Optimization of microflow LC–MS/MS and its utility in quantitative discovery bioanalysis

Bioanalysis ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1117-1127 ◽  
Author(s):  
Jun Zhang ◽  
Wilson Shou ◽  
Tairo Ogura ◽  
Shu Li ◽  
Harold Weller
Keyword(s):  
Quantitative Discovery

scholarly journals Uses and misuses of the fudge factor in quantitative discovery proteomics

PROTEOMICS ◽  
2016 ◽  
Vol 16 (14) ◽  
pp. 1955-1960 ◽  
Author(s):  
Quentin Giai Gianetto ◽  
Yohann Couté ◽  
Christophe Bruley ◽  
Thomas Burger
Keyword(s):  
Quantitative Discovery

scholarly journals Large-Scale Multiplexed Quantitative Discovery Proteomics Enabled by the Use of an18O-Labeled “Universal” Reference Sample

2009 ◽  
Vol 8 (1) ◽  
pp. 290-299 ◽  
Author(s):  
Wei-Jun Qian ◽  
Tao Liu ◽  
Vladislav A. Petyuk ◽  
Marina A. Gritsenko ◽  
Brianne O. Petritis ◽  
...  
Keyword(s):  
Large Scale ◽  
Reference Sample ◽  
Universal Reference ◽  
Quantitative Discovery

scholarly journals Identifying cell type specific driver genes in autism-associated copy number loci from cerebral organoids

2020 ◽  
Author(s):  
Elaine T. Lim ◽  
Yingleong Chan ◽  
Mannix J. Burns ◽  
Xiaoge Guo ◽  
Serkan Erdin ◽  
...  
Keyword(s):  
Neurodevelopmental Disorders ◽  
Cell Types ◽  
Great Promise ◽  
Molecular Processes ◽  
Cell Type ◽  
Driver Genes ◽  
Validation Framework ◽  
Neuroepithelial Cells ◽  
Cell Type Specific ◽  
Quantitative Discovery

ABSTRACTNeuropsychiatric and neurodevelopmental disorders have been particularly challenging to study using animal models, and recently, human-derived cerebral organoids demonstrate great promise for discovering molecular processes that are important in these disorders. However, several challenges remain in achieving robust phenotyping to discover cell type specific genes. We perform RNA sequencing on 71 samples comprising of 1,420 cerebral organoids from 25 donors, and describe a framework (Orgo-Seq) to identify cell type specific driver genes, for 16p11.2 deletions and 15q11-13 duplications. We identify neuroepithelial cells as critical cell types for 16p11.2 deletions, and discover novel and previously reported cell type specific driver genes. Finally, we validated our results that mutations in KCTD13 in the 16p11.2 locus lead to imbalances in the proportion of neuroepithelial cells, using CRISPR/Cas9-edited mosaic organoids. Our work presents a quantitative discovery and validation framework for identifying cell type specific driver genes associated with complex diseases using cerebral organoids.

The role and impact of quantitative discovery pathology

2010 ◽  
Vol 15 (21-22) ◽  
pp. 943-950 ◽  
Author(s):  
Steven J. Potts ◽  
G. David Young ◽  
Frank A. Voelker
Keyword(s):  
Quantitative Discovery

scholarly journals DAPAR & ProStaR: software to perform statistical analyses in quantitative discovery proteomics

2016 ◽  
Vol 33 (1) ◽  
pp. 135-136 ◽  
Author(s):  
Samuel Wieczorek ◽  
Florence Combes ◽  
Cosmin Lazar ◽  
Quentin Giai Gianetto ◽  
Laurent Gatto ◽  
...  
Keyword(s):  
Statistical Analyses ◽  
Quantitative Discovery

scholarly journals Determination of Burn Patient Outcome by Large-Scale Quantitative Discovery Proteomics

2013 ◽  
Vol 41 (6) ◽  
pp. 1421-1434 ◽  
Author(s):  
Celeste C. Finnerty ◽  
Marc G. Jeschke ◽  
Wei-Jun Qian ◽  
Amit Kaushal ◽  
Wenzhong Xiao ◽  
...  
Keyword(s):  
Patient Outcome ◽  
Large Scale ◽  
Quantitative Discovery ◽  
Burn Patient

scholarly journals Increased VLCFA-lipids and ELOVL4 underlie neurodegeneration in frontotemporal dementia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ying He ◽  
Katherine Phan ◽  
Surabhi Bhatia ◽  
Russell Pickford ◽  
YuHong Fu ◽  
...  
Keyword(s):  
Frontotemporal Dementia ◽  
Dependent Manner ◽  
Long Chain Fatty Acids ◽  
Strong Correlations ◽  
Lipid Species ◽  
Membrane Bound ◽  
The Impact ◽  
The Brain ◽  
Quantitative Discovery

AbstractRare, yet biologically critical, lipids that contain very long chain fatty acids (VLCFA-lipids) are synthesized in the brain by the enzyme ELOVL4. High levels of VLCFA-lipids are toxic to cells and excess VLCFA-lipids are actively removed by ABCD1 in an ATP-dependent manner. Virtually nothing is known about the impact of VLCFA-lipids in neurodegenerative diseases. Here, we investigated the possible role of VLCFA-lipids in frontotemporal dementia (FTD), which is a leading cause of younger-onset dementia. Using quantitative discovery lipidomics, we identified three VLCFA-lipid species that were significantly increased in FTD brain compared to controls, with strong correlations with ELOVL4. Increases in ELOVL4 expression correlated with significant decreases in the membrane-bound synaptophysin in FTD brain. Furthermore, increases in ABCD1 expression correlated with increases in VLCFA-lipids. We uncovered a new pathomechanism that is pertinent to understanding the pathogenesis of FTD.

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