Partial synthesis of phosphatidylethanolamines

SimiC enables the inference of complex gene regulatory dynamics across cell phenotypes

Single-cell RNA-Sequencing has the potential to provide deep biological insights by revealing complex regulatory interactions across diverse cell phenotypes at single-cell resolution. However, current single-cell gene regulatory network inference methods produce a single regulatory network per input dataset, limiting their capability to uncover complex regulatory relationships across related cell phenotypes. We present SimiC, a single-cell gene regulatory inference framework that overcomes this limitation by jointly inferring distinct, but related, gene regulatory dynamics per phenotype. We show that SimiC uncovers key regulatory dynamics missed by previously proposed methods across a range of systems, both model and non-model alike. In particular, SimiC was able to uncover CAR T cell dynamics after tumor recognition and key regulatory patterns on a regenerating liver, and was able to implicate glial cells in the generation of distinct behavioral states in honeybees. SimiC hence establishes a new approach to quantitating regulatory architectures between distinct cellular phenotypes, with far-reaching implications for systems biology.

A review on methods of phospholipase A determination

A short review is presented on methods to determine phospholipase A activities. On purpose, only those methods are covered which can, at least potentially, be used for assaying the low activities generally associated with intracellular phospholipases A. A selection was made of procedures which apply non-radioactive or radioactive substrates or chemically modified substrate analogs which can be used for spectrophotometric assays.