GSE184510
Spatial transcriptomics: The effect of consecutive slices data integration on accurate cell type annotation and clustering
In 10X Genomics Visium Spatial Gene Expression (ST), the resolution for distinguishing neighboring cells can be improved using data integration with single-cell/single-nuclei transcriptomics profiles. Besides, depending on the cell type and tissue, nuclei size may vary significantly to an extent that it may exceed the thickness of tissue slices. This may jeopardize capturing full transcriptomics profile of single slice due to the improper/incomplete incision of nuclei during cryosectioning process and this may cause drawbacks in downstream analysis.   To monitor the probable consequences, we monitored the effect of consecutive slices data integration (CSDI) on improvement of cell type clustering and annotation through transferring cell labels from a single-nuclei transcriptomics dataset to ST. To do so, two consecutive slices from the orbitofrontal neocortex and temporal neocortex of two post mortem brain samples were obtained and their spatial transcriptomics profiles were retrieved using 10x Genomics Visium Spatial Gene Expression protocol.     Using CSDI, not only the number of identified clusters were increased and the inconsistency between the pattern of clusters in consecutive slices was resolved, but the layered-structure of gray matter was unveiled. Besides, only after CSDI the transferred annotations from single-nuclei transcriptomics to ST could match the microscopic results.  CSDI can improve the ST clustering and cell type annotation by providing the full signals coming from all cell types of single slice of tissue. The codes in R programming language are publicly available at https://github.com/ElyasMo/ST_snRNA-seq Overall design: Four pairs of consecutive slices from Orbitofrontal and Temporal neocortex of post mortem brain samples