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Kim Y, Wang K, Lock RI, Nash TR, Fleischer S, Wang BZ, Fine BM, Vunjak-Novakovic G. BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs. IEEE Open J Eng Med Biol 2024; 5:238-249. [PMID: 38606403 PMCID: PMC11008807 DOI: 10.1109/ojemb.2024.3377461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 02/13/2024] [Accepted: 03/10/2024] [Indexed: 04/13/2024] Open
Abstract
Goal: Contractile response and calcium handling are central to understanding cardiac function and physiology, yet existing methods of analysis to quantify these metrics are often time-consuming, prone to mistakes, or require specialized equipment/license. We developed BeatProfiler, a suite of cardiac analysis tools designed to quantify contractile function, calcium handling, and force generation for multiple in vitro cardiac models and apply downstream machine learning methods for deep phenotyping and classification. Methods: We first validate BeatProfiler's accuracy, robustness, and speed by benchmarking against existing tools with a fixed dataset. We further confirm its ability to robustly characterize disease and dose-dependent drug response. We then demonstrate that the data acquired by our automatic acquisition pipeline can be further harnessed for machine learning (ML) analysis to phenotype a disease model of restrictive cardiomyopathy and profile cardioactive drug functional response. To accurately classify between these biological signals, we apply feature-based ML and deep learning models (temporal convolutional-bidirectional long short-term memory model or TCN-BiLSTM). Results: Benchmarking against existing tools revealed that BeatProfiler detected and analyzed contraction and calcium signals better than existing tools through improved sensitivity in low signal data, reduction in false positives, and analysis speed increase by 7 to 50-fold. Of signals accurately detected by published methods (PMs), BeatProfiler's extracted features showed high correlations to PMs, confirming that it is reliable and consistent with PMs. The features extracted by BeatProfiler classified restrictive cardiomyopathy cardiomyocytes from isogenic healthy controls with 98% accuracy and identified relax90 as a top distinguishing feature in congruence with previous findings. We also show that our TCN-BiLSTM model was able to classify drug-free control and 4 cardiac drugs with different mechanisms of action at 96% accuracy. We further apply Grad-CAM on our convolution-based models to identify signature regions of perturbations by these drugs in calcium signals. Conclusions: We anticipate that the capabilities of BeatProfiler will help advance in vitro studies in cardiac biology through rapid phenotyping, revealing mechanisms underlying cardiac health and disease, and enabling objective classification of cardiac disease and responses to drugs.
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Affiliation(s)
- Youngbin Kim
- Department of Biomedical EngineeringColumbia UniversityNew YorkNY10032USA
| | - Kunlun Wang
- Department of Biomedical EngineeringColumbia UniversityNew YorkNY10032USA
| | - Roberta I. Lock
- Department of Biomedical EngineeringColumbia UniversityNew YorkNY10032USA
| | - Trevor R. Nash
- Department of Biomedical EngineeringColumbia UniversityNew YorkNY10032USA
| | - Sharon Fleischer
- Department of Biomedical EngineeringColumbia UniversityNew YorkNY10032USA
| | - Bryan Z. Wang
- Department of Biomedical EngineeringColumbia UniversityNew YorkNY10032USA
| | - Barry M. Fine
- Department of MedicineDivision of CardiologyColumbia University Medical CenterNew YorkNY10032USA
| | - Gordana Vunjak-Novakovic
- Department of Biomedical EngineeringColumbia UniversityNew YorkNY10032USA
- Department of MedicineDivision of CardiologyColumbia University Medical CenterNew YorkNY10032USA
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Wang BZ, Nash TR, Zhang X, Rao J, Abriola L, Kim Y, Zakharov S, Kim M, Luo LJ, Morsink M, Liu B, Lock RI, Fleischer S, Tamargo MA, Bohnen M, Welch CL, Chung WK, Marx SO, Surovtseva YV, Vunjak-Novakovic G, Fine BM. Engineered cardiac tissue model of restrictive cardiomyopathy for drug discovery. Cell Rep Med 2023; 4:100976. [PMID: 36921598 PMCID: PMC10040415 DOI: 10.1016/j.xcrm.2023.100976] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 12/19/2022] [Accepted: 02/21/2023] [Indexed: 03/16/2023]
Abstract
Restrictive cardiomyopathy (RCM) is defined as increased myocardial stiffness and impaired diastolic relaxation leading to elevated ventricular filling pressures. Human variants in filamin C (FLNC) are linked to a variety of cardiomyopathies, and in this study, we investigate an in-frame deletion (c.7416_7418delGAA, p.Glu2472_Asn2473delinAsp) in a patient with RCM. Induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) with this variant display impaired relaxation and reduced calcium kinetics in 2D culture when compared with a CRISPR-Cas9-corrected isogenic control line. Similarly, mutant engineered cardiac tissues (ECTs) demonstrate increased passive tension and impaired relaxation velocity compared with isogenic controls. High-throughput small-molecule screening identifies phosphodiesterase 3 (PDE3) inhibition by trequinsin as a potential therapy to improve cardiomyocyte relaxation in this genotype. Together, these data demonstrate an engineered cardiac tissue model of RCM and establish the translational potential of this precision medicine approach to identify therapeutics targeting myocardial relaxation.
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Affiliation(s)
- Bryan Z Wang
- Department of Biomedical Engineering, Columbia University, New York, NY 10032, USA
| | - Trevor R Nash
- Department of Biomedical Engineering, Columbia University, New York, NY 10032, USA
| | - Xiaokan Zhang
- Department of Medicine, Division of Cardiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Jenny Rao
- Department of Medicine, Division of Cardiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Laura Abriola
- Yale Center for Molecular Discovery, Yale University, New Haven, CT 06520, USA
| | - Youngbin Kim
- Department of Biomedical Engineering, Columbia University, New York, NY 10032, USA
| | - Sergey Zakharov
- Department of Medicine, Division of Cardiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Michael Kim
- Department of Medicine, Division of Cardiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Lori J Luo
- Department of Biomedical Engineering, Columbia University, New York, NY 10032, USA
| | - Margaretha Morsink
- Department of Biomedical Engineering, Columbia University, New York, NY 10032, USA
| | - Bohao Liu
- Department of Biomedical Engineering, Columbia University, New York, NY 10032, USA
| | - Roberta I Lock
- Department of Biomedical Engineering, Columbia University, New York, NY 10032, USA
| | - Sharon Fleischer
- Department of Biomedical Engineering, Columbia University, New York, NY 10032, USA
| | - Manuel A Tamargo
- Department of Biomedical Engineering, Columbia University, New York, NY 10032, USA
| | - Michael Bohnen
- Department of Medicine, Division of Cardiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Carrie L Welch
- Department of Pediatrics, Columbia University, New York, NY 10032, USA
| | - Wendy K Chung
- Department of Pediatrics, Columbia University, New York, NY 10032, USA
| | - Steven O Marx
- Department of Medicine, Division of Cardiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Yulia V Surovtseva
- Yale Center for Molecular Discovery, Yale University, New Haven, CT 06520, USA
| | - Gordana Vunjak-Novakovic
- Department of Biomedical Engineering, Columbia University, New York, NY 10032, USA; Department of Medicine, Division of Cardiology, Columbia University Medical Center, New York, NY 10032, USA; College of Dental Medicine, Columbia University, New York, NY 10032, USA
| | - Barry M Fine
- Department of Medicine, Division of Cardiology, Columbia University Medical Center, New York, NY 10032, USA.
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Giangreco NP, Lebreton G, Restaino S, Farr M, Zorn E, Colombo PC, Patel J, Soni RK, Leprince P, Kobashigawa J, Tatonetti NP, Fine BM. Alterations in the kallikrein-kinin system predict death after heart transplant. Sci Rep 2022; 12:14167. [PMID: 35986069 PMCID: PMC9391369 DOI: 10.1038/s41598-022-18573-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 08/16/2022] [Indexed: 11/09/2022] Open
Abstract
Heart transplantation remains the definitive treatment for end stage heart failure. Because availability is limited, risk stratification of candidates is crucial for optimizing both organ allocations and transplant outcomes. Here we utilize proteomics prior to transplant to identify new biomarkers that predict post-transplant survival in a multi-institutional cohort. Microvesicles were isolated from serum samples and underwent proteomic analysis using mass spectrometry. Monte Carlo cross-validation (MCCV) was used to predict survival after transplant incorporating select recipient pre-transplant clinical characteristics and serum microvesicle proteomic data. We identified six protein markers with prediction performance above AUROC of 0.6, including Prothrombin (F2), anti-plasmin (SERPINF2), Factor IX, carboxypeptidase 2 (CPB2), HGF activator (HGFAC) and low molecular weight kininogen (LK). No clinical characteristics demonstrated an AUROC > 0.6. Putative biological functions and pathways were assessed using gene set enrichment analysis (GSEA). Differential expression analysis identified enriched pathways prior to transplant that were associated with post-transplant survival including activation of platelets and the coagulation pathway prior to transplant. Specifically, upregulation of coagulation cascade components of the kallikrein-kinin system (KKS) and downregulation of kininogen prior to transplant were associated with survival after transplant. Further prospective studies are warranted to determine if alterations in the KKS contributes to overall post-transplant survival.
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Affiliation(s)
- Nicholas P Giangreco
- Departments of Systems Biology, Biomedical Informatics, and Medicine, Columbia University, New York, NY, USA
| | - Guillaume Lebreton
- Chirurgie Thoracique et Cardiovasculaire, Pitíe-Salpetriere University Hospital, Paris, France
| | - Susan Restaino
- Department of Medicine, Division of Cardiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Maryjane Farr
- Department of Medicine, Division of Cardiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Emmanuel Zorn
- Center for Translational Immunology, Columbia University Irving Medical Center, New York, NY, USA
| | - Paolo C Colombo
- Department of Medicine, Division of Cardiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Jignesh Patel
- Cedars-Sinai Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Rajesh Kumar Soni
- Proteomics and Macromolecular Crystallography Shared Resource, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Pascal Leprince
- Chirurgie Thoracique et Cardiovasculaire, Pitíe-Salpetriere University Hospital, Paris, France
| | - Jon Kobashigawa
- Cedars-Sinai Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA, USA
| | - Nicholas P Tatonetti
- Departments of Systems Biology, Biomedical Informatics, and Medicine, Columbia University, New York, NY, USA
- Institute for Genomic Medicine, Columbia University, New York, NY, USA
| | - Barry M Fine
- Department of Medicine, Division of Cardiology, Columbia University Irving Medical Center, New York, NY, USA.
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Giangreco NP, Lebreton G, Restaino S, Jane Farr M, Zorn E, Colombo PC, Patel J, Levine R, Truby L, Soni RK, Leprince P, Kobashigawa J, Tatonetti NP, Fine BM. Plasma kallikrein predicts primary graft dysfunction after heart transplant. J Heart Lung Transplant 2021; 40:1199-1211. [PMID: 34330603 DOI: 10.1016/j.healun.2021.07.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 06/21/2021] [Accepted: 07/01/2021] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Primary graft dysfunction (PGD) is the leading cause of early mortality after heart transplant. Pre-transplant predictors of PGD remain elusive and its etiology remains unclear. METHODS Microvesicles were isolated from 88 pre-transplant serum samples and underwent proteomic evaluation using TMT mass spectrometry. Monte Carlo cross validation (MCCV) was used to predict the occurrence of severe PGD after transplant using recipient pre-transplant clinical characteristics and serum microvesicle proteomic data. Putative biological functions and pathways were assessed using gene set enrichment analysis (GSEA) within the MCCV prediction methodology. RESULTS Using our MCCV prediction methodology, decreased levels of plasma kallikrein (KLKB1), a critical regulator of the kinin-kallikrein system, was the most predictive factor identified for PGD (AUROC 0.6444 [0.6293, 0.6655]; odds 0.1959 [0.0592, 0.3663]. Furthermore, a predictive panel combining KLKB1 with inotrope therapy achieved peak performance (AUROC 0.7181 [0.7020, 0.7372]) across and within (AUROCs of 0.66-0.78) each cohort. A classifier utilizing KLKB1 and inotrope therapy outperforms existing composite scores by more than 50 percent. The diagnostic utility of the classifier was validated on 65 consecutive transplant patients, resulting in an AUROC of 0.71 and a negative predictive value of 0.92-0.96. Differential expression analysis revealed a enrichment in inflammatory and immune pathways prior to PGD. CONCLUSIONS Pre-transplant level of KLKB1 is a robust predictor of post-transplant PGD. The combination with pre-transplant inotrope therapy enhances the prediction of PGD compared to pre-transplant KLKB1 levels alone and the resulting classifier equation validates within a prospective validation cohort. Inflammation and immune pathway enrichment characterize the pre-transplant proteomic signature predictive of PGD.
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Affiliation(s)
- Nicholas P Giangreco
- Departments of Systems Biology, Biomedical Informatics, and Medicine, Columbia University, New York, New York
| | - Guillaume Lebreton
- Chirurgie Thoracique et Cardiovasculaire, Pitiíe-Salpetriere University Hospital, Paris, France
| | - Susan Restaino
- Department of Medicine, Division of Cardiology, Columbia University Irving Medical Center, New York, New York
| | - Mary Jane Farr
- Department of Medicine, Division of Cardiology, Columbia University Irving Medical Center, New York, New York
| | - Emmanuel Zorn
- Center for Translational Immunology, Columbia University Irving Medical Center, New York, New York
| | - Paolo C Colombo
- Department of Medicine, Division of Cardiology, Columbia University Irving Medical Center, New York, New York
| | - Jignesh Patel
- Cedars-Sinai Heart Institute, Cedars Sinai Medical Center, Los Angeles, California
| | - Ryan Levine
- Cedars-Sinai Heart Institute, Cedars Sinai Medical Center, Los Angeles, California
| | - Lauren Truby
- Department of Medicine, Division of Cardiology, Columbia University Irving Medical Center, New York, New York
| | - Rajesh Kumar Soni
- Proteomics and Macromolecular Crystallography Shared Resource, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, New York
| | - Pascal Leprince
- Chirurgie Thoracique et Cardiovasculaire, Pitiíe-Salpetriere University Hospital, Paris, France
| | - Jon Kobashigawa
- Cedars-Sinai Heart Institute, Cedars Sinai Medical Center, Los Angeles, California
| | - Nicholas P Tatonetti
- Departments of Systems Biology, Biomedical Informatics, and Medicine, Columbia University, New York, New York; Institute for Genomic Medicine, Columbia University, New York, New York
| | - Barry M Fine
- Department of Medicine, Division of Cardiology, Columbia University Irving Medical Center, New York, New York.
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Affiliation(s)
- B M Fine
- Genentech, Inc., South San Francisco, California, USA.
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Fine BM, Pande J, Lomakin A, Ogun OO, Benedek GB. Dynamic critical phenomena in aqueous protein solutions. Phys Rev Lett 1995; 74:198-201. [PMID: 10057733 DOI: 10.1103/physrevlett.74.198] [Citation(s) in RCA: 17] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
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