Spatial clustering and common regulatory elements correlate with coordinated gene expression.

Many cellular responses to surrounding cues require temporally concerted transcriptional regulation of multiple genes. In prokaryotic cells, a single-input-module motif with one transcription factor regulating multiple target genes can generate coordinated gene expression. In eukaryotic cells, transcriptional activity of a gene is affected by not only transcription factors but also the epigenetic modifications and three-dimensional chromosome structure of the gene. To examine how local gene environment and transcription factor regulation are coupled, we performed a combined analysis of time-course RNA-seq data of TGF-ß treated MCF10A cells and related epigenomic and Hi-C data. Using Dynamic Regulatory Events Miner (DREM), we clustered differentially expressed genes based on gene expression profiles and associated transcription factors. Genes in each class have similar temporal gene expression patterns and share common transcription factors. Next, we defined a set of linear and radial distribution functions, as used in statistical physics, to measure the distributions of genes within a class both spatially and linearly along the genomic sequence. Remarkably, genes within the same class despite sometimes being separated by tens of million bases (Mb) along genomic sequence show a significantly higher tendency to be spatially close despite sometimes being separated by tens of Mb along the genomic sequence than those belonging to different classes do. Analyses extended to the process of mouse nervous system development arrived at similar conclusions. Future studies will be able to test whether this spatial organization of chromosomes contributes to concerted gene expression.

Erratum to: Rapid, modular, and cost-effective generation of donor DNA constructs for CRISPR-based gene knock-in.

[This corrects the article DOI: 10.1093/biomethods/bpaa006.].

Rapid, modular, and cost-effective generation of donor DNA constructs for CRISPR-based gene knock-in.

Clustered regularly interspaced short palindromic repeats (CRISPR)-based gene editing techniques find applications in many fields, such as molecular biology, cancer biology, and disease modeling. In contrast to the knock-out procedure, a key step of CRISPR knock-in experiments is the homology-directed repair process that requires donor constructs as repair templates. Therefore, it is desirable to generate a series of donor templates efficiently and cost-effectively. In this study, we developed a new strategy that combines (i) Gibson assembly reaction, (ii) a linker pair composed of eight in silico screened restriction enzyme sites, and (iii) a hierarchical framework, to remarkably improve the efficiency of producing donor constructs for common genes as well as for the genes containing unbalanced guanine-cytosine content and requiring a selectable marker. Furthermore, the approach provides the ability of inserting additional elements into the donor templates, such as single guide RNA recognition sites that have been reported to enhance the efficiency of homology-directed repair. Conclusively, our modularized process is simple, fast, and cost-effective for making donor constructs and benefits the application of CRISPR knock-in methods.

Learn to segment single cells with deep distance estimator and deep cell detector.

Single cell segmentation is a critical and challenging step in cell imaging analysis. Traditional processing methods require time and labor to manually fine-tune parameters and lack parameter transferability between different situations. Recently, deep convolutional neural networks (CNN) treat segmentation as a pixel-wise classification problem and have become a general and efficient method for image segmentation. However, cell imaging data often possesses characteristics that adversely affect segmentation accuracy: absence of established training datasets, few pixels on cell boundaries, and ubiquitous blurry features. We developed a strategy that combines strengths of CNN and traditional watershed algorithm. First, we trained a CNN to learn Euclidean distance transform (EDT) of the mask corresponding to the input images (deep distance estimator). Next, we trained a faster R-CNN (Region with CNN) to detect individual cells in the EDT image (deep cell detector). Then, the watershed algorithm performed the final segmentation using the outputs of previous two steps. Tests on a library of fluorescence, phase contrast and differential interference contrast (DIC) images showed that both the combined method and various forms of the pixel-wise classification algorithm achieved similar pixel-wise accuracy. However, the combined method achieved significantly higher cell count accuracy than the pixel-wise classification algorithm did, with the latter performing poorly when separating connected cells, especially those connected by blurry boundaries. This difference is most obvious when applied to noisy images of densely packed cells. Furthermore, both deep distance estimator and deep cell detector converge fast and are easy to train.