Perturb-seq

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Perturb-seq combines multiplexed CRISPR mediated gene inactivations and single cell RNA sequencing to assess comprehensive gene expression phenotypes for each perturbation in a massively parallel fashion.

Background

Perturb-seq refers to a high throughput method of performing single cell RNA sequencing (scRNA-seq) on pooled genetic perturbation screens . Inferring a gene’s function by applying genetic perturbations to knockdown or knockout a gene and studying the resulting phenotype is known as reverse genetics. Perturb-seq is a reverse genetics approach that allows for the investigation of phenotypes at the level of the transcriptome, to elucidate gene functions in many cells, in a massively parallel fashion.

The Perturb-seq protocol uses CRISPR technology to inactivate specific genes and DNA barcoding of each guide RNA to allow for all perturbations to be pooled together and later deconvoluted, with assignment of each phenotype to a specific guide RNA . Droplet-based microfluidics platforms (or other cell sorting and separating techniques) are used to isolate individual cells and then scRNA-seq is performed to generate gene expression profiles for each cell. Upon completion of the protocol, bioinformatics analyses are conducted to associate each specific cell and perturbation with a transcriptomic profile that characterizes the consequences of inactivating each gene.

In the December 2016 issue of the Journal Cell, 2 companion papers were published that each introduced and described this technique . In addition, another paper describing a conceptually identical approach, which the authors termed CRISP-seq was also published in the same issue . While each paper shared the core principles of combining CRISPR mediated perturbation with scRNA-seq, their experimental, technological and analytical approaches differed in several aspects, to explore distinct biological questions, demonstrating the broad utility of this methodology.

Experimental Workflow

CRISPR Single Guide RNA Library Design/Selection

Pooled CRISPR libraries that enable gene inactivation can come in the form of either knockout or interference. Knockout libraries perturb genes through double stranded breaks that prompt the error prone non-homologous end joining repair pathway to introduce disruptive insertions or deletions. CRISPR interference (CRISPRi) on the other hand utilizes a catalytically inactive nuclease to physically block RNA polymerase to effectively prevent or halt transcription . Perturb-seq has been utilized with both the knockout and CRISPRi approaches in the Drixit et al paper and the Adamson et al paper , respectively.

Pooling all guide RNAs into a single screen relies on DNA barcodes that act as identifiers for each unique guide RNA. There are several commercially available pooled CRISPR libraries including the guide barcode library used in the study by Adamson et al . CRISPR libraries can also be custom made using tools for sgRNA design, many of which are listed on the CRISPR/cas9 tools wikipedia page.

Lentiviral Vectors

The sgRNA expression vector design will depend largely on the experiment performed but requires the following central components:

1. Promoter
2. Restriction sites
3. Primer Binding Sites
4. sgRNA
5. Guide Barcode
6. Reporter gene:
   • Fluorescent gene: vectors are often constructed to include a gene encoding a fluorescent protein, such that successfully transduced cells can be visually and quantitatively assessed by their expression.
   • Antibiotic resistance gene: similar to fluorescent markers, antibiotic resistance genes are often incorporated into vectors to allow for selection of successfully transduced cells.
7. CRISPR-associated endonuclease: Cas9 or other CRISPR-associated endonucleases such as Cpf1 must be introduced to cells that do not endogenously express them. Due to the large size of these genes, a two-vector system can be used to express the endonuclease separately from the sgRNA expression vector .

Transduction and Selection

Cells are typically transduced with a Multiplicity of Infection (MOI) of 0.4 to 0.6 lentiviral particles per cell to maximize the likelihood of obtaining the most amount of cells which contain a single guide RNA . If the effects of simultaneous perturbations are of interest, a higher MOI may be applied to increase the amount of transduced cells with more than one guide RNA. Selection for successfully transduced cells is then performed using a fluorescence assay or an antibiotic assay, depending on the reporter gene used in the expression vector.

Single-cell Library Prep

After successfully transduced cells have been selected for, isolation of single cells is needed to conduct scRNA-seq. Perturb-seq has been performed using droplet-based technology for single cell isolation , while the closely related CRISP-seq was performed with a microwell-based approach . Once cells have been isolated at the single cell level, reverse transcription, amplification and sequencing takes place to produce gene expression profiles for each cell. Many scRNA-seq approaches incorporate unique molecular identifiers (UMIs) and cell barcodes during the reverse transcription step to index individual RNA molecules and cells, respectively. These additional barcodes serve to help quantify RNA transcripts and to associate each of the sequences with their cell of origin.

Bioinformatics Analysis

Read alignment and processing are performed to map quality reads to a reference genome. Deconvolution of cell barcodes, guide barcodes and UMIs enables the association of guide RNAs with the cells that contain them, thus allowing the gene expression profile of each cell to be affiliated with a particular perturbation. Further downstream analyses on the transcriptional profiles will depend entirely on the biological question of interest. T-distributed Stochastic Neighbor Embedding (t-SNE) is a commonly used machine learning algorithm to visualize the high-dimensional data that results from scRNA-seq in a 2-dimensional scatterplot . The authors who first performed Perturb-seq developed an in-house computational framework called MIMOSCA that predicts the effects of each perturbation, using a linear model and is available on github.

Advantages and Limitations

Perturb-seq makes use of current technologies in molecular biology to integrate a multi-step workflow that couples high-throughput screening with complex phenotypic outputs. When compared to alternative methods used for gene knockdowns or knockouts, such as RNAi, zinc finger nucleases or transcription activator-like effector nucleases (TALENs), the application of CRISPR-based perturbations enables more specificity, efficiency and ease of use . Another advantage of this protocol is that while most screening approaches can only assay for simple phenotypes, such as cellular viability, scRNA-seq allows for a much richer phenotypic readout, with quantitative measurements of gene expression in many cells simultaneously.

However, while a large and comprehensive amount of data can be a benefit, it can also present a major challenge. Single cell RNA expression readouts are known to produce ‘noisy’ data, with a significant number of false positives . Both the large size and noise that is associated with scRNA-seq will likely require new and powerful computational methods and bioinformatics pipelines to better make sense of the resulting data. Another challenge associated with this protocol is the creation of large scale CRISPR libraries. The preparation of these extensive libraries depends upon a comparative increase in the resources required to culture the massive numbers of cells that are needed to achieve a successful screen of many perturbations .

Applications

Perturb-seq or conceptually similar protocols can potentially be used for a broad scope of biological functions and their applications will likely grow over time. Three papers on this topic, published in the December 2016 issue of the Journal Cell, demonstrated the utility of this method by investigating several distinct biological functions. In the paper, “Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens”, the authors used Perturb-seq to conduct knockouts of transcription factors related to the immune response in hundreds of thousands of cells to investigate the cellular consequences of their inactivation . They also explored the effects of transcription factors on cell states in the context of the cell cycle. In the study led by UCSF, “A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response” the researchers suppressed multiple genes in each cell to study the unfolded protein response (UPR) pathway . With a similar methodology, but using the term CRISP-seq instead of Perturb-seq, the paper "Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq" performed a proof of concept by using the technique to probe regulatory pathways related to innate immunity in mice .

While these publications used these protocols for answering complex biological questions, this technology can also be used as a validation assay to ensure the specificity of any CRISPR based knockdown or knockout; the expression levels of the target genes and other genes can be measured with single cell resolution to detect whether the perturbation was successful and to assess for off target effects of specific guide RNAs.