Biology subjects

Zikrin, S.

Publications and source records attributed to Zikrin, S..

3 recordsLinked to original sources

bioRxiv2024

Anti-correlation of LacI association and dissociation rates observed in living cells

The rate at which transcription factors (TFs) bind their cognate sites has long been assumed to be limited by diffusion, and thus independent of binding site sequence. Here, we systematically test this assumption using cell-to-cell variability in gene expression as a window into the in vivo association and dissociation kinetics of the model transcription factor LacI. Using a stochastic model of the relationship between gene expression variability and binding kinetics, we performed single-cell gene expression measurements to infer association and dissociation rates for a set of 35 different LacI binding sites. We found that both association and dissociation rates differed significantly between binding sites, and moreover observed a clear anticorrelation between these rates across varying binding site strengths. These results contradict the long-standing hypothesis that TF binding site strength is primarily dictated by the dissociation rate, but may confer the evolutionary advantage that TFs do not get stuck in near-operator sequences while searching.

biophysics↗

bioRxiv2023

Pooled optical screening in bacteria using chromosomally expressed barcodes

Optical pooled screening is an important tool to study dynamic phenotypes for libraries of genetically engineered cells. However, the desired engineering often requires that the barcodes used for in situ genotyping are expressed from the chromosome. This has not been possible in bacteria. Here we describe a method for in situ genotyping of libraries with genomic barcodes in Escherichia. coli. The method is applied to measure the intracellular maturation time of 81 red fluorescent proteins.

biophysics↗

bioRxiv2023

Three-dimensional localization of fluorescent proteins in living Escherichia coli

3D localization of fluorescent proteins (FPs) in living bacteria has been challenging due to the low signal-to-background ratio of the FPs and the relatively uncertain positioning of the cells in the optical reference system. Using mother-machine microfluidic devices together with deep learning, we present an approach that enables accurate 3D localization of FPs in Escherichia coli over long periods. We describe a method to simulate ground truth training data for the deep learning network based on background models generated from experimental data. We test the method by studying how chromosomal loci are relocated in 3D over the E. coli cell cycle. Since the cells are radially symmetric, we expect the same width and height distribution of fluorophores if the 3D positions are correctly determined. We observe this pattern experimentally for all the labelled loci on the chromosome. Interestingly, some loci are located exclusively in the periphery of the nucleoid, while others are more confined to the core of the nucleoid. This method enables studying any chromosomal loci inside living E. coli cells in high-throughput.

biophysics↗