Three-dimensional localization and tracking of chromosomal loci throughout the Escherichia coli cell cycle

The intracellular position of genes may impact their expression, but it has not been possible to accurately measure the 3D position of chromosomal loci. In 2D, loci can be tracked using arrays of DNA-binding sites for transcription factors (TFs) fused with fluorescent proteins. However, the same 2D data can result from different 3D trajectories. Here, we have developed a deep learning method for super-resolved astigmatism-based 3D localization of chromosomal loci in live E. coli cells which enables a precision better than 61 nm at a signal-to-background ratio of ~4 on a heterogeneous cell background. Determining the spatial localization of chromosomal loci, we find that some loci are at the periphery of the nucleoid for large parts of the cell cycle. Analyses of individual trajectories reveal that these loci are subdiffusive both longitudinally (x) and radially (r), but that individual loci explore the full radial width on a minute time scale.This dataset contains the raw data, analysis and code needed to generate the figures presented in the paper. The raw data consists of microscopy images. The analysis and code consists of analysis code, output from the analysis and its post-processing.Experimental data, analysis and plot scripts are all organized using unique IDs (UID; seen for example in the filenames of experimental data below). The README.txt file in the Analysis_of_data folder describes which UIDs for data, analysis & figure generation to combine for specific figures/tables.For the microscopy experiments (has the prefix Microscopy_Data_ in their filenames) there is information on the growth condition, strain genotypes, which positions corresponds to which genotype, and also which figure the data was used in. All experiments were performed at 30 degrees Celsius.Details on the output from microscopy experiments can be found in the file MicroscopyInformation.txt.

基因的细胞内位置可能影响其表达，然而，准确测量染色质位点的三维位置一直未能实现。在二维空间中，可通过融合荧光蛋白的转录因子（TFs）的DNA结合位点阵列来追踪位点。然而，相同的二维数据可能源自不同的三维轨迹。本研究中，我们开发了一种基于深度学习的超分辨率像差校正三维定位方法，用于活体大肠杆菌细胞中染色质位点的定位，该方法在信号与背景比约为4的情况下，实现了优于61纳米的精度。通过对染色质位点的空间定位进行分析，我们发现某些位点在细胞周期的大部分时间内位于核质的边缘。对个别轨迹的分析揭示，这些位点在纵向（x轴）和径向（r轴）上均呈现亚扩散特性，但个别位点在极短的时间尺度上探索了完整的径向宽度。本数据集包含了生成论文中展示的图表所需的原始数据、分析和代码。原始数据由显微镜图像组成。分析和代码包括分析代码、分析输出及其后处理。实验数据、分析和绘图脚本均采用唯一的标识符（UID；例如，在以下实验数据的文件名中可见）进行组织。数据分析和图表生成文件夹中的README.txt文件描述了为生成特定图表/表格需要组合的数据、分析和图表生成的UID。对于具有Microscopy_Data_前缀的显微镜实验，提供了关于生长条件、菌株基因型、哪些位置对应哪些基因型，以及哪些图表使用了这些数据的信息。所有实验均在30摄氏度下进行。显微镜实验的输出详细信息可在MicroscopyInformation.txt文件中找到。