In-depth serum proteomics reveals biomarkers of psoriasis severity and response to traditional Chinese medicine. Xu, M., Deng, J., Xu, K., Zhu, T., Han, L., Yan, Y., Yao, D., Deng, H., Wang, D., Sun, Y., Chang, C., Zhang, X., Dai, J., Yue, L., Zhang, Q., Cai, X., Zhu, Y., Duan, H., Liu, Y., Li, D., Zhu, Y., Radstake, T. R. D. J., Balak, D. M., Xu, D., Guo, T., Lu, C., & Yu, X. Theranostics, 9(9):2475–2488, April, 2019. Number: 9
In-depth serum proteomics reveals biomarkers of psoriasis severity and response to traditional Chinese medicine [link]Paper  doi  abstract   bibtex   
Serum and plasma contain abundant biological information that reflect the body's physiological and pathological conditions and are therefore a valuable sample type for disease biomarkers. However, comprehensive profiling of the serological proteome is challenging due to the wide range of protein concentrations in serum. Methods: To address this challenge, we developed a novel in-depth serum proteomics platform capable of analyzing the serum proteome across \textasciitilde10 orders or magnitude by combining data obtained from Data Independent Acquisition Mass Spectrometry (DIA-MS) and customizable antibody microarrays. Results: Using psoriasis as a proof-of-concept disease model, we screened 50 serum proteomes from healthy controls and psoriasis patients before and after treatment with traditional Chinese medicine (YinXieLing) on our in-depth serum proteomics platform. We identified 106 differentially-expressed proteins in psoriasis patients involved in psoriasis-relevant biological processes, such as blood coagulation, inflammation, apoptosis and angiogenesis signaling pathways. In addition, unbiased clustering and principle component analysis revealed 58 proteins discriminating healthy volunteers from psoriasis patients and 12 proteins distinguishing responders from non-responders to YinXieLing. To further demonstrate the clinical utility of our platform, we performed correlation analyses between serum proteomes and psoriasis activity and found a positive association between the psoriasis area and severity index (PASI) score with three serum proteins (PI3, CCL22, IL-12B). Conclusion: Taken together, these results demonstrate the clinical utility of our in-depth serum proteomics platform to identify specific diagnostic and predictive biomarkers of psoriasis and other immune-mediated diseases.
@article{xu_-depth_2019,
	title = {In-depth serum proteomics reveals biomarkers of psoriasis severity and response to traditional {Chinese} medicine},
	volume = {9},
	issn = {1838-7640},
	url = {http://www.thno.org/v09p2475.htm},
	doi = {10.7150/thno.31144},
	abstract = {Serum and plasma contain abundant biological information that reflect the body's physiological and pathological conditions and are therefore a valuable sample type for disease biomarkers. However, comprehensive profiling of the serological proteome is challenging due to the wide range of protein concentrations in serum.

Methods: To address this challenge, we developed a novel in-depth serum proteomics platform capable of analyzing the serum proteome across {\textasciitilde}10 orders or magnitude by combining data obtained from Data Independent Acquisition Mass Spectrometry (DIA-MS) and customizable antibody microarrays.

Results: Using psoriasis as a proof-of-concept disease model, we screened 50 serum proteomes from healthy controls and psoriasis patients before and after treatment with traditional Chinese medicine (YinXieLing) on our in-depth serum proteomics platform. We identified 106 differentially-expressed proteins in psoriasis patients involved in psoriasis-relevant biological processes, such as blood coagulation, inflammation, apoptosis and angiogenesis signaling pathways. In addition, unbiased clustering and principle component analysis revealed 58 proteins discriminating healthy volunteers from psoriasis patients and 12 proteins distinguishing responders from non-responders to YinXieLing. To further demonstrate the clinical utility of our platform, we performed correlation analyses between serum proteomes and psoriasis activity and found a positive association between the psoriasis area and severity index (PASI) score with three serum proteins (PI3, CCL22, IL-12B).

Conclusion: Taken together, these results demonstrate the clinical utility of our in-depth serum proteomics platform to identify specific diagnostic and predictive biomarkers of psoriasis and other immune-mediated diseases.},
	language = {en},
	number = {9},
	urldate = {2019-12-02},
	journal = {Theranostics},
	author = {Xu, Meng and Deng, Jingwen and Xu, Kaikun and Zhu, Tiansheng and Han, Ling and Yan, Yuhong and Yao, Danni and Deng, Hao and Wang, Dan and Sun, Yaoting and Chang, Cheng and Zhang, Xiaomei and Dai, Jiayu and Yue, Liang and Zhang, Qiushi and Cai, Xue and Zhu, Yi and Duan, Hu and Liu, Yuan and Li, Dong and Zhu, Yunping and Radstake, Timothy R. D. J. and Balak, Deepak M.W. and Xu, Danke and Guo, Tiannan and Lu, Chuanjian and Yu, Xiaobo},
	month = apr,
	year = {2019},
	note = {Number: 9},
	keywords = {Accessories, Application - Autoimmune Research, Application - Biomarker Discovery, Country - China, Country - Netherlands, Human, Sample Type - Protein, Sample Type - Serum},
	pages = {2475--2488},
}

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