系统生物学

High-throughput molecular screening of microbial colonies and DNA libraries are critical procedures that enable applications such as directed evolution, functional genomics, microbial identification, and creation of engineered microbial strains to produce high-value molecules. A promising chemical screening approach is the measurement of products directly from microbial colonies via optically guided matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). Measuring the compounds from microbial colonies bypasses liquid culture with a screen that takes approximately 5 s per sample. We describe a protocol combining a dedicated informatics pipeline and sample preparation method that can prepare up to 3,000 colonies in under 3 h. The screening protocol starts from colonies grown on Petri dishes and then transferred onto MALDI plates via imprinting. The target plate with the colonies is imaged by a flatbed scanner and the colonies are located via custom software. The target plate is coated with MALDI matrix, MALDI-MS analyzes the colony locations, and data analysis enables the determination of colonies with the desired biochemical properties. This workflow screens thousands of colonies per day without requiring additional automation. The wide chemical coverage and the high sensitivity of MALDI-MS enable diverse screening projects such as modifying enzymes and functional genomics surveys of gene activation/inhibition libraries.

Key features

• Mass spectrometry analyzes a range of compounds from E. coli colonies as a proxy for liquid culture testing enzyme mutant libraries.

• Colonies are transferred to a MALDI target plate by a simple imprinting method.

• The screen compares the ratio among several products or searches for the qualitative presence of specific compounds.

• The protocol requires a MALDI mass spectrometer.

Graphical overview

Many single nucleotide polymorphisms (SNPs) identified by genome-wide association studies exert their effects on disease risk as expression quantitative trait loci (eQTL) via allele-specific expression (ASE). While databases for probing eQTLs in tissues from normal individuals exist, one may wish to ascertain eQTLs or ASE in specific tissues or disease-states not characterized in these databases. Here, we present a protocol to assess ASE of two possible target genes (GPNMB and KLHL7) of a known genome-wide association study (GWAS) Parkinson’s disease (PD) risk locus in postmortem human brain tissue from PD and neurologically normal individuals. This was done using a sequence of RNA isolation, cDNA library generation, enrichment for transcripts of interest using customizable cDNA capture probes, paired-end RNA sequencing, and subsequent analysis. This method provides increased sensitivity relative to traditional bulk RNAseq-based and a blueprint that can be extended to the study of other genes, tissues, and disease states.

Key features

• Analysis of GPNMB allele-specific expression (ASE) in brain lysates from cognitively normal controls (NC) and Parkinson’s disease (PD) individuals.

• Builds on the ASE protocol of Mayba et al. (2014) and extends application from cells to human tissue.

• Increased sensitivity by enrichment for desired transcript via RNA CaptureSeq (Mercer et al., 2014).

• Optimized for human brain lysates from cingulate gyrus, caudate nucleus, and cerebellum.

Graphical overview

Dietary saturated fatty acids (SFAs) are upregulated in the blood circulation following digestion. A variety of circulating lipid species have been implicated in metabolic and inflammatory diseases; however, due to the extreme variability in serum or plasma lipid concentrations found in human studies, established reference ranges are still lacking, in addition to lipid specificity and diagnostic biomarkers. Mass spectrometry is widely used for identification of lipid species in the plasma, and there are many differences in sample extraction methods within the literature. We used ultra-high performance liquid chromatography (UPLC) coupled to a high-resolution hybrid triple quadrupole-time-of-flight (QToF) mass spectrometry (MS) to compare relative peak abundance of specific lipid species within the following lipid classes: free fatty acids (FFAs), triglycerides (TAGs), phosphatidylcholines (PCs), and sphingolipids (SGs), in the plasma of mice fed a standard chow (SC; low in SFAs) or ketogenic diet (KD; high in SFAs) for two weeks. In this protocol, we used Principal Component Analysis (PCA) and R to visualize how individual mice clustered together according to their diet, and we found that KD-fed mice displayed unique blood profiles for many lipid species identified within each lipid class compared to SC-fed mice. We conclude that two weeks of KD feeding is sufficient to significantly alter circulating lipids, with PCs being the most altered lipid class, followed by SGs, TAGs, and FFAs, including palmitic acid (PA) and PA-saturated lipids. This protocol is needed to advance knowledge on the impact that SFA-enriched diets have on concentrations of specific lipids in the blood that are known to be associated with metabolic and inflammatory diseases.

Key features

• Analysis of relative plasma lipid concentrations from mice on different diets using R.

• Lipidomics data collected via ultra-high performance liquid chromatography (UPLC) coupled to a high-resolution hybrid triple quadrupole-time-of-flight (QToF) mass spectrometry (MS).

• Allows for a comprehensive comparison of diet-dependent plasma lipid profiles, including a variety of specific lipid species within several different lipid classes.

• Accumulation of certain free fatty acids, phosphatidylcholines, triglycerides, and sphingolipids are associated with metabolic and inflammatory diseases, and plasma concentrations may be clinically useful.

Graphical overview

Information on RNA localisation is essential for understanding physiological and pathological processes, such as gene expression, cell reprogramming, host–pathogen interactions, and signalling pathways involving RNA transactions at the level of membrane-less or membrane-bounded organelles and extracellular vesicles. In many cases, it is important to assess the topology of RNA localisation, i.e., to distinguish the transcripts encapsulated within an organelle of interest from those merely attached to its surface. This allows establishing which RNAs can, in principle, engage in local molecular interactions and which are prevented from interacting by membranes or other physical barriers. The most widely used techniques interrogating RNA localisation topology are based on the treatment of isolated organelles with RNases with subsequent identification of the surviving transcripts by northern blotting, qRT-PCR, or RNA-seq. However, this approach produces incoherent results and many false positives. Here, we describe Controlled Level of Contamination coupled to deep sequencing (CoLoC-seq), a more refined subcellular transcriptomics approach that overcomes these pitfalls. CoLoC-seq starts by the purification of organelles of interest. They are then either left intact or lysed and subjected to a gradient of RNase concentrations to produce unique RNA degradation dynamics profiles, which can be monitored by northern blotting or RNA-seq. Through straightforward mathematical modelling, CoLoC-seq distinguishes true membrane-enveloped transcripts from degradable and non-degradable contaminants of any abundance. The method has been implemented in the mitochondria of HEK293 cells, where it outperformed alternative subcellular transcriptomics approaches. It is applicable to other membrane-bounded organelles, e.g., plastids, single-membrane organelles of the vesicular system, extracellular vesicles, or viral particles.

Key features

• Tested on human mitochondria; potentially applicable to cell cultures, non-model organisms, extracellular vesicles, enveloped viruses, tissues; does not require genetic manipulations or highly pure organelles.

• In the case of human cells, the required amount of starting material is ~2,500 cm² of 80% confluent cells (or ~3 × 10⁸ HEK293 cells).

• CoLoC-seq implements a special RNA-seq strategy to selectively capture intact transcripts, which requires RNases generating 5′-hydroxyl and 2′/3′-phosphate termini (e.g., RNase A, RNase I).

• Relies on nonlinear regression software with customisable exponential functions.

Graphical overview

T cells are endowed with T-cell antigen receptors (TCR) that give them the capacity to recognize specific antigens and mount antigen-specific adaptive immune responses. Because TCR sequences are distinct in each naïve T cell, they serve as molecular barcodes to track T cells with clonal relatedness and shared antigen specificity through proliferation, differentiation, and migration. Single-cell RNA sequencing provides coupled information of TCR sequence and transcriptional state in individual cells, enabling T-cell clonotype-specific analyses. In this protocol, we outline a computational workflow to perform T-cell states and clonal analysis from scRNA-seq data based on the R packages Seurat, ProjecTILs, and scRepertoire. Given a scRNA-seq T-cell dataset with TCR sequence information, cell states are automatically annotated by reference projection using the ProjecTILs method. TCR information is used to track individual clonotypes, assess their clonal expansion, proliferation rates, bias towards specific differentiation states, and the clonal overlap between T-cell subtypes. We provide fully reproducible R code to conduct these analyses and generate useful visualizations that can be adapted for the needs of the protocol user.

Key features

• Computational analysis of paired scRNA-seq and scTCR-seq data

• Characterizing T-cell functional state by reference-based analysis using ProjecTILs

• Exploring T-cell clonal structure using scRepertoire

• Linking T-cell clonality to transcriptomic state to study relationships between clonal expansion and functional phenotype

Graphical overview

Non-coding RNAs (ncRNAs) are defined as RNAs that do not encode proteins, but many ncRNAs do have the ability to regulate gene expression. These ncRNAs play a critical role in the epigenetic regulation of various physiological and pathological processes through diverse biochemical mechanisms. However, the existing screening methods to identify regulatory ncRNAs only focus on whole-cell expression levels and do not capture every ncRNA that targets certain genes. We describe a new method, chromatin-RNA in situ reverse transcription sequencing (CRIST-seq), that can identify all the ncRNAs that are associated with the regulation of any given gene. In this article, we targeted the ncRNAs that are associated with pluripotent gene Sox2, allowing us to catalog the ncRNA regulation network of pluripotency maintenance. This methodology is universally applicable for the study of epigenetic regulation of any genes by making simple changes on the CRISPR-dCas9 gRNAs.

Key features

• This method provides a new technique for screening ncRNAs and establishing chromatin interaction networks.

• The target gene for this method can be any gene of interest and any site in the entire genome.

• This method can be further extended to detect RNAs, DNAs, and proteins that interact with target genes.

Graphical overview

Non-alcoholic steatohepatitis (NASH) is a condition characterized by inflammation and hepatic injury/fibrosis caused by the accumulation of ectopic fats in the liver. Recent advances in lipidomics have allowed the identification and characterization of lipid species and have revealed signature patterns of various diseases. Here, we describe a lipidomics workflow to assess the lipid profiles of liver homogenates taken from a NASH mouse model. The protocol described below was used to extract and analyze the metabolites from the livers of mice with NASH by liquid chromatography–mass spectrometry (LC-MS); however, it can be applied to other tissue homogenate samples. Using this method, over 1,000 species of lipids from five classes can be analyzed in a single run on the LC-MS. Also, partial elucidation of the identity of neutral lipid (triacylglycerides and diacylglycerides) aliphatic chains can be performed with this simple LC-MS setup.

Key features

• Over 1,000 lipid species (sphingolipids, cholesteryl esters, neutral lipids, phospholipids, fatty acids) are analyzed in one run.

• Analysis of liver lipids in non-alcoholic steatohepatitis (NASH) mouse model.

• Normal-phase chromatography coupled to a triple quadrupole mass spectrometer.

Graphical overview

Schematic procedure for the homogenization and extraction of mouse liver tissue in preparation for LC-MS analysis (Created with BioRender.com)

Individual nucleotide resolution UV cross-linking and immunoprecipitation followed by high-throughput sequencing (iCLIP-seq) is a powerful technique that is used to identify RNA-binding proteins’ (RBP) binding sites on target RNAs and to characterize the molecular basis of posttranscriptional regulatory pathways. Several variants of CLIP have been developed to improve its efficiency and simplify the protocol [e.g., iCLIP2 and enhanced CLIP (eCLIP)]. We have recently reported that transcription factor SP1 functions in the regulation of alternative cleavage and polyadenylation through direct RNA binding. We utilized a modified iCLIP method to identify RNA-binding sites for SP1 and several of the cleavage and polyadenylation complex subunits, including CFIm25, CPSF7, CPSF100, CPSF2, and Fip1. Our revised protocol takes advantage of several features of the eCLIP procedure and also improves on certain steps of the original iCLIP method, including optimization of circularization of cDNA. Herein, we describe a step-by-step procedure for our revised iCLIP-seq protocol, that we designate as iCLIP-1.5, and provide alternative approaches for certain difficult-to-CLIP proteins.

Key features

• Identification of RNA-binding sites of RNA-binding proteins (RBPs) at nucleotide resolution.

• iCLIP-seq provides precise positional and quantitative information on the RNA-binding sites of RBPs in living cells.

• iCLIP facilitates the identification of sequence motifs recognized by RBPs.

• Allows quantitative analysis of genome-wide changes in protein-RNA interactions.

• Revised iCLIP-1.5 protocol is more efficient and highly robust; it provides higher coverage even for low-input samples.

Graphical overview

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.