementary Figure 7 S. pombe correlation of estimated copy numbers using 6-fraction ist-scx analysis to copy numbers reported in Marguerat et al 27. using 34 synthetic peptide standards (R 2 = 0.89).

7 Supplementary Figure 8 Distribution of median estimated copy numbers per cell for HeLa (21,057), S. pombe (5,137) and S. cerevisiae (822). Box plots show the median (stripe), the 25 th to 75 th percentile (interquartile range, box), 1.5x the interquartile range (1.5 IQR, whiskers), and outliers (open circles). Supplementary Figure 9 2D annotation enrichment score for GO terms based on estimated copy numbers between orthologs. (a) For S. pombe vs. S. cerevisiae, (b) for HeLa vs. S. cerevisiae and (c) HeLa vs. S. pombe. Representative GO terms showing anti-correlating behavior are depicted in red.

$reproducibility across four technical replicates of 6-fraction ist-scx analysis of HeLa proteome.$

8 Supplementary Figure 10 Multi-scatter plot of label-free quantification intensities depicting the reproducibility across four technical replicates of 6-fraction ist-scx analysis of HeLa proteome.

9 Supplementary Figure 11 Protein copy number distribution with respect to individual copy number bins. The bar plot represents the percentage of proteins present in the copy number bins (blue), neighbours with copy numbers within a factor 10 (grey, weighted average = 40%) or 100 (brown, weighted average = 64%).

10 Supplementary Note Interpretation of SILAC comparison plots The different conditions that were tested for the optimization of the sample preparation were carried out by using SILAC label swapping experiments (Supplementary Fig. 1-3). These plots show a cloud around the origin if there is no significant effect of the variable. However when the variable exerts a significant effect on the proteome detected, there will be a cloud along the diagonal line with slope = -1 as the ratios are inverted in a forward and reverse label swap experiment. Excluded fraction in density plots The color schemes of the density plots enable us to slice the data points in a discrete manner. We used excluded fraction based coloring. For example in figure 3c, the data points in black exclude 5% of the total data points and the point in black and grey exclude 25% of the total data points. Calculation of neighboring proteins For any protein, to calculate the percentage of total proteins that are within a factor 10 in copy number of each other, the proteins were divided into bins with each bin spanning an order of magnitude. The relative contribution in terms of number of proteins per bin was then calculated. The percentage of total proteins that are within one order of magnitude from a selected bin was determined by adding the relative contributions from the bins one order of magnitude above and below. The weighted average of this value from all the bins yields the percentage of proteins within one order of magnitude for any intensity bin. For the calculation of proteins within two orders of magnitude, two continuous bins above and below the bin of interest were added up.

Universal sample preparation method for proteome analysis

nature methods Universal sample preparation method for proteome analysis Jacek R Wi niewski, Alexandre Zougman, Nagarjuna Nagaraj & Matthias Mann Supplementary figures and text: Supplementary Figure 1