High-Throughput Proteomic Analysis of Fresh-Frozen Biopsy Tissue Samples Using Pressure Cycling Technology Coupled with SWATH Mass Spectrometry

Author(s):  
Yi Zhu ◽  
Tiannan Guo
2020 ◽  
Vol 19 (5) ◽  
pp. 1982-1990 ◽  
Author(s):  
Huanhuan Gao ◽  
Fangfei Zhang ◽  
Shuang Liang ◽  
Qiushi Zhang ◽  
Mengge Lyu ◽  
...  

2019 ◽  
Vol 13 (11) ◽  
pp. 2305-2328 ◽  
Author(s):  
Yi Zhu ◽  
Tobias Weiss ◽  
Qiushi Zhang ◽  
Rui Sun ◽  
Bo Wang ◽  
...  

2020 ◽  
Vol 16 (4) ◽  
pp. 364-376 ◽  
Author(s):  
Manveen K. Sethi ◽  
Margaret Downs ◽  
Joseph Zaia

A high-throughput & efficient protocol for mass spectrometry-based glycomic and proteomic analysis.


Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. SCI-13-SCI-13
Author(s):  
Sandeep S. Dave

High throughput sequencing is a revolutionary technology for the definition of the genomic features of tumors. This talk will provide a review of the relevant methodologies for non-experts in the field. The presentation will include a discussion of how high throughput sequencing is performed, its relative strengths and weaknesses, and how it is applicable to formalin-fixed and fresh/frozen tissue samples. The talk will also describe future directions in the genomic analysis of tumors. Disclosures No relevant conflicts of interest to declare.


2020 ◽  
Author(s):  
Yaoting Sun ◽  
Sathiyamoorthy Selvarajan ◽  
Zelin Zang ◽  
Wei Liu ◽  
Yi Zhu ◽  
...  

SUMMARYUp to 30% of thyroid nodules cannot be accurately classified as benign or malignant by cytopathology. Diagnostic accuracy can be improved by nucleic acid-based testing, yet a sizeable number of diagnostic thyroidectomies remains unavoidable. In order to develop a protein classifier for thyroid nodules, we analyzed the quantitative proteomes of 1,725 retrospective thyroid tissue samples from 578 patients using pressure-cycling technology and data-independent acquisition mass spectrometry. With artificial neural networks, a classifier of 14 proteins achieved over 93% accuracy in classifying malignant thyroid nodules. This classifier was validated in retrospective samples of 271 patients (91% accuracy), and prospective samples of 62 patients (88% accuracy) from four independent centers. These rapidly acquired proteotypes and artificial neural networks supported the establishment of an effective protein classifier for classifying thyroid nodules.


2011 ◽  
Vol 64 (4) ◽  
pp. 297-302 ◽  
Author(s):  
Lynda D Ralton ◽  
Graeme I Murray

The potential of proteomic approaches to elucidate disease pathogenesis and biomarker discovery is increasingly being recognised. These studies are usually based on the use of fresh tissue samples. Problems in obtaining and storing fresh frozen samples, especially either for the investigation of rare diseases or for the study of microscopic disease foci, have led to the investigation of the possible use of formalin fixed wax embedded tissue for proteomic biomarker detection Overcoming problems with protein cross-linking associated with formalin fixation of tissues, especially by using heat-mediated retrieval techniques combined with highly sensitive methods for protein separation and identification are now emerging, giving promise to the use of formalin fixed wax embedded tissues for proteomic analysis. Formalin fixed wax embedded tissues, together with their associated clinical and pathological information outcome may provide significant potential opportunities for proteomics research. Such studies of formalin fixed wax embedded tissue will allow access to already acquired clinical tissue samples which can be readily correlated with clinical, pathological and outcome data. It also provides access to rare types of tissue/diseases that would be either difficult to collect prospectively in a timely manner or are unlikely to be available as fresh samples. The purpose of this review is to provide an overview of the issues associated with the use of formalin fixed wax embedded tissues for proteomics.


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