Deepening into Intracellular Signaling Landscape through Integrative Spatial Proteomics and Transcriptomics in a Lymphoma Model.
Alicia Landeira-ViñuelaPaula DíezPablo Juanes-VelascoQuentin LécrevisseAlberto OrfaoJavier De Las RivasManuel FuentesPublished in: Biomolecules (2021)
Human Proteome Project (HPP) presents a systematic characterization of the protein landscape under different conditions using several complementary-omic techniques (LC-MS/MS proteomics, affinity proteomics, transcriptomics, etc.). In the present study, using a B-cell lymphoma cell line as a model, comprehensive integration of RNA-Seq transcriptomics, MS/MS, and antibody-based affinity proteomics (combined with size-exclusion chromatography) (SEC-MAP) were performed to uncover correlations that could provide insights into protein dynamics at the intracellular level. Here, 5672 unique proteins were systematically identified by MS/MS analysis and subcellular protein extraction strategies (neXtProt release 2020-21, MS/MS data are available via ProteomeXchange with identifier PXD003939). Moreover, RNA deep sequencing analysis of this lymphoma B-cell line identified 19,518 expressed genes and 5707 protein coding genes (mapped to neXtProt). Among these data sets, 162 relevant proteins (targeted by 206 antibodies) were systematically analyzed by the SEC-MAP approach, providing information about PTMs, isoforms, protein complexes, and subcellular localization. Finally, a bioinformatic pipeline has been designed and developed for orthogonal integration of these high-content proteomics and transcriptomics datasets, which might be useful for comprehensive and global characterization of intracellular protein profiles.
Keyphrases
- single cell
- rna seq
- ms ms
- mass spectrometry
- protein protein
- amino acid
- diffuse large b cell lymphoma
- binding protein
- endothelial cells
- genome wide
- label free
- gene expression
- social media
- quality improvement
- reactive oxygen species
- artificial intelligence
- drug delivery
- machine learning
- high resolution
- gas chromatography
- transcription factor
- induced pluripotent stem cells