Feature File 1. Feature Detection. Similarity Comparison (Precursor and product ions) Spectra Generation. Decoy spectra.

Transcription

1 Supplementary Figure 1. The workflow of Group-DIA. DIA Data Data File 1 Data File 2 Data File 3 Feature Detection Feature File 1 Feature File 2 Feature File 3 Retention Time Alignment Intensity Extraction Group-DIA Similarity Comparison (Precursor and product ions) Spectra Generation Spectra Target spectra Decoy spectra Database Searching Peak Rediscovery Interference Removal Result

2 Supplementary Figure 2. The precursor ions XICs of peptide HIDASGSINR which were identified in the yeast SGS dataset by Group-DIA in 30 data files. a Before alignment b After alignment Each line above indicates an XIC of a precursor ion in a data file. X-axis is the retention time (a) or relative retention time (b) and Y-axis is the intensity of XICs of precursor ions.

3 Supplementary Figure 3. Comparison of the numbers of peptides and proteins identified by DIA-Umpire and Group-DIA in analyzing 30 data files from yeast SGS dataset or human SGS dataset. Features in MS1 were extracted by the method of DIA-Umpire, and then concomitantly analyzed by DIA-Umpire and Group-DIA. The mgf files resulted(quality Tier (QT) 1 and QT2 mgf files are used for DIA-Umpire) were searched by Mascot, converted to pepxml file by Trans-Proteomics Pepline (TPP), validated by PeptideProphet, and then analyzed by iprophet, followed with the Proteinprophet analysis. The unique peptide ions with iprophet probability >= 0.9 and proteins with Proteinprophet probability >= 0.9 were shown.

4 Supplementary Figure 4. Comparison the performance of different database search engines in analyzing 30 data files from yeast SGS dataset or human SGS dataset. a Yeast SGS dataset b Human SGS dataset Features in MS1 were extracted by the method of DIA-Umpire, and then paralleled analyzed by DIA-Umpire and Group-DIA. The mgf files resulted (Quality Tier (QT) 1 and QT2 mgf files are used for DIA-Umpire) were searched by Mascot, Comet and X!Tandem with k-score, then converted to pepxml file by TPP, validated by PeptideProphet, and then analyzed by iprophet. The unique peptide ions with iprophet probability >= 0.9 were shown.

5 Supplementary Figure 5. Discriminating power of computed probabilities in analyzing 30 data files from yeast SGS dataset or human SGS dataset. a Yeast SGS dataset b Human SGS dataset (a) The number of correct peptide to spectrum matches (PSMs) as a function of FDR and iprophet probability in yeast SGS dataset. (b) The number of correct PSMs as a function of FDR and iprophet probability in human SGS dataset.

6 Supplementary Figure 6. Venn diagram of peptide ions identified by Group-DIA and DIA-Umpire in analyzing yeast or human SGS dataset. a Yeast SGS dataset Group-DIA DIA-Umpire b Human SGS dataset Group-DIA DIA-Umpire (a) Comparison of all the peptide ions identified in all 30 data files in yeast SGS dataset. (b) Comparison of all the peptide ions identified in all 30 data files in human SGS dataset. Features in MS1 were extracted by the method of DIA-Umpire and then analyzed by both DIA-Umpire and Group-DIA. The mgf files resulted (QT 1 and QT2 mgf files are used for DIA-Umpire) were searched by Mascot, converted to pepxml file by TPP, validated by PeptideProphet, and then analyzed by iprophet. The unique peptide ions with iprophet probability >= 0.9 were shown.

7 Supplementary Figure 7. The number of peptide hits per protein which was identified in SGS dataset by Group-DIA but not identified by DIA-Umpire. a Yeast SGS dataset b Human SGS dataset

8 Supplementary Figure 8. Comparison of DIA-Umpire and Group-DIA in the identification of SIS peptide ions from yeast or human SGS dataset. Features in MS1 were extracted by the method of DIA-Umpire, and then analyzed by both DIA- Umpire and Group-DIA. The mgf files resulted were searched by Mascot, converted to pepxml file by TPP, validated by PeptideProphet, and then analyzed by iprophet. The unique SIS peptide ions with iprophet probability >= 0.9 were shown.

9 Supplementary Figure 9. The XICs of peptide FLEPYDDSIQAQK which were identified in the yeast SGS dataset by Group-DIA but not identified by DIA- Umpire. The black line is the XIC of precursor ion and the colored lines are the XICs of product ions. X- axis is the retention time. Y-axis is the intensity of XICs of product ions. The 1X, 2X, 4X at the right side of the figure represent the dilution times for the SIS peptide. All the XICs of peptides which were identified in the yeast SGS dataset by Group-DIA but not identified by DIA-Umpire are in Supplementary Data.

10 Supplementary Figure 10. The XICs of peptide LTDVTFPTGQVSSFR which were identified in human SGS dataset by Group-DIA but not identified by DIA- Umpire. The black line is the XIC of precursor ion and the colored lines are the XICs of product ions. X- axis is the retention time. Y-axis is the intensity of XICs of product ions. The 1X, 2X, 4X at right side of figure represent the dilution times for the SIS peptide. All the XICs of peptides which were identified in the human SGS dataset by Group-DIA but not identified by DIA-Umpire are in Supplementary Data.

11 Supplementary Figure 11. The distribution of the intensity of product ions in target spectra and decoy spectra. A data file in the yeast SGS dataset was analyzed by Group-DIA. All the product ions in the pseudo-spectra (containing both target and decoy spectra) are extracted and analyzed.

12 Supplementary Figure 12. The distribution of the Mascot ion score of target and decoy spectra. Target spectra Decoy spectra A data file in the yeast SGS dataset was analyzed by Group-DIA. The spectra (containing both target and decoy spectra) were searched by Mascot. The database searching results were converted to pepxml file with TPP, and the Mascot ion scores were extracted from the pepxml file.

13 Supplementary Figure 13. The ROC plots of target and decoy spectra. Target vs. decoy spectra on target database Target vs. decoy spectra on decoy database A data file in the yeast SGS dataset was analyzed by Group-DIA. The spectra (containing both target and decoy spectra) were put into one file and searched together by Mascot. The database searching results were converted to pepxml file with TPP, validated by PeptideProphet, and then analyzed by iprophet.

14 Supplementary Figure 14. Comparative analysis of SIS peptide intensities quantified by Group-DIA or OpenSWATH in the yeast SGS dataset or human SGS dataset. Each dot in the figure represents the intensity of a SIS peptide in a data file. x and y-axis is the relative intensity, shown in log2 scale. SIS peptide intensities quantified by OpenSWATH are from Röst et al s publication on OpenSWATH 4.

15 Supplementary Figure 15. Comparison of the numbers of missing peptides in one replicate or two replicates out of three in the results obtained by Group- DIA and OpenSWATH in analyzing SGS dataset. Yeast SGS dataset Yeast SGS dataset Human SGS dataset Human SGS dataset After filtered at 1% FDR, peptides were extracted from the Group-DIA and OpenSWATH results. The numbers of peptides missing in one or two replicates out of the three were calculated. The percentage of the missing peptides for each time point was shown.

16 Supplementary Figure 16. Comparison of DIA-Umpire and Group-DIA in analyzing TNFR1 complex dataset. (a) Murine fibroblast cell line L929 were treated with flag-tnf for 0 min, 5 min, 15 min, 30 min, 45 min, 60 min and then harvested and immunoprecipitated with anti-flag antibody. The samples were analyzed by SWATH-MS. The features in MS1 were extracted by the method of DIA-Umpire and then analyzed by both DIA-Umpire and Group-DIA. The mgf files resulted (QT 1 and QT2 mgf files are used for DIA-Umpire) were searched by Mascot, converted to pepxml file by TPP, validated by PeptideProphet, and then analyzed by iprophet. The unique peptide ions with iprophet probability >= 0.9 were shown. (b) Comparison of the numbers of peptides identified by DIA-Umpire and Group-DIA in analyzing 1,2 and 3 replicates. Each replicate contain six data files.

17 Supplementary Figure 17. Venn diagram of peptide ions of TNFR1 complex dataset identified by Group-DIA and DIA-Umpire and in spectral library generated by shotgun MS. DIA-Umpire Group-DIA DDA Library Features in MS1 were extracted by the method of DIA-Umpire and then analyzed by both DIA- Umpire and Group-DIA. The mgf files resulted (QT 1 and QT2 mgf files are used for DIA- Umpire) were searched by Mascot and converted to pepxml file by TPP, validated by PeptideProphet, and then analyzed by iprophet. The peptide ions identified by Group-DIA and DIA-Umpire were filtered at an iprophet probability 0.9. The spectral library was generated from shotgun MS analysis of 18 IP samples and filtered at 1% FDR at protein level. Note: The amount of sample loading in shotgun MS was twice as that in SWATH-MS, therefore more peptides in DDA library were detected.

18 Supplementary Figure 18. Comparison of the numbers of missing peptides in one replicate or two replicates out of three obtained by Group-DIA and OpenSWATH in analyzing TNFR1 complex dataset. After filtered at 1% FDR, peptides were extracted from the Group-DIA and OpenSWATH results. The numbers of peptides missing in one or two replicates out of the three were calculated. The percentage of the missing peptides for each time point was shown.

19 Supplementary Figure 19. Survival curves of TNF-treated WT and RIPK3 KO L929 cells. WT and RIPK3 KO L929 cells were treated with 10 ng/ml TNF for 8 different time periods (WT: 0, 0.5, 1, 2, 2.5, 3, 3.5, 4 hour; RIPK3 KO: 0, 2, 4, 6, 8, 9, 11, 12 hour), and cells at different time points were collected. The cells were incubated with propidium iodide (PI) and then PI negative cells (living cells with integrated plasma membrane do not take in PI) were counted by BD flow cytometer.

20 Supplementary Figure 20. Overview of generation of spectral library of whole murine L929 proteome. Depth and coverage of murine L929 proteome Immunoprecipitation Single run HILIC fractionation SEC followed by HILIC fractionation All experiments combined Affinity purification Proteins extracted from cells Proteins extracted from cells Proteins extracted from cells All data are combined digestion digestion digestion SEC Peptides Peptides Peptides 8 fractions HILIC fractionation digestion C18 column separation and MS analysis C18 column separation and MS analysis ~40 fractions Peptides HILIC fractionation C18 column separation and MS analysis 10~40 HILIC fractions per SEC fraction C18 column separation and MS analysis

21 Supplementary Figure 21. Heat maps of the protein intensities quantified by Group-DIA and OpenSWATH in analyzing the whole L929 cell dataset. OpenSWATH WT RIPK3 KO WT RIPK3 KO WT RIPK3 KO GROA CCL2 CSF1 FEZ2 MBOA7 THIOM PRAF3 ZN326 COX7C AP1S1 NT503 PSB11 RT27 GEMI5 CNOT2 PAF1 PP1R8 RM37 DERC1 PRC1 GNAI3 MPPA DCA13 Group-DIA WT RIPK3 KO WT RIPK3 KO JUNB CCL2 GROA UBF1 PGAM5 NCOA5 WT RIPK3 KO HM13 CP2BJ SAR1A WDR5 NRX1A ALAT2 Software result Manual analysis result Fold change (log2 scale) Temporal profiles of the up-regulated proteins identified respectively by Group-DIA and OpenSWATH were shown in blue lines in the plots right to the heat maps, and manually checked results were shown in red lines in the same plots. The intensities were normalized by untreated control and were shown in log2 scale. The names of the proteins which were confirmed by manual check were underlined.

22 Supplementary Note 1 Overview of Group-DIA The Group-DIA method includes the following steps: a) Feature Detection. For each data file, all possible features at the MS1 level are found. b) Retention Time Alignment. Group-DIA aligns the retention time using the chromatographic signals extracted from MS1. For every data file in an experiment, Group-DIA aligns the retention time with all the other data files in the experiment. c) Intensity Extraction. The intensities of the precursor and product ions generated by the feature are extracted. d) Similarity Comparison. All the possible product ions are selected by comparing the similarity of the XIC of precursor ion and XICs of product ions. e) Spectra Generation. The pseudo target and decoy spectra are generated. f) Database Searching. The pseudo spectra are stored using mgf and mzml format, so that any database searching program designed for DDA can be used. In this step, both the target and decoy spectra are searched at the same time. g) Peak Rediscovery. The identification results are assigned to the features, and six product ions per precursor ion are chosen to determine the intensity of the transition. Then all possible peaks are picked and scored and the FDR are estimated. h) Interference Removal. The interfered transitions are found and fixed by comparing the intensities of the transitions across all data files. Feature Detection In this paper, we define a possible signal generated by a specific peptide as a feature. The data file in which the signal exists is defined as the reference data file. The peptide detected in the reference data file may or may not be detected in other data files. Any feature generated by third-party software in Feature XML format can be utilized by Group-DIA. Retention Time Alignment The retention time of a peptide may vary in different data files and therefore should be properly aligned. The

23 alignment method used in Group-DIA is modified from previously published method 1. Before the alignment, every MS1 scan is converted to arrays by binning m/z at a preset bin size (for SWATH-MS data, 50ppm is used). For each bin, the intensity is calculated by summing up the values of all intensities of ions that fall within the m/z bin. Then, assuming that two data files A and B with N and M full mass scans respectively will be aligned, a two-dimensional correlation matrix C with M rows and N columns is first calculated. In matrix C, the c i,j element is the correlation coefficient of the ith full scan X i of data file A, and the jth scan Y j of data file B,. Next, by using dynamic programming, a best path connecting the elements c 1,1 and c N,M is found to make the sum of correlation coefficients maximized. Then the path is smoothed by locally weighted scatterplot smoothing (LOWESS). Finally, the time alignment is determined using the smoothed path. Intensity Extraction When analyzing a DIA-MS data file, the Group-DIA sequentially analyzes each feature in the data file to extract the XICs of the precursor and product ions for the feature. For each feature, the program performs the following analyses: a) The retention time of the feature in the reference data file is determined first, and then, the XICs for the feature are extracted from the MS1 scans. b) All the possible product ions generated by the precursor ion are determined using follow steps: i. The apex retention time of the feature is computed from the XIC of the precursor ion, and then the MS2 spectrum at the apex retention time in the reference data file is extracted. ii. A Gaussian smoothing algorithm is applied to the extracted MS2 spectra. Then a peak finder algorithm 2 is used to find peaks in the smoothed spectra data. The m/z of remaining peaks is thought to be the m/z of all possible product ions. iii. Step ii will generate a very large number of product ions. Obviously, most of them are not generated by the precursor ion, so some product ions are removed first. The XICs of all the possible product ions are extracted from the MS2 scans. Then the correlation coefficient of XICs is calculated between the precursor ion and each product ion. Only product ions that show a greater correlation coefficient than a cutoff value are considered as possible product ions. The cutoff value was set through experimental test for optimal condition of peptide identification. c) The possible retention time of the feature in other data files is inferred using the retention time alignment result. Because the retention time alignment result may be imprecise, the program does not

24 assign an exact time but a time window instead. d) The XICs in the assigned time window of precursor ions and product ions in all other data files are extracted. e) The precise retention times in every data file are determined using the XICs of product ions 3. For each product ion i, the cross correlation coefficient is computed between the XIC at the reference data file and current data file, and the delay d i which has the maximal cross correlation coefficient is taken to be the best delay of this product ion. The intensity weighted mean of every product ions best delay is taken to be the real delays of the reference data file and current data file. After the alignment, in every data file, the XICs with exact retention time for precursor ion and product ions can be determined. f) The XIC of every ion is represented as the concatenation of XICs in every data file. For example, if the XIC of one ion in data file i is (I 1 i, I 2 i, I 3 i,, I n i ), the XIC in data file j is (I 1 j, I 2 j, I 3 j,, I m j ), the XIC of this ion is represented as (I 1 i, I 2 i, I 3 i,, I n i, I 1 j, I 2 j, I 3 j,, I m j ). Similarity Comparison All the product ions generated by a precursor ion may show the same elution pattern. To associate the right product ions with a precursor ion, Group-DIA clusters the product ions first: all product ions are divided into several groups, each containing five product ions. Then, the k-means clustering algorithm is used to cluster the product ions according to their XICs. For each cluster, the correlation coefficient between the XIC of precursor ion and the centroids of the cluster is calculated. Then, all the product ions in the cluster are selected as the candidate product ions from the clusters that have highest correlation to those having the lowest until the number of candidate product ions reaches a preset value. Spectra Generation The candidate product ions are taken as the fragments of the precursor ion, and this will form a target spectrum. The remaining product ions are not the fragments of the precursor ion, and a same number of remaining product ions are randomly selected to form a decoy spectrum. Database Searching Before database searching, all the spectra generated from the features existed in the same data file are reorganized into a spectra file. When performing database searching, all canonical database searching

25 programs can be used, and the results will be validated by PeptideProphet and iprophet. A pep XML file generated by iprophet will be used as the input file for the next step. The database searching program associates each spectrum with a specific peptide. Spectra mapped to the decoy database will not be used in the subsequent analysis. Peak Rediscovery In this step, Group-DIA filters the database searching result at 1% FDR at peptide spectrum match (PSM) level. Then, Group-DIA assigns the database searching result to all the features analyzed and finds all possible peaks and scores them. To perform the target-decoy FDR estimation strategy in Group-DIA, decoy transitions are created via the shuffle method as described 4. Then, a group library will be set up: for every identification result, the XICs of the precursor ion and product ions in every data file are re-extracted. Then, the correlation coefficient between the XIC of precursor ion and every product ion is calculated. And the product ions are selected according to the following criteria: i. The product ions have to be b-ions or y-ions. ii. The product ions have to be 1+ or 2+ iii. The correlation coefficient between the intensity of precursor ion and product ion should be greater than 0. iv. The product ions m/z has to be above a preset value. All product ions that satisfy the above criteria are chosen. Then, the product ions are sorted in descending order according to the correlation between the intensity of precursor ion and product ion, and the top 6 product ions are chosen. If the numbers of product ions are less than 4, the product ions that do not satisfy criteria iv are also chosen. For decoy transitions, the product ions satisfying criteria i and ii are chosen, and then 6 product ions are selected randomly. The intensity of ions in the group library is calculated by summing up all the intensities of ions in every data file. After generating the group library, all possible peaks are picked using the method of OpenSWATH 4. Then, the OpenSWATH scoring system is introduced to score every peak. In addition to the OpenSWATH score, two more scores are added: i. MS1 MS2 correlation score: the score is computed as the mean of correlations between the precursor ion intensity and product ions intensities.

26 ii. MS1 MS2 intensity ratio score: the score is computed as follows: s = log Ippp n ppp i=1 I i log Lppp n ppp i=1 L i Here I ppp is the intensity of precursor ion; I ppp i is the intensity of product ion i; n is the number of product ions; L ppp is the intensity of precursor ion in the library; L i ppp is the intensity of product ion i in the library. The statistical validation is performed by the similar method used in mprophet 5, but Group-DIA validates result in a group view. Before model training, all the dataset is re-organized into several parts, each part containing 60% of the full data. In the initial step, only features found in the reference data file are set to be the true training data, and all the decoy transitions are taken as the negative training data. In the iteration step, linear discriminant analysis (LDA) is used to train the data. Then, the linear combination of all the subscores is calculated as a discriminant score, and a Bayes probability is computed as follows: P(SSSSS TTTTTT)P(TTTTTT) P(Target Score) = P(SSSSS TTTTTT)P(TTTTTT) + P(SSSSS DDDDD)P(DDDDD) Here, P(SSSSS TTTTTT) and P(SSSSS DDDDD) are determined by the Gaussian distribution. And P(TTTTee) is determined as: P(TTTTTT) = P(TTTTTT GGGGG), ii a TSSGG eeeeee 0.5, ii a TSSGG dsse nnn eeeee P(TTTTTT GGGGG) = P mmm + DieTTnSS (P mmm P mmm ) Here, a group exists means that in the experiment, the peptide has been identified in other data files. That means among a group of biological samples in an experiment, if a peptide has been identified in at least one of these samples, the prior probability is different from the peptide which has not been identified. In Group-DIA, P mmm is set to 0.2 and P mmm is set to 0.8. DieTTnSS is the cosine similarity between the intensity of transition in the current data file and the intensity of transition in the reference data file. After the training step in Group-DIA, the results contain two different decoys: the decoy spectrum generated in the Spectrum Generation step, and the decoy transition generated in the Peak Rediscovery step. These results will be filtered at two levels: first, an appropriate P(Target Score) is found to make the result reach a preset FDR between target and decoy transitions, and only target transitions with high P(Target Score) are selected for the subsequent analysis. Next, remaining transitions contain those generated from the target spectra s result and decoy spectra s result, which will be reevaluated to a final score: SSSSS = P(Peptide Experiment) = P(Peptide Spectrum) P(Spectrum Experiment) The P(Peptide Spectrum) is the probability of a specific spectrum match to a peptide, which is equal to the

27 iprophet probability. The P(Spectrum Experiment) is the probability that the specific spectrum exists in the DIA-MS data file, which is equal to P(Target Score). The probability of a specific spectrum match to a peptide is independent from the specific spectrum exist in the DIA-MS data file. And remaining transitions are filtered with a preset FDR at the peptide level between transitions generated from the target spectra and decoy spectra. Interference Removal When analyzing biology samples, due to the high complexity, interference may exist in many of the product ion chromatograms, especially in DIA-MS with a large precursor ion mass window like SWATH-MS. So it is important to remove interferences when determining the intensity of a peptide. In Group-DIA, we use a group pattern to correct the interference. The ratio of the intensity of a product ion to the sum of intensity of other product ions is determined first as R i = I i n j=1 I j (j!=i), here n is the number of the product ions used for determining intensity and I i is the intensity of product ion i. R i should be similar across all data files. If R i is exceptionally high in a data file, that means there is an interference in the data file. Group-DIA first calculates the median and the median absolute deviation (MAD) of R i over all data files. If R i is greater than median+3*mad, it s thought that there exists an interference, and the intensity will be changed to make R i equal to the median of R i. Supplementary Note 2 Decoy strategy The Group-DIA generates pseudo product-ion spectra to identify the peptide. However, errors may be introduced in the generation of pseudo product-ion spectra. Group-DIA introduces decoy spectra to evaluate the quality of spectra. When generating a pseudo product ion spectrum, a decoy spectrum is generated at the same time. In the workflow of Group-DIA, three decoy approaches are introduced. The principles of them are similar. First, the decoy spectra are introduced to control the errors during the generation of pseudo spectra; second, the decoy databases are introduced to control the errors in search database 6 ; third, the decoy transitions are introduced to control the errors in statistical analyses 5.

28 Supplementary Note 3 To assess the performance of Group-DIA in analyzing SWATH-MS data from highly complex samples, we analyzed the whole cell lysates of wildtype (WT) and RIP3 KO L929 cells, the former undergo necroptosis while the latter undergo apoptosis upon TNF stimulation 7. In order to compare the cells with a same level of death in TNF-treated WT and RIP3 KO cells, different time periods of TNF treatment were used for WT and KO cells (0, 2, 4, 6, 8, 9, 11, 12 hour for RIP3 KO L929 cells; 0, 0.5, 1, 2, 2.5, 3, 3.5, 4 hour for WT cells) (Supplementary Fig. 19). Three biological replicates at each time point were collected, resulting in 48 samples in total. To generate L929 cell line whole proteome spectral library for OpenSWATH analysis, we used extensive fractionation techniques at protein and peptide levels to achieve deep proteome coverage (Supplementary Table 6-7, Supplementary Fig. 20 and Supplementary Data). Protein temporal profiles obtained by Group-DIA and OpenSWATH were shown (Supplementary Fig. 21 and Supplementary Table 8). Group-DIA and OpenSWATH revealed respectively 12 and 23 proteins which were significantly up-regulated in both WT and RIP3 KO cells after TNF treatment. We manually checked the XICs of these corresponding peptides, and found 5 of 12 proteins and 7 of 23 proteins are truly up-regulated proteins (Supplementary Table 9). Two of the truly up-regulated proteins were identified by both Group-DIA and OpenSWATH, suggesting a different coverage of the two pieces of software. It is clear that Group-DIA can be used for the analysis of DIA data from whole cell proteome. Based on the data of whole cell proteome analysis of L929 cells, Group-DIA can quantify proteins with high accuracy as long as their abundances are relatively high. On one hand, the housekeeping proteins in the whole L929 dataset were accurately quantified and those high-abundance proteins usually were unchanged (Supplementary Table 8). On the other hand, regulated proteins are mostly low-abundance. Since the low-abundance peptides were usually interfered by high-abundance ones, quantification accuracy of these low-abundance peptides was often compromised. The manual verification is required but 42% true positive rate by Group-DIA has made manual check feasible in our experiments because the number of regulated proteins in most cases is small.

29 Supplementary Methods Purification of TNFR1 complex, digestion, MS analysis and spectral library building L929 cell line was obtained from ATCC and tested for tested for mycoplasma contamination using Cell Line Authentication Service from Promega. L929 cells were seeded at 1X 10 7 cells per 15cm dish in DMEM supplemented with 10% FBS. After 24 hours, the cells were treated with 10 μg/ml 3X FLAG-TNF for various time points. We collected six time points (0, 5, 15, 30, 45 and 60 min) in biological triplicate, which resulted in 18 IP samples. For each time-point experiment, ten 15 cm dishes cells were collected. After TNF treatment, cells were immediately washed twice with PBS and harvested by scraping and centrifugation at 100 g for 10 min. The harvested cells were washed with PBS and lysed for 30 min on ice in HBS lysis buffer (12.5 mm HEPES, 150 mm NaCl, 1% Nonidet P-40, ph7.5) with protease inhibitor cocktail. Cell lysates were then spun down at 50,000 g for 30 min. The soluble fraction was collected, and immunoprecipitated overnight with anti-flag M2 antibody-conjugated agarose at 4 C. Resins containing protein complexes were washed three times with HBS lysis buffer. Proteins were then eluted twice with 0.15 mg/ml of 3 FLAG peptide in HBS lysis buffer for 30 min each time, and elutions were pooled for a final volume of 300 μl. Proteins in the elution were precipitated with 20% trichloroacetic acid (TCA) and the pellet washed two times with 1-ml cold acetone, and dried in speedvac. TCA-precipitated proteins were re-suspended in 50 μl 8 M urea in 50 mm NH 4 HCO 3, and the samples were shaken at 37 C for 30 min until protein pellets were completely dissolved. 5 mm DTT was added for cysteine reduction for 30 min at 37 C, and 15 mm iodoacetamide were added for alkylation for 30 min at room temperature in the dark. Excessive iodoacetamide was quenched with addition of another 5 mm DTT in the dark for 15 min at room temperature. Next, 8 M urea were diluted to 1.6 M urea with 50 mm NH 4 HCO 3 and trypsin (Washtington) was added at the protein:trypin ratio of 50:1. Digestion lasted for hour at 37 C. After digestion, 1% formic acid was added to stop the digestion reactions. The peptides were purified using C18 STAGETips. After desaltion, peptides were eluted with 70% acetonitrile /1% formic acid and dried. Peptides were dissolved in 0.1% formic acid and analyzed by mass spectrometry in IDA (information dependent acquisition) mode. MS analysis was performed on a TripleTOF 5600 (AB Sciex) mass spectrometry coupled to NanoLC Ultra 2D Plus (Eksigent) HPLC system. Peptides first bound to a 5 mm 500 μm trap column packed with Zorbax C18 5-μm 200- Å resin using 0.1% (V/V) formic acid/2% acetonitrile in H 2 O at 10 μl/min for 5 min, and then separated using 60-min gradient from 2-35% buffer B

30 (buffer A 0.1% (V/V) formic acid, 5% DMSO in H 2 O, buffer B 0.1% (V/V) formic acid, 5% DMSO in acetonitrile) on a 15 cm 75 μm in-house pulled emitter-integrated column packed with Magic C18 AQ 3-μm 200- Å resin. Peptides were analyzed in IDA and SWATH modes, and the amount of sample loading in IDA was twice of that in SWATH-MS. For IDA, MS1 spectra were collected in the range 350-1,250 m/z for 250 ms, and up to 20 most intense precursors with charge state 2-5 were selected for fragmentation, and MS2 spectra were collected in the range 100-1,800 m/z for 100 ms. Exclusion time for precursor ions selection is 20 s. For SWATH-MS, the mass spectrometer was operated such that a 100-ms survey scan (TOF-MS) which was collected in 350-1,500 m/z was performed followed by ms MS2 experiments which were collected in 100-1,800 m/z. These MS2 experiments used an isolation width of 26 m/z (containing 1 m/z for the window overlap) to cover the precursor mass range of 400-1,200 m/z. Ions were fragmented for MS2 experiment in the collision cell using a collision energy according to the equation of a doubly charged peptide, ramped ±15 V from the calculated collision energy. The wiff files from IDA were converted to centroided mzml format using the AB Sciex Data Converter v. 1.3 and then further converted to mzxml files using proteowizard MSConvert v mzxml files were converted to mgf format using Trans-Proteomics Pepline (TPP) v.4.7 convert tools 8. mzxml and mgf files were searched using X!Tandem (version , native and k-score plugin) 9 and Mascot (version 2.3) respectively against the full non-redundant, canonical mouse genome as annotated by UniprotKB/Swiss-Prot (downloaded in September, 2014) appended with common contaminants and irt peptide and reversed sequence decoys (33,864 sequences includes decoys). The database searching parameters were set as follows: carbamidomethylation (C) was set as fixed modification; methionine oxidation was set as variable modification, semi-tryptic peptides and peptides with up to two missed cleavages were allowed, and mass tolerance of the precursor ion was set at 30 ppm. Mass tolerance of the product ion was set at 75 ppm and 0.15 Da for X!Tandem (native and k-score plugin) and Mascot, respectively. Results from searching engines were converted to the pepxml files and validated with PeptideProphet 10, and combined and rescored using iprophet 11. We used MAYU 12 (version 1.07) to determine an iprophet probability, resulting in a protein FDR < In total 1,282 proteins were identified (Supplementary Table 4). The pepxml file was filtered at iprophet probability according to a protein FDR of 1% and converted to sptxt files with SpectraST 13 with CID-QTOF setting. Decoy and contaminant hits were removed. The retention times of peptides in sptxt files were replaced with irt times using spectrast2spectrast_irt.py script (downloaded from These sptxt files were then combined and made consensus

31 nonabundant spectral library with the irt retention times using spectrast. Time-course treatment of wildtype and RIP3-KO L929 cells, digestion and MS analysis Wildtype L929 cells and RIP3 knockout L929 cells were treated with 10 μg/ml TNF for eight time points (wildtype: 0, 0.5, 1, 2, 2.5, 3, 3.5, 4 hour; RIP3 knockout: 0, 2, 4, 6, 8, 9, 11, 12 hour) respectively, and treatment time was adjusted according to the same cell death extent for these two cell lines. After treatment, cells from sixteen time points were collected. Cells were immediately harvested on ice by pipetting in ice-cold PBS, followed by centrifugation for 3 min at 300 g at 4 C, and snap frozen. The time-course treatment was carried out in biological triplicate, using one 10 cm dish per condition. Cells were lysed using 8 M urea, 50 mm Tris-HCl, ph 8.0 containing a cocktail of protease inhibitors (1 tablet Roche complete mini per 10 ml, 1 mm PMSF) and phosphatase inhibitors (1 mm NaF, 1 mm β-glycrophosphate, 1 mm sodium orthovanadata, 10 mm sodium pyrophosphate). Protein assays were performed using the BCA method. Cysteines were reduced in 5 mm DTT for 30 min at 37 C. Free sulfhydryl groups were then alkylated in 15 mm iodoacetamide in the dark at room temperature for 30 min. 5 mm DTT was added to quench the excessive iodoacetamide in the dark for 15 min at room temperature. Next, the lysates were diluted to 1.6 M urea with 50 mm NH 4 HCO 3 and trypsin was added at the protein: trypsin ratio of 50:1. Digestion reactions were incubated overnight at 37 C, and acidified with formic acid to a concentration of 1% prior to solid phase extraction. Peptides were purified using Sep-Pak cartridges (Waters). After desaltion, peptides were eluted with 70%ACN/1%FA and irt peptides (Biognosys) for retention-time alignment were added followed by dryness. Each sample was analyzed using SWATH-MS. SWATH mass spectra were acquired using TripleTOF 5600 (AB Sciex) mass spectrometry interfaced to a NanoLC Ultra 2D Plus (Eksigent) HPLC system as described above. Samples were first trapped on a trap column (300 μm 0.5 cm) using 10 μl/min for 5 min, and then eluted using 180-min gradient from 2-35% buffer B (buffer A 0.1% (V/V) formic acid, 5% DMSO in H 2 O, buffer B 0.1% (V/V) formic acid, 5% DMSO in acetonitrile) after direct injection onto a 32 cm 75 μm in-house pulled emitter-integrated analytical column packed with Magic C18 AQ 3-μm 200- Å resin. For SWATH-MS, the mass spectrometer was operated with the same setting as described above. In addition to SWATH-MS analysis, the samples from each condition were also analyzed using shotgun data acquisition on TripleTOF The parameters of HPLC system are the same as described the above. MS1 spectra were collected in the range of 350-1,250 m/z for 250 ms, and up to 20 most intense precursors with

32 charge state 2-5 were selected for fragmentation, and MS2 spectra were collected in the range of 100-1,800 m/z for 50 ms. Exclusion time for precursor ions selection is 20 s. Generation of L929 cell spectral library Given the varied protein expression patterns in the cell, we chose two states of L929 cells to map murine L929 cell proteome. One was L929 cells with no treatment, and the other was TNF-treated cells. L929 cells were harvested after TNF treatment for 1 hour or no treatment (twenty 15 cm dishes per condition). Cells were lysed with 4% SDS in Tris-HCl ph 7.5, and protein concentration was measured using BCA method. We performed extensive fractionation for three samples. The first sample contained 10 mg proteins from non-treatment cells, the second contained 10 mg proteins from TNF-treated cells and the third contained 10 mg equally mixed proteins from both non-treatment cells and TNF-treated cells. 10 mg proteins from non-treatment cells or TNF-treated cells were first digested and the resulting peptides were then fractionated using HILIC (hydrophilic interaction liquid chromatography) technique, and 10 mg mixed proteins from basal and TNF-treated cells were fractionated using size-exclusion chromatography at protein level followed by HILIC fractionation at peptide level. Size-exclusion chromatography: 0.1 ml of the cell lysate containing 10 mg of total protein was loaded onto a Superdex /300 GL column (GE Healthcare Bio-Sciences AB, Uppsala) equilibrated with TNS buffer composed of 0.1 M Tris-HCl, ph 8.0 buffer, 0.1 M NaCl and 0.2% SDS. Proteins were eluted with TNS buffer and fractions were collected according to elution profile. Total 8 fractions were collected. Protein Digestion and Peptide fractionation: Proteins were digested using FASP (Filtered Aided Sample Preparation) 14. Briefly, the samples were loaded onto a 15 ml AmiconUltra Ultracel-30K filter (Millipore) and reduced in 10 mm DTT. Detergent was removed by urea followed by alkylation with 50 mm iodoacetamide for 30 min in darkness. The urea was then replaced with 50 mm NH 4 HCO 3. Trypsin was added at a protein:trypsin ratio of 50:1 overnight at 37 C. Peptides were collected from the filter by centrifugation followed by a wash with water. The peptide mixture was acidified by the addition of formic acid to a final concentration of 1% and then centrifuged. The supernatant was then desalted on SepPak C18 columns (Waters) and the desalted peptide was dried and stored at -80 C. HILIC was performed using a 1260 HPLC system (Agilent) with a TSKgel Amide-80 HILIC column ( mm, 5 μm; Tosoh Biosciences, Tokyo, Japan) at a flow rate of 150 μl/min. Two buffers were used for the gradient: buffer A, 90% ACN containing 0.005% TFA, and buffer B, 0.005% TFA. Peptides were resuspended

33 in 200 μl of 70% ACN and then injected into the HILIC Amide-80 column via a 200 μl loop with a flow rate of 150 μl/min. The gradient used is as follows: 0% buffer B at time 0 min, 11% buffer B at 5 min, 29% buffer B at 20 min, 95% buffer B at 45 min, hold 95% buffer for 5min, and finally 0% buffer B at 55 min. Fractions were collected according to elution profile and dried. Shotgun Mass Spectrometry: Peptides from each HILIC fraction were resuspended in 0.1% FA containing irt peptides and analyzed on TripleTOF 5600 mass spectrometry as described above. Peptides were first trapped on a trap column (300 μm 0.5 cm) and then separated on a homemade emitter-integrated analytical column (75 μm 15 cm ) packed with Magic C18 AQ 3-μm 200- Å resin. The gradient of 45-min from 2-35% buffer B (buffer A 0.1% (V/V) formic acid, 5% DMSO in H 2 O, buffer B 0.1% (V/V) formic acid, 5% DMSO in acetonitrile) was used. For DDA (information-dependent acquisition), survey scans were acquired in 350-1,250 m/z for 250 ms and 20 most intense product ions were collected in 100-1,800 for 50 ms. Depending on the sample complexity, each fraction was analyzed 1-2 times in shotgun mode. Data processing and spectral library building: The 296 wiff files from DDA were processed using the same method as described as above. Briefly, DDA data were searched with Mascot and X!Tandem (native and k-score plugin) and validated with PeptideProphet followed by iprophet analysis. An iprophet probability cutoff (p= ) was determined by MAYU (version 1.07), resulting in 106,601 unique peptides (FDR=0.15%) which correspond to 7,913 proteins (FDR=1.08%) (Supplementary Table 7). The pepxml file was filtered to 1% FDR at protein level and converted to sptxt file using SpectraST with CID-QTOF setting. The retention time of peptides in sptxt file were replaced with irt time using spectrast2spectrast_irt.py script (downloaded from After removing contaminant and decoy proteins hits, the sptxt file was made consensus nonabundant spectral library with the irt retention time using spectrast. File format conversion for SWATH-MS data In Group-DIA and OpenSWATH, the wiff raw files from the SWATH-MS were converted to mzxml format by the ProteoWizard package (version ) with the profile option, and the mzxml files resulted were applied with fix_swath_windows.py from msproteomics tools to fix the precursor isolation-window scheme, and split_mzxml_intoswath.py from msproteomicstools to split mzxml files into 33 individual files (32 SWATH MS2 files and 1 MS1 file). These mzxml files were further converted into mzml format by FileConverter from the OpenMS software. In DIA-Umpire, the wiff raw files from the SWATH-MS were converted to mzml format by the AB MS Data

34 Converter with the centroid option, and the resulting mzml files were further converted into mzxml format by msconvert.exe from the ProteoWizard package with the default parameters. Parallel Comparison between Group-DIA and DIA-Umpire for SGS dataset and TNFR1 dataset To compare the performance between Group-DIA and DIA-Umpire and avoid divergence caused by different feature-detection algorithm, the features were found by DIA-Umpire at MS1 level (spectra in Quality Tier 1 and Quality Tier 2). Then, the features were converted to files in Feature XML format with in-house scripts which were used later as the input feature files for Group-DIA. The two programs were run with the default parameter. In the step of database searching, the mgf files generated by the two pieces of software were searched with Mascot respectively. When analyzed with Group-DIA, only the target spectra were searched. When analyzed with DIA-Umpire, only the spectra generated from features found from MS1 (QT1.mgf and QT2.mgf files) were used. Database searching for pseudo-spectra For the pseudo-spectra generated by Group-DIA or DIA-Umpire, the database searching parameters were set as follows: carbamidomethylation (C) was set as fixed modification, methionine oxidation (M) was set as variable modification, and the mass tolerance of the precursor ion was set at 50 ppm and the mass tolerance of the product ion was set at 0.05 Da. For the SGS datasets, the 13C(6)15N(4) (R) and 13C(6)15N(2) (K) were also set as variable modification. The spectra files were searched against the full non-redundant, canonical yeast or mouse or human genome as annotated by UniprotKB/Swiss-Prot appended with common contaminants plus irt peptide and reversed sequence decoys and SIS peptides. The decoy sequences were generated by reverse transformation of the target sequence, and "DECOY" label was added to the name of proteins. After database searching, the result files were converted to pepxml file with TPP, and then validated by PeptideProphet with "-OAdPE -PPM -p0.05 -l7 -ddecoy" option(x!tandem 15 or Comet 16 result) or "-OAdP -PPM -p0.05 -l7 -ddecoy" option(mascot result), and subsequently combined and rescored using iprophet with "DECOY=DECOY" option. ProteinProphet was run with "IPROPHET option.

35 OpenSWATH analysis for yeast and human SGS dataset The yeast and human spectral libraries for OpenSWATH analysis of SGS dataset are from Olga et.al. 17 and George et.al. 18. Hela-specific assay library (phl004_sshela_s32.csv) was directly used for OpenSWATH analysis for human SGS dataset, and three DDA files (nselevse_l12037_001.wiff, nselevse_l120327_010.wiff, and nselevse_l120327_016.wiff) were used to build yeast spectral library. To obtain target-assay construction for OpenSWATH analysis, the consensus spectral library (the sptxt file) was first converted to a TSV file by selecting the six most abundant b or y product ions for each peptide precursor using spectrast2tsv.py script (downloaded from and then converted to TraML files using ConvertTSVToTraML tool in openms software. Finally decoy assays were included using OpenSwathDecoyGenerator tool in "shuffle" method in openms software. When the data was analyzed with OpenSWATH workflow, the retention time window was set to 300s. Then the results were analyzed by mprophet with Python implementation, and filtered at 1% FDR at peptide level. Analysis for TNFR1 dataset and whole L929 cell dataset When analyzed with Group-DIA workflow, the data was processed with default parameters. In the step of database searching, the mgf files and mzml files generated by Group-DIA were searched with Mascot and X!Tandem (native and k-score plugin) respectively. After database searching followed with PeptideProphet and iprophet analysis, the results were filtered to 1% FDR at peptide spectrum match (PSM) level. Then after the peak rediscovery step, the results were filtered to 1% FDR at peptide level. When analyzed with DIA-Umpire workflow, the data was processed with default parameters. Spectra in all Quality Tier 1, Quality Tier 2 and Quality Tier 3 were subjected to database searching. In the step of database searching, the mgf files generated by DIA-Umpire were converted to mzxml first, then the mgf and mzxml files were searched with Mascot and X!Tandem (native and k-score plugin) respectively. Then, the database searching results were validated by PeptideProphet respectively and analyzed by iprophet. Subsequently, the results were filtered to 1% FDR at peptide and protein levels. Protein profiles in TNFR1 dataset were from protein summary file of DIA-Umpire results. To obtain target-assay construction for OpenSWATH analysis, the consensus spectral library (the sptxt file) that was generated by shotgun MS was first converted to an assay library using the same method above (Supplementary Data). OpenSWATH workflow was implemented in the same way as described above, the retention time window was set to 600s and the results were filtered at 1% FDR at peptide level.

36 Data analysis and statistics for TNFR1 dataset and whole L929 cell dataset Peptide ions that met the 1% FDR threshold in three out of three biological replicates for any experimental condition were retained, and intensities for these peptides/transitions across the experiment were used for further analysis. The protein abundance was estimated using the best flyer peptide approach and was calculated by summing the six most intense transitions for the six most intense peptides. The protein intensities were normalized with the sum of all identified peptides. The log2 transformed intensity of all identified peptides were fitted to a Gaussian distribution, and the missing values were filled with the value in p= For TNFR1 complex dataset, since no TNFR1 complex components would interact with TNF in 0min, only proteins that changed significantly in term of amount (change fold in log2 scale >4) in 5min were considered. For whole L929 cell dataset, the one-way ANOVA test was used to identify proteins differentially expressed over time, and was corrected using the method of Benjamini-Hochberg to avoid multiple testing error. A Benjamini-Hochberg-corrected p-value < 0.05 was considered statistically significant. Then, Tukey's honest significance test was performed between the untreated control and every other sample at a confidence level of Only proteins that had p-value < 0.05 at least one time point was considered as significantly changed. Only significantly changed proteins which have larger than 4-fold change at 3 continuous time points were analyzed. Manual analysis of significantly changed proteins in TNFR1 and whole L929 cell dataset using PeakView For TNFR1 complex dataset, the consensus spectral library sptxt file built from DDA was converted to csv file using spectrast2tsv.py script with the same command line for OpenSWATH analysis except for peakview output. The csv file was imported into Peakview software (Version 2.1) integrated with SWATH quantitation plugin (Version 2.0), and the settings of SWATH plugin were: 10 peptides per protein, 6 transitions per peptide, 20 min for XIC extraction window, and 0.05 Da for XIC width. SWATH-MS wiff files were then imported into Peakview. After the normalization of retention time with irt across the entire experiment, the XICs for each product ion were manually inspected for correct peak group assignment. Peak areas for each product ion were directly retrieved in PeakView software. The sum of two most intense product ions represented the peptide abundance, and the sum of three most intense peptides represented the protein

37 abundance. Three most intense peptides of all peptides were used for normalization between different samples. For whole L929 cell dataset, the retention time normalization was performed using the most intense 11 peptides in the sample across entire experiment. Only the information of significantly changed proteins was extracted from the whole L929 proteome spectral library and imported into PeakView software and 30 min for XIC extraction window in SWTH plugin setting. Other steps were the same as described above. Data access and software availability The mass spectrometry raw SWATH-MS data of TNFR1 dataset are available via ProteomeXchange with identifier PXD002177, The mass spectrometry raw SWATH-MS data of whole L929 cell dataset are available via ProteomeXchange with identifier PXD The source code of Group-DIA is in Supplementary Software. References 1. Sadygov, R.G., Maroto, F.M. & Huhmer, A.F. ChromAlign: A two-step algorithmic procedure for time alignment of three-dimensional LC-MS chromatographic surfaces. Analytical chemistry 78, (2006). 2. Sturm, M. et al. OpenMS - an open-source software framework for mass spectrometry. BMC bioinformatics 9, 163 (2008). 3. Bernhardt, O. et al. A novel algorithm for protein profiling based on cross-run correlations in SWATH-MS data implemented in Spectronaut. F1000Posters Presented at the 61st American Society for Mass Spectrometry Conference, 9-13 Jun 2013 (2013). 4. Rost, H.L. et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nature biotechnology 32, (2014). 5. Reiter, L. et al. mprophet: automated data processing and statistical validation for large-scale SRM experiments. Nature methods 8, (2011). 6. Elias, J.E. & Gygi, S.P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nature methods 4, (2007). 7. Wu, X.N. et al. Distinct roles of RIP1-RIP3 hetero- and RIP3-RIP3 homo-interaction in mediating necroptosis. Cell death and differentiation 21, (2014). 8. Deutsch, E.W. et al. A guided tour of the Trans-Proteomic Pipeline. Proteomics 10, (2010). 9. Craig, R. & Beavis, R.C. A method for reducing the time required to match protein sequences with tandem mass spectra. Rapid communications in mass spectrometry : RCM 17, (2003). 10. Keller, A., Nesvizhskii, A.I., Kolker, E. & Aebersold, R. Empirical statistical model to estimate the accuracy of