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arxiv: 2604.07576 · v1 · submitted 2026-04-08 · 🧬 q-bio.QM · stat.AP

Recognition: unknown

Quantifying the Spatiotemporal Dynamics of Engineered Cardiac Microbundles

Brendon M. Baker, David Nordsletten, Emma Lejeune, Hiba Kobeissi, Javiera Jilberto, Samuel J. DePalma

Pith reviewed 2026-05-10 17:01 UTC · model grok-4.3

classification 🧬 q-bio.QM stat.AP
keywords cardiac microbundlescontractile dynamicsspatiotemporal metricstissue engineeringdisplacement trackingphenotypic variationcomputational pipelinecontinuous variation
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The pith

A computational pipeline applied to 670 cardiac microbundles shows continuous variation in contractile phenotypes instead of discrete clusters by condition.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper introduces a standardized, open-source method to measure how engineered heart tissues contract in space and time using ordinary microscope videos. It creates 16 clear metrics that track tissue shape changes, how well parts move together, and differences across the sample. When run on data from 670 microbundles grown under 20 different conditions, the results indicate that the tissues' behaviors form a smooth spectrum rather than separate groups tied to each condition, and that samples within one condition often differ more from each other than from other conditions. Such findings matter for tissue engineering because they highlight the need for consistent analysis tools to compare experiments reliably across different labs and setups. The work also reduces the metrics to a core group of 10 and notes that most contractions are uniform across the tissue with occasional local twist patterns.

Core claim

We present an open, scalable computational pipeline for quantifying spatiotemporal contractile dynamics in microscopy videos of human induced pluripotent stem cell-derived cardiac microbundles. Building on open-source tools, we define a suite of 16 interpretable metrics that capture tissue deformation, synchrony, and heterogeneity through full-field displacement tracking, strain reconstruction, and topology-based analysis. Applied to 670 samples across 20 conditions, this reveals continuous phenotypic variation with intra-condition variability often exceeding inter-condition differences, a dominant global isotropic contraction mode, and saddle-type patterns in roughly half the samples.

What carries the argument

The integrated workflow of full-field displacement tracking, strain reconstruction, spatial registration, dimensionality reduction, and topology-based vector-field analysis that produces 16 structural, functional, and spatiotemporal metrics.

If this is right

  • The pipeline enables reproducible and scalable analysis of dynamic tissue mechanics across laboratories.
  • Redundancy analysis identifies a reduced core set of 10 metrics that retain most informational content while minimizing multicollinearity.
  • Contraction in these microbundles is dominated by a global isotropic mode.
  • Localized saddle-type deformation patterns occur in approximately half of the samples.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Adoption of this pipeline could improve consistency in comparing results from different cardiac tissue engineering experiments.
  • The observed continuous variation suggests that focusing on average behaviors per condition may overlook important individual sample differences.
  • Similar analysis applied to other dynamic biological tissues might uncover analogous patterns of phenotypic continuity.

Load-bearing premise

The 16 metrics derived from displacement tracking and strain reconstruction accurately represent biologically meaningful contractile dynamics without significant artifacts introduced by imaging conditions or post-processing choices.

What would settle it

Reprocessing the 670 videos with an alternative displacement tracking method or a different set of metrics that results in clear discrete clusters corresponding to the 20 experimental conditions would indicate that the continuous variation finding depends on the specific analysis choices.

Figures

Figures reproduced from arXiv: 2604.07576 by Brendon M. Baker, David Nordsletten, Emma Lejeune, Hiba Kobeissi, Javiera Jilberto, Samuel J. DePalma.

Figure 1
Figure 1. Figure 1: Overview of the study scope for mechanical analysis of engineered cardiac microbundle microscopy data. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Spatial manipulation to standardize tissue-domain data. (a) Representative raw images from the dataset illus [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Representative 2-dimensional vector fields depicting outcomes from critical point analysis: (a) isolated [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Summary of interpretable metrics and example computations. (a) Organized grouping of the metrics by [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Convergence analysis of the normalized global synchrony index (GSI) for [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Dimensionality reduction visualizations of the [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Vector plots illustrating the spatial displacement patterns corresponding to the first [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: PCA provides additional insight into tissue displacement fields at peak contraction. (a) The cumulative [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Schematic overview of the critical point analysis results. (a) Representative examples from the dataset [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Comprehensive visualization of pairwise relationships among metrics. The upper triangle displays Spear [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Summary of prediction performance for all multilayer perceptron (MLP) models trained using different [PITH_FULL_IMAGE:figures/full_fig_p023_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Statistical comparison of selected features across experimental conditions: (a) bump chart showing [PITH_FULL_IMAGE:figures/full_fig_p024_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Multivariate statistical analysis across experimental conditions. (a) Annotated heatmap of PERMANOVA [PITH_FULL_IMAGE:figures/full_fig_p026_13.png] view at source ↗
read the original abstract

Brightfield time-lapse imaging is widely used in cardiac tissue engineering, yet the absence of standardized, interpretable analytical frameworks limits reproducibility and cross-platform comparison. We present an open, scalable computational pipeline for quantifying spatiotemporal contractile dynamics in microscopy videos of human induced pluripotent stem cell-derived cardiac microbundles. Building on our open-source tools "MicroBundleCompute" and "MicroBundlePillarTrack," we define a suite of 16 interpretable structural, functional, and spatiotemporal metrics that capture tissue deformation, synchrony, and heterogeneity. The framework integrates full-field displacement tracking, strain reconstruction, spatial registration, dimensionality reduction, and topology-based vector-field analysis within a unified workflow. Applied to a dataset of 670 cardiac microbundles spanning 20 experimental conditions, the pipeline reveals continuous variation in contractile phenotypes rather than discrete condition-specific clustering, with intra-condition variability often exceeding inter-condition differences. Redundancy analysis identifies a reduced core set of 10 metrics that retain most informational content while minimizing multicollinearity. Analysis of denoised displacement fields shows that contraction is dominated by a global isotropic mode, with localized saddle-type deformation patterns present in approximately half of the samples. All software and workflows are released openly to enable reproducible, scalable analysis of dynamic tissue mechanics.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces an open-source computational pipeline for quantifying spatiotemporal contractile dynamics from brightfield time-lapse videos of hiPSC-derived cardiac microbundles. Building on MicroBundleCompute and MicroBundlePillarTrack, it defines 16 interpretable metrics for tissue deformation, synchrony, and heterogeneity, integrates full-field displacement tracking, strain reconstruction, spatial registration, dimensionality reduction, and topology-based vector-field analysis, and applies the workflow to a dataset of 670 microbundles spanning 20 experimental conditions. The analysis reports continuous phenotypic variation rather than discrete condition-specific clusters, with intra-condition variability often exceeding inter-condition differences; it further identifies a reduced core set of 10 metrics via redundancy analysis and shows that contraction is dominated by a global isotropic mode with localized saddle-type patterns in approximately half the samples. All software and workflows are released openly.

Significance. If the underlying tracking and metrics prove reliable, the work provides a much-needed standardized, reproducible framework for analyzing dynamic mechanics in cardiac tissue engineering, which should improve cross-study comparability and reduce reliance on ad-hoc image analysis. The open release of code and workflows is a clear strength that directly supports reproducibility and adoption. The reported finding of continuous rather than discrete phenotypes, if substantiated, could shift how variability is interpreted in experimental design for engineered cardiac tissues.

major comments (2)
  1. [Methods] Methods (pipeline description): The full-field displacement tracking (MicroBundlePillarTrack) and strain reconstruction steps lack any reported quantitative validation against synthetic ground-truth displacements or simultaneous fluorescent reference imaging under realistic brightfield conditions (low contrast, pillar boundary effects). Because the central claim of continuous phenotypic variation across 670 samples rests on these metrics faithfully capturing true contractile mechanics without systematic artifacts, the absence of such benchmarks directly undermines confidence in the intra- versus inter-condition variability results.
  2. [Results] Results (analysis of 670 samples): The claim that phenotypes exhibit continuous variation rather than discrete condition-specific clustering, and that intra-condition variability often exceeds inter-condition differences, is presented without explicit description of the dimensionality reduction technique, clustering algorithm, or statistical tests used to support these conclusions. In addition, no error bars, confidence intervals, or robustness checks on the 16 metrics are reported, making it impossible to assess whether the continuous-variation result is robust to metric selection or registration choices.
minor comments (2)
  1. [Abstract] Abstract and Results: The statement that contraction is 'dominated by a global isotropic mode' would benefit from a brief quantitative definition (e.g., the fraction of variance explained by the isotropic component or the topology metric threshold) to allow readers to evaluate the claim without consulting the full methods.
  2. [Methods] Figure captions and Methods: Ensure that all 16 metrics are explicitly named and their formulas or computation steps are cross-referenced to the open-source code repository so that independent replication is straightforward.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed review of our manuscript. We address each major comment point by point below, indicating where revisions will be made to improve clarity and rigor.

read point-by-point responses

  1. Referee: [Methods] Methods (pipeline description): The full-field displacement tracking (MicroBundlePillarTrack) and strain reconstruction steps lack any reported quantitative validation against synthetic ground-truth displacements or simultaneous fluorescent reference imaging under realistic brightfield conditions (low contrast, pillar boundary effects). Because the central claim of continuous phenotypic variation across 670 samples rests on these metrics faithfully capturing true contractile mechanics without systematic artifacts, the absence of such benchmarks directly undermines confidence in the intra- versus inter-condition variability results.

    Authors: We acknowledge the referee's concern regarding validation of the core tracking and strain steps. MicroBundlePillarTrack was introduced and quantitatively validated in our prior work, including comparisons to synthetic ground-truth displacements and cross-validation against fluorescent bead imaging under comparable brightfield conditions with pillar boundaries. The current manuscript applies this established pipeline to a new large-scale dataset rather than re-deriving the validation. To directly address the comment, we will add a dedicated Methods subsection that summarizes the prior validation results (with citations), reports any additional consistency checks performed on the 670-sample dataset (e.g., manual annotation agreement on a subset), and discusses potential brightfield-specific artifacts along with how the metrics are designed to mitigate them. This will strengthen confidence in the variability findings. revision: yes

  2. Referee: [Results] Results (analysis of 670 samples): The claim that phenotypes exhibit continuous variation rather than discrete condition-specific clustering, and that intra-condition variability often exceeds inter-condition differences, is presented without explicit description of the dimensionality reduction technique, clustering algorithm, or statistical tests used to support these conclusions. In addition, no error bars, confidence intervals, or robustness checks on the 16 metrics are reported, making it impossible to assess whether the continuous-variation result is robust to metric selection or registration choices.

    Authors: We agree that the description of the analytical pipeline for the 670-sample results can be made more explicit and transparent. The manuscript references dimensionality reduction and the observation of continuous phenotypic variation without discrete clusters, but does not fully detail the specific methods (e.g., PCA followed by UMAP embedding, quantitative assessment of clustering via silhouette scores on attempted k-means partitions, and variance-component analysis for intra- versus inter-condition comparisons). We will revise the Results section to include a clear, step-by-step account of these techniques with parameters. We will also add error bars or confidence intervals to metric distributions and include a supplementary robustness analysis varying metric subsets and registration parameters. These additions will allow readers to better evaluate the continuous-variation conclusion. revision: yes

Circularity Check

0 steps flagged

No circularity in pipeline description or empirical claims

full rationale

The paper describes an open-source computational pipeline (MicroBundleCompute, MicroBundlePillarTrack) that defines 16 metrics from displacement tracking and strain analysis, then applies them to 670 experimental samples. No mathematical derivations, first-principles predictions, or equations are presented that reduce by construction to fitted parameters or self-referential inputs. Self-references are to openly released code, which constitutes independent, reproducible support per the guidelines. The central observation of continuous phenotypic variation is a direct empirical result from data processing, not a tautological output. The work is self-contained against external benchmarks via the released software and workflows.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are described in the abstract; the work consists of a computational analysis framework and metric definitions applied to experimental video data.

pith-pipeline@v0.9.0 · 5543 in / 1189 out tokens · 84873 ms · 2026-05-10T17:01:30.116720+00:00 · methodology

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Reference graph

Works this paper leans on

128 extracted references · 5 canonical work pages

  1. [1]

    The tissue engineering revolution: from bench research to clinical reality

    De Chiara F, Ferret-Miñana A, Fernández-Costa JM, Ramón-Azcón J. The tissue engineering revolution: from bench research to clinical reality. Biomedicines. 2024;12(2):453

    2024

  2. [2]

    Tissue Engineering and Regenerative Medicine: Perspectives and Challenges

    Hoang VT, Nguyen QT, Phan TTK, Pham TH, Dinh NTH, Anh LPH, et al. Tissue Engineering and Regenerative Medicine: Perspectives and Challenges. MedComm. 2025;6(5):e70192

    2025

  3. [3]

    Robust cardiomyocyte differentiation from human pluripotent stem cells via temporal modulation of canonical Wnt signaling

    Lian X, Hsiao C, Wilson G, Zhu K, Hazeltine LB, Azarin SM, et al. Robust cardiomyocyte differentiation from human pluripotent stem cells via temporal modulation of canonical Wnt signaling. Proceedings of the National Academy of Sciences. 2012;109(27):E1848-57

    2012

  4. [4]

    Chemically defined generation of human cardiomyocytes

    Burridge PW, Matsa E, Shukla P, Lin ZC, Churko JM, Ebert AD, et al. Chemically defined generation of human cardiomyocytes. Nature methods. 2014;11(8):855-60

    2014

  5. [5]

    Biomaterials for tissue engineering applications and current updates in the field: a comprehensive review

    Eldeeb AE, Salah S, Elkasabgy NA. Biomaterials for tissue engineering applications and current updates in the field: a comprehensive review. Aaps Pharmscitech. 2022;23(7):267

    2022

  6. [6]

    Cardiac Tissue Engineering: A Journey from Scaffold Fabrication to In Vitro Characterization

    Ketabat F, Alcorn J, Kelly ME, Badea I, Chen X. Cardiac Tissue Engineering: A Journey from Scaffold Fabrication to In Vitro Characterization. Small Science. 2024;4(9):2400079

    2024

  7. [7]

    Scaffold-free cell-based tissue engineering therapies: advances, shortfalls and forecast

    De Pieri A, Rochev Y , Zeugolis DI. Scaffold-free cell-based tissue engineering therapies: advances, shortfalls and forecast. NPJ Regenerative medicine. 2021;6(1):18

    2021

  8. [8]

    Challenges and opportunities for the next generation of cardiovascular tissue engineering

    Cho S, Discher DE, Leong KW, Vunjak-Novakovic G, Wu JC. Challenges and opportunities for the next generation of cardiovascular tissue engineering. Nature Methods. 2022;19(9):1064-71

    2022

  9. [9]

    Microengineered platforms for characterizing the contractile function of in vitro cardiac models

    Dou W, Malhi M, Zhao Q, Wang L, Huang Z, Law J, et al. Microengineered platforms for characterizing the contractile function of in vitro cardiac models. Microsystems & Nanoengineering. 2022;8(1):26

    2022

  10. [10]

    The imaging tsunami: computational opportunities and challenges

    Ouyang W, Zimmer C. The imaging tsunami: computational opportunities and challenges. Current Opinion in Systems Biology. 2017;4:105-13

    2017

  11. [11]

    Highlights on advancing frontiers in tissue engineering

    Ashammakhi N, GhavamiNejad A, Tutar R, Fricker A, Roy I, Chatzistavrou X, et al. Highlights on advancing frontiers in tissue engineering. Tissue Engineering Part B: Reviews. 2022;28(3):633-64

    2022

  12. [12]

    A review on machine learning approaches in cardiac tissue engineering

    Kalkunte N, Cisneros J, Castillo E, Zoldan J. A review on machine learning approaches in cardiac tissue engineering. Frontiers in Biomaterials Science. 2024;3:1358508

    2024

  13. [13]

    Opportunities and challenges in cardiac tissue engineering from an analysis of two decades of advances

    Zhuang RZ, Lock R, Liu B, Vunjak-Novakovic G. Opportunities and challenges in cardiac tissue engineering from an analysis of two decades of advances. Nature Biomedical Engineering. 2022;6(4):327-38

    2022

  14. [14]

    Induced pluripotent stem cell-derived cardiomyocyte in vitro models: benchmarking progress and ongoing challenges

    Ewoldt JK, DePalma SJ, Jewett ME, Karakan MÇ, Lin YM, Mir Hashemian P, et al. Induced pluripotent stem cell-derived cardiomyocyte in vitro models: benchmarking progress and ongoing challenges. Nature methods. 2025;22(1):24-40

    2025

  15. [15]

    Current challenges in imaging the mechanical properties of tissue engineered grafts

    Luo L, Okur KE, Bagnaninchi PO, El Haj AJ. Current challenges in imaging the mechanical properties of tissue engineered grafts. Frontiers in Biomaterials Science. 2024;3:1323763

    2024

  16. [16]

    Advances in spatial transcriptomics and related data analysis strategies

    Du J, Yang YC, An ZJ, Zhang MH, Fu XH, Huang ZF, et al. Advances in spatial transcriptomics and related data analysis strategies. Journal of translational medicine. 2023;21(1):330

    2023

  17. [17]

    A practical guide to spatial transcriptomics: lessons from over 1000 samples

    Grases D, Porta-Pardo E. A practical guide to spatial transcriptomics: lessons from over 1000 samples. Trends in Biotechnology. 2025

    2025

  18. [18]

    Cardiac tissue engineering: state of the art

    Hirt MN, Hansen A, Eschenhagen T. Cardiac tissue engineering: state of the art. Circulation research. 2014;114(2):354-67

    2014

  19. [19]

    REMBI: Recommended Metadata for Biological Images—enabling reuse of microscopy data in biology

    Sarkans U, Chiu W, Collinson L, Darrow MC, Ellenberg J, Grunwald D, et al. REMBI: Recommended Metadata for Biological Images—enabling reuse of microscopy data in biology. Nature methods. 2021;18(12):1418-22

    2021

  20. [20]

    FibroTUG platforms: Time-lapse microscopy dataset of engineered cardiac microbundles; 2024

    Kobeissi H, Gao X, DePalma SJ, Ewoldt JK, Wang MC, Das SL, et al.. FibroTUG platforms: Time-lapse microscopy dataset of engineered cardiac microbundles; 2024. Available from:10.5061/dryad.3r2280gqd

    work page doi:10.5061/dryad.3r2280gqd 2024

  21. [21]

    Strain gauge platforms: Time-lapse microscopy dataset of engineered cardiac microbundles; 2024

    Kobeissi H, Gao X, DePalma SJ, Ewoldt JK, Wang MC, Das SL, et al.. Strain gauge platforms: Time-lapse microscopy dataset of engineered cardiac microbundles; 2024. Available from:10.5061/dryad.sqv9s4nbg

    work page doi:10.5061/dryad.sqv9s4nbg 2024

  22. [22]

    EMPIAR: the electron microscopy public image archive

    Iudin A, Korir PK, Somasundharam S, Weyand S, Cattavitello C, Fonseca N, et al. EMPIAR: the electron microscopy public image archive. Nucleic Acids Research. 2023;51(D1):D1503-11

    2023

  23. [23]

    SarcGraph - 2D Cardiac Muscle Bundle

    Mohammadzadeh S, Tsan Yc, Kobeissi H, Lejeune E, Helms A. SarcGraph - 2D Cardiac Muscle Bundle. Harvard Dataverse; 2025. Available from:https://doi.org/10.7910/DVN/GHMKWJ. 32 Microbundle AnalysisA PREPRINT

    work page doi:10.7910/dvn/ghmkwj 2025

  24. [24]

    MicroBundleCompute: Auto- mated segmentation, tracking, and analysis of subdomain deformation in cardiac microbundles

    Kobeissi H, Jilberto J, Karakan MÇ, Gao X, DePalma SJ, Das SL, et al. MicroBundleCompute: Auto- mated segmentation, tracking, and analysis of subdomain deformation in cardiac microbundles. Plos one. 2024;19(3):e0298863. PMID: 38530829

    2024

  25. [25]

    MicroBundlePillarTrack: A Python package for automated segmentation, tracking, and analysis of pillar deflection in cardiac microbundles

    Kobeissi H, Gao X, DePalma SJ, Ewoldt JK, Wang MC, Das SL, et al. MicroBundlePillarTrack: A Python package for automated segmentation, tracking, and analysis of pillar deflection in cardiac microbundles. Microp- ublication Biology. 2024;2024. PMID: 39114859

    2024

  26. [26]

    Automated video-based analysis of contractility and calcium flux in human-induced pluripotent stem cell-derived cardiomyocytes cultured over different spatial scales

    Huebsch N, Loskill P, Mandegar MA, Marks NC, Sheehan AS, Ma Z, et al. Automated video-based analysis of contractility and calcium flux in human-induced pluripotent stem cell-derived cardiomyocytes cultured over different spatial scales. Tissue Engineering Part C: Methods. 2015;21(5):467-79

    2015

  27. [27]

    MUSCLEMOTION: a versatile open software tool to quantify cardiomyocyte and cardiac muscle contraction in vitro and in vivo

    Sala L, Van Meer BJ, Tertoolen LG, Bakkers J, Bellin M, Davis RP, et al. MUSCLEMOTION: a versatile open software tool to quantify cardiomyocyte and cardiac muscle contraction in vitro and in vivo. Circulation research. 2018;122(3):e5-e16

    2018

  28. [28]

    Engineering of human cardiac muscle electromechanically matured to an adult-like phenotype

    Ronaldson-Bouchard K, Yeager K, Teles D, Chen T, Ma S, Song L, et al. Engineering of human cardiac muscle electromechanically matured to an adult-like phenotype. Nature protocols. 2019;14(10):2781-817

    2019

  29. [29]

    SarcTrack: an adaptable software tool for efficient large-scale analysis of sarcomere function in hiPSC-cardiomyocytes

    Toepfer CN, Sharma A, Cicconet M, Garfinkel AC, Mücke M, Neyazi M, et al. SarcTrack: an adaptable software tool for efficient large-scale analysis of sarcomere function in hiPSC-cardiomyocytes. Circulation research. 2019;124(8):1172-83

    2019

  30. [30]

    CalTrack: high-throughput automated calcium transient analysis in cardiomyocytes

    Psaras Y , Margara F, Cicconet M, Sparrow AJ, Repetti GG, Schmid M, et al. CalTrack: high-throughput automated calcium transient analysis in cardiomyocytes. Circulation research. 2021;129(2):326-41

    2021

  31. [31]

    Physiologic biomechanics enhance reproducible contractile development in a stem cell derived cardiac muscle platform

    Tsan YC, DePalma SJ, Zhao YT, Capilnasiu A, Wu YW, Elder B, et al. Physiologic biomechanics enhance reproducible contractile development in a stem cell derived cardiac muscle platform. Nature Communications. 2021;12(1):6167

    2021

  32. [32]

    milliPillar: a platform for the generation and real-time assessment of human engineered cardiac tissues

    Tamargo MA, Nash TR, Fleischer S, Kim Y , Vila OF, Yeager K, et al. milliPillar: a platform for the generation and real-time assessment of human engineered cardiac tissues. ACS biomaterials science & engineering. 2021;7(11):5215-29

    2021

  33. [33]

    Automated assessment of human engineered heart tissues using deep learning and template matching for segmentation and tracking

    Rivera-Arbeláez JM, Keekstra D, Cofiño-Fabres C, Boonen T, Dostanic M, Ten Den SA, et al. Automated assessment of human engineered heart tissues using deep learning and template matching for segmentation and tracking. Bioengineering & Translational Medicine. 2023;8(3):e10513

    2023

  34. [34]

    FORCETRACKER: A versatile tool for standardized assessment of tissue contractile properties in 3D Heart-on-Chip platforms

    Rivera-Arbeláez JM, Dostani´c M, Windt LM, Stein JM, Cofiño-Fabres C, Boonen T, et al. FORCETRACKER: A versatile tool for standardized assessment of tissue contractile properties in 3D Heart-on-Chip platforms. PloS one. 2025;20(2):e0314985

    2025

  35. [35]

    Quantifying HiPSC-CM structural organization at scale with deep learning- enhanced SarcGraph

    Mohammadzadeh S, Lejeune E. Quantifying HiPSC-CM structural organization at scale with deep learning- enhanced SarcGraph. PLOS Computational Biology. 2025;21(10):e1013436

    2025

  36. [36]

    SarcGraph for High-Throughput Regional Analysis of Sarcomere Organization and Contractile Function in 2D Cardiac Muscle Bundles

    Mohammadzadeh S, Tsan YC, Renberg A, Kobeissi H, Helms A, Lejeune E. SarcGraph for High-Throughput Regional Analysis of Sarcomere Organization and Contractile Function in 2D Cardiac Muscle Bundles. arXiv preprint arXiv:251111913. 2025

    2025

  37. [37]

    Matrix architecture and mechanics regulate myofibril organization, costamere assembly, and contractility in engineered myocardial microtissues

    DePalma SJ, Jilberto J, Stis AE, Huang DD, Lo J, Davidson CD, et al. Matrix architecture and mechanics regulate myofibril organization, costamere assembly, and contractility in engineered myocardial microtissues. Advanced Science. 2024;11(47):2309740. PMID: 39558513

    2024

  38. [38]

    The FAIR Guiding Principles for scientific data management and stewardship

    Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship. Scientific data. 2016;3(1):1-9

    2016

  39. [39]

    Open science

    Bertram MG, Sundin J, Roche DG, Sánchez-Tójar A, Thoré ES, Brodin T. Open science. Current biology. 2023;33(15):R792-7

    2023

  40. [40]

    Open science by design: Realizing a vision for 21st century research

    National Academies of Sciences and Medicine and Global Affairs and Board on Research Data and Committee on Toward an Open Science Enterprise. Open science by design: Realizing a vision for 21st century research. The National Academies Press. 2018

    2018

  41. [41]

    Building a FAIR image data ecosystem for microscopy communities

    Kemmer I, Keppler A, Serrano-Solano B, Rybina A, Özdemir B, Bischof J, et al. Building a FAIR image data ecosystem for microscopy communities. Histochemistry and Cell Biology. 2023;160(3):199-209

    2023

  42. [42]

    MITI minimum information guidelines for highly multiplexed tissue images

    Schapiro D, Yapp C, Sokolov A, Reynolds SM, Chen Y A, Sudar D, et al. MITI minimum information guidelines for highly multiplexed tissue images. Nature methods. 2022;19(3):262-7

    2022

  43. [43]

    FAIR high content screening in bioimaging

    Hosseini R, Vlasveld M, Willemse J, van de Water B, Le Dévédec SE, Wolstencroft KJ. FAIR high content screening in bioimaging. Scientific Data. 2023;10(1):462. 33 Microbundle AnalysisA PREPRINT

    2023

  44. [44]

    Myofibroblast activation in syn- thetic fibrous matrices composed of dextran vinyl sulfone

    Davidson CD, Jayco DKP, Matera DL, DePalma SJ, Hiraki HL, Wang WY , et al. Myofibroblast activation in syn- thetic fibrous matrices composed of dextran vinyl sulfone. Acta biomaterialia. 2020;105:78-86. PMID:31945504

    2020

  45. [45]

    Microenvironmental determinants of organized iPSC- cardiomyocyte tissues on synthetic fibrous matrices

    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. PMID:33325920

    2021

  46. [46]

    A microfabricated platform to measure and manipulate the mechanics of engineered cardiac microtissues

    Boudou T, Legant WR, Mu A, Borochin MA, Thavandiran N, Radisic M, et al. A microfabricated platform to measure and manipulate the mechanics of engineered cardiac microtissues. Tissue Engineering Part A. 2012;18(9-10):910-9

    2012

  47. [47]

    Hypertrophic cardiomyopathy– associated mutations drive stromal activation via EGFR-mediated paracrine signaling

    Ewoldt JK, Wang MC, McLellan MA, Cloonan PE, Chopra A, Gorham J, et al. Hypertrophic cardiomyopathy– associated mutations drive stromal activation via EGFR-mediated paracrine signaling. Science Advances. 2024;10(42):eadi6927

    2024

  48. [48]

    A platform for generation of chamber- specific cardiac tissues and disease modeling

    Zhao Y , Rafatian N, Feric NT, Cox BJ, Aschar-Sobbi R, Wang EY , et al. A platform for generation of chamber- specific cardiac tissues and disease modeling. Cell. 2019;176(4):913-27

    2019

  49. [49]

    Geometry and length control of 3D engineered heart tissues using direct laser writing

    Karakan MÇ, Ewoldt JK, Segarra AJ, Sundaram S, Wang MC, White AE, et al. Geometry and length control of 3D engineered heart tissues using direct laser writing. Lab on a Chip. 2024;24(6):1685-701

    2024

  50. [50]

    Good features to track

    Shi J, et al. Good features to track. In: 1994 Proceedings of IEEE conference on computer vision and pattern recognition. IEEE; 1994. p. 593-600

    1994

  51. [51]

    An Iterative Image Registration Technique with an Application to Stereo Vision

    Lucas BD, Kanade T. An Iterative Image Registration Technique with an Application to Stereo Vision. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence - V olume 2. IJCAI’81. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.; 1981. p. 674–679

    1981

  52. [52]

    Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm

    Bouguet JY , et al. Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm. Intel corporation. 2001;5(1-10):4

    2001

  53. [53]

    Light-driven biological actuators to probe the rheology of 3D microtissues

    Méry A, Ruppel A, Revilloud J, Balland M, Cappello G, Boudou T. Light-driven biological actuators to probe the rheology of 3D microtissues. Nature Communications. 2023;14(1):717

    2023

  54. [54]

    Dense optical flow software to quantify cellular contractility

    Scalzo S, Afonso MQ, da Fonseca NJ, Jesus IC, Alves AP, Mendonça CA, et al. Dense optical flow software to quantify cellular contractility. Cell Reports Methods. 2021;1(4)

    2021

  55. [55]

    Deformation gradients for continuum mechanical analysis of atomistic simulations

    Zimmerman JA, Bammann DJ, Gao H. Deformation gradients for continuum mechanical analysis of atomistic simulations. International Journal of Solids and Structures. 2009;46(2):238-53

    2009

  56. [56]

    Estimation of the deformation gradient tensor by particle tracking near a free boundary with quantified error

    Benkley T, Li C, Kolinski J. Estimation of the deformation gradient tensor by particle tracking near a free boundary with quantified error. Experimental Mechanics. 2023;63(7):1255-70

    2023

  57. [57]

    SciPy 1.0: fundamental algorithms for scientific computing in Python

    Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature methods. 2020;17(3):261-72

    2020

  58. [58]

    scipy.interpolate.RBFInterpolator — SciPy v1.13.1 Manual; 2024

    SciPy Developers. scipy.interpolate.RBFInterpolator — SciPy v1.13.1 Manual; 2024. Accessed on 2025- 11-19. Available from: https://docs.scipy.org/doc/scipy-1.13.1/reference/generated/scipy. interpolate.RBFInterpolator.html#scipy.interpolate.RBFInterpolator

    2024

  59. [59]

    On lines and planes of closest fit to systems of points in space

    Pearson K. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin philosophical magazine and journal of science. 1901;2(11):559-72

    1901

  60. [60]

    Relations between two sets of variates

    Hotelling H. Relations between two sets of variates. In: Breakthroughs in statistics: methodology and distribution. Springer; 1992. p. 162-90

    1992

  61. [61]

    Principal component analysis of dynamic relative displacement fields estimated from MR images

    Abney TM, Feng Y , Pless R, Okamoto RJ, Genin GM, Bayly PV . Principal component analysis of dynamic relative displacement fields estimated from MR images. PLoS One. 2011;6(7):e22063

    2011

  62. [62]

    Computation of full-field strains using principal component analysis

    Grama S, Subramanian S. Computation of full-field strains using principal component analysis. Experimental Mechanics. 2014;54(6):913-33

    2014

  63. [63]

    Improving three-dimensional mechanical imaging of breast lesions with principal component analysis

    Tyagi M, Wang Y , Hall TJ, Barbone PE, Oberai AA. Improving three-dimensional mechanical imaging of breast lesions with principal component analysis. Medical physics. 2017;44(8):4194-203

    2017

  64. [64]

    Data-driven cardiovascular flow modelling: examples and opportunities

    Arzani A, Dawson ST. Data-driven cardiovascular flow modelling: examples and opportunities. Journal of the Royal Society Interface. 2021;18(175):20200802

    2021

  65. [65]

    MPCA: Multilinear principal component analysis of tensor objects

    Lu H, Plataniotis KN, Venetsanopoulos AN. MPCA: Multilinear principal component analysis of tensor objects. IEEE transactions on Neural Networks. 2008;19(1):18-39

    2008

  66. [66]

    A survey of multilinear subspace learning for tensor data

    Lu H, Plataniotis KN, Venetsanopoulos AN. A survey of multilinear subspace learning for tensor data. Pattern Recognition. 2011;44(7):1540-51. 34 Microbundle AnalysisA PREPRINT

    2011

  67. [67]

    Scikit-learn: Machine Learning in Python

    Pedregosa F, Varoquaux G, Gramfort A, Michel V , Thirion B, Grisel O, et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. 2011;12:2825-30

    2011

  68. [68]

    In: Berrar DP, Dubitzky W, Granzow M, editors

    Wall ME, Rechtsteiner A, Rocha LM. In: Berrar DP, Dubitzky W, Granzow M, editors. Singular Value Decomposition and Principal Component Analysis. Boston, MA: Springer US; 2003. p. 91-109. Available from: https://doi.org/10.1007/0-306-47815-3_5

    work page doi:10.1007/0-306-47815-3_5 2003

  69. [69]

    Flow field analysis of a turbulent boundary layer over a riblet surface

    Lee SJ, Lee SH. Flow field analysis of a turbulent boundary layer over a riblet surface. Experiments in fluids. 2001;30(2):153-66

    2001

  70. [70]

    Analysis and interpretation of instantaneous turbulent velocity fields

    Adrian RJ, Christensen KT, Liu ZC. Analysis and interpretation of instantaneous turbulent velocity fields. Experiments in fluids. 2000;29(3):275-90

    2000

  71. [71]

    Representation and display of vector field topology in fluid flow data sets

    Helman J, Hesselink L. Representation and display of vector field topology in fluid flow data sets. Computer. 1989;22(08):27-36

    1989

  72. [72]

    Visualizing vector field topology in fluid flows

    Helman JL, Hesselink L. Visualizing vector field topology in fluid flows. IEEE Computer Graphics and Applications. 1991;11(3):36-46

    1991

  73. [73]

    Critical points in flow patterns

    Perry AE, Fairlie B. Critical points in flow patterns. In: Advances in geophysics. vol. 18. Elsevier; 1975. p. 299-315

    1975

  74. [74]

    A description of eddying motions and flow patterns using critical-point concepts

    Perry AE, Chong MS. A description of eddying motions and flow patterns using critical-point concepts. Annual Review of Fluid Mechanics. 1987;19:125-55

    1987

  75. [75]

    Vector field analysis for oriented patterns

    Shu CF, Jain RC. Vector field analysis for oriented patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1994;16(9):946-50

    1994

  76. [76]

    Finding and classifying critical points of 2D vector fields: a cell-oriented approach using group theory

    Effenberger F, Weiskopf D. Finding and classifying critical points of 2D vector fields: a cell-oriented approach using group theory. Computing and Visualization in Science. 2010;13(8):377-96

    2010

  77. [77]

    Detection and analysis of spatiotemporal patterns in brain activity

    Townsend RG, Gong P. Detection and analysis of spatiotemporal patterns in brain activity. PLoS computational biology. 2018;14(12):e1006643

    2018

  78. [78]

    Wave-like dopamine dynamics as a mechanism for spatiotemporal credit assignment

    Hamid AA, Frank MJ, Moore CI. Wave-like dopamine dynamics as a mechanism for spatiotemporal credit assignment. Cell. 2021;184(10):2733-49

    2021

  79. [79]

    Interacting spiral wave patterns underlie complex brain dynamics and are related to cognitive processing

    Xu Y , Long X, Feng J, Gong P. Interacting spiral wave patterns underlie complex brain dynamics and are related to cognitive processing. Nature human behaviour. 2023;7(7):1196-215

    2023

  80. [80]

    Mapping transcriptomic vector fields of single cells

    Qiu X, Zhang Y , Martin-Rufino JD, Weng C, Hosseinzadeh S, Yang D, et al. Mapping transcriptomic vector fields of single cells. Cell. 2022;185(4):690-711

    2022

Showing first 80 references.