Biology subjects

Eisemann, E.

Publications and source records attributed to Eisemann, E..

2 recordsLinked to original sources

bioRxiv2019

Dynamic relocalization of the cytosolic type III secretion system components ensures specific protein secretion

Many bacterial pathogens use a type III secretion system (T3SS) to manipulate host cells. Protein secretion by the T3SS injectisome is activated upon contact to any host cell, and it has been unclear how premature secretion is prevented during infection. We found that in gastrointestinal pathogens, cytosolic injectisome components are temporarily released from the proximal interface of the injectisome at low external pH, preventing protein secretion in acidic environments, such as the stomach. In Yersinia enterocolitica, low external pH is detected in the periplasm and leads to a partial dissociation of the inner membrane injectisome component SctD, which in turn causes the dissociation of the cytosolic T3SS components. This effect is reversed upon restoration of neutral pH, allowing a fast activation of the T3SS at the native target regions within the host. These findings indicate that the cytosolic components form an adaptive regulatory interface, which regulates T3SS activity in response to environmental conditions.

microbiology↗

bioRxiv2017

Interactive Visual Analysis of Mass Cytometry Data by Hierarchical Stochastic Neighbor Embedding Reveals Rare Cell Types

Mass cytometry allows high-resolution dissection of the cellular composition of the immune system. However, the high-dimensionality, large size, and non-linear structure of the data poses considerable challenges for data analysis. In particular, dimensionality reduction-based techniques like t-SNE offer single-cell resolution but are limited in the number of cells that can be analysed. Here we introduce Hierarchical Stochastic Neighbor Embedding (HSNE) for the analysis of mass cytometry datasets. HSNE constructs a hierarchy of non-linear similarities that can be interactively explored with a stepwise increase in detail up to the single-cell level. We applied HSNE to a study on gastrointestinal disorders and three other available mass cytometry datasets. We found that HSNE efficiently replicates previous observations and identifies rare cell populations that were previously missed due to downsampling. Thus, HSNE removes the scalability limit of conventional t-SNE analysis, a feature that makes it highly suitable for the analysis of massive high-dimensional datasets.

bioinformatics↗