Data-Independent Acquisition

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DIA (Data-Independent Acquisition) is a non-discriminatory and non-random proteomic analysis technique that divides the full scan range of mass spectrometry into several windows, and then fragments and detects all ions in each window, achieving the collection and analysis of all ions in the sample without missing or discrepancy. This reduces the missing values of sample detection, improves quantitative accuracy and reproducibility, and achieves the highly stable and accurate quantitative proteomic analysis in large sample cohorts.

By a comprehensive analysis of the website database search results through multiple tools such as OpenSWATH, EncyclopeDIA and DIA-NN, Westlake Omics is able to increase the amount and accuracy of protein identifications. Besides, we developed a method to optimize specific spectral libraries.

Related patent: Data Independent Acquisition Mass Spectrometry Method based on Sub-Lib.

Patent No.: 202010773114.5.

Workflow

Application Scenarios:

DIA is appropriate for large sample scenarios, such as clinical cohorts.

 

Mass spectrometers:

Plasma/Serum: Sciex TripleTOF 5600, Sciex TripleTOF 6600 (Scanning SWATH)

Other tissues: Orbitrap Exploris™ 480, etc.

References:

1. Gillet et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics. 2012.11(6)

https://www.mcponline.org/article/S1535-9476(20)30442-4/fulltext

2. Röst et al. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nature Methods. 2016.13(9):741-748

https://www.nature.com/articles/nmeth.3959

3.Röst, et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nature Biotechnology. 2014.32:219-223

https://www.nature.com/articles/nbt.2841

4.Guo, et al. Rapid mass spectrometric conversion of tissue biopsy samples into permanent quantitative digital proteome maps. Nature Medicine. 2015.21(4):407–413.

https://www.nature.com/articles/nm.3807

5. Searle et al. Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry. Nature Communications. 2018. 9(1): 1-12

https://www.nature.com/articles/s41467-018-07454-w

6.Xu, et al. In-depth Serum Proteomics Reveals Biomarkers of Psoriasis Severity and Response to Traditional Chinese Medicine. Theranostics. 2019.9(9): 2475-2488.

https://www.thno.org/v09p2475.htm

7.Shao, et al. Comparative analysis of mRNA and protein degradation in prostate tissues indicates high stability of proteins. Nature Communications. 2019. 10(1):2524.

https://www.nature.com/articles/s41467-019-10513-5

8.Zhu, et al. High-throughput Proteomic analysis of FFPE tissue samples facilitates tumor stratification. Molecular Oncology. 2019 Sep;13(11): 2305-2328.

https://febs.onlinelibrary.wiley.com/doi/10.1002/1878-0261.12570

9.Zhang, et al. Data-Independent Acquisition Mass Spectrometry-Based Proteomics and Software Tools: A Glimpse in 2020. Proteomics. 2020.20(17-18): e1900276.

https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/pmic.201900276

10.Demichev, et al. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nature Methods. 2020.17:41-44

https://www.nature.com/articles/s41592-019-0638-x

11.Cai, et al. PulseDIA: Data-Independent Acquisition Mass Spectrometry Using Multi-Injection Pulsed Gas-Phase Fractionation. Journal of Proteome Research. 2021.20(1):279-288.

https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00381

12.Ge, et al. Computational Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage and Applications to 15 Tumors. Journal of Proteome Research. 2021

https://pubs.acs.org/doi/full/10.1021/acs.jproteome.1c00640

13.Liu, et al. DIA-based Proteomics Identifies IDH2 as a Targetable Regulator of Acquired Drug Resistance in Chronic Myeloid Leukemia. Mol Cell Prot. available at bioRxiv, 2021.

https://www.mcponline.org/article/S1535-9476(21)00159-6/fulltext#secsectitle0030

14.Shao, et al. Proteomics profiling of colorectal cancer progression identifies PLOD2 as a potential therapeutic target. Cancer Commun. 2021.

https://onlinelibrary.wiley.com/doi/10.1002/cac2.12240

15.Zhu, et al. Snapshot: Clinical proteomics. Cell. 2021.184(18): 4840-4840.

https://www.cell.com/cell/fulltext/S0092-8674(21)00985-5

DIA (Data-Independent Acquisition) is a non-discriminatory and non-random proteomic analysis technique that divides the full scan range of mass spectrometry into several windows, and then fragments and detects all ions in each window, achieving the collection and analysis of all ions in the sample without missing or discrepancy. This reduces the missing values of sample detection, improves quantitative accuracy and reproducibility, and achieves the highly stable and accurate quantitative proteomic analysis in large sample cohorts.

By a comprehensive analysis of the website database search results through multiple tools such as OpenSWATH, EncyclopeDIA and DIA-NN, Westlake Omics is able to increase the amount and accuracy of protein identifications. Besides, we developed a method to optimize specific spectral libraries.

Related patent: Data Independent Acquisition Mass Spectrometry Method based on Sub-Lib.

Patent No.: 202010773114.5.

Workflow

Application Scenarios:

DIA is appropriate for large sample scenarios, such as clinical cohorts.

Mass spectrometers:

Plasma/Serum: Sciex TripleTOF 5600, Sciex TripleTOF 6600 (Scanning SWATH)

Other tissues: Orbitrap Exploris™ 480, etc.

References:

1. Gillet et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics. 2012.11(6)

https://www.mcponline.org/article/S1535-9476(20)30442-4/fulltext

2. Röst et al. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nature Methods. 2016.13(9):741-748

https://www.nature.com/articles/nmeth.3959

3.Röst, et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nature Biotechnology. 2014.32:219-223

https://www.nature.com/articles/nbt.2841

4.Guo, et al. Rapid mass spectrometric conversion of tissue biopsy samples into permanent quantitative digital proteome maps. Nature Medicine. 2015.21(4):407–413.

https://www.nature.com/articles/nm.3807

5. Searle et al. Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry. Nature Communications. 2018. 9(1): 1-12

https://www.nature.com/articles/s41467-018-07454-w

6.Xu, et al. In-depth Serum Proteomics Reveals Biomarkers of Psoriasis Severity and Response to Traditional Chinese Medicine. Theranostics. 2019.9(9): 2475-2488.

https://www.thno.org/v09p2475.htm

7.Shao, et al. Comparative analysis of mRNA and protein degradation in prostate tissues indicates high stability of proteins. Nature Communications. 2019. 10(1):2524.

https://www.nature.com/articles/s41467-019-10513-5

8.Zhu, et al. High-throughput Proteomic analysis of FFPE tissue samples facilitates tumor stratification. Molecular Oncology. 2019 Sep;13(11): 2305-2328.

https://febs.onlinelibrary.wiley.com/doi/10.1002/1878-0261.12570

9.Zhang, et al. Data-Independent Acquisition Mass Spectrometry-Based Proteomics and Software Tools: A Glimpse in 2020. Proteomics. 2020.20(17-18): e1900276.

https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/pmic.201900276

10.Demichev, et al. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nature Methods. 2020.17:41-44

https://www.nature.com/articles/s41592-019-0638-x

11.Cai, et al. PulseDIA: Data-Independent Acquisition Mass Spectrometry Using Multi-Injection Pulsed Gas-Phase Fractionation. Journal of Proteome Research. 2021.20(1):279-288.

https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00381

12.Ge, et al. Computational Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage and Applications to 15 Tumors. Journal of Proteome Research. 2021

https://pubs.acs.org/doi/full/10.1021/acs.jproteome.1c00640

13.Liu, et al. DIA-based Proteomics Identifies IDH2 as a Targetable Regulator of Acquired Drug Resistance in Chronic Myeloid Leukemia. Mol Cell Prot. available at bioRxiv, 2021.

https://www.mcponline.org/article/S1535-9476(21)00159-6/fulltext#secsectitle0030

14.Shao, et al. Proteomics profiling of colorectal cancer progression identifies PLOD2 as a potential therapeutic target. Cancer Commun. 2021.

https://onlinelibrary.wiley.com/doi/10.1002/cac2.12240

15.Zhu, et al. Snapshot: Clinical proteomics. Cell. 2021.184(18): 4840-4840.

https://www.cell.com/cell/fulltext/S0092-8674(21)00985-5

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