Characterization of Glial Cells as a part of the Intracardiac Nervous System

https://doi.org/10.1007/s00392-024-02526-y

Amin Daryaie (Düsseldorf)1, K. Bekirî (Düsseldorf)1, C. Ungefug (Düsseldorf)1, Y.-W. Dai (Düsseldorf)1, J. A. Gomez-Sanchez (San Juan de Alicante)2, N. Erlenhardt (Düsseldorf)1, N. Klöcker (Düsseldorf)1, C. Meyer (Düsseldorf)3, K. Scherschel (Düsseldorf)1

1Universitätsklinikum Düsseldorf Institut für Neuro- und Sinnesphysiologie Düsseldorf, Deutschland; 2Universidad Miguel Hernández Instituto de Neurociencias San Juan de Alicante, Spanien; 3Evangelisches Krankenhaus Düsseldorf Klinik für Kardiologie Düsseldorf, Deutschland

 

Background:
The autonomic nervous system of the murine heart is a highly specialized network of approx. 1000 intracardiac neurons and glial cells. Independent of the species, innervation of cardiac regions vary in density in well-established gradients. Glia are heterogeneous cells assisting neurons in their function and initial evidence suggests they might modulate heart rhythm and rate. Despite their increasingly recognized significance, cardiac glia remain inadequately understood. Therefore, we aimed to characterize cell numbers and distribution patterns of intracardiac glial cells. 

Methods:
Hearts from reporter mice expressing the fluorescent protein tdTomato under the glial Plp1 promoter (both sexes; age 13-27 weeks) were sectioned (100 µm) from apex to base and digitalized. A software-based protocol using Fiji was established to quantify glia in absolute numbers as well as tdTomato-positive area from every third section in n=2 hearts and to subsequently visualize their distribution pattern. TdTomato-positive area was normalized to section size to calculate glial density. Costaining with the glial marker S100B and neuronal marker tyrosine hydroxylase (TH) was performed to assess neuro-glial distribution, quantified by the overlap coefficient r (range 0-1, r=1 with 1 indicating a 100% overlap) in Fiji. 

Results:
Cardiac cross sections contained 218-877 glia per section for heart 1 and 244-943 for heart 2. Cardiac glia show a specific distribution pattern, with a higher density on the posterior wall of the heart compared to anterior wall (0.21±0.01% vs 0.17±0.01%, p= 0.01 for heart 1 and 0.2±0.02% vs 0.17±0.02%, p= 0.03 for heart 2). Right ventricle contained a higher density of cardiac glia compared to left ventricle (0.29±0.01% vs 0.22±0.02%, p<0.001 for heart 1 and 0.27±0.01% vs 0.18±0.01%, p=0.0002 for heart 2), while septum had a lower density in comparison to the lateral wall (0.26±0.03% vs 0.32±0.02%, p=0.06 for heart 1 and 0.27±0.04% vs 0.39±0.03%, p=0.003 for heart 2). The total glial density across all regions was 0.23±0.006% for heart 1 and 0.21±0.006% for heart 2 (p=0.01). The overlap analysis showed a strong positive correlation between coexistence of glia marker S100B with neuronal marker TH (r =0.71). 

Conclusion: 
In conclusion, glial cells outnumber intracardiac neurons in the murine heart, while mirroring the distribution pattern of cardiac nerve gradients. Our findings highlight the need to further understand the role of glial cells in autonomic regulation of the heart.
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