A single cell transcriptomics map of paracrine networks in the intrinsic cardiac nervous system

Moss, Alison; Robbins, Shaina; Achanta, Sirisha; Kuttippurathu, Lakshmi; Turick, Scott; Nieves, Sean; Hanna, Peter; Smith, Elizabeth H.; Hoover, Donald B.; Chen, Jin; Cheng, Zixi; Ardell, Jeffrey L.; Shivkumar, Kalyanam; Schwaber, James S.; Vadigepalli, Rajanikanth

doi:10.1016/j.isci.2021.102713

Cited by 14 publications

(18 citation statements)

References 73 publications

(130 reference statements)

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“…Recent studies have explored the functional neuroanatomy of the cardiac intrinsic nervous system with particular attention to innervation of the SAN. In a recent single cell qPCR analysis of porcine heart, CCK-expressing neurons were identified as a subset of right atrial ganglionated plexus neurons and included cells that projected directly to the SAN 26 . Using a Cck Cre mouse line to drive reporter gene expression, we uncovered similar findings in mice, with Cre-expressing neurons observed in the RAGP and in projections within the SAN, findings that we confirmed with analysis of scRNA-seq data.…”

Section: Discussionmentioning

confidence: 99%

Cholecystokinin-A Signaling Regulates Automaticity of Pacemaker Cardiomyocytes

Ruan

Mandla

Ravi

et al. 2023

Preprint

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Aims: The behavior of pacemaker cardiomyocytes (PCs) in the sinoatrial node (SAN) is modulated by neurohormonal and paracrine factors, many of which signal through G-protein coupled receptors (GPCRs). The aims of the present study are to catalog GPCRs that are differentially expressed in the mammalian SAN and to define the acute physiological consequences of activating the cholecystokinin-A signaling system in the SAN and in isolated PCs. Methods and Results: Using bulk and single cell RNA sequencing datasets, we identify a set of GPCRs that are differentially expressed between SAN and right atrial tissue, including several whose roles in PCs and in the SAN have not been thoroughly characterized. Focusing on one such GPCR, Cholecystokinin-A receptor (CCKAR), we demonstrate expression of Cckar mRNA specifically in mammalian PCs, and further demonstrate that subsets of SAN fibroblasts and neurons within the cardiac intrinsic nervous system express cholecystokinin, the ligand for CCKAR. Using mouse models, we find that while baseline SAN function is not dramatically affected by loss of CCKAR, the firing rate of individual PCs is slowed by exposure to sulfated cholecystokinin-8 (sCCK-8), the high affinity ligand for CCKAR. The effect of sCCK-8 on firing rate is mediated by reduction in the rate of spontaneous phase 4 depolarization of PCs and is mitigated by activation of beta-adrenergic signaling. Finally, while perfusion of sCCK-8 onto the SAN ex-vivo reduces the beating rate modestly, exposure to sCCK-8 unexpectedly shortened the sinus node recovery time, a finding that has potential implications for treatment of SAN dysfunction. Conclusions: (1) PCs express many GPCRs whose specific roles in SAN function have not been characterized, (2) the cholecystokinin-A pathway constitutes a novel signaling system that regulates SAN function and PC electrophysiology.

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Section: Discussionmentioning

confidence: 99%

Cholecystokinin-A Signaling Regulates Automaticity of Pacemaker Cardiomyocytes

Ruan

Mandla

Ravi

et al. 2023

Preprint

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“…For single-cell ST data, the cell type with the maximum coefficient was assigned to each cell. For spot-based ST data, let M be the maximum cell number for each spot, which was set to 30 for 10x Visium data and was set to 1 for Slide-seq data according to a recent review 12 . In practice, the optimal cellular combination ω for each spot was determined by the following function: wherein and represent the integer and fractional parts of , respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Recent technological advances in spatially resolved transcriptomics (ST) benefiting from spatial barcoding and imaging-based approaches have enabled the measurement of whole or mostly whole transcriptomes while retaining the spatial information 10 , which have been increasingly adopted to generate useful insights in the biological and biomedical domains, with dramatically improved accuracy and reliability in the inference of spatially proximal cell–cell communications 11 . Given the space-constrained nature of juxtacrine and paracrine signaling, such spatial gene expression information is vital to understand cell–cell communications mediating tissue homeostasis, development, and disease 12 , 13 .…”

Section: Introductionmentioning

confidence: 99%

Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk

Shao

Yang

et al. 2022

Nat Commun

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Spatially resolved transcriptomics provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells by dissecting cell-type composition through a non-negative linear model and spatial mapping between single-cell transcriptomic and spatially resolved transcriptomic data. The benchmarked performance of SpaTalk on public single-cell spatial transcriptomic datasets is superior to that of existing inference methods. Then we apply SpaTalk to STARmap, Slide-seq, and 10X Visium data, revealing the in-depth communicative mechanisms underlying normal and disease tissues with spatial structure. SpaTalk can uncover spatially resolved cell-cell communications for single-cell and spot-based spatially resolved transcriptomic data universally, providing valuable insights into spatial inter-cellular tissue dynamics.

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“…Recent technological advances in spatially resolved transcriptomics (ST) benefiting from spatial barcoding and imaging-based approaches have enabled the measurement of whole or mostly whole transcriptomes while retaining the spatial information 10, 11 , which have been increasingly adopted to generate new insights in the biological and biomedical domains, with dramatically improved accuracy and reliability in the inference of spatially proximal cell-cell communications 12 . Given the space-constrained nature of juxtacrine and paracrine signaling, such spatial gene expression information is vital to understand cell-cell communications mediating tissue homeostasis, development, and disease 13, 14 .…”

Section: Introductionmentioning

confidence: 99%

Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk

Shao

Yang

et al. 2022

Preprint

View full text Add to dashboard Cite

Spatially resolved transcriptomics (ST) provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications from ST data, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells, decomposed from ST data through a non-negative linear model and spatial mapping between single-cell RNA-sequencing and ST data. The performance of SpaTalk benchmarked on public single-cell ST datasets was superior to that of existing cell-cell communication inference methods. SpaTalk was then applied to STARmap, Slide-seq, and 10X Visium data, revealing the in-depth communicative mechanisms underlying normal and disease tissues with spatial structure. SpaTalk can uncover spatially resolved cell-cell communications for single-cell and spot-based ST data universally, providing new insights into spatial inter-cellular dynamics.

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A single cell transcriptomics map of paracrine networks in the intrinsic cardiac nervous system

Cited by 14 publications

References 73 publications

Cholecystokinin-A Signaling Regulates Automaticity of Pacemaker Cardiomyocytes

Cholecystokinin-A Signaling Regulates Automaticity of Pacemaker Cardiomyocytes

Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk

Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk

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