About this Research Topic
Intracrinology is the study of hormonal communication between various cell types within tissues, as opposed to endocrine signaling which relies on circulating hormones as messengers between tissues. The endocrine field is robustly supported by clinical biochemical approaches to measure systemic hormones in blood, with the field led by gold-standard mass spectrometry approaches. At a tissue level, hormone action has been inferred from gene expression changes, but only recently have we been able to apply approaches which localize hormones or biomarkers of their downstream actions in a spatial manner. Techniques such as single-cell sequencing, spatial transcriptomics and mass spectrometry imaging are opening doors to localize hormone action within individual cells or cells clusters, but the field is still in its infancy. Hormone signaling in brain sub regions or developing tumors are only two examples of research areas which would benefit from regional datasets. Challenges are faced in the technical execution of these studies as well as analysis of the complex datasets. Integration of genomic and metabolomic datasets will offer rich insights into hormonal mechanisms of health and disease and this themed issue aims to highlight the potential of spatial analysis, while providing a true representation of the current state of play in our abilities to perform such studies.
Details for Authors:
We are seeking papers in the form of Original Research as well as review and mini-review articles. Methods papers and Technology and Code articles advancing the field are also welcome. Titles for reviews/mini-reviews should be submitted initially for approval to ensure diversity of content. Papers demonstrating the complexities hormonal action of in tissues with multi-compartmental architecture are of interest. We would like to receive in papers showing innovative approaches to visualising intracrine signaling at a transcriptomic or metabolomic level, including those showing the added value obtained from integrating omics datasets.
Keywords: Spatial Intracrinology, Transcriptomics, Mass Spectrometry Imaging, MALDI, Histology, Spatial Transcriptomics
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