Weiss, Z. | bioRxiv Science

bioRxiv2019

Mucoidy, a general mechanism for maintaining lytic phage in populations of bacteria

We present evidence that phage resistance resulting from overproduction of exopolysaccharides, mucoidy, provides a general answer to the longstanding question of how lytic viruses are maintained in populations dominated by bacteria upon which they cannot replicate. In serial transfer culture, populations of mucoid E. coli MG1655 that are resistant to lytic phages with different receptors, and thereby requiring independent mutations for surface resistance, are capable of maintaining these phages with little effect on their total density. Based on the results of our analysis of a mathematical model, we postulate that the maintenance of phage in populations dominated by mucoid cells can be attributed primarily to high rates of transition from the resistant mucoid states to susceptible non-mucoid states. Our tests with both population dynamic and single cell experiments as well as DNA sequence analysis are consistent with this hypothesis. We discuss reasons for the generalized resistance of these mucoid E. coli, and the genetic and molecular mechanisms responsible for the high rate of transition from mucoid to sensitive states responsible for the maintenance of lytic phage in mucoid populations of E. coli.

microbiology↗

bioRxiv2018

A Universal Algorithm to Detect Rare or Novel Cell Types in High-Throughput Single-Cell Gene Expression Data

Detecting rare cell types would allow early disease detection of cancers and infections, identification of new cell types, and a deepened understanding of cell differentiation. We developed a universal algorithm to identify rare cell types from a wide variety of single-cell omics data. We validated our algorithm on single-cell qPCR data from mouse hematopoietic cells and single-cell RNA-seq data from human glioblastoma tumors cells, both with expression values from an ample number of genes. We then applied our algorithm to seq-FISH data from mouse hippocampus cells containing expression values from only 121 genes. Our algorithm detected rare cell types including a putative new hippocampal cell type.\n\nAuthor summaryRare cell type detection would advance early disease diagnosis (e.g., cancer, infection), allow identification of new cell types, and increase understanding of cell differentiation. Current computational methods can detect common cell types, but it remains a challenge to detect rare cell types within cell populations, especially with expression data from a relatively small number of genes. We created a powerful algorithm to detect rare cell types in a population of cells. We validated our algorithm on data from mouse blood stem cells and human glioblastoma tumors cells. When we applied our algorithm to mouse hippocampus data containing expression values from only 121 genes, we detected a putative new brain cell type that no previous algorithm has identified. Our universal algorithm can now be applied to a wide range of data to detect the early onset of diseases and discover new cell types.

bioinformatics↗