Perturb-seq

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Perturb-seq (also known as CRISP-seq and CROP-seq) refers to a high-throughput method of performing single cell RNA sequencing (scRNA-seq) on pooled genetic perturbation screens. [1] [2] [3] Perturb-seq combines multiplexed CRISPR mediated gene inactivations with single cell RNA sequencing to assess comprehensive gene expression phenotypes for each perturbation. Inferring a gene’s function by applying genetic perturbations to knock down or knock out 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.

Contents

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. [1] [2] 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.

History

In the December 2016 issue of the Cell journal, two companion papers were published that each introduced and described this technique. [1] [2] A third paper describing a conceptually similar approach (termed CRISP-seq) was also published in the same issue. [4] In October 2016, the CROP-seq method for single-cell CRISPR screening was presented in a preprint on bioRxiv [5] and later published in the Nature Methods journal. [3] 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. For example, the CRISPR-seq paper demonstrated the feasibility of in vivo studies using this technology, and the CROP-seq protocol facilitates large screens by providing a vector that makes the guide RNA itself readable (rather than relying on expressed barcodes), which allows for single-step guide RNA cloning. [6] A June 2022 paper in Cell published results from one of the first genome-scale Perturb-seq screens, which uncovered new perturbations that promote chromosomal instability as well as variations in the expression of mitochondrially encoded transcripts in response to different forms of mitochondrial stress. [7]

Experimental workflow

CRISPR Single Guide RNA Library design and selection

Overview of Perturb-seq workflow Overview of Perturb-seq workflow.jpeg
Overview of Perturb-seq workflow

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, effectively preventing or halting transcription. [8] Perturb-seq has been utilized with both the knockout and CRISPRi approaches in the Dixit et al. paper [2] and the Adamson et al. paper, [1] 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. [1] 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. [9]

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 cells which contain a single guide RNA. [9] [10] 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 preparation

After successfully transduced cells have been selected for, isolation of single cells is needed to conduct scRNA-seq. Perturb-seq and CROP-seq have been performed using droplet-based technology for single cell isolation, [1] [2] [3] while the closely related CRISP-seq was performed with a microwell-based approach. [4] 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. [1] [4] [11] 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 an open software repository. [12]

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. [9] [13] 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. Perturb-seq can therefore combine the high throughput of forward genetics, in terms of the number of genetic perturbations, with the rich phenotype dimension of reverse genetics. [7]

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. [14] 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. [9]

In parallel to these single-cell methods, other approaches have been developed to reconstruct genetic pathways using whole-organism RNA-sequencing. These methods use a single aggregate statistic, called the transcriptome-wide epistasis coefficient, to guide pathway reconstruction. [15] In contrast with the statistical framework of the methods described above, this coefficient may be more robust to noise and is intuitively interpretable in terms of Batesonian epistasis. This approach was used to identify a new state in the life cycle of the nematode C. elegans. [16]

Applications

Perturb-seq or other conceptually similar protocols can be used to address a broad scope of biological questions and the applications of this technology 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 applying it to the investigation of 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. [2] 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. [1] 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 experiment by using the technique to probe regulatory pathways related to innate immunity in mice. [4] Lethality of each perturbation and epistasis analyses in cells with multiple perturbations was also investigated in these papers. Perturb-seq has so far been used with very few perturbations per experiment, but it can theoretically be scaled up to address the whole genome. Finally, the October 2016 preprint [5] and subsequent paper [3] demonstrate the bioinformatic reconstruction of the T cell receptor signaling pathway in Jurkat cells based on CROP-seq data.

Recently, the Perturb-seq (CROP-seq) workflow has been adapted to enable genome-scale CRISPRi (CRISPR interference) screens in Jurkat cells at single-cell resolution. [17] The first-of-its-kind genome-scale CRISPRi screen was conducted to verify factors involved in TCR signaling pathways. In more detail, a guide RNA library targeting 18,595 human genes was utilized for CRISPR-based gene knockdowns in Jurkat cells expressing the dCas9-KRAB fusion endonuclease. In total, one million Jurkat cells were processed for single-cell RNA sequencing allowing transcriptomic readouts of a final list of 374 marker genes involved in TCR signaling. The bioinformatic analysis confirmed more than 70 known activators and repressors of TCR signaling cascades, hence showcasing the potential of Perturb-seq (CROP-seq) screens to support translational research.

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 as well as others can be measured with single cell resolution in parallel, to detect whether the perturbation was successful and to assess the experiment for off target effects. Furthermore, these protocols make it possible to perform perturbation screens in heterogeneous tissues, while obtaining cell type specific gene expression responses.

Related Research Articles

A genetic screen or mutagenesis screen is an experimental technique used to identify and select individuals who possess a phenotype of interest in a mutagenized population. Hence a genetic screen is a type of phenotypic screen. Genetic screens can provide important information on gene function as well as the molecular events that underlie a biological process or pathway. While genome projects have identified an extensive inventory of genes in many different organisms, genetic screens can provide valuable insight as to how those genes function.

Gene knockdown is an experimental technique by which the expression of one or more of an organism's genes is reduced. The reduction can occur either through genetic modification or by treatment with a reagent such as a short DNA or RNA oligonucleotide that has a sequence complementary to either gene or an mRNA transcript.

<span class="mw-page-title-main">Functional genomics</span> Field of molecular biology

Functional genomics is a field of molecular biology that attempts to describe gene functions and interactions. Functional genomics make use of the vast data generated by genomic and transcriptomic projects. Functional genomics focuses on the dynamic aspects such as gene transcription, translation, regulation of gene expression and protein–protein interactions, as opposed to the static aspects of the genomic information such as DNA sequence or structures. A key characteristic of functional genomics studies is their genome-wide approach to these questions, generally involving high-throughput methods rather than a more traditional "candidate-gene" approach.

The transcriptome is the set of all RNA transcripts, including coding and non-coding, in an individual or a population of cells. The term can also sometimes be used to refer to all RNAs, or just mRNA, depending on the particular experiment. The term transcriptome is a portmanteau of the words transcript and genome; it is associated with the process of transcript production during the biological process of transcription.

<span class="mw-page-title-main">RNA-Seq</span> Lab technique in cellular biology

RNA-Seq is a technique that uses next-generation sequencing to reveal the presence and quantity of RNA molecules in a biological sample, providing a snapshot of gene expression in the sample, also known as transcriptome.

<span class="mw-page-title-main">Genetic engineering techniques</span> Methods used to change the DNA of organisms

Genetic engineering techniques allow the modification of animal and plant genomes. Techniques have been devised to insert, delete, and modify DNA at multiple levels, ranging from a specific base pair in a specific gene to entire genes. There are a number of steps that are followed before a genetically modified organism (GMO) is created. Genetic engineers must first choose what gene they wish to insert, modify, or delete. The gene must then be isolated and incorporated, along with other genetic elements, into a suitable vector. This vector is then used to insert the gene into the host genome, creating a transgenic or edited organism.

<span class="mw-page-title-main">Cas9</span> Microbial protein found in Streptococcus pyogenes M1 GAS

Cas9 is a 160 kilodalton protein which plays a vital role in the immunological defense of certain bacteria against DNA viruses and plasmids, and is heavily utilized in genetic engineering applications. Its main function is to cut DNA and thereby alter a cell's genome. The CRISPR-Cas9 genome editing technique was a significant contributor to the Nobel Prize in Chemistry in 2020 being awarded to Emmanuelle Charpentier and Jennifer Doudna.

<span class="mw-page-title-main">CRISPR interference</span> Genetic perturbation technique

CRISPR interference (CRISPRi) is a genetic perturbation technique that allows for sequence-specific repression of gene expression in prokaryotic and eukaryotic cells. It was first developed by Stanley Qi and colleagues in the laboratories of Wendell Lim, Adam Arkin, Jonathan Weissman, and Jennifer Doudna. Sequence-specific activation of gene expression refers to CRISPR activation (CRISPRa).

Single-cell sequencing examines the nucleic acid sequence information from individual cells with optimized next-generation sequencing technologies, providing a higher resolution of cellular differences and a better understanding of the function of an individual cell in the context of its microenvironment. For example, in cancer, sequencing the DNA of individual cells can give information about mutations carried by small populations of cells. In development, sequencing the RNAs expressed by individual cells can give insight into the existence and behavior of different cell types. In microbial systems, a population of the same species can appear genetically clonal. Still, single-cell sequencing of RNA or epigenetic modifications can reveal cell-to-cell variability that may help populations rapidly adapt to survive in changing environments.

No-SCAR genome editing is an editing method that is able to manipulate the Escherichia coli genome. The system relies on recombineering whereby DNA sequences are combined and manipulated through homologous recombination. No-SCAR is able to manipulate the E. coli genome without the use of the chromosomal markers detailed in previous recombineering methods. Instead, the λ-Red recombination system facilitates donor DNA integration while Cas9 cleaves double-stranded DNA to counter-select against wild-type cells. Although λ-Red and Cas9 genome editing are widely used technologies, the no-SCAR method is novel in combining the two functions; this technique is able to establish point mutations, gene deletions, and short sequence insertions in several genomic loci with increased efficiency and time sensitivity.

CRISPR activation (CRISPRa) is a type of CRISPR tool that uses modified versions of CRISPR effectors without endonuclease activity, with added transcriptional activators on dCas9 or the guide RNAs (gRNAs).

Off-target genome editing refers to nonspecific and unintended genetic modifications that can arise through the use of engineered nuclease technologies such as: clustered, regularly interspaced, short palindromic repeats (CRISPR)-Cas9, transcription activator-like effector nucleases (TALEN), meganucleases, and zinc finger nucleases (ZFN). These tools use different mechanisms to bind a predetermined sequence of DNA (“target”), which they cleave, creating a double-stranded chromosomal break (DSB) that summons the cell's DNA repair mechanisms and leads to site-specific modifications. If these complexes do not bind at the target, often a result of homologous sequences and/or mismatch tolerance, they will cleave off-target DSB and cause non-specific genetic modifications. Specifically, off-target effects consist of unintended point mutations, deletions, insertions inversions, and translocations.

<span class="mw-page-title-main">Spatial transcriptomics</span> Range of methods designed for assigning cell types

Spatial transcriptomics is a method for assigning cell types to their locations in the histological sections and can also be used to determine subcellular localization of mRNA molecules. First described in 2016 by Ståhl et al., it has since undergone a variety of improvements and modifications.

Multiplexed Accurate Genome Editing with Short, Trackable, Integrated Cellular barcodes (MAGESTIC) is a platform that builds on the CRISPR/Cas technique. It further improves CRISPR/Cas by making the gene-editing process more precise. It also increases cell survival during the editing process up to sevenfold.

<span class="mw-page-title-main">CRISPR gene editing</span> Gene editing method

CRISPR gene editing is a genetic engineering technique in molecular biology by which the genomes of living organisms may be modified. It is based on a simplified version of the bacterial CRISPR-Cas9 antiviral defense system. By delivering the Cas9 nuclease complexed with a synthetic guide RNA (gRNA) into a cell, the cell's genome can be cut at a desired location, allowing existing genes to be removed and/or new ones added in vivo.

CITE-Seq is a method for performing RNA sequencing along with gaining quantitative and qualitative information on surface proteins with available antibodies on a single cell level. So far, the method has been demonstrated to work with only a few proteins per cell. As such, it provides an additional layer of information for the same cell by combining both proteomics and transcriptomics data. For phenotyping, this method has been shown to be as accurate as flow cytometry by the groups that developed it. It is currently one of the main methods, along with REAP-Seq, to evaluate both gene expression and protein levels simultaneously in different species.

<span class="mw-page-title-main">Genome-wide CRISPR-Cas9 knockout screens</span> Research tool in genomics

Genome-wide CRISPR-Cas9 knockout screens aim to elucidate the relationship between genotype and phenotype by ablating gene expression on a genome-wide scale and studying the resulting phenotypic alterations. The approach utilises the CRISPR-Cas9 gene editing system, coupled with libraries of single guide RNAs (sgRNAs), which are designed to target every gene in the genome. Over recent years, the genome-wide CRISPR screen has emerged as a powerful tool for performing large-scale loss-of-function screens, with low noise, high knockout efficiency and minimal off-target effects.

<span class="mw-page-title-main">GESTALT</span> Method for lineage tracing using CRISPR-Cas9-edited barcodes

Genome editing of synthetic target arrays for lineage tracing (GESTALT) is a method used to determine the developmental lineages of cells in multicellular systems. GESTALT involves introducing a small DNA barcode that contains regularly spaced CRISPR/Cas9 target sites into the genomes of progenitor cells. Alongside the barcode, Cas9 and sgRNA are introduced into the cells. Mutations in the barcode accumulate during the course of cell divisions and the unique combination of mutations in a cell's barcode can be determined by DNA or RNA sequencing to link it to a developmental lineage.

References

  1. 1 2 3 4 5 6 7 8 Adamson, Britt; Norman, Thomas M.; Jost, Marco; Cho, Min Y.; Nuñez, James K.; Chen, Yuwen; Villalta, Jacqueline E.; Gilbert, Luke A.; Horlbeck, Max A. (2016). "A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response". Cell. 167 (7): 1867–1882.e21. doi:10.1016/j.cell.2016.11.048. PMC   5315571 . PMID   27984733.
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  4. 1 2 3 4 Jaitin, Diego Adhemar; Weiner, Assaf; Yofe, Ido; Lara-Astiaso, David; Keren-Shaul, Hadas; David, Eyal; Salame, Tomer Meir; Tanay, Amos; Oudenaarden, Alexander van (2016). "Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq". Cell. 167 (7): 1883–1896.e15. doi: 10.1016/j.cell.2016.11.039 . PMID   27984734.
  5. 1 2 Datlinger, Paul; Schmidl, Christian; Rendeiro, Andre F.; Traxler, Peter; Klughammer, Johanna; Schuster, Linda; Bock, Christoph (2016-10-27). "Pooled CRISPR screening with single-cell transcriptome read-out". bioRxiv   10.1101/083774 .
  6. ↑ "Pooled CRISPR screening with single-cell transcriptome readout". crop-seq.computational-epigenetics.org. Retrieved 2017-05-30.
  7. 1 2 Replogle, Joseph M.; Saunders, Reuben A.; Pogson, Angela N.; Hussmann, Jeffrey A.; Lenail, Alexander; Guna, Alina; Mascibroda, Lauren; Wagner, Eric J.; Adelman, Karen; Lithwick-Yanai, Gila; Iremadze, Nika (June 2022). "Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq". Cell. 185 (14): 2559–2575.e28. doi:10.1016/j.cell.2022.05.013. ISSN   0092-8674. PMC   9380471 . PMID   35688146.
  8. ↑ Larson, Matthew H; Gilbert, Luke A; Wang, Xiaowo; Lim, Wendell A; Weissman, Jonathan S; Qi, Lei S (2013). "CRISPR interference (CRISPRi) for sequence-specific control of gene expression". Nature Protocols. 8 (11): 2180–2196. doi:10.1038/nprot.2013.132. PMC   3922765 . PMID   24136345.
  9. 1 2 3 4 Shalem, Ophir; Sanjana, Neville E.; Hartenian, Ella; Shi, Xi; Scott, David A.; Mikkelsen, Tarjei S.; Heckl, Dirk; Ebert, Benjamin L.; Root, David E. (2014-01-03). "Genome-Scale CRISPR-Cas9 Knockout Screening in Human Cells". Science. 343 (6166): 84–87. Bibcode:2014Sci...343...84S. doi:10.1126/science.1247005. hdl:1721.1/111576. ISSN   0036-8075. PMC   4089965 . PMID   24336571.
  10. ↑ Wang, Tim; Wei, Jenny J.; Sabatini, David M.; Lander, Eric S. (2014-01-03). "Genetic Screens in Human Cells Using the CRISPR-Cas9 System". Science. 343 (6166): 80–84. Bibcode:2014Sci...343...80W. doi:10.1126/science.1246981. ISSN   0036-8075. PMC   3972032 . PMID   24336569.
  11. ↑ Wilson, Nicola K.; Kent, David G.; Buettner, Florian; Shehata, Mona; Macaulay, Iain C.; Calero-Nieto, Fernando J.; Castillo, Manuel Sánchez; Oedekoven, Caroline A.; Diamanti, Evangelia (2015). "Combined Single-Cell Functional and Gene Expression Analysis Resolves Heterogeneity within Stem Cell Populations". Cell Stem Cell. 16 (6): 712–724. doi:10.1016/j.stem.2015.04.004. PMC   4460190 . PMID   26004780.
  12. ↑ "GitHub - asncd/MIMOSCA: A repository for the design and analysis of pooled single cell RNA-seq perturbation experiments". GitHub . 22 October 2022.
  13. ↑ Boettcher, Michael; McManus, Michael T. (2015). "Choosing the Right Tool for the Job: RNAi, TALEN, or CRISPR". Molecular Cell. 58 (4): 575–585. doi:10.1016/j.molcel.2015.04.028. PMC   4441801 . PMID   26000843.
  14. ↑ Liu, Serena; Trapnell, Cole (2016-02-17). "Single-cell transcriptome sequencing: recent advances and remaining challenges". F1000Research. 5: 182. doi: 10.12688/f1000research.7223.1 . PMC   4758375 . PMID   26949524.
  15. ↑ Angeles-Albores, David; Puckett Robinson, Carmie; Williams, Brian A; Wold, Barbara J.; Sternberg, Paul W. (2018-03-27). "Reconstructing a metazoan genetic pathway with transcriptome-wide epistasis measurements". PNAS. 115 (13): E2930–E2939. Bibcode:2018PNAS..115E2930A. doi: 10.1073/pnas.1712387115 . PMC   5879656 . PMID   29531064.
  16. ↑ Angeles-Albores, David; Leighton, Daniel H.W.; Tsou, Tiffany; Khaw, Tiffany H.; Antoshechkin, Igor; Sternberg, Paul W. (2017-09-07). "The Caenorhabditis elegans Female-Like State: Decoupling the Transcriptomic Effects of Aging and Sperm Status". G3: Genes, Genomes, Genetics. 115 (9): 2969–2977. doi:10.1534/g3.117.300080. PMC   5592924 . PMID   28751504.
  17. ↑ "A genome-scale CROP-seq screen reveals mediators of T-cell signaling for target discovery". 10x Genomics. Retrieved 2023-03-16.
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