Methods for CRISPR selection

Abstract

Methods and systems for CRISPR positive selection are described, which use DNA sequencing to identify genes whose perturbation by CRISPR guide RNAs correlates with a phenotype.

Description

[Technical field]

[0001] The present invention relates to methods and systems for CRISPR selection. CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 62 / 820,106, filed March 18, 2019, which is incorporated by reference in its entirety. [Background technology]

[0002] Clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 technology has revolutionized genome engineering. In this system, a guide RNA (gRNA) directs the Cas9 nuclease to make a double-stranded break at a targeted genomic region. The 5' end of the gRNA contains a nucleotide sequence of about 20 nucleotides that is complementary to the targeted region. When the double-stranded break is repaired by non-homologous end joining (NHEJ), insertions and deletions occur at high frequency, thus efficiently knocking out the targeted genomic locus. The development of lentiviral delivery methods has enabled the creation of libraries of genome-scale CRISPR / Cas9 knockouts. These libraries allow both negative and positive selection screens to be performed on mammalian cell lines. In a CRISPR / Cas9 knockout screen, each gene is targeted by several gRNAs, and the mutant pool carrying different gene knockouts can be determined by high-throughput sequencing. CRISPR activation (CRISPRa) can also be used with gRNA libraries where activated genes can be determined by high-throughput sequencing.

[0003] Genome-wide CRISPR / Cas9 knockout or gene activation techniques are effective genetic perturbation screening techniques. The goal is to identify gRNAs associated with a phenotype and therefore the corresponding diseased genes. However, the data generated by these screens pose several challenges for computational analysis. CRISPR studies are often performed with multiple replicates. CRISPR is susceptible to variability in that each experiment may not use the same gRNA virus titer in the screening library, lentiviral infection kinetics may differ between experiments, and gRNAs may not target genes with the same efficiency between experiments. Thus, observed gRNA abundances are highly variable between experiments, even with cells of the same phenotype. Existing techniques rely on read counts to identify gRNAs associated with a phenotype, specifically using the mean and variance of normalized gRNA read counts to test whether gRNA abundances differ significantly between cells with or without the phenotype.

[0004] However, these techniques do not address the inter-experiment variability issues discussed above, but rather assume a high degree of homogeneity between pre- and post-selection experiments. These techniques do not address variability within a single CRISPR experiment and / or between CRISPR experiments. Summary of the Invention [Problem to be solved by the invention]

[0005] Thus, there is a need for technical improvements in computing technologies that address the issue of CRISPR variability in identifying genes via positive and negative selection screens. [Means for solving the problem]

[0006] It is to be understood that both the following general description and the following detailed description are exemplary and explanatory only and are not restrictive. In one embodiment, the method includes (A) infecting a first culture of cas9 positive cells with a library of viral vectors, the library including at least three guide RNAs (gRNAs) for cleaving target regions of DNA within the genome of the cells, sequencing the cells to obtain read counts for each of the gRNAs, and summing (Σ) the number of each of the gRNAs whose read counts exceed a background threshold per target region of DNA, where Σ=n, and summing (Σ) the total number of gRNAs, where Σ=N. The method includes (B) infecting a second culture of cas9 positive cells with the library of viral vectors, categorizing the cells of the second culture as either having a designated phenotype or not having a designated phenotype, selecting cells having the designated phenotype, sequencing the selected cells to obtain a post-selection read count for each of the gRNAs, summing the number of each of the gRNAs in the selected cells whose post-selection read count exceeds a background threshold per target region of DNA (Σ), where Σ=n′, and summing the total number of gRNAs in the selected cells whose read count exceeds the threshold over the entire target region (Σ), where Σ=N′. The method includes (C) calculating a total number of gRNAs for the target region of DNA using the formula

[0007]

number

[0008] calculating the probability of observing a n'gRNA for a target region in a selected cell by chance according to

[0009]

number

[0010] calculates the number of ways to select x objects from y objects, and for a target region of DNA containing a gene, the formula

[0011]

number

[0012] and calculating the probability of observing n' or more gRNAs for a gene in a selected cell by chance according to: In one embodiment, the method includes determining a number (n) of each of the multiple target regions of DNA whose read counts exceed a background threshold based on sequencing the first cell population after infection with a vector comprising a library of at least three guide RNAs (gRNAs) for each of the multiple target regions of DNA; determining a total number (N) of gRNAs present in the first cell population across all target regions of the multiple target regions of DNA whose read counts exceed a background threshold based on the respective numbers of gRNAs for each of the multiple target regions of DNA; determining a total number (N) of gRNAs present in the first cell population across all target regions of the multiple target regions of DNA whose read counts exceed a background threshold based on sequencing the second cell population after infection with a vector comprising a library of at least three gRNAs for each of the multiple target regions of DNA. determining a total number (N') of gRNAs present in the second cell population across all target regions of the plurality of target regions of DNA whose read counts exceed a background threshold based on the respective numbers of gRNAs for each of the plurality of target regions of DNA; determining, for each target region of the plurality of target regions, a probability of accidentally observing n' gRNAs for the target region in a selected cell based on n, N, n', and N'; determining, for a target region comprising a sequence of interest, a probability of accidentally observing n' or more gRNAs of the sequence of interest in the selected cell based on the probability of accidentally observing n' gRNAs for the target region in the selected cell; and identifying the sequence of interest as positively selected based on the probability of accidentally observing n' or more gRNAs of the sequence of interest in the selected cell.

[0013] Additional advantages will be set forth in part in the description that follows or may be learned by practice. These advantages will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims. [Brief description of the drawings]

[0014] [Figure 1] FIG. 1 is an exemplary method for CRISPR positive selection. [Diagram 2] FIG. 2 illustrates another exemplary method. [Diagram 3] FIG. 3 illustrates an exemplary operating environment. [Figure 4] FIG. 4 shows an exemplary data structure that stores artificial read counts for four hypothetical genes, with either three (A-C or D-F) or four (G-J or K-N) gRNAs per gene, whereby pre-selection and post-selection read counts for each gRNA are taken, whereby the pre-selection n and post-selection n′ read counts that exceed a threshold of 30 are identified and summed (N and N′), and the probability is determined according to the formula shown. [Diagram 5] 5 shows an exemplary data structure for storing artificial data and results of the disclosed method, where N gRNAs are used initially and N' gRNAs are present after 10 days of cell culture. As shown, target region 1 or target region 2 has n gRNAs, and the likelihood of n' gRNAs being present in the cells after 10 days of culture is presented (probability presented as p-value). [Figure 6] FIG. 6 shows the results of an experiment involving approximately 21,000 genes (G#), where the number of reads for each gRNA (g) from four pre-selection and three post-selection experiments are shown, the number of reads was summed (sum), and the number of reads above a threshold of 30 was identified (sum of presence) and summed (sum). [Figure 7]Figure 7 shows sample gRNA libraries (Gecko A and Geck B) that were used in several parallel experiments (xp#) of cell culture over three days (d03), six days (d06), and ten days (d10) after which tau aggregation as measured by FRET fluorescence was determined. The composition of the Gecko A and B libraries is shown as including specific gene-targeting gRNAs, specific microRNA-targeting gRNAs, and non-targeting gRNAs, as well as the approximate number of gRNAs per target. [Figure 8] FIG. 8 shows the pre-normalized read counts for each gRNA from the Gecko A library used in each experiment (xp#) over 3, 6, and 10 days, whereby the gRNA was used to direct target inactivation. [Figure 9] Figure 9 shows normalization of read counts for the Gecko A library from the experiment shown in Figure 8. Normalization is based on median read counts, day 10 post-selection. [Figure 10] Figure 10 shows that because targets may have different numbers of gRNAs, and some samples have more "present" gRNAs than others on day 10, another way to tally the frequency of "present" is to calculate the probability that a gene is "present." Probabilities were calculated for five different genes screened on day 10 (post-selection) using Gecko A library gRNAs. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate the embodiments and, together with the description, serve to explain the principles of the method and system. Prior to the disclosure and description of the present methods and systems, it is to be understood that the methods and systems are not limited to particular methods, components, or particular implementations. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

[0016] As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

[0017] As used herein, the terms "probe" and "guide RNA (gRNA)" and "guide" are used interchangeably. In one embodiment, the gRNA may also be provided in the form of DNA encoding the gRNA.

[0018] As used herein, a "Cas protein" may be a wild-type protein (i.e., one occurring in nature), a modified Cas protein (i.e., a Cas protein variant), or a fragment of a wild-type or modified Cas protein. A Cas protein may also be an active variant or fragment with respect to the catalytic activity of a wild-type or modified Cas protein.

[0019] In a first aspect, the disclosure features a method for identifying genes or gene products that regulate the expression of other genes or gene products, the method being useful, for example, for demonstrating positive selection following perturbation with CRISPR guides.

[0020] In one embodiment (shown in FIG. 1 ), the method includes steps of infecting 110 a first culture of cas9 positive cells with a library of viral vectors, the library comprising at least three guide RNAs for cleaving target regions of DNA in the genome of the cells; sequencing 120 the cells to obtain a number of reads for each of the gRNAs; summing the number of each of the gRNAs whose reads exceed a background threshold per target region of DNA (Σ) (Σ=n) and summing the total number of gRNAs whose reads exceed the background threshold over all target regions (Σ) (Σ=N) 130; infecting 140 a second culture of cas9 positive cells with the viral vectors, the library comprising at least three guide RNAs for cleaving target regions of DNA in the genome of the cells; Infecting with a library of vectors at step 140; categorizing the cells of the second culture as either having a designated phenotype or not having a designated phenotype at step 150; selecting cells having the designated phenotype and sequencing the selected cells to obtain a post-selection read count for each of the gRNAs at step 160; summing the count of each of the gRNAs in the selected cells whose post-selection read count exceeds a background threshold per target region of DNA (Σ)(Σ=n′) and summing the total number of gRNAs in the selected cells whose read count exceeds the threshold over the entire target region (Σ)(Σ=N′) at step 170; for the target region of DNA,

[0021]

number

[0022] According to Calculating 180 the probability of observing a n'gRNA for a target region in a selected cell by chance, comprising the formula:

[0023]

number

[0024] calculating the number of ways to select x objects from y objects; and, for a target region of DNA that contains a gene, calculating the formula

[0025]

number

[0026] and calculating 190 the probability of observing by chance n' or more gRNAs for a gene in a cell selected according to In one embodiment (also shown in FIG. 1 ), the method includes infecting 110 a first culture of cas9 positive cells with a library of viral vectors, the library including at least three guide RNAs (gRNAs) for enhancing transcription of a target region of DNA in the genome of the cells; sequencing 120 the cells to obtain a number of reads for each of the gRNAs; summing 130 the number of each of the gRNAs whose reads exceed a background threshold per target region of DNA (Σ) (Σ=n) and summing 131 the total number of gRNAs whose reads exceed the background threshold over all target regions (Σ) (Σ=N); and infecting 132 a second culture of cas9 positive cells with a library of viral vectors, the library including at least three guide RNAs (gRNAs) for enhancing transcription of a target region of DNA in the genome of the cells; , infecting with a library of viral vectors in step 140; categorizing the cells of the second culture as either having a designated phenotype or not having a designated phenotype in step 150; selecting cells having the designated phenotype and sequencing the selected cells to obtain a post-selection read count for each of the gRNAs in step 160; summing the count of each of the gRNAs in the selected cells whose post-selection read count exceeds a background threshold per target region of DNA (Σ)(Σ=n′) and summing the total number of gRNAs in the selected cells whose read count exceeds the threshold over the entire target region (Σ)(Σ=N′) in step 170; for the target region of DNA,

[0027]

number

[0028] According to Calculating 180 the probability of observing a n'gRNA for a target region in a selected cell by chance, comprising the formula:

[0029]

number

[0030] calculating the number of ways to select x objects from y objects; and, for a target region of DNA that contains a gene, calculating the formula

[0031]

number

[0032] and calculating 190 the probability of observing by chance n' or more gRNAs for a gene in a cell selected according to In one embodiment, the first culture can be infected according to CRISPR technology. In one embodiment, infecting the first culture of cas9 positive cells with a library of viral vectors (e.g., FIG. 2; 201) and infecting the second culture of cas9 positive cells with a library of viral vectors (e.g., FIG. 2; 205) includes using a CRISPR gRNA library. The CRISPR gRNA library can be a knockout library, including, for example, a genome-wide gRNA knockout library, including one or more gRNAs (e.g., sgRNAs) targeting each gene in a genome, where the genome can be any type of genome. In some embodiments, the gRNA library can include a pooled library. Non-limiting examples of pooled libraries include genome-scale CRISPR knockout (GeCKO) libraries. See, for example, Shalem O et al. (2014) Science 343:84-7 and Sanjana NE et al. (2014) Nat. Methods 11:783-4. The gRNAs in the library can target any number of target regions (e.g., genes) in DNA. For example, the gRNAs can target about 50 or more genes, about 100 or more genes, about 200 or more genes, about 300 or more genes, about 400 or more genes, about 500 or more genes, about 1000 or more genes, about 2000 or more genes, about 3000 or more genes, about 4000 or more genes, about 5000 or more genes, about 10000 or more genes, or about 20000 or more genes. In some libraries, the gRNAs can be selected to target genes of specific signaling pathways. The gRNA library can be administered using a wide range of multiplicities of infection (MOI). In some embodiments, a lower MOI can be used to facilitate an infection resulting in one gRNA per cell.

[0033] In one embodiment, the cas-positive cell may contain a cas protein for cleaving a target region of DNA, or a cas protein for regulating transcription (e.g., enhancing or suppressing transcription). The cas protein for cleaving a target region of DNA may contain an RNA binding domain and a nuclease domain. The cas protein for regulating transcription is inactivated so that it no longer has nuclease activity. The inactivated Cas (e.g., dCas-9) may be fused to a transcription activator or a transcription repressor. Thus, in one embodiment, a cas-9 positive cell is disclosed that contains a cas-9 with wild-type activity, or an inactivated cas-9. The inactivated cas-9 may be fused to, for example, at least one transcription activation domain. One or more gRNAs may bind to a target sequence upstream of the transcription start site of a gene of interest, and instead of cleaving DNA, the dCas-9 and the transcription regulator may play a role in either activating transcription or suppressing transcription of the gene of interest. When dCas-9 is fused to one or more transcriptional activators, the one or more transcriptional activators recruit the transcription factors to the transcription start site of a gene of interest, thereby activating or upregulating transcription. When dCas-9 is fused to a transcriptional repressor, transcription is inhibited, downregulated, or suppressed.

[0034] In one embodiment, the target region of DNA can be a gene. In one embodiment, the target region of DNA can be a promoter region or regulator region of a gene. In one embodiment, the target region of DNA regulates downstream genes or proteins. In one embodiment, regulating downstream genes or proteins includes activating or inhibiting downstream genes or proteins.

[0035] In some embodiments, the cells include a selection marker system. For example, as disclosed in the methods herein, the cas-9 positive cells of the first culture can be modified to contain one or more selection markers. The selection marker system can involve a marker protein fused or linked to a selection marker that is only activated with the protein being regulated. A "marker protein" can be any protein that can be regulated, where regulated means that the marker protein changes shape, binds to one or more other proteins or nucleic acids, has a change in activity, or has a change in expression level. The marker protein can be regulated by a gene targeted by one or more gRNAs, so that when the gene is cleaved by Cas9, the marker protein is not regulated and the selection marker is not activated. Thus, cells that do not have an activated selection marker can be selected as cells that contain a gene that regulates the marker protein fused or linked to a selection marker in the cell. For example, tau can be fused or linked to CFP. Another tau protein can be fused or linked to YFP. As tau protein aggregates, light that hits the CFP is emitted as blue light, and YFP is excited and emits yellow light. In the absence of tau protein aggregation, the blue light emitted from the CFP cannot excite the YFP of other tau proteins, so no yellow light is emitted. Thus, if a gene that regulates tau is targeted by one or more gRNAs and is therefore cleaved, no yellow light is emitted. Ultimately, this gene can be identified as a gene that regulates tau (e.g., causes tau aggregation). In CRISPRa, if the gRNA binds to the target region, downstream genes are activated, and thus cells with overexpression or excess amounts of the selection marker can be selected. For example, using the tau protein described above, an increased amount of yellow light compared to cells without gRNAs can indicate a gene that regulates tau.Any number of proteins that are present in known pathways, specifically disease pathways, can be used as marker proteins to identify genes that regulate the marker protein and may ultimately be found to be involved in a particular disease.

[0036] In one embodiment, the set of cells may be categorized (e.g., by phenotype) and selected (e.g., FIG. 2; 206), and may be performed some time after the initial infection, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 days post-infection, etc. In an embodiment, many types of screen / selection mechanisms may be used that utilize the selection markers contained in the set of cells. In one embodiment, the selection mechanism includes one or more of exposing the second cell population to a drug or exposing the second cell population to an agent that specifies protein activity or expression levels. In one embodiment, selecting cells having a specified phenotype includes sorting the cells based on one or more selection markers. In one embodiment, cell viability may be used for selection.

[0037] In one embodiment, categorizing the cells of the second culture as having or not having a designated phenotype comprises identifying the presence or absence of the designated phenotype in the cells. Categorizing the cells of the second culture as having or not having a designated phenotype may comprise applying a selection mechanism to the second cell culture. In one embodiment, once the phenotype is identified, selecting cells having the designated phenotype (or not having the designated phenotype) may be performed. A phenotype is any observable characteristic or functional effect that can be measured in an assay such as changes in cell growth, proliferation, morphology, enzyme function, signal transduction, expression patterns, downstream expression patterns, reporter gene activation, hormone release, growth factor release, neurotransmitter release, ligand binding, apoptosis, and product formation. In one embodiment, the designated phenotype may be fluorescence or cell survival.

[0038] Cells can be modified to convey a phenotype that can be directly selected, for example, by genomic integration of a marker, or by the presence of an intracellular marker that is not integrated in the genome. As used herein, "marker" most commonly refers to a biological feature or trait that, when present (e.g., expressed) in a cell, results in an attribute or phenotype that visualizes or identifies the cell as containing the marker. A variety of marker types are commonly used, and include, for example, visible markers, such as chromogenic, e.g., lacZ complementation (β-galactosidase), or fluorescent, e.g., green fluorescent protein (GFP) or GFP fusion proteins, RFP, BFP, luciferase, β-galactosidase, enhanced green fluorescent protein (eGFP), cyan fluorescent protein (CFP), yellow fluorescent protein (YFP), enhanced yellow fluorescent protein (eYFP), blue fluorescent protein (BFP), enhanced blue fluorescent protein (eBFP), DsRed, ZsGreen, MmGFP, mPlum, mCherry, tdTomato, mStrawberry, J-Red, mOrange, etc. The selectable markers may be phenotypic markers (growth rate, cell morphology, colony color or colony morphology, temperature sensitivity), auxotrophic markers (growth requirements), antibiotic sensitivity and resistance, molecular markers such as biomolecules distinguishable by antigen sensitivity (e.g., blood group antigens and histocompatibility markers), cell surface markers (e.g., H2KK), enzyme markers, and nucleic acid markers such as restriction fragment length polymorphisms (RFLPs), single nucleotide polymorphisms (SNPs), and various other amplifiable gene polymorphisms, such as expression of Di, mKO, mCitrine, Venus, YPet, Emerald, CyPet, Cerulean, T-Sapphire, and alkaline phosphatase. Thus, for example, one or more selectable markers may be a detectable enzyme, such as β-galactosidase or luciferase.

[0039] "Selection marker" or "screening marker" or "positive selection marker" refers to a marker that, when present in a cell, results in an attribute or phenotype that allows for the selection or separation of those cells from other cells that do not express the selection marker trait. A variety of genes can be used as selection markers, for example, genes encoding drug resistance or auxotrophic rescue are widely known. For example, kanamycin (neomycin) resistance can be used as a trait to select for bacteria that have taken up a plasmid carrying a gene encoding bacterial kanamycin resistance (e.g., the enzyme neomycin phosphotransferase II). Non-transfected cells will eventually die when the culture is treated with neomycin or a similar antibiotic. A set of cells can be used in a drug screen to identify genes that confer drug resistance. The cells can be treated with a drug of interest and the gRNA that is enriched will be associated with the gene that confers drug resistance when mutated. Screens for resistance to viral or bacterial pathogens can be used to identify genes that prevent infection or replication of the pathogen. As in drug resistance screens, survival after pathogen exposure provides a strong selection. In cancer, negative selection CRISPR screens could identify "oncogene dependencies" in specific cancer subtypes that could provide the basis for molecular targeted therapy. For developmental studies, screens in human and mouse pluripotent cells could pinpoint genes required for pluripotency or differentiation into distinct cell types.

[0040] In one embodiment, the cell comprises a selection marker system. The selection marker system may comprise a fluorescent protein FRET biosensor. For example, tau can be fused or linked to CFP. Another tau protein can be fused or linked to YFP. With the aggregation of tau protein, the light that strikes the CFP is emitted as blue light, which excites the YFP and emits yellow light. In the absence of aggregation of tau protein, the blue light emitted from the CFP cannot excite the YFP of other tau proteins, so no yellow light is emitted. Thus, if a gene that regulates tau is targeted by one or more gRNAs and thus cleaved, no yellow light is emitted. Ultimately, this gene can be identified as a gene that regulates tau (e.g., causes tau aggregation). In CRISPRa, if the gRNA binds to the target region, the downstream gene is activated, and thus cells with overexpression or excess amount of the selection marker can be selected. For example, using the tau protein FRET biosensor described above, an increased amount of yellow light compared to cells without gRNA can indicate a gRNA that is bound to a gene that regulates tau. Any number of proteins that are present in known pathways, specifically disease pathways, can be used as marker proteins to identify genes that regulate the marker protein and ultimately may be found to be involved in a particular disease pathway.

[0041] In one embodiment, the cells of the first culture and the selected cells of the second culture may be sequenced (e.g., FIG. 2; 202 and 207). The cells of the first culture and the selected cells of the second culture may be sequenced at different time points after infection. The cells of the first culture and the selected cells of the second culture may be sequenced at different time points after infection to generate pre-selection read counts (cells of the first culture) and post-selection read counts (selected cells of the second culture). For example, the cells of the first culture may be sequenced 3 days after infection and the selected cells of the second culture may be sequenced 10 days after infection. The cells may be sequenced using any available sequencing technology, such as NGS. The nucleic acids of the cells may be sequenced to generate sequence data. The sequence data may include read counts. The sequence data may include read counts for one or more gRNAs of the library. Sequencing the cells may generate sequence data that may be stored in a data structure. The data structure may include one or more nucleic acid sequences and / or sample identifiers.

[0042] The read counts resulting from sequencing suffer from traditional biases present in CRISPR screen analysis, including frequent absence of duplication, variability in gRNA knockout efficiency, and variability in read number distribution. These biases lead to poor results when analyzing read numbers according to the negative binomial approach, the log2-ratio approach, and the paired t-test approach. Such approaches require a certain homogeneity / concordance between gRNAs for the same gene, as well as between repeats. These existing approaches cannot handle the large variation between gRNAs and repeats for the same gene, which may result from, for example, different infection efficiencies, different gene editing efficiencies, initial virus numbers in the screening library, and the presence of other guides with the same phenotype. These biases may exist within a CRISPR experiment and between multiple CRISPR experiments. The process of determining the total number of gRNAs per target region and the total number of gRNAs across all target regions described herein represents a read number process that is robust to large variations in read numbers. One embodiment of the disclosed method offers advantages over existing approaches because it is based on the positive occurrence of guides per gene in individual experiments instead of the exact read counts of each guide.

[0043] The number of reads can be normalized. For example, the number of reads from different samples can be median normalized to adjust for the effects of library size and read number distribution. In an embodiment, a given N CRISPR / Cas9 knockout screening experiment is performed with a set of M gRNAs, and the number of reads for gRNA i in experiment j is x ij , 1≦i≦M, 1≦j≦N. Since the sequencing depth (or library size) may vary between experiments, the number of reads can be adjusted by applying the median ratio method across all experiments. In an embodiment, the adjusted number of reads x' ij is the equation:

[0044]

number

[0045] where S is, for j=1 to M,

[0046]

number

[0047] In another embodiment, the adjusted number of reads x' ij x ij / S j where s j is the size factor in experiment j, calculated as the median of all size factors calculated from the individual sgRNA read counts:

[0048]

number

[0049] During the ceremony,

[0050]

number

[0051] is the geometric mean of the number of reads for gRNA i.

[0052]

number

[0053] Alternatively, the reads per gRNA and the reads per gene may be normalized using either counts per million, total counts, or size factor normalization. See Anders, S. and Huber, W. (2010) Differential expression analysis for sequence count data. Genome Biol., 11, R106, which is incorporated herein by reference in its entirety.

[0054] In one embodiment, the disclosed method uses sequence data to determine the sum (Σ) of each number (n') of gRNAs in selected cells whose post-selection read counts exceed the background threshold per target region of DNA (e.g., FIG. 2; 203, 208). The method may also include determining the total number (N') of gRNAs in selected cells whose read counts exceed the background threshold across all target regions (e.g., FIG. 2; 204, 209).

[0055] Thus, the disclosed method can identify positive occurrences of gRNAs from a library in sequences of selected cells. Sequence data including read counts can be analyzed to determine a "total presence" or n' for each target region of DNA (e.g., gene) after selection. The "total presence", or the respective number of gRNAs present per target region of DNA, can be determined by comparing the individual read counts of each gRNA to a background threshold. Determining the respective number of gRNAs whose read counts exceed the background threshold per target region of DNA can be performed by a computing device. The background threshold can be any value sufficient to reduce background noise in the sequence data. For example, 30 can be used as the background threshold. Thus, the "total presence" indicates the number of gRNAs present (whose read counts exceed the background threshold) in contrast to the amount of gRNAs present as indicated by the read counts.

[0056] In one embodiment, the method comprises the steps of infecting a second culture of cas9 positive cells with a library of viral vectors, categorizing the cells of the second culture as either having a designated phenotype or not having the designated phenotype, selecting cells having the designated phenotype, sequencing the selected cells to obtain a post-selection read count for each of the gRNAs, summing (Σ) the number of each of the gRNAs in the selected cells whose post-selection read count exceeds a background threshold per target region of DNA, where Σ=n′, and summing (Σ) the total number of gRNAs in the selected cells whose read count exceeds the threshold across the entire target region, where Σ=N′ can be repeated any number of times.

[0057] In one embodiment, the method of the disclosure further comprises identifying the target region as positively selected based on the probability of observing n' or more gRNAs of the gene in the selected cell by chance (e.g., FIG. 2; 211). In some embodiments, identifying the target region as positively selected comprises determining that the probability of observing n' or more gRNAs of the sequence of interest in the selected cell by chance meets a threshold value.

[0058] In some embodiments, the disclosed method further comprises identifying the target region as a modifier of the second gene based on the probability of observing n' or more gRNAs of the gene in the selected cell by chance. In some embodiments, the disclosed method further comprises identifying the target region as a therapeutic target based on the probability of observing n' or more gRNAs of the gene in the selected cell by chance. In some embodiments, the disclosed method further comprises identifying the target region as correlated with a specified phenotype based on the probability of observing n' or more gRNAs of the gene in the selected cell by chance. In some embodiments, the disclosed method further comprises identifying the target region as exhibiting a protective effect based on the probability of observing n' or more gRNAs of the gene in the selected cell by chance. Thus, the disclosed method can aid in the identification of target regions (e.g., genes) involved in a disease pathway, target regions (e.g., genes) involved in the regulation of one or more other genes / proteins, and / or target regions (e.g., genes) associated with a phenotype. A region of DNA (eg, a candidate gene) may be "associated" with a selected phenotype if modulation of gene expression of the candidate gene results in a change in the selected phenotype.

[0059] In one embodiment, the disclosed method further comprises determining an enrichment score for each gRNA. In some embodiments, determining an enrichment score for each gRNA comprises evaluating N / N'. The enrichment score may be determined to be above a threshold. The threshold may be relative to other enrichment scores of other target regions. Target regions with high enrichment scores and low probability of the gRNA being present by chance may be used to identify target regions as associated with a phenotype.

[0060] In an exemplary embodiment, the method and system may be implemented on a computer 301 as shown in FIG. 3 and described below. Similarly, the method and system may utilize one or more computers to perform one or more functions at one or more locations. FIG. 3 is a block diagram illustrating an exemplary operating environment for implementing the method. This exemplary operating environment is only one example of an operating environment and is not intended to suggest any limitation as to the scope of use or functionality of the operating environment architecture. Neither should any operating environment be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment.

[0061] The method and system may be operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of computing systems, environments, and / or configurations that may be suitable for use with the system and method include, but are not limited to, personal computers, server computers, laptop devices, and multiprocessor systems. Additional examples include set-top boxes, programmable consumer electronics products, network PCs, minicomputers, mainframe computers, and distributed computing environments that include any of the above systems or devices.

[0062] The processing of the methods and systems may be implemented via software components. The systems and methods may be described in the general context of computer-executable instructions, such as program modules, executed via one or more computers or other devices. Generally, program modules include computer code, routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types. The methods may also be practiced in grid-based and distributed computing environments where tasks are performed via remote processing devices linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including memory storage devices.

[0063] Additionally, the systems and methods may be implemented via a computing device in the form of a computer 301. Components of the computer 301 may include, but are not limited to, one or more processors 303, a system memory 312, and a system bus 313 that couples various system components including the one or more processors 303 to the system memory 312. The system may utilize parallel computing.

[0064] The system bus 313 represents one or more of several possible types of bus structures, including a memory bus or memory controller, a bus for peripherals, an accelerated graphics port, or a local bus, using any of a variety of bus architectures. The bus 313, and all buses specified in this description, may also be implemented via a wired or wireless network connection, and each subsystem, including one or more processors 303, mass storage device 304, operating system 305, software 306, data 307, network adapter 308, system memory 312, input / output interface 310, display adapter 309, display device 311, and human-machine interface 302, may be contained in one or more remote computing devices 314a,b,c in physically separate locations connected via this form of bus, effectively implementing a fully distributed system.

[0065] Computer 301 typically includes a variety of computer readable media. Exemplary readable media may be any available media that can be accessed by computer 301, including, but not limited to, both volatile and nonvolatile media, removable and non-removable media. System memory 312 includes computer readable media in the form of volatile memory, such as random access memory (RAM), and / or nonvolatile memory, such as read only memory (ROM). System memory 312 typically includes data, such as data 307, and / or program modules, such as operating system 305 and software 306, that are immediately accessible to and / or presently being operated by the one or more processors 303.

[0066] In another embodiment, computer 301 may also include other removable / non-removable, volatile / non-volatile computer storage media. As an example, FIG. 3 illustrates a mass storage device 304 that may provide non-volatile storage of computer code, computer readable instructions, data structures, program modules, and other data for computer 301. For example, and without limitation, mass storage device 304 may be a hard disk, a removable magnetic disk, a removable optical disk, a magnetic cassette or other magnetic storage device, a flash memory card, a CD-ROM, a digital versatile disk (DVD) or other optical storage, a random access memory (RAM), a read-only memory (ROM), and / or an electrically erasable programmable read-only memory (EEPROM).

[0067] Optionally, any number of program modules may be stored on mass storage device 304, including, by way of example, operating system 305 and software 306. Each of operating system 305 and software 306 (or some combination thereof) may include elements of programming and software 306. Data 307 may also be stored on mass storage device 304. Data 307 may be stored in any of one or more databases. Examples of such databases include DB2®, MICROSOFT® Access, MICROSOFT® SQL Server, ORACLE®, and / or MYSQL®, POSTGRESQL®. The databases may be centralized or distributed across multiple systems. Data 307 may include sequencing data. Sequencing data may include sequencing read data (e.g., read counts). Computer 301 may receive, for example, first read count data generated in steps 120 and 202 of FIG. 1 and FIG. 2, respectively. Computer 301 may receive, for example, the second read count data generated in steps 160 and 207 of Figures 1 and 2, respectively.

[0068] In another embodiment, a user can enter commands and information into computer 301 through input devices (not shown). Examples of such input devices include, but are not limited to, a keyboard, a pointing device (e.g., a "mouse"), a microphone, a joystick, a scanner, a tactile input device such as a glove, and / or other body covering. These and other input devices can be connected to one or more processors 303 through a human-machine interface 302 that is coupled to a system bus 313, but can also be connected by other interface and bus structures such as a parallel port, a game port, an IEEE 1394 port (also known as a Firewire port), a serial port, or a universal serial bus (USB).

[0069] In yet another embodiment, a display device 311 can also be connected to the system bus 313 via an interface such as a display adapter 309. It is contemplated that more than one display adapter 309 can be provided on the computer 301, and more than one display device 311 can also be provided on the computer 301. For example, a display device can be a monitor, a liquid crystal display (LCD), or a projector. In addition to the display device 311, other output peripheral devices can include components such as speakers (not shown) and a printer (not shown), which can be connected to the computer 301 via the input / output interface 310. Any process and / or result of the method can be output in any form to an output device. Such output can be any form of visual representation, including but not limited to textual, graphical, animated, audio, and / or tactile. The display 311 and the computer 301 can be part of one device or can be separate devices.

[0070] The computer 301 can operate in a network environment using logical connections to one or more remote computing devices 314a, b, c. By way of example, the remote computing devices can be a personal computer, a portable computer, a smart phone, a server, a router, a network computer, a peer device or other common network node, and the like. The logical connections between the computer 301 and the remote computing devices 314a, b, c can be made through a network 315, such as a local area network (LAN) and / or a general wide area network (WAN). Such network connections can be through a network adapter 308. The network adapter 308 can be implemented in both wired and wireless environments. In one embodiment, the system memory 312 can store one or more objects that are made accessible to the one or more remote computing devices 314a, b, c via the network 315. Thus, the computer 301 can function as a cloud-based object storage. In another embodiment, one or more of the one or more remote computing devices 314a,b,c can store one or more objects that are authorized to be accessed by the computer 301 and / or one or more objects that are authorized to be accessed by other of the one or more remote computing devices 314a,b,c. Thus, the one or more remote computing devices 314a,b,c can also function as cloud-based object storage.

[0071] For convenience of illustration, application programs and other executable program components, such as the operating system 305, are illustrated herein as discrete blocks, although it will be appreciated that such programs and components may reside at various times in different storage components of the computing device 301 and be executed via one or more processors 303 of the computer. In one embodiment, at least a portion of the software 306 and / or data 307 may be stored and / or executed on one or more of the computing device 301, the remote computing devices 314a, b, c, and / or a combination thereof. Thus, the software 306 and / or data 307 may operate within a cloud computing environment, where access to the software 306 and / or data 307 may be effected via a network 315 (e.g., the Internet). Additionally, in one embodiment, the data 307 may be synchronized across one or more of the computing device 301, the remote computing devices 314a, b, c, and / or a combination thereof.

[0072] An implementation of the software 306 may be stored on or transmitted through some form of computer readable media. Any of the methods may be performed by computer readable instructions embodied on the computer readable media. The computer readable media may be any available medium accessible by a computer. By way of example, and not intended to be limiting, computer readable media may include "computer storage media" and "communications media." "Computer storage media" includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Exemplary computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVDs) or other optical storage, magnetic cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, or any other medium that can be used to store the desired information and that can be accessed by a computer.

[0073] The software 306 may be configured to perform some or all of the steps of the methods disclosed herein. In one embodiment, the software 306 performs a sequence analysis to determine a total number of gRNAs (N) present in the first cell population across all target regions of the multiple target regions of DNA, based on sequencing of the first cell population following infection with a vector comprising a library of at least three guide RNAs (gRNAs) for each of the multiple target regions of DNA, the number of reads of which exceeds a background threshold, based on sequencing of the second cell population following infection with a vector comprising a library of at least three gRNAs ... determine a total number (n') of gRNAs present in the second cell population across all target regions of the plurality of target regions of DNA whose read counts exceed a background threshold based on the respective number of gRNAs for each of the plurality of target regions of DNA; determine, for each target region of the plurality of target regions, a probability of accidentally observing n' gRNAs for the target region in the selected cell based on n, N, n', and N'; determine, for a target region comprising the sequence of interest, a probability of accidentally observing n' or more gRNAs of the sequence of interest in the selected cell based on the probability of accidentally observing n' gRNAs for the target region in the selected cell; and identify the sequence of interest as positively selected based on the probability of accidentally observing n' or more gRNAs of the sequence of interest in the selected cell.

[0074] Determining the total number (N) of gRNAs present in the first cell population across all target regions of the multiple target regions of DNA whose read counts exceed a background threshold based on the respective numbers of gRNAs for each of the multiple target regions of DNA may include counting each gRNA present in the first cell population whose read counts exceed the background threshold.

[0075] Determining the total number (N') of gRNAs present in the second cell population that span all target regions of the multiple target regions of DNA whose read counts exceed the background threshold based on the respective numbers of gRNAs for each of the multiple target regions of DNA may include counting each gRNA present in selected cells of the second cell population whose read counts exceed the background threshold.

[0076] For each target region of the plurality of target regions, determining a probability of observing n' guides by chance for the target region in the selected cell based on n, N, n', and N'

[0077]

number

[0078] wherein

[0079]

number

[0080] calculates the number of ways to select an x ​​object from a y object. Determining the probability of accidentally observing n' or more gRNAs for the sequence of interest in a selected cell based on the probability of accidentally observing n' guides for the target region in the selected cell for the target region that includes the sequence of interest,

[0081]

number

[0082] The method may include evaluating the following: Identifying a sequence of interest as positively selected based on the probability of observing n' or more gRNAs of the sequence of interest in a selected cell by chance may include determining that the probability of observing n' or more gRNAs of the sequence of interest in a selected cell by chance meets a threshold value.

[0083] The software 306 may further be configured to determine, for each gRNA, an enrichment score. Determining the enrichment score for each gRNA may include evaluating N / N'.

[0084] The software 306 may be configured to store the number of gRNA reads and the number of gRNAs present as data 307. FIG. 4 shows an exemplary data structure 410 representing an embodiment of the data 307. The data structure 410 may comprise one or more tables, arrays, etc. The data structure 410 may include multiple rows and multiple columns. The column "Gene #" may include identifiers of unique genes or target regions of DNA. The column "gRNAs per gene" includes identifiers of unique gRNAs that may bind to genes or target regions of DNA. The column "Number of gRNA reads (pre-selection)" includes a number of reads value. The column "n" includes the number of gRNAs present per target region of DNA (e.g., gene) from the column "Number of gRNA reads (pre-selection)". The column "Number of gRNA reads (post-selection)" includes a number of reads value. The column "n'" includes the number of gRNAs present per target region of DNA (e.g., gene) from the column "Number of gRNA reads (post-selection)".

[0085] The data structure 410 of FIG. 4 is shown with artificial values ​​stored as data 307 for illustrative purposes. For gene 1, gRNAs A, B, and C may be present in the library and may bind to gene 1. After sequencing, gRNAs A, B, and C were present in the cell with 100, 200, and 10 reads, respectively. A background threshold of 30 may be applied to the reads, and the positive occurrence of gRNAs from the library may be determined by the software 306 to be 2 (n1=3) since only gRNAs A and B have reads greater than 30. For gene 2, gRNAs D, E, and F may be present in the library and may bind to gene 2. After sequencing, gRNAs D, E, and F were present in the cell with 500, 300, and 200 reads, respectively. A background threshold of 30 may be applied to the number of reads and the positive occurrence of gRNAs from the library may be determined by the software 306 to be 3 (n2=3) since gRNAs D, E, and F have reads greater than 30. For gene 3, gRNAs G, H, I, and J may be present in the library and bind to gene 3. After sequencing, gRNAs G, H, I, and J were present in the cell with reads of 200, 250, 10, and 300, respectively. A background threshold of 30 may be applied to the number of reads and the positive occurrence of gRNAs from the library may be determined by the software 306 to be 3 (n3=3) since only gRNAs G, H, and J have reads greater than 30. For gene 4, gRNAs K, L, M, and N may be present in the library and bind to gene 4. After sequencing, gRNAs K, L, M, and N were present in the cell with reads of 100, 200, 200, and 100, respectively. A background threshold of 30 may be applied to the number of reads, and the positive occurrence of gRNAs from the library may be determined by the software 306 to be 4 (n4=4) since gRNAs K, L, M, and N have a number of reads greater than 30.

[0086] As shown in the data structure 410 of FIG. 4, the total number of gRNAs or N is 12. N is derived from 2+3+3+4 from the values ​​of n for each gene (n1+n2+n3+n4). Thus, of the original library of 14 different gRNAs (gRNAs A-N) used to infect the cell population, only 12 different gRNAs were present in amounts above the background threshold. The values ​​of n and N may be stored in the data structure. In addition to the values ​​of n and N, the data structure may include one or more nucleic acid sequences (e.g., target regions and / or gRNA sequences) and / or one or more gRNA identifiers.

[0087] The column "Gene#" contains identifiers of unique genes or target regions of DNA. The column "gRNAs per gene" contains identifiers of unique gRNAs that may bind to genes or target regions of DNA. The column "gRNA read count (post-selection)" contains a read count value. The column "n'" contains the number of gRNAs present per target region (e.g., gene) of DNA derived from the column "gRNA read count (post-selection)". As shown in the data structure 410 of FIG. 4, for gene 1, gRNAs A, B, and C may be present in the library and bind to gene 1. After selection and after sequencing, gRNAs A, B, and C were present in the cell with 0, 50, and 0 read counts, respectively. A background threshold of 30 may be applied to the read counts, and the positive occurrence of gRNAs from the library may be determined by the software 306 to be 1 (n'1=1) since only gRNA B has a read count greater than 30. For gene 2, gRNAs D, E, and F may be present in the library and bind to gene 2. After selection and sequencing, gRNAs D, E, and F were present in the cell with 100, 100, and 100 reads, respectively. A background threshold of 30 may be applied to the reads, and the positive occurrence of gRNAs from the library may be determined by the software 306 to be 3 (n'2=3) since gRNAs D, E, and F have reads greater than 30. For gene 3, gRNAs G, H, I, and J may be present in the library and bind to gene 3. After selection and sequencing, gRNAs G, H, I, and J were present in the cell with 0, 0, 0, and 20 reads, respectively. A background threshold of 30 may be applied to the reads, and the positive occurrence of gRNAs from the library may be determined by the software 306 to be 0 (n'3=0) since none of them have reads greater than 30. For gene 4, gRNAs K, L, M, and N may be present in the library and bind to gene 4. After selection and sequencing, gRNAs K, L, M, and N were present in the cells with 0, 0, 0, and 20 reads, respectively.A background threshold of 30 may be applied to the number of reads, and the positive occurrence of gRNAs from the library may be determined by the software 306 to be 0 (n'4=0) since no gRNA has a read count greater than 30.

[0088] The total number or N' of gRNAs across all target regions that are present in the selected cells whose read counts exceed the background threshold may be determined by the software 306. As shown in the data structure 410 of FIG. 4, the total number or N' of gRNAs is 4. N' is derived from 1+3+0+0 from the value of n for each gene (n'1+n'2+n'3+n'4). Thus, of the original library of 14 different gRNAs (gRNAs A-N) used to infect the cell population, only 4 different gRNAs were present in the selected cells in amounts above the background threshold. The values ​​of n' and N' may be stored in the data structure. In addition to the values ​​of n' and N', the data structure may include one or more nucleic acid sequences (e.g., target regions and / or gRNA sequences) and / or one or more gRNA identifiers.

[0089] In an embodiment, data 307 may also be configured to store one or more results of software 306. FIG. 5 shows an example of a results data structure 510 generated by software 306, for example, using data structure 410 as input. Data structure 510 may comprise one or more tables, arrays, etc. FIG. 5 shows artificial data and artificial results for illustrative purposes. A formal statistical p-value may be calculated for the number of guides across experimental repeats that are positively observed given library size, number of guides per gene, and total number of positive guides in each experiment. Data structure 510 shows an initial library (N) of 63,950 gRNAs. Data structure 510 shows that after selection, 4,946 gRNAs (N') (number of unique gRNAs, not quantity of gRNAs) remained in the cell population for experiment 1, and 13,606 gRNAs (N') (number of unique gRNAs, not quantity of gRNAs) remained in the cell population for experiment 2. As shown in data structure 510, target region 1 has three (3) gRNAs that can bind to at least a portion of target region 1. In experiment 1, the probability that three out of three gRNAs will be present by chance in the post-selection cell population is 0.000462378, given that 4,946 gRNAs remain in the cell population after selection. In experiment 2, the probability that three out of three gRNAs will be present by chance in the post-selection cell population is 0.009629, given that 13,606 gRNAs remain in the cell population after selection. Data structure 510 shows the results of the probability that two out of three, one out of three, and zero out of three gRNAs will be present by chance in the post-selection cell population.

[0090] As shown in data structure 510, target region 2 has four (4) gRNAs that can bind to at least a portion of target region 2. Data structure 510 shows results of the probability that 4 of 4, 3 of 4, 2 of 4, 1 of 4, and 0 of 4 gRNAs are present by chance in the cell population after selection. From the results shown in data structure 510, the probability may be determined by software 306 to be below a threshold (e.g., sufficiently small). For example, for target region 2, experiment 1, the probability that 4 of 4 gRNAs are present by chance is 3.57411E-05, indicating that 4 of 4 gRNAs may not have been present simply by chance. EXAMPLES

[0091] A. Example 1: Development of a genome-wide CRISPR / Cas9 screening platform to identify genetic modifiers of tau aggregation To identify genes and pathways that alter the process of abnormal tau protein aggregation, we developed a platform to conduct a genome-wide screen using CRISPR nuclease (CRISPRn) sgRNA libraries to identify genes that regulate the likelihood of cells to be "seeded" by tau disease-associated protein aggregates (i.e., genes that, when disrupted, render cells more susceptible to tau aggregate formation upon exposure to a source of tau fibrillary protein). The screen employed a tau biosensor human cell line consisting of HEK293T cells stably expressing tau 4 repeat domain, tau_4RD, containing the tau microtubule-binding domain (MBD) with the P301S pathogenic mutation, fused to either CFP or YFP. Briefly, the HEK293T cell line contains two transgenes stably expressing disease-associated protein variants fused to the fluorescent protein CFP or the fluorescent protein YFP: tau4RD-CFP / tau4RD-YFP (TCY), in which the tau repeat domain (4RD) contains the P301S pathogenic mutation.

[0092] In these biosensor lines, tau-CFP / tau-YFP protein aggregation produces a FRET signal, which is the result of the transfer of fluorescent energy from the donor CFP to the acceptor YFP. FRET-positive cells containing tau aggregates can be sorted and isolated by flow cytometry. At baseline, unstimulated cells express the reporter in a stable soluble state with minimal FRET signal. Upon stimulation (e.g., liposomal transfection of seed particles), the reporter protein forms aggregates and produces a FRET signal. Aggregate-containing cells can be isolated by FACS. A stably growing aggregate-containing cell line, Agg[+], can be isolated by clonal serial dilution of the Agg[-] cell line.

[0093] Several modifications were made to this tau biosensor cell line to make it useful for genetic screening. First, these tau biosensor cells were modified by introducing a Cas9-expressing transgene (SpCas9) via a lentiviral vector. Clonal transgenic cell lines expressing Cas9 were selected with blasticidin and isolated by clonal serial dilution to obtain single-cell-derived clones. Clones were assessed for levels of Cas9 expression by qRT-PCR and DNA cleavage activity by digital PCR.

[0094] Specifically, Cas9 mutation efficiency was assessed by digital PCR 3 and 7 days after transduction of lentivirus encoding gRNA against two selected target genes. Cleavage efficiency was limited by Cas9 levels in clones with lower expression. Clones with sufficient levels of Cas9 expression were necessary to achieve maximum activity. Some derived clones with lower Cas9 expression were unable to cleave the target sequence efficiently, whereas clones with higher expression (including those used for screening) were able to generate mutations in the target sequences of genes PERK and SNCA with approximately 80% efficiency after three days in culture. Efficient cleavage was already observed 3 days after gRNA transduction, with only a slight improvement after 7 days. Clone 7B10-C3 was selected as a high-performing clone to be used in the subsequent library screen.

[0095] Second, we developed reagents and methods to sensitize cells to tau seeding activity. Tau intercellular proliferation may result from tau aggregation activity secreted by aggregate-containing cells. To test the cellular proliferation of tau aggregates, we obtained a subclone of the tau-YFP cell line consisting of HEK293T cells stably expressing the tau repeat domain, tau_4RD, containing the tau microtubule-binding domain (MBD) with the P301S pathogenic mutation, fused to YFP.

[0096] Cells in which tau-YFP protein is stably present in an aggregated state (Agg[+]) were obtained by treating these tau-YFP cells with recombinant fibrillar tau mixed with Lipofectamine reagent to seed aggregates of tau-YFP protein stably expressed by these cells. The "seeded" cells were then serially diluted to obtain single cell derived clones. These clones were then expanded to identify clonal cell lines in which tau-YFP aggregates stably persisted in all cells over time and multiple passages in culture. One of these tau-YFP_Agg[+] clones was used to collect the medium in which tau-YFP_Agg[+] cells were confluent for four days to produce conditioned medium. The conditioned medium (CM) was then applied to naive biosensor tau-CFP / tau-YFP cells in a ratio of 3:1 CM:fresh medium to induce tau aggregation in a minority of these recipient cells. Lipofectamine was not used. Lipofectamine was not used in order to have as physiological an assay as possible without using lipofectamine to trick the recipient cells into forcing / increasing tau aggregation. Conditioned media consistently induced FRET in approximately 0.1% of cells, as measured by using flow cytometry to assess the percentage of cells producing a FRET signal as a measure of aggregation.

[0097] B. Example 2: Genome-wide CRISPR / Cas9 Screening to Identify Genetic Modifiers of Tau Aggregation To reveal modifier genes of tau aggregation as enriched sgRNAs in FRET(+) cells, Cas9-expressing tau-CFP / tau-YFP biosensor cells without aggregates (Agg[-]) were transduced with two human genome-wide CRISPR sgRNA libraries (GeCKO A and GeCKO B) (Figure 7) using a lentiviral delivery approach to introduce knockout mutations at each target gene. Each CRISPR sgRNA library targets 5' constitutive exons for functional knockout with an average coverage of approximately three sgRNAs per gene (total of six gRNAs per gene for the two libraries combined). The read number distribution (i.e., representation of each gRNA in the libraries) was normal and similar for each library. sgRNAs were designed to avoid off-target effects by avoiding sgRNAs with no more than two mismatches with off-target genomic sequences. This library covers 19,050 human genes and 1864 miRNAs with 1000 non-targeting control sgRNAs. The library was transduced at a multiple of infection (MOI) <0.3 with coverage of >300 cells per sgRNA. Tau biosensor cells were grown under puromycin selection to select for cells with unique sgRNA integration and expression per cell. Puromycin selection was initiated 24 hours post-transduction at 1 μg / mL. Five independent screening replicates were used in the primary screen.

[0098] Samples of the fully transduced cell population were taken at cell passage on days 3 and 6 after transduction. After passage on day 6, cells were grown in conditioned medium to make them susceptible to seeding activity. On day 10, a subpopulation of FRET[+] cells was specifically isolated using fluorescence-assisted cell sorting (FACS). The screen consisted of five replicate experiments. DNA isolation and PCR amplification of the integrated sgRNA constructs allowed for characterization by next-generation sequencing (NGS) of the sgRNA repertoire at each time point.

[0099] Statistical analysis of the NGS data allowed us to identify sgRNAs enriched in the day 10 FRET[+] subpopulation of five experiments compared to the sgRNA repertoire at earlier time points, day 3 and day 6. The first strategy to identify potential tau modifiers was to use DNA sequencing to generate sgRNA read counts in each sample using the DESeq algorithm to find sgRNAs that were more abundant at day 10 vs. day 3 or day 10 vs. day 6, but not day 6 vs. day 3 (fold change (fc) ≥ 1.5 and negative binomial test p < 0.01). Fc ≥ 1.5 means a ratio of (mean of day 10 counts) / (mean of day 3 or day 6 counts) ≥ 1.5. P < 0.01 means no statistical difference between day 10 and day 3 counts, or day 6 counts < 0.01. The DESeq algorithm is a widely used algorithm for "analysis of differential expression of sequence count data". See, for example, Anders et al. (2010), Genome Biology, 11:R106, incorporated herein by reference.

[0100] Specifically, two comparisons were used for each library to identify significant sgRNAs: day 10 vs. day 3, and day 10 vs. day 6. For each of these four comparisons, the DESeq algorithm was used and the cutoff threshold for being considered significant was a fold change ≧1.5 in addition to a negative binomial test p<0.01. Once significant guides were identified in each of these comparisons for each library, genes were considered significant if they met one of the following two criteria: (1) at least two sgRNAs corresponding to that gene were considered significant in one comparison (either day 10 vs. day 3, or day 10 vs. day 6); and (2) at least one sgRNA was significant in both comparisons (day 10 vs. day 3, and day 10 vs. day 6). Using this algorithm, five genes were identified as significant from the first library and four genes were identified from the second library. See Table 1.

[0101] [Table 1]

[0102] However, this first strategy requires a certain level of homogeneity of read numbers within each experimental group, which is extremely stringent. For the same sgRNA, many factors can cause variability in read numbers between samples within each experimental group (day 3, day 6, or day 10 samples), such as the initial virus number in the screening library, infection or gene editing efficiency, and relative growth rate after gene editing. Therefore, the second strategy was also used based on the positive occurrence (reads > 30) of guides per gene in each sample on day 10 (post-selection) instead of the exact read count.

[0103] The pre-selection CRISPR experiment was repeated four times. As shown in FIG. 6, for gene “G1” in pre-selection experiment “Exp1”, gRNAs “g1”, “g2”, and “g3” were present in the cell population with read counts of 121, 1000, and 302, respectively. For gene “G2”, gRNAs “g4”, “g5”, “g6”, and “g7” were present in the cell population with read counts of 443, 2012, 534, and 150, respectively. Such read count data is generated for genes “G1” through “G21,000”. For each gene, the “total presence” or n was determined. The “total presence”, which is the respective number of gRNAs present per targeted region of DNA, was determined by comparing the individual read counts of each gRNA to a background threshold. In this case, 30 was used as the background threshold. Thus, the “total presence” indicates the number of gRNAs qualitatively present, as opposed to the amount of gRNAs present as indicated by the read count. The sum of the occurrences of gRNAs corresponding to gene G1 is 3 because the number of reads for gRNAs g1, g2, and g3 are each above the background threshold of 30. The sum of the occurrences of gRNAs corresponding to gene G2 is 4 because the number of reads for gRNAs g4, g5, g6, and g7 are each above the background threshold of 30. The total number or N of gRNAs across all target regions present in the cell population with read counts above the background threshold is shown as 59,010. Thus, of the original library of approximately 64,000 different gRNAs used to infect the cell population, only approximately 59,000 different gRNAs were present in amounts above the background threshold.

[0104] The CRISPR experiment with phenotypic selection was repeated four times. However, before sequencing, the cells in the cell population were sorted according to phenotype using fluorescence technology (e.g., FRET fluorescence). If Cas9 / CRISPR cuts the target region (e.g., gene) of the cell and the cell does not fluoresce, the gene was successfully knocked out. If the cell fluoresces, the gene was not knocked out. The fluorescent cells can then be sequenced, and the non-fluorescent cells are not sequenced. The selected cells represent the cells that show a particular phenotype / marker.

[0105] As shown in Figure 6, for gene "G1" in post-selection experiment "Exp1", gRNAs "g1", "g2", and "g3" were present (or absent) in the cell population with read counts of 0, 8, and 12, respectively. For gene "G2", gRNAs "g4", "g5", "g6", and "g7" were present (or absent) in the cell population with read counts of 4, 25, 4, and 150, respectively. Such read count data is generated for genes "G1" through "G21,000". For each gene, the "total presence" or n' was determined. The "total presence", which is the respective number of gRNAs present per targeted region of DNA, was determined by comparing the individual read counts of each gRNA to a background threshold. In this case, 30 was used as the background threshold. Thus, the "total presence" indicates the number of gRNAs present versus the amount of gRNAs present as indicated by the read count. The sum of the presence of gRNAs corresponding to gene G1 is 0 because the number of reads for gRNAs g1, g2, and g3 each do not exceed the background threshold of 30. The sum of the presence of gRNAs corresponding to gene G2 is 1 because the number of reads for only gRNA g7 exceeds the background threshold of 30. The total number of gRNAs, or N', across all target regions present in the cell population whose number of reads exceeded the background threshold is shown as 4,320. Thus, of the original library of approximately 64,000 different gRNAs used to infect the cell population, only approximately 4,320 different gRNAs were present in amounts above the background threshold.

[0106] A formal statistical p-value was calculated for the number of guides in the post-selection sample that were positively observed, taking into account the library size, the number of guides per gene, and the total number of positive guides in the post-selection sample. Given the number of reads, the probability of observing n' guides to a target region (e.g., gene) in a selected cell (post-selection) by chance is calculated using the formula

[0107]

number

[0108] As an explanation,

[0109]

number

[0110] This determines the number of ways to select an x ​​object from a y object. After determining the probability of observing n' guides by chance for a target region (e.g., gene) in a selected cell (post-selection), the probability of observing n' or more gRNAs for a gene in a selected cell (post-selection) by chance is calculated using the formula

[0111]

number

[0112] It was decided in accordance with. Once the probability of observing n' or more gRNAs in a target region in a selected cell (post-selection) by chance was determined, the average enriched gRNA was determined at the target region level. The overall enrichment of the number of reads of the gene post-selection compared to pre-selection was used as an additional parameter to identify positive genes. The average enrichment was represented by the enrichment score. The enrichment score was determined by evaluating N / N'. Referring to Figure 6, the enrichment score is 59010 / 4320, or 13.66.

[0113] The probability of observing n' or more gRNAs of a target region in a selected cell (post-selection) by chance may be used to assess whether a target region is positively selected. The enrichment score may further be used to assess whether a target region is positively selected. Target regions with a probability of observing n' or more gRNAs of a target region in a selected cell that is significantly lower than the probability of observing n' or more gRNAs of a target region in a selected cell (post-selection) by chance may be identified as positively selected target regions. Additionally, target regions with enrichment scores above a threshold may indicate positively selected target regions.

[0114] Thus, this second strategy represents a new, more sensitive analysis method for CRISPR positive selection. The goal of CRISPR positive selection is to use DNA sequencing to identify genes whose perturbation by sgRNAs correlates with a phenotype. To reduce the noise background, multiple sgRNAs for the same gene are usually used in these experiments, along with experimental replicates. However, currently, commonly used statistical analysis methods that require some degree of homogeneity / concordance between sgRNAs for the same gene, as well as between technical replicates, do not work well. This is because these methods cannot handle large variations between sgRNAs and repeats for the same gene for many possible reasons (e.g., different infection or gene editing efficiencies, initial virus numbers in the screening library, and the presence of other sgRNAs with the same phenotype). In contrast, the method shown in this example is robust to large variations. It is based on the positive occurrence of guides per gene in individual experiments, rather than the exact number of reads for each sgRNA. A formal statistical p-value is calculated for the number of sgRNAs across experimental repeats that are positively observed, taking into account the library size, the number of sgRNAs per gene, and the total number of positive sgRNAs in each experiment. The relative sgRNA sequence read enrichment before and after phenotypic selection is also used as a parameter. This method performs better than the most widely used state-of-the-art methods, including DESeq, MAGECK, and others. Specifically, this method includes the following steps:

[0115] (1) For each experiment, identify any present guides in cells with a positive phenotype. (2) At the gene level, calculate the random probability (aka p-value) of the guide being present in each experiment. The overall probability of its presence across multiple experiments is calculated by Fisher's combined probability test (see Fisher, RA Fisher, RA (1948). Questions and Answers #14. The American Statistician). That is, first calculate the test statistic φ using the p-values ​​from multiple experiments:

[0116]

number

[0117] In the formula, p k is the p-value calculated for the kth experiment, where K is the total number of experiments. The combination of p-values ​​across the K experiments is then equal to the probability of observing a value of φ under a chi-squared distribution with 2*K degrees of freedom.

[0118] (3) Calculate the average enrichment of guides at the gene level: Enrichment score = relative abundance after selection / relative abundance before selection. Relative abundance = number of guide reads for a gene / number of reads for all guides.

[0119] (4) Selecting genes that are significantly lower than random chance of being present as well as above a certain enrichment score. C. Example 3 CRISPR / Cas9 activating and inactivating mutagenesis was used to screen for genetic modifiers of tau and α-synuclein fibrillization and proliferation using CRISPRn sgRNA libraries (hGeCKO-A and hGeCKO-B) targeting coding exons for functional knockout. Figure 7 shows the sample identifier, experiment number, time to sequencing, and identifies which library (Gecko A or Gecko B) was used for the experiment.

[0120] The Gecko A library consisted of approximately 63,950 gRNAs. Of the 63,950 gRNAs, 56,116 gRNAs targeted 18,874 genes, with the majority of genes targeted by three gRNAs. Of the 63,950 gRNAs, 6,834 gRNAs targeted 1,795 micoRNAs, with the majority of microRNAs targeted by four gRNAs.

[0121] The Gecko B library consisted of approximately 56,869 gRNAs. Of the 56,869 gRNAs, 55,869 gRNAs targeted 18,834 genes, with the majority of genes targeted by three gRNAs. Of the 56,869 gRNAs, no gRNAs targeted mycoRNAs. Of the 1,000 gRNAs, no gRNAs were targeted.

[0122] No phenotype was evident on days 3 and 6. On day 10, the sample was phenotype positive. Figure 8 shows the number of DNA reads from samples infected with the Gecko A library. Each bar represents a sample infected with virus. Each sample was sequenced on day 3 (d03), day 6 (d06), or day 10 (d10), and each sample represents the sequencing readout of a gRNA rather than the whole genome.

[0123] Figure 9 shows normalization of read counts from samples infected with the Gecko A library to the median. Normalization was done by dividing the number of gRNA reads by the sum of the number of reads in each sample, then dividing and multiplying by the median of the sum of the number of reads across all samples. The bar graph below is qualitative, showing the number of reads above a threshold of 30, day 10 post-selection. Considering similar sum of reads, day 3 and day 6 samples had a large variety of gRNAs, while day 10 samples had much fewer varieties of gRNAs.

[0124] Figure 10 shows the formal statistical p-value calculated for positively observing gRNA counts across experimental repeats, taking into account library size, number of gRNAs per gene, and total number of positive gRNAs in each experiment. p-values ​​are shown for five samples infected with the Gecko A library and sequenced on day 10.

[0125] Embodiment Embodiment 1. (A) infecting a first culture of cas9 positive cells with a library of viral vectors, the library comprising at least three guide RNAs (gRNAs) for cleaving target regions of DNA within the genome of the cells; and sequencing the cells to obtain read counts for each of the gRNAs; summing the number of each of the gRNAs whose read counts are above the background threshold per target region of DNA (Σ), where Σ=n; and summing the total number of gRNAs whose read counts are above the background threshold across all target regions (Σ), where Σ=N; (B) Infecting a second culture of cas9 positive cells with the library of viral vectors; categorizing the cells of the second culture as either having a designated phenotype or not having a designated phenotype; selecting the cells having the designated phenotype; and sequencing the selected cells to obtain a post-selection read count for each of the gRNAs. summing the number of each of the gRNAs per target region of DNA in the selected cells whose post-selection read counts exceed the background threshold (Σ), where Σ=n′; and (C) summing the total number of gRNAs of the selected cells whose read counts exceed a threshold across all target regions (Σ), where Σ=N′; (D) summing the total number of gRNAs of the selected cells whose read counts exceed a threshold across all target regions (Σ), where Σ=N′; (E) summing the total number of gRNAs of the selected cells whose read counts exceed a threshold across all target regions (Σ), where Σ=N′; (F) summing the total number of gRNAs of the selected cells whose read counts exceed a threshold across all target regions (Σ), where Σ=N′;

[0126]

number

[0127] Calculating the probability of observing a n'gRNA for a target region in a selected cell by chance according to the formula:

[0128]

number

[0129] Calculate the number of ways to select x objects from y objects, and for a target region of DNA that contains a gene, calculate the formula

[0130]

number

[0131] and calculating the probability of observing n' or more gRNAs for a gene in a selected cell by chance according to EMBODIMENT 2 (A) infecting a first culture of cas9 positive cells with a library of viral vectors, the library comprising at least three guide RNAs (gRNAs) for enhancing transcription of a target region of DNA within the genome of the cells; Sequencing the cells to obtain read numbers for each of the gRNAs; Summarizing (Σ) the number of each of the gRNAs whose read counts are above the background threshold per target region of DNA, where Σ=n; and Summarizing the total number of gRNAs whose read counts are above the background threshold across all target regions (Σ), where Σ=N; (B) Infecting a second culture of cas9 positive cells with the library of viral vectors; categorizing the cells of the second culture as either having a designated phenotype or not having a designated phenotype; selecting cells having a designated phenotype and sequencing the selected cells to obtain a post-selection read count for each of the gRNAs; summing the number of each of the gRNAs in the selected cells whose post-selection read counts exceed the background threshold per target region of DNA (Σ), where Σ=n′; and summing the total number of gRNAs of the selected cells whose read counts exceed a threshold across all target regions (Σ), where Σ=N′; (C) For a target region of DNA, the formula

[0132]

number

[0133] Calculating the probability of observing a n'gRNA for a target region in a selected cell by chance according to the formula:

[0134]

number

[0135] Calculate the number of ways to select x objects from y objects, and For a target region of DNA containing a gene, the formula

[0136]

number

[0137] and calculating the probability of observing n' or more gRNAs for a gene in a selected cell by chance according to Embodiment 3. The method according to any one of embodiments 1 to 2, wherein the first culture is infected according to CRISPR technology.

[0138] Embodiment 4. The method of any one of embodiments 1 to 3, wherein the cas-9 positive cells of the first culture are modified to contain one or more selectable markers. Embodiment 5. The method of any one of embodiments 1 to 4, wherein the designated phenotype is fluorescence.

[0139] Embodiment 6. The method of any one of embodiments 1 to 5, wherein the designated phenotype is cell survival.

[0023] Embodiment 7. The method of embodiment 4, wherein the one or more selectable markers comprises a fluorescent marker.

[0140] Embodiment 8. The method of embodiment 7, wherein the fluorescent marker is part of a FRET biosensor. Embodiment 9. The method of embodiment 4, wherein the one or more selectable markers is a detectable enzyme.

[0141] Embodiment 10. The method of embodiment 9, wherein the detectable enzyme is β-galactosidase.

[0023] Embodiment 11. The method of embodiment 9, wherein the detectable enzyme is luciferase.

[0142] Embodiment 12. The method of any one of embodiments 1 to 11, wherein the target region comprises a gene. Embodiment 13. The method of any of embodiments 1-12, wherein categorizing the cells of the second culture as either having the designated phenotype or not having the designated phenotype comprises applying a selection mechanism to the second cell culture.

[0143]

[0036] Embodiment 14. The method of embodiment 13, wherein the selection mechanism comprises one or more of exposing the second cell population to a drug or exposing the second cell population to an agent that specifies protein activity or expression levels.

[0144]

[0023] Embodiment 15. The method of embodiment 4, wherein selecting cells having a designated phenotype comprises sorting the cells based on one or more selectable markers. Embodiment 16. The method of any one of embodiments 1 to 15, further comprising identifying the target region as positively selected based on the probability of observing n' or more gRNAs of the gene in the selected cell by chance.

[0145]

[0023] Embodiment 17. The method of embodiment 16, wherein identifying the target region as positively selected comprises determining that the probability of observing n' or more gRNAs of the sequence of interest in the selected cell by chance meets a threshold value.

[0146]

[0031] Embodiment 18. The method of any one of embodiments 1 to 17, further comprising determining an enrichment score for each gRNA.

[0023] Embodiment 19. The method of embodiment 18, wherein for each gRNA, determining the enrichment score comprises evaluating N / N'.

[0147] Embodiment 20. The method of any one of embodiments 2 to 19, wherein the cas-9 positive cells contain inactivated cas-9. Embodiment 21. The method of embodiment 20, wherein the inactivated cas-9 is fused to at least one transcription activation domain.

[0148]

[0036] Embodiment 22. The method of any one of embodiments 1 to 21, wherein the target region of DNA regulates a downstream gene or protein.

[0046] Embodiment 23. The method of embodiment 22, wherein regulating the downstream gene or protein comprises activating or inhibiting the downstream gene or protein.

[0149] Embodiment 24. The method of any one of embodiments 1 to 23, further comprising identifying the target region as a modifier of a second gene based on the probability of observing n' or more gRNAs of the gene in the selected cell by chance.

[0150] Embodiment 25. The method of any one of embodiments 1 to 24, further comprising identifying the target region as a therapeutic target based on the probability of observing n' or more gRNAs of the gene in the selected cell by chance.

[0151]

[0031] Embodiment 26. The method of any one of embodiments 1 to 25, further comprising identifying the target region as correlated with the specified phenotype based on the probability of observing n' or more gRNAs of the gene in the selected cell by chance.

[0152] Embodiment 27. The method of any one of embodiments 1 to 26, further comprising identifying the target region as exhibiting a protective effect based on the probability of observing n' or more gRNAs of the gene in the selected cell by chance.

[0153]

[0046] Embodiment 28. A method comprising: determining, based on sequencing of a first cell population after infection with a vector comprising a library of at least three guide RNAs (gRNAs) for each of a plurality of target regions of DNA, a number of respective gRNAs (n) present for each of a plurality of target regions of DNA whose read count exceeds a background threshold; determining, based on the respective number of gRNAs for each of a plurality of target regions of DNA, a total number (N) of gRNAs present in the first cell population across all target regions of a plurality of target regions of DNA whose read count exceeds a background threshold; determining, based on sequencing of a second cell population after infection with a vector comprising a library of at least three gRNAs for each of a plurality of target regions of DNA, a number of respective gRNAs (n') present for each of a plurality of target regions of DNA whose read count exceeds a background threshold; determining a total number of gRNAs (N') present in the second population of cells across all target regions of the plurality of target regions of DNA whose read counts exceed a background threshold based on a respective number of gRNAs for each of the plurality of target regions of DNA; for each target region of the plurality of target regions, determining a probability of observing n' gRNAs by chance for the target region in a selected cell based on n, N, n', and N'; for a target region that includes a sequence of interest, determining a probability of observing n' or more gRNAs of the sequence of interest in the selected cell based on the probability of observing n' gRNAs by chance for the target region in the selected cell; and identifying the sequence of interest as positively selected based on the probability of observing n' or more gRNAs of the sequence of interest in the selected cell.

[0154]

[0036] Embodiment 29. The method of embodiment 28, wherein the first cell population and the second population comprise cas-9 positive cells.

[0046] Embodiment 30. The method of embodiment 29, wherein determining the respective number (n) of gRNAs present for each of a plurality of target regions of DNA whose read count exceeds a background threshold based on sequencing of the first cell population after infection with a vector comprising a library of at least three gRNAs for each of a plurality of target regions of DNA comprises infecting the first cell population with a library of viral vectors, sequencing cells of the first cell population to obtain a read count for each of the gRNAs, and counting each gRNA for each of the plurality of target regions of DNA whose read count exceeds a background threshold.

[0155]

[0041] Embodiment 31. The method of embodiment 30, wherein determining the total number (N) of gRNAs present in the first cell population across all target regions of the plurality of target regions of DNA whose read counts exceed a background threshold based on the respective numbers of gRNAs for each of the plurality of target regions of DNA comprises counting each gRNA present in the first cell population whose read counts exceed the background threshold.

[0156]

[0036] Embodiment 32 Based on sequencing the second cell population following infection with a vector comprising a library of at least three gRNAs for each of a plurality of target regions of DNA, determining the respective number (n') of gRNAs present for each of a plurality of target regions of DNA whose number of reads exceeds a background threshold, categorizing cells of the second cell population as either having a designated phenotype or not having a designated phenotype; 32. The method of embodiment 31, comprising selecting cells having a designated phenotype, sequencing the selected cells to obtain a read count for each of the gRNAs, and counting each gRNA whose read count exceeds a background threshold for each of a plurality of target regions of DNA.

[0157] Embodiment 33. The method of any one of embodiments 28 to 32, wherein determining the total number (N') of gRNAs present in the second cell population across all target regions of the multiple target regions of DNA whose read counts exceed the background threshold based on the respective numbers of gRNAs for each of the multiple target regions of DNA comprises counting each gRNA present in selected cells of the second cell population whose read counts exceed the background threshold.

[0158]

[0023] Embodiment 34: For each target region of the plurality of target regions, determining a probability of observing an n' gRNA for the target region in a selected cell by chance based on n, N, n', and N'

[0159]

number

[0160] wherein

[0161]

number

[0162] The method according to any one of embodiments 28 to 33, wherein the number of ways to select an x ​​object from a y object is calculated.

[0036] Embodiment 35. For a target region that includes a sequence of interest, determining the probability of by chance observing n' or more gRNAs for the sequence of interest in a selected cell based on the probability of by chance observing n' gRNAs for the target region in the selected cell comprises:

[0163]

number

[0164] 35. The method of any one of embodiments 28 to 34, comprising evaluating: Embodiment 36. The method of any one of embodiments 28 to 35, wherein identifying the target sequence as positively selected based on the probability of accidentally observing n' or more gRNAs of the target sequence in the selected cell comprises determining that the probability of accidentally observing n' or more gRNAs of the target sequence in the selected cell meets a threshold value.

[0165] Embodiment 37. The method of any one of embodiments 28 to 36, further comprising determining an enrichment score for each gRNA.

[0038] Embodiment 38. The method of embodiment 37, wherein for each gRNA, determining the enrichment score comprises evaluating N / N'.

[0166] Embodiment 39. The method of any one of embodiments 28 to 38, wherein the cas-9 positive cells contain inactivated cas-9. Embodiment 40. The method of embodiment 39, wherein the inactivated cas-9 is fused to at least one transcription activation domain.

[0167]

[0046] Embodiment 41. An apparatus comprising one or more processors and processor-executable instructions that, when executed by the one or more processors, cause the apparatus to: (A) receive first read count data, where the first read count data is generated by infecting a first culture of cas9 positive cells with a library of viral vectors, where the library comprises at least three guide RNAs (gRNAs) for cleaving a target region of DNA within the genome of the cells, and sequencing the cells to obtain a read count for each of the gRNAs; summing, based on the first read count data, a number of each of the gRNAs whose read counts exceed a background threshold per target region of DNA (Σ), where Σ=n, and summing, based on the first read count data, a total number of gRNAs whose read counts exceed the background threshold over the entire target region (Σ), where Σ=N. (B) receiving second read count data, where the second read count data is generated by infecting a second culture of cas9 positive cells with a library of viral vectors, categorizing the cells of the second culture as either having a designated phenotype or not having a designated phenotype, and selecting cells having the designated phenotype and sequencing the selected cells to obtain a post-selection read count for each of the gRNAs; summing, per target region of DNA based on the second read count data, the number of each of the gRNAs in the second cells whose post-selection read counts exceed a background threshold (Σ), where Σ=n', and summing, over the entire target region based on the second read count data, the total number of gRNAs in the selected cells whose read counts exceed the threshold (Σ), where Σ=N'; (C) for the target region of DNA, based on the formula

[0168]

number

[0169] Calculate the probability of observing a n'gRNA for a target region in a selected cell by chance according to:

[0170]

number

[0171] calculates the number of ways to select x objects from y objects, and for a target region of DNA containing a gene, the formula

[0172]

number

[0173] and a memory storing processor-executable instructions for calculating the probability of observing n' or more gRNAs for a gene in a selected cell by chance according to the following:

[0081] Embodiment 42. The apparatus of embodiment 41, wherein the first culture is infected according to CRISPR technology.

[0174] Embodiment 43. An apparatus described in any one of embodiments 41 to 42, wherein the cas-9 positive cells of the first culture are modified to contain one or more selectable markers. Embodiment 44. The device of any one of embodiments 41 to 43, wherein the target region comprises a gene.

[0175] Embodiment 45. An apparatus described in any one of embodiments 41 to 44, wherein categorizing the cells of the second culture as either having the specified phenotype or not having the specified phenotype comprises applying a selection mechanism to the second cell culture.

[0176]

[0081] Embodiment 46. The apparatus of embodiment 45, wherein the selection mechanism comprises one or more of exposing the second cell population to a drug or exposing the second cell population to an agent that identifies protein activity or expression levels.

[0177] Embodiment 47. An apparatus described in any one of embodiments 41 to 46, wherein selecting cells having a specified phenotype comprises sorting cells based on one or more selection markers.

[0178] Embodiment 48. The apparatus of any one of embodiments 41 to 47, further comprising identifying target regions as positively selected based on the probability of observing n' or more gRNAs of a gene in a selected cell by chance.

[0179]

[0046] Embodiment 49. The apparatus of embodiment 48, wherein identifying the target region as positively selected comprises determining that the probability of observing n' or more gRNAs of the sequence of interest in the selected cell by chance meets a threshold value.

[0180] Embodiment 50. The apparatus of any one of embodiments 41 to 49, further comprising determining an enrichment score for each gRNA.

[0036] Embodiment 51. The apparatus of embodiment 50, wherein for each gRNA, determining the enrichment score comprises evaluating N / N'.

[0181] Embodiment 52. A device described in any one of embodiments 41 to 51, wherein the cas-9 positive cells contain inactivated cas-9. Embodiment 53. The device described in embodiment 52, wherein the inactivated cas-9 is fused to at least one transcription activation domain.

[0182]

[0046] Embodiment 54. A non-transitory computer readable medium for determining a probability of observing one or more guide RNAs (gRNAs) by chance, comprising processor-executable instructions that, when executed by one or more processors, cause the one or more processors to: (A) receive first read count data, where the first read count data is generated by infecting a first culture of cas9 positive cells with a library of viral vectors, where the library comprises at least three guide RNAs (gRNAs) for cleaving a target region of DNA within the genome of the cells, and sequencing the cells to obtain a read count for each of the gRNAs; summing, based on the first read count data, the number of each of the gRNAs whose read count exceeds a background threshold per target region of DNA (Σ), where Σ=n, and calculating a probability of observing one or more gRNAs whose read count exceeds the background threshold over the entire target region based on the first read count data (Σ). (B) receiving second read count data, where the second read count data is generated by infecting a second culture of cas9 positive cells with a library of viral vectors, categorizing the cells of the second culture as either having a designated phenotype or not having a designated phenotype, and selecting cells having the designated phenotype, and sequencing the selected cells to obtain a post-selection read count for each of the gRNAs; summing the number of each of the gRNAs in the second cells whose post-selection read counts exceed a background threshold per target region of DNA based on the second read count data (Σ), where Σ=n', and summing the total number of gRNAs in the selected cells whose read counts exceed the threshold over the entire target region based on the second read count data (Σ), where Σ=N'; (C) receiving second read count data, where the second read count data is generated by infecting a second culture of cas9 positive cells with a library of viral vectors, categorizing the cells of the second culture as either having a designated phenotype or not having a designated phenotype, and selecting cells having the designated phenotype, and sequencing the selected cells to obtain a post-selection read count for each of the gRNAs;

[0183]

number

[0184] Calculate the probability of observing a n'gRNA for a target region in a selected cell by chance according to:

[0185]

number

[0186] Calculate the number of ways to select x objects from y objects, and for a target region of DNA containing a gene, calculate the number of ways to select the gene in the selected cell according to the formula n'.

[0187]

number

[0188] A non-transitory computer readable medium storing processor executable instructions for calculating the probability of observing more than one gRNA by chance.

[0023] Embodiment 55. The non-transitory computer-readable medium of embodiment 54, wherein the first culture is infected according to CRISPR technology.

[0189] Embodiment 56. The non-transitory computer readable medium of any one of embodiments 54-55, wherein the cas-9 positive cells of the first culture are modified to contain one or more selectable markers.

[0190]

[0046] Embodiment 57. The non-transitory computer readable medium of any one of embodiments 54-56, wherein the target region comprises a gene. Embodiment 58. The non-transitory computer readable medium of any one of embodiments 54 to 57, wherein categorizing the cells of the second culture as either having the specified phenotype or not having the specified phenotype comprises applying a selection mechanism to the second cell culture.

[0191]

[0046] Embodiment 59. The non-transitory computer readable medium of embodiment 58, wherein the selection mechanism comprises one or more of exposing the second cell population to a drug or exposing the second cell population to an agent that identifies protein activity or expression levels.

[0192] Embodiment 60. The non-transitory computer-readable medium of any one of embodiments 54 to 59, wherein selecting cells having a specified phenotype comprises sorting cells based on one or more selection markers.

[0193] Embodiment 61. The non-transitory computer readable medium of any one of embodiments 54 to 60, further comprising identifying the target region as positively selected based on the probability of observing n' or more gRNAs of the gene in the selected cell by chance.

[0194]

[0023] Embodiment 62. The non-transitory computer readable medium of embodiment 61, wherein identifying the target region as positively selected comprises determining that the probability of observing n' or more gRNAs of the sequence of interest in the selected cell by chance meets a threshold value.

[0195]

[0023] Embodiment 63. The non-transitory computer-readable medium of any one of embodiments 54 to 62, further comprising determining an enrichment score for each gRNA.

[0023] Embodiment 64. The non-transitory computer-readable medium of embodiment 63, wherein for each gRNA, determining the enrichment score comprises evaluating N / N'.

[0196] Embodiment 65. A non-transitory computer-readable medium described in any one of embodiments 54 to 64, wherein the cas-9 positive cells contain inactivated cas-9. Embodiment 66. The non-transitory computer-readable medium of embodiment 65, wherein the inactivated cas-9 is fused to at least one transcription activation domain.

[0197]

[0023] Embodiment 67. An apparatus comprising one or more processors and processor-executable instructions that, when executed by the one or more processors, cause the apparatus to determine, based on sequencing of a first cell population following infection with a vector comprising a library of at least three guide RNAs (gRNAs) for each of a plurality of target regions of DNA, a respective number (n) of gRNAs present for each of a plurality of target regions of DNA whose read count exceeds a background threshold; determine, based on sequencing of a second cell population following infection with a vector comprising a library of at least three gRNAs for each of a plurality of target regions of DNA, a total number (N) of gRNAs present in the first cell population across all target regions of the plurality of target regions of DNA whose read count exceeds a background threshold based on the respective number of gRNAs for each of the plurality of target regions of DNA; and determining a total number of gRNAs (N') present in the second population of cells across all target regions of the plurality of target regions of DNA whose read count exceeds a background threshold based on the respective numbers of gRNAs for each of the plurality of target regions of DNA; for each target region of the plurality of target regions, determining a probability of accidentally observing n' gRNAs for the target region in a selected cell based on n, N, n', and N'; for a target region that includes a sequence of interest, determining a probability of accidentally observing n' or more gRNAs of the sequence of interest in the selected cell based on the probability of accidentally observing n' gRNAs for the target region in the selected cell; and identifying a sequence of interest as positively selected based on the probability of accidentally observing n' or more gRNAs of the sequence of interest in the selected cell.

[0198]

[0046] Embodiment 68. The device of embodiment 67, wherein the first cell population and the second population comprise cas-9 positive cells. Embodiment 69. The apparatus of any one of embodiments 67-68, comprising processor-executable instructions that, when executed by one or more processors, cause the apparatus to determine, based on sequencing of a first cell population after infection with a vector comprising a library of at least three gRNAs for each of a plurality of target regions of DNA, a respective number (n) of gRNAs present for each of a plurality of target regions of DNA whose read counts exceed a background threshold; the processor-executable instructions cause the apparatus to receive first read count data generated by infecting a first cell population with a library of viral vectors and sequencing cells of the first cell population to obtain a read count for each of the gRNAs; and, based on the first read count data, count each gRNA for each of a plurality of target regions of DNA whose read counts exceed a background threshold.

[0199]

[0081] Embodiment 70. The apparatus of embodiment 69, further comprising processor-executable instructions that, when executed by one or more processors, cause the apparatus to determine a total number (N) of gRNAs present in the first cell population across all target regions of the plurality of target regions of DNA whose read count exceeds a background threshold based on the respective number of gRNAs for each of the plurality of target regions of DNA. The processor-executable instructions cause the apparatus to count each gRNA present in the first cell population whose read count exceeds the background threshold.

[0200] Embodiment 71. The apparatus of any one of embodiments 67-70, comprising processor-executable instructions that, when executed by one or more processors, cause the apparatus to determine, based on sequencing of a first cell population following infection with a vector comprising a library of at least three gRNAs for each of a plurality of target regions of DNA, a respective number (n') of gRNAs present for each of a plurality of target regions of DNA whose read counts exceed a background threshold; the processor-executable instructions cause the apparatus to receive second read count data generated by categorizing cells of a second cell population as either having a specified phenotype or not having a specified phenotype, selecting cells having the specified phenotype, and sequencing the selected cells to obtain a read count for each of the gRNAs; and counting, based on the second read count data, each gRNA for each of a plurality of target regions of DNA whose read counts exceed a background threshold.

[0201] Embodiment 72. A device described in any one of embodiments 67 to 71, comprising processor-executable instructions that, when executed by one or more processors, cause the device to determine a total number (N') of gRNAs present in a second cell population across all target regions of a plurality of target regions of DNA whose read counts exceed a background threshold based on the respective numbers of gRNAs for each of a plurality of target regions of DNA. The processor-executable instructions cause the device to count each gRNA present in selected cells of the second cell population whose read counts exceed the background threshold.

[0202]

[0023] Embodiment 73. Processor executable instructions that, when executed by one or more processors, cause the apparatus to determine, for each target region of a plurality of target regions, a probability of observing an n' gRNA for the target region in a selected cell by chance based on n, N, n', and N'.

[0203]

number

[0204] Evaluating the formula

[0205]

number

[0206] The apparatus according to any one of embodiments 67 to 72, wherein the apparatus calculates the number of ways to select an x ​​object from a y object.

[0023] Embodiment 74. Processor executable instructions that, when executed by one or more processors, cause the apparatus to determine a probability of by chance observing n' or more gRNAs of a sequence of interest in a selected cell for a target region that contains the sequence of interest based on the probability of by chance observing n' gRNAs for the target region in the selected cell.

[0207]

number

[0208] An apparatus described in any one of embodiments 67 to 73, which allows the following to be evaluated: Embodiment 75: A device described in any one of embodiments 67 to 74, wherein the processor-executable instructions, when executed by one or more processors, cause the device to identify a target sequence as positively selected based on the probability of accidentally observing n' or more gRNAs of the target sequence in a selected cell, the processor-executable instructions causing the device to evaluate and determine that the probability of accidentally observing n' or more gRNAs of the target sequence in a selected cell meets a threshold.

[0209] Embodiment 76. An apparatus described in any one of embodiments 67 to 76, further comprising processor-executable instructions that, when executed by one or more processors, cause the apparatus to determine an enrichment score for each gRNA.

[0210]

[0081] Embodiment 77. The apparatus of embodiment 76, wherein the processor-executable instructions, when executed by one or more processors, cause the apparatus to determine an enrichment score for each gRNA, the processor-executable instructions causing the apparatus to evaluate N / N'.

[0211] Embodiment 78. A device described in any one of embodiments 67 to 77, wherein the cas-9 positive cells contain inactivated cas-9. Embodiment 79. The device of embodiment 78, wherein the inactivated cas-9 is fused to at least one transcription activation domain.

[0212]

[0046] Embodiment 80. A non-transitory computer readable medium for determining a probability of observing one or more guide RNAs (gRNAs) by chance, comprising processor-executable instructions that, when executed by one or more processors, cause the one or more processors to determine, based on sequencing of a first cell population following infection with a vector comprising a library of at least three guide RNAs (gRNAs) for each of a plurality of target regions of DNA, a respective number (n) of gRNAs present for each of a plurality of target regions of DNA whose read counts exceed a background threshold; determine, based on the respective number of gRNAs for each of the plurality of target regions of DNA, a total number (N) of gRNAs present in the first cell population across all target regions of the plurality of target regions of DNA whose read counts exceed a background threshold; and determine, based on the respective number of gRNAs for each of the plurality of target regions of DNA, a total number (N) of gRNAs present in the first cell population across all target regions of the plurality of target regions of DNA whose read counts exceed a background threshold. determining a respective number (n') of gRNAs present for each of the plurality of target regions of DNA whose read counts exceed a background threshold based on sequencing the second cell population after infection with a vector containing the library; determining a total number (N') of gRNAs present in the second cell population across all target regions of the plurality of target regions of DNA whose read counts exceed a background threshold based on the respective numbers of gRNAs for each of the plurality of target regions of DNA; for each target region of the plurality of target regions, determining a probability of observing n' gRNAs by chance for the target region in a selected cell based on n, N, n', and N'; for a target region that includes a sequence of interest, determining a probability of observing n' or more gRNAs of the sequence of interest in a selected cell based on the probability of observing n' gRNAs by chance for the target region in a selected cell; and A non-transitory computer readable medium storing processor executable instructions for identifying a sequence of interest as positively selected based on the probability of observing n' or more gRNAs of the sequence of interest in a selected cell by chance.

[0213]

[0023] Embodiment 81. The non-transitory computer readable medium of embodiment 80, wherein the first cell population and the second population comprise cas-9 positive cells. Embodiment 82. The non-transitory computer readable medium of any one of embodiments 80-81, wherein the processor executable instructions, when executed by one or more processors, cause the device to determine a respective number (n) of gRNAs present for each of a plurality of target regions of DNA whose read counts exceed a background threshold based on sequencing of the first cell population after infection with a vector comprising a library of at least three gRNAs for each of a plurality of target regions of DNA; the processor executable instructions cause the one or more processors to receive first read count data generated by infecting the first cell population with a library of viral vectors and sequencing cells of the first cell population to obtain a read count for each of the gRNAs; and, based on the first read count data, count each gRNA for each of a plurality of target regions of DNA whose read counts exceed a background threshold.

[0214] Embodiment 83. A non-transitory computer readable medium according to any one of embodiments 80 to 82, comprising processor executable instructions that, when executed by one or more processors, cause the device to determine a total number (N) of gRNAs present in the first cell population across all target regions of the multiple target regions of DNA whose read counts exceed a background threshold based on the respective numbers of gRNAs for each of the multiple target regions of DNA. The processor executable instructions cause the one or more processors to count each gRNA present in the first cell population whose read counts exceed a background threshold.

[0215]

[0081] Embodiment 84. The non-transitory computer readable medium of any one of embodiments 80-83, comprising processor executable instructions that, when executed by one or more processors, cause the device to determine, based on sequencing of the second cell population following infection with a vector comprising a library of at least three gRNAs for each of a plurality of target regions of DNA, a respective number (n') of gRNAs present for each of a plurality of target regions of DNA whose read counts exceed a background threshold; the processor executable instructions cause the one or more processors to receive second read count data generated by categorizing cells of the second cell population as either having a specified phenotype or not having the specified phenotype, selecting cells having the specified phenotype, and sequencing the selected cells to obtain a read count for each of the gRNAs; and counting, based on the second read count data, each gRNA for which the read counts exceed a background threshold for each of a plurality of target regions of DNA.

[0216] Embodiment 85. A non-transitory computer readable medium according to any one of embodiments 80 to 84, comprising processor executable instructions that, when executed by one or more processors, cause the device to determine a total number (N') of gRNAs present in the second cell population across all target regions of the multiple target regions of DNA whose read counts exceed a background threshold based on the respective numbers of gRNAs for each of the multiple target regions of DNA, the processor executable instructions causing the one or more processors to count each gRNA present in selected cells of the second cell population whose read counts exceed the background threshold.

[0217]

[0023] Embodiment 86. Processor-executable instructions that, when executed by one or more processors, cause an apparatus to determine, for each target region of a plurality of target regions, a probability of observing an n' gRNA for the target region in a selected cell by chance based on n, N, n', and N'.

[0218]

number

[0219] Evaluating the formula

[0220]

number

[0221] 86. The non-transitory computer-readable medium of any one of embodiments 80 to 85, wherein:

[0023] Embodiment 87. Processor executable instructions that, when executed by one or more processors, cause an apparatus to determine a probability of by chance observing n' or more gRNAs of a sequence of interest in a selected cell for a target region that contains the sequence of interest based on the probability of by chance observing n' gRNAs for the target region in the selected cell.

[0222]

number

[0223] 87. A non-transitory computer-readable medium according to any one of embodiments 80 to 86, for evaluating: Embodiment 88. A non-transitory computer readable medium described in any one of embodiments 80 to 87, comprising processor executable instructions that, when executed by one or more processors, cause the device to identify a target sequence as positively selected based on the probability of accidentally observing n or more gRNAs of the target sequence in a selected cell, the processor executable instructions causing the one or more processors to evaluate and determine that the probability of accidentally observing n' or more gRNAs of the target sequence in a selected cell meets a threshold.

[0224] Embodiment 89. A non-transitory computer-readable medium according to any one of embodiments 80 to 88, further comprising processor-executable instructions that, when executed by one or more processors, cause the one or more processors to determine an enrichment score for each gRNA.

[0225]

[0046] Embodiment 90. The non-transitory computer-readable medium of embodiment 89, wherein the processor-executable instructions, which when executed by one or more processors, cause the device to determine an enrichment score for each gRNA, include causing the one or more processors to evaluate N / N'.

[0226] Embodiment 91. A non-transitory computer-readable medium described in any one of embodiments 80 to 90, wherein the cas-9 positive cells contain inactivated cas-9. Embodiment 92. The non-transitory computer-readable medium of embodiment 91, wherein the inactivated cas-9 is fused to at least one transcription activation domain.

[0227] Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the method and compositions described herein which equivalents are intended to be encompassed by the following claims.

Claims

1. (A) infecting a first culture of cas9 positive cells with a library of viral vectors, the library comprising at least three guide RNAs (gRNAs) associated with a target region of DNA; sequencing the cells to obtain a number of reads for each of the gRNAs; Summarizing the number (n) of each gRNA whose read count exceeds the background threshold per targeted region of DNA; and summing the total number (N) of gRNAs whose read counts exceed the background threshold across all target regions; (B) infecting a second culture of cas9 positive cells with the library of viral vectors; categorizing the cells of said second culture as either having a designated phenotype or not having said designated phenotype; selecting cells having the specified phenotype and sequencing the selected cells to obtain a post-selection read count for each of the gRNAs; summing the respective numbers (n') of gRNAs per targeted region of DNA in the selected cells whose post-selection read counts exceed the background threshold; and summing the total number (N') of gRNAs of the selected cells whose read counts exceed the background threshold across all target regions; (C) for a target region of DNA, formula [0010] calculating the probability of observing a n'gRNA for the target region in the selected cell by chance according to [0025] calculate the number of ways to select x objects from y objects; and for a target region of DNA containing a gene, the formula [0030] Calculating the probability of observing n' or more gRNAs of a gene in the selected cell by chance according to: and identifying the gene as associated with the specified phenotype based on the probability of observing n' or more gRNAs of the gene in the selected cells by chance.

2. The method of claim 1, wherein the first culture is infected according to the CRISPR technique.

3. The method of claim 1 or claim 2, wherein the cas9 positive cells contain inactivated cas9.

4. The method of claim 3, wherein the inactivated cas9 is fused to at least one transcription activation domain.

5. A method according to any one of claims 1 to 4, wherein the target region of the DNA comprising the gene regulates a downstream gene or protein, and regulating the downstream gene or protein comprises activating or inhibiting the downstream gene or protein.

6. 6. The method of any one of claims 1 to 5, wherein categorizing the cells of the second culture as either having the designated phenotype or not having the designated phenotype comprises applying a selection mechanism to the second culture.

7. 7. The method of claim 6, wherein the selection mechanism comprises one or more of exposing the second culture to a drug or exposing the second culture to an agent that specifies protein activity or expression levels.

8. The method of any one of claims 1 to 7, wherein the cas9 positive cells of the first culture are modified to contain one or more selectable markers, the one or more selectable markers comprising a fluorescent marker and / or a detectable enzyme.

9. 9. The method of claim 8, wherein selecting cells having the designated phenotype comprises sorting the cells based on the one or more selectable markers, and the designated phenotype is fluorescence and / or cell viability.

10. 10. The method of any one of claims 1 to 9, further comprising identifying the target region as positively selected based on the probability of observing n' or more gRNAs of the gene in the selected cells by chance.

11. The method of any one of claims 1 to 10, further comprising determining an enrichment score for each gRNA by evaluating N / N'.

12. 12. The method of any one of claims 1-11, further comprising identifying a target region of DNA that includes the gene as a modifier of a second gene based on the probability of observing n' or more gRNAs of the gene in the selected cells by chance.

13. 13. The method of any one of claims 1 to 12, further comprising identifying a target region of DNA comprising said gene as a therapeutic target based on the probability of observing n' or more gRNAs of said gene in said selected cells by chance.

14. 14. The method of any one of claims 1-13, further comprising identifying a target region of DNA comprising said gene as correlated with said designated phenotype based on the probability of observing n' or more gRNAs of said gene in said selected cells by chance.

15. 15. The method of any one of claims 1 to 14, further comprising identifying a target region of DNA comprising said gene as exhibiting a protective effect based on the probability of observing n' or more gRNAs of said gene in said selected cells by chance.

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