A Drop-on-Demand Bioprinting Approach to Spatially Arrange Multiple Cell Types and Monitor Their Cell-Cell Interactions towards Vascularization Based on Endothelial Cells and Mesenchymal Stem Cells

Spheroids, organoids, or cell-laden droplets are often used as building blocks for bioprinting, but so far little is known about the spatio-temporal cellular interactions subsequent to printing. We used a drop-on-demand bioprinting approach to study the biological interactions of such building blocks in dimensions of micrometers. Highly-density droplets (approximately 700 cells in 10 nL) of multiple cell types were patterned in a 3D hydrogel matrix with a precision of up to 70 μm. The patterns were used to investigate interactions of endothelial cells (HUVECs) and adipose-derived mesenchymal stem cells (ASCs), which are related to vascularization. We demonstrated that a gap of 200 μm between HUVEC and ASC aggregates led to decreased sprouting of HUVECs towards ASCs and increased growth from ASCs towards HUVECs. For mixed aggregates containing both cell types, cellular interconnections of ASCs with lengths of up to approximately 800 µm and inhibition of HUVEC sprouting were observed. When ASCs were differentiated into smooth muscle cells (dASCs), separate HUVEC aggregates displayed decreased sprouting towards dASCs, whereas no cellular interconnections nor inhibition of HUVEC sprouting were detected for mixed dASCs/HUVEC aggregates. These findings demonstrate that our approach could be applied to investigate cell–cell interactions of different cell types in 3D co-cultures

A Drop-on-Demand Bioprinting Approach to Spatially Arrange Multiple Cell Types and Monitor Their Cell-Cell Interactions towards Vascularization Based on Endothelial Cells and Mesenchymal Stem Cells

Spheroids, organoids, or cell-laden droplets are often used as building blocks for bioprinting, but so far little is known about the spatio-temporal cellular interactions subsequent to printing. We used a drop-on-demand bioprinting approach to study the biological interactions of such building blocks in dimensions of micrometers. Highly-density droplets (approximately 700 cells in 10 nL) of multiple cell types were patterned in a 3D hydrogel matrix with a precision of up to 70 μm. The patterns were used to investigate interactions of endothelial cells (HUVECs) and adipose-derived mesenchymal stem cells (ASCs), which are related to vascularization. We demonstrated that a gap of 200 μm between HUVEC and ASC aggregates led to decreased sprouting of HUVECs towards ASCs and increased growth from ASCs towards HUVECs. For mixed aggregates containing both cell types, cellular interconnections of ASCs with lengths of up to approximately 800 µm and inhibition of HUVEC sprouting were observed. When ASCs were differentiated into smooth muscle cells (dASCs), separate HUVEC aggregates displayed decreased sprouting towards dASCs, whereas no cellular interconnections nor inhibition of HUVEC sprouting were detected for mixed dASCs/HUVEC aggregates. These findings demonstrate that our approach could be applied to investigate cell-cell interactions of different cell types in 3D co-cultures

A Drop-on-Demand Bioprinting Approach to Spatially Arrange Multiple Cell Types and Monitor Their Cell-Cell Interactions towards Vascularization Based on Endothelial Cells and Mesenchymal Stem Cells

Spheroids, organoids, or cell-laden droplets are often used as building blocks for bioprinting, but so far little is known about the spatio-temporal cellular interactions subsequent to printing. We used a drop-on-demand bioprinting approach to study the biological interactions of such building blocks in dimensions of micrometers. Highly-density droplets (approximately 700 cells in 10 nL) of multiple cell types were patterned in a 3D hydrogel matrix with a precision of up to 70 μm. The patterns were used to investigate interactions of endothelial cells (HUVECs) and adipose-derived mesenchymal stem cells (ASCs), which are related to vascularization. We demonstrated that a gap of 200 μm between HUVEC and ASC aggregates led to decreased sprouting of HUVECs towards ASCs and increased growth from ASCs towards HUVECs. For mixed aggregates containing both cell types, cellular interconnections of ASCs with lengths of up to approximately 800 µm and inhibition of HUVEC sprouting were observed. When ASCs were differentiated into smooth muscle cells (dASCs), separate HUVEC aggregates displayed decreased sprouting towards dASCs, whereas no cellular interconnections nor inhibition of HUVEC sprouting were detected for mixed dASCs/HUVEC aggregates. These findings demonstrate that our approach could be applied to investigate cell–cell interactions of different cell types in 3D co-cultures

finDr: A web server for in silico D-peptide ligand identification

In the rapidly expanding field of peptide therapeutics, the short in vivo half-life of peptides represents a considerable limitation for drug action. D-peptides, consisting entirely of the dextrorotatory enantiomers of naturally occurring levorotatory amino acids (AAs), do not suffer from these shortcomings as they are intrinsically resistant to proteolytic degradation, resulting in a favourable pharmacokinetic profile. To experimentally identify D-peptide binders to interesting therapeutic targets, so-called mirror-image phage display is typically performed, whereby the target is synthesized in D-form and L-peptide binders are screened as in conventional phage display. This technique is extremely powerful, but it requires the synthesis of the target in D-form, which is challenging for large proteins. Here we present finDr, a novel web server for the computational identification and optimization of D-peptide ligands to any protein structure (https://findr.biologie.uni-freiburg.de/). finDr performs molecular docking to virtually screen a library of helical 12-mer peptides extracted from the RCSB Protein Data Bank (PDB) for their ability to bind to the target. In a separate, heuristic approach to search the chemical space of 12-mer peptides, finDr executes a customizable evolutionary algorithm (EA) for the de novo identification or optimization of D-peptide ligands. As a proof of principle, we demonstrate the validity of our approach to predict optimal binders to the pharmacologically relevant target phenol soluble modulin alpha 3 (PSMα3), a toxin of methicillin-resistant Staphylococcus aureus (MRSA). We validate the predictions using in vitro binding assays, supporting the success of this approach. Compared to conventional methods, finDr provides a low cost and easy-to-use alternative for the identification of D-peptide ligands against protein targets of choice without size limitation. We believe finDr will facilitate D-peptide discovery with implications in biotechnology and biomedicine