The authors acknowledge the members of the Centre in Regenerative Medicine at QUT, in particular, Antonia Horst and Pawel Mieszczanek for their helpful suggestions and feedback. This work was supported by the QUT&#8217;s Postgraduate Research Award for SE, and by the Australian Research Council (ARC) under grant agreement IC160100026 (ARC Industrial Transformation Training Centre in Additive Biomanufacturing). NB was supported by a National Health and Medical Research Council (NHMRC) Peter Doherty Early Career Research Fellowship (APP1091734).

CM and DWH are founders and shareholders of Gelomics Pty Ltd. CM is also the Director of Gelomics Pty Ltd. The authors declare no conflict of interests relevant to the subject of this article. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the article apart from those disclosed.

Pipetting of viscous materials, especially hydrogels for biomedical applications19,20,21,33,47, are routine tasks in many research labs to prepare a user-defined concentration or a dilution series with varying concentrations. Although it is repetitive and the execution is rather simple, it is mostly performed manually with low sample throughput18. This tutorial is introducing the operation of an open source workstation, which has been specifically designed for viscous materials, to enable automated mixing of viscous materials for reproducible generation of desired concentrations. This workstation is optimized for pipetting of hydrogels to enable automated and highly reliable handling by the integration of temperature docks for thermoresponsive materials, positive displacement pipettes for viscous materials, and an optional tip touch dock to remove excess material from the tip. The pipetting module has been specifically optimized to enable the processing of viscous material in a standardized and automated manner. In comparison to air cushion pipettes (Figure 5a), positive displacement pipettes (Figure 5b) dispense viscous materials without leaving residual material left in the tip, resulting in accurate aspirating and dispensing volumes. The optional tip touch dock removes excess sample material from the tip (Figure 5c,d), which is useful for gluey materials (e.g., 4% (w/v)&#160;alginate).
The protocol designer application has been specifically programmed for hydrogels and allows the dilution of up to four reagents with different concentrations and up to two diluents. The risk of errors in the calculation of final dilutions is prevented in this application, as users only choose the desired concentration or the serial dilution steps. Required aspirating and dispensing volumes are calculated automatically, saved in a separate documentation text file, and then filled into the protocol script. This protocol design application gives the user full control of all experimental parameters (e.g., pipetting speed) and ensures internal documentation of the important parameters. The protocol design app takes the filling level of the reservoir (e.g., well) into account and varies the aspirating/dispensing height to prevent unnecessary dipping into the viscous materials. This integrated feature avoids material accumulation on the outer wall of the tip, and, thereby, ensures reliable aspirating and dispensing tasks throughout the protocol. Although the protocol designer application has been developed for hydrogel dilution steps, it can be also used for dilution of nonviscous liquids, such as Orange G dyes. The protocol designer application, which is accessible via the repository under &#8216;/examples/publication-JoVE&#8217;, is the version which is explained in the protocol section and highlighted in the video. This version will not be updated. However, an updated version of the protocol designer application is available via the main repository page. The calibration terminal was initially developed by Sanderson48&#160;and has been optimized for the calibration of positive displacement pipettes.
As described in the protocol section 4, pipettes as well as containers must be calibrated initially. This calibration process is crucial to define and save the positions which are then used to calculate the movement increments. Therefore, successful protocol execution relies on well-defined calibration positions, as wrong calibration points could result in crashing of the tip into a container. Since the plunger positions of the pipettes must be calibrated manually, pipetting accuracy and precision depend greatly on the performed calibration. These calibration procedures depend highly on the user experience with the pipetting module, and, therefore, training with experienced staff is recommended at the beginning to ensure proper calibration procedures. In addition to the manual calibration on the pipetting module, the pipette itself must be calibrated to ensure accurate pipetting. It is recommended to calibrate the pipettes at least every 12 months to meet to acceptance criteria as specified in ISO 8655. To evaluate the pipette calibration internally, validation and verification are available as described by Stangegaard et al.16.
For the generation of a reliable data set, it is crucial to start with reagents of high quality. This is especially important for hydrogel processing tasks, as batch-to-batch variations might impact the generated results within this protocol. In addition to batch-to-batch variations, subtle changes in the preparation of small volumes may also contribute to property differences. To prevent this, preparation of larger volumes is recommended, which can be used for the entire experiments.
The validation and verification procedures rely on the usage of a dye to identify reliable mixtures. The presented protocol describes the application of Orange G, but the general protocol and analysis workflow can be also adapted to fluorescent dyes49,50. The usage of Orange G reduces the technical requirements of the spectrophotometer and eliminates precautions taken to prevent bleaching of the fluorescent dyes after exposure to light. Issues in the dissolving behavior or cluster formation of the dye have not been observed with the presented materials during experiments but might appear with other materials. Potential cluster formation and, therefore, the interaction between dye and material could be easily detected with a microscope.
The procedures and techniques presented in this tutorial add automation capability to current workflows for viscous materials to achieve highly reliable tasks with minimal human labor. The provided troubleshooting table (Table 2) includes identified issues and presents possible reasons as well as solutions to solve the problems. The presented workstation has been successfully applied to natural (gelatin, gellan gum, matrigel) and synthetic (e.g., poly(ethylene glycol) [PEG], Pluronic F127, Lutrol F127) polymeric materials for automated pipetting tasks. In particular, the combination of an open source workstation and an open source protocol design application designed for viscous materials will be very useful for researchers working in the fields of biomedical engineering, material science, and microbiology.

Reproducibility and replicability are of paramount importance in scientific work1,2,3,4. However, recent evidence has highlighted significant challenges in repeating high-impact biomedical studies in fundamental science as well as translational research4,5,6,7. Factors contributing to irreproducible results are complex and manifold, such as poor or biased study design6,8, insufficient statistical power3,9, missing compliance with reporting standards7,10,11, pressure to publish6, or unavailable methods or software code6,9. Amongst them, subtle changes in the protocol and &#65279;human errors in the execution of experiments have been identified as further elements accounting for irreproducibility4. For instance, manual pipetting tasks introduce intra- and inter-individual imprecision12,13 and increase the probability of human errors14. While commercial liquid handling robots are able to overcome these drawbacks and have demonstrated increased reliability for liquids15,16,17, automated handling of materials with significant viscous properties is still challenging.
Commercial liquid handling robots commonly use air cushion pipettes, also known as air piston or air displacement pipettes. The reagent and the piston are separated by an air cushion which shrinks during dispensing steps and expands during aspirating steps. Using air cushion pipettes, viscous materials &#8216;flow&#8217; only slowly into and out of the tip, and early withdrawal of the pipette from the reservoir may result in the aspiration of air bubbles. During dispensing tasks, the viscous material leaves a film on the inner tip wall which &#8216;flows&#8217; only slowly or not at all when being forced by air. To overcome these issues, positive displacement pipettes were introduced commercially to actively extrude the viscous material out of the tip using a solid piston. Although these positive displacement pipettes enable accurate and reliable handling of viscous materials, automated solutions with positive displacement pipettes are still too expensive for academic laboratory settings, and, therefore, most workflows with viscous materials rely solely on manual pipetting tasks18.
In general, viscosity is defined as the resistance of a fluid to flow, and viscous materials are further being defined as materials with a greater viscosity of water (0.89 mPa&#183;s s at 25 &#176;C). In the field of biomedical applications, experimental setups often contain multiple materials with a greater viscosity than water, such as dimethyl sulfoxide (DMSO; 1.99 mPa&#183;s at 25 &#176;C), glycerol (208.1 mPa&#183;s at 25 &#176;C for 90% glycerol [v/v]), Triton X-100 (240 mPa&#183;s at 25 &#176;C), and water-swollen polymers, referred to as hydrogels19,20. Hydrogels are hydrophilic polymer networks arranged in a physical or/and chemical mode used for various applications, including cell encapsulation, drug delivery, and soft actuators19,20,21,22. The viscosity of hydrogels depends on the polymer concentration and molecular weight19. Routinely used hydrogels for biomedical applications exhibit viscosity values between 1 and 1000 mPa&#183;s, while specific hydrogel systems have been reported with values of up to 6 x 107 mPa&#183;s19,23,24. However, viscosity measurements of hydrogels are not standardized in terms of measurement protocol and sample preparation, and, therefore, viscosity values between different studies are difficult to compare.
Since commercially available automated solutions specifically designed for hydrogels are either missing or too expensive, current workflows for hydrogel depend on manual handling18. To understand the limitations of the current manual-based workflow for pipetting of hydrogels, it is important to comprehend essential handling tasks18. For example, once a novel hydrogel material has been synthetized, a desired concentration or a dilution series with varying concentrations is generated to identify reliable synthesis protocols and crosslinking characteristics with subsequent analysis of the mechanical properties25,26,27,28. In general, a stock solution is prepared or purchased, and subsequently mixed with a diluent and/or other reagents to obtain a mixture. The mixing tasks are mostly not performed directly in a well plate (or any output format), and are rather performed in a separate reaction tube, which is commonly referred to as master mix. During these preparation tasks, various aspirating and dispensing steps are required to transfer the viscous material(s), mix the reagents, and transfer the mixture to an output format (e.g., a 96 well plate). These tasks require a high amount of human labor18, long experimental hours, and increase the probability of human errors which could potentially manifest as inaccurate results. Moreover, manual handling prevents efficient preparation of high sample numbers to screen various parameter combinations for detailed characterization. The manual processing also impedes the usage of hydrogels for high-throughput screening applications, such as the identification of promising compounds during drug development. The current manual-based preparation steps are not feasible to screen drug libraries consisting of thousands of drugs. For these reasons, automated solutions are required to provide an efficient development process and enable the successful translation of hydrogels for drug screening applications.
To move from manual-based workflows to automated processes, we have optimized a commercial open source pipetting robot for the handling of viscous materials by the integration of temperature docks for thermoresponsive materials, the usage of off-the-shelf positive displacement pipettes using capillary piston tips, and an optional tip touch dock for pipette tip cleaning. This pipetting robot has been further integrated as a pipetting module into a newly developed open source workstation, which consists of ready-to-install and customizable modules18,29. Detailed assembly instructions for the developed workstation including hardware and software files are freely accessible from the GitHub (https://github.com/SebastianEggert/OpenWorkstation) and the Zenodo repository (https://doi.org/10.5281/zenodo.3612757). In addition to the hardware development, an open source protocol design application has been programmed and released to guide the user through the parameter selection process and generate a ready-to-use protocol code (https://github.com/SebastianEggert/ProtocolDesignApp). This code runs on the commercial open source pipetting robot as well as on the developed open source workstation.
Herein, a comprehensive tutorial is provided on the operation of the open source workstation to automate mixing tasks for viscous materials (Figure 1). The tutorial-specific protocol steps can be carried out with the developed open source workstation as well as the commercial open source pipetting robot. Supported by an in-house developed open source protocol design application, automated mixing and preparation of required concentrations for glycerol, gelatin methacryloyl (GelMA) and alginate is demonstrated. Glycerol has been selected in this tutorial, since it is well characterized30,31, it is inexpensive and readily available, and, therefore, it is commonly used as viscous reference material for automated pipetting tasks. As examples for hydrogels used in biomedical applications, GelMA and alginate hydrogel precursor solutions have been applied for automated mixing experiments. GelMA presents one of the most commonly used hydrogels for cell encapsulation studies32,33, and alginate was selected in this study to demonstrate the ability to manufacture double network hydrogels34,35. Using Orange G as a dye, a fast and inexpensive procedure was implemented to validate and verify the mixing results with a spectrophotometer16.
A commercial open source pipetting robot has been integrated as a pipetting module into the developed open source workstation (Figure 2a), and therefore, the name &#8216;pipetting module&#8217; is further used to describe the pipetting robot. A detailed description of the installed hardware is beyond the scope of this protocol and is available via the provided repositories which also include step-by-step instructions for the general assembly of the open source platform. The pipetting module can be equipped with two pipettes (single- or 8-channel pipette) which are installed on axis A (right) and axis B (left) (Figure 2b). The pipetting module offers a 10-deck capacity according to American National Standards Institute/Society for Laboratory Automation and Screening (ANSI/SLAS) standards, and the following location positions are defined on the deck: A1, A2, B1, B2, C1, C2, D1, D2, E1, E2 (Figure 2c). To initiate photo-induced polymerization of hydrogel solutions, a separate crosslinker module is required and has been added to the workstation. The crosslinker module is equipped with LEDs with a wavelength of 400 nm and, therefore, substances that excite at a visible light wavelength can be used with the current systems, such as lithium phenyl-2,4,6 trimethylbenzoylphosphinate (LAP)36,37. The intensity (in mW/cm2) of the LEDs can be addressed by the user in the protocol design application to study the crosslinking behavior38. The workstation includes also a storage module to enable increased throughput studies; however, this module is not used within this study and, therefore, not further described. In general, it is recommended to operate the pipetting module in a biological safety cabinet to avoid sample contamination. The main power circuit to operate the pipetting module is a 12 V circuit, which is considered as a low-voltage application in most countries. All electrical components are based in a dedicated control box preventing users from coming into contact with the source of an electrical hazard.
By following these standardized mixing protocols, researchers are able to achieve reliable mixtures for viscous as well as non-viscous materials in an automated fashion. The open source approach allows users to optimize mixing sequences and share newly developed protocols with the community. Ultimately, this approach will facilitate the screening of multiple parameter combinations to investigate the interdependencies between different factors and, thereby, accelerate the reliable application and development of viscous materials for biomedical applications.

<table border="0" cellpadding="0" cellspacing="0" style="width:1065px;" width="1065">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:260px;">15 reaction tubes</td>
			<td style="width:301px;">Fisher Scientific, Inc. (USA)</td>
			<td style="width:203px;">14-959-53A</td>
			<td style="width:301px;"></td>
		</tr>
		<tr height="147">
			<td height="147" style="height:147px;width:260px;">5 mL tubes</td>
			<td style="width:301px;">Pacific Laboratory Products Australia Pty. Ltd. (Australia)</td>
			<td style="width:203px;">SCT-5ML</td>
			<td style="width:301px;">size depends on experimentl protocol; also Eppies (0.5, 1, 1.5 mL) or Falcon tubes (15, 50mL) can be used; product is manufactured by Axygen, Inc. https://www.pacificlab.com.au/shop/tubes-plastic/sct-5ml-tubewith-screwcap-blue-unassembled-5ml-self-standing/1/name</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:260px;">50 mL reaction tubes</td>
			<td style="width:301px;">Fisher Scientific, Inc. (USA)</td>
			<td style="width:203px;">14-432-22</td>
			<td style="width:301px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:260px;">&#65279;70% w/w Ethanol</td>
			<td style="width:301px;">LabChem, Inc. (USA)</td>
			<td style="width:203px;">aja726-5Lpl</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:260px;">96-well plate</td>
			<td style="width:301px;">Thermo Fisher Scientific, Inc. (USA)</td>
			<td style="width:203px;"></td>
			<td>https://www.thermofisher.com/order/catalog/product/168055</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:260px;">Alginate</td>
			<td style="width:301px;">NovaMatrix</td>
			<td align="right" style="width:203px;">4200001</td>
			<td>https://www.novamatrix.biz/store/pronova-up-lvg/</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:260px;">&#65279;Demineralized or ultrapure (MilliQ) water</td>
			<td style="width:301px;"></td>
			<td style="width:203px;"></td>
			<td></td>
		</tr>
		<tr height="105">
			<td height="105" style="height:105px;width:260px;">Gelatin methacryloyl (GelMA)</td>
			<td style="width:301px;">Synthetized in-house</td>
			<td style="width:203px;"></td>
			<td style="width:301px;">detailed protocol (incl materials and references) is available in Loessner et al. (2016), Nature Protocols. https://www.nature.com/articles/nprot.2016.037</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:260px;">Lithium phenyl-2,4,6-trimethylbenzoylphosphinate (LAP)</td>
			<td style="width:301px;">Sigma-Aldrich, Inc. (USA)</td>
			<td align="right" style="width:203px;">900889</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:260px;">M4 and M5 Allen key</td>
			<td style="width:301px;">OpenBuilds, inc. (USA)</td>
			<td style="width:203px;">179, 190</td>
			<td>also available in every hardware store. https://openbuildspartstore.com/allen-wrench/</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:260px;">OrangeG</td>
			<td style="width:301px;">Fisher Scientific (USA)</td>
			<td style="width:203px;">O267-25</td>
			<td>https://www.fishersci.com/shop/products/orange-g-certified-biological-stain-fisher-chemical/O26725</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:260px;">&#65279;Phosphate-buffered saline (PBS)</td>
			<td style="width:301px;">Thermo Fisher Scientific, Inc. (USA)</td>
			<td style="width:203px;">14190-144</td>
			<td style="width:301px;">alternativly: PBS tablets: 18912014 (Thermo Fisher Scientific)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:260px;">Equipment</td>
			<td style="width:301px;"></td>
			<td style="width:203px;"></td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:260px;">Aluminium blocks for temperature dock</td>
			<td style="width:301px;">Ratek Instruments Pty. Ltd. (Australia)</td>
			<td style="width:203px;">SB16</td>
			<td>blocks for different tube sizes are available. http://www.ratek.com.au/products/SB16-Block-with-12x16mm-holes.html</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:260px;">&#65279;Analytical balance</td>
			<td style="width:301px;">Sartorius AG (Germany)</td>
			<td style="width:203px;">ED224S</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:260px;">Open source liquid handling robot: commercial product</td>
			<td style="width:301px;">Opentrons Laboratories, Inc. (USA)</td>
			<td style="width:203px;">OT-One S Pro</td>
			<td>https://shop.opentrons.com/products/ot-one-pro</td>
		</tr>
		<tr height="45">
			<td>Open source liquid handling robot: open source hardware</td>
			<td>Assembled in-house following an open source approach</td>
			<td></td>
			<td>hardware and software files are freely accessible on GitHub and Zenodo (links provided); building instructions are provided. https://github.com/SebastianEggert/OpenWorkstation. https://zenodo.org/record/3612757#.XipEjBV7F24</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:260px;">Positive displacement pipette: MicromanE</td>
			<td style="width:301px;">Gilson, Inc. (USA)</td>
			<td style="width:203px;">FD10006</td>
			<td>depends on required size. https://www.gilson.com/default/shop-products/pipettes/positive-displacement.html</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:260px;">Spectrophotometer</td>
			<td style="width:301px;">BMG LABTECH GmbH (Germany)</td>
			<td style="width:203px;">CLARIOstar</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:260px;">Tips: capillary pistons</td>
			<td style="width:301px;">Gilson, Inc. (USA)</td>
			<td style="width:203px;">F148180</td>
			<td>depends on required size. https://www.gilson.com/default/shop-products/pipette-tips.html?technique_en_ww_lk=191</td>
		</tr>
	</tbody>
</table>

an open source technology platform to manufacture hydrogel-based 3d culture models in an automated and standardized fashion

Current mixing steps of viscous materials rely on repetitive and time-consuming tasks which are performed mainly manually in a low throughput mode. These issues represent drawbacks in workflows that can ultimately result in irreproducibility of research findings. Manual-based workflows are further limiting the advancement and widespread adoption of viscous materials, such as hydrogels used for biomedical applications. These challenges can be overcome by using automated workflows with standardized mixing processes to increase reproducibility. In this study, we present step-by-step instructions to use an open source protocol designer, to operate an open source workstation, and to identify reproducible mixtures. Specifically, the open source protocol designer guides the user through the experimental parameter selection and generates a ready-to-use protocol code to operate the workstation. This workstation is optimized for pipetting of viscous materials to enable automated and highly reliable handling by the integration of temperature docks for thermoresponsive materials, positive displacement pipettes for viscous materials, and an optional tip touch dock to remove excess material from the pipette tip. The validation and verification of mixtures are performed by a fast and inexpensive absorbance measurement of Orange G. This protocol presents results to obtain 80% (v/v) glycerol mixtures, a dilution series for gelatin methacryloyl (GelMA), and double network hydrogels of 5% (w/v) GelMA and 2% (w/v) alginate. A troubleshooting guide is included to support users with protocol adoption. The described workflow can be broadly applied to a number of viscous materials to generate user-defined concentrations in an automated fashion.

This tutorial presents results for experiments with glycerol (Figure 3) and GelMA with LAP and alginate (Figure 4).
The generation of an 80% (v/v) glycerol solution was investigated either without temperature control (room temperature, 22 &#176;C) and without tip touch (defined as setup 1), with temperature control (40 &#176;C) and without tip touch (setup 2), or with temperature control (40 &#176;C) and with tip touch (setup 3) (Figure 3a-i). These two temperature settings were chosen to evaluate the handling difference, since glycerol&#8217;s viscosity is decreasing almost by a factor of 3 when heated from 22 &#176;C (139.5 mPa&#183;s) to 40 &#176;C (46.6 mPa&#183;s)30. An 85% (v/v) stock solution of glycerol was diluted to a final concentration of 80% and uniformly distributed into a 96 well plate (n = 96 per setup). The experimental time, which includes the dispensing of each material into the mixture tube, the respective mixing tasks, and sample distribution into a 96 well plate, was 30 min 42 s. To identify differences between dilution mixtures, ultrapure water&#8211;as the diluent for glycerol&#8211;was prepared with 1 mg/mL Orange G. The absorbance readings highlight that the integration of the temperature control and the tip touch significantly impacts the mixtures (p &#60; 0.0001). In addition to the performed two-way analysis of variance (ANOVA), the CV values were calculated to evaluate the relative standard deviation. The coefficient of variation describes a standardized indicator to identify the degree of deviation in relation to the mean and is expressed as a percentage. If the sample means are not particularly the point of interest, but the variability within the measurements, the coefficient of variation provides additional insights to identify reproducible mixtures46. Within this experiment with three different setups, the absorbance values showed decreasing CV values from 5.6%, 4.2%, to 2.0% for setup 1, setup 2, and setup 3, respectively, demonstrating the significant influence of the temperature dock and the tip touch function on producing reliable results (Figure 3a-ii). Plotting of sample absorbance values for setup 3 (sample number #1 to #96 in a 96 well plate) yields no increasing or decreasing values throughout the experiment and, therefore, indicates no influence of the sample position on the absorbance values (Figure 3a-iii). Visualizing the data for each measured well plate with heat maps provides additional insights to identify heterogeneities for a specific row or column, or varying absorbance values throughout the dispensing tasks. The visualized heatmaps for the three setups display decreased heterogeneities across the entire well plates from setup 1 to setup 3 (Figure 3b). Finally, the replicability of the conducted mixing was evaluated within eight independent runs (Figure 3c-i,ii), where each run took 6 min 57 s. Single mixing runs showed low CV values between 1.1% to 2.6%, which indicate very reliable mixing and dispensing tasks for the individual runs. Absorbance values of all eight runs yielded a CV value of 3.3% and demonstrated the reproducibility of the established mixing protocol.
GelMA dilution series were prepared by diluting a 20% (w/v) stock solution with PBS to 14, 12, 10, 8, 6, 4, 2, and 0% (w/v) and adding LAP to a constant concentration of 0.15% (w/v) (Figure 4a-i), which took in total 55 min 12 s. As specified in the experimental protocol script, the hydrogel was crosslinking for 30 s with an intensity of 2.0 mW/cm2 at 400 nm. To evaluate the differences between the mixtures, PBS&#8213;as the diluent for GelMA and alginate&#8213;was prepared with 1 mg/mL Orange G. Hence, absorbance difference between samples within one mixture as well as between the serial dilutions are identified with a spectrophotometer. Measured absorbance values of each concentration step are significantly different (p &#60; 0.0001) and have very low CV values between 1.2% and 3.4% throughout the concentration steps (n = 12 per concentration step). Linear regression demonstrated high fit with an R&#178; value of 0.9869 (Figure 4a-ii) and a heatmap confirmed the homogenous distribution for each concentration and the difference between the concentrations (Figure 4a-iii). Automated mixing of four reagents was conducted for the generation of 5% (w/v) GelMA, 2%&#160;(w/v) alginate, 0.15% (w/v) LAP, and PBS as diluent without (setup 2) and with (setup 3) touch tip (n = 96 for each setup) with the same crosslinking parameters (30 s, 2.0 mW/cm2, 400 nm). Dispensing of the four materials, mixing, and distributing into a 96 well plate took 32 min 22 s. All experiments with GelMA and alginate were conducted at 37 &#176;C to prevent thermal gelling which prevents pipetting of GelMA. With the tip touch option, the CV value was reduced from 5.2% to 3.4% and, especially, outliers in the lower region were prevented by removing excess material from the tip (Figure 4b-i). Although the mean value of 1.927 and 1.944 for setup 2 and setup 3 are very close, the coefficient of variation highlights the decreasing deviation in relation to the mean. Single rows of the 96 well plate can be compared with each other using a heatmap visualization to detect row and/or column differences (Figure 4b-ii).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61261/61261fig1.jpg" /> 
Figure 1: Protocol workflow with individual tasks. The described workflow is divided into seven&#160;tasks, which are separated into setup, preparation, execution, and analysis. In the beginning, the software (task 1) as well as the hardware (task 2) must be set up. After the preparation of the materials (task 3) and the generation of the protocol script (task 4), the pipetting module is calibrated by defining the pipette and container positions (task 5). Next, the protocol script is executed on the workstation (task 6) and validation and verification (task 7) of mixtures are carried out to evaluate mixtures. <a href="https://www.jove.com/files/ftp_upload/61261/61261fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61261/61261fig2.jpg" /> 
Figure 2: Open source workstation and deck setup of the pipetting module. (a) The developed workstation is inspired by an assembly line approach, where samples are being transported through different modules, and consists of the following modules: pipetting, crosslinker, storage, transport, and computational module. (b) The deck of the pipetting module is set up depending on the experimental layout (e.g., well plate type, tube volume, etc.). The displayed deck setup was used for the presented experiments and consists of positive displacement pipettes with a range from 10&#8722;100 &#181;L (M100E) and 100&#8722;1,000 &#181;L (M1000E), the tip racks with capillary pistons (CP) for 100 &#181;L (CP1000) and 1,000 &#181;L (CP1000), a trash container, a mixing tray, and an input tray for the input reagents. (c) The available deck positions are defined with the displayed numbers. <a href="https://www.jove.com/files/ftp_upload/61261/61261fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/61261/61261fig3.jpg" /> 
Figure 3: Results for automated pipetting of glycerol mixtures. (a) The flexible workstation design enables the evaluation of three different setups (i) to identify optimal parameters for reproducible results. (ii) The addition of a tip touch and heating of the material resulted in a decreased coefficient of variance (CV) values and highly reproducible mixtures for setup 3. Each experiment was conducted with 96 samples. (iii) Plotting of single sample values showed no influence on the pipetting sequence. (b) Experimental results of each setup were visualized with heat maps to identify the influence on raw/column differences, edges, or master mixture. (c) The reproducibility of setup 3 was analyzed within eight independent runs using (i) median, standard deviation, CV value, and (ii) heatmaps. Data in panels a-ii (n = 96) and b-i (n = 12) are presented with the means and the single data points. Statistical significance was defined as ****p &#60; 0.0001 using two-way analysis of variance (ANOVA). <a href="https://www.jove.com/files/ftp_upload/61261/61261fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/61261/61261fig4.jpg" /> 
Figure 4: Results for mixing tasks with hydrogels. (a) From a gelatin metacryloyl (GelMA) 20% (w/v) stock solution, a serial dilution of 14, 12, 10, 8, 6, 4, 2, and 0% (w/v) was generated within one experimental run using a 96 well plate (n = 12 per concentration). (i) The coefficient of variance (CV) values varied between 1.2% and 3.4% throughout the prepared concentrations, and (ii) linear regression showed a high fit with an R&#178; value of 0.9869. (iii) Homogenous dilutions were confirmed visually with the generated heatmap. (b) Double network hydrogels were generated with 5% (w/v) GelMA, 2% (w/v) alginate, and 0.15% (w/v) LAP (i) with and without tip touch (n = 96 for each setup) and crosslinked for 30 s with an intensity of 2.0 mW/cm2 at 400 nm. The integration of the tip touch resulted in decreasing CV values from 5.2% to 3.4%. (ii,iii) Heatmaps confirm fewer deviations when using tip touch to remove excess material from tip. Data in panels a-i and b-i are presented with the means and the single data points. Statistical significance was defined as *p &#60; 0.05, ***p &#60; 0.001, and ****p &#60; 0.0001 using one-way analysis of variance (ANOVA). <a href="https://www.jove.com/files/ftp_upload/61261/61261fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/61261/61261fig5.jpg" /> 
Figure 5: Summary of the pipette type difference and issues with viscous biomaterials. (a) The reagent and the piston are separated by an air cushion that shrinks during dispensing steps and expands during aspirating steps. When aspirating and dispensing viscous materials, the slow &#8216;flow&#8217; introduces problems such as air bubbles and irregular pipetting behavior. (b) Positive displacement pipettes enable reliable aspirating and dispensing of viscous material by the usage of a piston inside the tip. (c) Pipetting of highly viscous materials (e.g., 4% (w/v)&#160;alginate) can result in the accumulation of excess material on the tip, which leads to inaccuracy throughout the experiments. (d) The implementation of a simple tip touch tray enables the removing of the excess material on the tip and results in accurate aspirating and dispensing volumes. This is realized by using the inner side of the well plate lid placed onto a tip rack container. <a href="https://www.jove.com/files/ftp_upload/61261/61261fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:font-size="9px" fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td>Material #1 (stock concentration)</td>
			<td>Final concentration of material #1</td>
			<td>Material #2 (stock concentration)</td>
			<td>Final concentration of material #2</td>
			<td>Materials #3 (stock concentration)</td>
			<td>Final concentration of material #3</td>
			<td>Diluent (Orange G working solution)</td>
			<td>Final Orange G concentration in mixture</td>
			<td>Displayed in figure</td>
		</tr>
		<tr>
			<td>Glycerol (85% (w/v))</td>
			<td>80% (w/v)</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td>water (1 mg/mL Orange G)</td>
			<td>0.059 mg/mL</td>
			<td>Figure 3a&#8722;c</td>
		</tr>
		<tr>
			<td>GelMA (20% (w/v))</td>
			<td>0% (w/v)</td>
			<td>LAP (3% (w/v))</td>
			<td>0.15% (w/v)</td>
			<td></td>
			<td></td>
			<td>PBS (1 mg/mL Orange G)</td>
			<td>1 mg/mL</td>
			<td>Figure 4a</td>
		</tr>
		<tr>
			<td>GelMA (20% (w/v))</td>
			<td>2% (w/v)</td>
			<td>LAP (3% (w/v))</td>
			<td>0.15% (w/v)</td>
			<td></td>
			<td></td>
			<td>PBS (1 mg/mL Orange G)</td>
			<td>0.85 mg/mL</td>
			<td>Figure 4a</td>
		</tr>
		<tr>
			<td>GelMA (20% (w/v))</td>
			<td>4% (w/v)</td>
			<td>LAP (3% (w/v))</td>
			<td>0.15% (w/v)</td>
			<td></td>
			<td></td>
			<td>PBS (1 mg/mL Orange G)</td>
			<td>0.75 mg/mL</td>
			<td>Figure 4a</td>
		</tr>
		<tr>
			<td>GelMA (20% (w/v))</td>
			<td>6% (w/v)</td>
			<td>LAP (3% (w/v))</td>
			<td>0.15% (w/v)</td>
			<td></td>
			<td></td>
			<td>PBS (1 mg/mL Orange G)</td>
			<td>0.65 mg/mL</td>
			<td>Figure 4a</td>
		</tr>
		<tr>
			<td>GelMA (20% (w/v))</td>
			<td>8% (w/v)</td>
			<td>LAP (3% (w/v))</td>
			<td>0.15% (w/v)</td>
			<td></td>
			<td></td>
			<td>PBS (1 mg/mL Orange G)</td>
			<td>0.55 mg/mL</td>
			<td>Figure 4a</td>
		</tr>
		<tr>
			<td>GelMA (20% (w/v))</td>
			<td>10% (w/v)</td>
			<td>LAP (3% (w/v))</td>
			<td>0.15% (w/v)</td>
			<td></td>
			<td></td>
			<td>PBS (1 mg/mL Orange G)</td>
			<td>0.45 mg/mL</td>
			<td>Figure 4a</td>
		</tr>
		<tr>
			<td>GelMA (20% (w/v))</td>
			<td>12% (w/v)</td>
			<td>LAP (3% (w/v))</td>
			<td>0.15% (w/v)</td>
			<td></td>
			<td></td>
			<td>PBS (1 mg/mL Orange G)</td>
			<td>0.35 mg/mL</td>
			<td>Figure 4a</td>
		</tr>
		<tr>
			<td>GelMA (20% (w/v))</td>
			<td>14% (w/v)</td>
			<td>LAP (3% (w/v))</td>
			<td>0.15% (w/v)</td>
			<td></td>
			<td></td>
			<td>PBS (1 mg/mL Orange G)</td>
			<td>0.25 mg/mL</td>
			<td>Figure 4a</td>
		</tr>
		<tr>
			<td>GelMA (20% (w/v))</td>
			<td>5% (w/v)</td>
			<td>Alginate (4% (w/v))</td>
			<td>2% (w/v)</td>
			<td>LAP (3% (w/v))</td>
			<td>0.15% (w/v)</td>
			<td>PBS (1 mg/mL Orange G)</td>
			<td>0.2 mg/mL</td>
			<td>Figure 4b</td>
		</tr>
	</tbody>
</table>
Table 1: Parameter overview for the conducted experiments.
<table border="1" fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td>Protocol step</td>
			<td>Problem</td>
			<td>Possible reason</td>
			<td>Solution</td>
		</tr>
		<tr>
			<td>1.1</td>
			<td>Software cannot be installed or updated</td>
			<td>Running out of disk space on SD card</td>
			<td>Check disk space on SD card. If required, remove unnecessary items, empty bin, or use an appropriately sized SD card</td>
		</tr>
		<tr>
			<td>1.2</td>
			<td>API cannot be installed</td>
			<td>Users ability for installation is restricted (no root user permission)</td>
			<td>Use &#8216;sudo&#8217; command in front of specified commands to gain admin rights. In Linux, this kind of access is known as the superuser.</td>
		</tr>
		<tr>
			<td>3.1</td>
			<td>Troubles with GelMA</td>
			<td>Functionalization, dialysis or lyophilization</td>
			<td>Detailed step-by-step protocol including troubleshooting list available in Loessner et al.33.</td>
		</tr>
		<tr>
			<td>5.1 and 6.2</td>
			<td>Workstation is not reacting to commands</td>
			<td>Connection issues</td>
			<td>Turn of everything and shut down the computer. Switch off the power supply for 10 s. Power computer and workstation back on.</td>
		</tr>
		<tr>
			<td>5.1 and 6.2</td>
			<td>Workstation is not reacting to commands</td>
			<td>Connection issues</td>
			<td>Check if the computer is recognizing the USB connection and the USB port is defined correctly. Make sure that firewall is not preventing the connection process (see the link below Table 2).</td>
		</tr>
		<tr>
			<td>5.1 and 6.2</td>
			<td>Cannot open file</td>
			<td>Wrong directory</td>
			<td>Check director (folder path) to ensure that the right path is being used. If a file (e.g., interface.py) cannot be found, it is likely that the wrong path is being used.</td>
		</tr>
		<tr>
			<td>6.6.2</td>
			<td>Tip is not properly attached or falls during movement</td>
			<td>Calibration issue</td>
			<td>Repeat calibration steps for the pipette and make sure that the capillary piston is connected properly with the pipette.</td>
		</tr>
		<tr>
			<td>6.6.2</td>
			<td>Tip is not properly attached or falls during movement</td>
			<td>Attachment issue</td>
			<td>Pipette is not properly connected to the pipette axis and moves during the movement steps. Tighten screws firmly to prevent this.</td>
		</tr>
		<tr>
			<td>6.6.2</td>
			<td>Tip is aspirating above material</td>
			<td>Calibration issue</td>
			<td>Repeat calibration of this tray type to define height properly.</td>
		</tr>
		<tr>
			<td>6.6.2</td>
			<td>Tip is aspirating above material</td>
			<td>Calibration issue</td>
			<td>Check volume in tube and make sure that the volume equals the volume defined in the protocol designer application.</td>
		</tr>
		<tr>
			<td>6.6.2</td>
			<td>Material is dipping during movement</td>
			<td>Too much excess material on tip</td>
			<td>Add tip touch dock option; optionally, also the time for tip touch can be increased.</td>
		</tr>
		<tr>
			<td>6.6.2</td>
			<td>Material is solid or too viscous for pipetting</td>
			<td>Thermoresponsive behaviour of material</td>
			<td>Check thermoresponsive material characterization and adjust heating/cooling temperature of temperature dock accordingly.</td>
		</tr>
		<tr>
			<td colspan="4">https://support.opentrons.com/en/articles/2687601-c-having-trouble-connecting-try-this-basic-troubleshooting</td>
		</tr>
	</tbody>
</table>
Table 2: Troubleshooting table with identified issues, possible reasons as well as solutions to solve the problems.
Supplemental File.&#160;<a href="https://www.jove.com/files/ftp_upload/61261/supplementary_template-analysis.xlsx" target="_blank">Please click here to download this file.</a>

Watch this Scientific Journal Video about An Open Source Technology Platform to Manufacture Hydrogel-Based 3D Culture Models in an Automated and Standardized Fashion at JoVE.com

An Open Source Technology Platform to Manufacture Hydrogel-Based 3D Culture Models in an Automated and Standardized Fashion

This protocol serves as a comprehensive tutorial for standardized and reproducible mixing of viscous materials with a novel open source automation technology. Detailed instructions are provided on the operation of a newly developed open source workstation, the usage of an open source protocol designer, and the validation and verification to identify reproducible mixtures.

Current mixing steps of viscous materials rely on repetitive and time-consuming tasks which are performed mainly manually in a low throughput mode. These ...

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4.3K Views.  Queensland University of Technology. This protocol serves as a comprehensive tutorial for standardized and reproducible mixing of viscous materials with a novel open source automation technology. Detailed instructions are provided on the operation of a newly developed open source workstation, the usage of an open source protocol designer, and the validation and verification to identify reproducible mixtures.

Video: An Open Source Technology Platform to Manufacture Hydrogel-Based 3D Culture Models in an Automated and Standardized Fashion

GENPLAT: an Automated Platform for Biomass Enzyme Discovery and Cocktail Optimization

The high cost of enzymes for biomass deconstruction is a major impediment to the economic conversion of lignocellulosic feedstocks to liquid transportation fuels such as ethanol. We have developed an integrated high throughput platform, called GENPLAT, for the discovery and development of novel enzymes and enzyme cocktails for the release of sugars from diverse pretreatment/biomass combinations. GENPLAT comprises four elements: individual pure enzymes, statistical design of experiments, robotic pipeting of biomass slurries and enzymes, and automated colorimeteric determination of released Glc and Xyl. Individual enzymes are produced by expression in Pichia pastoris or Trichoderma reesei, or by chromatographic purification from commercial cocktails or from extracts of novel microorganisms. Simplex lattice (fractional factorial) mixture models are designed using commercial Design of Experiment statistical software. Enzyme mixtures of high complexity are constructed using robotic pipeting into a 96-well format. The measurement of released Glc and Xyl is automated using enzyme-linked colorimetric assays. Optimized enzyme mixtures containing as many as 16 components have been tested on a variety of feedstock and pretreatment combinations.
GENPLAT is adaptable to mixtures of pure enzymes, mixtures of commercial products (e.g., Accellerase 1000 and Novozyme 188), extracts of novel microbes, or combinations thereof. To make and test mixtures of &tilde;10 pure enzymes requires less than 100 &mu;g of each protein and fewer than 100 total reactions, when operated at a final total loading of 15 mg protein/g glucan. We use enzymes from several sources. Enzymes can be purified from natural sources such as fungal cultures (e.g., Aspergillus niger, Cochliobolus carbonum, and Galerina marginata), or they can be made by expression of the encoding genes (obtained from the increasing number of microbial genome sequences) in hosts such as E. coli, Pichia pastoris, or a filamentous fungus such as T. reesei. Proteins can also be purified from commercial enzyme cocktails (e.g., Multifect Xylanase, Novozyme 188). An increasing number of pure enzymes, including glycosyl hydrolases, cell wall-active esterases, proteases, and lyases, are available from commercial sources, e.g., Megazyme, Inc. (www.megazyme.com), NZYTech (www.nzytech.com), and PROZOMIX (www.prozomix.com).
Design-Expert software (Stat-Ease, Inc.) is used to create simplex-lattice designs and to analyze responses (in this case, Glc and Xyl release). Mixtures contain 4-20 components, which can vary in proportion between 0 and 100%. Assay points typically include the extreme vertices with a sufficient number of intervening points to generate a valid model. In the terminology of experimental design, most of our studies are "mixture" experiments, meaning that the sum of all components adds to a total fixed protein loading (expressed as mg/g glucan). The number of mixtures in the simplex-lattice depends on both the number of components in the mixture and the degree of polynomial (quadratic or cubic). For example, a 6-component experiment will entail 63 separate reactions with an augmented special cubic model, which can detect three-way interactions, whereas only 23 individual reactions are necessary with an augmented quadratic model. For mixtures containing more than eight components, a quadratic experimental design is more practical, and in our experience such models are usually statistically valid.
All enzyme loadings are expressed as a percentage of the final total loading (which for our experiments is typically 15 mg protein/g glucan). For "core" enzymes, the lower percentage limit is set to 5%. This limit was derived from our experience in which yields of Glc and/or Xyl were very low if any core enzyme was present at 0%. Poor models result from too many samples showing very low Glc or Xyl yields. Setting a lower limit in turn determines an upper limit. That is, for a six-component experiment, if the lower limit for each single component is set to 5%, then the upper limit of each single component will be 75%. The lower limits of all other enzymes considered as "accessory" are set to 0%. "Core" and "accessory" are somewhat arbitrary designations and will differ depending on the substrate, but in our studies the core enzymes for release of Glc from corn stover comprise the following enzymes from T. reesei: CBH1 (also known as Cel7A), CBH2 (Cel6A), EG1(Cel7B), BG (&beta;-glucosidase), EX3 (endo-&beta;1,4-xylanase, GH10), and BX (&beta;-xylosidase).
<div id="myEventWatcherDiv">
	 </div>

GENPLAT (GLBRC Enzyme Platform) is an automated platform for discovery and optimization of enzyme cocktails for biomass degradation. It can be adapted to multiple feedstocks and mixtures of enzymes containing multiple components.

The high cost of enzymes for biomass deconstruction is a major impediment to the economic conversion of lignocellulosic feedstocks to liquid ...

Development of an In Vitro Ocular Platform to Test Contact Lenses

Currently, in vitro evaluations of contact lenses (CLs) for drug delivery are typically performed in large volume vials,1-6 which fail to mimic physiological tear volumes.7 The traditional model also lacks the natural tear flow component and the blinking reflex, both of which are defining factors of the ocular environment. The development of a novel model is described in this study, which consists of a unique 2-piece design, eyeball and eyelid piece, capable of mimicking physiological tear volume. The models are created from 3-D printed molds (Polytetrafluoroethylene or Teflon molds), which can be used to generate eye models from various polymers, such as polydimethylsiloxane (PDMS) and agar. Further modifications to the eye pieces, such as the integration of an explanted human or animal cornea or human corneal construct, will permit for more complex in vitro ocular studies. A commercial microfluidic syringe pump is integrated with the platform to emulate physiological tear secretion. Air exposure and mechanical wear are achieved using two mechanical actuators, of which one moves the eyelid piece laterally, and the other moves the eyeballeyepiece circularly. The model has been used to evaluate CLs for drug delivery and deposition of tear components on CLs.

Development of an In Vitro Ocular Platform to Test Contact Lenses

Current in vitro models for evaluating contact lenses (CLs) and other eye-related applications are severely limited. The presented ocular platform simulates physiological tear flow, tear volume, air exposure and mechanical wear. This system is highly versatile and can be applied to various in vitro analyses with CLs.

Currently, in vitro evaluations of contact lenses (CLs) for drug delivery are typically performed in large volume vials,1-6 which fail to mimic ...

An In Vitro Organ Culture Model of the Murine Intervertebral Disc

Intervertebral disc (IVD) degeneration is a significant contributor to low back pain. The IVD is a fibrocartilaginous joint that serves to transmit and dampen loads in the spine. The IVD consists of a proteoglycan-rich nucleus pulposus (NP) and a collagen-rich annulus fibrosis (AF) sandwiched by cartilaginous end-plates. Together with the adjacent vertebrae, the vertebrae-IVD structure forms a functional spine unit (FSU). These microstructures contain unique cell types as well as unique extracellular matrices. Whole organ culture of the FSU preserves the native extracellular matrix, cell differentiation phenotypes, and cellular-matrix interactions. Thus, organ culture techniques are particularly useful for investigating the complex biological mechanisms of the IVD. Here, we describe a high-throughput approach for culturing whole lumbar mouse FSUs that provides an ideal platform for studying disease mechanisms and therapies for the IVD. Furthermore, we describe several applications that utilize this organ culture method to conduct further studies including contrast-enhanced microCT imaging and three-dimensional high-resolution finite element modeling of the IVD.

An In Vitro Organ Culture Model of the Murine Intervertebral Disc

Whole organ culture of the intervertebral disc (IVD) preserves the native extracellular matrix, cell phenotypes, and cellular-matrix interactions. Here we describe an IVD culture system using mouse lumbar and caudal IVDs in their functional spinal units and several applications utilizing this system.

Intervertebral disc (IVD) degeneration is a significant contributor to low back pain. The IVD is a fibrocartilaginous joint that serves to transmit and ...

An Additive Manufacturing Technique for the Facile and Rapid Fabrication of Hydrogel-based Micromachines with Magnetically Responsive Components

Polyethylene glycol (PEG)-based hydrogels are biocompatible hydrogels that have been approved for use in humans by the FDA. Typical PEG-based hydrogels have simple monolithic architectures and often function as scaffolding materials for tissue engineering applications. More sophisticated structures typically take a long time to fabricate and do not contain moving components. This protocol describes a photolithography method that allows for facile and rapid microfabrication of PEG structures and devices. This strategy involves an in-house developed fabrication stage that allows for the rapid fabrication of 3D structures by building upwards in a layer-by-layer fashion. Independent moving components can also be aligned and assembled onto support structures to form integrated devices. These independent components are doped with superparamagnetic iron oxide nanoparticles that are sensitive to magnetic actuation. In this manner, the fabricated devices can be actuated using external magnets to yield movement of the components within. Hence, this technique allows for the fabrication of sophisticated MEMS-like devices (micromachines) that are composed entirely out of a biocompatible hydrogel, able to function without an onboard power source, and respond to a contact-less method of actuation. This manuscript describes the fabrication of both the fabrication set-up as well as the step-by-step method for the microfabrication of these hydrogels-based MEMS-like devices.

An additive manufacturing strategy for processing UV-crosslinkable hydrogels has been developed. This strategy allows for the layer-by-layer assembly of microfabricated hydrogel structures as well as the assembly of independent components, yielding integrated devices containing moving components that are responsive to magnetic actuation.

Polyethylene glycol (PEG)-based hydrogels are biocompatible hydrogels that have been approved for use in humans by the FDA. Typical PEG-based hydrogels ...

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Sequencing technologies have undergone a paradigm shift from bulk to single-cell resolution in response to an evolving understanding of the role of cellular heterogeneity in biological systems. However, single-cell sequencing of large populations has been hampered by limitations in processing genomes for sequencing. In this paper, we describe a method for single-cell genome sequencing (SiC-seq) which uses droplet microfluidics to isolate, amplify, and barcode the genomes of single cells. Cell encapsulation in microgels allows the compartmentalized purification and tagmentation of DNA, while a microfluidic merger efficiently pairs each genome with a unique single-cell oligonucleotide barcode, allowing &gt;50,000 single cells to be sequenced per run. The sequencing data is demultiplexed by barcode, generating groups of reads originating from single cells. As a high-throughput and low-bias method of single-cell sequencing, SiC-seq will enable a broader range of genomic studies targeted at diverse cell populations.

Single-cell sequencing reveals genotypic heterogeneity in biological systems, but current technologies lack the throughput necessary for the deep profiling of community composition and function. Here, we describe a microfluidic workflow for sequencing &gt;50,000 single-cell genomes from diverse cell populations.

Sequencing technologies have undergone a paradigm shift from bulk to single-cell resolution in response to an evolving understanding of the role of ...

A Novel Surgical Technique As a Foundation for In Vivo Partial Liver Engineering in Rat

Organ engineering is a novel strategy to generate liver organ substitutes that can potentially be used in transplantation. Recently, in vivo liver engineering, including in vivo organ decellularization followed by repopulation, has emerged as a promising approach over ex vivo liver engineering. However, postoperative survival was not achieved. The aim of this study is to develop a novel surgical technique of in vivo selective liver lobe perfusion in rats as a prerequisite for in vivo liver engineering. We generate a circuit bypass only through the left lateral lobe. Then, the left lateral lobe is perfused with heparinized saline. The experiment is performed with 4 groups (n = 3 rats per group) based on different perfusion times of 20 min, 2 h, 3 h, and 4 h. Survival, as well as the macroscopically visible change of color and the histologically determined absence of blood cells in the portal triad and the sinusoids, is taken as an indicator for a successful model establishment. After selective perfusion of the left lateral lobe, we observe that the left lateral lobe, indeed, turned from red to faint yellow. In a histological assessment, no blood cells are visible in the branch of the portal vein, the central vein, and the sinusoids. The left lateral lobe turns red after reopening the blocked vessels. 12/12 rats survived the procedure for more than one week. We are the first to report a surgical model for in vivo single liver lobe perfusion with a long survival period of more than one week. In contrast to the previously published report, the most important advantage of the technique presented here is that perfusion of 70% of the liver is maintained throughout the whole procedure. The establishment of this technique provides a foundation for in vivo partial liver engineering in rats, including decellularization and recellularization.

A Novel Surgical Technique As a Foundation for In Vivo Partial Liver Engineering in Rat

We establish a novel surgical technique for an in vivo single liver lobe perfusion model in rat as a prerequisite for further studying in vivo partial liver engineering in the future.

Organ engineering is a novel strategy to generate liver organ substitutes that can potentially be used in transplantation. Recently, in vivo liver ...

Design of an Open-Source, Low-Cost Bioink and Food Melt Extrusion 3D Printer

Three-dimensional (3D) printing is an increasingly popular manufacturing technique that allows highly complex objects to be fabricated with no retooling costs. This increasing popularity is partly driven by falling barriers to entry such as system set-up costs and ease of operation. The following protocol presents the design and construction of an Additive Manufacturing Melt Extrusion (ADDME) 3D printer for the fabrication of custom parts and components. ADDME has been designed with a combination of 3D-printed, laser-cut, and online-sourced components. The protocol is arranged into easy-to-follow sections, with detailed diagrams and parts lists under the headings of framing, y-axis and bed, x-axis, extrusion, electronics, and software. The performance of ADDME is evaluated through extrusion testing and 3D printing of complex objects using viscous cream, chocolate, and Pluronic F-127 (a model for bioinks). The results indicate that ADDME is a capable platform for the fabrication of materials and constructs for use in a wide range of industries. The combination of detailed diagrams and video content facilitates access to low-cost, easy-to-operate equipment for individuals interested in 3D printing of complex objects from a wide range of materials.

The aim of this work is to design and construct a reservoir-based melt extrusion three-dimensional printer made from open-source and low-cost components for applications in the biomedical and food printing industries.

Three-dimensional (3D) printing is an increasingly popular manufacturing technique that allows highly complex objects to be fabricated with no retooling ...

Protocols of 3D Bioprinting of Gelatin Methacryloyl Hydrogel Based Bioinks

Gelatin methacryloyl (GelMA) has become a popular biomaterial in the field of bioprinting. The derivation of this material is gelatin, which is hydrolyzed from mammal collagen. Thus, the arginine-glycine-aspartic acid (RGD) sequences and target motifs of matrix metalloproteinase (MMP) remain on the molecular chains, which help achieve cell attachment and degradation. Furthermore, formation properties of GelMA are versatile. The methacrylamide groups allow a material to become rapidly crosslinked under light irradiation in the presence of a photoinitiator. Therefore, it makes great sense to establish suitable methods for synthesizing three-dimensional (3D) structures with this promising material. However, its low viscosity restricts GelMA's printability. Presented here are methods to carry out 3D bioprinting of GelMA hydrogels, namely the fabrication of GelMA microspheres, GelMA fibers, GelMA complex structures, and GelMA-based microfluidic chips. The resulting structures and biocompatibility of the materials as well as the printing methods are discussed. It is believed that this protocol may serve as a bridge between previously applied biomaterials and GelMA as well as contribute to the establishment of GelMA-based 3D architectures for biomedical applications.

Presented here is a method for the 3D bioprinting of gelatin methacryloyl.

Gelatin methacryloyl (GelMA) has become a popular biomaterial in the field of bioprinting. The derivation of this material is gelatin, which is hydrolyzed ...

Anterior Segment Organ Culture Platform for Tracking Open Globe Injuries and Therapeutic Performance

Open globe injuries have poor visual outcomes, often resulting in permanent loss of vision. This is partly due to an extended delay between injury and medical intervention in rural environments and military medicine applications where ophthalmic care is not readily available. Untreated injuries are susceptible to infection after the eye has lost its watertight seal, as well as loss of tissue viability due to intraocular hypotension. Therapeutics to temporarily seal open globe injuries, if properly developed, may be able to restore intraocular pressure and prevent infection until proper ophthalmic care is possible. To facilitate product development, detailed here is the use of an anterior segment organ culture open globe injury platform for tracking therapeutic performance for at least 72 h post-injury. Porcine anterior segment tissue can be maintained in custom-designed organ culture dishes and held at physiological intraocular pressure. Puncture injuries can be created with a pneumatic-powered system capable of generating injury sizes up to 4.5 mm in diameter, similar to military-relevant injury sizes. Loss of intraocular pressure can be observed for 72 h post-injury confirming proper injury induction and loss of the eye&#39;s watertight seal. Therapeutic performance can be tracked by application to the eye after injury induction and then tracking intraocular pressure for multiple days. Further, the anterior segment injury model is applicable to widely used methods for functionally and biologically tracking anterior segment physiology, such as assessing transparency, ocular mechanics, corneal epithelium health, and tissue viability. Overall, the method described here is a necessary next step toward developing biomaterial therapeutics for temporarily sealing open globe injuries when ophthalmic care is not readily available.

Open globe eye injuries may go untreated for multiple days in rural or military-relevant scenarios, resulting in blindness. Therapeutics are needed to minimize loss of vision. Here, we detail an organ culture open globe injury model. With this model, potential therapeutics for stabilizing these injuries can be properly evaluated.

Open globe injuries have poor visual outcomes, often resulting in permanent loss of vision. This is partly due to an extended delay between injury and ...

Large-Scale, Automated Production of Adipose-Derived Stem Cell Spheroids for 3D Bioprinting

Adipose-derived stromal/stem cells (ASCs) are a subpopulation of cells found in the stromal vascular fraction of human subcutaneous adipose tissue recognized as a classical source of mesenchymal stromal/stem cells. Many studies have been published with ASCs for scaffold-based tissue engineering approaches, which mainly explored the behavior of these cells after their seeding on bioactive scaffolds. However, scaffold-free approaches are emerging to engineer tissues in vitro and in vivo, mainly by using spheroids, to overcome the limitations of scaffold-based approaches.
Spheroids are 3D microtissues formed by the self-assembly process. They can better mimic the architecture and microenvironment of native tissues, mainly due to the magnification of cell-to-cell and cell-to-extracellular matrix interactions. Recently, spheroids are mainly being explored as disease models, drug screening studies, and building blocks for 3D bioprinting. However, for 3D bioprinting approaches, numerous spheroids, homogeneous in size and shape, are necessary to biofabricate complex tissue and organ models. In addition, when spheroids are produced automatically, there is little chance for microbiological contamination, increasing the reproducibility of the method.
The large-scale production of spheroids is considered the first mandatory step for developing a biofabrication line, which continues in the 3D bioprinting process and finishes in the full maturation of the tissue construct in bioreactors. However, the number of studies that explored the large-scale ASC spheroid production are still scarce, together with the number of studies that used ASC spheroids as building blocks for 3D bioprinting. Therefore, this article aims to show the large-scale production of ASC spheroids using a non-adhesive micromolded hydrogel technique spreading ASC spheroids as building blocks for 3D bioprinting approaches.

Here, we describe the large-scale production of adipose-derived stromal/stem cell (ASC) spheroids using an automated pipetting system to seed the cell suspension, thus ensuring homogeneity of spheroid size and shape. These ASC spheroids can be used as building blocks for 3D bioprinting approaches.