Dataset Information

Sample Size-comparable Spectral Library Enhances Data-Independent Acquisition-based Proteome Coverage of Low-input Cells

ABSTRACT: Despite advancement of data-independent acquisition mass spectrometry (DIA-MS) to provide comprehensive and reproducible quantitative proteome profiling, its utility in very low-input samples is limited. For low-input samples at microscale, the proteome composition and overall peptide ion patterns are different from the conventional sample size. Thus, the conventional LC-MS/MS acquisition and widely used large proteome-scale DIA libraries may not be suitable for the microscale sample. In this study, we report a sample size-comparable library-based DIA approach for enhanced proteome coverage of low-input samples at nanoscale to near single-cell levels (i.e. nanogram cells,~5-50 cells). With these spectral libraries made freely available, we envision that such single-shot DIA strategy and the DIA digital map will advance quantitative proteomics applications by enabling improved deep proteome profiling for mass-limited samples.

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Yu-Ju Chen

PROVIDER: PXD027679 | JPOST Repository | Mon Nov 07 00:00:00 GMT 2022

REPOSITORIES: jPOST

ACCESS DATA

Similar Datasets

Project description:Data independent acquisition-mass spectrometry (DIA-MS) coupled with liquid chromatography is a promising approach for rapid, automatic sampling of MS/MS data in untargeted metabolomics. However, wide isolation windows in DIA-MS generate MS/MS spectra containing a mixed population of fragment ions together with their precursor ions. This precursor-fragment ion map in a comprehensive MS/MS spectral library is crucial for relative quantification of fragment ions uniquely representative of each precursor ion. However, existing reference libraries are not sufficient for this purpose since the fragmentation patterns of small molecules can vary in different instrument setups. Here we developed a bioinformatics workflow called MetaboDIA to build customized MS/MS spectral libraries using a user's own data dependent acquisition (DDA) data and to perform MS/MS-based quantification with DIA data, thus complementing conventional MS1-based quantification. MetaboDIA also allows users to build a spectral library directly from DIA data in studies of a large sample size. Using a marine algae data set, we show that quantification of fragment ions extracted with a customized MS/MS library can provide as reliable quantitative data as the direct quantification of precursor ions based on MS1 data. To test its applicability in complex samples, we applied MetaboDIA to a clinical serum metabolomics data set, where we built a DDA-based spectral library containing consensus spectra for 1829 compounds. We performed fragment ion quantification using DIA data using this library, yielding sensitive differential expression analysis. </br></br> Serum metabolome of 40 age-related macular degeneration patients and 20 control samples was analyzed using untargeted mass spectrometry. We used data dependent acquisition data to build a MS/MS spectral assay library for more than 1,000 compounds and performed targeted extraction of MS2 ion chromatograms from data independent acquisition analysis.

Dataset Information

Sample Size-comparable Spectral Library Enhances Data-Independent Acquisition-based Proteome Coverage of Low-input Cells

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure