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DIA-based Proteomic Profiling of FFPE Medulloblastoma Tissues from Indian Patient Cohort

ABSTRACT: This dataset includes Data-Independent Acquisition (DIA) raw files generated from FFPE medulloblastoma tissue samples collected at NIMHANS, India. Samples were subgrouped into WNT, SHH and non-WNT/non-SHH (NWS) based on immunohistochemical profiling. Deparrafinized tissues were processed using a Filter-Aided Sample Preparation (FASP) protocol, followed by LC-MS/MS analysis on a high-resolution Thermo Scientific Orbitrap Fusion mass spectrometer. DIA acquisition was performed using optimized m/z windows for the samples, and spectral library construction included pooled fraction runs across separate precursor m/z windows. QC pools were run to monitor runwise consistency.

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Prof. Sanjeeva Srivastava

PROVIDER: PXD063870 | JPOST Repository | Tue Jul 28 00:00:00 BST 2026

REPOSITORIES: jPOST

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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.

			Action	DRS
	04122023_MGJ_Nim_03.raw	Raw
	04122023_MGJ_Nim_04.raw	Raw
	04122023_MGJ_Nim_06.raw	Raw
	04122023_MGJ_Nim_QC_Pool1.raw	Raw
	05122023_MGJ_Nim_07.raw	Raw

Dataset Information

DIA-based Proteomic Profiling of FFPE Medulloblastoma Tissues from Indian Patient Cohort

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