High-throughput single-cell CRISPR-Cas9 gene editing.

Single-cell CRISPR-Cas9 screening is a powerful and innovative technology that allows researchers to perform high-throughput, precise gene editing at the single-cell level. This approach combines the advantages of CRISPR-Cas9 gene editing with the resolution of single-cell analysis, providing unparalleled insights into gene function and cellular heterogeneity, particularly in cancer research.

The CRISPR-Cas9 system enables targeted modification of specific genes, allowing researchers to knockout, knockin, or modify genes of interest. By integrating this with single-cell RNA sequencing (scRNA-seq), scientists can investigate the effects of gene perturbations on gene expression profiles across individual cells. This is particularly valuable in cancer research, where tumor heterogeneity and the presence of diverse cell populations within a single tumor can significantly influence treatment outcomes.

In a single-cell CRISPR-Cas9 screen, a library of single-guide RNAs (sgRNAs) is introduced into a population of cells. Each sgRNA directs the Cas9 protein to a specific genomic location, inducing double-strand breaks and resulting in gene knockout or modification. Following this, single-cell sequencing is performed to capture the transcriptomic changes in individual cells. By analyzing these changes, researchers can identify genes that are essential for cancer cell survival, proliferation, drug resistance, and other phenotypic traits.

This method allows for the identification of novel gene targets for cancer therapy and the elucidation of genetic networks involved in cancer progression and treatment resistance. It also facilitates the discovery of biomarkers that can predict responses to specific drugs, enabling more personalized and effective treatment strategies.

Additionally, single-cell CRISPR-Cas9 screening can be combined with advanced computational tools, such as machine learning and network analysis, to interpret complex data and uncover new therapeutic opportunities. For example, drug repurposing knowledge graphs can be integrated with single-cell CRISPR-Cas9 data to identify existing drugs that could be repurposed to target newly discovered vulnerabilities in cancer cells.

Overall, single-cell CRISPR-Cas9 screening represents a significant advancement in cancer research, offering a comprehensive approach to understanding the genetic underpinnings of cancer and developing targeted therapies. Its ability to provide high-resolution insights into cellular function and heterogeneity makes it an invaluable tool for advancing precision medicine in oncology.

1. Progress Report

2. The goal of a pooled CRISPR screen is to use CRISPR to perturb the expression of a list of pre-identified genes and quantitatively measure the effects of those perturbations on the transcriptome of the cells of interest. The cells are typically transfected in a pooled fashion with a number of plasmids that code for guide RNAs that target the pre-selected genes of interest. Since the assay captures both transcripts and transfected guide RNAs from each cell, one can correlate the changes in the transcriptome with the perturbations received by each sub-group of cells.

3. Multiple guide RNAs per target gene. In general, it is hard to predict the functional efficacy of a guide RNA construct purely from its in silico design. In order to mitigate the risk of non-functional guide RNA molecules that do not perturb the expression of their target genes significantly, pooled CRISPR screens typically employ 2-5 guide RNA constructs per target gene. Non-targeting guide RNAs that function as negative controls. In order to measure the effectiveness of a particular guide RNA construct in perturbing the expression of its target gene, or the effects of such a perturbation on the rest of the transcriptome, one would need to perform a differential expression analysis where the cells expressing the relevant guide RNA(s) are compared with control cells. The experimental design typically includes control guide RNA constructs that are explicitly designed not to target any annotated genes in the reference transcriptome; these guide RNAs are called "non-targeting" guides. The control cells used in the differential expression analyses are typically cells identified as containing only (some combination of) non-targeting guides. In order to account for possible error in the design or transfection of these non-targeting guide RNA constructs, typically more than one such construct (usually 2-5) are used in the experiment. Carefully designed and validated transfection protocol. Based on the particular transfection protocol used in the assay, the distribution of guide RNA constructs among cells can vary widely, from as few as a median of 1 guide per cell to as high as 15 per cell. The transfection protocol is usually carefully designed based on the requirements imposed by the biological questions of interest, such as the median number of guide RNA constructs per cell or the number of cells required per perturbation of interest. In addition, typically the transfection protocol is validated by some combination of PCR-based techniques and next-generation sequencing (see Methods sections of the References).

4. In pooled CRISPR screens, two central questions arise. First, to what extent did the expression of the target genes change amongst those cells expressing the guide RNAs that targeted those genes ("Perturbation Efficiency")? Second, what effects did these perturbations have on the transcriptome of those cells ("Perturbation Effects")? Both questions rely on differential expression analyses. As with Gene Expression, Cell Ranger uses the quick and simple method sSeq (Yu, Huber, & Vitek, 2013) in order to find differentially expressed genes between the perturbed cells and the control cells (cells that only contain guide RNAs designed specifically to be non-targeting). For details on the implementation of sSeq within Cell Ranger, see Gene Expression. To quantify Perturbation Efficiency, we report the log2-fold-change in the expression of each target gene. To address transcriptome-wide Perturbation Effects, we provide a list of top perturbed genes for each perturbation, in addition to a list of how every gene in the reference transcriptome changed under each perturbation. Each of the above results are calculated "by feature," where the cells are grouped based on the combinations of guide RNAs they contain, or "by target," where they are grouped based on the combinations of genes targeted by those guide RNAs. (The latter can lead to increased statistical power in cases where each gene is targeted by multiple guides, since cells where the same combinations of genes are perturbed may be grouped together.)

5. What are sequenced-Hit identification using Illumina Next Seq Sequencing Yau, Edwin H., and Tariq M. Rana. "Next-generation sequencing of genome-wide CRISPR Screens." Next Generation Sequencing. Humana Press, New York, NY, 2018. 203-216. NGS libraries generated by two-step PCR first PCR second PCR

6. Combining CRISPR and Single Cells : Perturb-seq/CRISPR-seq

7. Combining CRISPR and Single Cells ; Perturb-seq/CRISPR-seq Dixit et al 2016 Cell 167, 1853-1866