cARLA: a small molecule cocktail for robust induction of blood-brain barrier properties

Gergo Porkolab 1,2 , Maria Meszaros 1 , Aniko Szecsko 1,2 , Judit P. Vigh 1,2 , Fruzsina R. Walter 1 , Ricardo Figueiredo 3 ,  Ildiko  Kalomista 4 ,  Gaszton  Vizsnyiczai 1 ,  Jeng-Shiung  Jan 5 ,  Fabien Gosselet 6 , Monika Vastag 4 , Szilvia Veszelka 1  & Maria A. Deli 1.
1 Institute  of  Biophysics,  Biological  Research  Centre,  Eötvös  Loránd  Research  Network, Szeged, Hungary
2 Doctoral School of Biology, University of Szeged, Szeged, Hungary
3 GenXPro GmbH, Frankfurt am Main, Germany
4 In vitro Metabolism Laboratory, Gedeon Richter Plc., Budapest, Hungary
5 Department of Chemical Engineering, National Cheng Kung University, Tainan, Taiwan
6 Laboratoire de la Barriére Hémato-Encéphalique (LBHE), Université d’Artois, Lens, France

Correspondence: Gergo Porkolab – <This email address is being protected from spambots. You need JavaScript enabled to view it.>

Blood-brain barrier (BBB) models derived from human stem cells are powerful tools to improve our understanding of human cerebrovascular diseases and facilitate drug development for the brain.  Yet providing  endothelial  cells  with  the  appropriate  molecular  cues  to  both  retain  a vascular  identity  and  acquire  BBB  characteristics  remains  challenging.  Here  we  present cARLA,  an  easy-to-use  and  affordable  small  molecule  cocktail  that  robustly  induces  BBB properties in vitro. By activating cyclic AMP and Wnt/β-catenin signaling while inhibiting the transforming growth factor beta (TGF-β) pathway, cARLA synergistically enhances barrier tightness in a range of BBB models. We demonstrate that, upon cARLA treatment, human stem cell-derived endothelial cells have lower rates of transcytosis, higher glycocalyx density and increased efflux pump activity with a shift in gene expressional profile towards the in vivo brain endothelial signature. Our work provides mechanistic insight into how endothelial signaling is orchestrated  during  BBB  maturation  and leverages  this  to  advance  the  prediction  of  drug delivery to the human brain.