Spatial Cellular Networks from omics data with SpaCeNet
- Stefan Schrod1,
- Niklas Lück1,
- Robert Lohmayer2,
- Stefan Solbrig3,
- Dennis Völkl3,
- Tina Wipfler3,
- Katherine H. Shutta4,
- Marouen Ben Guebila4,
- Andreas Schäfer3,
- Tim Beißbarth1,
- Helena U. Zacharias5,
- Peter Oefner6,
- John Quackenbush4 and
- Michael Altenbuchinger1,7
- 1 University Medical Center Göttingen;
- 2 Leibniz Institute for Immunotherapy;
- 3 Institute of Theoretical Physics, University of Regensburg;
- 4 Harvard T.H. Chan School of Public Health;
- 5 Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover Medical School;
- 6 Institute of Functional Genomics, University of Regensburg
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 relations between captured variables within individual cells and by disentangling these from conditional independence relations between variables of different cells.
- Received February 15, 2024.
- Accepted August 27, 2024.
- Published by Cold Spring Harbor Laboratory Press
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