Dataset Information

Improved Sensitivity in Low-Input Proteomics using Micro-Pillar Array-based Chromatography

ABSTRACT: Capitalizing on the massive increase in sample concentrations which are produced by extremely low elution volumes, nano-LC-ESI-MS/MS is currently the most sensitive analytical technology for the comprehensive characterization of complex protein samples. However, despite tremendous technological improvements made in the production and the packing of monodisperse spherical particles for nano-flow HPLC, current state-of-the-art systems still suffer from limits in operation at the maximum potential of the technology. With the recent introduction of the µPAC system, which provides perfectly ordered micro-pillar array based chromatographic support materials, completely new chromatographic concepts for optimization towards the needs of ultra-sensitive proteomics become available. Here we report on a series of benchmarking experiments comparing the performance of a commercially available 50 cm micro-pillar array column to a widely used nano-flow HPLC column for the proteomics analysis of 10 ng tryptic HeLa cell digest. Comparative analysis of LC-MS/MS-data corroborated that micro-pillar array cartridges provide outstanding chromatographic performance and excellent retention time stability, which substantially increase sensitivity in the analysis of low-input proteomics samples, and thus repeatedly yielded almost twice as many unique peptide and unique protein group identifications when compared to conventional nano-flow HPLC columns.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Hela Cell

SUBMITTER: Otto Hudecz

LAB HEAD: Karl Mechtler

PROVIDER: PXD014124 | Pride | 2019-10-23

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	20190202_QExHFX2_RSLC8_PST_HeLa_10ng_1ulLoop_muPAC_1hr_15k_7.pdResult	Other
	20190202_QExHFX2_RSLC8_PST_HeLa_10ng_1ulLoop_muPAC_1hr_15k_7.raw	Raw
	20190202_QExHFX2_RSLC8_PST_HeLa_10ng_1ulLoop_muPAC_1hr_15k_8.pdResult	Other
	20190202_QExHFX2_RSLC8_PST_HeLa_10ng_1ulLoop_muPAC_1hr_15k_8.raw	Raw
	20190202_QExHFX2_RSLC8_PST_HeLa_10ng_1ulLoop_muPAC_1hr_15k_9.pdResult	Other

Items per page:

1 - 5 of 137

Publications

Improved Sensitivity in Low-Input Proteomics Using Micropillar Array-Based Chromatography.

Stadlmann Johannes J Hudecz Otto O Krššáková Gabriela G Ctortecka Claudia C Van Raemdonck Geert G Op De Beeck Jeff J Desmet Gert G Penninger Josef M JM Jacobs Paul P Mechtler Karl K

Analytical chemistry 20191031 22

Capitalizing on the massive increase in sample concentrations which are produced by extremely low elution volumes, nanoliquid chromatography-electrospray ionization-tandem mass spectrometry (nano-LC-ESI-MS/MS) is currently one of the most sensitive analytical technologies for the comprehensive characterization of complex protein samples. However, despite tremendous technological improvements made in the production and the packing of monodisperse spherical particles for nanoflow high-pressure liq ...[more]

PMID: 31612716

Similar Datasets

Project description:This a model from the article: Critical study of and improvements in chromatographic methods for the analysis of type B trichothecenes. Mateo JJ, Llorens A, Mateo R, Jimenez M. J Chromatogr A 2001 May 18;918(1):99-112 11403460 , Abstract: Various analytical methods used in the analysis of type B trichothecenes (deoxynivalenol, nivalenol, 3- and 15-acetyldeoxynivalenol) in cereals were compared and optimised in this work. These methods use either GC-electron-capture detection (ECD) of trimethylsilyl, trifluoroacetyl and heptafluorobutyryl derivatives or HPLC with UV or photodiode array detection of analytes. A new HPLC procedure using fluorescence detection prior derivatisation with coumarin-3-carbonyl chloride has been also tested. Five extraction solvents and two solid-phase extraction cartridges (silica, Florisil) plus a especial clean-up column (MycoSep 225) were compared in order to obtain the best recovery of the mycotoxins with minimal presence of coextractives in the chromatograms. The chosen extraction solvent was a mixture of acetonitrile-water (84:16, v/v). The MycoSep 225 column was chosen as the best alternative for clean-up of grain samples. For GC-ECD analysis, derivatisation of analytes with heptafluorobutyric anhydride prior the final determination was chosen as the most suitable procedure. HPLC-photodiode array (at 221 nm) analysis was more suitable for determination of type B trichothecenes than HPLC of the fluorescent coumarin-3-carbonyl derivatives. Recoveries obtained in spiked corn, rice and wheat are reported. The utility of the proposed methodology was assayed in cereal cultures of various Fusarium strains. This model was taken from the CellML repository and automatically converted to SBML. The original model was: Mateo JJ, Llorens A, Mateo R, Jimenez M. (2001) - version=1.0 The original CellML model was created by: Catherine Lloyd c.lloyd@auckland.ac.nz The University of Auckland This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

Project description:In the light of the ongoing single-cell revolution, scientific disciplines are combining forces with the ultimate goal to retrieve as much relevant data as possible from trace amounts of biological material. For single cell proteomics, this implies optimizing the entire workflow from initial cell isolation down to sample preparation, LC separation, MS/MS data acquisition and data analysis. To demonstrate the potential for single cell and limited sample proteomics, we report on a series of benchmarking experiments where we combine LC separation on a new generation of micro pillar array columns with state-of-the-art Orbitrap MS/MS detection and FAIMS. As compared to the currently commercially available pillar array columns, this dedicated limited sample column has a reduced cross section (factor of 2) and micro pillar dimensions that have been further downscaled (also by a factor of 2; 2.5 µm instead of 5 µm diameter), resulting in improved chromatography at reduced void times. A dilution series ranging from 5 to 0.05 ng/µL was prepared using trace amounts of PEG in the sample solvent to reduce adsorptive effects in the autosampler vial, sample stability up to 24h after dilution was demonstrated. Comparative processing of the MS/MS data with different database search algorithms (with and without second search feature activated and with and without rescoring based on fragmentation pattern prediction) pointed out the benefits of using Sequest HT together with INFERYS when analyzing samples at a concentration below 1 ng/µL. On average (data from quadruplicate runs), we were able to successfully identify 2855 unique protein groups from just 1 ng of HeLa lysate and this using a 60 min non-linear LC solvent gradient at a flow rate of 250 nL/min, hereby increasing detection sensitivity as compared to a previous contribution by a factor well above 10 (2436 proteins identified from 10 ng of HeLa lysate in 2019). By successfully identifying 1486 and 2210 proteins (average values, quadruplicates) from as little as 250 and 500 pg of HeLa lysate respectively, we demonstrate outstanding sensitivity with great promise for use in limited sample proteomics workflows.