In OsteoNano project the training-through-research is targeted at the developing of biomimetic surfaces to study and control the osteogenic differentiation of human Mesenchymal Stem Cells (hMSCs). Osteogenic differentiation of hMSCs can be guided by growth factors, such as bone morphogenetic protein 2 (BMP-2). Currently, recombinantly expressed BMP-2 is applied clinically to enhance the healing of fractured sites. However, the absence of a control over the growth factor surface density and adhesion of cells induce side effects such as ectopic bone formation. The first aim of this project is to present BMP-2 to hMSCs in a spatially controlled manner by applying surface sensitive and high-resolution techniques. The growth factors will be linked to the surface using glycosamminoglycans (GAGs) such as heparan sulphate (HS), and combined with adhesives molecules such as cyclic RGD sequences, which are known to increase the osteogenic effects of BMP-2 by activation of Integrin alphaV-beta3 and alpha5beta1. Our approach will be to design biomimetic surfaces with stepwise increasing complexity, by binding each component in a controlled manner, in terms of orientation, surface density and spatial arrangement. By using these functionalized substrates for hMSCs osteogenic differentiation we will answer several fundamental questions regarding the role of BMP-2 and its interaction with HS and RGD sequences on the osteogenic commitment of hMSCs. The biomimetic surfaces proposed here, present several fundamental studies, advantages and breakthroughs (i) reduced use of BMP-2 bound on the surface: indeed (i) the binding prevents the cellular internalization of the growth factors; (ii) possibility to enhance the osteogenic differentiation by surface co-presentation of BMPs or/and of RGDs; (iii) surface versatility with respect to stiffness which could positively impact osteogenic differentiation.

A key problem in Mental Health is that up to one third of patients suffering from major mental disorders develop resistance against drug therapy. However, patients showing early signs of treatment resistance (TR) do not receive adequate early intensive pharmacological treatment but instead they undergo a stepwise trial-and-error treatment approach. This situation originates from three major knowledge and translation gaps: i.) we lack effective methods to identify individuals at risk for TR early in the disease process, ii.) we lack effective, personalized treatment strategies grounded in insights into the biological basis of TR, and iii.) we lack efficient processes to translate scientific insights about TR into clinical practice, primary care and treatment guidelines. It is the central goal of PSYCH-STRATA to bridge these gaps and pave the way for a shift towards a treatment decision-making process tailored for the individual at risk for TR. To that end, we aim to establish evidence-based criteria to make decisions of early intense treatment in individuals at risk for TR across the major psychiatric disorders of schizophrenia, bipolar disorder and major depression. PSYCH-STRATA will i.) dissect the biological basis of TR and establish criteria to enable early detection of individuals at risk for TR based on the integrated analysis of an unprecedented collection of genetic, biological, digital mental health, and clinical data. ii.) Moreover, we will determine effective treatment strategies of individuals at risk for TR early in the treatment process, based on pan-European clinical trials in SCZ, BD and MDD. These efforts will enable the establishment of novel multimodal machine learning models to predict TR risk and treatment response. Lastly, iii.) we will enable the translation of these findings into clinical practice by prototyping the integration of personalized treatment decision support and patient-oriented decision-making mental health boards.