Time-dependent pulsing of microfluidic pumps to enhance 3D bioprinting of peptide bioinks

Abstract

Using bioinks for 3D bioprinting of cellular constructs remains a challenge due to factors including viscosity, fluid dynamics and shear stress. The encapsulation of cells within the bioinks directly affects the quality of 3D bioprinting and microfluidic pumping is a commonly used supporting approach. The accuracy of microfluidic pumps can be further improved by introducing various mixing techniques. However, many of these techniques introduce complex geometries or external fields. In this study, we used a simple control technique of time-dependent pulsing for instant gelation of the peptide bioinks and observed its effect during the bioprinting process. Various time-dependent periodic signals are imposed on to a stable flow cycle and the effects are analyzed. The microfluidic pumps are programmed with different flow patterns represented by low frequency sinusoidal pulses, ramp inputs, and duty cycle pulses. Different combinations of these pulses are tested to achieve an optimal pulse for improved quality of printed constructs. Time-varied pulsing of microfluidic pumps, particularly as square waveforms, is found to provide better continuous flow and avoid material buildup within the extruder unit when compared to pumping at a constant flow rate with manual tuning. Clogging is avoided since the gelation rate is periodically reduced which avoids gel clumps in the printed constructs. This study substantially improves the use of suitable peptide bioinks, standardizes the 3D bioprinting process, and reduces clogging and clumping during printing. Our findings allow for printing of more accurate and complex constructs for applications in tissue engineering, such as skin grafting, and other regenerative medical applications.

A Parkinson’s disease model composed of 3D bioprinted dopaminergic neurons within a biomimetic peptide scaffold

“…Another technique employed to better facilitate 3D bioprinting was automated pulsing using the 3D bioprinting system's microfluidic pumps. As reported in [17], this technique allows for smoother printing by regulating the flowrate at different time intervals. By observing the user's manual adjustments to the pump flow during printing, a square pulse wave profile was created to mimic the adjustments through automation.…”

“…Another key factor in 3D bioprinting of the mouse brain model to scale was maintaining seamless flow of peptide bioink through the nozzle without excess gelation or inconsistencies. To achieve this, a protocol involving automated time-dependent pulsing was implemented with microfluidic pumps; this protocol had previously been optimized for printing lines and basic grid shapes [17]. Pumping with a square pulse flow profile was found to assist in maintaining continuous flow by alternately reducing peptide and PBS flow rates at specific time points while maintaining the overall volume in the nozzle at any given time constant at 80 µl min −1 .…”

“…In this method, the printing resolution relies on the properties of the material used and is often enhanced by the incorporation of a support material. Due to its property of instantaneous gelation, the use of peptide bioink without the use of a support material can present challenges [17], especially when the aim is to develop complex multilayer constructs. However, incorporating support material during 3D bioprinting of cells can be undesirable, as it may require a multi-material extruder or increase printing time.…”

“…Parameter optimization of duty cycles and pulse periods can improve the 3D bioprinting process by reducing flow interruptions and nozzle blockage. A previous study reported optimized parameters for time-dependent pulsing of microfluidic pumps for 3D bioprinting of simple grid structures [17]. In this study, we aimed to implement these parameters for the first time to print mouse brain-inspired 3D vm DA neuron constructs at a scale of 1:1 with high precision and resolution.…”

A predictive machine learning model to optimize flow rates on an integrated microfluidic pumping system for peptide-based 3D bioprinting

“…To ensure a thorough sampling of bioprinting experiments for the dataset, the data was generated by multiple users and printing stations, using ultrashort peptides of varied batches, sequences, and concentrations, as well as different concentrations of PBS. Different flow profiles were also taken into account to incorporate manual and automated flow cases 24 . Each of these parameters were bounded by optimization ranges previously established for precise printing with our system 11,14,15 .…”