Sampling strategies to capture single-cell heterogeneity

Microbial individuality: how single-cell heterogeneity enables population level strategies.

Much of our knowledge of microbial life is only a description of average population behaviours, but modern technologies provide a more inclusive view and reveal that microbes also have individuality. It is now acknowledged that isogenic cell-to-cell heterogeneity is common across organisms and across different biological processes. This heterogeneity can be regulated and functional, rather than...

Heterogeneity of single-cell mechanical responses to tumorigenic factors

Tumor development progresses through a complex path of biomechanical changes leading first to cell growth and contraction followed by cell de-adhesion, scattering and invasion. Tumorigenic factors may act specifically on one of these steps or have wider spectrum of actions, leading to a variety of effects and thus sometimes to apparent contradictory outcomes. Here we used micropatterned lines o...

Using single-cell multiple omics approaches to resolve tumor heterogeneity

It has become increasingly clear that both normal and cancer tissues are composed of heterogeneous populations. Genetic variation can be attributed to the downstream effects of inherited mutations, environmental factors, or inaccurately resolved errors in transcription and replication. When lesions occur in regions that confer a proliferative advantage, it can support clonal expansion, subclona...

Capture–recapture sampling designs

Detecting heterogeneity in single-cell RNA-Seq

21 Single-cell RNA-Sequencing (scRNA-Seq) is a cutting edge technology that enables the 22 understanding of biological processes at an unprecedentedly high resolution. However, 23 well suited bioinformatics tools to analyze the data generated from this new technology 24 are still lacking. Here we have investigated the performance of non-negative matrix 25 factorization (NMF) method to analyze a...