Dataset Information

Temporal Insights into Molecular and cellular responses during rAAV production in HEK293T cells

ABSTRACT: To understand the dynamic role of HEK293T cells at different cellular states post-transfection we have performed a comprehensive comparative proteome profiling of the host cell system post-transfection at 24, 48, and 72 hpt. For this study, we developed an in-house label-free HEK293T-AAV spectral library

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Suspension Culture

SUBMITTER: Alok Tanala Patra

LAB HEAD: Dr. Bi Xuezhi

PROVIDER: PXD046574 | Pride | 2024-08-09

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	20210906_Hek_4ug.mzML	Mzml
	20210909_DDA_HEK293_F1.mzML	Mzml
	20210913_HEK293_F10_4ug.mzML	Mzml
	20210913_HEK293_F11_4ug.mzML	Mzml
	20210913_HEK293_F1_4ug.mzML	Mzml

Items per page:

1 - 5 of 70

Publications

Temporal insights into molecular and cellular responses during rAAV production in HEK293T cells.

Patra Alok Tanala AT Tan Evan E Kok Yee Jiun YJ Ng Say Kong SK Bi Xuezhi X

Molecular therapy. Methods & clinical development 20240608 3

The gene therapy field seeks cost-effective, large-scale production of recombinant adeno-associated virus (rAAV) vectors for high-dosage therapeutic applications. Although strategies like suspension cell culture and transfection optimization have shown moderate success, challenges persist for large-scale applications. To unravel molecular and cellular mechanisms influencing rAAV production, we conducted an SWATH-MS proteomic analysis of HEK293T cells transfected using standard, sub-optimal, and ...[more]

PMID: 39022743

Similar Datasets

Project description:Background and Aim: Crohn's disease (CD) and ulcerative colitis (UC) are the two major chronic inflammatory bowel diseases (IBD). Although their symptoms are similar, their pathological features and clinical treatments differ. Currently, distinguishing between these diseases involves invasive procedures such as colonoscopy and histopathology, causing discomfort and inconvenience to patients. The use of fecal proteins as non-invasive biomarkers offers a promising alternative due to their stability and proximity to inflamed tissues. This study focuses on using high-throughput data-independent acquisition (DIA) mass spectrometry to develop accurate biomarker signatures from complex stool samples. Methods: Stool samples obtained from 46 active CD patients and 23 active UC patients were analyzed. Using DIA-based SWATH mass spectrometry, we explored the stool proteome, identifying and quantifying approximately 1,250 proteins. The samples were divided into training and testing groups. After data processing, various feature selection algorithms were applied on training group to determine proteins that were significantly different between the CD and UC groups. Additionally, six machine learning algorithms including k-Nearest Neighbors, Naive Bayes, eXtreme Gradient Boosting, Random Forest, Support Vector Machine, and glmnet were evaluated to identify the best-performing classifiers. Results: Sixteen proteins were selected based of several feature selection algorithms and the six ML models trained based on them. According to performance metrics of each algorithm on the training dataset, Naïve Bayes model was selected. For performance validation, the final predictive model was applied to 16 prospective samples as the test dataset. Remarkably, the model achieved an AUC of 0.95 on training dataset and AUC of 0.96 on the test dataset, demonstrating its robustness and lack of overfitting. Conclusion: This study demonstrates the effectiveness of SWATH-based proteomics and machine learning in developing predictive models to classify CD and UC. Further future validation on a larger cohort using targeted MRM mass spectrometry would be served to establish the clinical utility and reliability of this approach.

Project description:Chronic lymphocytic leukaemia (CLL) is renowned for its variable clinical course and response to therapy, suggesting a role for precision medicine. However, the molecular basis of CLL variability remains incompletely understood. Recent developments in data independent acquisition (DIA) -MS technologies, such as SWATH (Sequential Windowed Acquisition of all THeoretical fragments), provide an opportunity to study the pathophysiology of CLL at the proteome level. This report describes the sample replication and data handling requirements for SWATH-MS analysis of clinical samples. A CLL-specific spectral library compromising over 1,500,000 spectra with digital information for over 150,000 peptides has been generated to enable the quantification of up to 7736 proteins. To assess the reproducibility of the assay, SWATH-MS analysis was performed on 6 cryopreserved CLL patient samples, incorporating biological (IGHV mutational status), sample preparation and MS technical replicates. Quantitative information was obtained for 5169 proteins (<1% FDR) across 54 SWATH-MS acquisitions. These analyses showed that SWATH-MS is a highly reproducible technique. However, replicate sample preparations on different days was identified as the main source of variation. To overcome the effect of variation between sample preparation batches, different computational approaches for batch correction were tested. All approaches successfully removed technical variability whilst retaining proteomic differences between clinical subgroups. Functional enrichment analysis of proteins associated to IGHV mutational status highlighted the importance of metabolic remodelling in the biology of CLL and overlapped significantly with previous studies based on gene expression profiling. Finally, an approach to perform statistical power analysis in proteomics studies was developed. This approach could be used to drive the design of future CLL SWATH-MS studies. These fundamental requirements for DIA approaches should be widely applicable to other clinical proteomics studies.