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Heart-on-a-Chip Model with Integrated Extra- and Intracellular Bioelectronics for Monitoring Cardiac Electrophysiology under Acute Hypoxia

  • Haitao Liu
    Haitao Liu
    Department of Biomedical Engineering, Tufts University, Medford, Massachusetts 02155, United States
    School of Materials Science and Technology, Beijing Key Laboratory of Materials Utilization of Nonmetallic Minerals and Solid Wastes, National Laboratory of Mineral Materials, China University of Geosciences, Beijing 100083, PR China
    More by Haitao Liu
  • Olurotimi A. Bolonduro
    Olurotimi A. Bolonduro
    Department of Biomedical Engineering, Tufts University, Medford, Massachusetts 02155, United States
  • Ning Hu
    Ning Hu
    Department of Biomedical Engineering, Tufts University, Medford, Massachusetts 02155, United States
    State Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Guangdong Province Key Laboratory of Display Material and Technology, The First Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, Guangzhou 510275, PR China
    More by Ning Hu
  • Jie Ju
    Jie Ju
    Department of Biomedical Engineering, Tufts University, Medford, Massachusetts 02155, United States
    More by Jie Ju
  • Akshita A. Rao
    Akshita A. Rao
    Department of Biomedical Engineering, Tufts University, Medford, Massachusetts 02155, United States
  • Breanna M. Duffy
    Breanna M. Duffy
    Department of Biomedical Engineering, Tufts University, Medford, Massachusetts 02155, United States
  • Zhaohui Huang
    Zhaohui Huang
    School of Materials Science and Technology, Beijing Key Laboratory of Materials Utilization of Nonmetallic Minerals and Solid Wastes, National Laboratory of Mineral Materials, China University of Geosciences, Beijing 100083, PR China
  • Lauren D. Black
    Lauren D. Black
    Department of Biomedical Engineering, Tufts University, Medford, Massachusetts 02155, United States
    Department of Cell, Molecular & Developmental Biology, School of Graduate Biomedical Sciences, Tufts University, Boston, Massachusetts 02111, United States
  • , and 
  • Brian P. Timko*
    Brian P. Timko
    Department of Biomedical Engineering, Tufts University, Medford, Massachusetts 02155, United States
    *E-mail: [email protected]
Cite this: Nano Lett. 2020, 20, 4, 2585–2593
Publication Date (Web):February 24, 2020
https://doi.org/10.1021/acs.nanolett.0c00076

Copyright © 2020 American Chemical Society. This publication is licensed under these Terms of Use.

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Abstract

We demonstrated a bioelectronic heart-on-a-chip model for studying the effects of acute hypoxia on cardiac function. A microfluidic channel enabled rapid modulation of medium oxygenation, which mimicked the regimes induced by a temporary coronary occlusion and reversibly activated hypoxia-related transduction pathways in HL-1 cardiac model cells. Extracellular bioelectronics provided continuous readouts demonstrating that hypoxic cells experienced an initial period of tachycardia followed by a reduction in beat rate and eventually arrhythmia. Intracellular bioelectronics consisting of Pt nanopillars temporarily entered the cytosol following electroporation, yielding action potential (AP)-like readouts. We found that APs narrowed during hypoxia, consistent with proposed mechanisms by which oxygen deficits activate ATP-dependent K+ channels that promote membrane repolarization. Significantly, both extra- and intracellular devices could be multiplexed, enabling mapping capabilities unachievable by other electrophysiological tools. Our platform represents a significant advance toward understanding electrophysiological responses to hypoxia and could be applicable to disease modeling and drug development.

Cardiovascular disease (CVD) is the leading cause of death worldwide, with CVD due to ischemic disease accounting for a large proportion of the patient population. (1,2) Cardiac ischemia occurs when an artery supplying blood to the heart muscle is partially or fully blocked. This blockage reduces blood flow to downstream muscle, creating a region of hypoxia. If severe or for an extended time, the ischemia can cause myocardial infarction (MI), leading to tissue death, abnormal cardiac electrophysiology, and functional decline. Despite decades of research, deaths due to ischemic heart disease have increased between 1990 and 2013 by 41.7%. (1) Therefore, there is a clear need for improved preclinical models to develop novel therapeutics.

Bioelectrical activity is a critical component of healthy myocardial function, as it is this organized electrical propagation that induces synchronized pumping. It is known that changes in oxygen level in the microcellular environment induce a number of functional responses among cardiomyocytes (CMs), including changes in resting membrane potential, release of neurotransmitters, shifts in gene expression, altered metabolic functions, and activated ion channels. (3−7) Electrical measurement, such as the shape and duration of action potentials (AP), provides key information on the metabolic state, type, and density of various ion channels within a given cell. (8,9) Many of these changes occur rapidly upon induction of hypoxia, requiring continuous and multiplexed measurement strategies that can record cellular electrophysiology in real time. (7) Furthermore, study of AP changes prior to, during, and after a hypoxic event require stable intracellular probing methods. Current preclinical models for cardiovascular disease, including in vitro cell assays and in vivo animal models, allow for limited to no potential to monitor acute intracellular electrical responses to hypoxic stress. Recent heart-on-a-chip models have effectively recapitulated the structure and function of native myocardium (10) including under hypoxia. (11) However, current models for hypoxia do not provide readouts of electrophysiological activity, which changes rapidly during both hypoxia and recovery. Further studies could better inform future therapeutic development through disease modeling and drug screening applications.

The ideal electrophysiological platform should provide stable, multiplexed bioelectronic readouts while exerting minimal disturbances to cellular function. (12) Patch clamp electrophysiology is considered a gold standard for short-term measurements, but patch pipettes are generally difficult to multiplex and moreover present mechanical mismatches with cell membranes that preclude long-term study. Optical imaging using exogenous or genetically encoded dyes offers spatial mapping of AP propagation, but these techniques require instrumentation that precludes continuous, long-term monitoring, and they may be limited in temporal resolution. Bioelectronic devices such as multielectrode arrays (MEAs) (13−15) or field-effect transistors (FETs) (16−18) offer a compelling alternative, providing electrophysiological readouts with high temporal resolution and at multiple length scales, ranging from subcellular to tissue level. In extracellular configurations, these devices record spikes that only approximate the AP. Nevertheless, they are powerful, because they can be readily multiplexed up to a level of thousands of devices (19) and moreover support long-term cultures. For these reasons, extracellular bioelectronics have enabled fundamental insights into cardiac signaling, disease modeling, and pharmacology. More recently, 3D bioprobes including freestanding nanowire-FETs, (20−22) nanopillars, (23,24) nanomushrooms, (25) and nanovolcanoes (26) have been achieved. These structures could stably enter the cytosol following mechanical, electrical, or chemical perturbations, allowing bioelectronic readouts that were similar or identical to the AP.

In this report, we demonstrate a heart-on-a-chip model consisting of a microfluidic channel and cell culture area with integrated extra- or intracellular bioelectronic devices (Figure 1a). The microfluidic channel enabled temporal modulation of medium oxygenation that we could switch between normoxic (21% O2), hypoxic (1–4% O2), and recovery/reperfusion (21% O2) conditions that mimicked coronary occlusion followed by unblocking. (27) As a cell model, we chose immortalized mouse atrial HL-1 cells, which have been used to study cardiac signaling, electrical coupling, and transcriptional regulation. (28) Moreover, HL-1 cells demonstrated similar responses to ischemia and reperfusion as primary cardiomyocytes. (29) The central innovation of our work relates to the bioelectronic interfaces, which provided continuous readouts of cardiac signaling. The multiplexed signals from extracellular devices enabled us to monitor beat rates, rhythmicity, and wavefront propagation velocities, which changed rapidly during hypoxia and recovery. Our intracellular probes consisted of Pt nanopillar arrays that entered the cytosol following localized membrane poration. (23) These devices provided accurate readouts of the cardiac AP, which contained details about ion channel functions that were not available from extracellular measurements but were crucial to understanding adaptive responses to hypoxia. Significantly, these intracellular probes provided both single-cell resolution and multiplexing capabilities, thereby enabling us to observe spatial heterogeneities in cellular function that are innate in biological systems but could not have been readily recorded using other electrophysiological techniques.

Figure 1

Figure 1. Overview of the heart-on-a-chip platform. (a) (top) Optical image and (bottom) scheme representing fully assembled chip with integrated recording elements, reference electrode, and PDMS channel for media delivery. (b) Representative optical image of an extracellular recording element coated with Pt black (red arrow). (c) Representative optical image of an intracellular recording element comprised of an underlying Au pad with five vertical Pt nanopillars (blue arrow). (d) (top) SEM detail of five vertical nanopillars corresponding to the location marked by the blue arrow in panel c. (inset) Schematic representation of a single nanopillar cross section. (bottom) Cross section of a single nanopillar after etching with FIB. Note that the Pt nanopillar fully penetrated the SiO2 layer to form a junction with the underlying Au layer. (e) Immunostaining of the HL-1 cell monolayer cultured in a PDMS channel at 4 DIV showing α-actinin cytoskeleton (green), Cx-43 gap junction proteins (red), and nuclei (blue, DAPI). (f) Stitched immunofluorescence image showing continuous HL-1 monolayer across the lateral direction of the microfluidic channel. Yellow dotted lines denote edges of the PDMS boundary.

We fabricated extra- or intracellular bioelectronic devices using top-down photolithography and nanofabrication techniques. All devices were fabricated as 2 × 8 element arrays, with 600 or 1000 μm spacing between devices (Figure S1). For extracellular measurements, we fabricated planar electrodes consisting of 30 μm diameter Au pads and interconnects passivated by a 2 μm thick layer of SU-8. We subsequently coated these electrodes with Pt black, which increased the surface area (30) to yield devices with an impedance of ∼20 kΩ (1 kHz) and filtered baseline noise of <20 μVpp (Figure 1b). For intracellular measurements, we fabricated Pt nanopillar arrays, where each nanopillar was 150–200 nm in diameter and 1.5–2.0 μm in height. These nanopillars were fabricated in groups of 5, e.g., 5 nanopillars on each of 16 underlying Au pads, enabling 16 independently addressable intracellular probes. Each underlying Au pad was passivated with a 200 nm insulating SiO2 layer; we then used focused ion beam (FIB) to mill holes through the passivation followed by Pt deposition. (23) We chose that strategy to ensure that only the nanopillar portion of the device—that is, the portion that could access the intercellular space—would form a bioelectronic interface. A cross section view achieved by FIB/SEM confirmed that the nanopillars fully penetrated the SiO2 layer to make electrical contact with the underlying Au layer (Figure 1d). We also performed electrical impedance measurements to verify the electrical integrity of our SiO2 layer; our nanopillar arrays had impedances of ∼300 kΩ (1 kHz), which was much larger than we recorded for planar devices and consistent with our expectation of a much smaller unpassivated surface area: 4–6 μm2 compared to ∼700 μm2 for planar devices (Figure S2).

We next fabricated fluidic device assemblies by permanently bonding a poly(dimethylsiloxane) (PDMS) channel layer onto the surface of our bioelectronic device chips (Figure 1a). These channels were coated with fibronectin and then seeded with HL-1 cells. After seeding, we flowed medium at a rate of 40 μL/h, which provided sufficient nutrient and oxygen delivery to support a healthy cell culture. (30,31) HL-1 cells demonstrate pacemaker activity; (32) we found that by 3–4 days in vitro (DIV), our cells formed confluent monolayers that beat spontaneously (Movie S1). Immunohistochemistry demonstrated well-defined actinin filaments and significant connexin-43 protein expression, which are associated with cytoskeletal mechanics and electrical coupling, respectively (Figure 1e,f). Generally, connexin-43 proteins were distributed uniformly across cell membranes, which is consistent with other works involving HL-1 cells (33) and attributed to the relatively early stage in culture; we however did note several locations where these proteins were localized at cell–cell junctions as is typical of primary CMs (Figure S3). Our platform maintained healthy cultures for at least 7 DIV.

Cells undergo an adaptive response when exposed to acute hypoxic stress where hypoxia-inducible factor-1-alpha (HIF-1α), a transcriptional regulator, activates in order to increase oxygen delivery and facilitate metabolic adaptation to hypoxia. (34) At the onset of mild hypoxia, HIF-1α translocates into the nucleus, where it binds the HIF-1β subunit and upregulates the relevant genes. (35,36) To assess the ability of our platform activate these signaling pathways, we exposed HL-1 cells at 4 DIV to hypoxic medium (1% O2) for up to 5 h. Immunostaining revealed that by 2.5 h, HIF-1α was strongly expressed and had localized in the nucleus (Figure S4a). This expression was constant throughout the remainder of the hypoxic episode. However, upon applying a normoxic recovery medium for 90 min, HIF-1α returned to basal levels (Figure S4b); HIF-1α degrades with a <5 min half-life posthypoxia. (37) These results demonstrate the ability of our platform to model normoxia, hypoxia, and early stages of recovery.

To validate the ability of our platform to achieve bioelectronic readouts, we first monitored electrophysiology using extracellular electrodes, which yielded ≥85% functional bioelectronic interfaces and enabled continuous recording over the course of the experiment. Prior to inducing hypoxic stress, HL-1 cells beat with a frequency of 3.0 ± 0.5 Hz across N = 8 distinct cultures, consistent with previously reported results for HL-1 cultures in static conditions. (32) To induce a period of acute hypoxia, we switched normoxic media with hypoxic (1% O2) media for 5 h, according to the protocol depicted schematically in Figure 2a. These conditions induced an initial period of tachycardia, with beating frequency increasing from 3.2 to 4.2 Hz, followed by a gradual reduction in frequency until arrhythmia with much longer firing intervals, 4.9 ± 1.5 s. In a separate culture (Figure S5), we induced ischemia gradually by first perfusing 4% O2 medium for 20 min before further reducing O2 to 1%. In that case, we also observed tachycardia, albeit at a slower rate of increase before cresting and then returning to basal. Similar electrophysiological patterns—that is, hypoxia-induced tachycardia followed by a decrease in beat rate to below basal—were also observed in primary murine CMs that were exposed to hypoxic media in static conditions. (38) Arrhythmia is also a symptom of ischemia. (39) We maintained hypoxia for either 5.5 h (Figure 2a) or 20 h (Figure S5); in both cases, rhythmic beating recovered within 30 min after reintroducing normoxic media. The tachycardia present at that time is consistent with reperfusion injury, which is common following ischemia. (40)

Figure 2

Figure 2. Extracellular bioelectronic readouts before, during, and after hypoxia. (a) (top) Scheme of media delivery protocol with distinct regions of normoxia, hypoxia, and recovery and (bottom) HL-1 firing rate. The red dotted box highlights the transition from rhythmic beating to arrhythmia. (b) Representative signals from a single device recorded during (I) normoxia, (II) hypoxia, upon onset of arrhythmia, and (III) recovery. These traces correspond to the points noted in panel a. (inset) Single peak expansions of (black) overlaid individual traces and (red) average of individual traces. (c) Scheme of electrode layout (black dots) and representative multiplexed readouts from a chip with 14 out of 16 functional bioelectronic interfaces. (d,e) Isochronal maps representing signal propagation at two time points each during (d) normoxia and (e) hypoxia for ∼1 h. Black arrow overlays represent the gradient of the isochrones. The area of each map is 1000 μm wide × 4200 μm tall.

The high yield of multiplexed readouts enabled us to further assess cell–cell communication. Figure 2c shows typical signals from a chip with 14 out of 16 functioning bioelectronic interfaces. Cross-correlation analysis enabled us to construct isochronal maps that provided information about wavefront propagation velocities. We found that under normoxia, wavefronts propagated uniformly with an average speed of 18.9 ± 5.0 mm/s, which is typical for HL-1 cells. (41) These characteristics were stable: another data set collected 10 min later showed a nearly identical propagation pattern and speed, 19.3 ± 5.0 mm/s (Figure 2d). In contrast, 1% O2 hypoxia for 1 h resulted in turbid propagation patterns that changed significantly between successive analyses performed 10 min apart (Figure 2e). We also found that the average propagation speeds were reduced to 11.9 ± 11.0 and 13.3 ± 8.5 mm/s at the two time points shown. While these data represent snapshots at just four time points, we emphasize that they are representative of 6 time points analyzed during normoxia and 14 during a 5 h hypoxic episode. We found that propagation speeds became progressively slower over the course of hypoxia, and normoxic and hypoxic data sets were significantly different (p < 0.001, Welch’s t test) (Figure S6). Collectively, these characteristics—tachycardia, arrhythmia, and reduced propagation velocities—are consistent with cardiac responses to hypoxia, whereby excitation thresholds are decreased and Cx-43 gap junction expression is diminished, leading to reentrant arrhythmias and an increase in tissue impedance. (39) Our results demonstrate the ability of our platform to achieve ischemia-like conditions and moreover establish a time frame for hypoxic responses.

We next sought to achieve intracellular readouts from our microfluidic platform using Pt nanopillar electrodes as shown in Figures 1c,d and S1b. Prior to electroporation, nanopillars recorded extracellular signals with an average magnitude of ∼60 μV and baseline noise of 20 μVpp (Figure 3a). Immediately following electroporation (3 Vpp, 200 μs biphasic square pulses for 2–3 s), we found that nanopillar electrodes recorded intracellular signals with an initial magnitude of 2.3 mV (Figure 3b). The signal shape resembled that of a slow AP, with well-defined rising (phase 0) and falling (phase 3) edges consistent with inward Ca2+ and outward K+ currents, respectively, followed by a refractory period and resting phase (phase 4). These characteristics are consistent with previous reports (8,23) and moreover expected given that HL-1 cells are derived from atrial cardiomyocytes. (42,43) We observed intracellular signals for at least 10 min following electroporation, and we found that their amplitude decreased to ∼7% of the initial value during that time, consistent with sealing of the transient pores. (23) However, both the AP duration at 50% repolarization (APD50) as well as signal shape remained constant during that period (Figure 3c–e).

Figure 3

Figure 3. Electrophysiology using nanopillar electrodes in normoxic media. (a) Representative extracellular signal recorded prior to electroporation. Inset shows expansion of single representative peak. (b) Intracellular signals recorded immediately after electroporation. (c) (blue square) Normalized action potential amplitude and (red circle) APD50 as a function of time after electroporation. Within 2 min, the amplitude of the action potentials decreased to around 24% of the maximum, while APD50 was unchanged. (d) Expansions of peaks shown in panel b at locations noted by red, blue, and yellow arrows, plotted on (left) absolute and (right) normalized scales. Note that the baseline of these peaks is offset for clarity.

To analyze the intracellular electrophysiology of HL-1 cells under hypoxia, we investigated the APD50, AP duration at 90% repolarization (APD90), and depolarization time (Figure 4a). We chose to study these parameters under normoxia (0 h) and at three time points under 1% O2 hypoxia (2, 4, and 6 h) given the time course of responses observed with extracellular readouts. By the 6 h time point, our intracellular probes recorded arrhythmia with long firing intervals (Figure 4b) similar to those shown in Figure 2b,II. We observed a substantial reduction in mean APD50 (−46%), APD90 (−34%), and depolarization time (−44%) by the 2 h time point, which persisted through 4 and 6 h (Figure 4d–g). These changes are expected given that we also observed HIF-1α activation by 2 h as shown in Figure S4. AP shortening is also expected given that hypoxia lowers intracellular ATP and activates ATP-dependent potassium channels, which promote membrane repolarization. (44)

Figure 4

Figure 4. Intracellular electrophysiology of HL-1 cells during 1% O2 hypoxic stress. (a) Schematic representation of typical HL-1 AP highlighting key parameters. (b) Intracellular recording showing arrhythmic beating after 6 h of hypoxic stress. (c) Representative examples of AP recordings following 0, 2, 4, or 6 h of hypoxic stress. Note that the 0 h time point represents normoxia. (d–f) Summary statistics representing (d) APD50, (e) APD90, and (f) depolarization time corresponding to each time point represented in panel c. (g) Percentage change for APD50, APD90, and depolarization time throughout hypoxia. Statistics are from N = 14 different cells in 4 different cultures. *P < 0.05, **P < 0.01, ***P < 0.0005, ****P < 0.0001 in Welch’s t test. All error bars denote s.d.

A distinct advantage of our nanopillar approach is that it not only enables accurate AP measurements but also does so at the single-cell level. This functionality opens avenues to spatially map intracellular features within the same sample, thereby providing insights into heterogeneities that would not be revealed by lower-resolution or ensemble techniques. To explore this possibility, we recorded intracellular signals from five devices simultaneously, under normoxia or after 2 h of exposure to hypoxia (1% O2), as shown in Figure 5a. Propagation maps of signals along these linear device arrays showed qualitative characteristics similar to those presented in Figure 2, with uniform propagation in the normoxic sample compared to nonuniform propagation in the hypoxic sample. The corresponding propagation speeds are 22.7 ± 0.6 and 27.3 ± 5.2 mm/s, respectively (Figure 5b). (45) Heat maps representing the electrophysiological properties of each trace demonstrate that APD50, APD90, and depolarization time generally decrease with hypoxia (Figure 5c), consistent with the statistical analysis presented in Figure 4. Interestingly, however, each group includes one clear outlier: an abnormally short AP in the normoxia group and an abnormally long AP in the hypoxia group. These outliers represent the innate variabilities in biological systems: for example, HL-1 cells exhibit substantial phenotypic/electrophysiological variability, (46,47) while nuclear HIF-1α is activated nonuniformly, particularly at early stages of hypoxia. (48) Similar variability gradients have been observed among and within clusters of human embryonic stem-cell-derived cardiac cell clusters, using optical dyes. (49)

Figure 5

Figure 5. Multiplexed intracellular recordings. (a) Representative APs simultaneously recorded under (left) normoxia and (right) hypoxia after 2 h. The color-coded legend represents the corresponding device arrangement; spacing between devices is 600 μm. (b) Propagation maps correlating to each trace and spatial location shown in panels a and b. Each map is 3600 μm tall and represents propagation along the direction of the linear device array. (c) Heat maps representing APD50, APD90, and depolarization time corresponding to each trace and spatial location shown in panels a and b.

We have demonstrated an ischemia-on-a-chip model with integrated extra- or intracellular bioelectronic devices. These devices provided electrophysiological readouts with complementary attributes: extracellular devices recorded stable signals with high device yield, which enabled us to continuously monitor beat frequency and wavefront propagation, while intracellular devices provided accurate recordings of the AP. While this work focused solely on the effects of hypoxia, other factors that are relevant to ischemia including acidosis, hyperkalemia, nutrient deprivation, and waste accumulation (27) could be incorporated into the model by modulating the composition or flow of the medium. Our proof-of-concept multiplexing capabilities—limited to 16 devices here—could also be extended to much larger or denser arrays. For example, >1000-element nanoelectrode arrays capable of parallel, network-level intracellular recording from CMs (50) and connected neurons (51) have been demonstrated. Improvements upon our nanofabrication techniques to achieve probes with nanoscale concavities, (25) smaller diameters, (20−22) and/or biochemical (26,52) surface ligands could promote membrane integration and yield more stable and long-term intracellular recordings. We could also adapt our platform to include 3D cardiac tissue constructs with embedded bioelectronic devices, (14,18,53) which would more adequately recapitulate endogenous tissues. The distinct advantages of our platform could be applicable to a wide variety of conditions relevant to hypoxia or ischemia. For example, multiplexed outputs are relevant to assessing responses to oxygen concentration gradients and borderzone infarcts, while continuous, real-time readouts are relevant to understanding the rapid changes that occur during reperfusion injuries. Further studies could yield fundamental insights into cardiac signaling pathways or provide a high-throughput platform for assessing therapeutics.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.nanolett.0c00076.

  • Movie S1. Spontaneous beating of cells that formed confluent monolayers (MP4)

  • Materials and Methods; Scheme S1. Illustration of microfluidic chip with integrated nanopillar microelectrode arrays; Scheme S2. Strategy to generate hypoxic medium flow; Figure S1. Design of MEA devices; Figure S2. Representative electrical impedance spectra of planar and nanopillar bioelectronic devices; Figure S3. Gap junction localization; Figure S4. HIF-1α validation of heart-on-a-chip; Figure S5. Extracellular bioelectronic readouts before, during, and after hypoxia; Figure S6. Summary of wavefront propagation speeds derived from isochronal map (PDF)

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Author Information

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  • Corresponding Author
  • Authors
    • Haitao Liu - Department of Biomedical Engineering, Tufts University, Medford, Massachusetts 02155, United StatesSchool of Materials Science and Technology, Beijing Key Laboratory of Materials Utilization of Nonmetallic Minerals and Solid Wastes, National Laboratory of Mineral Materials, China University of Geosciences, Beijing 100083, PR ChinaOrcidhttp://orcid.org/0000-0003-1824-0304
    • Olurotimi A. Bolonduro - Department of Biomedical Engineering, Tufts University, Medford, Massachusetts 02155, United States
    • Ning Hu - Department of Biomedical Engineering, Tufts University, Medford, Massachusetts 02155, United StatesState Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Guangdong Province Key Laboratory of Display Material and Technology, The First Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, Guangzhou 510275, PR ChinaOrcidhttp://orcid.org/0000-0001-7178-3952
    • Jie Ju - Department of Biomedical Engineering, Tufts University, Medford, Massachusetts 02155, United States
    • Akshita A. Rao - Department of Biomedical Engineering, Tufts University, Medford, Massachusetts 02155, United States
    • Breanna M. Duffy - Department of Biomedical Engineering, Tufts University, Medford, Massachusetts 02155, United States
    • Zhaohui Huang - School of Materials Science and Technology, Beijing Key Laboratory of Materials Utilization of Nonmetallic Minerals and Solid Wastes, National Laboratory of Mineral Materials, China University of Geosciences, Beijing 100083, PR ChinaOrcidhttp://orcid.org/0000-0001-7188-0875
    • Lauren D. Black - Department of Biomedical Engineering, Tufts University, Medford, Massachusetts 02155, United StatesDepartment of Cell, Molecular & Developmental Biology, School of Graduate Biomedical Sciences, Tufts University, Boston, Massachusetts 02111, United StatesOrcidhttp://orcid.org/0000-0002-7486-5811
  • Notes
    The authors declare no competing financial interest.

Acknowledgments

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This work was supported by a Tufts Collaborates grant (to B.P.T.), a Tufts Research Advancement Fund award (to B.P.T.), a Department of Defense Grant W81XWH-16-1-0304 (to L.D.B.), an American Heart Association Grant-in-Aid Award 16GRNT27760100 (to L.D.B.), a Student National Scholarship (to H.L.), and a China National Scholarship (to N.H). This work was performed in part at the Center for Nanoscale Systems (CNS), a member of the National Nanotechnology Coordinated Infrastructure Network (NNCI), which is supported by the National Science Foundation under NSF award no. 1541959. CNS is part of Harvard University.

Abbreviations

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AP

action potential

APD50

action potential duration, 50% repolarization

APD90

action potential duration, 90% repolarization

CM

cardiomyocyte

DIV

days in vitro

FIB

focused ion beam

HIF-1α

hypoxia-inducible factor-1α

MEA

mutielectrode array

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  • Abstract

    Figure 1

    Figure 1. Overview of the heart-on-a-chip platform. (a) (top) Optical image and (bottom) scheme representing fully assembled chip with integrated recording elements, reference electrode, and PDMS channel for media delivery. (b) Representative optical image of an extracellular recording element coated with Pt black (red arrow). (c) Representative optical image of an intracellular recording element comprised of an underlying Au pad with five vertical Pt nanopillars (blue arrow). (d) (top) SEM detail of five vertical nanopillars corresponding to the location marked by the blue arrow in panel c. (inset) Schematic representation of a single nanopillar cross section. (bottom) Cross section of a single nanopillar after etching with FIB. Note that the Pt nanopillar fully penetrated the SiO2 layer to form a junction with the underlying Au layer. (e) Immunostaining of the HL-1 cell monolayer cultured in a PDMS channel at 4 DIV showing α-actinin cytoskeleton (green), Cx-43 gap junction proteins (red), and nuclei (blue, DAPI). (f) Stitched immunofluorescence image showing continuous HL-1 monolayer across the lateral direction of the microfluidic channel. Yellow dotted lines denote edges of the PDMS boundary.

    Figure 2

    Figure 2. Extracellular bioelectronic readouts before, during, and after hypoxia. (a) (top) Scheme of media delivery protocol with distinct regions of normoxia, hypoxia, and recovery and (bottom) HL-1 firing rate. The red dotted box highlights the transition from rhythmic beating to arrhythmia. (b) Representative signals from a single device recorded during (I) normoxia, (II) hypoxia, upon onset of arrhythmia, and (III) recovery. These traces correspond to the points noted in panel a. (inset) Single peak expansions of (black) overlaid individual traces and (red) average of individual traces. (c) Scheme of electrode layout (black dots) and representative multiplexed readouts from a chip with 14 out of 16 functional bioelectronic interfaces. (d,e) Isochronal maps representing signal propagation at two time points each during (d) normoxia and (e) hypoxia for ∼1 h. Black arrow overlays represent the gradient of the isochrones. The area of each map is 1000 μm wide × 4200 μm tall.

    Figure 3

    Figure 3. Electrophysiology using nanopillar electrodes in normoxic media. (a) Representative extracellular signal recorded prior to electroporation. Inset shows expansion of single representative peak. (b) Intracellular signals recorded immediately after electroporation. (c) (blue square) Normalized action potential amplitude and (red circle) APD50 as a function of time after electroporation. Within 2 min, the amplitude of the action potentials decreased to around 24% of the maximum, while APD50 was unchanged. (d) Expansions of peaks shown in panel b at locations noted by red, blue, and yellow arrows, plotted on (left) absolute and (right) normalized scales. Note that the baseline of these peaks is offset for clarity.

    Figure 4

    Figure 4. Intracellular electrophysiology of HL-1 cells during 1% O2 hypoxic stress. (a) Schematic representation of typical HL-1 AP highlighting key parameters. (b) Intracellular recording showing arrhythmic beating after 6 h of hypoxic stress. (c) Representative examples of AP recordings following 0, 2, 4, or 6 h of hypoxic stress. Note that the 0 h time point represents normoxia. (d–f) Summary statistics representing (d) APD50, (e) APD90, and (f) depolarization time corresponding to each time point represented in panel c. (g) Percentage change for APD50, APD90, and depolarization time throughout hypoxia. Statistics are from N = 14 different cells in 4 different cultures. *P < 0.05, **P < 0.01, ***P < 0.0005, ****P < 0.0001 in Welch’s t test. All error bars denote s.d.

    Figure 5

    Figure 5. Multiplexed intracellular recordings. (a) Representative APs simultaneously recorded under (left) normoxia and (right) hypoxia after 2 h. The color-coded legend represents the corresponding device arrangement; spacing between devices is 600 μm. (b) Propagation maps correlating to each trace and spatial location shown in panels a and b. Each map is 3600 μm tall and represents propagation along the direction of the linear device array. (c) Heat maps representing APD50, APD90, and depolarization time corresponding to each trace and spatial location shown in panels a and b.

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    • Movie S1. Spontaneous beating of cells that formed confluent monolayers (MP4)

    • Materials and Methods; Scheme S1. Illustration of microfluidic chip with integrated nanopillar microelectrode arrays; Scheme S2. Strategy to generate hypoxic medium flow; Figure S1. Design of MEA devices; Figure S2. Representative electrical impedance spectra of planar and nanopillar bioelectronic devices; Figure S3. Gap junction localization; Figure S4. HIF-1α validation of heart-on-a-chip; Figure S5. Extracellular bioelectronic readouts before, during, and after hypoxia; Figure S6. Summary of wavefront propagation speeds derived from isochronal map (PDF)


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