This work is supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health, grant number R01 HL129008. The authors especially thank the LMNA gene mutation family members for their participation in the study. We also would like to thank Linda McCarthy for her assistance with cell culture and maintaining the lab spaces, Nasam Chokr for her participation in cell imaging and the nuclei data analysis, and Michael A. Grosberg for his pertinent advice with setting up our initial Microsoft Access database as well as answering other technical questions.

NOTE: See Table of Materials for the software versions used in this protocol.
1. Evaluate if the data would benefit from a database organization scheme
<ol>
	<li>Download the example codes and databases (see Supplemental Coding Files, which are summarized in Table 1).</li>
	<li>Use Figure 1 to evaluate if the data set of interest is "multi-dimensional". 
	NOTE: Figure 1 is a graphical representation of a multi-dimensional database provided for the example data set.</li>
	<li>If the data can be visualized in a "multi-dimensional" form like the example and if the ability to relate a specific experimental outcome to any of the dimensions (i.e., conditions) would allow for greater scientific insight into the available data, proceed to construct a relational database.</li>
</ol>
2. Organize the database structure
NOTE: Relational databases store information in the form of tables. Tables are organized in schema of rows and columns, similar to spreadsheets, and can be used to link identifying information within the database.
<ol>
	<li>Organize the data files, so they have well thought out unique names. Good practice with file naming conventions and folder-subfolder structures, when done well, allow for broad database scalability without compromising the readability of accessing files manually. Add date files in a consistent format, such as &#34;20XX-YY-ZZ&#34;, and name subfolders according to metadata is one such example.</li>
	<li>As the data-base structure is designed, draw relationships between the fields in different tables. Thus, multi-dimensionality is handled by relating different fields (i.e., columns in the tables) in individual tables to each other.</li>
	<li>Create readme documentation that describes the database and relationships that were created in step 2.2. Once an entry between different tables is linked, all associated information is related to that entry and can be used to call complex queries to filter down to the desired information. 
	NOTE: Readme documents are a common solution for providing supplemental information and database structural information about a project without adding non-uniform data to the structure.</li>
	<li>Following steps 2.1&#8722;2.3, make the end result similar to this example where the differing characteristics of individuals (Figure 2A) are related to associated experimental data of those individuals (Figure 2B). The same was done through relating columns of pattern types (Figure 2C) and data types (Figure 2D) to matching entries in the main data values table to explain various shorthand notations (Figure 2B).</li>
	<li>Determine all the essential and merely helpful data points that need to be recorded for long range data collection. 
	NOTE: A key advantage of using databases over spreadsheet programs, as mentioned earlier, is scalability: additional data points can be trivially added at any point and calculations, such as averages, are instantly updated to reflect newly added data points.
	<ol>
		<li>Identify the necessary information for creating distinct data points prior to beginning. Leave raw data untouched, instead of modifying or saving over it, so that reanalysis is possible and accessible. 
		NOTE: For the given example (Figure 2), the "Designator" corresponding to an individual, "Pattern type", "Coverslip #", and "Variable type" were all vital fields for distinctness of the associated value.</li>
		<li>If desired, add other helpful, non-vital information such as the "Total # of Coverslips" to indicate the number of repetitions conducted and help determine if data points are missing in this example.</li>
	</ol>
	</li>
</ol>
3. Set up and organize the pipeline
<ol>
	<li>Identify all the various experiments and data analysis methods that might lead to data collection along with the normal data storage practices for each data type. Work with open source version control software such as GitHub to ensure necessary consistency and version control while minimizing user burden.</li>
	<li>If possible, create procedure for consistent naming and storing of data to allow for an automated pipeline. 
	NOTE: In the example, outputs were all consistently named, thus creating a data-pipeline that looked for specific attributes was straightforward once the files were selected. If consistent naming is not possible, the tables in the database will need to be populated manually, which is not recommended.</li>
	<li>Use any convenient programming language to generate new data entries for the database.
	<ol>
		<li>Create small "helper" tables (files #8&#8722;#10 in Table 1) in separate files that can guide automated selection of data. These files serve as a template of possibilities for the pipeline to operate under and are easy to edit.</li>
		<li>To generate new data entries for the data-pipeline (Figure 3D), program the code (LocationPointer.m, file #1 in Table 1) to use the helper tables as inputs to be selected by the user (files #8&#8722;#10 in Table 1).</li>
		<li>From here, assemble a new spreadsheet of file locations by combining the new entries with the previous entries (Figure 3E). Create a code to automate this step as shown in LocationPointerCompile.m (file #2 in Table 1).</li>
		<li>Afterwards, check this merged spreadsheet for duplicates, which should be automatically removed. Create a code to automate this step as shown in LocationPointer_Remove_Duplicates.m (file #3 in Table 1).</li>
		<li>Additionally, check the spreadsheet for errors, and notify the user of their reason and location (Figure 3F). Create a code to automate this step as shown in BadPointerCheck.m (file #4 in Table 1). Alternatively, write a code that will check the compiled database and identify duplicates in one step as shown in LocationPointer_Check.m (file #5 in Table 1).</li>
		<li>Create a code to let the user manually remove bad points without losing the integrity of the database as shown in Manual_Pointer_Removal.m (file #6 in Table 1).</li>
		<li>Then use the file locations to generate a data value spreadsheet (Figure 3G, file #12 in Table 1) as well as to create a most updated list of entries that can be accessed to identify file locations or merged with future entries (Figure 3H). Create a code to automate this step as shown in Database_Generate.m (file #7 in Table 1).</li>
	</ol>
	</li>
	<li>Double check that the pipeline adds to the experimental rigor by checking for inclusion of rigorous naming conventions, automated file assembly codes, and automated error checks as previously described.</li>
</ol>
4. Create the database and queries
NOTE: If tables store information in databases, then queries are requests to the database for information given specific criteria. There are two methods to create the database: starting from a blank document or starting from the existing files. Figure 4 shows a sample query using SQL syntax that is designed to run using the database relationships shown in Figure 2.
<ol>
	<li>Method 1: Starting from scratch in creating the database and queries
	<ol>
		<li>Create a blank database document.</li>
		<li>Load the helper tables (files #8&#8722;#10 in Table 1) by selecting External Data | Text File Import | Choose File (files #8&#8722;#10) | Delimited | First Row Contains Headers, Comma | leave default | Choose My Own Primary Key (Designator for Cell Lines File #8, Variable Name for Data Types File #9, Pat Name for Pattern Type File #10) | leave default | Finish.</li>
		<li>Load the Data value table (file #12 in Table 1) by selecting External Data | Text File Import | Choose File (file #12) | Delimited | First Row Contains Headers, Comma | leave default | Let Access Add primary key | Import to Table: DataValues | Finish.</li>
		<li>Create the relationships by selecting Database Tools | Relationships | Drag all Tables to the board | Edit Relationships | Create New | Match the DataValue fields with Helper Tables Designators | Joint Type 3.</li>
		<li>Select Create | Query Design.</li>
		<li>Select or drag all relevant tables into the top window. In this example &#8216;Cell Lines', &#8216;Data Values', &#8216;Data Types', and &#8216;Pattern Type'. The relationships should automatically set up based on the previous Relationship design.</li>
		<li>Fill out the query columns for desired results, for example:
		<ol>
			<li>Click on Show | Totals.</li>
			<li>Fill out the first column (Table: DataValues, Field: DataVar, Total: GroupBy, Criteria: &#34;Act_OOP&#34;), the second column (Table: DataValues, Field: PatVar, Total: GroupBy, Criteria: &#34;Lines&#34;), and the third column (Table: Cell_Lines, Field: Designator, Total: GroupBy, Sort: Ascending).</li>
			<li>Fill out the fourth column (Table: DataValues, Field: Parameter, Total: Ave), the fifth column (Table: DataValues, Field: Parameter, Total: StDev), and the sixth column (Table: DataValues, Field: Parameter, Total: Count).</li>
		</ol>
		</li>
		<li>Run the query.</li>
	</ol>
	</li>
	<li>Alternatively, use the provided example database as a basis for examples. Open the database file Database_Queries.accdb (file #13 in Table 1) that was downloaded earlier. Use it as a template by replacing existing tables with the data of interest.</li>
</ol>
5. Move the output tables to a statistical software for significance analysis
<ol>
	<li>For this sample experimental data, use the one-way analysis of variance (ANOVA) using Tukey's test for mean comparisons between various conditions. 
	NOTE: Values of p &#60; 0.05 were considered statistically significant.</li>
</ol>

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The authors have nothing to disclose.

Technical discussion of the protocol 
The first step when considering the use of databases is to evaluate if the data would benefit from such an organization.
The next essential step is to create an automated code that will ask the minimum input from the user and generate the table data structure. In the example, the user entered the category of data type (cell nuclei or structural measurements), cell lines' subject designator, and number of files being selected. The rele...

In an era where scientific progress is heavily driven by technology, handling large amounts of data has become an integral facet of research across all disciplines. The emergence of new fields such as computational biology and genomics underscores how critical the proactive utilization of technology has become. These trends are certain to continue due to Moore's law and steady progress gained from technological advances1,2. One consequence, however, is the rising quantities of generated data that exceed the capabilities of previously viable organization methods. Although most academic laboratories have sufficient computational resources for handling complex data sets, many groups lack the technical expertise necessary to construct custom systems suited for developing needs3. Having the skills to manage and update such data sets remains critical for efficient workflow and output. Bridging the gap between data and expertise is important for efficiently handling, re-updating, and analyzing a broad spectrum of multifaceted data.
Scalability is an essential consideration when handling large data sets. Big data, for instance, is a flourishing area of research that involves revealing new insights from processing data characterized by huge volumes, large heterogeneity, and high rates of generation, such as audio and video4,5. Using automated methods of organization and analysis is mandatory for this field to appropriately handle torrents of data. Many technical terms used in big data are not clearly defined, however, and can be confusing; for instance, "high velocity" data is often associated with millions of new entries per day whereas "low velocity" data might only be hundreds of entries per day, such as in an academic lab setting. Although there are many exciting findings yet to be discovered using big data, most academic labs do not require the scope, power, and complexity of such methods for addressing their own scientific questions5. While it is undoubtable that scientific data grows increasingly complex with time6, many scientists continue to use methods of organization that no longer meet their expanding data needs. For example, convenient spreadsheet programs are frequently used to organize scientific data, but at the cost of being unscalable, error prone, and time inefficient in the long run7,8. Conversely, databases are an effective solution to the problem as they are scalable, relatively cheap, and easy to use in handling varied data sets of ongoing projects.
Immediate concerns that arise when considering schemas of data organization are cost, accessibility, and time investment for training and usage. Frequently used in business settings, database programs are more economical, being either relatively inexpensive or free, than the funding required to support use of big data systems. In fact, a variety of both commercially available and open source software exists for creating and maintaining databases, such as Oracle Database, MySQL, and Microsoft (MS) Access9. Many researchers would also be encouraged to learn that several MS Office academic packages come with MS Access included, further minimizing cost considerations. Furthermore, nearly all developers provide extensive documentation online and there is a plethora of free online resources such as Codecademy, W3Schools, and SQLBolt to help researchers understand and utilize structured query language (SQL)10,11,12. Like any programming language, learning how to use databases and code using SQL takes time to master, but with the ample resources available the process is straightforward and well worth the effort invested.
Databases can be powerful tools for increasing data accessibility and ease of aggregation, but it is important to discern which data would most benefit from a greater control of organization. Multi-dimensionality refers to the number of conditions that a measurement can be grouped against, and databases are most powerful when managing many different conditions13. Conversely, information with low dimensionality is simplest to handle using a spreadsheet program; for example, a data set containing years and a value for each year has only one possible grouping (measurements against years). High dimensional data such as from clinical settings would require a large degree of manual organization in order to effectively maintain, a tedious and error-prone process beyond the scope of spreadsheet programs13. Non-relational (NoSQL) databases also fulfill a variety of roles, primarily in applications where data does not organize well into rows and columns14. In addition to being frequently open source, these organizational schemas include graphical associations, time series data, or document-based data. NoSQL excels at scalability better than SQL, but cannot create complex queries, so relational databases are better in situations that require consistency, standardization, and infrequent large-scale data changes15. Databases are best at effectively grouping and re-updating data into the large array of conformations often needed in scientific settings13,16.
The main intent of this work, therefore, is to inform the scientific community about the potential of databases as scalable data management systems for "medium sized", low velocity data as well as to provide a general template using specific examples of patient sourced cell-line experiments. Other similar applications include geospatial data of river beds, questionnaires from longitudinal clinical studies, and microbial growth conditions in growth media17,18,19. This work highlights common considerations for and utility of constructing a database coupled with a data-pipeline necessary to convert raw data into structured formats. The basics of database interfaces and coding for databases in SQL are provided and illustrated with examples to allow others to gain the knowledge applicable to building basic frameworks. Finally, a sample experimental data set demonstrates how easily and effectively databases can be designed to aggregate multifaceted data in a variety of ways. This information provides context, commentary, and templates for assisting fellow scientists on the path towards implementing databases for their own experimental needs.
For the purposes of creating a scalable database in a research laboratory setting, data from experiments using human fibroblast cells was collected over the past three years. The primary focus of this protocol is to report on the organization of computer software to enable the user to aggregate, update, and manage data in the most cost- and time-efficient manner possible, but the relevant experimental methods are provided as well for context.
Experimental setup 
The experimental protocol for preparing samples has been described previously20,21, and is presented briefly here. Constructs were prepared by spin-coating rectangular glass coverslips with a 10:1 mixture of polydimethylsiloxane (PDMS) and curing agent, then applying 0.05 mg/mL fibronectin, in either unorganized (isotropic) or 20 &#181;m lines with 5 &#181;m gap micropatterned arrangements (lines). Fibroblast cells were seeded at passage 7 (or passage 16 for positive controls) onto the coverslips at optimal densities and left to grow for 48 h with media being changed after 24 h. The cells were then fixed using 4% paraformaldehyde (PFA) solution and 0.0005% nonionic surfactant, followed by the coverslips being immunostained for cell nuclei (4',6'-diaminodino-2-phenylinodole [DAPI]), actin (Alexa Fluor 488 phalloidin), and fibronectin (polycloncal rabbit anti-human fibronectin). A secondary stain for fibronectin using goat anti-rabbit IgG antibodies (Alexa Fluor 750 goat anti-rabbit) was applied and preservation agent was mounted onto all coverslips to prevent fluorescent fading. Nail polish was used to seal coverslips onto microscope slides then left to dry for 24 h.
Fluorescence images were obtained as described previously20 using a 40x oil immersion objective coupled with a digital charge coupled device (CCD) camera mounted on an inverted motorized microscope. Ten randomly selected fields of view were imaged for each coverslip at 40x magnification, corresponding to a 6.22 pixels/&#181;m resolution. Custom-written codes were used to quantify different variables from the images describing the nuclei, actin filaments, and fibronectin; corresponding values, as well as organization and geometry parameters, were automatically saved in data files.
Cell lines 
More extensive documentation on all sample data cell lines can be found in prior publications20. To describe briefly, the data collection was approved and informed consent was performed in accordance with UC Irvine Institutional Review Board (IRB # 2014-1253). Human fibroblast cells were collected from three families of different variations of the lamin A/C (LMNA) gene mutation: heterozygous LMNA splice-site mutation (c.357-2A&#62;G)22 (family A); LMNA nonsense mutation (c.736 C&#62;T, pQ246X) in exon 423 (family B); and LMNA missense mutation (c.1003C&#62;T, pR335W) in exon 624 (family C). Fibroblast cells were also collected from other individuals in each family as related mutation-negative controls, referred to as "Controls", and others were purchased as unrelated mutation-negative controls, referred to as "Donors". As a positive control, fibroblast cells from an individual with Hutchinson-Gliford progeria (HGPS) were purchased and grown from a skin biopsy taken from an 8-year-old female patient with HGPS possessing a LMNA G608G point mutation25. In total, fibroblasts from 22 individuals were tested and used as data in this work.
Data types 
Fibroblast data fell into one of two categories: cellular nuclei variables (i.e., percentage of dysmorphic nuclei, area of nuclei, nuclei eccentricity)20 or structural variables stemming from the orientational order parameter (OOP)21,26,27 (i.e., actin OOP, fibronectin OOP, nuclei OOP). This parameter is equal to the maximum eigenvalue of the mean order tensor of all the orientation vectors, and it is defined in detail in previous publications26,28. These values are aggregated into a variety of possible conformations, such as values against age, gender, disease status, presence of certain symptoms, etc. Examples of how these variables are used can be found in the results section.
Example codes and files 
The example codes and other files based on the data above can be downloaded with this paper, and their names and types are summarized in Table 1.

<table><tbody><tr><td>4&#039;,6&#039;-diaminodino-2-phenylinodole (DAPI)</td><td>Life Technologies, Carlsbad, CA</td><td></td><td></td></tr><tr><td>Alexa Fluor 488 Phalloidin</td><td>Life Technologies, Carlsbad, CA</td><td></td><td></td></tr><tr><td>Alexa Fluor 750 goat anti-rabbit</td><td>Life Technologies, Carlsbad, CA</td><td></td><td></td></tr><tr><td>digital CCD camera ORCAR2 C10600-10B</td><td>Hamamatsu Photonics, Shizuoka Prefecture, Japan</td><td></td><td></td></tr><tr><td>fibronectin</td><td>Corning, Corning, NY</td><td></td><td></td></tr><tr><td>IX-83 inverted motorized microscope</td><td>Olympus America, Center Valley, PA</td><td></td><td></td></tr><tr><td>Matlab R2018b</td><td>Mathworks, Natick, MA</td><td></td><td></td></tr><tr><td>MS Access</td><td>Microsoft, Redmond, WA</td><td></td><td></td></tr><tr><td>paraformaldehyde (PFA)</td><td>Fisher Scientific Company, Hanover Park, IL</td><td></td><td></td></tr><tr><td>polycloncal rabbit anti-human fibronectin</td><td>Sigma Aldrich Inc., Saint Louis, MO</td><td></td><td></td></tr><tr><td>polydimethylsiloxane (PDMS)</td><td>Ellsworth Adhesives, Germantown, WI</td><td></td><td></td></tr><tr><td>Prolong Gold Antifade</td><td>Life Technologies, Carlsbad, CA</td><td></td><td></td></tr><tr><td>rectangular glass coverslips</td><td>Fisher Scientific Company, Hanover Park, IL</td><td></td><td></td></tr><tr><td>Triton-X</td><td>Sigma Aldrich Inc., Saint Louis, MO</td><td></td><td></td></tr></tbody></table>

databases to efficiently manage medium sized, low velocity, multidimensional data in tissue engineering

Science relies on increasingly complex data sets for progress, but common data management methods such as spreadsheet programs are inadequate for the growing scale and complexity of this information. While database management systems have the potential to rectify these issues, they are not commonly utilized outside of business and informatics fields. Yet, many research labs already generate "medium sized", low velocity, multi-dimensional data that could greatly benefit from implementing similar systems. In this article, we provide a conceptual overview explaining how databases function and the advantages they provide in tissue engineering applications. Structural fibroblast data from individuals with a lamin A/C mutation was used to illustrate examples within a specific experimental context. Examples include visualizing multidimensional data, linking tables in a relational database structure, mapping a semi-automated data pipeline to convert raw data into structured formats, and explaining the underlying syntax of a query. Outcomes from analyzing the data were used to create plots of various arrangements and significance was demonstrated in cell organization in aligned environments between the positive control of Hutchinson-Gilford progeria, a well-known laminopathy, and all other experimental groups. In comparison to spreadsheets, database methods were enormously time efficient, simple to use once set up, allowed for immediate access of original file locations, and increased data rigor. In response to the National Institutes of Health (NIH) emphasis on experimental rigor, it is likely that many scientific fields will eventually adopt databases as common practice due to their strong capability to effectively organize complex data.

Multi-dimensionality of the data 
In the context of the example data-set presented here, the subjects, described in the Methods section, were divided into groups of individuals from the three families with the heart disease-causing LMNA mutation (&#34;Patients&#34;), related non-mutation negative controls (&#34;Controls&#34;), unrelated non-mutation negative controls (&#34;Donors&#34;), and an individual with Hutchinson-Gilford progeria syndrome (HGPS) as a positive control<sup class="xref"...

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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Many researchers generate "medium-sized", low-velocity, and multi-dimensional data, which can be managed more efficiently with databases rather than spreadsheets. Here we provide a conceptual overview of databases including visualizing multi-dimensional data, linking tables in relational database structures, mapping semi-automated data pipelines, and using the database to elucidate data meaning.

Science relies on increasingly complex data sets for progress, but common data management methods such as spreadsheet programs are inadequate for the ...

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Research

JoVE Journal

Bioengineering

6.2K Views.  University of California, Irvine. Wetenschappelijke gegevens zijn de afgelopen decennia steeds complexer en rijker geworden, maar wetenschappers blijven organisaties gebruiken die niet meer voldoen aan hun groeiende databehoeften. Het belangrijkste voordeel van een techniek beschreven in deze video is dat het mogelijk maakt voor een database die een rigoureuze datapijplijn en opslag geeft met behoud van flexibiliteit voor gegevensanalyse. Download de voorbeeldcodes en databases in deze tabel om te beginnen met de evaluatie va...

Video: Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Design of a Biaxial Mechanical Loading Bioreactor for Tissue Engineering

We designed a loading device that is capable of applying uniaxial or biaxial mechanical strain to a tissue engineered biocomposites fabricated for transplantation. While the device primarily functions as a bioreactor that mimics the native mechanical strains, it is also outfitted with a load cell for providing force feedback or mechanical testing of the constructs. The device subjects engineered cartilage constructs to biaxial mechanical loading with great precision of loading dose (amplitude and frequency) and is compact enough to fit inside a standard tissue culture incubator. It loads samples directly in a tissue culture plate, and multiple plate sizes are compatible with the system. The device has been designed using components manufactured for precision-guided laser applications. Bi-axial loading is accomplished by two orthogonal stages. The stages have a 50 mm travel range and are driven independently by stepper motor actuators, controlled by a closed-loop stepper motor driver that features micro-stepping capabilities, enabling step sizes of less than 50 nm. A polysulfone loading platen is coupled to the bi-axial moving platform. Movements of the stages are controlled by Thor-labs Advanced Positioning Technology (APT) software. The stepper motor driver is used with the software to adjust load parameters of frequency and amplitude of both shear and compression independently and simultaneously. Positional feedback is provided by linear optical encoders that have a bidirectional repeatability of 0.1 &mu;m and a resolution of 20 nm, translating to a positional accuracy of less than 3 &mu;m over the full 50 mm of travel. These encoders provide the necessary position feedback to the drive electronics to ensure true nanopositioning capabilities. In order to provide the force feedback to detect contact and evaluate loading responses, a precision miniature load cell is positioned between the loading platen and the moving platform. The load cell has high accuracies of 0.15% to 0.25% full scale.

We designed a novel mechanical loading bioreactor that can apply uniaxial or biaxial mechanical strain to a cartilage biocomposite prior to transplantation into an articular cartilage defect.

We  designed a loading device that is capable of applying uniaxial or biaxial  mechanical strain to a tissue engineered biocomposites fabricated for  ...

Tissue Engineering of a Human 3D in vitro Tumor Test System

Cancer is one of the leading causes of death worldwide. Current therapeutic strategies are predominantly developed in 2D culture systems, which inadequately reflect physiological conditions in vivo. Biological 3D matrices provide cells an environment in which cells can self-organize, allowing the study of tissue organization and cell differentiation. Such scaffolds can be seeded with a mixture of different cell types to study direct 3D cell-cell-interactions. To mimic the 3D complexity of cancer tumors, our group has developed a 3D in vitro tumor test system.	
	Our 3D tissue test system models the in vivo situation of malignant peripheral nerve sheath tumors (MPNSTs), which we established with our decellularized porcine jejunal segment derived biological vascularized scaffold (BioVaSc). In our model, we reseeded a modified BioVaSc matrix with primary fibroblasts, microvascular endothelial cells (mvECs) and the S462 tumor cell line. For static culture, the vascular structure of the BioVaSc is removed and the remaining scaffold is cut open on one side (Small Intestinal Submucosa SIS-Muc). The resulting matrix is then fixed between two metal rings (cell crowns).	
	Another option is to culture the cell-seeded SIS-Muc in a flow bioreactor system that exposes the cells to shear stress. Here, the bioreactor is connected to a peristaltic pump in a self-constructed incubator. A computer regulates the arterial oxygen and nutrient supply via parameters such as blood pressure, temperature, and flow rate. This setup allows for a dynamic culture with either pressure-regulated pulsatile or constant flow.	
	In this study, we could successfully establish both a static and dynamic 3D culture system for MPNSTs. The ability to model cancer tumors in a more natural 3D environment will enable the discovery, testing, and validation of future pharmaceuticals in a human-like model.

Tissue Engineering of a Human 3D in vitro Tumor Test System

Methods to create human 3D tumor tissues as test systems are described. These technologies are based on a decellularized Biological Vascularized Scaffold (BioVaSc), primary human cells and a tumor cell line, which can be cultured under static as well as under dynamic conditions in a flow bioreactor.

Cancer is one of the leading causes of death worldwide.  Current therapeutic strategies are predominantly developed in 2D culture  systems, which ...

Longitudinal Measurement of Extracellular Matrix Rigidity in 3D Tumor Models Using Particle-tracking Microrheology

The mechanical microenvironment has been shown to act as a crucial regulator of tumor growth behavior and signaling, which is itself remodeled and modified as part of a set of complex, two-way mechanosensitive interactions. While the development of biologically-relevant 3D tumor models have facilitated mechanistic studies on the impact of matrix rheology on tumor growth, the inverse problem of mapping changes in the mechanical environment induced by tumors remains challenging. Here, we describe the implementation of particle-tracking microrheology (PTM) in conjunction with 3D models of pancreatic cancer as part of a robust and viable approach for longitudinally monitoring physical changes in the tumor microenvironment, in situ. The methodology described here integrates a system of preparing in vitro 3D models embedded in a model extracellular matrix (ECM) scaffold of Type I collagen with fluorescently labeled probes uniformly distributed for position- and time-dependent microrheology measurements throughout the specimen. In vitro tumors are plated and probed in parallel conditions using multiwell imaging plates. Drawing on established methods, videos of tracer probe movements are transformed via the Generalized Stokes Einstein Relation (GSER) to report the complex frequency-dependent viscoelastic shear modulus, G*(&#969;). Because this approach is imaging-based, mechanical characterization is also mapped onto large transmitted-light spatial fields to simultaneously report qualitative changes in 3D tumor size and phenotype. Representative results showing contrasting mechanical response in sub-regions associated with localized invasion-induced matrix degradation as well as system calibration, validation data are presented. Undesirable outcomes from common experimental errors and troubleshooting of these issues are also presented. The 96-well 3D culture plating format implemented in this protocol is conducive to correlation of microrheology measurements with therapeutic screening assays or molecular imaging to gain new insights into impact of treatments or biochemical stimuli on the mechanical microenvironment.

Particle-tracking microrheology can be used to non-destructively quantify and spatially map changes in extracellular matrix mechanical properties in 3D tumor models.

The mechanical microenvironment has been shown to act as a crucial regulator of tumor growth behavior and signaling, which is itself remodeled and ...

Application of Design Aspects in Uniaxial Loading Machine Development

In terms of accurate and precise mechanical testing, machines run the continuum. Whereas commercial platforms offer excellent accuracy, they can be cost-prohibitive, often priced in the $100,000 - $200,000 price range. At the other extreme are stand-alone manual devices that often lack repeatability and accuracy (e.g., a manual crank device). However, if a single use is indicated, it is over-engineering to design and machine something overly elaborate. Nonetheless, there are occasions where machines are designed and built in-house to accomplish a motion not attainable with the existing machines in the laboratory. Described in detail here is one such device. It is a loading platform that enables pure uniaxial loading. Standard loading machines typically are biaxial in that linear loading occurs along the axis and rotary loading occurs about the axis. During testing with these machines, a load is applied to one end of the specimen while the other end remains fixed. These systems are not capable of conducting pure axial testing in which tension/compression is applied equally to the specimen ends. The platform developed in this paper enables the equal and opposite loading of specimens. While it can be used for compression, here the focus is on its use in pure tensile loading. The device incorporates commercial load cells and actuators (movers) and, as is the case with machines built in-house, a frame is machined to hold the commercial parts and fixtures for testing.

Here we present a protocol to develop a pure uniaxial loading machine. Critical design aspects are employed to ensure accurate and reproducible testing results.

In terms of accurate and precise mechanical testing, machines run the continuum. Whereas commercial platforms offer excellent accuracy, they can be ...

Engineering

A Multi-Cue Bioreactor to Evaluate the Inflammatory and Regenerative Capacity of Biomaterials under Flow and Stretch

The use of resorbable biomaterials to induce regeneration directly in the body is an attractive strategy from a translational perspective. Such materials induce an inflammatory response upon implantation, which is the driver of subsequent resorption of the material and the regeneration of new tissue. This strategy, also known as in situ tissue engineering, is pursued to obtain cardiovascular replacements such as tissue-engineered vascular grafts. Both the inflammatory and the regenerative processes are determined by the local biomechanical cues on the scaffold (i.e., stretch and shear stress). Here, we describe in detail the use of a custom-developed bioreactor that uniquely enables the decoupling of stretch and shear stress on a tubular scaffold. This allows for the systematic and standardized evaluation of the inflammatory and regenerative capacity of tubular scaffolds under the influence of well-controlled mechanical loads, which we demonstrate on the basis of a dynamic co-culture experiment using human macrophages and myofibroblasts. The key practical steps in this approach&#8212;the construction and setting up of the bioreactor, preparation of the scaffolds and cell seeding, application and maintenance of stretch and shear flow, and sample harvesting for analysis&#8212;are discussed in detail.

The goal of this protocol is to execute a dynamic co-culture of human macrophages and myofibroblasts in tubular electrospun scaffolds to investigate material-driven tissue regeneration, using a bioreactor which enables the decoupling of shear stress and cyclic stretch.

The use of resorbable biomaterials to induce regeneration directly in the body is an attractive strategy from a translational perspective. Such materials ...

Nondestructive Monitoring of Degradable Scaffold-Based Tissue-Engineered Blood Vessel Development Using Optical Coherence Tomography

Engineered vascular grafts with structural and mechanical properties similar to natural blood vessels are expected to meet the growing demand for arterial bypass. Characterization of the growth dynamics and remodeling process of degradable polymer scaffold-based tissue-engineered blood vessels (TEBVs) with pulsatile stimulation is crucial for vascular tissue engineering. Optical imaging techniques stand out as powerful tools for monitoring vascularization of engineered tissue enabling high-resolution imaging in real-time culture. This paper demonstrates a nondestructive and fast real-time imaging strategy to monitor the growth and remodeling of TEBVs in long-term culture by using optical coherence tomography (OCT). Geometric morphology is evaluated, including vascular remodeling process, wall thickness, and comparison of TEBV thickness in different culture time points and presence of pulsatile stimulation. Finally, OCT provides practical possibilities for real-time observation of the degradation of polymer in the reconstructing tissues under pulsatile stimulation or not and in each vessel segment, by compared with the assessment of polymer degradation using scanning electron microscopic(SEM) and polarized microscope.

A step by step protocol for nondestructive and long-period monitoring the process of vascular remodeling and scaffold degradation in real-time culture of biodegradable polymeric scaffold-based tissue-engineered blood vessels with pulsatile stimulation using optical coherence tomography is described here.

Engineered vascular grafts with structural and mechanical properties similar to natural blood vessels are expected to meet the growing demand for arterial ...

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

Functional recovery rarely occurs&#160;following injury or disease-induced degeneration within the central nervous system (CNS)&#160;due to the inhibitory environment and the limited capacity for neurogenesis. We are developing&#160;a strategy to simultaneously address&#160;neuronal and axonal pathway loss within the damaged CNS. This manuscript presents the fabrication protocol for micro-tissue engineered neural networks (micro-TENNs), implantable constructs consisting of neurons and aligned axonal tracts spanning the extracellular matrix (ECM) lumen of a preformed hydrogel cylinder hundreds of microns in diameter that may extend centimeters in length. Neuronal aggregates are delimited to the extremes of the three-dimensional encasement and are spanned by axonal projections. Micro-TENNs are uniquely poised as a strategy for CNS reconstruction, emulating aspects of brain connectome cytoarchitecture and potentially providing means for network replacement. The neuronal aggregates may synapse with host tissue to form new functional relays to restore and/or modulate missing or damaged circuitry. These constructs may also act as pro-regenerative&#160;&#34;living scaffolds&#34; capable of exploiting developmental mechanisms for cell migration and axonal pathfinding, providing synergistic structural and soluble cues based on the state of regeneration. Micro-TENNs are fabricated by pouring liquid hydrogel into a cylindrical mold containing a longitudinally centered needle. Once the hydrogel has gelled, the needle is removed, leaving a hollow micro-column. An ECM solution is added to the lumen to provide an environment suitable for neuronal adhesion and axonal outgrowth. Dissociated neurons are mechanically aggregated for precise seeding within one or both ends of the micro-column. This methodology reliably produces self-contained miniature constructs with long-projecting axonal tracts that may recapitulate features of brain neuroanatomy. Synaptic immunolabeling and genetically encoded calcium indicators suggest that micro-TENNs possess extensive synaptic distribution and intrinsic electrical activity. Consequently, micro-TENNs represent a promising strategy for targeted neurosurgical reconstruction of brain pathways and may also be applied as biofidelic models to study neurobiological phenomena in vitro.

This manuscript details the fabrication of micro-tissue engineered neural networks: three-dimensional micron-sized constructs comprised of long aligned axonal tracts spanning aggregated neuronal population(s) encased in a tubular hydrogel. These living scaffolds can serve as functional relays to reconstruct or modulate neural circuitry or as biofidelic test-beds mimicking gray-white matter neuroanatomy.

Functional recovery rarely occurs&#160;following injury or disease-induced degeneration within the central nervous system (CNS)&#160;due to the inhibitory ...

Neuroscience

Fibroblast Derived Human Engineered Connective Tissue for Screening Applications

Fibroblasts are phenotypically highly dynamic cells, which quickly transdifferentiate into myofibroblasts in response to biochemical and biomechanical stimuli. The current understanding of fibrotic processes, including cardiac fibrosis, remains poor, which hampers the development of new anti-fibrotic therapies. Controllable and reliable human model systems are crucial for a better understanding of fibrosis pathology. This is a highly reproducible and scalable protocol to generate engineered connective tissues (ECT) in a 48-well casting plate to facilitate studies of fibroblasts and the pathophysiology of fibrotic tissue in a 3-dimensional (3D) environment. ECT are generated around the poles with tunable stiffness, allowing for studies under a defined biomechanical load. Under the defined loading conditions, phenotypic adaptations controlled by cell-matrix interactions can be studied. Parallel testing is feasible in the 48-well format with the opportunity for the time-course analysis of multiple parameters, such as tissue compaction and contraction against the load. From these parameters, biomechanical properties such as tissue stiffness and elasticity can be studied.

Presented here is a protocol to generate engineered connective tissues for a parallel culture of 48 tissues in a multi-well plate with double poles, suitable for mechanistic studies, disease modeling, and screening applications. The protocol is compatible with fibroblasts from different organs and species and is exemplified here with human primary cardiac fibroblasts.

Fibroblasts are phenotypically highly dynamic cells, which quickly transdifferentiate into myofibroblasts in response to biochemical and biomechanical ...

Preclinical Drug Testing in Scalable 3D Engineered Muscle Tissues

Accurately modeling healthy and disease conditions in vitro is vital for the development of new treatment strategies and therapeutics. For cardiac and skeletal muscle diseases, contractile force and kinetics constitute key metrics for assessing muscle function. New and improved methods for generating engineered muscle tissues (EMTs) from induced pluripotent stem cells have made in vitro disease modeling more reliable for contractile tissues; however, reproducibly fabricating tissues from suspended cell cultures and measuring their contractility is challenging. Such techniques are often plagued with high failure rates and require complex instrumentation and customized data analysis routines. A new platform and device that utilizes 3D EMTs in conjunction with a label-free, highly-parallel, and automation-friendly contractility assay circumvent many of these obstacles. The platform enables facile and reproducible fabrication of 3D EMTs using virtually any cell source. Tissue contractility is then measured via an instrument that simultaneously measures 24 tissues without the need for complex software analysis routines. The instrument can reliably measure micronewton changes in force, allowing for dose-dependent compound screening to measure the effect of a drug or therapeutic on contractile output. Engineered tissues made with this device are fully functional, generating twitch and tetanic contractions upon electrical stimulation, and can be analyzed longitudinally in culture over weeks or months. Here, we show data from cardiac muscle EMTs under acute and chronic dosing with known toxicants, including a drug (BMS-986094) that was pulled from clinical trials after patient fatalities due to unanticipated cardiotoxicity. Altered skeletal muscle function in engineered tissues in response to treatment with a myosin inhibitor is also presented. This platform enables the researcher to integrate complex, information-rich bioengineered model systems into their drug discovery workflow with minimal additional training or skills required.

This protocol provides methods for generating 3D engineered cardiac and skeletal muscle tissues and describes their use in preclinical drug screening modalities. The described methods utilize a magnetic sensing system to facilitate the simultaneous assessment of 24 tissues in parallel.

Accurately modeling healthy and disease conditions in vitro is vital for the development of new treatment strategies and therapeutics. For cardiac and ...

Designing a Bioreactor to Improve Data Acquisition and Model Throughput of Engineered Cardiac Tissues

Heart failure remains the leading cause of death worldwide, creating a pressing need for better preclinical models of the human heart. Tissue engineering is crucial for basic science cardiac research; in vitro human cell culture eliminates the interspecies differences of animal models, while a more tissue-like 3D environment (e.g., with extracellular matrix and heterocellular coupling) simulates in vivo conditions to a greater extent than traditional two-dimensional culture on plastic Petri dishes. However, each model system requires specialized equipment, for example, custom-designed bioreactors and functional assessment devices. Additionally, these protocols are often complicated, labor-intensive, and plagued by the failure of the small, delicate tissues.
This paper describes a process for generating a robust human engineered cardiac tissue (hECT) model system using induced pluripotent stem-cell-derived cardiomyocytes for the longitudinal measurement of tissue function. Six hECTs with linear strip geometry are cultured in parallel, with each hECT&#160;suspended from a pair of force-sensing polydimethylsiloxane (PDMS) posts attached to PDMS racks. Each post is capped with a black PDMS stable post tracker (SPoT), a new feature that improves the ease of use, throughput, tissue retention, and data quality. The shape allows for the reliable optical tracking of post deflections, yielding improved twitch force tracings with absolute active and passive tension. The cap geometry eliminates tissue failure due to hECTs slipping off the posts, and as they involve a second step after PDMS rack fabrication, the SPoTs can be added to existing PDMS post-based designs without major changes to the bioreactor fabrication process.
The system is used to demonstrate the importance of measuring hECT function at physiological temperatures and shows stable tissue function during data acquisition. In summary, we describe a state-of-the-art model system that reproduces key physiological conditions to advance the biofidelity, efficiency, and rigor of engineered cardiac tissues for in vitro applications.

Three-dimensional cardiac tissues bioengineered using stem-cell-derived cardiomyocytes have emerged as promising models for studying healthy and diseased human myocardium in vitro while recapitulating key aspects of the native cardiac niche. This manuscript describes a protocol for fabricating and analyzing high-content engineered cardiac tissues generated from human induced pluripotent stem-cell-derived cardiomyocytes.

Heart failure remains the leading cause of death worldwide, creating a pressing need for better preclinical models of the human heart. Tissue engineering ...

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

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