Computational Biology and Bioinformatics

Accessible chromatin regions modulate gene expression by acting as cis-regulatory elements. Understanding the epigenetic landscape by mapping accessible regions of DNA is therefore imperative to decipher mechanisms of gene regulation under specific biological contexts of interest. The assay for transposase-accessible chromatin sequencing (ATAC-seq) has been widely used to detect accessible chromatin and the recent introduction of single-cell technology has increased resolution to the single-cell level. In a recent study, we used droplet-based, single-cell ATAC-seq technology (scATAC-seq) to reveal the epigenetic profile of the transit-amplifying subset of thymic epithelial cells (TECs), which was identified previously using single-cell RNA-sequencing technology (scRNA-seq). This protocol allows the preparation of nuclei from TECs in order to perform droplet-based scATAC-seq and its integrative analysis with scRNA-seq data obtained from the same cell population. Integrative analysis has the advantage of identifying cell types in scATAC-seq data based on cell cluster annotations in scRNA-seq analysis.

Understanding how genes are differentially expressed across tissues is key to reveal the etiology of human diseases. Genes are never expressed in isolation, but rather co-expressed in a community; thus, they co-act through intricate but well-orchestrated networks. However, existing approaches cannot coalesce the full properties of gene–gene communication and interactions into networks. In particular, the unavailability of dynamic gene expression data might impair the application of existing network models to unleash the complexity of human diseases. To address this limitation, we developed a statistical pipeline named DRDNetPro to visualize and trace how genes dynamically interact with each other across diverse tissues, to ascertain health risk from static expression data. This protocol contains detailed tutorials designed to learn a series of networks, with the illustration example from the Genotype-Tissue Expression (GTEx) project. The proposed toolbox relies on the method developed in our published paper (Chen et al., 2022), coding all genes into bidirectional, signed, weighted, and feedback looped networks, which will provide profound genomic information enabling medical doctors to design precise medicine.

Graphical abstract

Flowchart illustrating the use of DRDNetPro.
The left panel contains the summarized pipeline of DRDNetPro and the right panel contains one pseudo-illustrative example. See the Equipment and Procedure sections for detailed explanations.

CRISPR/Cas9 screening has revolutionized functional genomics in biomedical research and is a widely used approach for the identification of genetic dependencies in cancer cells. Here, we present an efficient and versatile protocol for the cloning of guide RNAs (gRNA) into lentiviral vectors, the production of lentiviral supernatants, and the transduction of target cells in a 96-well format. To assess the effect of gene knockouts on cellular fitness, we describe a competition-based cell proliferation assay using flow cytometry, enabling the screening of many genes at the same time in a fast and reproducible manner. This readout can be extended to any parameter that is accessible to flow-based measurements, such as protein expression and stability, differentiation, cell death, and others. In summary, this protocol allows to functionally assess the effect of a set of 50–300 gene knockouts on various cellular parameters within eight weeks.

Graphical abstract

R-loops, or RNA:DNA hybrids, are structures that arise co-transcriptionally when a nascent RNA hybridizes back with the template ssDNA, leading to a displaced ssDNA. Because accumulation of R-loops can lead to genomic instability and loss of cellular homeostasis, it is important to determine the genome-wide distribution of R-loops in different physiological conditions. Current R-loop mapping strategies are based on R-loop enrichment—mediated by the S9.6 antibody, such as DRIP-seq, or by the exonuclease RNase H1, such as MapR—or the latest R-loop CUT&Tag, based on an artificial R-loop sensor derived from an RNase H1 sub-domain. Because some of these techniques often require high input material or expensive reagents, we sought to apply MapR, which does not require expensive reagents and has been shown to be compatible with low input samples. Importantly, we demonstrate that incorporation of improved CUT&RUN steps into the MapR protocol yields R-loop-enriched DNA when using low input Drosophila nuclei.

Graphical abstract:

Workflow for mapping tissue-specific, genome-wide R-loops in Drosophila.

Purify GST-tagged and catalytically inactive RNase H1 tethered MapR enzymes, GST-ΔRH-MNase, and GST-MNase, from transformed E. coli. Perform tissue-specific nuclei immuno-enrichment from UAS-EGFP.KASH-Msp300 Drosophila using magnetic bead–bound green fluorescent protein (GFP) antibody. Incubate isolated nuclei with MapR enzymes and activate MNase DNA cleavage with low salt/high calcium buffers. Purify released, R-loopenriched DNA fragments and generate sequencing-ready libraries. Align MapR data to reference genome and compare R-loop enrichment peaks in genome browser.

In eukaryotic cells, RNA Polymerase II (RNAP2) is the enzyme in charge of transcribing mRNA from DNA. RNAP2 possesses an extended carboxy-terminal domain (CTD) that gets dynamically phosphorylated as RNAP2 progresses through the transcription cycle, therefore regulating each step of transcription from recruitment to termination. Although RNAP2 residue-specific phosphorylation has been characterized in fixed cells by immunoprecipitation-based assays, or in live cells by using tandem gene arrays, these assays can mask heterogeneity and limit temporal and spatial resolution. Our protocol employs multi-colored complementary fluorescent antibody-based (Fab) probes to specifically detect the CTD of the RNAP2 (CTD-RNAP2), and its phosphorylated form at the serine 5 residue (Ser5ph-RNAP2) at a single-copy HIV-1 reporter gene. Together with high-resolution fluorescence microscopy, single-molecule tracking analysis, and rigorous computational modeling, our system allows us to visualize, quantify, and predict endogenous RNAP2 phosphorylation dynamics and mRNA synthesis at a single-copy gene, in living cells, and throughout the transcription cycle.

Graphical abstract:

Schematic of the steps for visualizing, quantifying, and predicting RNAP2 phosphorylation at a single-copy gene.

Subcellular structures exhibit diverse behaviors in different cellular processes, including changes in morphology, abundance, and relative spatial distribution. Faithfully tracking and quantifying these changes are essential to understand their functions. However, most freely accessible methods lack integrated features for tracking multiple objects in different spectral channels simultaneously. To overcome these limitations, we have developed TRACES (Tracking of Active Cellular Structures), a customizable and open-source pipeline capable of detecting, tracking, and quantifying fluorescently labeled cellular structures in up to three spectral channels simultaneously at single-cell level. Here, we detail step-by-step instructions for performing the TRACES pipeline, including image acquisition and segmentation, object identification and tracking, and data quantification and visualization. We believe that TRACES will be a valuable tool for cell biologists, enabling them to track and measure the spatiotemporal dynamics of subcellular structures in a robust and semi-automated manner.

The quantification of labeled cells in tissue sections is crucial to the advancement of biological knowledge. Traditionally, this was a tedious process, requiring hours of careful manual counting in small portions of a larger tissue section. To overcome this, many automated methods for cell analysis have been developed. Recent advances in whole slide scanning technologies have provided the means to image cells in entire tissue sections. However, common automated analysis tools do not have the capacity to deal with the large image files produced. Herein, we present a protocol for the quantification of two fluorescently labeled cell populations, namely pericytes and microglia, in whole brain tissue sections. This protocol uses custom-made scripts within the open source software QuPath to provide a framework for the careful optimization and validation of automated cell detection parameters. Images obtained from a whole-slide scanner are first loaded into a QuPath project. Manual counts are performed on small sample regions to optimize cell detection parameters prior to automated quantification of cells across entire brain regions. Even though we have quantified pericytes and microglia, any fluorescently labeled cell with clear labeling in and around the nucleus can be analyzed using these methods. This protocol provides a user-friendly and cost-effective framework for the automated analysis of whole tissue sections.

In most biomedical labs, researchers gather metadata (i.e., all details about the experimental data) in paper notebooks, spreadsheets, or, sometimes, electronic notebooks. When data analyses occur, the related details usually go into other notebooks or spreadsheets, and more metadata are available. The whole thing rapidly becomes very complex and disjointed, and keeping track of all these things can be daunting. Organizing all the relevant data and related metadata for analysis, publication, sharing, or deposit into archives can be time-consuming, difficult, and prone to errors. By having metadata in a centralized system that contains all details from the start, the process is greatly simplified. While lab management software is available, it can be costly and inflexible. The system described here is based on a popular, freely available, and open-source wiki platform. It provides a simple but powerful way for biomedical research labs to set up a metadata management system linking the whole research process. The system enhances efficiency, transparency, reliability, and rigor, which are key factors to improving reproducibility. The flexibility afforded by the system simplifies implementation of specialized lab requirements and future needs. The protocol presented here describes how to create the system from scratch, how to use it for gathering basic metadata, and provides a fully functional version for perusal by the reader.

Graphical abstract:

Lab Metadata Management System.

Recent advancements in 3D microscopy have enabled scientists to monitor signals of multiple cells in various animals/organs. However, segmenting and tracking the moving cells in three-dimensional time-lapse images (3D + T images), to extract their dynamic positions and activities, remains a considerable bottleneck in the field. We developed a deep learning-based software pipeline called 3DeeCellTracker, which precisely tracks cells with large movements in 3D + T images, obtained from different animals or organs, using highly divergent optical systems. In this protocol, we explain how to set up the computational environment, the required data, and the procedures to segment and track cells with 3DeeCellTracker. Our protocol will help scientists to analyze cell activities/movements in 3D + T image datasets that have been difficult to analyze.

Graphic abstract:

The flowchart illustrating how to use 3DeeCellTracker.

See the Equipment and Procedure sections for detailed explanations.