Aug 26, 2022
Protocol for Data Independent Acquisition - Mass spectrometry analysis – a DIA-based Organelle Proteomics
- Raja Sekhar Nirujogi1,2,
- Rotimi Fasimoye1,2,
- Toan K Phung1,2,
- Dario R Alessi1,2
- 1Medical Research Council Protein Phosphorylation and Ubiquitylation Unit, College of Life Sciences, University of Dundee, Dow Street, Dundee, DD1 5EH;
- 2Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
External link: https://doi.org/10.1073/pnas.2219953120
Protocol Citation: Raja Sekhar Nirujogi, Rotimi Fasimoye, Toan K Phung, Dario R Alessi 2022. Protocol for Data Independent Acquisition - Mass spectrometry analysis – a DIA-based Organelle Proteomics. protocols.io https://dx.doi.org/10.17504/protocols.io.kxygxzrokv8j/v1
Manuscript citation:
Fasimoye R, Dong W, Nirujogi RS, Rawat ES, Iguchi M, Nyame K, Phung TK, Bagnoli E, Prescott AR, Alessi DR, Abu-Remaileh M, Golgi-IP, a tool for multimodal analysis of Golgi molecular content. Proceedings of the National Academy of Sciences of the United States of America 120(20). doi: 10.1073/pnas.2219953120
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: August 18, 2022
Last Modified: May 31, 2024
Protocol Integer ID: 68832
Keywords: Mass spectrometry, Proteomics, Orbitrap exploris, ASAPCRN
Funders Acknowledgement:
Aligning Science Across Parkinson’s (ASAP) initiative
Grant ID: 000463
UK Medical Research Council
Grant ID: MC_UU_00018/1
Abstract
Purification of intact organelles by previously described methods (dx.doi.org/10.17504/protocols.io.bybjpskn; dx.doi.org/10.17504/protocols.io.6qpvrdjrogmk/v1) allows to profile the organelle proteome using quantitative mass spectrometry. Here we provide a detailed protocol for the Data Independent Acquisition (DIA)-based mass spectrometry (MS) data acquisition method for proteomic profiling of the Golgi. This includes a description of how to construct the nano Liquid chromatography and DIA MS methods as well as a Data Dependent Acquisition (DDA) strategy to generate deep spectral libraries to be able to use in searching the DIA data. In addition, we provide detailed search parameters for database search for both DDA and DIA and downstream MS data analysis.
Attachments
ivejbgrdf.docx
822KB
Materials
Reagents/Buffers:
- Acetonitrile ≥99.9%VWR AvantorCatalog #1.00030.2500
- LC-grade Formic acidSigma – AldrichCatalog #695076
- LC-grade WaterFisher ScientificCatalog #10777404
- Ammonium formateSigma AldrichCatalog #70221-25G-F
- Acetonitrile ≥99.9%VWR AvantorCatalog #1.00030.2500
- Ammonium Hydroxide (Acros #42305000)
- X100 20 mL Amber Glass EPA Vial (Cole Parmer # 11533750)
- LC- vials (VWR #548-0120)
- Snap ring caps (548-0016)
- High Recovery Vials & InsertsAgilent TechnologiesCatalog #5181-8872
- Acclaim 2cm trap column (C18, 5µm, 100Ao, 100µ, 2cm Nano-viper column # 164564, Thermo Scientific)
- 50cm analytical column (C18, 5µm, 50cm, 100Ao Easy nano spray column # ES903, Thermo Scientific)
- Water’s XBridge Peptide BEH C18 Column (130 Ao, 3.5 µm, 1mm X 100mm. Waters #186003561).
- 96-Well DeepWell™ Polypropylene MicroplatesThermo FisherCatalog #12566120
- pH indicator strips mid rangeVWR international LtdCatalog #1.09584.0001
- Biognosys iRT peptides mix (https://biognosys.com/product/irt-kit/)
- High-pH RPLC buffer: Solvent-A: 10 mM Ammonium formate (w/v) in LC-MS grade water. Solvent-B: 10 mM Ammonium Bicarbonate (w/v) in 80% ACN (v/v). Adjust pH to 10.0 using Ammonium Hydroxide
- LC buffer: 3% ACN (v/v) in 0.1% Formic acid (v/v).
Note
Note: Prepare a stock of 40ml in Amber glass vials and store at room temperature. The buffer can be stored up to 2 months. Avoid using any autoclaved pipette tips in aliquoting the buffer.
Equipment/Software:
- Dionex Ultimate 3000 nano-liquid chromatography system.
- Orbitrap Exploris 480 mass spectrometer.
- Dionex Ultimate 3000 liquid chromatography system with UV detector and fraction collector modules.
- Thermomixer.
- Speedvac (Thermo Scientific #SPD140DDA).
- MaxQuant software suite.
- Biognosys Spectronaut Software suite (Optional: DIA-NN Software suite).
High-pH Reversed-phase Liquid Chromatography fractionation of pooled Golgi-tag IP peptides to generate Spectral library:
High-pH Reversed-phase Liquid Chromatography fractionation of pooled Golgi-tag IP peptides to generate Spectral library:
Take ~5 µg of peptide digest from each of the Golgi-tag IP and Control-IP sample.
Vacuum dry the pooled samples.
Dissolve the peptide digest by adding 120 µL of High-pH Solvent-A (10 millimolar (mM) Ammonium formate 10.0 ). Place the sample on a Thermomixer with an agitation at 1800 rpm for 00:30:00 .
30m
Centrifuge the sample at high speed (17000 x g ) for 00:05:00 at Room temperature .
5m
Take 0.5 µL of the sample and verify the pH and transfer the sample into LC-vial.
Ensure the LC-solvent are as Solvent-A (10 millimolar (mM) Ammonium formate 10.0 ); Solvent-B (90% ACN (v/v) in 10 millimolar (mM) Ammonium formate 10.0 ).
Note
Note: Adjust the pH with 30% Ammonium Hydroxide.
Prepare the LC method by following the below gradient:
| A | B | C | |
| Time (minutes) | Nano pump Flow rate (µl/min) | % Of Solvent-B | |
| 0.0 | 0.100 | 3.0 | |
| 5.0 | 0.100 | 7.0 | |
| 5.5 | 0.100 | 7.0 | |
| 10.0 | 0.100 | 10.0 | |
| 50.0 | 0.100 | 40.0 | |
| 55.0 | 0.100 | 90.0 | |
| 62.0 | 0.100 | 90.0 | |
| 62.5 | 0.100 | 3.0 | |
| 70.0 | 0.100 | 3.0 | |
| 70.1 | 0.0100 | 3.0 |
Set the fraction collection time as Start time 00:05:05 and End time 01:02:00 .
1h 7m 5s
Collect a total of 45 fractions by keeping the fraction collection for 00:01:15 for each fraction.
1m 15s
Transfer the fractions into a pre-labelled 1.5 mL protein lo binding tubes.
Vacuum dry the samples and freeze in -20 freezer until the LC-MS/MS analysis.
Single shot DIA acquisition on Orbitrap Exploris 480:
Single shot DIA acquisition on Orbitrap Exploris 480:
Dissolve vacuum dried peptides in 60 µL of LC buffer (3% ACN in 0.1% Formic acid) and place the samples on a Thermomixer and mix them at 1800 rpm at Room temperature for about 00:30:00 .
30m
Take 4 µg equivalent of peptide digest and spike 1 µL of iRT peptide mix. Adjust the total volume of the sample anywhere between 5 µL to 15 µL but don’t exceed 15 µL . Transfer the sample into glass insert and place them on LC vial.
Note
Note: Strictly avoid using any autoclaved pipette tips. If possible, use dedicated Pipette set for MS analysis.
Construct LC and vDIA MS method as described below using Xaclibur software integrated in Thermo Orbitrap Exploris 480 MS acquisition software suite.
Ensure 2 cm trap column (C18, 5 μm , 100A°, 100 µ, 2 cm Nano-viper column # 164564, Thermo Scientific) and 50 cm analytical column (C18, 5 micromolar (µM) , 50 cm , 100Aº Easy nano spray column # ES903, Thermo Scientific) are equilibrated and verify the column performance by injecting 50 ng HeLa or another standard digest.
Nano LC gradient for 02:25:00 DIA analysis:
| A | B | C | |
| Time (minutes) | Nano pump Flow rate (µl/min) | % Of Solvent-B | |
| 0.0 | 0.250 | 3.0 | |
| 12.0 | 0.250 | 7.0 | |
| 115.0 | 0.250 | 25.0 | |
| 129.0 | 0.250 | 37.0 | |
| 130.0 | 0.250 | 95.0 | |
| 135.30 | 0.250 | 95.0 | |
| 135.80 | 0.250 | 3.0 | |
| 145.0 | 0.250 | 3.0 | |
| 145.0 | Stop Run | ||
2h 25m
Mass spectrometer parameters: Refer below settings to construct variable DIA method:
| A | B | C | D | |
| Method duration | 145 min | |||
| MS Global settings: | ||||
| Infusion mode: | Liquid Chromatography | |||
| Expected LC peak width (s): | 20 | |||
| Advanced Peak determination: | TRUE | |||
| Default charge state: | 3 | |||
| Internal mass calibration: | off | Note: If needed enable user defined calibrant ion (Polysilaxolane 445.120025 or enable Easy-IC option | ||
| Full scan settings: | ||||
| Orbitrap resolution: | 120000 | |||
| Scan range (m/z): | 375-1500 | |||
| RF lens (%): | 40 | |||
| AGC target: | Custom | |||
| Normalized AGC target (%): | 300 | |||
| Maximum injection Time mode: | Custom | |||
| Maximum injection Time (ms): | 30 | |||
| Micro scans: | 1 | |||
| Data type: | Profile | |||
| tMS2 or DIA settings | Isolation offset: | Off | ||
| Collision Energy Mode: | Stepped | |||
| Collision Energy Type: | Normalized | |||
| HCD Collision Energy (%): | 25, 28, 32 | |||
| Orbitrap resolution: | 30000 | |||
| Scan range mode: | Define m/z range | |||
| Scan Range (m/z): | 200 - 1200 | Note: Maximum of the matched fragement ions (b series and y-series) fall within this range and if needed this can be modified. | ||
| RF Lens (%): | 50 | |||
| AGC target: | Custom | |||
| Normalized AGC target (%): | 3000 | Note: It is recommended to fill the trap with a maximum accumulation of ions (3000% = 3E6 ions) for each of the DIA window to increase the sensitivity | ||
| Maximum injection Time mode: | Custom | |||
| Maximum injection Time (ms): | 70 | |||
| Micro scans: | 1 | |||
| Data type: | Profile | |||
| Polarity: | Positive | |||
| Loop control: | N | |||
| N (Number of Spectra): | 24 | We include one full MS1 scan after every 24 DIA scans to accommodate maximum possible MS1 scans | ||
| Dynamic RT: | Off | |||
| Time Mode: | Unscheduled |
| A | B | C | D | |
| Scheme of vDIA windows mass list table: | ||||
| m/z | z | Isolation Window (mz) | ||
| 383.375 | 3 | 66.8 | ||
| 423 | 3 | 13.5 | ||
| 435 | 3 | 11.5 | ||
| 446.5 | 3 | 12.5 | ||
| 458 | 3 | 11.5 | ||
| 469 | 3 | 11.5 | ||
| 480 | 3 | 11.5 | ||
| 490.5 | 3 | 10.5 | ||
| 501 | 3 | 11.5 | ||
| 512 | 3 | 11.5 | ||
| 523 | 3 | 11.5 | ||
| 533.5 | 3 | 10.5 | ||
| 544 | 3 | 11.5 | ||
| 554.5 | 3 | 10.5 | ||
| 565 | 3 | 11.5 | ||
| 575.5 | 3 | 10.5 | ||
| 586 | 3 | 11.5 | ||
| 597.5 | 3 | 12.5 | ||
| 609.5 | 3 | 12.5 | ||
| 621.5 | 3 | 12.5 | ||
| 633 | 3 | 11.5 | ||
| 645 | 3 | 13.5 | ||
| 657.5 | 3 | 12.5 | ||
| 670.5 | 3 | 14.5 | ||
| 684 | 3 | 13.5 | ||
| 697 | 3 | 13.5 | ||
| 710.5 | 3 | 14.5 | ||
| 725.5 | 3 | 16.5 | ||
| 741 | 3 | 15.5 | ||
| 756.5 | 3 | 16.5 | ||
| 773.5 | 3 | 18.5 | ||
| 791 | 3 | 17.5 | ||
| 808.5 | 3 | 18.5 | ||
| 827 | 3 | 19.5 | ||
| 846.5 | 3 | 20.5 | ||
| 866.5 | 3 | 20.5 | ||
| 887.5 | 3 | 22.5 | ||
| 910.5 | 3 | 24.5 | ||
| 935.5 | 3 | 26.5 | ||
| 962.5 | 3 | 28.5 | ||
| 992 | 3 | 31.5 | ||
| 1025 | 3 | 35.5 | ||
| 1063 | 3 | 41.5 | ||
| 1108.5 | 3 | 50.5 | ||
| 1391.625 | 3 | 516.8 |
Export the MS raw data for database searches by library-free (direct DIA) or library-based as illustrated in the workflow with Biognosys Spectronaut software suite.
Note
Optional: As the Biognosys Spectronaut is a commercial software suite if you don’t have access to it then you could use an open-source software suite such as DIA-NN.
Data Dependent Acquisition (DDA) MS analysis to generate Spectral library:
Data Dependent Acquisition (DDA) MS analysis to generate Spectral library:
Dissolve vacuum dried peptides of each fraction in 60 µL of LC buffer (3% ACN in 0.1% Formic acid) and place the samples on a Thermomixer and mix them at 1800 rpm at Room temperature for about 00:30:00 .
30m
Take 1 µg equivalent of peptide digest and Spike 1 µL of iRT peptide mix. Adjust the total volume of the sample anywhere between 5 µL to 15 µL but don’t exceed 15 µL . Transfer the sample into glass insert and place them on LC vial.
Note
Note: Strictly avoid using any autoclaved pipette tips. If possible, use dedicated Pipette set for MS analysis.
Ensure 2 cm trap column (C18, 5 μm , 100Ao, 100 µ, 2 cm Nano-viper column # 164564, Thermo Scientific) and 50 cm analytical column (C18, 5 micromolar (µM) , 50 cm , 100Ao Easy nano spray column # ES903, Thermo Scientific) are equilibrated and verify the column performance by injecting 50 ng HeLa or another standard digest.
Nano LC gradient for 01:25:00 DDA analysis:
| A | B | C | |
| Time (minutes) | Nano pump Flow rate (µl/min) | % Of Solvent-B | |
| 0.0 | 0.300 | 3.0 | |
| 7.0 | 0.300 | 7.0 | |
| 60.0 | 0.300 | 22.0 | |
| 70.0 | 0.300 | 35.0 | |
| 71.0 | 0.300 | 95.0 | |
| 78.0 | 0.300 | 95.0 | |
| 79.0 | 0.300 | 3.0 | |
| 85.0 | 0.300 | 3.0 | |
| 85.0 | Stop Run |
1h 25m
Mass spectrometer parameters: Refer below settings to construct DDA method:
| A | B | C | |
| Method duration | 85 min | ||
| MS Global settings: | |||
| Infusion mode: | Liquid Chromatography | ||
| Expected LC peak width (s): | 15 | ||
| Advanced Peak determination: | TRUE | ||
| Default charge state: | 2 | ||
| Internal mass calibration: | off | ||
| Full scan settings: | |||
| Orbitrap resolution: | 60000 | ||
| Scan range (m/z): | 350-1200 | ||
| RF lens (%): | 40 | ||
| AGC target: | Custom | ||
| Normalized AGC target (%): | 300 | ||
| Maximum injection Time mode: | Custom | ||
| Maximum injection Time (ms): | 28 | ||
| Micorscans: | 1 | ||
| Data type: | Profile | ||
| Polarity: | Positive | ||
| Filters: | |||
| MIPS | Monoisotopic peak determination: | Peptide | |
| Relax restrictions when too few precursors are found: | FALSE | ||
| Intensity | Filter Type: | ntensity Threshold | |
| Intensity Threshold: | 1.00E+04 | ||
| Charge State | Include charge state(s): | 2 to 6 | |
| Include undetermined charge states: | False | ||
| Dynamic Exclusion | Dynamic Exclusion Mode: | Custom | |
| Exclude after n times: | 1 | ||
| Exclusion duration (s): | 45 | ||
| Mass Tolerance: | ppm | ||
| Low: | 10 | ||
| High | 10 | ||
| Exclude isotopes: | TRUE | ||
| Perform dependent scan on single charge state per precursor only: | FALSE | ||
| Data Dependent | Data Dependent Mode: | Cycle Time | |
| Time between Master Scans (sec): | 3 | ||
| ddMS2 settings | Isolation Window (m/z): | 1.2 | |
| Isolation Offset: | Off | ||
| Collision Energy Mode: | Fixed | ||
| Collision Energy Type: | Normalized | ||
| HCD Collision Energy (%): | 30 | ||
| Orbitrap resolution: | 15000 | ||
| Scan range mode: | Auto | ||
| Scan Range (m/z): | 200 - 1200 | ||
| AGC target: | Custom | ||
| Normalized AGC target (%): | 100 | ||
| Maximum injection Time mode: | Custom | ||
| Maximum injection Time (ms): | 85 | ||
| Micorscans: | 1 | ||
| Data type: | Centroid | ||
| Polarity: | Positive |
Database searches with MaxQuant for Data Dependent Acquisition (DDA) MS analysis to generate Spectral library:
Database searches with MaxQuant for Data Dependent Acquisition (DDA) MS analysis to generate Spectral library:
Export Raw MS data to a Windows server to perform database searches using MaxQuant. Refer the below search parameters for the search.
Note
Note: It is recommended to have a good computational capability for a faster and successful MaxQuant search. We used the configuration: Intel® Xeon® Silver 421R CPU @ 2.40GHz and 2.39 GHz (2 processors), 384 GB RAM, 64-bit Windows OS with 1TB SSD drive.
| A | B | |
| Value | ||
| Version | 1.6.10.0 | |
| Include contaminants | TRUE | |
| PSM FDR | 0.01 | |
| PSM FDR Crosslink | 0.01 | |
| Protein FDR | 0.01 | |
| Site FDR | 0.01 | |
| Use Normalized Ratios for Occupancy | TRUE | |
| Min. peptide Length | 7 | |
| Min. score for unmodified peptides | 0 | |
| Min. score for modified peptides | 40 | |
| Min. delta score for unmodified peptides | 0 | |
| Min. delta score for modified peptides | 6 | |
| Min. unique peptides | 0 | |
| Min. razor peptides | 1 | |
| Min. peptides | 1 | |
| Use only unmodified peptides and | TRUE | |
| Modifications included in protein quantification | Oxidation (M);Acetyl (Protein N-term) | |
| Peptides used for protein quantification | Razor | |
| Discard unmodified counterpart peptides | TRUE | |
| Label min. ratio count | 2 | |
| Use delta score | FALSE | |
| iBAQ | TRUE | |
| iBAQ log fit | TRUE | |
| Match between runs | TRUE | |
| Matching time window [min] | 0.7 | |
| Match ion mobility window [indices] | 0.05 | |
| Alignment time window [min] | 20 | |
| Alignment ion mobility window [indices] | 1 | |
| Find dependent peptides | FALSE | |
| Fasta file | D:\Database\20200723-Human-Uniprot.fasta | |
| Decoy mode | revert | |
| Include contaminants | TRUE | |
| Advanced ratios | TRUE | |
| Fixed andromeda index folder | ||
| Temporary folder | ||
| Combined folder location | ||
| Second peptides | TRUE | |
| Stabilize large LFQ ratios | FALSE | |
| Separate LFQ in parameter group | FALSE | |
| Require MS/MS for LFQ comparisons | FALSE | |
| Calculate peak properties | FALSE | |
| Main search max. combinations | 200 | |
| Advanced site intensities | TRUE | |
| Write msScans table | TRUE | |
| Write msmsScans table | TRUE | |
| Write ms3Scans table | FALSE | |
| Write allPeptides table | TRUE | |
| Write mzRange table | TRUE | |
| Write pasefMsmsScans table | FALSE | |
| Write accumulatedPasefMsmsScans table | FALSE | |
| Max. peptide mass [Da] | 4600 | |
| Min. peptide length for unspecific search | 8 | |
| Max. peptide length for unspecific search | 25 | |
| Razor protein FDR | TRUE | |
| Disable MD5 | FALSE | |
| Max mods in site table | 3 | |
| Match unidentified features | FALSE | |
| Epsilon score for mutations | ||
| Evaluate variant peptides separately | TRUE | |
| Variation mode | None | |
| MS/MS tol. (FTMS) | 20 ppm | |
| Top MS/MS peaks per Da interval. (FTMS) | 12 | |
| Da interval. (FTMS) | 100 | |
| MS/MS deisotoping (FTMS) | TRUE | |
| MS/MS deisotoping tolerance (FTMS) | 7 | |
| MS/MS deisotoping tolerance unit (FTMS) | ppm | |
| MS/MS higher charges (FTMS) | TRUE | |
| MS/MS water loss (FTMS) | TRUE | |
| MS/MS ammonia loss (FTMS) | TRUE | |
| MS/MS dependent losses (FTMS) | TRUE | |
| MS/MS recalibration (FTMS) | TRUE | |
| MS/MS tol. (ITMS) | 0.5 Da | |
| Top MS/MS peaks per Da interval. (ITMS) | 8 | |
| Da interval. (ITMS) | 100 | |
| MS/MS deisotoping (ITMS) | FALSE | |
| MS/MS deisotoping tolerance (ITMS) | 0.15 | |
| MS/MS deisotoping tolerance unit (ITMS) | Da | |
| MS/MS higher charges (ITMS) | TRUE | |
| MS/MS water loss (ITMS) | TRUE | |
| MS/MS ammonia loss (ITMS) | TRUE | |
| MS/MS dependent losses (ITMS) | TRUE | |
| MS/MS recalibration (ITMS) | FALSE | |
| MS/MS tol. (TOF) | 40 ppm | |
| Top MS/MS peaks per Da interval. (TOF) | 10 | |
| Da interval. (TOF) | 100 | |
| MS/MS deisotoping (TOF) | TRUE | |
| MS/MS deisotoping tolerance (TOF) | 0.01 | |
| MS/MS deisotoping tolerance unit (TOF) | Da | |
| MS/MS higher charges (TOF) | TRUE | |
| MS/MS water loss (TOF) | TRUE | |
| MS/MS ammonia loss (TOF) | TRUE | |
| MS/MS dependent losses (TOF) | TRUE | |
| MS/MS recalibration (TOF) | FALSE | |
| MS/MS tol. (Unknown) | 20 ppm | |
| Top MS/MS peaks per Da interval. (Unknown) | 12 | |
| Da interval. (Unknown) | 100 | |
| MS/MS deisotoping (Unknown) | TRUE | |
| MS/MS deisotoping tolerance (Unknown) | 7 | |
| MS/MS deisotoping tolerance unit (Unknown) | ppm | |
| MS/MS higher charges (Unknown) | TRUE | |
| MS/MS water loss (Unknown) | TRUE | |
| MS/MS ammonia loss (Unknown) | TRUE | |
| MS/MS dependent losses (Unknown) | TRUE | |
| MS/MS recalibration (Unknown) | FALSE | |
| Site tables | Deamidation (NQ)Sites.txt;Oxidation (M)Sites.txt;Phospho (STY)Sites.txt |
Database searches with Biognosys Spectronaut for Data Dependent Independent Acquisition (DIA) MS analysis (Library free and Library-based search):
Database searches with Biognosys Spectronaut for Data Dependent Independent Acquisition (DIA) MS analysis (Library free and Library-based search):
Import the msms.txt file form the MaxQuant search output files into Spectronaut to generate a Spectral library.
Note
Make sure to provide a correct path of the DDA raw data.
Alternatively perform a Pulsar search of DDA data to generate a library.
As illustrated in the workflow we recommend doing a direct-DIA or Library free search using Human Uniprot FAST file to construct a hybrid library. Enable search archive option during the direct-DIA search.
Merge the direct-DIA search archive and DDA library to construct a hybrid library and use this library to perform library-based search of the DIA data.
Use the below settings for the library-based DIA search within Spectronaut.
| A | B | |
| Spectronaut 15.7.220308.50606 | ||
| Computer Name: MRC-DRI-2 | ||
| User Domain Name: LIFESCI-AD | ||
| User Name: rnirujogi | ||
| Analysis Mode: UI | ||
| Analysis Type: Peptide-Centric | ||
| Settings Used: RN_DIA_Default | ||
| Data Extraction | ||
| MS1 Mass Tolerance Strategy: | Dynamic | |
| Correction Factor: | 1 | |
| MS2 Mass Tolerance Strategy: | Dynamic | |
| Correction Factor: | 1 | |
| Intensity Extraction MS1: | Maximum Intensity | |
| Intensity Extraction MS2: | Maximum Intensity | |
| XIC Extraction | ||
| XIC IM Extraction Window: | Dynamic | |
| Correction Factor: | 1 | |
| XIC RT Extraction Window: | Dynamic | |
| Correction Factor: | 1 | |
| Calibration | ||
| Calibration Mode: | Automatic | |
| MS1 Mass Tolerance Strategy: | System Default | |
| MS2 Mass Tolerance Strategy: | System Default | |
| Precision iRT: | TRUE | |
| iRT <-> RT Regression Type: | Local (Non-Linear) Regression | |
| Exclude Deamidated Peptides: | TRUE | |
| MZ Extraction Strategy: | Maximum Intensity | |
| Allow source specific iRT Calibration: | TRUE | |
| Used Biognosys' iRT Kit: | TRUE | |
| Calibration Carry-Over: | FALSE | |
| Identification | ||
| Generate Decoys: | TRUE | |
| Decoy Limit Strategy: | Dynamic | |
| Library Size Fraction: | 0.1 | |
| Decoy Method: | Mutated | |
| Preferred Fragment Source: | NN Predicted Fragments | |
| Machine Learning: | Per Run | |
| Exclude Duplicate Assays: | TRUE | |
| Precursor PEP Cutoff: | 0.2 | |
| Protein Qvalue Cutoff (Experiment): | 0.01 | |
| Protein Qvalue Cutoff (Run): | 0.05 | |
| Exclude Single Hit Proteins: | TRUE | |
| Pvalue Estimator: | Kernel Density Estimator | |
| Precursor Qvalue Cutoff: | 0.01 | |
| Single Hit Definition: | By Stripped Sequence | |
| Quantification | ||
| Interference Correction: | TRUE | |
| MS1 Min: | 2 | |
| MS2 Min: | 3 | |
| Exclude All Multi-Channel Interferences: | TRUE | |
| Only Identified Peptides: | TRUE | |
| Protein LFQ Method: | Automatic | |
| Major (Protein) Grouping: | by Protein Group Id | |
| Minor (Peptide) Grouping: | by Stripped Sequence | |
| Minor Group Top N: | TRUE | |
| Min: | 1 | |
| Max: | 3 | |
| Minor Group Quantity: | Mean precursor quantity | |
| Major Group Top N: | TRUE | |
| Min: | 1 | |
| Max: | 3 | |
| Major Group Quantity: | Mean peptide quantity | |
| Quantity MS-Level: | MS2 | |
| Quantity Type: | Area | |
| Proteotypicity Filter: | None | |
| Data Filtering: | Qvalue | |
| Cross Run Normalization: | TRUE | |
| Row Selection: | Automatic | |
| Normalization Strategy: | None | |
| Normalization Filter Type: | None | |
| PTM Workflow | ||
| PTM Localization: | TRUE | |
| Probability Cutoff: | 0.75 | |
| PTM Analysis: | TRUE | |
| Multiplicity: | TRUE | |
| Run Clustering: | FALSE | |
| PTM Consolidation: | Sum | |
| Flanking Region: | 7 | |
| Workflow | ||
| In-Silico Library Optimization: | FALSE | |
| Profiling Strategy: | iRT Profiling | |
| Profiling Row Selection: | Minimum Qvalue Row Selection | |
| Qvalue Threshold: | 0.01 | |
| Profiling Target Selection: | Automatic Selection | |
| Carry-over exact Peak Boundaries: | FALSE | |
| Unify Peptide Peaks Strategy: | None | |
| Multi-Channel Workflow Definition: | From Library Annotation | |
| Fallback Option: | Labeled | |
| Protein Inference | ||
| Protein Inference Workflow: | Automatic | |
| Inference Algorithm: | IDPicker | |
| Post Analysis | ||
| Calculate Sample Correlation Matrix: | TRUE | |
| Calculate Explained TIC: | None | |
| Gene Ontology: | geneOntology/Ontologies\bgs_default_go basic.obo | |
| Differential Abundance Grouping: | Major Group (Quantification Settings) | |
| Smallest Quantitative Unit: | Major Group (Quantification Settings) | |
| Use All MS-Level Quantities: | FALSE | |
| Differential Abundance Testing: | Un-Paired t-test | |
| Assume Equa Variance: | FALSE | |
| Group-Wise Testing Correction: | FALSE | |
| Run Clustering: | TRUE | |
| Distance Metric: | Manhattan Distance | |
| Linkage Strategy: | Ward's Method | |
| Z-score transformation: | FALSE | |
| Order Runs by Clustering: | TRUE | |
| Pipeline Mode | ||
| Post Analysis Reports: | ||
| Scoring Histograms: | TRUE | |
| Data Completeness Bar Chart: | TRUE | |
| Run Identifications Bar Chart: | TRUE | |
| CV Density Line Chart: | TRUE | |
| CVs Below X Bar Chart: | TRUE | |
| Generate SNE File: | TRUE | |
| Store Iontraces in SNE: | FALSE | |
| Report Schema: | PTMSiteReport (Pivot), RN_PG_Pivot (Pivot), MSStats Report (v 3.7.3)(Normal), Protein Quant (Normal), Protein Quant (Pivot), BGS Factory Report(Normal) | |
| Reporting Unit: | Across Experiment |
Data analysis of DIA data and data visualization:
Data analysis of DIA data and data visualization:
Export Protein group tables from Spectronaut in PG Pivot format.
For the Golgi-tag IP data, annotate using a complied list of know Golgi proteins from a resource e.g. (https://compartments.jensenlab.org/Search) and Uniprot-GO terms.
Note
Note: Annotate Golgi proteins by using the VLOOKUP function in Excel from the compiled known Golgi-tag proteins. Similarly in case of Mito IP use Mito carta resource.
Prepare the data for differential analysis and this can be done using Perseus software suite (https://maxquant.net/perseus/). The basic functionalities of the software and various workflows can be adopted from the published literature (PMID: 27348712) and available tutorials (http://coxdocs.org/doku.php?id=perseus:start) on Youtube (https://www.youtube.com/c/MaxQuantChannel/featured)
Follow the Perseus workflow illustrated in Figure 2.
Figure: 2
Figure 2: Workflow describing the DIA data analysis using Perseus software package to identify enriched Golgi proteins and subsequent data visualization.
The T-test results can be exported and could be analysed using other software suites such as curtain tool to visualize the volcano plot and associated protein raw intensities for all the conditions, protein domain architecture, STRING interaction prediction and alpha fold prediction.
Optional: In addition to using the Perseus other data quality can be done using custom R or Python Scripts (Provided below) and other relevant packages.
Figure: 1
Figure1: Workflow of DIA MS data acquisition: Workflow describing the data acquisition of Golgi-tag IP, control IP and whole cell extracts of the DIA data and subsequent database search using Spectronaut. The library can be generated using DDA strategy and which can be searched using MaxQuant or Pulsar within Spectronaut
Scripts - R correlation plot
Scripts - R correlation plot
library(corrplot)
filename <- "//mrc-smb.lifesci.dundee.ac.uk/mrc-group-folder/ALESSI/Toan/For
Golgitag_Paper/For_Pearson_Corr_02.txt"
df <- read.table(filename, header = TRUE, sep="\t")
df <- df[colnames(df)[1:which(colnames(df) == "HA.WCL_06")]]
cor_mat <- cor(as.matrix(df), use="everything")
pdf("//mrc-smb.lifesci.dundee.ac.uk/mrc-group-folder/ALESSI/Toan/For
Golgitag_Paper/For_Pearson_Corr_02.txt.pdf")
corrplot(cor_mat, order="hclust", type="lower", method="ellipse")
dev.off()
Scripts - Python Network Interaction with Cytoscape and Plotly Dash
Scripts - Python Network Interaction with Cytoscape and Plotly Dash
import dash
import dash_cytoscape as cyto
from dash.dependencies import Input, Output
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
cyto.load_extra_layouts()
app = dash.Dash(__name__)
server = app.server
def add_individual_protein(df, source, elements):
highest = df["Difference"].max()
n = 0
for i, r in df.iterrows():
if n < 15:
opacity = r["Difference"]/highest
elements.append({'data': {'id': r["Gene.names"], 'label': r["Gene.names"], 'color': f"rgba(136, 86, 167,{opacity})", "opacity": opacity}, 'classes': 'protein'})
elements.append(
{'data': {'source': source, 'target': r["Gene.names"], 'color': f"rgba(136, 86, 167,{opacity})", "opacity": opacity}, 'classes': 'protein-edge'},)
else:
break
n += 1
def add_groups_enriched(edf, elements):
edf = edf.sort_values(by="Difference", ascending=False)
golgi = edf[(edf["Golgi"] == "+")]
#golgi = edf[(edf["C: Golgi"] == "+")]
golgi_count = len(golgi.index)
print(golgi_count)
glyco = golgi[golgi["Glycosylation"] == "+"]
#glyco = golgi[golgi["Glycosylation genes"] == "+"]
glyco_count = len(glyco.index)
print(glyco_count)
phospha = golgi[golgi["Phosphatases"] == "+"]
phospha_count = len(phospha.index)
kinases = golgi[(golgi["Kinases"] == "+") | (golgi["Dark.kinase"] == "+")]
#kinases = golgi[(golgi["Kinases"] == "+") | (golgi["Dark Kinases"] == "+")]
kinases_count = len(kinases.index)
ubi = golgi[golgi["Ub.Pathway"] == "+"]
ubi_count = len(ubi.index)
l = [
{'data': {'id': 'enriched-golgi', 'label': f'Golgi: {golgi_count}', "size": golgi_count * block},
'classes': 'golgi enriched'},
#{'data': {'source': 'significant', 'target': 'enriched-golgi'}, 'classes': 'significant-edge'},
{'data': {'id': 'enriched-glyco', 'label': f'Glycosylation genes: {glyco_count}',
"size": glyco_count * block}, 'classes': 'golgi enriched'},
{'data': {'id': 'enriched-phospha', 'label': f'Phosphatases: {phospha_count}',
"size": phospha_count * block}, 'classes': 'golgi enriched'},
{'data': {'id': 'enriched-kinase', 'label': f'Kinases: {kinases_count}', "size": kinases_count * block},
'classes': 'golgi enriched'},
{'data': {'id': 'ubi', 'label': f'Ubiquitin components: {ubi_count}', "size": ubi_count * block},
'classes': 'golgi enriched'},
{'data': {'source': 'enriched-golgi', 'target': 'enriched-glyco'}, 'classes': 'golgi-edge enriched'},
{'data': {'source': 'enriched-golgi', 'target': 'enriched-phospha'},
'classes': 'golgi-edge enriched'},
{'data': {'source': 'enriched-golgi', 'target': 'enriched-kinase'},
'classes': 'golgi-edge enriched'},
{'data': {'source': 'enriched-golgi', 'target': 'ubi'},
'classes': 'golgi-edge enriched'},
]
for i in l:
elements.append(i)
add_individual_protein(glyco, "enriched-glyco", elements)
add_individual_protein(phospha, "enriched-phospha", elements)
add_individual_protein(kinases, "enriched-kinase", elements)
add_individual_protein(ubi, "ubi", elements)
def add_groups_not_enriched(edf, elements):
edf = edf.sort_values(by="Difference", ascending=False)
golgi = edf[(edf["Golgi"] != "+")]
#golgi = edf[(edf["C: Golgi"] != "+")]
golgi_count = len(golgi.index)
print(golgi_count)
glyco = golgi[golgi["Glycosylation"] == "+"]
#glyco = golgi[golgi["Glycosylation genes"] == "+"]
glyco_count = len(glyco.index)
print(glyco_count)
phospha = golgi[golgi["Phosphatases"] == "+"]
phospha_count = len(phospha.index)
kinases = golgi[(golgi["Kinases"] == "+") | (golgi["Dark.kinase"] == "+")]
#kinases = golgi[(golgi["Kinases"] == "+") | (golgi["Dark Kinases"] == "+")]
kinases_count = len(kinases.index)
ubi = golgi[golgi["Ub.Pathway"] == "+"]
ubi_count = len(ubi.index)
l = [
{'data': {'id': 'non-enriched-golgi', 'label': f'Non-golgi: {golgi_count}', "size": golgi_count * block}, 'classes': 'not-golgi not-enriched'},
#{'data': {'source': 'significant', 'target': 'non-enriched-golgi'}, 'classes': 'not-golgi significant-edge'},
{'data': {'id': 'non-enriched-glyco', 'label': f'Glycosylation genes: {glyco_count}',
"size": glyco_count * block}, 'classes': 'not-golgi not-enriched'},
{'data': {'id': 'non-enriched-phospha', 'label': f'Phosphatases: {phospha_count}',
"size": phospha_count * block}, 'classes': 'not-golgi not-enriched'},
{'data': {'id': 'non-enriched-kinase', 'label': f'Kinases: {kinases_count}', "size": kinases_count * block},
'classes': 'not-golgi not-enriched'},
{'data': {'id': 'non-enriched-ubi', 'label': f'Ubiquitin components: {ubi_count}', "size": ubi_count * block},
'classes': 'not-golgi not-enriched'},
{'data': {'source': 'non-enriched-golgi', 'target': 'non-enriched-glyco'}, 'classes': 'not-golgi-edge not-enriched'},
{'data': {'source': 'non-enriched-golgi', 'target': 'non-enriched-phospha'}, 'classes': 'not-golgi-edge not-enriched'},
{'data': {'source': 'non-enriched-golgi', 'target': 'non-enriched-kinase'}, 'classes': 'not-golgi-edge not-enriched'},
{'data': {'source': 'non-enriched-golgi', 'target': 'non-enriched-ubi'},
'classes': 'not-golgi-edge not-enriched'},
]
for i in l:
elements.append(i)
add_individual_protein(glyco, "non-enriched-glyco", elements)
add_individual_protein(phospha, "non-enriched-phospha", elements)
add_individual_protein(kinases, "non-enriched-kinase", elements)
add_individual_protein(ubi, "non-enriched-ubi", elements)
block = 0.2
#df = pd.read_csv(r"C:\Users\toanp\Downloads\All enriched_For Network.txt", sep="\t")
#df = pd.read_csv(r"C:\Users\toanp\Downloads\GT-IP_Mock-IP_tTest.txt", sep="\t")
df = pd.read_csv(r"C:\Users\toanp\Downloads\GT-IP_WCL_tTest.txt", sep="\t")
df = df[(df["Significant"]=="+")&(df["Difference"] >= 1)]
elements = [
#{'data': {'id': 'significant', 'label': f'Significant: {len(df.index)}', "size": len(df.index) * block}, 'classes': 'significant'},
]
add_groups_enriched(df, elements)
add_groups_not_enriched(df, elements)
app.layout = html.Div([
cyto.Cytoscape(
id='cytoscape',
elements=elements,
layout={'name': 'cose', 'idealEdgeLength': 20},
style={'width': '2000px', 'height': '2000px'},
stylesheet=[
{
'selector': '.significant',
'style': {
'shape': 'ellipse',
'background-color': 'rgb(173, 218, 226)',
}
},
{
'selector': '.not-golgi',
'style': {
'shape': 'ellipse',
'background-color': 'rgb(255, 154, 162)',
}
},
{
'selector': '.not-golgi-edge',
'style': {
'curve-style': 'straight-triangle',
"width": 5,
'line-color': 'rgb(255, 154, 162)',
}
},
{
'selector': '.golgi',
'style': {
'shape': 'ellipse',
'background-color': 'rgb(255, 218, 193)',
}
},
{
'selector': '.golgi-edge',
'style': {
'curve-style': 'straight-triangle',
"width": 5,
'line-color': 'rgb(255, 218, 193)',
}
},
{
'selector': '.protein',
'style': {
'shape': 'ellipse',
'background-color': 'data(color)',
'background-opacity': 'data(opacity)',
'line-color': 'black'
}
},
{
'selector': '.protein-edge',
'style': {
'line-color': 'data(color)',
'opacity': 'data(opacity)',
}
},
{
'selector': 'node',
'style': {
"content": "data(label)",
"width": "data(size)",
"height": "data(size)",
}
},
{
'selector': '.enriched',
'style': {
'shape': 'ellipse',
'background-color': 'rgb(255, 154, 162)',
'line-color': 'rgb(255, 154, 162)',
}
},
{
'selector': '.not-enriched',
'style': {
'shape': 'ellipse',
'background-color': 'rgb(255, 218, 193)',
'line-color': 'rgb(255, 218, 193)',
}
}
]
),
html.Div([html.Button("as svg", id="btn-get-svg")])
])
print(elements)
@app.callback(
Output('image-text', 'children'),
Input('cytoscape', 'imageData'),
)
def put_image_string(data):
return data
@app.callback(
Output("cytoscape", "generateImage"),
[
Input("btn-get-svg", "n_clicks"),
])
def get_image(get_svg_clicks):
# File type to output of 'svg, 'png', 'jpg', or 'jpeg' (alias of 'jpg')
# 'store': Stores the image data in 'imageDataf' !only jpg/png are supported
# 'download'`: Downloads the image as a file with all data handling
# 'both'`: Stores image data and downloads image as file.
ctx = dash.callback_context
if ctx.triggered:
input_id = ctx.triggered[0]["prop_id"].split(".")[0]
if input_id != "tabs":
action = "download"
ftype = input_id.split("-")[-1]
return {
'type': 'svg',
'action': 'download'
}
return {
'type': 'png',
'action': 'store'
}
if __name__ == "__main__":
app.run_server(debug=True)