Biology subjects

Liu, N.

Publications and source records attributed to Liu, N..

At least 109 records · Page 6Linked to original sources

bioRxiv2023

Predicting biomarker for the acute pulmonary embolism by using gene ontology and machine learning

Key PointsEarly and Accurate Diagnosis Essential: Acute pulmonary embolism (PE) is a critical condition that demands prompt and precise diagnosis for effective treatment. Limitations of Current Diagnostics: Existing diagnostic methods like Computed Tomography Pulmonary Angiography (CTPA) have certain limitations, leading to the exploration of alternative approaches. Potential of Blood-Based Biomarkers: A recent study focused on identifying blood-based biomarkers for PE. This involved using gene ontology analysis and machine learning methods to analyze gene expression data from both PE patients and healthy controls. Gene Selection and Analysis: The study selected 20 genes for detailed analysis. These included various coagulation factors, fibrinolytic genes, and inflammation markers. Gene Ontology enrichment analysis was performed to understand the biological processes and molecular functions of these genes. Machine Learning for Diagnosis: Supervised machine learning algorithms were utilized to create classification models using the expression levels of these 20 genes. The models demonstrated promising results in distinguishing PE patients from healthy individuals. Acute pulmonary embolism (PE) is a life-threatening condition requiring early and accurate diagnosis. Current diagnostic methods like CTPA have limitations, and a study aimed to identify potential blood-based biomarkers for PE using gene ontology analysis and machine learning methods. Gene expression data of PE patients and healthy controls were obtained from the Gene Expression Omnibus database. A total of 20 genes were selected for further analysis, including coagulation factors F7, F10, F12, fibrinolytic genes PLAT, SERPINE1 and SERPINE2, and inflammation markers SELE, VCAM1 and ICAM. Gene Ontology enrichment analysis was performed to identify biological processes and molecular functions overrepresented among the candidate genes. Supervised machine learning algorithms were applied to build classification models using the expression levels of the 20 genes as features. Nested cross-validation was employed to assess model performance. The RF model achieved the highest area under the receiver operating characteristic curve of 0.89, indicating excellent discrimination between PE patients and controls based on the gene expression signature. Validation in larger cohorts is warranted to clinically translate these findings into a non-invasive diagnostic test for PE.

genomics↗

bioRxiv2023

Unbiased Complete Estimation of Chloroplast Number in Plant Cells Using Deep Learning Methods

Chloroplasts are essential organelles in plants that are involved in plant development and photosynthesis. Accurate quantification of chloroplast numbers is important for understanding the status and type of plant cells, as well as assessing photosynthetic potential and efficiency. Traditional methods of counting chloroplasts using microscopy are time-consuming and face challenges such as the possibility of missing out-of-focus samples or double counting when adjusting the focal position. Here, we developed an innovative approach called Detecting- and-Counting-chloroplasts (D&Cchl) for automated detection and counting of chloroplasts. This approach utilizes a deep-learning-based object detection algorithm called You-Only-Look-Once (YOLO), along with the Intersection Over Union (IOU) strategy. The application of D&Cchl has shown excellent performance in accurately identifying and quantifying chloroplasts. This holds true when applied to both a single image and a three-dimensional (3D) structure composed of a series of images. Furthermore, by integrating Cellpose, a cell-segmentation tool, we were able to successfully perform single-cell 3D chloroplast counting. Compared to manual counting methods, this approach improved the accuracy of detection and counting to over 95%. Together, our work not only provides an efficient and reliable tool for accurately analyzing the status of chloroplasts, enhancing our understanding of plant photosynthetic cells and growth characteristics, but also makes a significant contribution to the convergence of botany and deep learning. One-sentence summaryThis deep learning-based approach enables the accurate complete detection and counting of chloroplasts in 3D single cells using microscopic image stacks, and showcases a successful example of utilizing deep learning methods to analyze subcellular spatial information in plant cells. The authors responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (https://academic.oup.com/plcell/) is: Zhao Dong (dongzhao@hebeu.edu.cn), Shaokai Yang, (shaokai1@ualberta.ca), Ningjing Liu (liuningjing1@yeah.net), and Qiong Zhao (qzhao@bio.ecnu.edu.cn).

cell biology↗

bioRxiv2023

Parallel Molecular Data Storage by Printing Epigenetic Bits on DNA

DNA storage has shown potential to transcend current silicon-based data storage technologies in storage density, lifetime and energy consumption. However, writing large-scale data directly into DNA sequences by de novo synthesis remains uneconomical in time and cost. Inspired by the natural genomic modifications, in this work, we report an alternative, parallel strategy that enables the writing of arbitrary data on DNA using premade nucleic acids. With epigenetic modifications as information bits, our system employed DNA self-assembly guided enzymatic methylation to perform movable-type printing on universal DNA templates. By programming with a finite set of 700 DNA movable types and 5 templates, we achieved the synthesis-free writing of [~]270000 bits on an automated platform with 350 bits written per reaction. The data encoded in complex epigenetic patterns were retrieved high-throughput by nanopore sequencing, and algorithms were developed to finely resolve 240 modification patterns per sequencing reaction. Our framework presents a new modality of DNA-based data storage that is parallel, programmable, stable and scalable. Such a modality opens up avenues towards practical data storage and dual-mode data functions in biomolecular systems.

biochemistry↗

bioRxiv2023

INHBA/Activin A promotes tumor growth and induces resistance to anti-PD-L1 therapy by suppressing IFN-γ signaling

Inhibin beta A (INHBA) and its homodimer activin A have pleiotropic effects on modulation of immune responses and tumor progression, respectively, but it remains uncertain whether tumors may release activin A to regulate anti-tumor immunity. As evidenced by our RNA-Seq and in vitro results, the interferon-{gamma} (IFN-{gamma}) signaling pathway was significantly down-regulated by tumor intrinsic activin A. Tumor INHBA deficiency led to lower expression of PD-L1 induced by IFN-{gamma}, resulting in poor responsiveness to anti-PD-L1 therapy. On the other hand, decreased secretion of IFN-{gamma}-stimulated chemokines, including C-X-C motif chemokine 9 (CXCL9) and 10 (CXCL10), impaired the infiltration of effector T cells into the tumor microenvironment. Furthermore, the activin A-specific antibody garetosmab improved anti-tumor immunity and its combination with the anti-PD-L1 antibody atezolizumab showed a superior therapeutic effect to monotherapy. Our findings reveal that INHBA/activin A is involved in anti-tumor immunity by inhibiting the IFN-{gamma} signaling pathway and considered to be a potential target to overcome anti-PD-L1 resistance in clinical cancer treatment.

immunology↗

bioRxiv2023

Comparative analysis of methodologies for detecting extrachromosomal circular DNA

Extrachromosomal circular DNA (eccDNA) is crucial in oncogene amplification, gene transcription regulation, and intratumor heterogeneity. While various analysis pipelines and experimental methods have been developed for eccDNA identification, their detection efficiencies have not been systematically assessed. To address this, we evaluated the performance of 7 analysis pipelines using three simulated datasets, in terms of accuracy, similarity, duplication rate, and computational resource consumption. We also compared the eccDNA detection efficiency of 7 experimental methods through twenty-one real sequencing datasets. Our results identified Circle-Map and CReSIL as the most effective pipelines for eccDNA detection through short-read and long-read sequencing data, respectively. Moreover, third-generation sequencing-based Circle-Seq showed superior efficiency in detecting copy number-amplified eccDNA over 10 kb in length. These results offer valuable insights for researchers in choosing the most suitable methods for eccDNA research.

cancer biology↗

bioRxiv2023

Polygonum Cillinerve polysaccharide inhibits Transmissible gastroenteritis virus by regulating microRNA-181

Transmissible gastroenteritis virus (TGEV) is an important pathogen that can cause changes in the expression profile of cell miRNA. In this study, PCP could treat piglets infected with TGEV by in vivo experiment. High-throughput sequencing technology was used to detect that 9 miRNAs were up-regulated and 17 miRNAs were down-regulated during PCP inhibition of TGEV infection in PK15 cells. Meanwhile, miR-181 was related to the target genes of key proteins of apoptosis pathway. PK15 was treated with different PCP after transfection of miR-181 mimic or inhibitor. Real-time PCR was used to detect the effect of TGEV replication, electron microscopy, TEM and Hoechst fluorescence staining were used to detect the biological function of the treated cells, and western blot (WB) was used to detect the expression of key signaling factors cyt C, caspase 9 and P53 in the apoptotic signaling pathway. The results showed that compared with the control group, 250 g/mL PCP significantly inhibited the replication of TGEV gRNA and gene N (P < 0.01). PK15 cells in PCP groups (250 and 125 g/mL) had uniform cell morphology and a small number of floating cells by microscopic, no typical virus structure was observed in 250 g/mL PCP group under TEM, and apoptosis staining showed that 250 g/mL PCP significantly reduced the number of apoptotic cells. PCP may inhibit TGEV-induced apoptosis through Caspase-dependent mitochondrial pathway after transfection of miR-181. The above results provided a theoretical basis for further research on the mechanism of PCP anti-TGEV.

pharmacology and toxicology↗

bioRxiv2023

Functional connectome through the human life span

The lifespan growth of the functional connectome remains unknown. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals aged 32 postmenstrual weeks to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively. After constructing a fine-grained, lifespan-wide suite of system-level brain atlases, we show distinct maturation timelines for functional segregation within different systems. Lifespan growth of regional connectivity is organized along a primary-to-association cortical axis. These connectome-based normative models reveal substantial individual heterogeneities in functional brain networks in patients with autism spectrum disorder, major depressive disorder, and Alzheimers disease. These findings elucidate the lifespan evolution of the functional connectome and can serve as a normative reference for quantifying individual variation in development, aging, and neuropsychiatric disorders.

neuroscience↗

bioRxiv2023

Next-generation anti-PD-L1/IL-15 immunocytokine elicits superior antitumor immunity in cold tumors with minimal toxicity

Immunocytokines, such as anti-PD-L1/IL-15, have shown promising efficacy in preclinical studies, but their clinical development still faces severe safety concerns, with the problem not easily overcome by simply reducing the cytokine activity. We proposed a next-generation immunocytokine concept of designing a tumor-conditional anti-PD-L1/IL-15 prodrug (LH05), which innovatively masks IL-15 with steric hindrance of its flanking moieties of anti-PD-L1 and IL-15R-sushi domain. The design successfully attenuated the cytokine sink effect of IL-15 and resulted in a significantly reduced systemic toxicity when compared to wild-type anti-PD-L1/IL-15. LH05 would be specifically cleaved in the tumor microenvironment (TME) to release the active IL-15/IL-15R-sushi domain (ILR) in a proteolytic cleavage-dependent manner and exhibited potent antitumor effects in mouse syngeneic models. Mechanistically, the antitumor efficacy of LH05 was dependent on both innate and adaptive immunity, which altered the TME to Th1-type by recruiting and stimulating both NK and CD8+ T cells and fired up cold tumors. LH05 also showed superior efficacy in restoring immunotherapy response in a refractory U251 xenograft model. Collectively, we introduced a novel next-generation immunocytokine strategy for tumor immunotherapy, contributing to the establishment of optimal treatment for patients with resistance to immune checkpoint inhibitors or cold tumors.

bioengineering↗

bioRxiv2023

Pontiella agarivorans sp. nov., a novel marine anaerobic bacterium capable of degrading macroalgal polysaccharides and fixing nitrogen

Marine macroalgae produce abundant and diverse polysaccharides which contribute substantially to the organic matter exported to the deep ocean. Microbial degradation of these polysaccharides plays an important role in the turnover of macroalgal biomass. Various members of the Planctomycetes-Verrucomicrobia-Chlamydia (PVC) superphylum are degraders of recalcitrant polysaccharides in widespread anoxic environments. In this study, we isolated a novel anaerobic bacterial strain NLcol2T from microbial mats on the surface of marine sediments offshore Santa Barbara, California, USA. Based on 16S rRNA gene and phylogenomic analyses, strain NLcol2T represents a novel species within the Pontiella genus in the Kiritimaitellaeota phylum (within the PVC superphylum). Strain NLcol2T is able to utilize various monosaccharides, disaccharides, and macroalgal polysaccharides such as agar and iota-carrageenan. A near-complete genome also revealed an extensive metabolic capacity for anaerobic degradation of sulfated polysaccharides, as evidenced by 202 carbohydrate-active enzymes (CAZymes) and 165 sulfatases. Additionally, its ability of nitrogen fixation was confirmed by nitrogenase activity detected during growth on nitrogen-free medium, and the presence of nitrogenases (nifDKH) encoded in the genome. Based on the physiological and genomic analyses, this strain represents a group of bacteria that may play an important role in the degradation of macroalgal polysaccharides and with relevance to the biogeochemical cycling of carbon, sulfur, and nitrogen in marine environments. Strain NLcol2T (= DSM 113125T = MCCC 1K08672) is proposed to be the type strain of a novel species in Pontiella genus, and the name Pontiella agarivorans sp. nov. is proposed.

microbiology↗

bioRxiv2023

standR: a Bioconductor package for analysing transcriptomic Nanostring GeoMx DSP data

To gain a better understanding of the complexity of gene expression in normal and diseased tissues it is important to account for the spatial context and identity of cell in situ. State-of-the-art spatial profiling technologies, such as the Nanostring GeoMx Digital Spatial Profiler (DSP), now allow quantitative spatially resolved measurement of the transcriptome in tissues. However, the bioinformatics pipelines currently used to analyse GeoMx data often fail to successfully account for the technical variability within the data and the complexity of experimental designs, thus limiting the accuracy and reliability of subsequent analysis. Carefully designed quality control workflows, that include in-depth experiment-specific investigations into technical variation and appropriate adjustment for such variation can address this issue. Here we present standR, a R/Bioconductor package that enables an end-to-end analysis of GeoMx DSP data. With four case studies from previously published experiments, we demonstrate how the standR workflow can enhance the statistical power of GeoMx DSP data analysis and how application of standR enables scientists to develop in-depth insights into the biology of interest.

bioinformatics↗

bioRxiv2023

Epitranscriptome analysis of NAD-capped RNA by spike-in-based normalization

The hub metabolite, nicotinamide adenine dinucleotide (NAD), can be used as an initiating nucleotide in RNA transcription to result in NAD-capped RNA (NAD-RNA). NAD-RNA that intimately connects metabolite with gene expression can be developed as novel biomarkers for aging and disease. Epitranscriptome-wide profiling of NAD-RNAs involves chemo-enzymatic labeling and affinity-based enrichment; yet currently available computational analysis cannot adequately remove variations associated with capture procedures. Here, we propose a spike-in-based normalization and data-driven evaluation framework, enONE, for the omic-level analysis of NAD-capped RNAs. We demonstrate that carefully designed spike-in RNAs, together with modular normalization procedures and evaluation metrics, can lead to the optimal normalization that maximally removes unwanted variations, empowering quantitative and comparative assessment of NAD-RNAs from different datasets. Using enONE and a human aging cohort, we reveal critical features of NAD-capped RNAs that occur with normal age. enONE facilitates the discovery of NAD-capped RNAs that are responsive to physiological changes, laying a critical foundation for functional investigations into this modification.

bioinformatics↗

bioRxiv2023

Library size confounds biology in spatial transcriptomics data

Spatial molecular technologies have revolutionised the study of disease microenvironments by providing spatial context to tissue heterogeneity. Recent spatial technologies are increasing the throughput and spatial resolution of measurements, resulting in larger datasets. The added spatial dimension and volume of measurements poses an analytics challenge that has, in the short-term, been addressed by adopting methods designed for the analysis of single-cell RNA-seq data. Though these methods work well in some cases, not all necessarily translate appropriately to spatial technologies. A common assumption is that total sequencing depth, also known as library size, represents technical variation in single-cell RNA-seq technologies, and this is often normalised out during analysis. Through analysis of several different spatial datasets, we noted that this assumption does not necessarily hold in spatial molecular data. To formally assess this, we explore the relationship between library size and independently annotated spatial regions, across 23 samples from 4 different spatial technologies with varying throughput and spatial resolution. We found that library size confounded biology across all technologies, regardless of the tissue being investigated. Statistical modelling of binned total transcripts shows that tissue region is strongly associated with library size across all technologies, even after accounting for cell density of the bins. Through a benchmarking experiment, we show that normalising out library size leads to sub-optimal spatial domain identification using common graph-based clustering algorithms. On average, better clustering was achieved when library size effects were not normalised out explicitly, especially with data from the newer sub-cellular localised technologies. Taking these results into consideration, we recommend that spatial data should not be specifically corrected for library size prior to analysis unless strongly motivated. We also emphasise that spatial data are different to single-cell RNA-seq and care should be taken when adopting algorithms designed for single cell data.

bioinformatics↗

bioRxiv2023

Myosin VIII and XI isoforms interact with Agrobacterium VirE2 protein and help direct transport from the plasma membrane to the perinuclear region during plant transformation

Virulent Agrobacterium strains transfer single-strand T-DNA (T-strands) and virulence effector proteins into plant cells. VirE2, one of these virulence effectors, enters the plant cell and is thought to bind T-strands, protecting them from nuclease degradation and helping guide them to the nucleus. How VirE2 is trafficked inside the plant cell is not fully understood. Using bimolecular fluorescence complementation, in vitro pull-down, yeast two-hybrid, and in vivo co-immunoprecipitation assays, we found that VirE2 binds directly to the cargo binding domains of several myosin VIII family members, and to myosin XI-K. We observed reduced susceptibility of several Arabidopsis actin mutants and a myosin VIII-1/2/a/b quadruple mutant to Agrobacterium-mediated transformation. Expression of cargo binding domains of myosin VIII-1, VIII-2, VIII-A, or VIII-B in transgenic plants inhibits Arabidopsis root transformation. However, none of the myosin VIII proteins contribute to the intracellular trafficking of VirE2. Expression of myosin VIII-2, -A, -B, but not VIII-1, cDNAs in the myosin VIII-1/2/a/b mutant partially restored transformation. Furthermore, functional fluorescently-tagged VirE2, synthesized in plant cells, relocalized from the cellular periphery into the cytoplasm after delivery of T-strands from Agrobacterium. Surprisingly, mutation of myosin XI-k and expression of the myosin XI-K cargo binding domain had no effect on transformation, although it blocked VirE2 movement along actin filaments. We hypothesize that myosin VIII proteins facilitate VirE2 tethering to the plasma membrane and are required for efficient localization of VirE2 to membrane sites from which they bind incoming T-strands. Myosin XI-K is important for VirE2 movement through the cytoplasm towards the nucleus.

plant biology↗

bioRxiv2023

Spatial transcriptomic atlas of shoot organogenesis in tomato callus

Callus is a reprogrammed transitional cell mass during plant regeneration. Pluripotent callus cells develop into fertile shoots through de novo shoot organogenesis. This study represents a pioneering effort in exploring the spatial transcriptome of tomato callus during shoot regeneration, using technologies including BGI Stereo-seq, BMKMANU S1000, and 10x Visium. The results indicate that the callus comprises highly heterogeneous cells, classified into various cell types based on spatial gene expression and histological observation, including epidermis, shoot primordium, vascular tissue, inner callus, and outgrowth shoots. The developmental trajectories from shoot primordium to outgrowth shoot are traced, and vascular tissue development is characterized. The single-cell resolution spatial approach reveals the origin of shoot primordia from the sub-epidermis. The spatial full length RNA sequencing shows high incompletely spliced (IS) ratios in the shoot primordium cells. These findings enhance our knowledge of plant organogenesis and highlight the significance of spatial biology in plant research.

cell biology↗

bioRxiv2023

Sequential restoring O2 then unloading CO2 is beneficial against reperfusion injury: role of CO2 in metabolism

CO2 is one of main byproducts during mitochondrial oxidation. Under the acute occlusion of coronary artery situation, the intra-tissue pCO2 of heart could be extremely high. This CO2 accumulation will be acutely unloaded and discharged by blood reperfusion. However, the effect of this intra-tissue CO2 accumulation then unloading process on cardiac ischemic reperfusion injury has not been well investigated yet. In the present study, we show that the perfusion with a high level of pCO2 and normal pO2 in the initial 30min followed by a 30min normal pCO2 and normal pO2 is better than the perfusion with 1h normal pCO2 and normal pO2 simultaneously during the reperfusion after a 45min global ischemia in isolated rat hearts. To observe the effect of high pCO2 on cellular metabolism, we exposed C2C12 cells under about 370mmHg pCO2 to observe the mitochondrial substrate switch and TCA cycle flux change, by using 13C tracers. We show that a short time exposure to the extremely high level of pCO2 is not completely destructive for cellular metabolism but has specific effects. The high pCO2 inhibits pyruvate transport into mitochondria and the next oxidation, switching to more reliance on fatty acid oxidation and enhancing the glutamine oxidation to maintain the TCA cycle. Intriguingly, the high pCO2 significantly activates the reductive carboxylation from glutamine, fixation of mitochondrial excessive CO2. The mechanism under the beneficial effect of the high-then-low CO2 sequential reperfusion strategy is discussed further.

biochemistry↗

bioRxiv2023

Compressed phenotypic screens for complex multicellular models and high-content assays

High-throughput phenotypic screens leveraging biochemical perturbations, high-content readouts, and complex multicellular models could advance therapeutic discovery yet remain constrained by limitations of scale. To address this, we establish a method for compressing screens by pooling perturbations followed by computational deconvolution. Conducting controlled benchmarks with a highly bioactive small molecule library and a high-content imaging readout, we demonstrate increased efficiency for compressed experimental designs compared to conventional approaches. To prove generalizability, we apply compressed screening to examine transcriptional responses of patient-derived pancreatic cancer organoids to a library of tumor-microenvironment (TME)-nominated recombinant protein ligands. Using single-cell RNA-seq as a readout, we uncover reproducible phenotypic shifts induced by ligands that correlate with clinical features in larger datasets and are distinct from reference signatures available in public databases. In sum, our approach enables phenotypic screens that interrogate complex multicellular models with rich phenotypic readouts to advance translatable drug discovery as well as basic biology.

systems biology↗

bioRxiv2022

Decoding frontotemporal and cell type-specific vulnerabilities to neuropsychiatric disorders and psychoactive drugs

Abnormalities in temporal and frontal lobes (TL and FL) have been linked to cognition and neuropsychiatric disorders. While structural and functional differences between the brain lobes have been documented in disease, the cellular heterogeneity in FL and TL and its impact to the vulnerability to genetic risk factors for neuropsychiatric disorders is not well studied. We hypothesize that intrinsic cellular-level differences between TL and FL explain the vulnerability of specific cell types to genetic risk factors and psychoactive drugs. To test this, we integrated single-nucleus transcriptome analysis in fresh human FL and TL with data related to genetic susceptibility and gene dysregulation in neuropsychiatric disease, and response to psychoactive drugs. We also investigate how these differences are associated with gene dysregulation in disease brain. Neuronal cell populations were the most vulnerable to psychiatric genetic risk factors, and more specifically parvalbumin interneurons (PVALB neurons). These PVALB-expressed genetic risk factors were mostly upregulated in the TL compared with FL, and dysregulated in the brain of patients with obsessive-compulsive disorder, bipolar disorder and schizophrenia. We found GRIN2A and HCN1, implicated in schizophrenia by genome-wide association studies, to be significantly upregulated in PVLAB from the TL and in brain cortex from schizophrenia patients. Our analysis provides comprehensive evidence for PVALB neurons as the most vulnerable cell type that is implicated in several psychiatric disorders. PVALB neurons showed the highest vulnerability to psychoactive drug response, which was 3.6-fold higher than the vulnerability to genetic risk factors. In summary, we show high vulnerability of PVALB neurons that is specific to the temporal lobe, implying that differences between TL and FL greatly influence the cell vulnerability to genetic risk factors as well as the response to psychoactive drugs. These findings offer insights into how regional brain differences affect the cell type vulnerabilities in neuropsychiatric disorders.

neuroscience↗

bioRxiv2022

Graphene Biointerface for Cardiac Arrhythmia Diagnosis and Treatment

Heart rhythm disorders, known as arrhythmias, cause significant morbidity and are one of the leading causes of mortality. Cardiac arrhythmias are primarily treated by implantable devices, such as pacemakers and defibrillators, or by ablation therapy guided by electroanatomical mapping. Pharmacological treatments are mostly ineffective. Both implantable and ablation therapies require sophisticated biointerfaces for electrophysiological measurements of electrograms and delivery of therapeutic stimulation or ablation energy. In this work, we report for the first time on graphene biointerface for in vivo cardiac electrophysiology. Leveraging sub-micrometer thick tissue-conformable graphene arrays, we demonstrate sensing and stimulation of the open mammalian heart both in vitro and in vivo. Furthermore, we demonstrate graphene pacemaker treatment of a pharmacologically-induced arrhythmia, AV block. The arrays show effective electrochemical properties, namely interface impedance down to 40 Ohmxcm2 at 1kHz, charge storage capacity up to 63.7 mC/cm2, and charge injection capacity up to 704 C/cm2. Transparency of the graphene structures allows for simultaneous optical mapping of cardiac action potentials and optogenetic stimulation while performing electrical measurements and stimulation. Our report presents evidence of the significant potential of graphene biointerfaces for the future clinical device- and catheter-based cardiac arrhythmias therapies.

bioengineering↗