Each cell’s individual phenotype together with its location in space relative to other cells captures information about this process. This organization is orchestrated via signals that include both physical interactions via cell-cell contact, chemical signals, and even exosome-mediated transfer of RNAs between cells. Ĭells organize themselves spatially within tissues and organisms in order to carry out specific functions. Although spatial omics technologies are in their infancy, the launch of commercial products such as the 10X Genomics Visium platform has led to increased interest in methods that will allow the analysis and interpretation of data generated. More recently, sci-Space has allowed single-cell RNA sequencing, but it is only capable of capturing a small proportion of cells in a tissue. Threedimensional intact tissue sequencing in single cells has been achieved by STARmap (spatially-resolved transcript amplicon readout mapping), which is capable of measuring simultaneously the expression of about 1000 genes at single-cell resolution. In situ hybridization or sequencing methods can measure the expression of many genes with single-cell resolution (and even subcellular resolution), but these methods require complex instrumentation and long imaging times. A drawback of these early methods was that mRNAs from multiple cells in a small region could contribute to the observed signal, masking differences between cells and cellcell interactions.
Methods such as “spatial tran-scriptomics” and Slide-Seq used molecular barcoding to count mRNAs aggregated in small regions and integrated those with images to produce a transcript map. Measurements of spatially resolved RNA or protein expression patterns open unprecedented opportunities to study questions in areas such as developmental biology and pathophysiology, where interactions between cells are known to influence a wide range of processes. 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. “SpaCeNet” is a method designed to elucidate both the intracellular molecular networks (capturing how molecular variables affect each other within the cell) and the intercellular molecular networks (how cells affect molecular variables in their neighbors). 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.
These interactions and their responses shape the individual molecular phenotype of a cell in a given microenvironment. Cells send chemical and mechanical signals which are received by other cells and transduced through signaling cascades from the membrane to the nucleus, where they can subsequently initiate context-specific gene regulatory responses. 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.
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