Brief summary of dataset contents, contextualized in experimental procedures and results.

Abstract

The "Microbundle Time-lapse Dataset" contains $24$ experimental time-lapse images of cardiac microbundles using three distinct types of experimental testbeds. Of the $24$ experimental time-lapse images, $23$ examples are brightfield videos, and a single example is a phase contrast video. We categorize the different experimental testbeds into $3$ types, where "Type 1" includes movies obtained from standard experimental microbundle platforms termed microbundle strain gauges [1,2,3]. We refer to data collected from non-standard platforms termed FibroTUGs [4] as "Type 2" data, and "Type 3" data represents a highly versatile and diverse nanofabricated experimental platform [5,6].

Methods:

We include here a brief description of the 3 data types. For "Type 1" testbeds, each experimental tissue well consisted of $2$ pillars with rectangular cross sections and spherical caps cast from poly(dimethylsiloxane) (PDMS) using a 3D printed mold. Following treatment to promote cell attachment to the spherical caps, hiPSC-CMs, differentiated and purified, were seeded with human ventricular cardiac fibroblasts. And after $5-7$ days of seeding, time-lapse videos of tissue contractions were acquired.

As for "Type 2" experimental platforms, arrays of PDMS cantilevers were fabricated by soft lithography. Then, fiber matrices were generated by selective photo-crosslinking of electrospun dextran vinyl sulfone (DVS) fibers and suspended between pairs of cantilevers. After functionalizing the electrospun fiber matrices, iPSC-CMs, differentiated and purified, were patterned onto matrices using microfabricated seeding masks cast from 3D-printed molds. Finally, time-lapse videos of the microtissues spontaneous contractions were taken after $7$ days of seeding.

Finally, seeding platforms from "Type 3" were generated using a combination of soft lithography and two-photon direct laser writing (DLW). Briefly, the process involves printing negative master molds using DLW, casting PDMS onto the molds, followed by sandwiching, curing and demolding. Then, cage-like micron-scale structures were printed using DLW on the sides of the wells of the demolded PDMS devices to facilitate cell attachment. Differentiated and purified hiPSC-CMs were seeded into the wells and time-lapse videos of the tissue contractions were obtained $4-9$ days after seeding. Of note, these platforms allow for mechanical actuation of the microbundle, and within this dataset, we include "Example_01" which is subjected to sawtooth actuation.

We note that more details about testbed fabrication, tissue preparation protocols, and imaging procedures for each type of the 3 experimental platforms are included in the accompanying file "Microbundle_metadata.pdf" and its machine-readable version in ".csv" format. We note that cells containing "N/A" indicate that the details provided for the other tissue engineering platform types are not applicable to this specific type.

Usage notes:

Within this dataset, we include 11 examples of "Type 1" tissue, 7 examples of "Type 2" tissue, and 6 examples of "Type 3" tissue, totaling to 24 different examples of these experimental data. In addition to the raw videos shared in ".tif" format, we include the tissue masks, whether generated automatically via our computational pipeline [7] or manually via tracing in ImageJ [8], that were used to run the "MicroBundleCompute" software [7] for analyzing these data. These masks are included within the "masks" subfolders, where each mask text fileis a two-dimensional array in which the tissue domain is denoted by 1 and the background domain is denoted by 0. We include the "mask.tif" files for visualization purposes only.

In brief, this dataset was used to showcase the functionality of the "MicroBundleCompute" analysis software [7] including pillar tracking and analysis of heterogeneous displacement and strain fields. To reproduce the results shown in [9], the manuscript introducing the "MicroBundleCompute" software, only a single pre-processing step is required. Specifically, the single ".tif" file for each experiment needs to be converted into a series of individual images saved in the ".TIF" format in the "movie" folder. The code [7] to reproduce all results on this data is available in the GitHub repository associated with this manuscript 9.

References:

[1] Boudou T, Legant WR, Mu A, Borochin MA, Thavandiran N, Radisic M, Zandstra PW, Epstein JA, Margulies KB, Chen CS. A microfabricated platform to measure and manipulate the mechanics of engineered cardiac microtissues. Tissue Engineering Part A. 2012 May 1;18(9-10):910-9.
[2] Xu F, Zhao R, Liu AS, Metz T, Shi Y, Bose P, Reich DH. A microfabricated magnetic actuation device for mechanical conditioning of arrays of 3D microtissues. Lab on a Chip. 2015;15(11):2496-503.
[3] Bielawski KS, Leonard A, Bhandari S, Murry CE, Sniadecki NJ. Real-time force and frequency analysis of engineered human heart tissue derived from induced pluripotent stem cells using magnetic sensing. Tissue Engineering Part C: Methods. 2016 Oct 1;22(10):932-40.
[4] DePalma SJ, Davidson CD, Stis AE, Helms AS, Baker BM. Microenvironmental determinants of organized iPSC-cardiomyocyte tissues on synthetic fibrous matrices. Biomaterials science. 2021;9(1):93-107.
[5] Jayne RK, Karakan M, Zhang K, Pierce N, Michas C, Bishop DJ, Chen CS, Ekinci KL, White AE. Direct laser writing for cardiac tissue engineering: a microfluidic heart on a chip with integrated transducers. Lab on a Chip. 2021;21(9):1724-37.
[6] Karakan M. A Direct-Laser-Written Heart-on-a-Chip Platform for Generation and Stimulation of Engineered Heart Tissues (Doctoral dissertation, Boston University, 2023).
[7] Kobeissi H, & Lejeune E (2023). MicroBundleCompute [Computer software]. https://github.com/HibaKob/MicroBundleCompute
[8] Bourne R. Fundamentals of digital imaging in medicine. Springer Science & Business Media; 2010 Jan 18.
[9] Kobeissi H, Jilberto, J, Karakan M, Gao X, DePalma SJ, Das SL, Quach L, Urquia J, Baker BM, Chen CS, Nordsletten D, Lejeune E. MicroBundleCompute: Automated segmentation, tracking, and analysis of sub-domain deformation in cardiac microbundles, in preparation (2023).

Related work

MicroBundleCompute: https://github.com/HibaKob/MicroBundleCompute