The significance of single cell transcription resides not only in the cumulative expression strength of the cell population but also in its heterogeneity. Researchers at the Baylor Institute for Immunology Research propose a new model that improves the detection of changes in the transcriptional heterogeneity pattern of RNAseq data using two heterogeneity parameters: “burst proportion” and “burst magnitude”, whose changes are validated using RNA FISH. Transcriptional “cobursting” governed by distinct mechanisms during myoblast proliferation and differentiation, is described here.
Single-cell RNA-Seq reveals transcriptional ‘co-bursting’. (a) The identified coexpression network for human myoblast differentiation at T0 from single-cell RNA-Seq data. The color bar represents the degree of bimodality, where red color denotes π1 is close to 0.5 (strong bimodality) and white color denotes that π1 is close to 0 (rare expression) or 1 (housekeeping expression). (b) The identified co-expression network for human myoblast differentiation at T24 from single-cell RNA-Seq data. (c) Heatmap of co-bursting genes in T0. (d) Heatmap of co-bursting genes in T24. (e) Top enriched motifs for co-bursting genes in T0. (f) Top enriched motifs for co-bursting genes in T24.
Availability – The R package of Sphinx method is freely available at: