Dataset Information

Label free proteomics of the IPF Lung

ABSTRACT: Idiopathic pulmonary fibrosis is a debilitating disease leading ultimately to death without existing treatment. Here we profiled the proteome of 30 IPF cores and 10 control cores. The goal was to validate findings of snRNA-seq and new informatics means to mine the data (UNAGI) and evaluate in silico drugs that could present efficiency against IPF. The samples were extracted using the MPLEx method, peptides were reduced, alkylated, and digested with trypsin and 5 ul of 0.1 ug/ul were injected on LC-MS/MS on a Lumos instrument. The analysis was performed in DDA label free mode.

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Geremy Clair

PROVIDER: MSV000093129 | MassIVE | Mon Oct 16 20:57:00 BST 2023

REPOSITORIES: MassIVE

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Publications

Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases.

Zheng Yumin Y Schupp Jonas C JC Adams Taylor T Clair Geremy G Justet Aurelien A Ahangari Farida F Yan Xiting X Hansen Paul P Carlon Marianne M Cortesi Emanuela E Vermant Marie M Vos Robin R De Sadeleer Laurens J LJ Rosas Ivan O IO Pineda Ricardo R Sembrat John J Königshoff Melanie M McDonough John E JE Vanaudenaerde Bart M BM Wuyts Wim A WA Kaminski Naftali N Ding Jun J

Research square 20231218

Human diseases are characterized by intricate cellular dynamics. Single-cell sequencing provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in-silico drug interventions. Here, we introduce UNAGI, a deep generative neural network tailored to analyze time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modeling and d ...[more]

PMID: 38196613

Dataset Information

Label free proteomics of the IPF Lung

Dataset's files

Publications

Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases.

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