Dataset Information

Surpassing 10,000 identified and quantified proteins in a single run by optimizing current LC-MS instrumentation and data analysis strategy

ABSTRACT: Comprehensive proteome quantification is crucial for a better understanding of underlying mechanisms of diseases. Liquid chromatography mass spectrometry (LC-MS) has become the method of choice for comprehensive proteome quantification due to its power and versatility. Even though great advances have been made in recent years, full proteome coverage for complex samples remains challenging due to the high dynamic range of protein expression. Additionally, when studying diseases regulatory proteins, biomarkers or potential drug targets are often low abundant, such as for instance kinases and transcription factors. Here, we show that with improvements in chromatography and data analysis the single shot proteome coverage can go beyond 10,000 proteins in human tissue. In a testis cancer study, we quantified 11,200 proteins using data independent acquisition (DIA). This depth was achieved with a false discovery rate of 1% which was experimentally validated using a two species test. We introduce the concept of hybrid libraries which combine the strength of direct searching of DIA data as well as the use of large project-specific or published DDA data sets. Remarkably deep proteome coverage is possible using hybrid libraries without the additional burden of creating a project-specific library.

INSTRUMENT(S): Q Exactive HF-X

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Testis, Cell Culture

DISEASE(S): Testicular Cancer

SUBMITTER: Jan Muntel

LAB HEAD: Lukas Reiter

PROVIDER: PXD013658 | Pride | 2019-08-12

REPOSITORIES: Pride

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Surpassing 10 000 identified and quantified proteins in a single run by optimizing current LC-MS instrumentation and data analysis strategy.

Muntel Jan J Gandhi Tejas T Verbeke Lynn L Bernhardt Oliver M OM Treiber Tobias T Bruderer Roland R Reiter Lukas L

Molecular omics 20191001 5

Comprehensive proteome quantification is crucial for a better understanding of underlying mechanisms of diseases. Liquid chromatography mass spectrometry (LC-MS) has become the method of choice for comprehensive proteome quantification due to its power and versatility. Even though great advances have been made in recent years, full proteome coverage for complex samples remains challenging due to the high dynamic range of protein expression. Additionally, when studying disease regulatory proteins ...[more]

PMID: 31465043

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Project description:The sensitivity of single-cell proteomics (SCP) has increased dramatically in recent years due to advances in experimental design, sample preparation, separations and mass spectrometry instrumentation. However, further increasing the sensitivity of SCP methods and instrumentation will enable the study of proteins within single cells that are expressed at copy numbers too small to be measured by current methods. Here we further increase SCP sensitivity by combining efficient nanoPOTS sample preparation and ultra-low-flow liquid chromatography with a newly developed data acquisition and analysis scheme termed wide window acquisition (WWA) to extend the achievable proteome coverage for single cells to >3,000 for single-cell data analyses. WWA is based on data-dependent acquisition (DDA) but employs larger precursor isolation windows to intentionally co-isolate and co-fragment additional precursors along with the selected precursor. The resulting chimeric MS2 spectra are then resolved using the CHIMERYS search engine within Proteome Discoverer 3.0. Compared to standard DDA workflows, WWA employing isolation windows of 8-12 Th increases peptide and proteome coverage by ~28% and ~39%, respectively. For a 40 min LC gradient operated at ~15 nL/min, we identified an average of 2,150 proteins per single-cell-sized aliquots of protein digest directly from MS2 spectra, which increased to an average 3,524 proteins including proteins identified with MS1-level feature matching. Reducing the active gradient to 20 min resulted in only a 10% decrease in proteome coverage. We also compared performance of WWA with DIA underperformed relative to WWA in terms of proteome coverage, especially with faster separations. Average proteome coverage for single HeLa and K562 cells was respectively 1,758 and 1,642 based on MS2 identifications with 1% false discovery rate and 3042 and 2891 with MS1 feature matching. As such, WWA combined with efficient sample preparation and rapid separations extends the depths of the proteome that can be studied at the single-cell level.

Dataset Information

Surpassing 10,000 identified and quantified proteins in a single run by optimizing current LC-MS instrumentation and data analysis strategy

Publications

Surpassing 10 000 identified and quantified proteins in a single run by optimizing current LC-MS instrumentation and data analysis strategy.

Similar Datasets

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