Fig. 2: Live-seq enables the stratification of cell type and state (treatment). | Nature

Fig. 2: Live-seq enables the stratification of cell type and state (treatment).

From: Live-seq enables temporal transcriptomic recording of single cells

Fig. 2

a, Experimental setup. 100 nM LPS or PBS was used for RAW_LPS and RAW_Mock, respectively. A chemical cocktail was used to differentiate ASPCs (Methods). ASPC_Pre and ASPC_Post denotes ASPCs pre- and post- (2 days)-adipogenic differentiation induction. b, t-SNE projection of Live-seq data coloured by cell type/states. n = 61 for ASPC_Pre, n = 37 for ASPC_Post, n = 44 for IBA, n = 102 for RAW_Mock, n = 50 for RAW_LPS. c, t-SNE projection of scRNA-seq data (Smart-seq2) coloured by cell type or state. n = 60 for ASPC_Pre, n = 35 for ASPC_Post, n = 153 for IBA, n = 157 for RAW_Mock, n = 149 for RAW_LPS. d, Gene expression correlation (Pearson’s r) between all the cells from both scRNA-seq and Live-seq. e, A direct comparison of the log fold gene expression change derived from Live-seq and scRNA-seq data when comparing the cluster of cells corresponding to a focal cell state (here, left, RAW_Mock and left, RAW_LPS) to the rest of all the cells (Methods). For the correlation, P = 2.2 × 10−16 for all conditions (All genes, DE scRNA-seq and DE both), two-sided F-test. f, g, Visualization of Live-seq and scRNA-seq data after anchor-based data integration (Methods) reveals no obvious molecular differences. t-SNE projection of the integrated Live-seq and scRNA-seq data according to cell type and state (treatment) (f) and approach (g).

Back to article page