MassIVE MSV000095211

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Proteomics to decipher subcellular protein features of mouse heart

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Description

Protein compartmentalisation to distinctive subcellular niches is critical for cardiac function and homeostasis. Here, we employed a rapid and robust workflow based on differential centrifugal-based fractionation with mass spectrometry (MS)-based proteomics and bioinformatic analyses for systemic mapping of the subcellular proteome of mouse heart. Using supervised machine learning (ML) of 450 hallmark protein markers from 16 subcellular niches, we further refined the subcellular information of 2083 proteins with high confidence. Our data validation focused on specific subcellular niches such as mitochondria, cell surface, cardiac dyad, and myofibril including heart tissue-enriched nuclear fraction, underscoring the significance of protein localisation in cardiac biology. This study provides novel insights into the molecular landscape of different subcellular niches of inside the heart and will serve as a draft map for heart subcellular proteome. [doi:10.25345/C5Z02ZM32] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: spatial ; subcellular proteomics ; heart ; tissue

Contact

Principal Investigators:
(in alphabetical order) 		David Greening, Baker Heart & Diabetes Institute, Australia
Submitting User: dwgree

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Proteomics to decipher subcellular protein features of mouse heart.
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Number of distinct biological replicates across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct technical replicates across all analyses (original submission and reanalyses) associated with this dataset.

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Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.

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Number of distinct peptide sequences (including modified variants or peptidoforms) reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct proteins quantified across all analyses (original submission and reanalyses) associated with this dataset.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

A protein is differentially abundant if its change in abundance across conditions is found to be statistically significant with an adjusted p-value <= 0.05 and lists no issues associated with statistical tests for differential abundance.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

"N/A" means no results of this type were submitted.
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