系统生物学

Genome-wide CRISPR-based screening is a powerful tool in forward genetics, enabling biologic discovery by linking a desired phenotype to a specific genetic perturbation. However, hits from a genome-wide screen require individual validation to reproduce and accurately quantify their effects outside of a pooled experiment. Here, we describe a step-by-step protocol to rapidly assess the effects of individual sgRNAs from CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa) systems. All steps, including cloning, lentivirus generation, cell transduction, and phenotypic readout, can be performed entirely in 96-well plates. The system is highly flexible in both cell type and selection system, requiring only that the phenotype(s) of interest be read out via flow cytometry. We expect that this protocol will provide researchers with a rapid way to sift through potential screening hits, and prioritize them for deeper analysis in more complex in vitro or even in vivo systems.

Graphical abstract

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.

Chemical proteomics focuses on the drug–target–phenotype relationship for target deconvolution and elucidation of the mechanism of action—key and bottleneck in drug development and repurposing. Majorly due to the limits of using chemically modified ligands in affinity-based methods, new, unbiased, proteome-wide, and MS-based chemical proteomics approaches have been developed to perform drug target deconvolution, using full proteome profiling and no chemical modification of the studied ligand. Of note among them, thermal proteome profiling (TPP) aims to identify the target(s) by measuring the difference in melting temperatures between each identified protein in drug-treated versus vehicle-treated samples, with the thermodynamic interpretation of “protein melting” and curve fitting of all quantified proteins, at all temperatures, in each biological replicate. Including TPP, all the other chemical proteomics approaches often fail to provide target deconvolution with sufficient proteome depth, statistical power, throughput, and sustainability, which could hardly fulfill the final purpose of drug development. The proteome integral solubility alteration (PISA) assay provides no thermodynamic interpretation, but a throughput 10–100-fold compared to the other proteomics methods, high sustainability, much lower time of analysis and sample amount requirements, high confidence in results, maximal proteome coverage (~10,000 protein IDs), and up to five drugs / test molecules in one assay, with at least biological triplicates of each treatment. Each drug-treated or vehicle-treated sample is split into many fractions and exposed to a gradient of heat as solubility perturbing agent before being recomposed into one sample; each soluble fraction is isolated, then deep and quantitative proteomics is applied across all samples. The proteins interacting with the tested molecules (targets and off-targets), the activated mechanistic factors, or proteins modified during the treatment show reproducible changes in their soluble amount compared to vehicle-treated controls. As of today, the maximal multiplexing capability is 18 biological samples per PISA assay, which enables statistical robustness and flexible experimental design accommodation for fuller target deconvolution, including integration of orthogonal chemical proteomics methods in one PISA assay. Living cells for studying target engagement in vivo or, alternatively, protein extracts to identify in vitro ligand-interacting proteins can be studied, and the minimal need in sample amount unlocks target deconvolution using primary cells and their derived cultures.

Graphical abstract:

Genome-wide screens using yeast or phage displays are powerful tools for identifying protein–ligand interactions, including drug or vaccine targets, ligand receptors, or protein–protein interactions. However, assembling libraries for genome-wide screens can be challenging and often requires unbiased cloning of 10⁵–10⁷ DNA fragments for a complete representation of a eukaryote genome. A sub-optimal genomic library can miss key genomic sequences and thus result in biased screens. Here, we describe an efficient method to generate genome-wide libraries for yeast surface display using Gibson assembly. The protocol entails genome fragmentation, ligation of adapters, library cloning using Gibson assembly, library transformation, library DNA recovery, and a streamlined Oxford nanopore library sequencing procedure that covers the length of the cloned DNA fragments. We also describe a computational pipeline to analyze the library coverage of the genome and predict the proportion of expressed proteins. The method allows seamless library transfer among multiple vectors and can be easily adapted to any expression system.

Chromatin accessibility is a key determinant of gene expression that can be altered under different physiological and disease conditions. Skeletal muscle is made up of myofibers that are highly plastic and adaptive. Therefore, assessing the genome-wide chromatin state of myofibers under various conditions is very important to gain insight into the epigenetic state of myonuclei. The rigid nature of myofibers, as well as the low number of myonuclei that they contain, have rendered genome-wide studies with myofibers challenging. In recent years, ATAC-Seq from whole muscle and single nucleus ATAC-Seq have been performed. However, these techniques cannot distinguish between different fiber and cell types present in the muscle. In addition, due to the limited depth capacity obtained from single nucleus ATAC-Seq, an extensive comparative analysis cannot be performed. Here, we introduce a protocol where we combine the isolation of a single myofiber with OMNI ATAC-Seq. This protocol allows for genome-wide analysis of accessible chromatin regions of a selected single myofiber at a sufficient depth for comparative analysis under various physiological and disease conditions. This protocol can also allow for a specific myofiber to be selected, such as a regenerating myofiber. In the future, this protocol can help identify global changes in chromatin state under various conditions, as well as between different types of myofibers.

Graphical abstract:

Populations of some bumble bee species are in decline, prompting the need to better understand bumble bee biology and for assessing the effects of environmental stressors on these important pollinators. Microcolonies have been successfully used for investigating a range of endpoints, including behavior, gut microbiome, nutrition, development, pathogens, and the effects of pesticide exposure on bumble bee health. Here, we present a step-by-step protocol for initiating, maintaining, and monitoring microcolonies with Bombus impatiens. This protocol has been successfully used in two pesticide exposure-effects studies and can be easily expanded to investigate other aspects of bumble bee biology.

DNA methylation is a conserved chemical modification, by which methyl groups are added to the cytosine of DNA molecules. Methylation can influence gene expression without changing the sequence of a particular gene. This epigenetic effect is an intriguing phenomenon that has puzzled biologists for years. By probing the temporal and spatial patterns of DNA methylation in genomes, it is possible to learn about the biological role of cytosine methylation, as well as its involvement in gene regulation and transposon silencing. Advances in whole-genome sequencing have led to the widespread adoption of methods that examine genome-wide patterns of DNA methylation. Achieving sufficient sequencing depth in these types of experiments is costly, particularly for pilot studies in organisms with large genome sizes, or incomplete reference genomes. To overcome this issue, assays to determine site-specific DNA methylation can be used. Although often used, these assays are rarely described in detail. Here, we describe a pipeline that applies traditional TA cloning, Sanger sequencing, and online tools to examine DNA methylation. We provide an example of how to use this protocol to examine the pattern of DNA methylation at a specific transposable element in maize.

Due to overlapping sequences with linear cognates, identifying internal sequences of circular RNA (circRNA) remains a challenge. Recently, we have developed a full-length circRNA sequencing method (circFL-seq) and computational pipeline, to profile ordinary and fusion circRNA at the isoform level. Compared to short-read RNA-seq, rolling circular reverse transcription and nanopore long-read sequencing of circFL-seq make circRNA reads more than tenfold enriched, and show advantages for identification of both short (<100 nt) and long (>2,000 nt) circRNA transcripts. circFL-seq allows identification of differential alternative splicing suggested wide application prospects for functional studies of internal sequences in circRNAs. In addition, the experimental protocol and computational pipeline of circFL-seq shows better sensitivity and precision for identification of back-splicing junctions than current long-read sequencing methods. Together, the accurate identification and quantification of full-length circRNAs makes circFL-seq a potential tool for large-scale screening of functional circRNAs.

In recent years, DNA methylation research has been accelerated by the advent of nanopore sequencers. However, read length has been limited by the constraints of base conversion using the bisulfite method, making analysis of chromatin content difficult. The read length of the previous method combining bisulfite conversion and long-read sequencing was ~1.5 kb, even using targeted PCR. In this study, we have improved read length (~5 kb), by converting unmethylated cytosines to uracils with APOBEC enzymes, to reduce DNA fragmentation. The converted DNA was then sequenced using a PromethION nanopore sequencer. We have also developed a new analysis pipeline that accounts for base conversions, which are not present in conventional nanopore sequencing, as well as errors produced by nanopore sequencing.