Methods for modulating RNA processing

Abstract

Provided herein are methods and materials for performing pooled screening, modulating RNA processing events, and / or analyzing a sample to identify an RNA processing event or RNA processing target associated with a condition. For example, an RNA processing event can be alternative splicing, constitutive splicing, polyadenylation, methylation, folding, degradation, pseudouridylation, and / or adenosine to inosine RNA editing. In some embodiments, the method includes selecting for a phenotype of interest.

Description

[0001] METHODS FOR MODULATING RNA PROCESSING

[0002] CLAIM OF PRIORITY

[0003] This application claims the benefit of U.S. Provisional Application Serial No. 63 / 549,205, filed on February 2, 2024. The entire contents of the foregoing are incorporated herein by reference.

[0004] SEQUENCE LISTING

[0005] This application contains a Sequence Listing that has been submitted electronically as an XML file named “15670-0394WO1 ST26 SL.XML.” The XML file, created on January 29, 2025, is 5,573 bytes in size. The material in the XML file is hereby incorporated by reference in its entirety.

[0006] FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0007] This invention was made with Government support under Grant No. HG004659 and NS103172 awarded by the National Institutes of Health. The Government has certain rights in the invention.

[0008] TECHNICAL FIELD

[0009] The present disclosure relates to methods and materials for performing pooled screening, modulating RNA processing events, and / or analyzing a sample to identify an RNA processing event or RNA processing target associated with a condition.

[0010] BACKGROUND

[0011] The current state of art for targeting RNA processing events (e.g., splice modulation) includes DNA-targeting Clustered regularly interspaced short palindromic repeats (CRISPR), RNA targeting CRISPR, antisense oligonucleotides (ASOs), or small molecule inhibitors. Using DNA-targeting CRISPR systems (e.g., paired guide RNA) pose the risk of excising portions of DNA that may encode transcriptional regulatory elements such as enhancers and insulators; thus, may alter the length of the RNA transcript, which ultimately may affect the processing of RNA at other regions in the transcript. Other DNA targeting methods using base editing methods are limited by PAM requirement, large editing regions, and similar to pgRNA may lead to skipping of an entire splice site (Tang et al. 2022; Thomas et al. 2020; Kluesner et al. 2021). Additionally, the complete removal of alternative exons is often not as biologically or therapeutically effective. Other methods such as RNA-targeting CRISPR systems have been shown to modulate splicing through steric hindrance and by tethering serin e / arginine (SR)-rich proteins. However, RNA-targeting CRISPR comprises protein domains with bacterial origin that may trigger immunogenic responses in cells and tissues. Thus, there remains an unmet need for an improved method of targeting RNA processing or RNA processing events.

[0012] SUMMARY

[0013] The present disclosure provides methods and materials for using nuclear expressed antisense RNA (NEAR) constructs. In some embodiments, the NEAR constructs are used to perform pooled NEAR screening, modulate RNA processing, and / or identify an RNA processing event or RNA processing target associated with a condition.

[0014] Provided herein are methods for pooled screening of RNA processing events in a plurality of cells, the method including: a) introducing a plurality of nuclear expressed antisense RNA (NEAR) constructs to the plurality of cells; b)selecting cells from the plurality of cells that express the plurality of NEAR constructs; and c) detecting an output signal from the cells from (b). Also provided herein are methods of modulating RNA processing in a cell, the method including: a) introducing a plurality of nuclear expressed antisense RNA (NEAR) constructs to the plurality of cells; b) selecting cells from the plurality of cells that express the plurality of NEAR constructs; and c) detecting an output signal from the cells from (b). Also provided herein are methods of analyzing a sample containing a plurality of cells, the method including: a) introducing a plurality of nuclear expressed antisense RNA (NEAR) constructs to the cells in the sample; b) selecting cells from the plurality of cells in the sample that express the plurality of NEAR constructs; and c) detecting an output signal from the cells from (b) to identify an RNA processing event or RNA processing target associated with a condition.

[0015] Also provided herein are methods for pooled screening of RNA processing events in a plurality of cells, the method including: a) introducing a plurality of nuclear expressed antisense RNA (NEAR) constructs to the plurality of cells; b) selecting cells from the plurality of cells that express the plurality of NEAR constructs; c) selecting cells that express a phenotype of interest; and d) detecting an output signal from the selected cells from. Also provided herein are methods of modulating RNA processing in a cell, the method including: a) introducing a plurality of nuclear expressed antisense RNA (NEAR) constructs to the plurality of cells; b) selecting cells from the plurality of cells that express the plurality of NEAR constructs; c) selecting cells that express a phenotype of interest; and d) detecting an output signal from the selected cells. Also provided herein are methods of analyzing a sample containing a plurality of cells, the method including: a) introducing a plurality of nuclear expressed antisense RNA (NEAR) constructs to the cells in the sample; b) selecting cells from the plurality of cells in the sample that express the plurality of NEAR constructs; c) selecting cells that express a phenotype of interest; and d) detecting an output signal from the selected cells to identify an RNA processing event or RNA processing target associated with a condition.

[0016] In some embodiments, the method further includes selecting cells from step (b) that exhibit a phenotype of interest. In some embodiments, the NEAR construct includes: i) a NEAR guide RNA including an antisense sequence; and ii) a ribonucleoprotein recruiting motif.

[0017] In some embodiments, the NEAR construct further includes a barcode. In some embodiments, the plurality of NEAR constructs includes two or more pluralities of NEAR constructs, including each NEAR construct includes a different antisense sequence. In some embodiments, the barcode includes a single stranded barcode, an RNA barcode, the NEAR antisense region, the NEAR guide region, or the integrated NEAR lentivirus genome. In some embodiments, the antisense sequence includes a portion that is antisense to a region of a pre-mRNA or adjacent to an alternative splice site.

[0018] In some embodiments, the ribonucleoprotein recruiting motif is attached to a 5’ end and / or a 3’ end of the antisense sequence. In some embodiments, the ribonucleoprotein recruiting motif can recruit any combination of endogenous mammalian nuclear ribonucleoproteins including U snRNP components, hnRNP components, Sm core components, and / or any other nuclear expressed ribonucleoprotein.

[0019] In some embodiments, the NEAR construct is in the form of a vector. In some embodiments, the vector is a viral vector or a non-viral vector. In some embodiments, the viral vector includes a lentivirus, an AAV, an adenovirus, an adeno-associated virus, retrovirus, or a herpes simplex virus. In some embodiments, the non-viral vector is a liposome, exosome, an extracellular vesicle, a polymer, a nanoparticle, a peptide, or a dendrimer. In some embodiments, selecting the cells that express the plurality of NEAR constructs includes identifying an antibiotic resistance polypeptide, a fluorescent polypeptide, a bioluminescent polypeptide, and / or a polypeptide that causes a color change in the cells.

[0020] In some embodiments, selecting cells that express the phenotype of interest includes antibiotic selection, antibody staining, fluorescence-activated cell sorting (FACS), magnetic- acted cell sorting (MACS), enrichment over time, depletion over time, a cell surface marker screen, a poison exon screen, cell division, population expansion, cell fitness, an image based phenotyping, or drug selection.

[0021] In some embodiments, the phenotype of interest includes a fluorescence marker, a cell surface polypeptide, an intracellular polypeptide, a fluorescent RNA aptamer, a fluorescent DNA aptamer, an imaging based marker, a cell fitness marker, a cell death marker, a cell morphology marker, a cell proliferation marker, an antibiotic resistance marker, or a drug resistance marker.

[0022] In some embodiments, the output signal identifies the presence, absence, or degree of an RNA processing event and / or an RNA processing target. In some embodiments, the RNA processing event includes alternative splicing, constitutive splicing, polyadenylation, methylation, folding, degradation, pseudouridylation, and / or adenosine to inosine RNA editing.

[0023] In some embodiments, an RNA processing target includes an exon. In some embodiments, the RNA processing includes: a) excluding an exon; b) including an exon; c) suppressing polyadenylation; or d) targeting a poison exon. In some embodiments, the NEAR construct targets a pre-mRNA. In some embodiments, excluding an exon includes targeting the NEAR construct to the 3 ’ splice site of a target exon. In some embodiments, including an exon includes targeting the NEAR construct to the 5’ splice site of a target exon. In some embodiments, an exon includes targeting the NEAR construct to an RBP binding site. In some embodiments, targeting poison exons includes inducing the expression of poison exons or inhibiting the expression of poison exons.

[0024] In some embodiments, the sample is a biological sample. In some embodiments, the biological sample is a cell or a tissue.

[0025] In some embodiments, the condition includes a neurodegenerative disease, a rare disease, a cancer, and / or an immune disorder. In some embodiments, the output signal includes a fluorescence signal, a single cell sequencing read out, a DROP-Seq read out, a CROP-seq, MARS-seq, CytoSeq, and / or an in- situ sequencing screen.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Methods and materials are described herein for use in the present invention; other, suitable methods and materials known in the art can also be used. The materials, methods, and examples are illustrative only and not intended to be limiting. All publications, patent applications, patents, sequences, database entries, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control.

[0027] Other features and advantages of the invention will be apparent from the following detailed description and figures, and from the claims.

[0028] DESCRIPTION OF DRAWINGS

[0029] FIGS. 1A-1B. Schematics illustrating the pooled NEAR screen. FIG. 1A: Flow chart showing the steps for performing a NEAR screen. FIG. IB: Schematics demonstrating the composition of a NEAR cassette and a NEAR lentiviral construct.

[0030] FIGS. 2A-2G. Using NEARs to modulate surface antigen expression. FIG. 2A: Schematic showing a generic alternative splicing event in a transcript of interest wherein inclusion of the alternative exon leads to the presentation of a surface antigen detectable by antibody. Exclusion of this exon leads to a transcript that does not yield the presentation of a surface antigen that is detectable by that antibody. These splicing events are also referred to as antigen determinant exons (ADE). FIG. 2B: Schematic showing modulation of ADEs using NEARs to promote their inclusion or exclusion with flow cytometry as a readout for increasing or decreasing FITC fluorescence, respectively. FIG. 2C: NEAR construct targeting ADE exon 6 in FAS modulated the surface detection of FAS antigen. Flow cytometry plot shows the reduction of fluorescence with guide 3 that targeted FAS. FIG. 2D: Bar plot showing mean fluorescent intensity of stained cells treated with either targeting NEAR construct or non-targeting NEAR construct, showing significant reduction of FAS detection. FIG. 2E: rtPCR gel of FAS exon 6 in cells treated with either targeting NEAR construct or non-targeting construct. The top band at 150 bp is the normal isoform of FAS with exon 6 included, encoding a transmembrane domain. The bottom band is the exon 6 excluded isoform, where no transmembrane domain was encoded, and no surface antigen resulted from this isoform. FIGS. 2F and 2G: NEAR constructs were designed to modulate exon inclusion in CD45 exon 4, and cells were stained with CD45RA and CD45RB antibodies. The bar plot depicts the mean fluorescent intensity of cells transduced with each NEAR relative to a nontargeting control. Statistics: One-way Anova with Dunnett’s correction; *P < 0.05; **P < 0.005; ***P < 0.0005.

[0031] FIGS. 3A-C. Schematic illustrating Poison Exon Screening method. FIG. 3A: Diagram outlining the nature of poison exons, wherein exon inclusion can lead to nonsense mediated decay of the transcript and thus, knocking down the expression of the transcript and the protein. FIG. 3B: Schematic outlining the overview method of poison exon screening. NEAR constructs targeting poison exons in essential transcripts can reduce cell fitness thus causing depletion. Additionally, the screen also identified NEAR constructs that can target poison exons for exon exclusion leading to enrichment FIG. 3C: Schematic depicting the overview method of lentiviral transduction of NEAR constructs pre-phenotype selection (3- 30 days post transduction).

[0032] FIGS. 4A-4D: Overall Poison Exon Screening metrics. FIG. 4A: Negative inverse RRA versus DepMap Score. Inverse RRA negative is a metric of total depletion of a gene in a poison exon screen, where higher score indicates greater depletion. DepMap score is a scoring of the relative essentiality of each gene to a cell as determined by depletion after shRNA knock down of the gene. Plotted are the 50 genes from the poison exon screen. Genes with greater DepMap score on the left had higher Inverse RRA negative, whereas genes with no effect on proliferation had lower Inverse RRA negative scores. FIG. 4B: Positive inverse RRA versus DepMap Score. Positive Inverse RRA is a metric of total enrichment of a gene in the poison exon screen, where higher score indicates greater enrichment. Plotted are the 50 genes in the poison exon screen. Some perturbed genes showed increased proliferation, even those that have lower DepMap scores, whereas genes with no effect on proliferation showed lower Positive Inverse RRA scores. FIG. 4C: Depletion scores by guide length. Violin plot characterizing all NEAR guides by length (25 bp, 35 bp, 45 bp) with log2 fold change value on the Y axis comparing the plasmid sequencing to day 15 post transduction. Each group of guides was centered on 25 base pairs, with a wide distribution, suggesting greater efficacy for splicing modulation. FIG. 4D: Comparison of day 15 and day 30 NEAR enrichment within a single replicate. Scatter plot showing the relative count of each member of the poison exon library’s enrichment profile compared to the plasmid library. R squared was used to measure correlation. R2value of 0.79 between the two datasets indicated that even with 15 days between the timepoints, genetic drift did not significantly change the underlying distribution of NEAR gRNA.

[0033] FIGS. 5A-5D: Individual gene highlights. Distance of the start of the guide from the 5’ splice site (5’SS) of the poison exon is shown on the X axis and log 2 fold change from plasmid stock to day 30 is shown on the Y axis. FIG. 5A: Scatter plot showing NEAR constructs, colored by gRNA length, targeting small nuclear ribonucleoprotein U 1 subunit 70 (SNRNP70) poison exon. Boxed are efficacious guides that were enriched (top panel) and depleted (bottom panel). FIG. 5B: Scatter plot showing NEAR constructs, colored by gRNA length, targeting U2 small nuclear RNA auxiliary factor 2 (U2AF2) poison. Boxed are the efficacious guides that were depleted upstream of 5’SS. Mutating the efficacious targeting region caused a reduction in the poison exon in U2AF2. A web based splice predicting model confirmed that U2AF2 is involved in regulation of splicing. FIG. 5C: Scatter plot showing NEAR constructs, colored by gRNA length, targeting proteasome 26S subunit, non-ATPase 7 (PSMD7) poison exon. Boxed are efficacious guides that were enriched at 5’SS. FIG. 5D: Scatter plot showing NEAR constructs, colored by gRNA length, targeting COP9 signalosome subunit 5 (COPS5) poison exon. Boxed are efficacious guides that were depleted at 5’SS.

[0034] FIGS. 6A-6D. snRNAs modulated inclusion of alternative exon in MAP3K7. FIG. 6A: PUF60 was shown to be a splicing regulator in the triple negative breast cancer (TNBC) line, MDA-MB231. FIG. 6B: Identification of PUF60 splicing targets in TNBC lines. FIG. 6C: PUF60 knockdown led to the exclusion of exon 2 in MAP27K in the MDA- MB231 cell line. FIG. 6D: NEAR constructs targeting exon 2 of MAP2K7 recapitulated the change in splicing and excluded exon 2 at levels similar to PUF60 knock down.

[0035] DETAILED DESCRIPTION

[0036] The current state of art for targeting RNA processing events (e.g., splice modulation) includes DNA-targeting Clustered regularly interspaced short palindromic repeats (CRISPR), RNA targeting CRISPR, antisense oligonucleotides (ASOs), or small molecule inhibitors. Although DNA-targeting CRISPR systems (e.g., paired guide RNA) have been previously used to modulate splicing, it poses the risk of excising portions of DNA that encode transcriptional regulatory elements such as enhancers and insulators; thus, may alter the length of the RNA transcript, which may affect the processing of RNA at other regions in the transcript. Other DNA targeting methods using base editing methods are limited by PAM requirement, large editing regions, and similar to pgRNA may lead to skipping of an entire splice site (Tang et al. 2022; Thomas et al. 2020; Kluesner et al. 2021). The complete removal of alternative exons is often not as biologically or therapeutically effective compared to modulation that can be achieved by targeting RNA. RNA-targeting CRISPR systems have been shown to modulate splicing through steric hindrance and by tethering serine / arginine (SR)-rich proteins. However, RNA-targeting CRISPR includes protein domains with bacterial origin that may trigger immunogenic responses in cells and tissues, or may get cleared by the immune system by pre-existing adaptive immunity (Tang et al. 2022).

[0037] Provided herein are methods and materials for using nuclear expressed antisense RNAs (NEARs) to identify RNA processing events such as alternative RNA splicing, constitutive RNA splicing, or polyadenylation. The present method describes a pooled screening method using NEAR constructs to identify a phenotype of interest in a sample (e.g., a cell or a tissue), RNA processing events, or RNA progressing targets associated with a condition (e.g., neurodegenerative disease, a rare disease, a cancer, and / or an immune disorder). Further, the methods described herein can be used to modulate an RNA processing in a sample or analyze a sample to identify an RNA processing event or an RNA processing target associated with a condition.

[0038] In summary, described herein are methods and materials for using NEAR constructs (e.g., in a pooled NEAR screening) to modulate RNA processing or analyze a sample to identify a phenotype of interest, an RNA processing event or an RNA processing target associated with a condition.

[0039] Nuclear expressed antisense RNAs (NEARs)

[0040] Provided herein are NEAR constructs that can be used to perform pooled NEAR screening to modulate and / or analyze RNA processing events or RNA processing targets (e.g., an exon) associated with a condition. Similar to a small nuclear RNA (snRNA), NEAR can modulate RNA processing events (e.g., alternative splicing, constitutive splicing, polyadenylation, methylation, folding, degradation, pseudouridylation, and / or adenosine to inosine RNA editing). snRNAs are mostly found within the nucleus of eukaryotic cells. They are a key component of nuclear ribonucleoprotein complexes which can form spliceosome, the cell machinery responsible for regulating the mRNA maturation process. snRNAs are thus involved in precursor mRNA splicing, which removes introns from RNA transcripts before translation. Non-limiting examples of spliceosomal snRNA comprise Ul, U2, U4, U5, U6, U4ATAC, U6ATAC, U7, Ul 1 and U12, due to the generous amount of uridylic acid they contain (Mattaj et al., 1993, FASEB J, 15, 7:47-53) An individual snRNA can be about less than 250 nucleotides (e.g., less than about 225 nucleotides, less than about 200 nucleotides, less than about 175 nucleotides, less than about 150 nucleotides, less than about 125 nucleotides, less than about 100 nucleotides, less than about 75 nucleotides, or less than about 50 nucleotides). For example, U1 snRNA is about 164 nucleotides in length and is encoded by genes that occur in several copies within the human genome. U 1 snRNA represents the ribonucleic component of the nuclear particle U 1 snRNP. The U 1 snRNA can bind by complementary base pairing with a splicing donor site on a pre-mRNA molecule. (Horowitz et al., 1994, Trends Genet., 10(3): 100-6.)

[0041] NEAR uses similar mechanism as a snRNA by recruiting mammalian nuclear ribonucleoprotein such as U snRNP components, hnRNP components, Sm core components, and / or any other nuclear expressed ribonucleoprotein. A NEAR construct can include a NEAR guide RNA and a ribonucleoprotein recruiting motif. The NEAR guide RNA can include an antisense sequence that is antisense to region of interest on a pre-mRNA or adjacent to an alternative splice site. In some embodiments, a NEAR construct includes two or more pluralities of NEAR constructs, wherein each NEAR construct includes a different antisense sequence.

[0042] In some embodiments, an antisense sequence includes about 20 base pair (bp) to about 45 bp. For example, an antisense sequence can comprise about 20 bp to about 45 bp, about 20 bp to about 40 bp, about 20 bp to about 35 bp, about 20 bp to about 30 bp, about 20 bp to about 25 bp, about 25 bp to about 45 bp, about 25 bp to about 40 bp, about 25 bp to about 35 bp, about 25 bp to about 30 bp, about 30 bp to about 45 bp, about 30 bp to about 40 bp, about 30 bp to about 35 bp, about 35 bp to about 45 bp, about 35 bp to about 40 bp, or about 40 bp to about 45 bp.

[0043] In some cases, the ribonucleoprotein recruiting motif can be attached to a 5 ’end and / or a 3 'end of the antisense sequence. The ribonucleoprotein recruiting motif can recruit any combination of endogenous mammalian nuclear ribonucleoproteins. Non-limiting example of a nuclear ribonucleoprotein includes U snRNP components, hnRNP components, Sm core components, and / or any other nuclear expressed ribonucleoprotein. In some embodiments, the ribonucleoprotein recruiting motif is attached to a 5 ’end and / or a 3 ’end of the antisense sequence.

[0044] In some embodiments, the NEAR construct described herein includes a NEAR cassette. A NEAR cassette can comprise a promoter operably linked to a guide RNA, a NEAR backbone, and a terminator. In some cases, a promoter can be operably linked to a NEAR backbone, a guide RNA, and a terminator. In some embodiments, a NEAR backbone includes a ribonucleoprotein recruiting structure.

[0045] In some embodiments, the NEAR constmct further includes a barcode. The barcode can help identify specific NEAR guide RNA in a cell. In some embodiments, the barcode includes a single stranded barcode, an RNA barcode, the NEAR antisense region, the NEAR guide region, or the integrated NEAR lentivirus genome.

[0046] In some embodiments, the NEAR construct described herein can be packaged in a vector. For example, a vector can be a viral vector or a nonviral vector. Non-limiting examples of a viral vector includes a lentivirus, an AAV, an adenovirus, an adeno-associated virus, retrovirus, or a herpes simplex virus. Non-limiting examples of a non-viral vector includes a liposome, exosome, an extracellular vesicle, a polymer, a nanoparticle, a peptide, or a dendrimer. In some embodiments, a NEAR construct is packaged in a lentivirus as a NEAR lentiviral construct. In some cases, a NEAR lentiviral construct can comprise a 5 'long terminal repeat (LTR), a promoter operably linked to lentiviral selection marker (e.g., expression of a fluorescent protein such as green fluorescent protein or an antibiotic resistance gene such as puromycin), a NEAR cassette for the expression of NEAR, and a 3'LTR.

[0047] In some embodiments, vectors can include a selection marker, appropriate restriction sites to facilitate cloning of the desired gene and the ability to enter and / or replicate in eukaryotic or prokaryotic cells. Numerous expression vector systems may be employed. For example, one class of vector utilizes DNA elements which are derived from animal viruses such as bovine papilloma virus, polyoma virus, adenovirus, vaccinia virus, baculovirus, retroviruses (RSV, MMTV or MOMLV) or SV40 vims. Others can involve the use of polycistronic systems with internal ribosome binding sites.

[0048] In some embodiments, vectors may be introduced into an appropriate host cell. Introduction of the vector into a host cell can be accomplished by various techniques well known to those of skill in the art. These include, but are not limited to, transfection including lipotransfection using, e.g., Fugene® or lipofectamine, protoplast fusion, calcium phosphate precipitation, cell fusion with enveloped DNA, microinjection, and infection with intact vims. Typically, plasmid introduction into the host is via standard calcium phosphate coprecipitation method.

[0049] A variety of host-expression vector systems may be utilized to express the NEAR constmct for use in the methods described herein. Such host-expression systems represent vehicles by which the coding sequences of interest may be produced and subsequently purified, but also represent cells which may, when transformed or transfected with the appropriate nucleotide coding sequences, express NEAR constructs described herein in situ. These include but are not limited to microorganisms such as bacteria (e.g., E. coli, B. subtilis) transformed with recombinant bacteriophage DNA, plasmid DNA or cosmid DNA expression vectors containing antibody coding sequences; yeast (e.g., Saccharomyces, Pichia) transformed with recombinant yeast expression vectors containing antibody coding sequences; insect cell systems infected with recombinant virus expression vectors (e.g., baculovirus) containing appropriate nucleotide coding sequences; plant cell systems infected with recombinant virus expression vectors (e.g., cauliflower mosaic virus, CaMV; tobacco mosaic virus, TMV) or transformed with recombinant plasmid expression vectors (e.g., Ti plasmid) containing appropriate nucleotide coding sequences; or mammalian cell systems (e.g., COS, CHO, NSO, BLK, 293, 3T3 cells) harboring expression constructs containing promoters derived from the genome of mammalian cells (e.g., metallothionein promoter) or from mammalian viruses (e.g., the adenovirus late promoter; the vaccinia virus 7.5K promoter).

[0050] In some embodiments, a NEAR construct is a CRISPR droplet (CROP)-seq NEAR construct. In some cases, a CROP-seq NEAR construct is used to perform RNA sequencing and related assays. A CROP-seq NEAR construct can include a 5’LTR, a 5’LTR bisected by a NEAR cassette, a NEAR cassette, a promoter operably linked to a lentiviral selection marker (e.g., GFP and / or a puromycin resistance gene), a 3’LTR, a NEAR cassette, and a 3 ’LTR bisected by a NEAR cassette.

[0051] In some embodiments, a NEAR can be used to target an exon of interest (e.g., an exon driving the phenotype of interest). In some embodiments an exon of interest can be targeted using about 3 to about 300 different NEARs (e.g., about 3 to about 250 NEARS, about 3 to about 200 NEARS, about 3 to about 150 NEARS, about 3 to about 100 NEARS, about 3 to about 50 NEARS, about 3 to about 10 NEARS, about 10 to about 300 NEARS, about 10 to about 250 NEARS, about 10 to about 200 NEARS, about 10 to about 150 NEARS, about 10 to about 100 NEARS, about 10 to about 50 NEARS, about 50 to about 300 NEARS, about 50 to about 250 NEARS, about 50 to about 200 NEARS, about 50 to about 150 NEARS, about 50 to about 100 NEARS, about 100 to about 300 NEARS, about 100 to about 250 NEARS, about 100 to about 200 NEARS, about 100 to about 150 NEARS, about 150 to about 300 NEARS, about 150 to about 250 NEARS, about 150 to about 200 NEARS, about 200 to about 300 NEARS, about 200 to about 250 NEARS, or about 250 to about 300 NEARS).

[0052] Methods of Use

[0053] Provided herein are methods of using NEAR constructs to perform a pooled screening of RNA processing events in a cell. Also provided herein are methods of using NEAR constructs for modulating RNA processing in a cell. In another aspect, this document provides methods of using NEAR constructs for analyzing a sample (e.g., a cell or a tissue) to identify an RNA processing event or RNA processing target associated with a condition.

[0054] In some embodiments, the methods include introducing a plurality of NEAR constructs to a plurality of cells. In some embodiments, the methods include selecting cells from the plurality of cells that express the plurality of NEAR constructs. For example, the selection of cells for the expression of NEAR constructs can include identifying an antibiotic resistance polypeptide (e.g., resistance to puromycin, kanamycin, tetracycline, hygromycin, neomycin, zeocin, chloramphenicol, or other antibiotics known in the art), a fluorescent polypeptide (e.g., GFP, BFP, YFP, and / or RFP), a bioluminescent polypeptide, and / or a polypeptide that causes a color change in the cells.

[0055] In some embodiments, the cells can be selected for the expression of a phenotype of interest. For example, a phenotype of interest can be a fluorescence marker, a cell surface polypeptide, an intracellular polypeptide, a fluorescent RNA aptamer, a fluorescent DNA aptamer, an imaging based marker, a cell fitness marker, a cell death marker, a cell morphology marker, a cell proliferation marker, an antibiotic resistance marker, or a drug resistance marker. In some embodiments, selecting cells for the expression of phenotype of interest includes antibiotic selection, antibody staining, fluorescence-activated cell sorting (FACS), magnetic-acted cell sorting (MACS), enrichment over time, depletion over time, a cell surface marker screen, a poison exon screen, cell division, population expansion, cell fitness, an image based phenotyping, or drug selection. In some embodiments, cells are not selected for the expression of a phenotype of interest described herein.

[0056] In some embodiments, the methods include detecting one or more output signals from the cells that were selected based on the expression of NEAR constructs. For example, an output signal can identify the presence, absence, or degree of an RNA processing event and / or an RNA processing target. In some embodiments, the output signal is a fluorescence signal, a single cell sequencing read out, a Droplet (DROP)-Seq read out, a CROP-seq, Massively Parallel Single-Cell RNA (MARS)-seq, CytoSeq, and / or an in-situ sequencing screen. Based on the selection markers provided herein, one or more output signals can identify one or more RNA processing events or one or more RNA processing targets. For example, one or more output signals can be a single cell RNA sequence data readout identifying multiple splice modulating events, splice modulation sites, RNA processing targets, RNA processing events, and / or genes involved in RNA processing events and splice modulation.

[0057] In some embodiments, an output signal is used to determine which guide RNAs are enriched, depleted, or not selected at all. For example, a barcode can be used to measure guide RNA enrichment or depletion in the cells. In some embodiments, the barcodes are gRNA specific, hi some embodiments, the barcodes are cell specific. In some embodiments, an output signal can be a sequencing data. The sequencing data can be used to determine the role of different splicing events in transcriptome and their effects on modulating gene networks. In some embodiments, the output signal is used to elucidate the effects of individual exons on gene expression, transcriptome, and splicing. In some embodiments, an output signal is identification of regulatory network of exons in a high throughput manner. In some embodiments, an output signal is identification of therapeutic targets in a condition (e.g., poison exons in cancer specific transcripts).

[0058] In some embodiments, the methods include: i) introducing a plurality of NEAR constructs to a plurality of cells, ii) selecting NEAR construct expressing cells, iii) selecting cells that express a phenotype of interest described herein (e.g., be a fluorescence marker, a cell surface polypeptide, an intracellular polypeptide, a fluorescent RNA aptamer, a fluorescent DNA aptamer, an imaging based marker, a cell fitness marker, a cell death marker, a cell morphology marker, a cell proliferation marker, an antibiotic resistance marker, or a drug resistance marker), and iv) detecting an output signal described herein (e.g., fluorescence signal, a single cell sequencing read out, a Droplet (DROP)-Seq read out, a CROP-seq, Massively Parallel Single-Cell RNA (MARS)-seq, CytoSeq, and / or an in-situ sequencing screen) from cells selected to express the phenotype of interest described herein.

[0059] In some embodiments, the methods include identifying an RNA processing event or an RNA processing target associated with a condition. For example, an RNA processing event can be alternative splicing, constitutive splicing, polyadenylation, methylation, folding, degradation, pseudouridylation, and / or adenosine to inosine RNA editing.

[0060] In some embodiments, an RNA processing target is an exon. In some embodiments, the exon is involved in development of a condition. For example, the condition can include a neurodegenerative disease, a rare disease, a cancer, and / or an immune disorder. In some embodiments, the condition is associated with splice modulation, mis-splicing, and / or switching of RNA isoforms.

[0061] In some embodiments, the RNA processing includes excluding an exon, including an exon, suppressing polyadenylation, or targeting a poison exon. In some embodiments, excluding an exon includes targeting the NEAR construct to a 3 ’splice site across the exon junction or to the 5 ’of a target exon (e.g., a poison exon) junction. In some embodiments, including an exon includes targeting a NEAR construct to the 5 'splice site spanning a target exon junction, or downstream of a target exon junction. In some embodiments, including an exon includes targeting the NEAR construct to a cis regulatory elements (e.g., ribonucleoprotein binding site). In some cases, the cis regulatory element is known to suppress splicing. In some embodiments, the methods include targeting poison exons. For example, targeting poison exon can include inducing the expression of a poison exon or inhibiting the expression of a poison exon.

[0062] In some embodiments, the methods include using NEAR constructs or pooled NEAR screening to analyze a sample for the identification of an RNA processing event or RNA processing target associated with a condition described herein. In some embodiments, the sample is a biological sample. For example, the biological sample can be a cell or a tissue from a subject. In some embodiments, the subject has an increased risk of developing a condition associated with RNA processing or RNA processing events. For example, the subject may have a family history of genetic disorders, an identified mutation associated with a condition, an identified polymorphism associated with a condition, or an identified perturbation of an RNA processing machinery. In some embodiments, the subject is at increased risk of developing a condition associated with abnormal splicing, splicing defects, splice isoforms, and / or aberrant splicing of an exon. Examples of methods for identifying the subject as having an increased risk of developing a condition associated with RNA processing or RNA processing event include, without limitation, genetic tests, laboratory tests (e.g., blood, plasma, or urine), mobility tests, cognitive tests, and / or physical examination.

[0063] EXAMPLES

[0064] The invention is further described in the following examples, which do not limit the scope of the invention described in the claims. Example 1: Methods and Materials for NEAR pooled screening

[0065] Identification of exon of interest

[0066] Exon(s) of interest can be identified using various methods well known in the art. For example, exon of interest can be identified using RNA-seq datasets or enhanced version of the crosslinking and immunoprecipitation (eCLIP)-seq data.

[0067] The number of exons to be screened using the methods described herein depends on the objective of the study. A larger screen allows for screening of a large number of exons with higher statistical power with reduced signal to noise ratio, whereas a smaller screen allows for a more targeted screening for the study of interest.

[0068] NEAR library Design

[0069] To perform the inclusion and exclusion of exon studies, 3-5 guide RNAs were used against the 5’ and 3’ splice sites each. To block RNA binding sites, tiling was performed across the entirety of the exon, with a NEAR placed every 1-10 base pairs (FIGS. 2C and 3 C). Guide RNAs were 20-45 base pairs long.

[0070] Clone library into Lend NEAR backbone

[0071] After designing the NEAR library, an oligonucleotide pool of gRNAs having the following construction was used:

[0072] 5’ adapter - Esp3I cut site - overhang - guide sequence - overhang - Esp3I cut site - 3’ adapter

[0073] The following sequence was used for poison exon screening:

[0074] GGTTCTTCG - cgtctct - CTGC - N(x20-45) - TGAG - agagacg - Tgctgccag (SEQ ID NO: 1)

[0075] To amplify the library, a forward primer with the nucleotide sequence of GGTTCTTCGcgtctctCTGC (SEQ ID NO: 2)and a reverse primer with the nucleotide sequence of CTGGCAGCacgtctctCTC (SEQ ID NO: 3)were used. A PCR was performed for 11 cycles. The resulting DNA was run on and extracted from a polyacrylamide gel. The extracted gel was purified using standard gel extraction and purification techniques.

[0076] The NEAR backbone was digested using Esp3I / BsmBI restriction enzyme. For the CROP-seq NEAR backbone, BbsI restriction enzyme was used. The NEAR backbone was ran on a gel. The linearized NEAR backbone was extracted and purified using a Qiagen Gel extraction kit.

[0077] The PCR amplified, purified guide region and the digested backbone were mixed together in a reactive tube with Esp3I restriction enzyme and T4 Ligase. To cut the guide insert and ligate the plasmid and the gRNA, the reaction was ran for 16 cycles switching from 16°C to 37°C. Ethanol precipitation was used to precipitate and concentrate the resulting plasmids. The plasmids were then transformed into an electrocompetent cell line, followed by plating onto an ampicillin LB-Agar plate. It was ensured that there was a lOOOx representation of the library. The colonies were scraped and purified using a maxiprep. To amplify the inserted gRNA region for sequencing, the following primers were used. Forward: AATGATACGGCGACCACCGAGATCTACACTACCATTGACACTCTTTCCCTACACG ACGCTCTTCCGATCTGGTTCTTCGcgtctct (SEQ ID NO: 4) Reverse: CAAGCAGAAGACGGCATACGAGATTAGGCTAAGTGACTGGAGTTCAGACGTGTG CTCTTCCGATCCTGGCAGCacgtctc (SEQ ID NO: 5)

[0078] The resulting plasmid stock at lOOOx representation was sequenced. Using the MagEcK software, the distribution of the gRNAs in the library was assessed to ensure no gRNA was overrepresented.

[0079] Lentiviral Transduction

[0080] Lentiviral vector was used to transduce the cells with the NEAR library at a multiplicity of infection (MOI) < 0.1 -0.5. The number of cells for transduction was determined using the following calculation.

[0081] Fold representation x guide number / MOI = total starting number of cells

[0082] The number of transduction units per volume unit of cells was determined by transducing a small population of cells with serial dilutions of the lentiviras.

[0083] The transduction unit was determined by assessing the percent green fluorescent protein or blue fluorescent protein. The transduction units per microliter was calculated by selecting conditions that were 10%-30% transduced. The number of cells originally transduced was determined by multiplying the original cell number at transduction by the fraction transduced (10,000 cells seeded and 30% transduced is 3,000 transduction units). This number was divided by the microliters of vims added to get the transduction units per microliter (TU / pL)

[0084] After determining the TU per microliter, the transduction unit was calculated by dividing the total number of cells above the TU / pL. Using the same method as described above, the goal cell number was transduced with the calculated volume of the vims.

[0085] Select cells post transduction

[0086] For phenotype of interest such as cell death or depletion, the sample after 3 days of transduction can be used as a control. Alternatively, an inducible vector and an uninduced control can be used. Selection of cells post transduction is cell line dependent because some cells cannot tolerate certain antibiotics. For example, in the Poison Exon Screen, Hela cells subjected to 1 pg / mL of puromycin selection for 4 days removed all the non-transduced cells. Depending on the lentiviral construct used and the selection markers used in therein, flow cytometer was used to sort cells for the expression of that marker (e.g., GFP+ or BFP + cells).

[0087] Select cells for a phenotype of interest

[0088] Cells were selected for the expression CD45RA and RB and Fas proteins. Unselected cells were also stored for downstream applications.

[0089] Sequence integrated cassette for guide identity

[0090] In the Poison Exon Screen, Fast Library Insert (FLI)-seq probes were used to purify the NEAR expression cassette from the genome before PCR amplification was performed. The libraries were then sequenced on an Illumina sequencer at a length that ensures the entire guide is read through. For the poison exon screen, lOObp single end was used for 10 million reads per sample. To decode the gRNA and analyze the results, single-cell RNA sequencing was performed using a standard single-cell RNA sequencing methods such as 1 OxGenomics or Parse Bio.

[0091] Analysis

[0092] MagEck was used to analyze the results from the Poison Exon Screen. The sequencing data from both the selected and unselected NEAR guides generated above were used to determine which guides were enriched, depleted, or not selected at all. MagEck generated score target events based on enrichment and depletion by aggregating the score of all guides targeting that event. Enriched guides correlated with targets that were not depleted, or caused faster division. Depleted guides correlated with targets that reduced cellular fitness or division.

[0093] Example 2: Modulating antigen determinant exon (ADE) using NEAR construct

[0094] To test if the NEAR constructs are capable of pooled phenotypic screening of exons involved in RNA splice modulation, a NEAR construct was designed against three separate exons containing alternative exons that are integral to the expression of their respective surface antigens. The NEAR construct was then tested in conditions that mimicked those of a pooled phenotypic screen (e.g., at low MOI and performed flow cytometry). These exons, FAS exon 6, CD45 exon 4, and CD45 exon 5, when spliced in results in mature messenger (mRNA) transcripts that encode for a surface receptor detectable by antibody (FIG. 2A, top panel). When spliced out, the mRNA transcripts do not produce the antigen (FIG. 2A, bottom panel).

[0095] FAS exon 6 encodes the transmembrane domain of FAS protein, and when excluded, results in a soluble protein that is released into the extracellular space. CD45 exon 4 and CD45 exon 5 encode the glycosylated domains of the CD45 protein on the cell surface, creating antigens CD45RA and CD45 RB respectively. To target FAS exon 6, a NEAR construct was designed against FAS 3’ splice site (the 5’ of the alternative exon) for exon exclusion (FIG. 2B). To target CD45RA exon 4, a NEAR construct was designed against 3’ splice site for exon exclusion or a cis splicing inhibitor (ISS 1) for exon inclusion (FIG. 2 C). To target CD45RB exon 5, a NEAR construct was designed against the 5’ splice site (3’ of the alternative exon) for exon inclusion (FIG. 2CD).

[0096] Example 3: Poison gene screening

[0097] A pooled depletion screen targeting poison exons in essential genes in Hela cells was performed. Fifty poison exons in essential genes were targeted in this screen. The results showed depletion of guide RNAs targeting the 5 'splice site, as shown for COPS5 and SNRNP70 in FIGS. 5A-5D, as well as upstream events that target RBP regulatory regions, and the 3’ splice site in SNRNP70 and U2AF2. Additional findings showed enrichment of guides targeting the 3’ splice site in SNRNP70 and the 5’ splice site of PSMD7, demonstrating that poison exon modulation can increase cell fitness in SNRNP70 and PSMD7. The depletion and enrichment of these guide RNAs suggests that a single NEAR is capable of modulating splicing events with a phenotypic outcome that surpasses the noise ill these assays. Overview of the poison exon screening and metrics used to perform the screen are shown in FIGS 3A-3C and FIGS. 4A-4D.

[0098] Example 4: NEAR construct modulated inclusion of potential cancer dependent alternative exon in MAP3K7

[0099] In triple negative breast cancer (TNBC) line MDA-MB-231, knocking down PUF60 caused exon exclusion within PUF60 eCLIP targets including MAP2K7. It was shown that 6 out of the 7 gRNAs of NEARs successfully perturbed MAP2K7 exon 2 (FIGS. 6A-6D). Although the study was intended to exclude the exon to determine whether the PUF60- regulated inclusion of MAP2K.7 exon 2 contributes to arrested proliferation, the result showed that 1 out of the 7 gRNA modestly included the exon, an unintended effect of these NEARs (FIGS. 6A-6D). These findings suggest that NEARs are capable of performing both inclusion and exclusion of exon. Moreover, it shows that a pooled NEAR screen can be performed on the splicing targets of PUF60 to investigate the determinants regulating the role of PUF60 in triple negative breast cancer.

[0100] Example 5: Role of alternative exons in CD45 isoform switching in T cell activation

[0101] To investigate the alternative exons that are determinants of CD45 iso form switching in T cell activation, a screen is generated where sequencing data from phorbol 12-myristate 13-acetate (PMA) stimulated and unstimulated Jurkat cells. One of the signatures of T cell activation is the iso form switching of CD45, where exon 4 is excluded. In a small screen, alternative splicing events of the T cell receptor signaling pathway are selected, including Zap-70, RasGRP, Ras, Raf, MEK1 / 2, Erkl / 2, and Fos. For each alternative exon, approximately 3 guides are designed to promote inclusion and exclusion of exons. Cells are transduced with lentiviral NEAR expression vector and treated with 1 OOng / mL of PMA for 24 and 48 hours. The cells are then stained with CD45RA, C45RB, or CD45RO antibodies and sorted into quartiles based on high, medium, or low expression (FIG. 2B). MagEck is used to deconvolute the results.

[0102] OTHER EMBODIMENTS

[0103] It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

Claims

WHAT IS CLAIMED IS:

1. A method for pooled screening of RNA processing events in a plurality of cells, the method comprising: a. introducing a plurality7of nuclear expressed antisense RNA (NEAR) constructs to the plurality of cells; b. selecting cells from the plurality of cells that express the plurality of NEAR constructs; and c. detecting an output signal from the cells from (b).

2. A method of modulating RNA processing in a cell, the method comprising: a. introducing a plurality of nuclear expressed antisense RNA (NEAR) constructs to the plurality of cells; b. selecting cells from the plurality of cells that express the plurality of NEAR constructs; and c. detecting an output signal from the cells from (b).

3. A method of analyzing a sample containing a plurality of cells, the method comprising: a. introducing a plurality of nuclear expressed antisense RNA (NEAR) constructs to the cells in the sample; b. selecting cells from the plurality of cells in the sample that express the plurality’ of NEAR constructs; and c. detecting an output signal from the cells from (b) to identify an RNA processing event or RNA processing target associated with a condition.

4. The method of any one of claims 1-3, further comprising selecting cells from step (b) that exhibit a phenotype of interest.

5. A method for pooled screening of RNA processing events in a plurality’ of cells, the method comprising: a. introducing a plurality' of nuclear expressed antisense RNA (NEAR) constructs to the plurality’ of cells; b. selecting cells from the plurality of cells that express the plurality of NEAR constructs;c. selecting cells that express a phenotype of interest; and d. detecting an output signal from the selected cells from.

6. A method of modulating RNA processing in a cell, the method comprising: a. introducing a plurality of nuclear expressed antisense RNA (NEAR) constructs to the plurality of cells; b. selecting cells from the plurality of cells that express the plurality of NEAR constructs; c. selecting cells that express a phenotype of interest; and d. detecting an output signal from the selected cells.

7. A method of analyzing a sample containing a plurality of cells, the method comprising: a. introducing a plurality of nuclear expressed antisense RNA (NEAR) constructs to the cells in the sample; b. selecting cells from the plurality of cells in the sample that express the plurality of NEAR constructs; c. selecting cells that express a phenotype of interest; and d. detecting an output signal from the selected cells to identify an RNA processing event or RNA processing target associated with a condition.

8. The method of any one of the above claims, wherein the NEAR construct includes: i. a NEAR guide RNA comprising an antisense sequence; and ii. a ribonucleoprotein recruiting motif.

9. The method of claim 8, wherein the NEAR construct further includes a barcode.

10. The method of any one of the above claims, wherein the plurality of NEAR constructs includes two or more pluralities of NEAR constructs, wherein each NEAR construct includes a different antisense sequence.

11. The method of claim 9. wherein the barcode includes a single stranded barcode, an RNA barcode, the NEAR antisense region, the NEAR guide region, or the integrated NEAR lentivirus genome.

12. The method of claim 8, wherein the antisense sequence includes a portion that is antisense to a region of a pre-mRNA or adjacent to an alternative splice site.

13. The method of claim 8, wherein the ribonucleoprotein recruiting motif is attached to a 5’ end and / or a 3’ end of the antisense sequence.

14. The method of any one of the above claims, wherein the ribonucleoprotein recruiting motif can recruit any combination of endogenous mammalian nuclear ribonucleoproteins including U snRNP components, hnRNP components, Sm core components, and / or any other nuclear expressed ribonucleoprotein.

15. The method of any one of the above claims, wherein the NEAR construct is in the form of a vector.

16. The method of claim 15, wherein the vector is a viral vector or a non-viral vector.

17. The method of claim 16, wherein the viral vector includes a lentivirus, an AAV, an adenovirus, an adeno-associated virus, retrovirus, or a herpes simplex virus.

18. The method of claim 16, wherein the non-viral vector is a liposome, exosome, an extracellular vesicle, a polymer, a nanoparticle, a peptide, or a dendrimer.

19. The method of any one of the above claims, wherein selecting the cells that express the plurality of NEAR constructs includes identifying an antibiotic resistance polypeptide, a fluorescent polypeptide, a bioluminescent polypeptide, and / or a polypeptide that causes a color change in the cells.

20. The method of any one of the above claims, wherein selecting cells that express the phenotype of interest includes antibiotic selection, antibody staining, fluorescence-activated cell sorting (FACS), magnetic-acted cell sorting (MACS), enrichment over time, depletion over time, a cell surfacemarker screen, a poison exon screen, cell division, population expansion, cell fitness, an image based phenotyping, or drug selection.

21. The method of any one of the above claims, wherein the phenotype of interest includes a fluorescence marker, a cell surface polypeptide, an intracellular polypeptide, a fluorescent RNA aptamer, a fluorescent DNA aptamer, an imaging based marker, a cell fitness marker, a cell death marker, a cell morphology' marker, a cell proliferation marker, an antibiotic resistance marker, or a drug resistance marker.

22. The method of any one of the above claims, wherein the output signal identifies the presence, absence, or degree of an RNA processing event and / or an RNA processing target.

23. The method of any one of the above claims, wherein the RNA processing event includes alternative splicing, constitutive splicing, polyadenylation, methylation, folding, degradation, pseudouridylation, and / or adenosine to inosine RNA editing.

24. The method of any one of the above claims, wherein an RNA processing target includes an exon.

25. The method of any of the above claims, wherein the RNA processing includes: a. excluding an exon; b. including an exon; c. suppressing polyadenylation; or d. targeting a poison exon.

26. The method of any one of the above claims, wherein the NEAR construct targets a pre-mRNA.

27. The method of claim 25, wherein excluding an exon includes targeting the NEAR construct to the 3’ splice site of a target exon.

28. The method of claim 25, wherein including an exon includes targeting the NEAR construct to the 5’ splice site of a target exon.

29. The method of claim 25, wherein including an exon includes targeting the NEAR construct to an RBP binding site.

30. The method of claim 25, wherein targeting poison exons includes inducing the expression of poison exons or inhibiting the expression of poison exons.

31. The method of any one of the above claims, wherein the sample is a biological sample.

32. The method of claim 31. wherein the biological sample is a cell or a tissue.

33. The method of any one of the above claims, wherein the condition includes a neurodegenerative disease, a rare disease, a cancer, and / or an immune disorder.

34. The method of any of the above claims, wherein the output signal includes a fluorescence signal, a single cell sequencing read out, a DROP-Seq read out, a CROP-seq, MARS-seq, CytoSeq, and / or an in-situ sequencing screen.

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