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IA技術(shù)與DDA技術(shù)在蛋白質(zhì)組學(xué)中的差異

1.1: DDA技術(shù)

數(shù)據(jù)依賴型采集（DDA）是一種傳統(tǒng)的蛋白質(zhì)組學(xué)質(zhì)譜分析方法。在DDA中，在串聯(lián)質(zhì)譜的第二階段，選擇少量肽段進(jìn)行碎裂，并且這些肽段是在質(zhì)荷比（m/z）信號(hào)強(qiáng)度的狹窄范圍內(nèi)選擇[1]。

1.2: DIA技術(shù)

數(shù)據(jù)無(wú)關(guān)型采集（DIA）技術(shù)是另一種蛋白質(zhì)組學(xué)質(zhì)譜分析方法。DIA技術(shù)將質(zhì)譜儀的整個(gè)全掃描范圍分為幾個(gè)窗口，然后同時(shí)碎裂每個(gè)窗口內(nèi)的所有肽前體，生成全面的MS2譜[2][3]。

1.3: 差異與優(yōu)勢(shì)

DIA技術(shù)相對(duì)于DDA技術(shù)具有以下優(yōu)勢(shì)：

DIA能夠提供更全面的蛋白質(zhì)組覆蓋，因?yàn)樗梢詸z測(cè)和定量樣品中的所有肽段，無(wú)論其豐度或m/z值如何[4]。

DIA技術(shù)類似于多反應(yīng)監(jiān)測(cè)（MRM）或并行反應(yīng)監(jiān)測(cè)（PRM），能夠?qū)崿F(xiàn)可重復(fù)的定量[4]。

DDA技術(shù)更適用于發(fā)現(xiàn)型蛋白質(zhì)組學(xué)，而DIA技術(shù)更適用于定量目的，可以識(shí)別樣品中盡可能多的蛋白質(zhì)，而不僅僅是對(duì)其進(jìn)行定量[1]。

DIA技術(shù)在乙酰化修飾蛋白質(zhì)組學(xué)中的應(yīng)用

2.1: 乙酰化修飾

乙酰化是一種常見(jiàn)的蛋白質(zhì)翻譯后修飾，發(fā)生在蛋白質(zhì)的賴氨酸殘基上。

2.2: DIA技術(shù)在乙酰化修飾蛋白質(zhì)組學(xué)中的作用

DIA技術(shù)在乙酰化修飾蛋白質(zhì)組學(xué)中的應(yīng)用主要包括：

DIA能夠檢測(cè)和定量樣品中的所有乙酰化肽段，無(wú)論其豐度或m/z值如何，從而提高定量的準(zhǔn)確性[5]。

DIA技術(shù)能夠提供更全面的蛋白質(zhì)組覆蓋，有助于鑒定新的乙酰化位點(diǎn)并理解乙酰化的功能意義[5]。

DIA技術(shù)能夠減少對(duì)修飾肽段先驗(yàn)知識(shí)的需求，成為發(fā)現(xiàn)型蛋白質(zhì)組學(xué)的有用工具[5]。

DIA技術(shù)如何提高乙酰化修飾定量的準(zhǔn)確性

3.1: 蛋白質(zhì)組覆蓋的提高

DIA技術(shù)通過(guò)提供更全面的蛋白質(zhì)組覆蓋來(lái)提高乙酰化修飾定量的準(zhǔn)確性。它可以檢測(cè)和定量樣品中的所有乙酰化肽段，無(wú)論其豐度或m/z值如何[4][5]。

3.2: 減少偏差的發(fā)生

DIA技術(shù)的應(yīng)用可以減少其他方法（如DDA）可能存在的對(duì)高豐度肽段的偏向[1]。這樣可以避免在定量過(guò)程中的偏差。

3.3: 發(fā)現(xiàn)型蛋白質(zhì)組學(xué)的工具

DIA技術(shù)能夠減少對(duì)修飾肽段先驗(yàn)知識(shí)的需求，使其成為發(fā)現(xiàn)型蛋白質(zhì)組學(xué)的有用工具[5]。

結(jié)論

DIA技術(shù)在乙酰化修飾蛋白質(zhì)組學(xué)中具有巨大的潛力，通過(guò)提供更全面的蛋白質(zhì)組覆蓋和準(zhǔn)確的定量，有助于提高對(duì)乙酰化及其在生物系統(tǒng)中功能意義的理解。

Citations:

[1] Hu A, Noble WS, Wolf-Yadlin A. Technical advances in proteomics: new developments in data-independent acquisition. F1000Res. 2016;5(F1000 Faculty Rev):419. doi: 10.12688/f1000research.7042.1. https://f1000research.com/articles/5-419

[2] Kawashima Y, Watanabe E, Umeyama T, et al. Optimization of data-independent acquisition mass spectrometry for deep and highly sensitive proteomic analysis. Int. J. Mol. Sci. 2019;20(23):E5932. doi: 10.3390/ijms20235932.
https://www.mdpi.com/1422-0067/20/23/5932

[3] Yuefan Wang, Optimized data-independent acquisition approach for proteomic analysis at single-cell level Clinical Proteomics volume 19, Article number: 24 (2022) doi: 10.1186/s12014-022-09359-9
https://clinicalproteomicsjournal.biomedcentral.com/articles/10.1186/s12014-022-09359-9

[4] Hu A, Noble WS, Wolf-Yadlin A. Technical advances in proteomics: new developments in data-independent acquisition. F1000Res. 2016 Mar 31;5:F1000 Faculty Rev-419. doi: 10.12688/f1000research.7042.1.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4821292/

[5] Koopmans F, Ho JTC, Smit AB, Li KW. Comparative Analyses of Data Independent Acquisition Mass Spectrometric Approaches: DIA, WiSIM-DIA, and Untargeted DIA. Proteomics. 2018 Jan;18(1):1700304. doi: 10.1002/pmic.201700304. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5817406/

--------------------
英文原稿

## How does DIA technology differ from DDA technology in proteomics?

Data-dependent acquisition (DDA) and data-independent acquisition (DIA) are two approaches used in proteomics for mass spectrometry analysis. DDA is a traditional method that selects a few peptides for fragmentation during the second stage of tandem MS, and these peptides are chosen within a narrow range of mass-to-charge (m/z) signal intensity[1]. In contrast, DIA technology divides the entire full scan range of the mass spectrometer into several windows, and all the peptide precursors within each window are fragmented simultaneously to produce comprehensive MS2 spectra[2][3]. DIA can provide more comprehensive coverage of the proteome, as it can detect and quantify all peptides in a sample, regardless of their abundance or m/z values[4]. DIA also allows for reproducible quantification, similar to multiple reaction monitoring (MRM) or parallel reaction monitoring (PRM) [4]. In contrast, DDA is better suited for discovery proteomics, where the goal is to identify as many proteins as possible, rather than quantifying them[1].

## Application of DIA (Data-independent acquisition) in acetylated modified proteomics?

Acetylation is a common post-translational modification that occurs on lysine residues in proteins. DIA technology has been applied to acetylated modified proteomics to improve the accuracy of quantification. DIA can detect and quantify all acetylated peptides in a sample, regardless of their abundance or m/z values, which is important for accurate quantification of modified peptides[5]. DIA can also provide more comprehensive coverage of the proteome, which is important for identifying novel acetylation sites and understanding the functional significance of acetylation[5]. In addition, DIA can reduce the need for prior knowledge of the modified peptides, making it a useful tool for discovery proteomics[5].

## How DIA technology improves accuracy in acetylation modification quantification?

DIA technology improves accuracy in acetylation modification quantification by providing more comprehensive coverage of the proteome and detecting and quantifying all acetylated peptides in a sample, regardless of their abundance or m/z values[4][5]. This is important for accurate quantification of modified peptides, as it reduces the bias towards highly abundant peptides that can occur with other methods, such as DDA[1]. In addition, DIA can reduce the need for prior knowledge of the modified peptides, making it a useful tool for discovery proteomics[5]. Overall, DIA technology has the potential to improve our understanding of acetylation and its functional significance in biological systems.

Citations:

[1] https://www.technologynetworks.com/proteomics/lists/data-dependent-vs-data-independent-proteomic-analysis-331712

[2] https://www.creative-proteomics.com/ngpro/pct-dia-services.html

[3] https://clinicalproteomicsjournal.biomedcentral.com/articles/10.1186/s12014-022-09359-9

[4] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4821292/

[5] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5817406/

IA技術(shù)與DDA技術(shù)在蛋白質(zhì)組學(xué)中的差異

1.1: DDA技術(shù)

數(shù)據(jù)依賴型采集（DDA）是一種傳統(tǒng)的蛋白質(zhì)組學(xué)質(zhì)譜分析方法。在DDA中，在串聯(lián)質(zhì)譜的第二階段，選擇少量肽段進(jìn)行碎裂，并且這些肽段是在質(zhì)荷比（m/z）信號(hào)強(qiáng)度的狹窄范圍內(nèi)選擇[1]。

1.2: DIA技術(shù)

數(shù)據(jù)無(wú)關(guān)型采集（DIA）技術(shù)是另一種蛋白質(zhì)組學(xué)質(zhì)譜分析方法。DIA技術(shù)將質(zhì)譜儀的整個(gè)全掃描范圍分為幾個(gè)窗口，然后同時(shí)碎裂每個(gè)窗口內(nèi)的所有肽前體，生成全面的MS2譜[2][3]。

1.3: 差異與優(yōu)勢(shì)

DIA技術(shù)相對(duì)于DDA技術(shù)具有以下優(yōu)勢(shì)：

DIA能夠提供更全面的蛋白質(zhì)組覆蓋，因?yàn)樗梢詸z測(cè)和定量樣品中的所有肽段，無(wú)論其豐度或m/z值如何[4]。

DIA技術(shù)類似于多反應(yīng)監(jiān)測(cè)（MRM）或并行反應(yīng)監(jiān)測(cè)（PRM），能夠?qū)崿F(xiàn)可重復(fù)的定量[4]。

DDA技術(shù)更適用于發(fā)現(xiàn)型蛋白質(zhì)組學(xué)，而DIA技術(shù)更適用于定量目的，可以識(shí)別樣品中盡可能多的蛋白質(zhì)，而不僅僅是對(duì)其進(jìn)行定量[1]。

DIA技術(shù)在乙酰化修飾蛋白質(zhì)組學(xué)中的應(yīng)用

2.1: 乙酰化修飾

乙酰化是一種常見(jiàn)的蛋白質(zhì)翻譯后修飾，發(fā)生在蛋白質(zhì)的賴氨酸殘基上。

2.2: DIA技術(shù)在乙酰化修飾蛋白質(zhì)組學(xué)中的作用

DIA技術(shù)在乙酰化修飾蛋白質(zhì)組學(xué)中的應(yīng)用主要包括：

DIA能夠檢測(cè)和定量樣品中的所有乙酰化肽段，無(wú)論其豐度或m/z值如何，從而提高定量的準(zhǔn)確性[5]。

DIA技術(shù)能夠提供更全面的蛋白質(zhì)組覆蓋，有助于鑒定新的乙酰化位點(diǎn)并理解乙酰化的功能意義[5]。

DIA技術(shù)能夠減少對(duì)修飾肽段先驗(yàn)知識(shí)的需求，成為發(fā)現(xiàn)型蛋白質(zhì)組學(xué)的有用工具[5]。

DIA技術(shù)如何提高乙酰化修飾定量的準(zhǔn)確性

3.1: 蛋白質(zhì)組覆蓋的提高

DIA技術(shù)通過(guò)提供更全面的蛋白質(zhì)組覆蓋來(lái)提高乙酰化修飾定量的準(zhǔn)確性。它可以檢測(cè)和定量樣品中的所有乙酰化肽段，無(wú)論其豐度或m/z值如何[4][5]。

3.2: 減少偏差的發(fā)生

DIA技術(shù)的應(yīng)用可以減少其他方法（如DDA）可能存在的對(duì)高豐度肽段的偏向[1]。這樣可以避免在定量過(guò)程中的偏差。

3.3: 發(fā)現(xiàn)型蛋白質(zhì)組學(xué)的工具

DIA技術(shù)能夠減少對(duì)修飾肽段先驗(yàn)知識(shí)的需求，使其成為發(fā)現(xiàn)型蛋白質(zhì)組學(xué)的有用工具[5]。

結(jié)論

DIA技術(shù)在乙酰化修飾蛋白質(zhì)組學(xué)中具有巨大的潛力，通過(guò)提供更全面的蛋白質(zhì)組覆蓋和準(zhǔn)確的定量，有助于提高對(duì)乙酰化及其在生物系統(tǒng)中功能意義的理解。

Citations:

[1] Hu A, Noble WS, Wolf-Yadlin A. Technical advances in proteomics: new developments in data-independent acquisition. F1000Res. 2016;5(F1000 Faculty Rev):419. doi: 10.12688/f1000research.7042.1. https://f1000research.com/articles/5-419

[2] Kawashima Y, Watanabe E, Umeyama T, et al. Optimization of data-independent acquisition mass spectrometry for deep and highly sensitive proteomic analysis. Int. J. Mol. Sci. 2019;20(23):E5932. doi: 10.3390/ijms20235932.
https://www.mdpi.com/1422-0067/20/23/5932

[3] Yuefan Wang, Optimized data-independent acquisition approach for proteomic analysis at single-cell level Clinical Proteomics volume 19, Article number: 24 (2022) doi: 10.1186/s12014-022-09359-9
https://clinicalproteomicsjournal.biomedcentral.com/articles/10.1186/s12014-022-09359-9

[4] Hu A, Noble WS, Wolf-Yadlin A. Technical advances in proteomics: new developments in data-independent acquisition. F1000Res. 2016 Mar 31;5:F1000 Faculty Rev-419. doi: 10.12688/f1000research.7042.1.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4821292/

[5] Koopmans F, Ho JTC, Smit AB, Li KW. Comparative Analyses of Data Independent Acquisition Mass Spectrometric Approaches: DIA, WiSIM-DIA, and Untargeted DIA. Proteomics. 2018 Jan;18(1):1700304. doi: 10.1002/pmic.201700304. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5817406/

--------------------
英文原稿

## How does DIA technology differ from DDA technology in proteomics?

Data-dependent acquisition (DDA) and data-independent acquisition (DIA) are two approaches used in proteomics for mass spectrometry analysis. DDA is a traditional method that selects a few peptides for fragmentation during the second stage of tandem MS, and these peptides are chosen within a narrow range of mass-to-charge (m/z) signal intensity[1]. In contrast, DIA technology divides the entire full scan range of the mass spectrometer into several windows, and all the peptide precursors within each window are fragmented simultaneously to produce comprehensive MS2 spectra[2][3]. DIA can provide more comprehensive coverage of the proteome, as it can detect and quantify all peptides in a sample, regardless of their abundance or m/z values[4]. DIA also allows for reproducible quantification, similar to multiple reaction monitoring (MRM) or parallel reaction monitoring (PRM) [4]. In contrast, DDA is better suited for discovery proteomics, where the goal is to identify as many proteins as possible, rather than quantifying them[1].

## Application of DIA (Data-independent acquisition) in acetylated modified proteomics?

Acetylation is a common post-translational modification that occurs on lysine residues in proteins. DIA technology has been applied to acetylated modified proteomics to improve the accuracy of quantification. DIA can detect and quantify all acetylated peptides in a sample, regardless of their abundance or m/z values, which is important for accurate quantification of modified peptides[5]. DIA can also provide more comprehensive coverage of the proteome, which is important for identifying novel acetylation sites and understanding the functional significance of acetylation[5]. In addition, DIA can reduce the need for prior knowledge of the modified peptides, making it a useful tool for discovery proteomics[5].

## How DIA technology improves accuracy in acetylation modification quantification?

DIA technology improves accuracy in acetylation modification quantification by providing more comprehensive coverage of the proteome and detecting and quantifying all acetylated peptides in a sample, regardless of their abundance or m/z values[4][5]. This is important for accurate quantification of modified peptides, as it reduces the bias towards highly abundant peptides that can occur with other methods, such as DDA[1]. In addition, DIA can reduce the need for prior knowledge of the modified peptides, making it a useful tool for discovery proteomics[5]. Overall, DIA technology has the potential to improve our understanding of acetylation and its functional significance in biological systems.

Citations:

[1] https://www.technologynetworks.com/proteomics/lists/data-dependent-vs-data-independent-proteomic-analysis-331712

[2] https://www.creative-proteomics.com/ngpro/pct-dia-services.html

[3] https://clinicalproteomicsjournal.biomedcentral.com/articles/10.1186/s12014-022-09359-9

[4] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4821292/

[5] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5817406/