Dataset Information

Integrative Multi-omics Characterization of 12 Syngeneic Mouse Models

ABSTRACT: Mouse syngeneic models serve as indispensable tools for elucidating tumor-immune interactions and assessing immunotherapy efficacy. In this study, we first conducted a comprehensive evaluation of six label-free protein quantification pipelines across 12 mouse syngeneic models, revealing that data-independent acquisition (DIA) significantly outperforms data-dependent acquisition (DDA) in terms of data coverage, reproducibility, and inter-model discrimination. We next performed an integrative multi-omics analysis to uncover molecular mechanisms associated with treatment response. Our analysis identified Dnmt3a and Igf2r, which are correlated with resistance to immune checkpoint inhibitors (ICIs), and highlighted key pathways including interferon signaling and oxidative phosphorylation that distinguish responders from non-responders. To facilitate broader research applications, we have developed an interactive web resource that shares our multi-omics datasets and analytical results, equipped with user-friendly tools for further exploration. It is freely accessible at https://muscreen.crownbio.com/syngeneic_omics. This resource aims to accelerate preclinical research and contribute to the development of personalized cancer therapies.

INSTRUMENT(S): maXis

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Spleen, Lung, Liver, Skin, Colon, Kidney, Blood

SUBMITTER: Zihan Xu

LAB HEAD: Zihan Xu

PROVIDER: PXD064638 | Pride | 2025-07-28

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	AE133DA_P22SZ_101_Slot1-44_1_1611.d.zip	Other
	AE133DA_P22SZ_102_Slot1-45_1_1612.d.zip	Other
	AE133DA_P22SZ_103_Slot1-46_1_1613.d.zip	Other
	AE133DA_P22SZ_112_Slot1-47_1_1615.d.zip	Other
	AE133DA_P22SZ_114_Slot1-48_1_1616.d.zip	Other

Items per page:

1 - 5 of 38

Publications

Integrative multi-omics characterization of 12 syngeneic mouse models.

Xu Zihan Z Mao Binchen B Liu Hengyuan H Wang Shijia S Chen Xiaobo X Guo Sheng S

iScience 20250627 8

Mouse syngeneic models serve as indispensable tools for elucidating tumor-immune interactions and assessing immunotherapy efficacy. In this study, we first conducted a comprehensive evaluation of six label-free protein quantification pipelines across 12 mouse syngeneic models, revealing that data-independent acquisition (DIA) significantly outperforms data-dependent acquisition (DDA) in terms of data coverage, reproducibility, and inter-model discrimination. We next performed an integrative mult ...[more]

PMID: 40686606

Similar Datasets

Project description:A quadrupole time-of-flight mass spectrometry coupled with a trapped ion mobility mass spectrometer operated in parallel accumulation-serial fragmentation mode has recently emerged as proteome profiling platform capable of providing four dimensional features of elution time, collision cross section, precursor and fragment ion masses of peptides. The PASEF mode has demonstrated its superior performance by providing nearly 100% ion sampling efficiency both in data-dependent acquisition and data-independent acquisition modes without sacrificing sensitivity. In addition, the substantial gain in selectivity has been proven in targeted measurements using PASEF integrated parallel reaction monitoring mode. However, because these are emerging technologies in the field of proteomics, their applicability to clinical samples has been less investigated. Cerebrospinal fluid is in direct contact with brain and has been shown to contain biomarkers related to a variety of neurological diseases although the proteomics data have been mainly generated using orbitrap-based mass spectrometers. Thus, we acquired in-depth proteome profiles of human CSF in DDA mode to generate a spectral library of 3,478 proteins. The benefit of applying the spectral library to PRM experiments was demonstrated by using CSF samples from 15 Alzheimer’s disease patients and 19 controls and utilizing the spectral library to determine elution times and ion mobility values of targeting peptide. Further, we acquired DIA data from the same CSF samples, and analyzed using the spectral library without additional efforts to generate study-specific library, which also resulted in sensitive protein identification compared to a library-free approach. Overall, we established resource of CSF proteome profiles with 4D features and demonstrated the significant gain for designing and analyzing targeted and DIA studies, which is expected to facilitate 4D proteomics of CSF to study various neurological disorders.