Unit:
Faculty of MedicineLibrary of the School of Health Sciences
Author:
Tzaferis Christos
Dissertation committee:
Κόλλιας Γεώργιος, Καθηγητής, Ιατρική Σχολή, ΕΚΠΑ
Χατζηγεωργίου Αντώνιος, Αναπληρωτής Καθηγητής, Ιατρική Σχολή, ΕΚΠΑ
Παυλόπουλος Γεώργιος, Ερευνητής Α’, Ε.ΚΕ.Β.Ε «Αλέξανδρος Φλέμιγκ»
Σφηκάκης Πέτρος, Καθηγητής, Ιατρική Σχολή, ΕΚΠΑ
Παληκαράς Κωνσταντίνος, Επίκουρος Καθηγητής, Ιατρική Σχολή, ΕΚΠΑ
Αθανασιάδης Εμμανουήλ, Επίκουρος Καθηγητής, Πανεπιστήμιο Δυτικής Αττικής
Νικολάου Χριστόφορος, Ερευνητής Β’, Ε.ΚΕ.Β.Ε «Αλέξανδρος Φλέμιγκ»
Original Title:
A computational approach towards a more accurate characterization and annotation of a cell population’s transcriptome based on single-cell RNA sequencing technologies
Translated title:
A computational approach towards a more accurate characterization and annotation of a cell population’s transcriptome based on single-cell RNA sequencing technologies
Summary:
Single-cell technologies, including scRNA-seq and scATAC-seq, have revolutionized biomedical research by providing unprecedented insights into the genome, transcriptome, proteome, and epigenome at single-cell resolution across various tissues in both homeostasis and disease contexts. The rapid expansion of these assays has driven the development of numerous software tools for data analysis and visualization, supporting processes ranging from quality control and dimensionality reduction to cell clustering and the integration of RNA and ATAC data. To streamline the use of these diverse tools, we developed SCALA, an integrated, user-friendly platform available online and as a standalone application. SCALA was applied to analyze scRNA-seq and scATAC-seq data from synovial fibroblasts in the hTNFtg mouse model of arthritis, revealing cellular heterogeneity, dynamic population shifts, active regulatory networks, as well as similarities and differences between the mouse model and human RA patients. Our open-source software package not only provides an advanced workflow for data analysis and exploration but also enables researchers to gain critical biological insights, with potential implications for future diagnostic and therapeutic approaches.
Main subject category:
Health Sciences
Keywords:
scRNA-seq, scATAC-seq, Bionformatics analysis, Synovial fibroblasts, Rheumatoid arthritis
Number of references:
140
