技术介绍：

DIA（Data-Independent Acquisition，数据非依赖性采集）是一种无歧视性和无随机性的蛋白质组分析技术， 将质谱全扫描范围分为若干个窗口，然后对每个窗口中的所有离子进行检测、碎裂，从而无遗漏、无差异地获得样本中所有离子的信息, 降低样本检测的缺失值，同时提高定量准确性和重复性，实现大样本队列中高稳定，高精准的蛋白质组定量分析。

技术优势：

1. 西湖欧米结合PCT样本前处理技术，可以实现临床微量样本 (如FFPE、穿刺活检、泪液等）的高深度蛋白质定量分析，组织样本最低送样量只需0.1mg。

2. 使用多种蛋白质组学搜库软件，包括OpenSWATH, EncyclopeDIA, DIA-NN等，并能够综合分析结果，提高蛋白的鉴定量和定量准确度。

3. 开发了优化特异性谱图库的方法，发明专利：基于优化数据库（Sub-Lib）的数据非依赖性质谱检测方法，专利号：202010773114.5。

项目案例：

Cell Discovery | 千人队列蛋白质组学建立甲状腺结节良恶性辅助判别模型

该研究针对1724例超大临床队列甲状腺结节FFPE样本，通过PCT-DIA方法采集其蛋白质组学定量表达数据，构建涵盖五种组织类型的甲状腺表达图谱。利用已构建的蛋白质组学图谱与遗传算法和深度学习模型，筛选出19个蛋白质特征用以区分甲状腺结节良恶性。本模型在发现集中判定甲状腺恶性结节的准确率高于91%。随后，研究者从国内、国外共计12个独立的临床中心回顾性、前瞻性地取材独立测试样本，结果显示，在回顾性测试集的288例FFPE样本队列与前瞻性数据集的294例FNA样本队列中，模型可实现准确率分别高达89%和85%。本研究展示了高通量蛋白质组学与AI技术的深度碰撞，AI技术在海量的蛋白质表达数据中能够挖掘至关重要的信息，助力疾病研究的向前发展。

参考文献：

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.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

12.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

13.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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