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>

<ol>
	<li>Cavin, R. K., Lugli, P., Zhirnov, V. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Science+and+engineering+beyond+Moore's+law.">Science and engineering beyond Moore's law.</a> Proceedings of the IEEE. 100, 1720-1749 (2012).</li><li>Mast, F. D., Ratushny, A. V., Aitchison, J. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systems+cell+biology.">Systems cell biology.</a> The Journal of Cell Biology. 206 (6), 695-706 (2014).</li><li>Barone, L., Williams, J., Micklos, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Unmet+needs+for+analyzing+biological+big+data:+A+survey+of+704+NSF+principal+investigators.">Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators.</a> PLoS Computational Biology. 13 (10), 1005755 (2017).</li><li>Gandomi, A., Haider, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Beyond+the+hype:+Big+data+concepts,+methods,+and+analytics.">Beyond the hype: Big data concepts, methods, and analytics.</a> International Journal of Information Management. 35 (2), 137-144 (2015).</li><li>Siddiqa, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+survey+of+big+data+management:+Taxonomy+and+state-of-the-art.">A survey of big data management: Taxonomy and state-of-the-art.</a> Journal of Network and Computer Applications. 71, 151-166 (2016).</li><li>Anderson, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+End+of+Theory:+The+Data+Deluge+Makes+the+Scientific+Method+Obsolete.">The End of Theory: The Data Deluge Makes the Scientific Method Obsolete.</a> Wired Magazine. , (2008).</li><li>Broman, K. W., Woo, K. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Data+Organization+in+Spreadsheets.">Data Organization in Spreadsheets.</a> The American Statistician. 72 (1), 2-10 (2018).</li><li>Lee, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+I+do+it:+a+practical+database+management+system+to+assist+clinical+research+teams+with+data+collection,+organization,+and+reporting.">How I do it: a practical database management system to assist clinical research teams with data collection, organization, and reporting.</a> Academic Radiology. 22 (4), 527-533 (2015).</li><li>Bassil, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparative+study+on+the+performance+of+the+Top+DBMS+systems.">A comparative study on the performance of the Top DBMS systems.</a> Journal of Computer Science &amp; Research. 1 (1), 20-31 (2012).</li><li>. Learn SQL - Codeacademy Available from: <a target="_blank" href="https://www.codecademy.com/learn/learn-sql">https://www.codecademy.com/learn/learn-sql</a> (2018)</li><li>. SQL Tutorial - w3schools.com Available from: <a target="_blank" href="https://www.w3schools.com/sql">https://www.w3schools.com/sql</a> (2018)</li><li>. Introduction to SQL - SQLBolt Available from: <a target="_blank" href="https://sqlbolt.com">https://sqlbolt.com</a> (2018)</li><li>Pedersen, T. B., Jensen, C. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multidimensional+database+technology.">Multidimensional database technology.</a> Computer. 34 (12), 40-46 (2001).</li><li>Gy&#337;r&#246;di, C., Gyorodi, R., Sotoc, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Comparative+Study+of+Relational+and+Non-Relational+Database+Models+in+a+Web-+Based+Application.">A Comparative Study of Relational and Non-Relational Database Models in a Web- Based Application.</a> International Journal of Advanced Computer Science and Applications. 6 (11), 78-83 (2015).</li><li>Nayak, A., Poriya, A., Poojary, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Type+of+NOSQL+databases+and+its+comparison+with+relational+databases.">Type of NOSQL databases and its comparison with relational databases.</a> International Journal of Applied Information Systems. 5 (4), 16-19 (2013).</li><li>Lei, C., Feng, D., Wei, C., Ai-xin, Z., Zhen-hu, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+application+of+multidimensional+data+analysis+in+the+EIA+database+of+electric+industry.">The application of multidimensional data analysis in the EIA database of electric industry.</a> Procedia Environmental Sciences. 10, 1210-1215 (2011).</li><li>Soranno, P. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Building+a+multi-scaled+geospatial+temporal+ecology+database+from+disparate+data+sources:+fostering+open+science+and+data+reuse.">Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse.</a> GigaScience. 4, 28 (2015).</li><li>Edwards, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Questionnaires+in+clinical+trials:+guidelines+for+optimal+design+and+administration.">Questionnaires in clinical trials: guidelines for optimal design and administration.</a> Trials. 11, 2 (2010).</li><li>Richards, M. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MediaDB:+A+Database+of+Microbial+Growth+Conditions+in+Defined+Media.">MediaDB: A Database of Microbial Growth Conditions in Defined Media.</a> PLoS ONE. 9 (8), 103548 (2014).</li><li>Core, J. Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Age+of+heart+disease+presentation+and+dysmorphic+nuclei+in+patients+with+LMNA+mutations.">Age of heart disease presentation and dysmorphic nuclei in patients with LMNA mutations.</a> PLoS ONE. 12 (11), 0188256 (2017).</li><li>Drew, N. K., Johnsen, N. E., Core, J. Q., Grosberg, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiscale+Characterization+of+Engineered+Cardiac+Tissue+Architecture.">Multiscale Characterization of Engineered Cardiac Tissue Architecture.</a> Journal of Biomechanical Engineering. 138 (11), 111003 (2016).</li><li>Zaragoza, M. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exome+Sequencing+Identifies+a+Novel+LMNA+Splice-Site+Mutation+and+Multigenic+Heterozygosity+of+Potential+Modifiers+in+a+Family+with+Sick+Sinus+Syndrome,+Dilated+Cardiomyopathy,+and+Sudden+Cardiac+Death.">Exome Sequencing Identifies a Novel LMNA Splice-Site Mutation and Multigenic Heterozygosity of Potential Modifiers in a Family with Sick Sinus Syndrome, Dilated Cardiomyopathy, and Sudden Cardiac Death.</a> PLoS ONE. 11 (5), 0155421 (2016).</li><li>Zaragoza, M., Nguyen, C., Widyastuti, H., McCarthy, L., Grosberg, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dupuytren's+and+Ledderhose+Diseases+in+a+Family+with+LMNA-Related+Cardiomyopathy+and+a+Novel+Variant+in+the+ASTE1+Gene.">Dupuytren's and Ledderhose Diseases in a Family with LMNA-Related Cardiomyopathy and a Novel Variant in the ASTE1 Gene.</a> Cells. 6 (4), 40 (2017).</li><li>Zaragoza, M. V., Hakim, S. A., Hoang, V., Elliott, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Heart-hand+syndrome+IV:+a+second+family+with+LMNA-related+cardiomyopathy+and+brachydactyly.">Heart-hand syndrome IV: a second family with LMNA-related cardiomyopathy and brachydactyly.</a> Clinical Genetics. 91 (3), 499-500 (2017).</li><li>Eriksson, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recurrent+de+novo+point+mutations+in+lamin+A+cause+Hutchinson-Gilford+progeria+syndrome.">Recurrent de novo point mutations in lamin A cause Hutchinson-Gilford progeria syndrome.</a> Nature. 423 (6937), 293-298 (2003).</li><li>Drew, N. K., Eagleson, M. A., Baldo, D. B., Parker, K. K., Grosberg, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metrics+for+Assessing+Cytoskeletal+Orientational+Correlations+and+Consistency.">Metrics for Assessing Cytoskeletal Orientational Correlations and Consistency.</a> PLoS Computational Biology. 11 (4), 1004190 (2015).</li><li>Hamley, I. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Introduction to Soft Matter: Synthetic and Biological Self-Assembling Materials. , (2013).</li><li>Grosberg, A., Alford, P. W., McCain, M. L., Parker, K. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ensembles+of+engineered+cardiac+tissues+for+physiological+and+pharmacological+study:+Heart+on+a+chip.">Ensembles of engineered cardiac tissues for physiological and pharmacological study: Heart on a chip.</a> Lab Chip. 11 (24), 4165-4173 (2011).</li><li>Hey, T., Trefethen, A., Berman, F., Fox, G., Hey, A. J. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Data+Deluge:+An+e-Science+Perspective.">The Data Deluge: An e-Science Perspective.</a> Grid Computing: Making the Global Infrastructure a Reality. , (2003).</li><li>Wardle, M., Sadler, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+to+set+up+a+clinical+database.">How to set up a clinical database.</a> Practical Neurology. 16 (1), 70-74 (2016).</li><li>Kerr, W. T., Lau, E. P., Owens, G. E., Trefler, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+future+of+medical+diagnostics:+large+digitized+databases.">The future of medical diagnostics: large digitized databases.</a> The Yale Journal of Biology and Medicine. 85 (3), 363 (2012).</li><li>Laulederkind, S. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Rat+Genome+Database+curation+tool+suite:+a+set+of+optimized+software+tools+enabling+efficient+acquisition,+organization,+and+presentation+of+biological+data.">The Rat Genome Database curation tool suite: a set of optimized software tools enabling efficient acquisition, organization, and presentation of biological data.</a> Database. 2011, (2011).</li><li>Harris, P. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Research+electronic+data+capture+(REDCap)--a+metadata-driven+methodology+and+workflow+process+for+providing+translational+research+informatics+support.">Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.</a> Journal of Biomedical Informatics. 42 (2), 377-381 (2009).</li><li>Panko, R. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=What+we+know+about+spreadsheet+errors.">What we know about spreadsheet errors.</a> Journal of Organizational and End User Computing (JOEUC). 10 (2), 15-21 (1998).</li><li>Ziemann, M., Eren, Y., El-Osta, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gene+name+errors+are+widespread+in+the+scientific+literature.">Gene name errors are widespread in the scientific literature.</a> Genome Biology. 17 (1), 177 (2016).</li><li>Enhancing Reproducibility through Rigor and Transparency. NIH Available from: <a target="_blank" href="https://grants.nih.gov/reproducibility/index.htm">https://grants.nih.gov/reproducibility/index.htm</a> (2018)</li><li>Hofseth, L. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Getting+rigorous+with+scientific+rigor.">Getting rigorous with scientific rigor.</a> Carcinogenesis. 39 (1), 21-25 (2017).</li><li>. SQL Training and Tutorials - Lynda.com Available from: <a target="_blank" href="https://www.lynda.com/SQL-training-tutorials/446-0.html">https://www.lynda.com/SQL-training-tutorials/446-0.html</a> (2018)</li></ol>

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&#39;,6&#39;-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"...

Watch this Scientific Journal Video about Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering at JoVE.com

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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Bioengineering

6.2K Views.  University of California, Irvine. 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.

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

Elastomeric PGS Scaffolds in Arterial Tissue Engineering

Cardiovascular disease is one of the leading cause of mortality in the US and especially, coronary artery disease increases with an aging population and increasing obesity1. Currently, bypass surgery using autologous vessels, allografts, and synthetic grafts are known as a commonly used for arterial substitutes2. However, these grafts have limited applications when an inner diameter of arteries is less than 6 mm due to low availability, thrombotic complications, compliance mismatch, and late intimal hyperplasia3,4. To overcome these limitations, tissue engineering has been successfully applied as a promising alternative to develop small-diameter arterial constructs that are nonthrombogenic, robust, and compliant. Several previous studies have developed small-diameter arterial constructs with tri-lamellar structure, excellent mechanical properties and burst pressure comparable to native arteries5,6. While high tensile strength and burst pressure by increasing collagen production from a rigid material or cell sheet scaffold, these constructs still had low elastin production and compliance, which is a major problem to cause graft failure after implantation. Considering these issues, we hypothesized that an elastometric biomaterial combined with mechanical conditioning would provide elasticity and conduct mechanical signals more efficiently to vascular cells, which increase extracellular matrix production and support cellular orientation.
The objective of this report is to introduce a fabrication technique of porous tubular scaffolds and a dynamic mechanical conditioning for applying them to arterial tissue engineering. We used a biodegradable elastomer, poly (glycerol sebacate) (PGS)7 for fabricating porous tubular scaffolds from the salt fusion method. Adult primary baboon smooth muscle cells (SMCs) were seeded on the lumen of scaffolds, which cultured in our designed pulsatile flow bioreactor for 3 weeks. PGS scaffolds had consistent thickness and randomly distributed macro- and micro-pores. Mechanical conditioning from pulsatile flow bioreactor supported SMC orientation and enhanced ECM production in scaffolds. These results suggest that elastomeric scaffolds and mechanical conditioning of bioreactor culture may be a promising method for arterial tissue engineering.

Elastomeric PGS scaffolds with vascular smooth muscle cells cultured in a pulsatile flow bioreactor may lead to promising small-diameter arterial constructs with native ECM production in a relatively short culture period.

Cardiovascular disease is one of the leading cause of mortality in the US and especially, coronary artery disease increases with an aging population and ...

Directed Cellular Self-Assembly to Fabricate Cell-Derived Tissue Rings for Biomechanical Analysis and Tissue Engineering

Each year, hundreds of thousands of patients undergo coronary artery bypass surgery in the United States.1 Approximately one third of these patients do not have suitable autologous donor vessels due to disease progression or previous harvest. The aim of vascular tissue engineering is to develop a suitable alternative source for these bypass grafts. In addition, engineered vascular tissue may prove valuable as living vascular models to study cardiovascular diseases. Several promising approaches to engineering blood vessels have been explored, with many recent studies focusing on development and analysis of cell-based methods.2-5 Herein, we present a method to rapidly self-assemble cells into 3D tissue rings that can be used in vitro to model vascular tissues.
To do this, suspensions of smooth muscle cells are seeded into round-bottomed annular agarose wells. The non-adhesive properties of the agarose allow the cells to settle, aggregate and contract around a post at the center of the well to form a cohesive tissue ring.6,7 These rings can be cultured for several days prior to harvesting for mechanical, physiological, biochemical, or histological analysis. We have shown that these cell-derived tissue rings yield at 100-500 kPa ultimate tensile strength8 which exceeds the value reported for other tissue engineered vascular constructs cultured for similar durations (&lt;30 kPa).9,10 Our results demonstrate that robust cell-derived vascular tissue ring generation can be achieved within a short time period, and offers the opportunity for direct and quantitative assessment of the contributions of cells and cell-derived matrix (CDM) to vascular tissue structure and function.

This article outlines a versatile method to create cell-derived tissue rings by cellular self-assembly. Smooth muscle cells seeded into ring-shaped agarose wells aggregate and contract to form robust three-dimensional (3D) tissues within 7 days. Millimeter-scale tissue rings are conducive to mechanical testing and serve as building blocks for tissue assembly.

Each year, hundreds of thousands of patients undergo coronary artery bypass surgery in the United States.1 Approximately one third of these patients do ...

Tissue Engineering of the Intestine in a Murine Model

Tissue-engineered small intestine (TESI) has successfully been used to rescue Lewis rats after massive small bowel resection, resulting in return to preoperative weights within 40 days.1 In humans, massive small bowel resection can result in short bowel syndrome, a functional malabsorptive state that confers significant morbidity, mortality, and healthcare costs including parenteral nutrition dependence, liver failure and cirrhosis, and the need for multivisceral organ transplantation.2 In this paper, we describe and document our protocol for creating tissue-engineered intestine in a mouse model with a multicellular organoid units-on-scaffold approach. Organoid units are multicellular aggregates derived from the intestine that contain both mucosal and mesenchymal elements,3 the relationship between which preserves the intestinal stem cell niche.4 In ongoing and future research, the transition of our technique into the mouse will allow for investigation of the processes involved during TESI formation by utilizing the transgenic tools available in this species.5The availability of immunocompromised mouse strains will also permit us to apply the technique to human intestinal tissue and optimize the formation of human TESI as a mouse xenograft before its transition into humans. Our method employs good manufacturing practice (GMP) reagents and materials that have already been approved for use in human patients, and therefore offers a significant advantage over approaches that rely upon decellularized animal tissues. The ultimate goal of this method is its translation to humans as a regenerative medicine therapeutic strategy for short bowel syndrome.

This article and the accompanying video present our protocol for generating tissue-engineered intestine in the mouse, using an organoid units-on-scaffold approach.

Tissue-engineered small intestine (TESI) has successfully been used to rescue Lewis rats after massive small bowel resection, resulting in return to ...

Capillary Force Lithography for Cardiac Tissue Engineering

Cardiovascular disease remains the leading cause of death worldwide1. Cardiac tissue engineering holds much promise to deliver groundbreaking medical discoveries with the aims of developing functional tissues for cardiac regeneration as well as in vitro screening assays. However, the ability to create high-fidelity models of heart tissue has proven difficult. The heart&#8217;s extracellular matrix (ECM) is a complex structure consisting of both biochemical and biomechanical signals ranging from the micro- to the nanometer scale2. Local mechanical loading conditions and cell-ECM interactions have recently been recognized as vital components in cardiac tissue engineering3-5.
A large portion of the cardiac ECM is composed of aligned collagen fibers with nano-scale diameters that significantly influences tissue architecture and electromechanical coupling2. Unfortunately, few methods have been able to mimic the organization of ECM fibers down to the nanometer scale. Recent advancements in nanofabrication techniques, however, have enabled the design and fabrication of scalable scaffolds that mimic the in vivo structural and substrate stiffness cues of the ECM in the heart6-9.
Here we present the development of two reproducible, cost-effective, and scalable nanopatterning processes for the functional alignment of cardiac cells using the biocompatible polymer poly(lactide-co-glycolide) (PLGA)8 and a polyurethane (PU) based polymer. These anisotropically nanofabricated substrata (ANFS) mimic the underlying ECM of well-organized, aligned tissues and can be used to investigate the role of nanotopography on cell morphology and function10-14.
Using a nanopatterned (NP) silicon master as a template, a polyurethane acrylate (PUA) mold is fabricated. This PUA mold is then used to pattern the PU or PLGA hydrogel via UV-assisted or solvent-mediated capillary force lithography (CFL), respectively15,16. Briefly, PU or PLGA pre-polymer is drop dispensed onto a glass coverslip and the PUA mold is placed on top. For UV-assisted CFL, the PU is then exposed to UV radiation (&#955; = 250-400 nm) for curing. For solvent-mediated CFL, the PLGA is embossed using heat (120 &#176;C) and pressure (100 kPa). After curing, the PUA mold is peeled off, leaving behind an ANFS for cell culture. Primary cells, such as neonatal rat ventricular myocytes, as well as human pluripotent stem cell-derived cardiomyocytes, can be maintained on the ANFS2.

In this protocol, we demonstrate the fabrication of biomimetic cardiac cell culture substrata made from two distinct polymeric materials using capillary force lithography. The described methods provide a scalable, cost-effective technique to engineer the structure and function of macroscopic cardiac tissues for in vitro and in vivo applications.

Cardiovascular disease remains the leading cause of death worldwide1. Cardiac tissue engineering holds much promise to deliver groundbreaking medical ...

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 ...

Tissue Engineering of Tumor Stromal Microenvironment with Application to Cancer Cell Invasion

3D organotypic cultures of epithelial cells on a matrix embedded with mesenchymal cells are widely used to study epithelial cell differentiation and invasion. Rat tail type I collagen and/or matrix derived from Engelbreth-Holm-Swarm mouse sarcoma cells have been traditionally employed as the substrates to model the matrix or stromal microenvironment into which mesenchymal cells (usually fibroblasts) are populated. Although experiments using such matrices are very informative, it can be argued that due to an overriding presence of a single protein (such as in type I Collagen) or a high content of basement membrane components and growth factors (such as in matrix derived from mouse sarcoma cells), these substrates do not best reflect the contribution to matrix composition made by the stromal cells themselves. To study native matrices produced by primary dermal fibroblasts isolated from patients with a tumor prone, genetic blistering disorder (recessive dystrophic epidermolysis bullosa), we have adapted an existing native matrix protocol to study tumor cell invasion. Fibroblasts are induced to produce their own matrix over a prolonged period in culture. This native matrix is then detached from the culture dish and epithelial cells are seeded onto it before the entire coculture is raised to the air-liquid interface. Cellular differentiation and/or invasion can then be assessed over time. This technique provides the ability to assess epithelial-mesenchymal cell interactions in a 3D setting without the need for a synthetic or foreign matrix with the only disadvantage being the prolonged period of time required to produce the native matrix. Here we describe the application of this technique to assess the ability of a single molecule expressed by fibroblasts, type VII collagen, to inhibit tumor cell invasion.

Tissue engineered fibroblast-derived native matrix is an emerging tool to generate a stromal substrate which supports epithelial cell proliferation and differentiation. Here a protocol applying this methodology to assess the impact of different stromal cell types on tumor cell biology is presented.

3D organotypic cultures of epithelial cells on a matrix embedded with mesenchymal cells are widely used to study epithelial cell differentiation and ...

Engineering 3D Cellularized Collagen Gels for Vascular Tissue Regeneration

Synthetic materials are known to initiate clinical complications such as inflammation, stenosis, and infections when implanted as vascular substitutes. Collagen has been extensively used for a wide range of biomedical applications and is considered a valid alternative to synthetic materials due to its inherent biocompatibility (i.e., low antigenicity, inflammation, and cytotoxic responses). However, the limited mechanical properties and the related low hand-ability of collagen gels have hampered their use as scaffold materials for vascular tissue engineering. Therefore, the rationale behind this work was first to engineer cellularized collagen gels into a tubular-shaped geometry and second to enhance smooth muscle cells driven reorganization of collagen matrix to obtain tissues stiff enough to be handled.
The strategy described here is based on the direct assembling of collagen and smooth muscle cells (construct) in a 3D cylindrical geometry with the use of a molding technique. This process requires a maturation period, during which the constructs are cultured in a bioreactor under static conditions (without applied external dynamic mechanical constraints) for 1 or 2 weeks. The &#8220;static bioreactor&#8221; provides a monitored and controlled sterile environment (pH, temperature, gas exchange, nutrient supply and waste removal) to the constructs. During culture period, thickness measurements were performed to evaluate the cells-driven remodeling of the collagen matrix, and glucose consumption and lactate production rates were measured to monitor the cells metabolic activity. Finally, mechanical and viscoelastic properties were assessed for the resulting tubular constructs. To this end, specific protocols and a focused know-how (manipulation, gripping, working in hydrated environment, and so on) were developed to characterize the engineered tissues.

In this work, we present a technique for the rapid fabrication of living vascular tissues by direct culturing of collagen, smooth muscle cells and endothelial cells. In addition, a new protocol for the mechanical characterization of engineered vascular tissues is described.

Synthetic materials are known to initiate clinical complications such as inflammation, stenosis, and infections when implanted as vascular substitutes. ...

Design and Construction of Artificial Extracellular Matrix (aECM) Proteins from Escherichia coli for Skin Tissue Engineering

Recombinant technology is a versatile platform to create novel artificial proteins with tunable properties. For the last decade, many artificial proteins that have incorporated functional domains derived from nature (or created de novo) have been reported. In particular, artificial extracellular matrix (aECM) proteins have been developed; these aECM proteins consist of biological domains taken from fibronectin, laminins and collagens and are combined with structural domains including elastin-like repeats, silk and collagen repeats. To date, aECM proteins have been widely investigated for applications in tissue engineering and wound repair. Recently, Tjin and coworkers developed integrin-specific aECM proteins designed for promoting human skin keratinocyte attachment and propagation. In their work, the aECM proteins incorporate cell binding domains taken from fibronectin, laminin-5 and collagen IV, as well as flanking elastin-like repeats. They demonstrated that the aECM proteins developed in their work were promising candidates for use as substrates in artificial skin. Here, we outline the design and construction of such aECM proteins as well as their purification process using the thermo-responsive characteristics of elastin.

Design and Construction of Artificial Extracellular Matrix aECM Proteins from Escherichia coli for Skin Tissue Engineering

Recombinant technologies have enabled material designers to create novel artificial proteins with customized functionalities for tissue engineering applications. For example, artificial extracellular matrix proteins can be designed to incorporate structural and biological domains derived from native ECMs. Here, we describe the construction and purification of aECM proteins containing elastin-like repeats.

Recombinant technology is a versatile platform to create novel artificial proteins with tunable properties. For the last decade, many artificial proteins ...

Sandwich-like Microenvironments to Harness Cell/Material Interactions

Cell culture has been traditionally carried out on bi-dimensional (2D) substrates where cells adhere using ventral receptors to the biomaterial surface. However in vivo, most of the cells are completely surrounded by the extracellular matrix (ECM), resulting in a three-dimensional (3D) distribution of receptors. This may trigger differences in the outside-in signaling pathways and thus in cell behavior.
This article shows that stimulating the dorsal receptors of cells already adhered to a 2D substrate by overlaying a film of a new material (a sandwich-like culture) triggers important changes with respect to standard 2D cultures. Furthermore, the simultaneous excitation of ventral and dorsal receptors shifts cell behavior closer to that found in 3D environments. Additionally, due to the nature of the system, a sandwich-like culture is a versatile tool that allows the study of different parameters in cell/material interactions, e.g., topography, stiffness and different protein coatings at both the ventral and dorsal sides. Finally, since sandwich-like cultures are based on 2D substrates, several analysis procedures already developed for standard 2D cultures can be used normally, overcoming more complex procedures needed for 3D systems.

The following protocol describes the procedure to assemble sandwich-like cultures to be used as an intermediate stage between bi-dimensional (2D) and three-dimensional (3D) cellular environments. The engineered systems can have applications in microscopy, biomechanics, biochemistry and cell biology assays.

Cell culture has been traditionally carried out on bi-dimensional (2D) substrates where cells adhere using ventral receptors to the biomaterial surface. ...

Tissue Engineering by Intrinsic Vascularization in an In Vivo Tissue Engineering Chamber

In reconstructive surgery, there is a clinical need for an alternative to the current methods of autologous reconstruction which are complex, costly and trade one defect for another. Tissue engineering holds the promise to address this increasing demand. However, most tissue engineering strategies fail to generate stable and functional tissue substitutes because of poor vascularization. This paper focuses on an in vivo tissue engineering chamber model of intrinsic vascularization where a perfused artery and a vein either as an arteriovenous loop or a flow-through pedicle configuration is directed inside a protected hollow chamber. In this chamber-based system angiogenic sprouting occurs from the arteriovenous vessels and this system attracts ischemic and inflammatory driven endogenous cell migration which gradually fills the chamber space with fibro-vascular tissue. Exogenous cell/matrix implantation at the time of chamber construction enhances cell survival and determines specificity of the engineered tissues which develop. Our studies have shown that this chamber model can successfully generate different tissues such as fat, cardiac muscle, liver and others. However, modifications and refinements are required to ensure target tissue formation is consistent and reproducible. This article describes a standardized protocol for the fabrication of two different vascularized tissue engineering chamber models in vivo.

Tissue Engineering by Intrinsic Vascularization in an In Vivo Tissue Engineering Chamber

This is a guideline for constructing in vivo vascularized tissue using a microsurgical arteriovenous loop or a flow-through pedicle configuration inside a tissue engineering chamber. The vascularized tissues generated can be employed for organ regeneration and replacement of tissue defects, as well as for drug testing and disease modeling.

In reconstructive surgery, there is a clinical need for an alternative to the current methods of autologous reconstruction which are complex, costly and ...