# Vavassori_LNP2022_Proteomics

Vavassori V, Ferrari S, Beretta S, et al.
**Lipid nanoparticles improve ex vivo gene editing of human hematopoietic cells.**
2022.

Mass-Spectrometry based proteomic analysis on T cells derived from 3 healthy donors, constituting 3 biological replicates for CD40LG editing treated with LNPs. As controls, cells untreated (UT), mock electroporated or treated with LNPs formulated without RNA (Empty LNPs) were employed.
- PMID: [37294917](https://www.ncbi.nlm.nih.gov/pubmed/37294917)
- DOI: [10.1182/blood.2022019333](https://doi.org/10.1182/blood.2022019333)
- ProteomeXchange: [PXD037529](http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD037529)

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### Analyses ###

Acquired raw data were analysed using MaxQuant, using the Andromeda search engine and a Human Fasta Database downloaded from UniprotKB.
For both group-specific and global parameters, all values were kept as default.
The LFQ intensity calculation was enabled, as well as the match between runs (MBRs) feature61. All proteins and peptides matching the reversed database were filtered out.
Resulting data were analyzed using the R/Bioconductor package DEP.
In details, protein quantification matrix from MaxQuant was processed and filtered keeping only proteins present in all replicates of at least one condition.
Then, after normalization using a variance stabilizing transformation (vsn), the imputation of missing values was done using random draws from a Gaussian distribution centered around a minimal value.
Differential Expressed Proteins (DEPs) among different tested conditions.

Differential Expressed Proteins (DEPs) among different tested conditions were finally identified and the R/Bioconductor package ClusterProfiler was employed to perform pre-ranked Gene Set Enrichment Analysis (GSEA) on the Hallmark categories from the MSigDB.

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### Directories and Files ###

- _paper_plots.R_: script used to produce the plots of the paper;
- _paper_data_: folder with data used to produce the plots of the paper.