Spatial Cellular Networks from omics data with SpaCeNet
- Stefan Schrod1,9,
- Niklas Lück1,9,
- Robert Lohmayer2,
- Stefan Solbrig3,
- Dennis Völkl3,
- Tina Wipfler3,
- Katherine H. Shutta4,5,
- Marouen Ben Guebila4,
- Andreas Schäfer3,
- Tim Beißbarth1,6,
- Helena U. Zacharias7,
- Peter J. Oefner8,
- John Quackenbush4,5 and
- Michael Altenbuchinger1
- 1Department of Medical Bioinformatics, University Medical Center Göttingen, 37077 Göttingen, Germany;
- 2Leibniz Institute for Immunotherapy, 93053 Regensburg, Germany;
- 3Institute of Theoretical Physics, University of Regensburg, 93053 Regensburg, Germany;
- 4Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA;
- 5Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA;
- 6Campus Institute Data Science (CIDAS), University of Göttingen, 37077 Göttingen, Germany;
- 7Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover Medical School, 30625 Hannover, Germany;
- 8Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany
Abstract
Advances in omics technologies have allowed spatially resolved molecular profiling of single cells, providing a window not only into the diversity and distribution of cell types within a tissue, but also into the effects of interactions between cells in shaping the transcriptional landscape. Cells send chemical and mechanical signals which are received by other cells, where they can subsequently initiate context-specific gene regulatory responses. These interactions and their responses shape the individual molecular phenotype of a cell in a given microenvironment. RNAs or proteins measured in individual cells, together with the cells' spatial distribution, provide invaluable information about these mechanisms and the regulation of genes beyond processes occurring independently in each individual cell. “SpaCeNet” is a method designed to elucidate both the intracellular molecular networks (how molecular variables affect each other within the cell) and the intercellular molecular networks (how cells affect molecular variables in their neighbors). This is achieved by estimating conditional independence (CI) relations between captured variables within individual cells and by disentangling these from CI relations between variables of different cells.
Footnotes
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↵9 The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.279125.124.
- Received February 15, 2024.
- Accepted August 27, 2024.
This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
