Exciting news! Scientists have developed a revolutionary toolkit, SPACEL, for Spatial Transcriptomics to uncover the mysteries of mRNA expression in individual cells while maintaining their spatial coordinates. This cutting-edge toolkit, created by Prof. Qu Kun and their team from the University of Science and Technology of the Chinese Academy of Sciences, boasts three modules—Spoint, Splane, and Scube—which combine to create a 3D panorama of tissues automatically.
The first module, Sprint, tackles the cell-type deconvolution task. It predicts the spatial distribution of cell types using a combination of simulated pseudo-spots, neural network modeling, and statistical recovery of expression profiles. This makes predictions accurate and powerful. The second module, Splane, utilizes a graph convolutional network (GCN) approach and an adversarial learning algorithm to identify special domains by jointly analyzing multiple ST slices. Splane uses adversarial training to remove batch effects over several slices and uses cell-type composition as input. Splane stands out for its innovative method of efficiently identifying spatial domains. The third module, Scube, automates the alignment of slices and constructs a stacked 3D architecture of the tissue. This is crucial in overcoming the challenges posed by the limitations of experimental ST techniques, allowing for a comprehensive understanding of the tissue’s three-dimensional structure.
The researchers applied SPACEL to 11 ST datasets totaling 156 slices and utilized technologies like 10X Visium, STARmap, MERFISH, Stereo-seq, and Spatial Transcriptomics. Their results? SPACEL outperformed previous techniques in three fundamental analytical tasks—cell type distribution prediction, spatial domain identification, and three-dimensional tissue reconstruction. With its superior performance and simplified approach, SPACEL is a major step forward in Spatial Transcriptomics!
This incredible toolkit is revolutionizing the way scientists examine tissues by analyzing the expression levels of genes in individual cells using a technique known as spatial transcriptomics (ST). Thanks to its three modules, SPACEL allows for accurate 3D tissue alignment, cell type predictions, and efficient spatial domain identification. This means researchers can gain insights into cells’ spatial organization and function by measuring the quantity of RNA in specific locations within a tissue.
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