Deaminase-based RNA sensors

Abstract

Disclosed herein is an RNA editing tool for use in a system designed to measure RNA and manipulate specific cell types in vivo.The RNA sensor system comprises: a) a single-stranded RNA (ssRNA) sensor that comprises a stop codon and a payload, and optionally further comprises a normalization gene; and b) an adenosine deaminase that acts on RNA (ADAR), where the sensor can bind to target ssRNA to form a double-stranded RNA (dsRNA) that is a substrate for ADAR deaminase; the substrate comprises a mismatch within the stop codon; the mismatch can be edited by ADAR deaminase, and the edit can effectively remove the stop codon, allowing the payload to be translated and expressed.Also disclosed is a method for quantifying ribonucleic acid (RNA) levels using the RNA sensor system.

Description

[Technical field]

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 267,177, filed January 26, 2022, and U.S. Provisional Application No. 63 / 210,829, filed June 15, 2021, the entireties of which are incorporated herein by reference.

[0002] STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT This invention was made with government support awarded by the National Institutes of Health (NIH) under Grant No. 5DP5OD024583. The Federal Government has certain rights in this invention. [Background technology]

[0003] In recent years, the ability to edit nucleic acids in a precise and programmable manner has been refined. New technologies allow this high-precision editing in vivo, opening the possibility of treating patients at the genotype level. However, there are no viable tools to measure and track RNA levels in vivo without genetic engineering or tagging. Genetic engineering often requires genome engineering that has unforeseen consequences on expression and whole cell activity, rather than being a strictly observable sensor system where changes can be measured. Furthermore, engineering at the genome level to fully integrate sensors requires transgenic organisms, which is an impractical approach in many experimental designs. Summary of the Invention [Problem to be solved by the invention]

[0004] Recent advances have enabled the determination of many specific cell types, but the ability to track and manipulate these cells is still lacking. The present disclosure relates to RNA editing tools for use in systems designed to measure RNA and manipulate specific cell types in vivo. [Means for solving the problem]

[0005] Summary of the Invention The present disclosure is directed to an RNA sensor system. The present disclosure presents an RNA sensor system comprising: a) a single-stranded RNA (ssRNA) sensor comprising a stop codon and a payload, and optionally further comprising a normalization gene; and b) an adenosine deaminase acting on RNA (ADAR); the sensor can bind to target ssRNA to form double-stranded RNA (dsRNA) that is a substrate for ADAR deaminase; the substrate comprises a mismatch within the stop codon; the mismatch can be edited by ADAR deaminase, and the edit can effectively remove the stop codon so that the payload can be translated and expressed.

[0006] The present disclosure presents an RNA sensor system in which mispairing between the ssRNA sensor and the target ssRNA involves an adenine:cytidine mispairing within the dsRNA duplex.

[0007] The present disclosure provides an RNA sensor system, in which the mismatch between the ssRNA sensor and the target ssRNA comprises adenine:cytidine mismatch, and ADAR deaminase edits the adenine into inosine in the mismatch of the dsRNA duplex.In one embodiment, the RNA system comprises more than one mismatch.

[0008] The sensor strand of the RNA sensor system may include a payload that includes a reporter protein, a transcription factor, an enzyme, a transgene protein, or a therapeutic protein. The present disclosure also provides an RNA sensor system in which the payload includes a fluorescent reporter. The present disclosure also provides an RNA sensor system in which the payload includes an EGFP reporter or a luciferase reporter. The present disclosure also provides an RNA sensor system in which the payload includes a caspase.

[0009] The present disclosure also provides an RNA sensor system in which the ADAR is an endogenous ADAR or an exogenous ADAR. The present disclosure also provides an RNA sensor system in which the ADAR is a modified ADAR.

[0010] The present disclosure also provides an RNA sensor system in which the ADAR comprises a programmable A to I(G) replacement RNA editing (REPAIR) molecule, a Cas13b-ADAR fusion molecule, a Cas13d-ADAR fusion molecule, a Cas7-11-ADAR fusion molecule and an MS2-ADAR fusion molecule, the deaminase domain of ADAR2, full-length ADAR2 or a truncated ADAR2.

[0011] The present disclosure also provides an RNA sensor system comprising a plurality of RNA sensors.

[0012] The disclosure also provides an AND gate comprising: a) an ssRNA sensor that includes one or more payloads and multiple stop codons that are complementary to different target ssRNAs, where the ssRNA sensor is capable of binding to the target ssRNA to form a double-stranded RNA (dsRNA) that is a substrate for an ADAR deaminase; the substrate includes a mismatch within each stop codon; and the mismatch within each stop codon is editable by an ADAR deaminase, where the editing can effectively remove the stop codon to allow translation and expression of one or more payloads; and / or b) an AND gate that includes multiple independent ssRNA sensors that are capable of binding to the target ssRNA to form a double-stranded RNA (dsRNA) that is a substrate for an ADAR deaminase; the substrate includes a mismatch within each stop codon; and the mismatch within each stop codon is editable by an ADAR deaminase, where the editing can effectively remove the stop codon to allow translation and expression of one or more payloads; Also presented is a cellular logic system that includes an OR gate that includes multiple independent ssRNA sensors, each of the sensors including a payload and a stop codon that is complementary to one or more different target RNAs, where each ssRNA sensor is capable of binding to a different target ssRNA to form a double-stranded RNA (dsRNA) that is a substrate for an ADAR deaminase; the substrate includes a mismatch within each stop codon; and the mismatch within each stop codon is editable by an ADAR deaminase, where the editing can effectively remove the stop codon to allow translation and expression of one or more payloads.The disclosure also provides an AND gate comprising: a) one or more payloads and a ssRNA sensor that includes multiple stop codons that are complementary to different target ssRNAs, where the ssRNA sensor is capable of binding to the target ssRNA to form a double-stranded RNA (dsRNA) that is a substrate for an ADAR deaminase; the substrate includes a mismatch within each stop codon; and the mismatch within each stop codon is editable by an ADAR deaminase, where the editing can effectively remove the stop codons to allow translation and expression of the one or more payloads; or b) a payload comprising a ssRNA sensor that includes a plurality of stop codons that are complementary to different target ssRNAs, where the ssRNA sensor is capable of binding to the target ssRNA to form a double-stranded RNA (dsRNA) that is a substrate for an ADAR deaminase; the substrate includes a mismatch within each stop codon; and the mismatch within each stop codon is editable by an ADAR deaminase, where the editing can effectively remove the stop codons to allow translation and expression of the one or more payloads; Also disclosed is a cellular logic system that includes an OR gate that includes multiple independent ssRNA sensors that include a load and a stop codon that is complementary to one or more different target RNAs, where each ssRNA sensor is capable of binding to a target ssRNA to form a double-stranded RNA (dsRNA) that is a substrate for an ADAR deaminase; the substrate includes a mismatch within each stop codon; and the mismatch within each stop codon is editable by an ADAR deaminase, where the editing can effectively remove the stop codon to allow translation and expression of one or more payloads.

[0013] The present disclosure provides a method for detecting or quantifying ribonucleic acid (RNA) levels with an RNA sensor system, comprising the steps of: a) providing a single-stranded RNA (ssRNA) sensor comprising a stop codon and a payload, and optionally further comprising a normalization gene; and b) providing an adenosine deaminase acting on RNA (ADAR); the sensor is capable of binding to a target ssRNA of interest to form a double-stranded RNA (dsRNA) that is a substrate for the ADAR deaminase; the substrate comprises a mismatch within the stop codon; the mismatch is editable by the ADAR deaminase, which editing can effectively remove the stop codon to allow translation and expression of the payload.

[0014] The present disclosure also provides an RNA sensor system in which the mispairing comprises an adenine:cytidine mispairing within a dsRNA duplex.

[0015] The present disclosure provides a method for detecting or quantifying ribonucleic acid (RNA) levels by an RNA sensor system in which mismatches include adenine mismatches with cytidine, and ADAR deaminase edits adenine to inosine in a dsRNA duplex.The present disclosure provides a method for detecting or quantifying ribonucleic acid (RNA) levels by an RNA sensor system that includes more than one mismatch.

[0016] The present disclosure provides methods for detecting or quantifying ribonucleic acid (RNA) levels by RNA sensor systems that contain payloads translated into reporter proteins, transcription factors, enzymes, transgene proteins, or therapeutic proteins.

[0017] The present disclosure also provides an RNA sensor system, wherein the ADAR is an endogenous ADAR or an exogenous ADAR. In some embodiments, the ADAR is a modified ADAR. In some embodiments, the ADAR is an ADAR that is endogenous to the cell type in which the sensor is used.

[0018] The present disclosure also provides an RNA sensor system comprising: a) a single-stranded RNA (ssRNA) sensor comprising at least a first stop codon and a payload, and optionally further comprising a normalization gene; and b) an adenosine deaminase acting on RNA (ADAR); the sensor is capable of binding to a target ssRNA to form a double-stranded RNA (dsRNA) that is a substrate for the ADAR deaminase; the substrate comprises a mismatch within the first stop codon; the mismatch is editable by the ADAR deaminase, which editing can effectively remove the stop codon to allow translation and expression of the payload.

[0019] The present disclosure also provides an RNA sensor system, wherein the single-stranded RNA sensor comprises more than one stop codon.The present disclosure also provides an RNA sensor system, wherein the single-stranded RNA sensor further comprises a second stop codon.The present disclosure also provides an RNA sensor system, wherein the single-stranded RNA sensor further comprises a third stop codon.

[0020] The present disclosure also provides an RNA sensor system in which the mismatch comprises CCA on the target strand and TAG / UAG on the sensor strand. The present disclosure also provides an RNA sensor system in which the sensor strand comprises a stop codon, TAG / UAG, but does not mismatch with the CCA codon on the target strand. The present disclosure also provides an RNA sensor system in which the sensor strand comprises a stop codon that can create a match or mismatch with the codon on the target strand, selected from the group consisting of ACA, ACT, ACC, ACG, TCA, TCT, TCC, TCG, GCA, GCT, GCC, GCG, CCA, CCT, CCC, and CCG.

[0021] The present disclosure also provides an RNA sensor system comprising: a) a single-stranded RNA (ssRNA) sensor comprising a stop codon and a payload, and optionally further comprising a normalization gene; and b) an adenosine deaminase acting on RNA (ADAR); the sensor can bind to target ssRNA to form a double-stranded RNA (dsRNA) that is a substrate for ADAR deaminase; the substrate comprises a stop codon that can be edited by ADAR deaminase, and editing can effectively remove the stop codon to allow the payload to be translated and expressed. In some embodiments, the ssRNA sensor comprises a stop codon TAG / UAG. In some embodiments, the stop codon TAG / UAG forms a dsRNA duplex with target ssRNA at a codon with the formula nCn, where n is any nucleotide and C is cytidine.

[0022] The present disclosure also provides an RNA sensor system as described herein, wherein the ssRNA sensor is 50 nucleotides or more, 100 nucleotides or more, 150 nucleotides or more, 200 nucleotides or more, 250 nucleotides or more, 300 nucleotides or more, or 500 nucleotides or more. In some embodiments, the ssRNA sensor is 51 nucleotides. In some embodiments, the ssRNA sensor is 81 nucleotides. In some embodiments, the ssRNA sensor is 171 nucleotides. In some embodiments, the ssRNA sensor is 225 nucleotides. In some embodiments, the ssRNA sensor is 279 nucleotides. In some embodiments, the ssRNA sensor is longer than 279 nucleotides.

[0023] The present disclosure also provides an RNA sensor system as described herein, wherein the ssRNA sensor is a circular sensor. In some embodiments, the circular sensor is a rolling circle translation sensor. In some embodiments, the circular sensor is a regular circular sensor.

[0024] The present disclosure also provides the RNA sensor system described herein, wherein ssRNA sensor comprises two stop codons.In some embodiments, ssRNA sensor comprises three stop codons.In some embodiments, ssRNA sensor comprises two stop codons, and in this case, only one stop codon is targeted for ADAR editing.In some embodiments, ssRNA sensor comprises three stop codons, and in this case, only one stop codon is targeted for ADAR editing.

[0025] The present disclosure also provides the RNA sensor system described herein, wherein the ssRNA sensor comprises at least one avidity binding region. In some embodiments, the ssRNA sensor comprises at least three avidity binding regions. In some embodiments, the ssRNA sensor comprises at least five avidity binding regions. In some embodiments, the ssRNA sensor comprises at least seven avidity binding regions. In some embodiments, the ssRNA sensor comprises more than seven avidity binding regions. In some embodiments, the avidity binding regions are separated by MS2 hairpin regions.

[0026] The present disclosure also provides the RNA sensor system described herein, wherein the payload comprises Cre recombinase. In some embodiments, the payload comprises Cas protein. In some embodiments, the payload comprises Cas9. In some embodiments, the payload comprises a transcription factor. In some embodiments, the payload comprises a payload ADAR. In some embodiments, the payload is a reporter for cell stress response.

[0027] The present disclosure also provides a composition comprising the RNA sensor system described herein and a delivery vehicle.In some embodiments, the composition comprises an RNA sensor system and lipid nanoparticles, where the RNA sensor system comprises a) a single-stranded RNA (ssRNA) sensor comprising a stop codon and a payload, and optionally further comprising a normalization gene; and b) an adenosine deaminase that acts on RNA (ADAR); the sensor can bind to target ssRNA to form double-stranded RNA (dsRNA) that is a substrate for ADAR deaminase; the substrate comprises a stop codon that can be edited by ADAR deaminase, and editing can effectively remove the stop codon to allow the translation and expression of the payload, and the RNA sensor system is encapsulated in lipid nanoparticles.

[0028] The disclosure also provides a method of killing a specific cell or a specific cell type, comprising providing a single-stranded RNA (ssRNA) sensor or guide comprising a stop codon and a payload, and optionally further comprising a normalization gene; the payload is a self-dimerizing caspase, and the ssRNA sensor or guide is capable of binding to the target ssRNA to form a double-stranded RNA (dsRNA) that is a substrate for adenosine deaminase acting on RNA (ADAR), and the target ssRNA is enriched for expression in the specific cell or in the specific cell type.

[0029] The present disclosure also provides an RNA sensor system comprising: a) an RNA sensor comprising a stop codon and a payload, and optionally further comprising a normalization gene; and b) an adenosine deaminase acting on RNA (ADAR); the sensor can bind to target RNA to form a double-stranded RNA (dsRNA) region that is a substrate for ADAR deaminase; the substrate comprises a mismatch within the stop codon; the mismatch can be edited by ADAR deaminase, and the edit can effectively remove the stop codon so that the payload can be translated and expressed. In some embodiments, the RNA sensor is a single-stranded RNA. In some embodiments, the RNA sensor comprises one or more double-stranded RNA (dsRNA) domains. In some embodiments, the target RNA is a single-stranded RNA. In some embodiments, the RNA comprises one or more double-stranded RNA (dsRNA) domains.

[0030] The present disclosure also provides an RNA sensor system as described herein, wherein the RNA sensor is 50 nucleotides or more, 100 nucleotides or more, 150 nucleotides or more, 200 nucleotides or more, 250 nucleotides or more, 300 nucleotides or more, or 500 nucleotides or more. In some embodiments, the RNA sensor is 51 nucleotides. In some embodiments, the ssRNA sensor is 81 nucleotides. In some embodiments, the ssRNA sensor is 171 nucleotides. In some embodiments, the ssRNA sensor is 225 nucleotides. In some embodiments, the ssRNA sensor is 279 nucleotides. In some embodiments, the ssRNA sensor is longer than 279 nucleotides.

[0031] The present disclosure also provides the RNA sensor system described herein, wherein the ssRNA sensor is a circular sensor. In some embodiments, the circular sensor is a rolling circle translation sensor. In some embodiments, the circular sensor is a conventional circular sensor. In some embodiments, the RNA sensor comprises two stop codons. In some embodiments, the RNA sensor comprises three stop codons. In some embodiments, the RNA sensor comprises two stop codons, in which case only one stop codon is targeted for ADAR editing. In some embodiments, the RNA sensor comprises three stop codons, in which case only one stop codon is targeted for ADAR editing.

[0032] The present disclosure also provides an RNA sensor system as described herein, wherein the RNA sensor comprises at least one avidity binding region. In some embodiments, the RNA sensor comprises at least three avidity binding regions. In some embodiments, the RNA sensor comprises at least five avidity binding regions. In some embodiments, the RNA sensor comprises at least seven avidity binding regions. In some embodiments, the RNA sensor comprises more than seven avidity binding regions. In some embodiments, the avidity binding regions are separated by MS2 hairpin regions.

[0033] In some embodiments, the payload comprises a Cre recombinase. In some embodiments, the payload comprises a Cas protein. In some embodiments, the payload comprises Cas9. In some embodiments, the payload comprises a transcription factor. In some embodiments, the payload comprises a payload ADAR. In some embodiments, the payload is a reporter for a cellular stress response.

[0034] In some embodiments, the ADAR is selected from the group consisting of ADAR2, ADAR1, ADAR1 p150, ADAR1 p110, ADAR2 R455G, ADAR2 R455G, ADAR2 S486T, ADAR2 T375G E488Q T490A, ADAR2 T375G, ADAR2 T375S, ADAR2 N473D, ADAR2 deaminase domain, ADAR2 T490S, ADAR2 T490A, MCP-ADAR2 deaminase domain, ADAR2 R455E, ADAR2 T375G T490A, ADAR2 E488Q, MCP-ADAR2 deaminase domain E488Q T490A, ADAR2 R510E, ADAR2 R455S, ADAR2 V351L, and derivatives or engineered variants thereof. In some embodiments, the ADARs are endogenously expressed in the target cells in which the RNA sensor system may be used. [Brief description of the drawings]

[0035] [Figure 1] 1 is a graphical illustration of how an RNA sensor using ADAR technology can generate new gene output. The sensor RNA contains an optional marker protein, a guide RNA region with a stop codon (red octagon) and a downstream payload protein. The sensor association with the target RNA forms a duplex with an AC mismatch, which is used as a substrate for RNA editing by the ADAR protein (brown). For example, RNA editing can convert the stop codon UAG to UIG, allowing the translation of the payload (green protein). [Figure 2A] Figure 2A is a graphical illustration of dual transcript ADAR sensor design. Luciferase dual transcript ADAR sensor contains a normalization protein under constitutive expression and a payload protein under the control of ADAR sensor. The fold change ratio can be calculated by calculating the activation (gluc / cluc) of normalization sensor and then normalizing to the ratio value in the absence of target. Target can be delivered via exogenous transfection under the control of doxycycline-inducible promoter. Sensor can recruit endogenous ADAR to sense target EGFP transcript, or utilize exogenously delivered ADAR to sense target transcript with enhanced sensitivity. [Figure 2B] FIG. 2B is a graphical illustration of a comparison of the fold increase in activation of luciferase levels in HEK293FT cells transfected with an eGFP plasmid or a control plasmid, as well as a sensor strand that recognizes eGFP, in the presence or absence of a supplemented ADAR. [Figure 2C] FIG. 2C is a graphical illustration comparing luciferase values ​​of a non-targeted sensor, a targeted sensor, or a constitutively active plasmid in the presence or absence of supplemented ADAR. [Figure 2D]FIG. 2D is a graphical illustration of next generation sequencing results quantifying editing of the stop codon UAG in targeted sensors, non-targeted sensors, and constitutively active plasmids. [Figure 3A] 3A is a schematic diagram showing a fluorescent ADAR sensor with a single transcript design containing a constitutively expressed normalizing fluorescent protein (mCherry) upstream of an ADAR sensor guide that controls a second fluorescent protein (mNeon). Due to dual fluorescence on a single transcript, a non-functional eGFP must be used with this format. [Figure 3B] Figure 3B is a series of representative images of HEK293FT cells transfected with targeted or non-targeted sensors, non-functional eGFP, with or without supplemented ADAR, and Figure 3C is quantification of fold increase in mNeon activation by measuring fluorescence. [Figure 3C] FIG. 3C is a quantification of the fold increase in mNeon activation by measuring fluorescence. [Figure 3D] FIG. 3D is a graphical illustration of next-generation sequencing results quantifying editing of the stop codon UAG in the presence of recruited ADARs for both non-targeted and targeted sensors. [Figure 4A] Graphical illustration of luciferase expression (A) and fold increase in luciferase expression compared to negative control (B) in HEK293FT cells. Fold change ratios can be calculated by calculating normalized sensor activation (gluc / cluc) and then normalizing to the ratio value in the absence of target. (A) Expression of the Gluc luciferase gene was quantified in HEK293FT cells transfected with two distinct guide strands (design 2 and design 4) targeting EGFP or a negative control scrambled sequence (negative control). (B) Increase in EGFP expression was quantified compared to the negative control guide strand. [Figure 4B]Graphical illustration of luciferase expression (A) and fold increase in luciferase expression compared to negative control (B) in HEK293FT cells. Fold change ratios can be calculated by calculating normalized sensor activation (gluc / cluc) and then normalizing to the ratio value in the absence of target. (A) Expression of the Gluc luciferase gene was quantified in HEK293FT cells transfected with two distinct guide strands (design 2 and design 4) targeting EGFP or a negative control scrambled sequence (negative control). (B) Increase in EGFP expression was quantified compared to the negative control guide strand. [Diagram 5] Graphical illustration of the increase in luciferase expression by experimental guide strands 1-4 targeting EGFP transcripts in the presence of endogenous ADAR2 (Figure 3, blue bars), endogenous ADAR2 supplemented with exogenously supplied ADAR2 deaminase domain (ADAR2dd; Figure 2, white bars), or in the presence of a fusion construct expressing the catalytically inactive enzyme Cas13b fused to the ADAR2 deaminase domain (dPspCas13b-ADAR2dd; Figure 2, red bars). All fold increases are compared to a scrambled guide designed not to target EGFP, used as a negative control. [Figure 6A]Visual representation of ADAR mutants and catalytic domain mutation screen. (A) From left to right, schematic diagrams of different tested ADARs, including ADAR1p150, ADAR1p110, ADAR2, and MS2 coat protein (MCP)-ADAR fusion protein (MCP-ADAR). fl=full length. DD=deaminase domain. Catalytic domain mutations are not shown in the schematic diagram, but all reside within the deaminase domain. (B) Bar graph showing activation of exogenously transfected sensors in the presence of exogenously transfected iRFP and different ADAR mutants. ADAR mutants selected for target-wide screening are shown in red with RNA sequencing data showing conversion of the stop codon TAG to TIG in the presence and absence of target. Error bars indicate standard deviation of technical replicates with n=3. [Figure 6B] Visual representation of ADAR mutants and catalytic domain mutation screen. (A) From left to right, schematic diagrams of different tested ADARs, including ADAR1p150, ADAR1p110, ADAR2, and MS2 coat protein (MCP)-ADAR fusion protein (MCP-ADAR). fl=full length. DD=deaminase domain. Catalytic domain mutations are not shown in the schematic diagram, but all reside within the deaminase domain. (B) Bar graph showing activation of exogenously transfected sensors in the presence of exogenously transfected iRFP and different ADAR mutants. ADAR mutants selected for target-wide screening are shown in red with RNA sequencing data showing conversion of the stop codon TAG to TIG in the presence and absence of target. Error bars indicate standard deviation of technical replicates with n=3. [Figure 7A]7A-7C are graphical representations of activation of ADAR sensors by targets eGFP and iRFP. (A) Testing of ADAR mutants on a 69 nucleotide iRFP sensor. The indicated fold change indicates the fluorescence ratio value (mNeon / mCherry) in the presence of target divided by the ratio value in the absence of target. (B) Non-normalized mNeon / mCherry fluorescence ratio value for the data shown in FIG. 7A. (C) Testing of ADAR mutants on a 51 nucleotide eGFP sensor. The indicated fold change indicates the fluorescence ratio value (mNeon / mCherry) in the presence of target divided by the ratio value in the absence of target. (D) Non-normalized mNeon / mCherry fluorescence ratio value for the data shown in FIG. 7C. Error bars indicate standard deviation of technical replicates with n=3. [Figure 7B] 7A-7C are graphical representations of activation of ADAR sensors by targets eGFP and iRFP. (A) Testing of ADAR mutants on a 69 nucleotide iRFP sensor. The indicated fold change indicates the fluorescence ratio value (mNeon / mCherry) in the presence of target divided by the ratio value in the absence of target. (B) Non-normalized mNeon / mCherry fluorescence ratio value for the data shown in FIG. 7A. (C) Testing of ADAR mutants on a 51 nucleotide eGFP sensor. The indicated fold change indicates the fluorescence ratio value (mNeon / mCherry) in the presence of target divided by the ratio value in the absence of target. (D) Non-normalized mNeon / mCherry fluorescence ratio value for the data shown in FIG. 7C. Error bars indicate standard deviation of technical replicates with n=3. [Figure 7C]7A-7C are graphical representations of activation of ADAR sensors by targets eGFP and iRFP. (A) Testing of ADAR mutants on a 69 nucleotide iRFP sensor. The indicated fold change indicates the fluorescence ratio value (mNeon / mCherry) in the presence of target divided by the ratio value in the absence of target. (B) Non-normalized mNeon / mCherry fluorescence ratio value for the data shown in FIG. 7A. (C) Testing of ADAR mutants on a 51 nucleotide eGFP sensor. The indicated fold change indicates the fluorescence ratio value (mNeon / mCherry) in the presence of target divided by the ratio value in the absence of target. (D) Non-normalized mNeon / mCherry fluorescence ratio value for the data shown in FIG. 7C. Error bars indicate standard deviation of technical replicates with n=3. [Figure 7D] 7A-7C are graphical representations of activation of ADAR sensors by targets eGFP and iRFP. (A) Testing of ADAR mutants on a 69 nucleotide iRFP sensor. The indicated fold change indicates the fluorescence ratio value (mNeon / mCherry) in the presence of target divided by the ratio value in the absence of target. (B) Non-normalized mNeon / mCherry fluorescence ratio value for the data shown in FIG. 7A. (C) Testing of ADAR mutants on a 51 nucleotide eGFP sensor. The indicated fold change indicates the fluorescence ratio value (mNeon / mCherry) in the presence of target divided by the ratio value in the absence of target. (D) Non-normalized mNeon / mCherry fluorescence ratio value for the data shown in FIG. 7C. Error bars indicate standard deviation of technical replicates with n=3. [Figure 8A] Graphical depiction of editing rates at stop codons of the sensor panel in targeted and untargeted groups for (A) exogenous recruitment with MCP-ADAR2dd, (B) exogenous recruitment with ADAR1 p150 isoform, (C) exogenous recruitment with ADAR2, and (D) no exogenous ADAR recruitment. [Figure 8B]Graphical depiction of editing rates at stop codons of the sensor panel in targeted and untargeted groups for (A) exogenous recruitment with MCP-ADAR2dd, (B) exogenous recruitment with ADAR1 p150 isoform, (C) exogenous recruitment with ADAR2, and (D) no exogenous ADAR recruitment. [Figure 8C] Graphical depiction of editing rates at stop codons of the sensor panel in targeted and untargeted groups for (A) exogenous recruitment with MCP-ADAR2dd, (B) exogenous recruitment with ADAR1 p150 isoform, (C) exogenous recruitment with ADAR2, and (D) no exogenous ADAR recruitment. [Figure 8D] Graphical depiction of editing rates at stop codons of the sensor panel in targeted and untargeted groups for (A) exogenous recruitment with MCP-ADAR2dd, (B) exogenous recruitment with ADAR1 p150 isoform, (C) exogenous recruitment with ADAR2, and (D) no exogenous ADAR recruitment. [Figure 9] This is a heat map showing the results of an experiment in which HEK293FT cells were transfected with plasmids expressing ADAR p150 and the target transcripts and target-sensing ADAR sensor constructs in combination, as shown on the y-axis and x-axis, respectively. Data shown are fold changes calculated as the fluorescence ratio (mNeon / mCherry) in the condition with target divided by the fluorescence ratio in the condition without target (pUC19). All conditions represent data from technical replicates with n=3. [Figure 10A](A) Selected ADAR variants screened against four different targets combined with their respective RNA sensors. Numbers in heatmaps represent fold change ratios. All conditions represent data from technical replicates with n=3. (B) Representative images are shown for neuropeptide Y (NPY), a target for ADAR1 p150. Cells were transfected with combinations of NPY sensor, ADAR variants and targets, listed around the image. 10x image data and 4x digitally enhanced image data for HEK293 cells obtained via confocal microscopy. [Figure 10B] (A) Selected ADAR variants screened against four different targets combined with their respective RNA sensors. Numbers in heatmaps represent fold change ratios. All conditions represent data from technical replicates with n=3. (B) Representative images are shown for neuropeptide Y (NPY), a target for ADAR1 p150. Cells were transfected with combinations of NPY sensor, ADAR variants and targets, listed around the image. 10x image data and 4x digitally enhanced image data for HEK293 cells obtained via confocal microscopy. [Figure 11A]Graphical illustration of normalized (A, C, E, G) and non-normalized (B, D, F, H) fluorescence values ​​for target and ADAR sensor combinations as examined in HEK293FT cells. Test targets were iRFP (A, B), eGFP (C, D), neuropeptide Y (E, F), and dCas9 (G, H). (A, C, E, G): Fold change in sensor fluorescence ratio (mNeon / mCherry) representing the target condition normalized to the target-free condition for each target and sensor combination. (B, D, F, H): Non-normalized mNeon / mCherry fluorescence ratio values ​​for each sensor and target combination with different ADAR variants. Next generation sequencing data of RNA sensor for UAG to UIG conversion for targets iRFP and EGFP. Edit % indicates A→I edited read %. All conditions represent data from n=3 technical replicates. [Figure 11B] Graphical illustration of normalized (A, C, E, G) and non-normalized (B, D, F, H) fluorescence values ​​for target and ADAR sensor combinations as examined in HEK293FT cells. Test targets were iRFP (A, B), eGFP (C, D), neuropeptide Y (E, F), and dCas9 (G, H). (A, C, E, G): Fold change in sensor fluorescence ratio (mNeon / mCherry) representing the target condition normalized to the target-free condition for each target and sensor combination. (B, D, F, H): Non-normalized mNeon / mCherry fluorescence ratio values ​​for each sensor and target combination with different ADAR variants. Next generation sequencing data of RNA sensor for UAG to UIG conversion for targets iRFP and EGFP. Edit % indicates A→I edited read %. All conditions represent data from n=3 technical replicates. [Figure 11C]Graphical illustration of normalized (A, C, E, G) and non-normalized (B, D, F, H) fluorescence values ​​for target and ADAR sensor combinations as examined in HEK293FT cells. Test targets were iRFP (A, B), eGFP (C, D), neuropeptide Y (E, F), and dCas9 (G, H). (A, C, E, G): Fold change in sensor fluorescence ratio (mNeon / mCherry) representing the target condition normalized to the target-free condition for each target and sensor combination. (B, D, F, H): Non-normalized mNeon / mCherry fluorescence ratio values ​​for each sensor and target combination with different ADAR variants. Next generation sequencing data of RNA sensor for UAG to UIG conversion for targets iRFP and EGFP. Edit % indicates A→I edited read %. All conditions represent data from n=3 technical replicates. [Figure 11D] Graphical illustration of normalized (A, C, E, G) and non-normalized (B, D, F, H) fluorescence values ​​for target and ADAR sensor combinations as examined in HEK293FT cells. Test targets were iRFP (A, B), eGFP (C, D), neuropeptide Y (E, F), and dCas9 (G, H). (A, C, E, G): Fold change in sensor fluorescence ratio (mNeon / mCherry) representing the target condition normalized to the target-free condition for each target and sensor combination. (B, D, F, H): Non-normalized mNeon / mCherry fluorescence ratio values ​​for each sensor and target combination with different ADAR variants. Next generation sequencing data of RNA sensor for UAG to UIG conversion for targets iRFP and EGFP. Edit % indicates A→I edited read %. All conditions represent data from n=3 technical replicates. [Figure 11E]Graphical illustration of normalized (A, C, E, G) and non-normalized (B, D, F, H) fluorescence values ​​for target and ADAR sensor combinations as examined in HEK293FT cells. Test targets were iRFP (A, B), eGFP (C, D), neuropeptide Y (E, F), and dCas9 (G, H). (A, C, E, G): Fold change in sensor fluorescence ratio (mNeon / mCherry) representing the target condition normalized to the target-free condition for each target and sensor combination. (B, D, F, H): Non-normalized mNeon / mCherry fluorescence ratio values ​​for each sensor and target combination with different ADAR variants. Next generation sequencing data of RNA sensor for UAG to UIG conversion for targets iRFP and EGFP. Edit % indicates A→I edited read %. All conditions represent data from n=3 technical replicates. [Figure 11F] Graphical illustration of normalized (A, C, E, G) and non-normalized (B, D, F, H) fluorescence values ​​for target and ADAR sensor combinations as examined in HEK293FT cells. Test targets were iRFP (A, B), eGFP (C, D), neuropeptide Y (E, F), and dCas9 (G, H). (A, C, E, G): Fold change in sensor fluorescence ratio (mNeon / mCherry) representing the target condition normalized to the target-free condition for each target and sensor combination. (B, D, F, H): Non-normalized mNeon / mCherry fluorescence ratio values ​​for each sensor and target combination with different ADAR variants. Next generation sequencing data of RNA sensor for UAG to UIG conversion for targets iRFP and EGFP. Edit % indicates A→I edited read %. All conditions represent data from n=3 technical replicates. [Figure 11G]Graphical illustration of normalized (A, C, E, G) and non-normalized (B, D, F, H) fluorescence values ​​for target and ADAR sensor combinations as examined in HEK293FT cells. Test targets were iRFP (A, B), eGFP (C, D), neuropeptide Y (E, F), and dCas9 (G, H). (A, C, E, G): Fold change in sensor fluorescence ratio (mNeon / mCherry) representing the target condition normalized to the target-free condition for each target and sensor combination. (B, D, F, H): Non-normalized mNeon / mCherry fluorescence ratio values ​​for each sensor and target combination with different ADAR variants. Next generation sequencing data of RNA sensor for UAG to UIG conversion for targets iRFP and EGFP. Edit % indicates A→I edited read %. All conditions represent data from n=3 technical replicates. [Figure 11H] Graphical illustration of normalized (A, C, E, G) and non-normalized (B, D, F, H) fluorescence values ​​for target and ADAR sensor combinations as examined in HEK293FT cells. Test targets were iRFP (A, B), eGFP (C, D), neuropeptide Y (E, F), and dCas9 (G, H). (A, C, E, G): Fold change in sensor fluorescence ratio (mNeon / mCherry) representing the target condition normalized to the target-free condition for each target and sensor combination. (B, D, F, H): Non-normalized mNeon / mCherry fluorescence ratio values ​​for each sensor and target combination with different ADAR variants. Next generation sequencing data of RNA sensor for UAG to UIG conversion for targets iRFP and EGFP. Edit % indicates A→I edited read %. All conditions represent data from n=3 technical replicates. [Figure 12A]Representative images of full-screen 10x images with insets without target (FIG. 12A) or with target (FIG. 12B) for the ADAR p150 image shown in FIG. 10B. Scale bar is 100 μm. [Figure 12B] Representative images of full-screen 10x images with insets without target (FIG. 12A) or with target (FIG. 12B) for the ADAR p150 image shown in FIG. 10B. Scale bar is 100 μm. [Figure 13A] Graphical comparison of normalized luciferase values ​​of the sensor panel in targeted and non-targeted groups for (A) exogenous supplementation with MCP-ADAR2dd, (B) exogenous supplementation with ADAR1 p150 isoform, (C) exogenous supplementation with ADAR2, and (D) no exogenous ADAR supplementation. [Figure 13B] Graphical comparison of normalized luciferase values ​​of the sensor panel in targeted and non-targeted groups for (A) exogenous supplementation with MCP-ADAR2dd, (B) exogenous supplementation with ADAR1 p150 isoform, (C) exogenous supplementation with ADAR2, and (D) no exogenous ADAR supplementation. [Figure 13C] Graphical comparison of normalized luciferase values ​​of the sensor panel in targeted and non-targeted groups for (A) exogenous supplementation with MCP-ADAR2dd, (B) exogenous supplementation with ADAR1 p150 isoform, (C) exogenous supplementation with ADAR2, and (D) no exogenous ADAR supplementation. [Figure 13D] Graphical comparison of normalized luciferase values ​​of the sensor panel in targeted and non-targeted groups for (A) exogenous supplementation with MCP-ADAR2dd, (B) exogenous supplementation with ADAR1 p150 isoform, (C) exogenous supplementation with ADAR2, and (D) no exogenous ADAR supplementation. [Figure 14]Heat map of titration of exogenously supplemented MCP-ADAR2dd sensor versus IL6-targeted sensor against 20 ng of tetracycline-inducible human IL6 transgene transfected into IL6. Fold change represents normalized luciferase ratio between target and non-target groups. [Figure 15] 15 is a graphical illustration of the fold increase in luciferase expression for guide strand 1 and guide strand 2 targeted to EGFP compared to a scrambled guide strand designed not to specifically target EGFP. Each guide strand was introduced into HEK293T cells. Cells were then examined using Cas13b (Programmable A to I(G) Replacement RNA Editing (REPAIR)), an enzyme fused to the deaminase domain of ADAR2, or its catalytically inactive form (REPAIR K370A). Guide strands were examined in cells containing only endogenous ADAR2 (FIG. 15, blue bar), cells containing endogenous ADAR2 in addition to exogenous catalytically inactive REPAIR molecules (REPAIR K370A; FIG. 15, white bar), or cells containing catalytically active REPAIR molecules (REPAIR; FIG. 15, red bar). [Figure 16] Heatmap showing fold increase in luciferase activation (white: lowest fold increase to dark blue: highest fold increase) when examined in HEK293FT cells. The y-axis presents guide strands targeting EGFP with several different designs, varying in length and mismatch. The x-axis presents the exogenous ADAR molecules tested (none=endogenous only; ADAR2 fl=full length ADAR2, REPAIR=Cas13b enzyme fused to the deaminase domain of ADAR2; MS2-ADAR2dd=MS2 binding protein fused to ADAR2dd; dDisCas7-11-ADAR2dd=catalytically inactive Cas7-11 fused to the deaminase domain of ADAR2). [Figure 17A](A) Schematic diagram showing sensors of different lengths screened against the target iRFP transcript. (B) Bar graph showing increasing sensor activation with increasing sensor length. Sensor activation refers to the normalized fluorescence (mNeon / mCherry) value in the presence of target divided by the normalized fluorescence (mNeon / mCherry) value in the absence of target for each sensor. (C) mNeon positive cell fraction indicates the proportion of cells with expression higher than a given threshold. All conditions represent data from n=3 technical replicates. [Figure 17B] (A) Schematic diagram showing sensors of different lengths screened against the target iRFP transcript. (B) Bar graph showing increasing sensor activation with increasing sensor length. Sensor activation refers to the normalized fluorescence (mNeon / mCherry) value in the presence of target divided by the normalized fluorescence (mNeon / mCherry) value in the absence of target for each sensor. (C) mNeon positive cell fraction indicates the proportion of cells with expression higher than a given threshold. All conditions represent data from n=3 technical replicates. [Figure 17C] (A) Schematic diagram showing sensors of different lengths screened against the target iRFP transcript. (B) Bar graph showing increasing sensor activation with increasing sensor length. Sensor activation refers to the normalized fluorescence (mNeon / mCherry) value in the presence of target divided by the normalized fluorescence (mNeon / mCherry) value in the absence of target for each sensor. (C) mNeon positive cell fraction indicates the proportion of cells with expression higher than a given threshold. All conditions represent data from n=3 technical replicates. [Figure 18A]Single cell image analysis similar to fluorescence cytometry for iRFP-targeting ADAR sensor with guide lengths of 69, 249 and 600 nucleotides. Histograms show population density of mNeon expression across all cells for conditions with targeted iRFP (blue) and without targeted iRFP (pink). Dotted lines indicate intensity thresholds that are constant across all conditions to gate individual cells as mNeon (+) or mNeon (-). Colored boxes show % mNeon positive cells for conditions with targeted iRFP (blue) and without targeted iRFP (pink). (B) Representative images are shown for (A). Cells were transfected with combinations of targeted iRFP, ADAR p150 and different ADAR sensor guide lengths, as listed around the image. Scale bar: 100 microns. [Figure 18B] Single cell image analysis similar to fluorescence cytometry for iRFP-targeting ADAR sensor with guide lengths of 69, 249 and 600 nucleotides. Histograms show population density of mNeon expression across all cells for conditions with targeted iRFP (blue) and without targeted iRFP (pink). Dotted lines indicate intensity thresholds that are constant across all conditions to gate individual cells as mNeon (+) or mNeon (-). Colored boxes show % mNeon positive cells for conditions with targeted iRFP (blue) and without targeted iRFP (pink). (B) Representative images are shown for (A). Cells were transfected with combinations of targeted iRFP, ADAR p150 and different ADAR sensor guide lengths, as listed around the image. Scale bar: 100 microns. [Figure 19A] Representative image of a full-screen 10x image with an inset for the image shown in Figure 18B. Scale bar is 100 μm. [Figure 19B] Representative image of a full-screen 10x image with an inset for the image shown in Figure 18B. Scale bar is 100 μm. [Figure 20] 13 is a graphical comparison of different exogenously recruited ADAR variants on an IL6-targeted sensor transiently transfected with a tetracycline-inducible human IL6 transgene. [Figure 21A] A series of graphs showing normalized luciferase values ​​of the sensor panel in targeted and non-targeted groups for (A) exogenous supplementation with MCP-ADAR2dd(E488Q, T490A), (B) exogenous supplementation with ADAR1 p150 isoform, (C) exogenous supplementation with ADAR2, and (D) no exogenous ADAR supplementation. [Figure 21B] A series of graphs showing normalized luciferase values ​​of the sensor panel in targeted and non-targeted groups for (A) exogenous supplementation with MCP-ADAR2dd(E488Q, T490A), (B) exogenous supplementation with ADAR1 p150 isoform, (C) exogenous supplementation with ADAR2, and (D) no exogenous ADAR supplementation. [Figure 21C] A series of graphs showing normalized luciferase values ​​of the sensor panel in targeted and non-targeted groups for (A) exogenous supplementation with MCP-ADAR2dd(E488Q, T490A), (B) exogenous supplementation with ADAR1 p150 isoform, (C) exogenous supplementation with ADAR2, and (D) no exogenous ADAR supplementation. [Figure 21D] A series of graphs showing normalized luciferase values ​​of the sensor panel in targeted and non-targeted groups for (A) exogenous supplementation with MCP-ADAR2dd(E488Q, T490A), (B) exogenous supplementation with ADAR1 p150 isoform, (C) exogenous supplementation with ADAR2, and (D) no exogenous ADAR supplementation. [Figure 22]1 is a graphical illustration of a comparison between sensors containing a conventional guide and sensors containing a guide containing multiple binding sites and an MS2 hairpin loop for the target human IL6 with endogenous ADAR1, exogenously supplemented ADAR1 p150 isoform, full-length ADAR2, or MCP-ADAR2dd (E488Q, T490A) in HEK293 cells. Fold changes are calculated by normalizing luciferase values ​​(Gluc / Cluc) of the target condition to the non-target condition. [Figure 23] 1 is a visual representation of the engineering of the ADAR sensor with an MS2 hairpin loop and avidity region. The addition of the MS2 hairpin loop and avidity enhances the sensitivity and dynamic range of the ADAR sensor. [Figure 24-1] Schematic diagram of the step-by-step creation of a three-avidity ADAR sensor with 5 nucleotide spacing between the avidity guide regions. [Figure 24-2] Schematic diagram of the step-by-step creation of a three-avidity ADAR sensor with 5 nucleotide spacing between the avidity guide regions. [Figure 25A] (A) Schematic of the variation of linker length outside the target region. (B) Graphical representation of the effect of linker length between avidity regions. Linker lengths of 5, 30 and 50 nucleotides between the avidity regions of the MS2 hairpin-connected 5-avidity sensor for IL6 were investigated. [Figure 25B] (A) Schematic of the variation of linker length outside the target region. (B) Graphical representation of the effect of linker length between avidity regions. Linker lengths of 5, 30 and 50 nucleotides between the avidity regions of the MS2 hairpin-connected 5-avidity sensor for IL6 were investigated. [Figure 26A](A) Schematic diagram of the double / single stop codon avidity / MS2 hairpin sensor. (B) Comparison of sensor activation fold between a conventional MS2 hairpin connected 7 avidity sensor and a double stop codon 7 avidity sensor with the insertion of a 3' downstream stop codon in the most posterior avidity region. [Figure 26B] (A) Schematic diagram of the double / single stop codon avidity / MS2 hairpin sensor. (B) Comparison of sensor activation fold between a conventional MS2 hairpin connected 7 avidity sensor and a double stop codon 7 avidity sensor with the insertion of a 3' downstream stop codon in the most posterior avidity region. [Figure 27A] (A) Comparison of background vs. activation for avidity sensors versus naive ("long") sensors. (B) Scatter plot of fold change versus background luciferase values ​​for avidity sensors versus naive ("long") sensors. [Figure 27B] (A) Comparison of background vs. activation for avidity sensors versus naive ("long") sensors. (B) Scatter plot of fold change versus background luciferase values ​​for avidity sensors versus naive ("long") sensors. [Figure 28A] (A) Bar graph showing a comparison of the 5-binding site avidity sensor versus the 7-binding site avidity double stop codon sensor across MCP-ADAR2dd (E488Q, T490A) and ADAR1 p150. (B) Bar graph showing a comparison of the activation signal versus the background signal for the 7-binding site avidity single stop codon sensor versus the 7-binding site avidity double stop codon sensor. [Figure 28B](A) Bar graph showing a comparison of the 5-binding site avidity sensor versus the 7-binding site avidity double stop codon sensor across MCP-ADAR2dd (E488Q, T490A) and ADAR1 p150. (B) Bar graph showing a comparison of the activation signal versus the background signal for the 7-binding site avidity single stop codon sensor versus the 7-binding site avidity double stop codon sensor. [Figure 29A] Comparison of target mismatch tolerance across all 16 possible mismatches (16 targets containing 5' or 3' nucleotide changes from conventional CCA) between the naive 51 bp sensor, 3 avidity type sensor designs, and 5 avidity type sensor designs. (A) Schematic representation of mismatch tolerance. (B) Heatmap showing log activation fold (blue) and log10 normalized tolerance (red) of different target mismatches compared to the native CCA target for all three sensor designs across the 16 target mismatches. [Figure 29B] Comparison of target mismatch tolerance across all 16 possible mismatches (16 targets containing 5' or 3' nucleotide changes from conventional CCA) between the naive 51 bp sensor, 3 avidity type sensor designs, and 5 avidity type sensor designs. (A) Schematic representation of mismatch tolerance. (B) Heatmap showing log activation fold (blue) and log10 normalized tolerance (red) of different target mismatches compared to the native CCA target for all three sensor designs across the 16 target mismatches. [Diagram 30] 13 is a heat map showing the normalized preference of sensor designs among each target mismatch combination between the naive 51 bp sensor, the triple-binding site type sensor, and the five-binding site type sensor. [Figure 31A](A) Visual representation of the creation and activation of circular sensors. A conventional circular sensor is created with a twister ribozyme backbone driven by a U6 promoter for self-circularization in vitro. Self-circularization of the sensor-HiBit tag utilizes mammalian cell RtcB ligase. A rolling circle translation form of the circular sensor is created by deleting a stop codon at the C-terminus of the HiBit protein and inserting a T2A peptide that allows for cyclic read-through of the ribosome. (B) Sensors of various lengths, between 50 and 120 nucleotides, are compared for fold change in sensor activation upon induction of the transgene target (human IL6). [Figure 31B] (A) Visual representation of the creation and activation of circular sensors. A conventional circular sensor is created with a twister ribozyme backbone driven by a U6 promoter for self-circularization in vitro. Self-circularization of the sensor-HiBit tag utilizes mammalian cell RtcB ligase. A rolling circle translation form of the circular sensor is created by deleting a stop codon at the C-terminus of the HiBit protein and inserting a T2A peptide that allows for cyclic read-through of the ribosome. (B) Sensors of various lengths, between 50 and 120 nucleotides, are compared for fold change in sensor activation upon induction of the transgene target (human IL6). [Figure 32A] (A) Schematic diagram of assessed RNA modifications. (B) Heat map comparing different mRNA modifications for synthetic mRNA ADAR sensor detecting IL6 transgene expression in HEK293FT cells supplemented with MCP-ADAR2dd(E488Q, T490A) by transient transfection of plasmid 24 hours prior to mRNA sensor transfection. (C) Heat map comparing different mRNA modifications for synthetic mRNA ADAR sensor detecting IL6 transgene expression in HEK293FT cells supplemented with MCP-ADAR2dd(E488Q, T490A) mRNA at the time of sensor transfection. [Figure 32B] (A) Schematic diagram of assessed RNA modifications. (B) Heat map comparing different mRNA modifications for synthetic mRNA ADAR sensor detecting IL6 transgene expression in HEK293FT cells supplemented with MCP-ADAR2dd(E488Q, T490A) by transient transfection of plasmid 24 hours prior to mRNA sensor transfection. (C) Heat map comparing different mRNA modifications for synthetic mRNA ADAR sensor detecting IL6 transgene expression in HEK293FT cells supplemented with MCP-ADAR2dd(E488Q, T490A) mRNA at the time of sensor transfection. [Figure 32C] (A) Schematic diagram of assessed RNA modifications. (B) Heat map comparing different mRNA modifications for synthetic mRNA ADAR sensor detecting IL6 transgene expression in HEK293FT cells supplemented with MCP-ADAR2dd(E488Q, T490A) by transient transfection of plasmid 24 hours prior to mRNA sensor transfection. (C) Heat map comparing different mRNA modifications for synthetic mRNA ADAR sensor detecting IL6 transgene expression in HEK293FT cells supplemented with MCP-ADAR2dd(E488Q, T490A) mRNA at the time of sensor transfection. [Figure 33A] Graphical illustrations depicting EGFP expression (FIG. 33A, 6B) and fold increase in GFP expression (FIG. 33C) in HEK293FT cells. Expression of EGFP was constitutively expressed or expressed as a gradient using a doxycycline-inducible EGFP construct. HEK203T cells were then exposed to doxycycline at concentrations ranging from 8 ng / mL to 200 ng / mL. [Figure 33B]Graphical illustrations depicting EGFP expression (FIG. 33A, 6B) and fold increase in GFP expression (FIG. 33C) in HEK293FT cells. Expression of EGFP was constitutively expressed or expressed as a gradient using a doxycycline-inducible EGFP construct. HEK203T cells were then exposed to doxycycline at concentrations ranging from 8 ng / mL to 200 ng / mL. [Figure 33C] Graphical illustrations depicting EGFP expression (FIG. 33A, 6B) and fold increase in GFP expression (FIG. 33C) in HEK293FT cells. Expression of EGFP was constitutively expressed or expressed as a gradient using a doxycycline-inducible EGFP construct. HEK203T cells were then exposed to doxycycline at concentrations ranging from 8 ng / mL to 200 ng / mL. [Figure 34A] Graphical illustration depicting dose-dependent luciferase activity of guide strand 1 (A) and guide strand 3 (B) as a function of doxycycline dose in HEK293FT cells simultaneously exposed to full-length ADAR2 and a guide strand targeting EGFP under the control of a doxycycline-inducible promoter. [Figure 34B] Graphical illustration depicting dose-dependent luciferase activity of guide strand 1 (A) and guide strand 3 (B) as a function of doxycycline dose in HEK293FT cells simultaneously exposed to full-length ADAR2 and a guide strand targeting EGFP under the control of a doxycycline-inducible promoter. [Figure 35A] Graphical illustration depicting the luciferase activity of guide strand 1 (A) and guide strand 3 (B) as a function of GFP fluorescence in HEK293FT cells simultaneously exposed to full-length ADAR2 and a guide strand targeting EGFP under the control of a doxycycline-inducible promoter. [Figure 35B]Graphical illustration depicting the luciferase activity of guide strand 1 (A) and guide strand 3 (B) as a function of GFP fluorescence in HEK293FT cells simultaneously exposed to full-length ADAR2 and a guide strand targeting EGFP under the control of a doxycycline-inducible promoter. [Figure 36A] (B) A visual representation of the results of combined treatment with tetracycline-inducible IL6 and stable lentiviral integration. The relative expression of IL6 was then quantified using a double stop codon 7 avidity IL6 sensor, and the corresponding luciferase fold change is plotted against the Cq value for IL6 expression detected by quantitative polymerase chain reaction (QCPR). [Figure 36B] (B) A visual representation of the results of combined treatment with tetracycline-inducible IL6 and stable lentiviral integration. The relative expression of IL6 was then quantified using a double stop codon 7 avidity IL6 sensor, and the corresponding luciferase fold change is plotted against the Cq value for IL6 expression detected by quantitative polymerase chain reaction (QCPR). [Figure 37] Scatter plot showing that the double stop codon 7 avidity type IL6 sensor is useful for quantifying the relative expression of IL6 with a large dynamic range. Target expression range is created through the combination of tetracycline-inducible IL6 transient overexpression and stable integration of tetracycline-IL6 cassette into HEK293FT cells by lentivirus. The fold change of ADAR sensor compared to basal state is plotted against the change in IL6 gene expression as determined by quantitative polymerase chain reaction (qPCR). [Figure 38] FIG. 11 is a scatter plot displaying the linear regression of the fold change in sensor activation against the fold change in gene expression detected by qPCR. [Figure 39] Corresponding to FIG. 38, adenosine editing within the sensor stop codon UAG across different IL6 gene expression levels. [Figure 40A](A) Schematic representation of the AND gate, (B) Schematic representation of the OR gate, (C) Graphical illustration comparing activation fold change of naive 51 nucleotide-guided and 5 avidity-guided AND gate sensors across all four combinations of targeted IL6 / EGFP induction. [Figure 40B] (A) Schematic representation of the AND gate, (B) Schematic representation of the OR gate, (C) Graphical illustration comparing activation fold change of naive 51 nucleotide-guided and 5 avidity-guided AND gate sensors across all four combinations of targeted IL6 / EGFP induction. [Figure 40C] (A) Schematic representation of the AND gate, (B) Schematic representation of the OR gate, (C) Graphical illustration comparing activation fold change of naive 51 nucleotide-guided and 5 avidity-guided AND gate sensors across all four combinations of targeted IL6 / EGFP induction. [Figure 41A] (A) Graphical illustration of normalized sensor activation of an AND-gated ADAR sensor for EGFP / IL6 transcript input across all four possible target combinations (B) Graphical illustration of normalized sensor activation of an OR-gated ADAR sensor for EGFP / IL6 transcript input across different target combinations. [Figure 41B] (A) Graphical illustration of normalized sensor activation of an AND-gated ADAR sensor for EGFP / IL6 transcript input across all four possible target combinations (B) Graphical illustration of normalized sensor activation of an OR-gated ADAR sensor for EGFP / IL6 transcript input across different target combinations. [Figure 42A](A) Schematic diagram of IL6-responsive caspase using ADAR sensor with 5 avidity type sensor targeting human IL6 transcript. Activation of the sensor expresses FKBP, which causes caspase 9 to self-dimerize. (B) Graphical illustration of fold change in cell death (apoptosis) in response to activation of ADAR sensor by detection of IL6 transcript. Positive control sensor has scrambled guide sequence in front of iCaspase without stop codon in frame. Fold change in cell death is determined by calculating fold change in cell viability in target condition compared to no target condition. (C) Bar graph comparing percent cell viability values ​​by ADAR sensor and no stop codon control for IL6-responsive iCaspase with target and no target group. [Figure 42B] (A) Schematic diagram of IL6-responsive caspase using ADAR sensor with 5 avidity type sensor targeting human IL6 transcript. Activation of the sensor expresses FKBP, which causes caspase 9 to self-dimerize. (B) Graphical illustration of fold change in cell death (apoptosis) in response to activation of ADAR sensor by detection of IL6 transcript. Positive control sensor has scrambled guide sequence in front of iCaspase without stop codon in frame. Fold change in cell death is determined by calculating fold change in cell viability in target condition compared to no target condition. (C) Bar graph comparing percent cell viability values ​​by ADAR sensor and no stop codon control for IL6-responsive iCaspase with target and no target group. [Figure 42C](A) Schematic diagram of IL6-responsive caspase using ADAR sensor with 5 avidity type sensor targeting human IL6 transcript. Activation of the sensor expresses FKBP, which causes caspase 9 to self-dimerize. (B) Graphical illustration of fold change in cell death (apoptosis) in response to activation of ADAR sensor by detection of IL6 transcript. Positive control sensor has scrambled guide sequence in front of iCaspase without stop codon in frame. Fold change in cell death is determined by calculating fold change in cell viability in target condition compared to no target condition. (C) Bar graph comparing percent cell viability values ​​by ADAR sensor and no stop codon control for IL6-responsive iCaspase with target and no target group. [Figure 43A] Visual representation of an experiment investigating the efficiency of ADAR sensors in heat shock assays. (A) Heat shock to Hela cells at 42°C is used to induce upregulation of HSP40 / HSP70 gene expression. Hela cells are transfected with HSP40 / HSP70 targeting sensors alone or with MCP-ADAR2dd (E488Q, T490A) and then maintained at 42°C or 37°C for 24 hours. (B) qPCR verification of upregulated HSP40 / HSP70 levels after 24 hours of heat shock. (C) Sensor activation is calculated between 42°C and 37°C groups and normalized to sensors with scrambled non-targeting guides to account for changes in protein degradation. [Figure 43B]Visual representation of an experiment investigating the efficiency of ADAR sensors in heat shock assays. (A) Heat shock to Hela cells at 42°C is used to induce upregulation of HSP40 / HSP70 gene expression. Hela cells are transfected with HSP40 / HSP70 targeting sensors alone or with MCP-ADAR2dd (E488Q, T490A) and then maintained at 42°C or 37°C for 24 hours. (B) qPCR verification of upregulated HSP40 / HSP70 levels after 24 hours of heat shock. (C) Sensor activation is calculated between 42°C and 37°C groups and normalized to sensors with scrambled non-targeting guides to account for changes in protein degradation. [Figure 43C] Visual representation of an experiment investigating the efficiency of ADAR sensors in heat shock assays. (A) Heat shock to Hela cells at 42°C is used to induce upregulation of HSP40 / HSP70 gene expression. Hela cells are transfected with HSP40 / HSP70 targeting sensors alone or with MCP-ADAR2dd (E488Q, T490A) and then maintained at 42°C or 37°C for 24 hours. (B) qPCR verification of upregulated HSP40 / HSP70 levels after 24 hours of heat shock. (C) Sensor activation is calculated between 42°C and 37°C groups and normalized to sensors with scrambled non-targeting guides to account for changes in protein degradation. [Figure 44A]Visual representation of the analysis of SERPINA1 in three cell types that differentially express SERPINA1. (A) Bar graph comparing SERPINA1 expression across HEK293FT, HepG2 and Hela cells. (B) SERPINA1 sensing 5 avidity type sensor is transfected into three different cell types (HEK293FT, Hela and HepG2 cells) with or without exogenous MCP-ADAR2dd (E488Q, T490A). (C) Bar graph comparing fold change in sensor activation between Hela and HepG2 cells across SERPINA1 sensors targeting different CCA sites. (D) Sensor activation is determined by calculating raw luciferase values ​​from the SERPINA1 sensor normalized to the scrambled non-targeting guide sensor to account for differences in protein production / secretion and background ADAR activity between cell types, followed by normalization to the gluc / cluc ratio in HEK293FT cells. [Figure 44B] Visual representation of the analysis of SERPINA1 in three cell types that differentially express SERPINA1. (A) Bar graph comparing SERPINA1 expression across HEK293FT, HepG2 and Hela cells. (B) SERPINA1 sensing 5 avidity type sensor is transfected into three different cell types (HEK293FT, Hela and HepG2 cells) with or without exogenous MCP-ADAR2dd (E488Q, T490A). (C) Bar graph comparing fold change in sensor activation between Hela and HepG2 cells across SERPINA1 sensors targeting different CCA sites. (D) Sensor activation is determined by calculating raw luciferase values ​​from the SERPINA1 sensor normalized to the scrambled non-targeting guide sensor to account for differences in protein production / secretion and background ADAR activity between cell types, followed by normalization to the gluc / cluc ratio in HEK293FT cells. [Figure 44C] Visual representation of the analysis of SERPINA1 in three cell types that differentially express SERPINA1. (A) Bar graph comparing SERPINA1 expression across HEK293FT, HepG2 and Hela cells. (B) SERPINA1 sensing 5 avidity type sensor is transfected into three different cell types (HEK293FT, Hela and HepG2 cells) with or without exogenous MCP-ADAR2dd (E488Q, T490A). (C) Bar graph comparing fold change in sensor activation between Hela and HepG2 cells across SERPINA1 sensors targeting different CCA sites. (D) Sensor activation is determined by calculating raw luciferase values ​​from the SERPINA1 sensor normalized to the scrambled non-targeting guide sensor to account for differences in protein production / secretion and background ADAR activity between cell types, followed by normalization to the gluc / cluc ratio in HEK293FT cells. [Fig.44D]Visual representation of the analysis of SERPINA1 in three cell types that differentially express SERPINA1. (A) Bar graph comparing SERPINA1 expression across HEK293FT, HepG2 and Hela cells. (B) SERPINA1 sensing 5 avidity type sensor is transfected into three different cell types (HEK293FT, Hela and HepG2 cells) with or without exogenous MCP-ADAR2dd (E488Q, T490A). (C) Bar graph comparing fold change in sensor activation between Hela and HepG2 cells across SERPINA1 sensors targeting different CCA sites. (D) Sensor activation is determined by calculating raw luciferase values ​​from the SERPINA1 sensor normalized to the scrambled non-targeting guide sensor to account for differences in protein production / secretion and background ADAR activity between cell types, followed by normalization to the gluc / cluc ratio in HEK293FT cells. [Diagram 45] FIG. 13 is a graphical representation of normalized fold change in editing rates in five avidity sensors for SERPINA1 in different cell types (HEK293, Hela, and HepG2). [Diagram 46] 13 is a bar graph comparing the fold change in mRNA SERPINA1 sensor activation targeting different CCA sites on the SERPINA1 transcript in transiently transfected tetracycline induced SERPINA1 expression Hepa1-6 cells. [Figure 47A](A) Schematic illustration of an in vivo sensing experiment for human SERPINA1 transcript using mADAR sensor construct for SERPINA1. SERPINA1 targeting sensor mRNA with Akaluc output is made in vitro with 25% 5-methylcytosine and 0% pseudouridine. Constitutive Akaluc sensor construct (without stop codon) and non-targeting guide sensor construct (with stop codon) are synthesized by the same protocol. All mRNAs are packaged with lipid nanoparticles and tail-vein injected into wild-type or NSG-Piz mice with human SERPINA1 PiZ mutant cassette. In vivo sensor activation is measured 18 hours after injection. (B) Representative images of sensor activation are presented for various synthetic mRNA ADAR sensor constructs. [Figure 47B] (A) Schematic illustration of an in vivo sensing experiment for human SERPINA1 transcript using mADAR sensor construct for SERPINA1. SERPINA1 targeting sensor mRNA with Akaluc output is made in vitro with 25% 5-methylcytosine and 0% pseudouridine. Constitutive Akaluc sensor construct (without stop codon) and non-targeting guide sensor construct (with stop codon) are synthesized by the same protocol. All mRNAs are packaged with lipid nanoparticles and tail-vein injected into wild-type or NSG-Piz mice with human SERPINA1 PiZ mutant cassette. In vivo sensor activation is measured 18 hours after injection. (B) Representative images of sensor activation are presented for various synthetic mRNA ADAR sensor constructs. [Figure 48A](A) Graphical illustration of Akaluc-generated radiance calculated for liver and compared between wild-type and NSG-PiZ mutant mice. Fold change between NGS-PiZ and WT mice is calculated for each ADAR sensor construct. Significance is determined via two-tailed t-test, N=2 mice. *: p<0.05. p-values ​​less than 0.05 are indicated by an asterisk for statistical significance. (B) Bar graph comparing Akaluc-generated radiance in NSG-PiZ and WT mice across non-targeted, constitutive, SERPINA1 CCA35-targeted and SERPINA1 CCA30-targeted sensors. [Figure 48B] (A) Graphical illustration of Akaluc-generated radiance calculated for liver and compared between wild-type and NSG-PiZ mutant mice. Fold change between NGS-PiZ and WT mice is calculated for each ADAR sensor construct. Significance is determined via two-tailed t-test, N=2 mice. *: p<0.05. p-values ​​less than 0.05 are indicated by an asterisk for statistical significance. (B) Bar graph comparing Akaluc-generated radiance in NSG-PiZ and WT mice across non-targeted, constitutive, SERPINA1 CCA35-targeted and SERPINA1 CCA30-targeted sensors. [Figure 49A] Graphical illustration of differential gene analysis for 37 tissues using the Human Protein Atlas and GTEX datasets for the minimum number of genes required to classify tissues according to gene (A) as well as the number of protein-coding genes enriched in specific tissues, enhanced in specific tissues, or less specific (B). (C) Heatmap showing the relative transcript abundance of 34 tissue-specific mRNAs across 34 different tissue types. [Figure 49B]Graphical illustration of differential gene analysis for 37 tissues using the Human Protein Atlas and GTEX datasets for the minimum number of genes required to classify tissues according to gene (A) as well as the number of protein-coding genes enriched in specific tissues, enhanced in specific tissues, or less specific (B). (C) Heatmap showing the relative transcript abundance of 34 tissue-specific mRNAs across 34 different tissue types. [Figure 49C] Graphical illustration of differential gene analysis for 37 tissues using the Human Protein Atlas and GTEX datasets for the minimum number of genes required to classify tissues according to gene (A) as well as the number of protein-coding genes enriched in specific tissues, enhanced in specific tissues, or less specific (B). (C) Heatmap showing the relative transcript abundance of 34 tissue-specific mRNAs across 34 different tissue types. [Figure 50A] Graphical illustrations showing the characterization of RADARS safety with respect to immune response and endogenous RNA knockdown. (A, B) Effect of sensor-target duplex formation on the innate antiviral pathway. RADARS sensors were transfected in the presence or absence of complementary target sequences. Total RNA was analyzed using quantitative PCR (qPCR) to determine relative expression levels of MDA5 (A) and IFN-β (B). (C, D) Effect of sensor-target duplex formation on the abundance of endogenous target transcripts. Relative abundance of NEFM and PPIP transcripts upon transfection of complementary or non-targeting RADARS sensors was assessed by qPCR. Data are presented as mean ± sd (n = 4); non-significant by unpaired two-tailed Student's t-test: p > 0.05. [Figure 50B]Graphical illustrations showing the characterization of RADARS safety with respect to immune response and endogenous RNA knockdown. (A, B) Effect of sensor-target duplex formation on the innate antiviral pathway. RADARS sensors were transfected in the presence or absence of complementary target sequences. Total RNA was analyzed using quantitative PCR (qPCR) to determine relative expression levels of MDA5 (A) and IFN-β (B). (C, D) Effect of sensor-target duplex formation on the abundance of endogenous target transcripts. Relative abundance of NEFM and PPIP transcripts upon transfection of complementary or non-targeting RADARS sensors was assessed by qPCR. Data are presented as mean ± sd (n = 4); non-significant by unpaired two-tailed Student's t-test: p > 0.05. [Figure 50C] Graphical illustrations showing the characterization of RADARS safety with respect to immune response and endogenous RNA knockdown. (A, B) Effect of sensor-target duplex formation on the innate antiviral pathway. RADARS sensors were transfected in the presence or absence of complementary target sequences. Total RNA was analyzed using quantitative PCR (qPCR) to determine relative expression levels of MDA5 (A) and IFN-β (B). (C, D) Effect of sensor-target duplex formation on the abundance of endogenous target transcripts. Relative abundance of NEFM and PPIP transcripts upon transfection of complementary or non-targeting RADARS sensors was assessed by qPCR. Data are presented as mean ± sd (n = 4); non-significant by unpaired two-tailed Student's t-test: p > 0.05. [Figure 50D]Graphical illustrations showing the characterization of RADARS safety with respect to immune response and endogenous RNA knockdown. (A, B) Effect of sensor-target duplex formation on the innate antiviral pathway. RADARS sensors were transfected in the presence or absence of complementary target sequences. Total RNA was analyzed using quantitative PCR (qPCR) to determine relative expression levels of MDA5 (A) and IFN-β (B). (C, D) Effect of sensor-target duplex formation on the abundance of endogenous target transcripts. Relative abundance of NEFM and PPIP transcripts upon transfection of complementary or non-targeting RADARS sensors was assessed by qPCR. Data are presented as mean ± sd (n = 4); non-significant by unpaired two-tailed Student's t-test: p > 0.05. [Figure 51A] Graphical illustration showing the decay of RADARS signal corresponding to endogenous target knockdown. (A) Schematic diagram of endogenous transcript knockdown by siRNA. (B) qPCR and fluorescent RADARS detected differential expression of siRNA targeting PPIB or NEFM versus control non-targeting siRNA in HEK293FT cells. RADAR sensor activation was calculated for siRNA targeting and normalized to control siRNA. Data are mean ± sd of technical replicates (n > 3). [Figure 51B] Graphical illustration showing the decay of RADARS signal corresponding to endogenous target knockdown. (A) Schematic diagram of endogenous transcript knockdown by siRNA. (B) qPCR and fluorescent RADARS detected differential expression of siRNA targeting PPIB or NEFM versus control non-targeting siRNA in HEK293FT cells. RADAR sensor activation was calculated for siRNA targeting and normalized to control siRNA. Data are mean ± sd of technical replicates (n > 3). [Figure 52]Graphical comparison of fold activation of engineered guide RNAs for #CCA8 IL6 with a 171 nucleotide guide and four MS2 loops when used with exogenously supplemented ADAR1p150 or endogenous ADAR. Data are mean ± sem of technical replicates (n = 3). [Figure 53A] Figure 53A is a graphical comparison of fold activation of mRNA RADARS sensor activation in detecting IL6 transcripts following transfection of ADAR1p150 with plasmid. Synthetic mRNA sensors are synthesized with different chemically modified bases at different incorporation levels ranging from 0 to 100%. Data are means ± sem of technical replicates (n = 3). [Figure 53B] Figure 53B is a graphical comparison of fold activation of mRNA RADARS activation in detecting IL6 transcripts that utilize endogenous ADAR when synthesized with different chemically modified bases at different incorporation levels ranging from 0 to 100%. Data are means ± sem of technical replicates (n = 3). [Figure 53C] Figure 53C is a graphical comparison of the induction of interferon beta responses resulting from transfection of mRNA RADARS. Synthetic mRNAs are synthesized with different levels of chemically modified bases, and the interferon response is measured by a plasmid (1 avidity Glo luciferase) reporter assay (Gentili et al., 2015). [Figure 54A]Figure 54A is a visual characterization of the evolution of different RADARS sensor designs when used with exogenous ADAR1p150 supplementation. The inset depicts the scaffold for the different RADARS designs. RADARS fold activation is calculated as the ratio of Gaussia luciferase (Gluc) luminescence to Cypridina luciferase (Cluc) constitutive luminescence in the presence of target IL6 compared to the absence of target (Gluc / Cluc) (see Methods). Sensor, target (IL6) and ADAR1p150 are co-delivered via transient transfection. Data are the mean ± sem of technical replicates (n = 3). [Figure 54B] Figure 54B is a graphical comparison of the Gluc / Cluc ratio between on-target and off-target conditions for #CCA8 IL6-targeting engineered guide RNAs with different lengths and different MS2 hairpin loops while retaining a 5' peptide length of 0 amino acids. Error bars indicate standard error of the mean (n=3 technical replicates). [Figure 55A] Figure 55A is a graphical comparison of the Gluc / Cluc ratio between targeted and untargeted conditions for 5-avidity binding site (4 MS2 loops) and 9-avidity binding site (8 MS2 loops) engineered guide RNAs with varying 5' peptide lengths. Error bars indicate standard error of the mean (n=3 technical replicates). [Figure 55B] Figure 55B is a graphical comparison of the Gluc / Cluc ratio between no out-of-frame stop codon and two out-of-frame stop codons added for 5 avidity binding site-based engineered guide RNAs with 200 amino acids of 5' peptide residues. The last column represents constitutive gluc driven under the Ef1-alpha promoter. Error bars indicate standard error of the mean (n=3 technical replicates). [Figure 56A]Figure 56A is a graphical depiction of the fold activation of IL6-, EGFP-, and NPY-targeting RADARS with supplementation of exogenous ADAR1p150. For each transcript, 12 engineered guide RNAs were engineered to target different CCA sites across the transcript. The depicted CCA site numbers follow the convention that #CCAx indicates the number of CCA triplicates counted from the 5' end of the transcript coding region. Each dot represents the average of three technical replicates for an individual sensor. The solid horizontal line represents the average value of all 12 engineered guide RNAs. [Figure 56B] Figure 56B graphically depicts the percentage editing of the target adenosine within the stop codon UAG for non-targeting engineered guide RNAs and 14 IL6-targeting engineered guide RNAs tiling the CCA site on IL6 using the RADARSv2 design with exogenous ADAR1p150 supplementation in the presence and absence of the targeted IL6 transcript. Error bars indicate standard error of the mean (n=3 technical replicates). [Figure 57] Figure 1 is a graphical depiction of RADARSv2 with engineered guide RNA targeting high TPM gene (RPS5), engineered guide RNA targeting low TPM gene (KRAS), or non-targeting scrambled sequence used with exogenous ADAR1p150 supplementation or endogenous ADAR to sense downregulation of their corresponding genes via gene-specific siRNA. Fold activation is calculated by the activation of payload in the targeting siRNA group below that of the non-targeting siRNA group. Data are the mean ± sem of technical replicates (n = 3). [Figure 58A]Figure 58A is a visual representation of a schematic showing a fluorescent output RADARS construct containing a constitutively expressed normalizing fluorescent protein (mCherry) (top) upstream of a RADARS-driven guide RNA that controls the mNeon fluorescent protein, and an image of a fluorescent RADARS showing HEK293FT cells expressing the mNeon payload only in the presence of a target transcript (out-of-frame EGFP). HEK293FT cells are transfected with EGFP-targeting RADARS, ADAR1p150, and with or without a target (out-of-frame EGFP) as indicated (bottom). Scale bar: 100 microns. [Figure 58B] FIG. 58B is a visual representation of flow cytometry analysis for fluorescent RADARS showing histograms of mNeon / mCherry fluorescence for HEK293FT cells transfected as in FIG. 58A, with beige and blue distributions indicating absence and presence of target, respectively. [Figure 59A] Figure 59A is a visual representation of the gating strategy used for flow cytometry analysis of fluorescent RADARS in HEK293 cells. Gates are set using a control population transfected with pUC19 plasmid. [Figure 59B] FIG. 59B is a visual representation of the gates placed on the population of cells transfected with EGFP-targeted RADARS, ADARp150 and pUC19 plasmids. [Figure 59C] FIG. 59C is a visual representation of the gates placed on the populations of cells transfected with EGFP-targeted RADARS, ADARp150 and EGFP target (frameshift) plasmids. [Figure 60A]Figure 60A is a visual depiction of fold activation of RADARS over basal conditions (0 ng / mL doxycycline in integrated HEK293FT cells) plotted against changes in IL6 gene expression as determined by quantitative polymerase chain reaction (qPCR) on a log10-log10 scale. The dashed blue line represents the linear regression results for the data. Data are the mean ± sem of technical replicates (n = 3). [Figure 60B] Figure 60B is a visual depiction of the RADARS corresponding raw Cq values ​​and fold activation of the IL6 transgene normalized to the Cq numbers of the GAPDH gene by subtraction. Error bars indicate the standard error of the mean (n=3 biological replicates). [Figure 60C] Figure 60C is a visual depiction of the effect of the best titration of RADARS engineered guide RNA sensor for IL6 under target conditions on the resulting activation and total protein production (gluc / cluc ratio). For conditions below 40ng, the remaining plasmid amount was replaced by pUC19 plasmid. Error bars indicate standard error of the mean (n=3 technical replicates). [Figure 61] Figure 1 shows a visual depiction of the results of a validation experiment of siRNA knockdown of 10 endogenous transcripts as measured by qPCR expression. Fold change is calculated by gene expression of the target transcript in the targeted siRNA group below that of the non-targeted siRNA group (n=3 biological replicates). [Figure 62A]Figure 62A (top) is a visual depiction of gene expression in transcripts per million (TPM) shown on a logarithmic scale across 10 genes ranging from 10,381 TPM (RSP5) to 13 TPM (KRAS). Bottom: Detection of transcripts by RADARSv2 in cells treated with 100 nM targeted or non-targeted siRNA pools. Bars represent fold activation (Gluc / Cluc ratio of RADARS in the targeted siRNA group compared to the non-targeted siRNA group) of targeted versus non-targeted RADARS constructs. Significance is determined via unpaired t-tests with Welch's correction assuming separate variances for each group between targeted and non-targeted RADARS (*: p<0.5; **: p<0.01; ***: p<0.001; ****: p<0.0001). [Figure 62B] Figure 62B is a graphical depiction of the editing rate of the stop codon UAG in the best performing sensor in each gene group according to Figure 62A. Unpaired one-tailed t-test was performed between the targeting siRNA group and the non-targeting siRNA group (*: p<0.05; **: p<0.01; ***: p<0.001; ****: p<0.0001). Error bars indicate the standard error of the mean (n=3 technical replicates). [Figure 63A] Figure 63A is a graphical depiction of the performance of 8 randomly selected engineered guide RNAs and 8 randomly selected non-targeting engineered guide RNAs targeting 10 endogenous transcripts. RADARS activation fold is calculated by the Gluc / Cluc ratio in the targeted siRNA group below the non-targeting siRNA group. The best performing targeted sensor for each gene is labeled as a yellow sensor and discussed in Figure 63. The horizontal lines represent the average value for each group. [Figure 63B]Figure 63B is a visual depiction of RADARSv2 targeting RPL41, GAPDH, ACTB, HSP90AA1, PPIB and KRAS, tracking the expression of these transcripts over a range of siRNA concentrations. The blue and beige curves represent the RADARS fold activation (Gluc / Cluc) ratios compared to 0 nM siRNA and the fold change in expression quantified by qPCR, respectively. The grey curve represents the fold activation of non-targeting engineered guide RNAs (non-complementary to the target transcript). Data are the mean ± sem of technical replicates (n = 3) (R values ​​represent the Pearson correlation between qPCR and targeted RADARS, *: p < 0.05; **: p < 0.01; ***: p < 0.001). [Fig. 64A] FIG. 64A is a visual schematic of the upregulation of heat shock protein family gene, HSP70, upon heat shock at 42 degrees Celsius. [Fig. 64B] Figure 64B is a visual depiction of results from an experiment in which HeLa cells were transfected with four HSP70-targeting engineered guide RNAs and a scrambled non-targeting (NT) engineered guide RNA, all targeting different CCA sites, with supplementation of exogenous ADAR1 p150, followed by 24 hours at 42°C or 37°C. qPCR and RADARSv2 detected differential expression of HSP70 between the 37°C (control) and 42°C (heat shock) groups. Sensor activation was calculated between the 42°C and 37°C groups and normalized to the NT condition. Data are the mean ± sem of technical replicates (n = 3). [Figure 65A] FIG. 65A is a visual schematic diagram of a two-input AND gate in RADARS. [Figure 65B] Figure 65B is a graphical depiction of normalized sensor activation by AND-gated RADARS for EGFP / IL6 transcript input across all four possible target combinations. Data are the mean ± sem of technical replicates (n = 3). [Figure 66A] FIG. 66A is a visual schematic diagram of a two-input OR logic gate in RADARS. [Figure 66B] Figure 66B is a graphical depiction of sensor activation by the OR-gated RADARS for all possible EGFP / IL6 transcript input combinations. Data are means ± sem of technical replicates (n = 3). [Figure 67A] FIG. 67A is a schematic representation of SERPINA1-targeting RADARS with an inducible caspase-9 payload. [Figure 67B] Figure 67B is a graphical depiction of cell viability of A549, Hela and HepG2 cells after transfection of RADARS that senses SERPINA1 and expresses iCaspase9 in combination with exogenous ADAR1p150. The non-targeting control engineered guide RNA contains a scrambled sequence with a stop codon in front of the payload. Data are the mean ± sem of technical replicates (n = 3). [Figure 67C] Figure 67C is a graphical depiction of cell viability of HepG2, Hela, and A549 cells determined using an MTS assay 48 hours after transfection of iCaspase9 constructs and SERPINA1 or non-targeted RADARS expressing ADAR1p150, normalized to a control transfected with only a plasmid expressing GFP. [Figure 68]1 is a graphical depiction of the results of an experiment in which HeLa cells were transfected with the best HSP70-targeted RADARS constructs and non-targeted (NT) scrambled RADARS constructs against HSP70 transcripts without exogenous ADARs, followed by 24 hours at 42°C or 37°C. qPCR and RADARSv2 detected the difference in HSP70 expression between the 37°C (control) and 42°C (heat shock) groups. Sensor activation was calculated between the 42°C and 37°C groups and normalized to the NT condition, a sensor with scrambled non-targeted (NT) engineered guide RNA, to compare changes in protein production. Data are the mean ± sem of technical replicates (n=3). [Figure 69A] Figure 69A is a visual schematic of the double loxP EGFP Cre reporter and IL6 RADARS-CRE. Right: Fluorescence from HEK293FT 48 hours after transfection of the double loxP EGFP reporter, ADAR1p150 and IL6 targeted RADARS with Cre payload with or without targeted IL6. Images are shown for untargeted and targeted conditions. White scale bar represents 100 microns. [Figure 69B] FIG. 69B is a visual depiction of the results of an experiment in which cells from FIG. 69A were harvested for flow cytometry analysis for expression of EGFP. [Figure 70A] FIG. 70A is a visual schematic of a SERPINA1-targeted RADARS construct with a Cre payload. [Figure 70B] Figure 70B is a visual depiction of the results of percent GFP+ cells analyzed by flow cytometry for Hela, HepG2, and A549 cells 48 hours after transfection of SERPINA1-targeting RADARS constructs expressing Cre together with exogenous ADAR1p150. Data are the mean ± sem of technical replicates (n = 3). [Figure 70C] Figure 70C is a visual depiction of EGFP expression quantified by flow cytometry 48 hours after transfection of IL6-targeted RADARS with CRE reporter, ADAR1p150 and Cre payload with or without target IL6. The distribution of EGFP signal is analyzed by flow cytometry for Hela, A549 and HepG2 cells. For all three cell types, GFP-positive cells are defined as the cell population with EGFP intensity in the FITC channel greater than 107. [Figure 71A] FIG. 71A is a visual representation of bioluminescence images of sensor activation for various synthetic mRNA RADARS constructs. [Figure 71B] Figure 71B is a graphical comparison of Akaluc luminescence radiance across substrate background, constitutive sensor, and #CCA32 SERPINA1-targeted RADARS in livers of NSG-PiZ and NSG-WT mice. Data are mean ± sem of technical replicates (n = 3). Significance is determined between radiance of NSG-WT samples and radiance of NSG-PiZ samples via unpaired two-tailed t-test with N = 3 mice (non-significant: p > 0.05; **: p < 0.01). [Figure 71C] Figure 71C is a graphical comparison of Akaluc luminescence radiance calculated for liver and compared between wild type and NSG-PiZ mutant mice. Fold activation between NGS-PiZ and NSG-WT mice is calculated for each RADARS construct. Significance is tested for each group compared to substrate background via unpaired two-tailed t-test with N=3 mice (non-significant: p>0.05; ***: p<0.001). [Figure 72A]Figure 72A is a graphical depiction of the transcript expression levels of 10 endogenous genes from HEK293FT cells transfected with targeted or non-targeted (NT) RADARS constructs with exogenous ADAR1p150 supplementation, as quantified by qPCR. Data shown is normalized to targeted RADARS. Significance between targeted and non-targeted RADARS is determined via unpaired t-test (non-significant: p>0.05) with Welch's correction assuming separate variances for each group. [Fig. 72B] Figure 72B is a graphical depiction of endogenous ACTB / PPIB protein expression in HEK293FT cells transfected with ACTB / PPIB targeted RADARS or non-targeted RADARS (with supplementation of ADAR1p150) as quantified by Western blot. Significance between targeted and non-targeted RADARS is determined by unpaired t-test (non-significant: p>0.05) with Welch's correction assuming separate variances for each group. [Fig. 72C] Figures 72C and 72D are visual depictions of the effect of sensor-target hybridization on protein production analyzed by Western blot. Protein levels of ACTB (Figure 72C) and PPIB (Figure 72D) in response to hybridization with RADARS are shown, with GAPDH used as a normalization protein control. [Fig. 72D] Figures 72C and 72D are visual depictions of the effect of sensor-target hybridization on protein production analyzed by Western blot. Protein levels of ACTB (Figure 72C) and PPIB (Figure 72D) in response to hybridization with RADARS are shown, with GAPDH used as a normalization protein control. [Figure 73A]Figure 73A is a graphical depiction of the expression levels of interferon beta, OAS1, RIG-1 and MDA5 detected by qPCR upon transfection of RADARS constructs targeting IL6, ACTB, RPS5 or PPIB, non-targeting (NT) RADARS and high molecular weight poly(I:C). Significance is determined by one-way ANOVA test between untreated, RADARS and poly(I:C) groups (non-significant: p>0.05; ****: p<0.0001). [Figure 73B] Figure 73B is a graphical depiction of the fold change in gene expression of four dsRNA-responsive genes (IFNb, OAS1, MDA5, and RIG-1) in response to RADARS in HEK293FT cells detected by qPCR, using ACTB as a normalization gene. [Figure 73C] Figure 73C is a graphical depiction of the fold change in gene expression of four dsRNA-responsive genes (IFNb, OAS1, MDA5, and RIG-1) in response to RADARS or poly(I:C) in HepG2 cells as detected by qPCR, with GAPDH as the normalizing gene. [Fig. 74A] Figure 74A is a visual depiction of the quantification of RNA editing within the 200 bp hybridization region of RADARS targeting transcripts for engineered guide RNAs for ACTB, PPIB, and non-targeting engineered guide RNAs. A to I(G) conversion is depicted in the heatmap (non-significant: p>0.05). [Fig. 74B]Figure 74B is a scatter plot analysis of transcriptome-wide off-target editing. The scatter plot shows the allele fraction (AF) of A→G mutations in (i) with overexpression of ADAR1p150 with PPIB sensor versus non-transfected HEK293T cells (n=3.17×106 sites); (ii) same as (i) but with IL6 sensor (n=3.07×106 sites); (iii) same as (i) but without ADAR overexpression but with non-targeting RADAR sensor (n=3.14×106 sites). Sites are colored by FDR (false discovery rate) corrected p-value (color bar on the right). For i and iii, experiments were performed in triplicate and independent replicates. [Fig. 74C] Figure 74C is a scatter plot analysis of transcriptome-wide off-target editing by MCP-ADAR2(E488Q) using the dataset from NCBI Geo accession number: GSE123905 (Katrekar et al., 2019). The scatter plot shows the AF (allele fraction) of A→G mutations in MCP-ADAR2(E488Q) overexpression (n=2.35×106 sites) versus non-transfected HEK293T cells. Sites are colored by FDR-corrected p-value (color bar on the right). [Fig. 75A] Figure 75A is a graphical representation of the analysis of sequence homology between transcriptome off-targets (n=23 sites) and targeted PPIB homology regions, with corresponding FDR-corrected p-values ​​(Monte Carlo permutation test) for the significance of local alignment between each off-target site and 200 bp surrounding the PPIB homology region targeted by the engineered guide RNA (red curve indicates p=0.05). [Fig. 75B] Figure 75B is a visual depiction of sequence logo analysis for off-target editing sites with overexpression of ADAR1p150 together with PPIB-targeting RADARS. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0036] The present disclosure presents systems and sensors for detecting and quantifying RNA. The present disclosure also presents systems and methods for gene editing. Also disclosed is a system for in vivo imaging of RNA expression. The present disclosure presents a sensor system that converts adenine to inosine, which is recognized by the translation machinery as guanosine (G). The conversion of adenine to inosine is the result of hydrolytic deamination of adenosine (Cox et al., Science, 358(6366):1019-1027 (2017)). Thus, the adenosine deaminase acting on RNA (ADAR) family of enzymes can convert codons within a transcript such that the translation product is functionally altered. The present disclosure presents, inter alia, the editing of a transcript to remove the functional alteration, the stop codon, thereby allowing expression of a payload.

[0037] definition Unless otherwise stated, the terms and techniques used within this application have the meanings commonly known to those skilled in the art.

[0038] As used herein, the term "about" is understood to modify the specified value. Unless expressly stated otherwise, the term "about" is understood to modify the specified value by ±10%. As used herein, when applied to a range, the term "about" modifies both endpoints of the range. For purposes of example, the range "about 5 to 10" is understood to mean "about 5 to about 10".

[0039] As used herein, the terms "sensor" and "sensor strand" are used interchangeably. As used herein, "sensor" or "sensor strand" is understood to refer to a single strand of RNA that includes at least a stop codon, where the RNA strand is capable of hybridizing or forming a duplex with another RNA strand. The terms "sensor" strand and "guide" strand may be used interchangeably throughout this disclosure.

[0040] As used herein, the terms "ADAR", "ADAR enzyme" and "deaminase" are used interchangeably unless expressly stated otherwise. Thus, within the present disclosure, "when the ADAR enzyme is a prokaryotic RNA editing enzyme" is understood to also mean "when the deaminase is a prokaryotic RNA editing enzyme".

[0041] As used herein, the term "RNA sensor system" or "sensor system" is understood to mean the minimum components required for (i) hybridization of a single-stranded RNA with a transcript of interest such that the resulting hybridized RNA contains at least one mismatch and at least one stop codon, (ii) recognition of the hybridized RNA as a substrate, and (iii) editing of the single-stranded RNA to remove the stop codon.

[0042] As used herein, the term "cellular logic system" or "logic system" refers to a system composed of multiple individual sensor systems. The cellular logic system or logic system of the present disclosure is a composite system that may be composed of one or more individual RNA sensor systems. When integrated into a larger cellular logic system, the individual RNA sensor systems may depend on separate RNA sensor systems in the same cell for activation. Alternatively, multiple individual RNA sensor systems may be integrated into a cellular logic system such that an individual RNA sensor system is not required for another RNA sensor system.

[0043] Unless expressly stated otherwise, the term "payload" as used herein is generally understood to mean a portion of a single-stranded RNA that may hybridize with another single-stranded RNA, may be an invading strand to an already duplexed RNA molecule, and may be translated to express a protein. Thus, when an embodiment of the present disclosure states, for purposes of example, that "the payload comprises a therapeutic protein," it is generally understood that the payload is a fragment or portion of a single-stranded RNA that can be translated to express a therapeutic protein.

[0044] As used herein, "cell-specific," "cell type-specific," and "activatable by a specific cell type" will be understood by those of skill in the art to mean that activation of the RNA sensor requires the presence of factors that are present at substantially higher levels in the specific cell type than in other cell types. Those of skill in the art will recognize that expression of these factors may occur in other cell types such that activation of the RNA sensor is possible, but is less likely.

[0045] As used herein, unless otherwise stated, "avidity region" or "avidity binding region" may be used interchangeably to describe a region on the guide strand that has a degree of complementarity to target a transcript. Avidity refers to the design of multiple binding sites in the guide. These binding sites may be separated by linkers. The avidity region may be separated from the main sensor region of the guide strand by one or more secondary structures, optionally including hairpin structures. In some embodiments, the hairpin structure is an MS2 hairpin. The avidity region may optionally contain a stop codon that is not targeted for editing by ADAR. In some embodiments, the avidity region comprises a linker sequence. In some embodiments, the avidity region comprises one or more linker sequences.

[0046] Adenosine deaminase acting on RNA (ADAR) and other RNA-editing enzymes The ADAR enzyme is evolutionarily conserved among animals. Mammals have three known ADAR enzymes: ADAR1, ADAR2, and ADAR3. ADAR1 and ADAR2 are known to be catalytically active. In contrast, ADAR3 is generally considered to be catalytically inactive, despite its substantial similarity to ADAR2 (Savva et al., Genome Biol., 13(12):252 (2012)). The ADAR enzymes envisioned in the present disclosure include mammalian ADAR enzymes or modified enzymes derived from mammalian ADARs. In some embodiments, the ADAR RNA sensor enzyme is human ADAR1. In some embodiments, the ADAR RNA sensor enzyme is modified human ADAR1. In some embodiments, the ADAR RNA sensor enzyme is human ADAR2. In some embodiments, the ADAR RNA sensor enzyme is modified human ADAR2. In some embodiments, the ADAR RNA sensor enzyme is modified human ADAR3. In some embodiments, the ADAR RNA sensor enzyme is a synthetic enzyme. In some embodiments, the RNA sensor enzyme ADAR is an enzyme, a non-mammalian ADAR.

[0047] The ADAR enzyme of the present disclosure includes modified enzymes. The ADAR enzyme envisaged in the present disclosure includes enzymes modified to increase the affinity of the enzyme to the sensor strand. In some embodiments, the ADAR is modified to include an additional RNA binding domain. In some embodiments, the ADAR is modified to exclude one or more non-catalytic domains. In some embodiments, the ADAR enzyme of the sensor system includes an ADAR2 deaminase domain. In some embodiments, the ADAR is composed of an ADAR2 deaminase domain. In some embodiments, the ADAR is comprised of an ADAR2 deaminase domain fused to an MS2 binding protein. In some embodiments, the ADAR is composed of an ADAR2 deaminase domain fused to an MS2 binding protein. In some embodiments, the ADAR is fused to a Cas (CRISPR-associated system) protein or a fragment or derivative thereof. In some embodiments, the ADAR is fused to a modified Cas protein. In some embodiments, the modified Cas protein is mutated to lack catalytic activity. In some embodiments, the ADAR is fused to modified Cas13. In some embodiments, the ADAR is fused to Cas13b that contains a mutation at the amino acid corresponding to K370. In some embodiments, the ADAR is fused to Cas13b that contains the mutation K370A. In some embodiments, the ADAR is fused to modified Cas13d. In some embodiments, the ADAR is fused to modified Cas7-11.

[0048] The enzyme ADAR of the present disclosure can be endogenous to the cell that the sensor is delivered to.The enzyme ADAR envisioned in the present disclosure can be exogenous and can be delivered to the cell simultaneously with the sensor or can be delivered to the cell separately from the sensor.In some embodiments, the exogenous ADAR is delivered separately from the sensor.In some embodiments, the exogenous ADAR is delivered simultaneously with the sensor.In some embodiments, the exogenous ADAR can be used to supplement the endogenous ADAR.In some embodiments, more than one exogenous ADAR is provided to the cell.

[0049] Further RNA-editing enzymes In the method of the present disclosure, additional deamination enzymes may be used. In some embodiments, the deamination enzyme may be a modified ADAR enzyme. The modified ADAR enzyme may include an ADAR enzyme modified to increase cytidine deamination activity, such as RESCUE (Abudayyeh et al., Science, 365, 382-386 (2019)).

[0050] In some embodiments, the deaminase is a modified enzyme that edits cytidine to uracil. In some embodiments, the deaminase may be a member of the APOBEC (apolipoprotein B mRNA-editing enzyme, catalytic polypeptide like) family of cytidine deaminases. In some embodiments, the deaminase is a modified APOBEC1. In some embodiments, the deaminase is a modified APOBEC2. In some embodiments, the deaminase is a modified APOBEC3. In some embodiments, the deaminase is a modified APOBEC3A. In some embodiments, the deaminase is a modified APOBEC3B. In some embodiments, the deaminase is a modified APOBEC3C. In some embodiments, the deaminase is a modified APOBEC3D. In some embodiments, the deaminase is a modified APOBEC3E. In some embodiments, the deaminase is a modified APOBEC3F. In some embodiments, the deaminase is a modified APOBEC3G. In some embodiments, the deaminase is a modified APOBEC3H.

[0051] In some embodiments, the deaminase may be a prokaryotic RNA editing enzyme, hi some embodiments, the deaminase is derived from Escherichia coli (E. coli).

[0052] Sensor / Sensor Chain The sensor of the present disclosure comprises at least one stop codon. The sensor can be located on the same RNA strand as the payload, the normalization gene, or both the payload and the normalization gene. The sensor of the present disclosure can be designed such that when the single-stranded RNA (ssRNA) sensor strand binds to the target ssRNA strand to create a double-stranded RNA (dsRNA), the duplex contains a mismatch in the region corresponding to the stop codon in the sensor strand. The disclosed sensor can be modified in a number of ways. In some embodiments, the sensor is administered to a cell as a DNA template that can then be transcribed into a single-stranded RNA sensor molecule.

[0053] The present disclosure also provides a sensor strand that includes more than one stop codon. In some embodiments, the sensor strand includes one, two, three, four, five, six, seven, eight, nine, or ten stop codons. In some embodiments, the sensor strand includes more than ten stop codons. In some embodiments, the sensor strand includes two stop codons. In some embodiments, the sensor strand includes three stop codons. In some embodiments, the sensor strand includes four stop codons. In some embodiments, the sensor strand includes five stop codons. In some embodiments, the sensor strand includes six stop codons. In some embodiments, the sensor strand includes seven stop codons. In some embodiments, the sensor strand includes eight stop codons. In some embodiments, the sensor strand includes nine stop codons. In some embodiments, the sensor strand includes ten stop codons.

[0054] The present disclosure also provides a sensor / guide strand that contains one or more avidity binding regions.In some embodiments, the sensor strand comprises three avidity regions.In some embodiments, the sensor strand comprises five avidity regions.In some embodiments, the sensor strand comprises seven avidity regions.The avidity region can incorporate a stop codon that is not the target for ADAR editing.The avidity binding region can also contain a stop codon that is the target for ADAR editing.In some embodiments, the avidity binding region comprises the stop codon for intended ADAR editing.

[0055] Also provided within the present disclosure are sensor / guide strands incorporating one or more MS2 hairpins. In some embodiments, the sensor strand comprises two MS2 hairpins. In some embodiments, the sensor strand comprises three MS2 hairpins.

[0056] In some embodiments, the sensor / guide strand comprises both an avidity region and an MS2 hairpin region.

[0057] In some embodiments, the sensor / guide strand comprises an RNA modification. In some embodiments, the modified RNA comprises 5-methylcytosine. In some embodiments, the modified RNA comprises pseudouridine.

[0058] payload In some embodiments, the payload comprises a reporter transcript. In some embodiments, the payload consists of a reporter transcript. In some embodiments, the reporter transcript is a fluorescent reporter. In some embodiments, the reporter transcript comprises a luciferase transcript. In some embodiments, the reporter transcript comprises a GFP transcript.

[0059] The sensor systems of the present disclosure can be designed to deliver a payload that encodes a therapeutic protein, which in some embodiments can be used in conjunction with another therapeutic agent.

[0060] In some embodiments, the payload comprises a transcription factor, in some embodiments, the payload comprises an enzyme, in some embodiments, the payload comprises a transgene protein.

[0061] In some embodiments, the payload comprises a protein for use in editing the genome of a cell. In some embodiments, the payload comprises a Cas protein. In some embodiments, the payload comprises a Cas9 protein.

[0062] In some embodiments, the payload comprises a protein capable of converting one cell type into another cell type.

[0063] In some embodiments, the payload comprises an ADAR, hi some embodiments, the payload comprises an ADAR capable of inducing a positive feedback loop.

[0064] In some embodiments, the payload comprises a protein capable of killing a specific cell type. In some embodiments, the payload comprises a protein capable of killing a tumor cell. In some embodiments, the payload comprises an immune modulating protein.

[0065] Logic Gates The present disclosure also relates to multiplexed multi-sensor reporter systems. In some embodiments, these multi-sensor reporter systems utilize logic gates. These logic gates may consist of AND gates, OR gates, or AND / OR gates as separate decision points within the same reporter system.

[0066] An AND gate for use in the reporter system of the present disclosure can be implemented by having multiple guide strand binding compartments on the same ssRNA sensor. In this type of AND gate, each individual guide compartment senses a separate endogenous transcript in the cell. The "activation" of the AND gate in this type of gate is the same as the activation of the entire ssRNA sensor strand; that is, removal of the stop codon and expression of the terminal payload. This type of logic gate requires that each guide compartment interacts with a target sequence and an ADAR or other deamination molecule. Deamination of the stop codon in each guide compartment allows full expression of the payload. In some embodiments, each guide compartment is further separated by a separate reporter. In some embodiments, each guide compartment is further separated by a separate, distinct reporter. In some embodiments, each reporter on the ssRNA sensor for use in the AND gate is a distinct fluorescent reporter.

[0067] In some embodiments, the AND gate can operate sequentially. In this type of AND gate, each of the multiple guide strand binding compartments is placed on a separate ssRNA sensor. The activation of the gate in this type of AND gate involves the activation of multiple sensors in a defined sequence. In this type of AND gate, the activation of the first sensor results in the expression of an intermediate payload. This intermediate payload allows the expression of a second RNA sensor in a specific cellular environment. In such a system, the cascade of RNA sensor activation can only occur in the context of a specifically determined cellular stimulus upon activation.

[0068] OR gates for use in the reporter systems of the present disclosure can be implemented through the use of multiple independent ssRNA sensors in the same cell, each of which can deliver a payload without activation of another sensor.

[0069] Delivery System The present disclosure also provides a system for delivering an ADAR sensor.

[0070] In some embodiments, the ADAR sensor is delivered directly to a cell. In some embodiments, the ADAR sensor is encapsulated in a lipid nanoparticle. In some embodiments, the ADAR sensor is delivered via a viral vector.

[0071] In some embodiments, the ADAR sensor is a circular RNA.

[0072] Methods of the Disclosure The present disclosure also provides methods for using the sensor systems described herein.

[0073] In some embodiments, the RNA sensor delivers a payload that can be optically observed. In some embodiments, the RNA sensor is tracked via an imaging system. The imaging system can be any suitable imaging system that is compatible with the system. In some embodiments, the imaging system uses fluorescent molecules. In some embodiments, the RNA sensor system includes multiple fluorescent molecules. In other embodiments, the imaging system is a non-invasive imaging system. In some embodiments, the RNA sensor system is compatible with a fluorescence-activated cell sorting (FACS) system.

[0074] In some embodiments, the RNA sensor system is tracked via a non-invasive imaging system. In some embodiments, the imaging system tracks Deep Red luciferase.

[0075] The present disclosure also provides a method for quantifying RNA in vivo.The quantification method of the present disclosure can rely on the incorporation of a normalization gene on the sensor strand.The translation of this normalization gene occurs independently of RNA editing.The quantification of the normalization gene provides a reference standard for the total sensor strand delivered to each individual cell.The reference to this normalization gene allows the determination of the activated RNA sensor as a percentage of the total sensor delivered.

[0076] The present disclosure also contemplates live cell imaging. In some embodiments, fluorescent reporters are visualized. In some embodiments, multiple fluorescent reporters are tracked. The present disclosure also presents long-term cell lineage tracking. In some embodiments, activation of the RNA sensor system results in a permanent change in the expression of the reporter molecule, so that cells in which the sensor system has already been activated can be identified at a later time point.

[0077] In some embodiments, the RNA sensor system can be used to target specific cell types. In some embodiments, the RNA sensor system is engineered to be activated in specific cell types. In some embodiments, the RNA sensor system targets specific cell types. In some embodiments, the RNA sensor system targets tumor cells. In some embodiments, the RNA sensor system delivers a payload that kills specific cell types. In some embodiments, the RNA sensor system delivers a payload that transforms one cell type into another cell type. In some embodiments, the RNA sensor system delivers a payload that edits the genome of a cell.

[0078] In some embodiments, the RNA sensor system is druggable. In some embodiments, the RNA sensor system is drug sensitive. In some embodiments, the RNA sensor system is activated only in the presence of a single drug or compound. In some embodiments, the RNA sensor system is activated only in the presence of multiple drugs or compounds.

[0079] The present disclosure also contemplates the use of the RNA sensor system described herein for use in in vitro diagnostic assays.For example, the RNA sensor system can be an RNA sensor system in diagnostic assays in which the payload comprises a fluorescent protein, a luciferase protein, an antigen or an epitope.In some embodiments, the diagnostic assay is a lateral flow strip.

[0080] Now that the present technology has been described in detail, the present technology will be more clearly understood by referring to the following examples.The following examples are incorporated for illustrative purposes only and are not considered to limit the embodiments of the present technology.All patents and publications mentioned in this specification are expressly incorporated by reference. EXAMPLES

[0081] [Example 1] Example method Unless otherwise indicated, the following experimental methods were utilized in the examples presented herein.

[0082] Measurement of luciferase activity Media containing secreted luciferase was harvested 48 hours after transfection unless otherwise noted. 20 μL of media was used to measure luciferase activity using the Targeting Systems Cypridinia luciferase assay kit and the Targeting systems Gaussia luciferase assay kit (Targeting Systems) on a Biotek Synergy 4 plate reader with an injection protocol. All replicates were performed as biological replicates.

[0083] Transfection for fluorescent sensors The day before transfection, cells were seeded at 10,000 in Corning 96-well tissue culture treated plates (black), resulting in approximately 40-50% confluency on the day of transfection. For all fluorescent sensors, HEK293FT cells were transfected with 100 ng of total plasmid DNA using TransIT-LT1 according to the manufacturer's specifications (ratio of 1 μg DNA:3 μL Trans Reagent). Unless otherwise specified, ADAR sensor, ADAR and target plasmids were mixed at equal concentrations (33.3 ng per condition); experiments without one or more of the foregoing were substituted with pUC19, thereby keeping the total concentration of DNA at 100 ng.

[0084] Confocal microscopy on fluorescent ADAR sensors 48 hours after transfection, all wells were measured via confocal microscopy with the following settings: For each well, 2x2 images at 10x magnification were collected around the center point and stitched together. Images were collected in the 488nm channel (32.8% magnification, 100ms exposure), 561nm channel (35.2% magnification, 100ms exposure), 640nm channel (80% magnification, 100ms exposure) and brightfield channel (25ms exposure).

[0085] Quantification of fluorescent signals from images Images were opened in Matlab and segmented in the mCherry channel via Watershed. Total pixel area and average pixel intensity were calculated for the mNeon (488 nm), mCherry (561 nm) and iRFP (640 nm) channels for each segmented cell and exported to a tabulated csv file. The csv files were batch processed in R with the following steps: merge all csv files, merge conditions with small tabulated areas (few cells or conditions not transfected with sensor), subtract the fluorescence background for each channel from all conditions in this channel, and divide the tabulated value for each condition by the area to obtain the average fluorescence intensity. Standard deviations were calculated by comparing the average values ​​in triplicate technical replicates for transfection. For mNeon / mCherry ratio values, the average mNeon fluorescence intensity for a condition was divided by the average mCherry value for this same condition. For fluorescence ratio and fold change ratio values, error propagation was performed using the formula:

[0086]

number

[0087] Quantification of the percentage of mNeon positive cells in confocal images We observed some consistent leakage of mNeon into the fluorescent sensor due to very low levels of plasmid contamination or ribosome slippage. Therefore, we gated on detection of mNeon positive cells 30 arbitrary units above background and determined the percentage of mCherry positive cells under conditions where mNeon was expressed above this threshold. The mNeon values ​​were plotted as a histogram with kernel density estimation by base 10 logarithm to generate the plot in Figure 2E.

[0088] RNA extraction and next-generation sequencing for ADAR sensors To calculate the editing rate of the sensor ADAR sensor, cells were harvested 48 hours after transfection after imaging. Total RNA was extracted using the RNeasy 96 Kit (Qiagen) with DNase treatment. cDNA was prepared with SuperScript IV reverse transcriptase (Invitrogen) and sensor-specific primers. The sensor guide regions were amplified, indexed, and sequenced on an Illumina MiSeq platform. Reads were demultiplexed and aligned to each sensor, and A-to-I editing rates were calculated using an in-house MATLAB pipeline.

[0089] Quantification of protein expression Two days after transfection into HEK293FT cells, the Nano-Glo HiBiT Lytic Detection System (Promega) was used to quantify HiBiT tags in cell lysates. For preparation of Nano-Glo HiBit Lytic Reagent, Nano-Glo HiBit Lytic Buffer (Promega) was mixed with Nano-Glo HiBit Lytic Substrate (Promega) and LgBiT Protein (Promega) according to the manufacturer's protocol. The volume of Nano-Glo HiBit Lytic Reagent added was equal to the culture medium present in each well, and the samples were shaken for 3 min at 600 rpm on an orbital shaker. After 10 min of incubation at room temperature, readout was performed using a plate reader (Biotek Synergy Neo 2) with a gain of 125 and an integration time of 2 s. The control background was subtracted from the final measurement.

[0090] Synthesis of mRNA Prior to in vitro transcription, DNA templates were obtained by PCR with a targeting-type forward primer containing a T7 promoter. Sensor mRNA and MCP-ADAR2dd mRNA were transcribed and polyA-tailed using the HiScribe™ T7 ARCA mRNA kit (NEB, E2065S) with 50% supplementation of 5-methyl CTP and pseudo UTP (Jena Biosciences) according to the manufacturer's protocol. Then, the mRNA was cleaned up using the MEGAclear™ Transcription Clean-Up Kit (Thermo Fisher, AM1908).

[0091] Harvesting of total RNA and quantitative PCR For gene expression experiments in mammalian cells, 48 ​​hours after transfection, cells were harvested and reverse transcription to generate cDNA was performed using a modification of the commercially available Cells-to-Ct kit (Thermo Fisher Scientific) previously described (Joung et al., 2017). Expression of transcripts was then quantified by qPCR using Fast Advanced Master Mix (Thermo Fisher Scientific) and TaqMan qPCR probes (Thermo Fisher Scientific) with GAPDH control probes (Thermo Fisher Scientific). All qPCR reactions were performed in duplicate technical replicates in 10 μl reactions in a 384-well format and read using a LightCycler 480 Instrument II (Roche). For multiplexed targeting reactions, reads of different targets were performed in separate wells. Expression levels were calculated by subtracting the cycle threshold (Ct) of the housekeeping control (GAPDH) from the target Ct value and normalizing to the total input, resulting in ΔCt levels. Relative transcript abundance is calculated as 2 -ΔCt All replicates were performed as biological replicates.

[0092] Automated production of avidity sensors The avidity sensor was created using the python script in the following repository: https: / / github.com / abugoot-lab / ADAR SENSOR. A schematic for the creation of a typical three-avidity guided ADAR sensor with two MS2 hairpin loops and a 5 nucleotide spacing between the guide region (on target) is shown in FIG.

[0093] Animal care and protocols All experiments were performed on female B6(Cg)-Tyrc-2J / J (Albino B6) mice and female NOD.Cg-Prkdcscid Il2rgtm1Wjl Tg(SERPINA1*E342K)#Slcw / SzJ (NSG-PiZ) mice (Jackson Laboratory) with food and water ad libitum. NSG-PiZ mice express mutant human SERPINA1 on an immunodeficient NOD scid gamma background. All mice were housed in individually ventilated cages (IVCs) in a temperature-controlled animal facility (usually 12:12 h light / dark cycle) and used in accordance with procedures approved by the Committee on Animal Care at MIT.

[0094] RADARSv2 Design Stop codons were engineered in the +1 and +2 frames following the engineered guide RNA region to capture translating ribosomes across all frames. These out-of-frame stop codon designs synergized with the long 5' peptide to significantly reduce background and result in approximately 200-fold activation. We chose this sensor design, called RADARSv2, incorporating a structured guide, upstream peptide, and out-of-frame stop codon as a unifying structure for future sensors (Figure 54A).

[0095] [Example 2] Development of luciferase / fluorescence sensor and detection of EGFP transcripts

[0096] Cloning of luciferase sensors Luciferase sensors were cloned by Gibson assembly of PCR products. The sensor backbone was created by cloning Cypridinia luciferase (Cluc) under the expression of the CMV promoter and Gaussia luciferase (Gluc) under the expression of the EF1-a promoter, both on a single vector. Expression of both luciferases on a single vector allowed one luciferase to be used as a dosing control for normalization of the knockdown of the other luciferase, controlling for variations due to transfection conditions. The short form sensors were ordered as primers, then phosphorylated and annealed using T4 polynucleotide kinase. The annealed oligos are ligated into the backbone using T4 DNA ligase (NEB) for 20 minutes at room temperature in a typical 10 μL ligation reaction with 1 μL T4 DNA ligase, 30 ng insert, 50 ng backbone and 1 μL 10x ligation buffer. The long avidity sensor regions were ordered directly from Ingegrated DNA Technologies (IDT) as Eblocks. The PCR products were purified by gel extraction (Monarch gel extraction kit, NEB) and assembled into the backbone using the NEB HiFi DNA Assembly master mix kit with 2.5 μL mastermix, 30 ng backbone and 5 ng insert in a 5 μL reaction. The reaction was incubated at 50°C in a thermocycler for 30 minutes, and 20 μL of competent Stbl3 produced by Mix and Go! competency kit (Zymo) was transformed with 2 μL of the assembled reaction and plated onto an agar plate supplemented with the appropriate antibiotic. After overnight growth at 37°C, colonies were picked into Terrific Broth (Thermo Fisher Scientific) and incubated at 37°C with shaking for 24 hours.Cultures were harvested using a QIAprep Spin Miniprep kit (Qiagen) according to the manufacturer's instructions.

[0097] This ADAR sensor for luciferase contains a 51 nucleotide EGFP transcript sensing guide and a Gaussia luciferase (Gluc) payload (Figure 2A). A constitutive Cypridiana luciferase (Cluc) was incorporated on a separate transcript to allow ratiometric control of transfection variability. This dual reporter dual transcript luciferase reporter system targets functional eGFP under the control of a doxycycline-inducible promoter. We investigated this ADAR sensor design in the presence or absence of exogenous ADAR2 deaminase domain with the hyperactive mutation E488Q and specific mutant T490A (MCP-ADAR2dd(E488Q, T490A)) fused to the MS2 coat protein (Kuttan and Bass, 2012; Cox et al., 2017), along with a scrambled guide control. HEK293FT cells were co-transfected with a plasmid expressing ADAR sensor and a plasmid expressing EGFP or a control plasmid. We observed that the ADAR sensor resulted in a 5-fold increase in normalized luciferase value when relying only on endogenous ADAR, and a 51-fold activation of signal (fold change of luciferase expression in the presence of target / in the absence of target) when supplemented with exogenous MCP-ADAR2dd (E488Q, T490A) (Figure 2B). In addition, we observed that the luciferase signal upon induction of the ADAR sensor supplemented with exogenous ADAR is equivalent to the constitutively expressed transcript without upstream stop codon (about 78%, Figure 2C). Thus, this high protein production upon activation of the ADAR sensor confirms the validity of the ADAR sensor for applications that require high absolute payload expression.To confirm that payload expression is dependent on RNA editing, we harvested RNA from cells and quantified editing by next-generation sequencing, and observed that editing of the stop codon UAG was increased approximately 24-fold in the EGFP-targeted sensor, whereas the increase in editing with the non-targeted sensor was negligible (Figure 2D).

[0098] Cloning of fluorescent sensors The parent fluorescent ADAR sensors were cloned in three pieces via Gibson assembly using HindIII and NotI cut pcDNA3.1(+) as backbone. mCherry was amplified from Addgene vector 109427 and T2A mNeon was ordered as gBlock from IDT. All fluorescent ADAR sensors were subcloned into the parent fluorescent plasmid via Golden Gate cloning using the enzyme Esp3I (an isoschizomer of BsmBI). Inserts were ordered as complementary strands with overhangs and annealed in phosphorylated form or generated via PCR. Golden Gate reactions were assembled from components using the BsmBIv2 golden gate assembly kit from NEB or in a 20 μL reaction containing 25 ng of vector and 2 μL of 1:200 diluted insert (approximately 5-10 ng). The reaction was thermocycled for 1 hour, alternating between 25°C and 37°C for 5 minutes each, and then 12.5 μL of Zymo Mix and Go Competent Cells was transformed with 0.75 μL of the reaction mix. The transformed cells were diluted 1:1 with SOC medium and 10 μL was streaked onto a carbenicillin agar plate at 50 μg / mL. After overnight incubation at 37°C, a single colony was picked into 4 mL of Luria broth (LB) supplemented with 50 μg / mL carbenicillin. Plasmids were prepared from the cultures as described above for the luciferase sensor.

[0099] This dual reporter single transcript fluorescent sensor contains a single transcript fluorescent reporter, which constitutively expresses mCherry upstream of the 51bp eGFP sensor, and activates the mNeon reporter downstream only when interacting with the target (Figure 3A). HEK293 cells were transfected with non-functional eGFP under the control of a doxycycline-inducible promoter. In the presence of 1 μg / mL doxycycline, cells were also transfected with dual reporter single transcript targeting sensor or non-targeting sensor. Figure 3B shows representative images of experiments with and without targets and experiments with and without exogenous ADAR. Figure 3C presents the quantification of the fold change in EGFP fluorescence upon induction by the target, and the fold change ratio indicates the mNeon / mCherry fluorescence value (fluorescence ratio value) in the presence of the target divided by the fluorescence ratio value in the absence of the target for the ADAR mutant. In the presence of exogenous MCP-ADAR2dd (E488Q, T490A), the targeted ADAR sensor exhibited a >21-fold increase in activation. In addition, low background activation was observed in the absence of the targeted ADAR sensor. The TAG→TIG editing rate of the sensor codon TAG in the presence or absence of the target was also measured. In the presence of exogenous ADAR and the presence of the targeted ADAR sensor, the stop codon UAG was edited at an editing rate of 9.4%, whereas in the absence of the targeted ADAR sensor, the stop codon UAG was edited at an editing rate of 0.2%, suggesting that target-driven editing drives the expression of the fluorescent payload.

[0100] To further establish the proof of principle for the biological luciferase sensor, the three guide strands were introduced into HEK293FT cells simultaneously with the reporter exogenous EGFP transcript. The ADAR sensor was a dual transcript with Gaussia luciferase (Gluc) / Cypridiana luciferase (Cluc) transcript, allowing ratiometric control of transfection variability. The dual reporter dual transcript luciferase reporter system targeted the exogenous eGFP reporter transcript. Design 2 and Design 4 are different guides targeting the EGFP transcript. No exogenous ADAR was introduced into the cells. The negative control was a guide strand that does not recognize EGFP. When EGFP expression was analyzed after the introduction of the three guide strands, both Design 2 and Design 4 showed a significant increase in luciferase expression levels (Figure 4A). Both Design 2 and Design 4 guide strands exhibited a significant increase in luciferase signal compared to the negative control scrambled guide (Figure 4B).

[0101] Example 3: Increased transcript expression following administration of exogenous ADAR2 To determine whether the introduction of additional ADAR molecules would increase luciferase expression, five guide strands were introduced into HEK293FT cells simultaneously with the reporter exogenous EGFP transcript. Guides 1-4 are different guides that target the EGFP transcript. The negative control is a scrambled control designed not to recognize EGFP. Each guide strand was examined in three experimental conditions. First, each guide was introduced into HEK293FT cells that had no exogenous ADAR introduced (Figure 5, blue bars). Next, each guide was introduced into HEK293FT cells simultaneously with the deaminase domain of ADAR2 (ADARdd) (Figure 5, white bars). Finally, each guide was introduced into HEK293FT cells simultaneously with dPspCas13b-ADAR2dd, a transcript that overexpresses ADAR2dd (Figure 5, red bars).

[0102] Guide strands 1-4 exhibited between a 1.5-fold and 2-fold increase in luciferase expression without the addition of exogenous ADAR molecules when normalized to negative controls (Figure 5, blue bars). When the deaminase domain of ADAR2 (ADAR2dd) was co-introduced into cells, guide 1, guide 2, and guide 4 exhibited similar increases in luciferase expression as the same guides in the presence of endogenous ADAR (Figure 5, white bars). Guide strand 3 exhibited a 3-fold increase in luciferase expression in the presence of an additional ADAR2dd molecule (Figure 5, white bars).

[0103] When a vector overexpressing ADAR2dd was introduced simultaneously with guide strands 1-4, luciferase expression was increased at least two-fold (Figure 5, red bars). Guide strand 3, notably, presented a four-fold increase in luciferase expression when compared to the negative control. This data highlights the possibility of utilizing exogenous / modified ADAR molecules to enhance the capacity of the sensor.

[0104] Example 4: ADAR optimization and length screening reduces background and increases activation of ADAR sensors in the presence of target During validation of these ADAR sensors, we observed that for some guides, activation can occur in the presence of exogenous ADARs, despite the absence of target RNA (Figure 2C, Figure 3B). We attempted to determine whether ADAR activity can be optimized to increase activation and reduce background. To optimize ADAR sensors and minimize this background, we selected and examined a panel of different ADAR1 / ADAR2 mutants combined with 69 nucleotide guides that target frameshift EGFP or iRFP transcripts (Figure 6, Figure 7). Figure 6A shows, from left to right, a schematic diagram of different tested ADARs, including the p150 isoform of ADAR1, the p110 isoform of ADAR1, ADAR2, and MS2 coat protein (MCP)-ADAR fusion protein (MCP-ADAR). fl=full length. DD=deaminase domain. The catalytic domain mutations are not shown in the schematic, but all mutations are within the deaminase domain.

[0105] We screened full-length human ADAR isoforms (ADAR1 p110, ADAR1 p150, and ADAR2) (Galipon et al., 2017; Merkle et al., 2019) and their catalytic deaminase domains along with specific mutants designed to destabilize ADAR-dsRNA interactions and reduce nonspecific editing (Cox et al., 2017; Matthews et al., 2016). Our initial exogenous ADAR selection, MCP-ADAR2dd(E488Q, T490A), performed best against a frameshifted EGFP transcript, but several candidates in our screen also provided comparable activation upon co-transfection with the target (Figure 6B) and reduced background against the two sets of targets (Figure 7). We also examined the editing rate of the stop codon by various sensors in the following cases: (Figure 8A) exogenous recruitment by MCP-ADAR2dd, (Figure 8B) exogenous recruitment by ADAR1 p150 isoform, (Figure 8C) exogenous recruitment by ADAR2, and (Figure 8D) no exogenous ADAR recruitment. The editing rate of these candidates was also examined. The editing rate is calculated through RNA sequencing data showing the conversion of the stop codon UAG to UIG in the presence and absence of the target of the ADAR mutant selected for further screening (Figure 8).

[0106] Because guide selection can affect the overall sensitivity of the sensor, we screened the top ADAR candidates against multiple guide sequences and targets in an orthogonal panel (Figure 10). First, we observed activation above background only in properly matched ADAR sensors and target transcripts (Figure 9). ADAR1 p150 generally provided the highest fold activation for three of the four targets, driven by a low overall background signal in the absence of target, while MCP-ADAR2dd (E488Q, T490A) performed best against the target EGFP due to its generally high level of absolute signal, but suffered from high background against other targets, reducing its overall activation (Figure 6, Figure 10, Figure 11, Figure 12).

[0107] Analysis of activation of the sensor panel by various sensors versus background was also performed for (Figure 13A) exogenous recruitment with MCP-ADAR2dd, (Figure 13B) exogenous recruitment with ADAR1 p150 isoform, (Figure 13C) exogenous recruitment with ADAR2, and (Figure 13D) no recruitment with exogenous ADAR.

[0108] The optimal exogenous ADAR amount was also examined (FIG. 14). In a titration experiment for tetracycline-inducible IL6, with the target (IL6) amount fixed at 20 ng, HEK293 cells were transfected with varying amounts of MCP-ADAR2dd ranging from 10 ng to 100 ng as well as three-site avidity-linked IL6 sensor chain ranging from 10 ng to 100 ng. The fold change represents the normalized luciferase ratio between the group with and without the target.

[0109] Example 5: Use of the Programmable A to I(G) Replacement RNA Editing (REPAIR) System for Biological Sensors We next assessed the feasibility of the programmable A to I(G) replacement RNA editing (REPAIR) system, which could be utilized as a mechanism to trigger these genetic sensors. The REPAIR system consists of a fusion of a catalytically active enzyme, Cas13b molecule, fused to the deaminase domain of ADAR2. A catalytically inactive enzyme, Cas13b, incorporating the mutation K370A, was also fused to the deaminase domain of the ADAR2 molecule to form a fusion protein without Cas13b activity (REPAIR K370A).

[0110] Two guide strands designed to target EGFP, as well as a negative control designed not to target EGFP, were introduced into HEK293FT cells without the addition of exogenous ADAR molecules (Figure 15, blue bars). Guide strands were also introduced into HEK293FT cells simultaneously with REPAIR molecules (Figure 15, red bars) or catalytically inactive REPAIR K370A molecules (Figure 15, white bars). Guide 1 and guide 2, designed to be targeted by Cas13b, did not show increased luciferase expression in cells compared to cells that relied solely on endogenous ADAR expression. However, when Cas13 activity was turned off (REPAIR K370A), guide 1 showed a four-fold increase in luciferase expression compared to cells that relied solely on endogenous ADAR expression. This increased expression supports the potential of the REPAIR system to be used with the genetic sensor of the present disclosure.

[0111] [Example 6] Examination of variation in guide strand characteristics to increase luciferase expression Next, guide strand design features are varied to determine which variables can be adjusted to increase gene sensor efficiency and expression. Figure 16 presents a heat map showing luciferase expression fold against the variation of guide / ADAR combination. Exogenously introduced full-length ADAR (column 2) consistently exhibits the highest fold increase in luciferase expression. However, this is not the case for the guide strand designed as MS2 agonist, which exhibits almost consistent expression regardless of introduced ADAR.

[0112] By selecting the low background level from Example 4, we started further optimization of the sensor with the ADAR1 p150 construct. To improve both binding stability and target search time, increasing guide length around the premature stop codon (Qu et al., 2019) was examined against the constitutive target iRFP. As guide length increased from 51 nucleotides to 600 nucleotides, sensor activation improved from 2.2-fold to 18.22-fold (Figure 17B). In addition, in the presence of target, a significant shift in the distribution of mNeon expression levels per cell was observed for all guide lengths (Figure 17C, Figure 18A), with substantial mNeon(+) populations increasing with increasing guide length. Meanwhile, in the absence of target, the percentage of mNeon(+) cells remained consistently <5% for all guide lengths. With a guide length of 600 nucleotides, 66.7% mNeon(+) cells were observed in the presence of the target and 1.6% mNeon(+) cells in the absence of the target, suggesting a robust ability to separate cell populations based on expression of the target mRNA.

[0113] The two best performing ADARS from Example 4, ADAR1 p150 and MCP-ADAR2dd(E488Q, T490A), achieved optimal signals through reduced background or increased activation. Because engineering the enzyme ADAR, MCP-ADAR2dd(E488Q, T490A) resulted in the highest activation by the luciferase sensor ( FIG. 20 ), we hypothesized that a guide engineering strategy that reduces background would result in maximum activation of the optimal sensor when coupled with MCP-ADAR2dd(E488Q, T490A).

[0114] We designed a new sensor targeting IL6 mRNA, a virtually unexpected transcript in HEK293FT cells (Uhlen et al., 2015), allowing us to both supplement IL6 mRNA via exogenous transfection and generate an integrated diagram of IL6 mRNA under the control of a doxycycline-inducible promoter that modulates low levels of IL6 expression for sensitivity testing (see Figure 20).

[0115] Due to increased background signal in the absence of target, potentially due to readthrough of a stop codon within the long guide region (Figure 21 (A) MCP-ADAR2dd(E488Q, T490A) exogenous recruitment, (B) exogenous recruitment with ADAR1 p150 isoform, (C) exogenous recruitment with ADAR2, and (D) no exogenous ADAR recruitment), we engineered the guide region to block aberrant translation by introducing an MS2 hairpin loop (Chao et al., 2008), which provides the added benefit of recruiting the MCP-ADAR2dd(E488Q, T490A) protein to the guide:target duplex (Figure 23). We have repeated the search for increasing the guide region with luciferase sensor targeting IL6 transcripts, and found a significant reduction in the fold change of ADAR sensor activation over 81 nucleotide guides using MCP-ADAR2dd(E488Q, T490A) construct (Figure 22) when only one binding site is present on the guide strand. However, structural additions and modifications to the guide strand design, which were carried out to determine whether the addition of MS2 hairpin loops and further manipulation of the guide binding region (referred to as "avidity binding region") on the sensor / guide strand enhanced sensitivity, showed a significant increase in ADAR sensor activation (Figure 22). Figure 22 presents the results of an experiment to determine the fold change for guide / sensor strands with different formats. HEK293 cells were transfected (Lipofectamine 3000, Thermo Fisher Scientific) with naive reverse complements (reverse complements of the target IL6) and sensors containing MS2 hairpins with additional avidity regions (x-axis: 51 bp sensor, sensor with 171 bp continuous binding region, sensor with 171 bp avidity region separated by two MS2 hairpins, sensor with 225 bp single continuous binding region, sensor with 225 bp binding region separated by four MS2 hairpins, sensor with 279 bp continuous binding region, sensor with 279 bp binding region separated by six MS2 hairpins and non-targeted sensor).Separation of the binding regions by an MS2 hairpin significantly increased target expression in all sensors with varying avidity region lengths. Several types of ADAR proteins were examined, including full-length ADAR2 (ADAR2FL), the p150 isoform of ADAR1, fusion proteins of MS2 coat binding protein fused to the deaminase domain of endogenous ADAR1 and human ADAR2. Fold change in expression (y-axis) is calculated by raw luciferase value under target condition relative to raw luciferase value under target-free condition.

[0116] Activation of ADAR sensors was highest with 5-site avidity-linked guides, achieving approximately 70-fold activation over uninterrupted guide designs and substantially lower background (Figure 27). Avidity-linked guides improved performance for all exogenous ADAR constructs, but supplementation with MCP-ADAR2dd showed the greatest increase in performance. Avidity-linked guides with 5 or 7 binding sites can provide detectable activation relying solely on endogenous ADAR. Figure 23 presents several formats of possible MS2 hairpin / avidity modifications, and Figure 24 provides design guidelines for avidity sensors and an easy-to-use software program (github.com / abugoot-lab / ADARSENSOR) that automatically creates avidity sensors for input target sequences.

[0117] We investigated whether the best performing IL6-targeting engineered guide RNA could utilize endogenous ADARs to sense IL6, a synthetic target transfected into cells. We found that supplementation with exogenous ADAR1p150 improved the performance of RADARSv2, but observed over 50-fold activation of the payload by endogenous ADARs (Figure 52).

[0118] To further explore the concept of avidity-linked guides, we varied the spacing between binding sites (5, 30, and 50 nucleotides). The length between binding sites represents the number of nucleotides on the guide strand starting just before the MS2 hairpin and ending at another complementary region. Closely spaced binding sites on the target transcript resulted in the highest degree of activation (Figure 25).

[0119] We also explored whether avidity-linked guide improvement could be combined with orthogonal methods to block translational read-through, such as additional stop codons. We compared a single stop codon 7 avidity region sensor or a double stop codon 7 avidity region sensor with an additional stop codon in the most posterior avidity region (Figure 26). Figure 26B shows the fold change of luciferase payload between the single stop codon sensor and the double stop codon sensor. The double stop codon sensor showed a significant increase in fold change compared to the single stop codon sensor that relies on ADAR. We found that the additional stop codon increased the activation fold for the 7-site avidity-linked guide over the performance of the 5-site avidity-linked guide (Figure 28A). This improvement was driven by both a reduction in background activation rates and an increase in stop codon editing rates in the presence of target (Figure 28B, Figure 8).

[0120] Despite the abundance of codons, CCA, on potential target transcripts, we explored whether incremental manipulation of avidity guide design could improve mismatch tolerance to increase targeting flexibility. We also investigated target mismatch tolerance (Figure 29A) at 16 possible mismatches (derived from canonical CCA) between 51 bp naive, 3-avidity, and 5-avidity sensors. We designed 16 targets (nCn) covering all nucleotide changes to 5' cytosine or 3' adenosine. Looking beyond these codon variations to guides containing UAG, we found that mismatches with guanine or cytosine were generally better tolerated than mismatches with adenosine or uridine (Figure 29B). Furthermore, with the exception of targets ACA and ACU, the five-site avidity-linked guide-ADAR sensor design yielded the best activation fold change (Figure 30).

[0121] The modularity of protein payloads also allows for small payloads, such as HiBit payloads (Schwinn et al., 2018), which allow for the circularization of transcripts in vivo. Circular RNAs present a platform for enhanced residence time and minimal immunotoxicity (Katrekar et al., 2019), and we hypothesized that ADAR sensors with small payloads could be circularized to exploit these properties (Figure 31A). First, two forms of short circular sensors were developed. The canonical circular sensor is a scaffold of a dual twister ribozyme system (Litke and Jaffrey, 2019) driven by a U6 promoter, and circularizes in vitro in the presence of RtcB ligase. The canonical circular sensor also contains a HiBit tag, along with a stop codon at the C-terminus of HiBit. We found that the circular ADAR sensor expressed HiBit in a target (IL6) specific manner (Figure 31B). To amplify the signal, we enhanced these circular ADAR sensors as endless ADAR sensors by removing the stop codon at the end of the payload and inserting 2A peptides at either end of the HiBit tag to enable expression via rolling circle translation (RCT) (Abe et al., 2015). These rolling circle translation sensors are similar to conventional circular sensors, except that the stop codon in HiBit is removed and a T2ToA peptide is inserted to enable circular read-through by the ribosome. HEK293 cells were transfected with these circular sensors (targeting IL6) and sensors of varying lengths were compared for sensor activation. The long sensor consistently increased the fold change in sensor activation. We found that rolling circle sensors can direct proteins to target-specific expression with minimal background leakage (FIG. 31B).

[0122] We investigated the effect of mRNA modification on the fold change of sensor activation. Synthetic mRNAs have emerged as useful therapeutic modalities, but there is no way to control their payload expression in a transcript-specific manner. We explored the application of synthetic mRNA ADAR sensors for transcript-specific expression in a mouse model expressing human SERPINA1 transcripts in mouse hepatocytes. When delivering mRNA, the incorporation of base modifications such as 5' methylcytosine (5mc) and pseudouridine (Ψ) is essential to reduce immune responses by the host (Kauffman et al., 2016), but these modifications may interfere with ADAR activity and affect the sensor function of mADAR.

[0123] The incorporation of 5-methylcytosine and pseudouridine was analyzed in HEK293 cells. 24 hours before mRNA transfection, HEK293 cells were supplemented with MCP-ADAR2dd as a plasmid or directly as mRNA. IL6 sensor with tetracycline-inducible IL6 was also used. We found that increasing the amount of Ψ reduced the activation of ADAR sensor, whereas 5mc was more tolerated, and 25% incorporation of 5mc resulted in the highest signal activation (Figure 32).

[0124] Using our inducible IL6 system to measure sensor activation, we further assayed the effect on the activation of mRNA RADARS for different incorporation levels of a large panel of chemically modified bases, transfecting modified IL6-sensing mRNA RADARS with exogenous ADAR1p150 (Figure 53A) or endogenous ADAR (Figure 53B). We found that all tested modifications reduced the activation of mRNA RADARS, likely due to interference with the ability of ADAR1p150 to edit modified mRNA. Among the modifications, we found that in the case of exogenous ADARp150, 50% incorporation of modified bases, such as 5-methylcytosine or 5-methyluridine, was best tolerated, and 100% incorporation of 5-methylcytosine resulted in the highest activation by endogenous ADAR. To determine whether this level of modification was sufficient to reduce the host immune response, we assayed the induction of interferon beta-related genes by chemically modified mRNA RADARS. We observed that even at a 25% incorporation level of modified bases, we achieved minimal induction of inflammatory genes (Figure 53C).

[0125] We investigated a sensor called RADARSv1, which contains a 51-nucleotide IL6 transcript sensing guide in front of a Gaussia luciferase (Gluc) payload, in combination with a constitutive Cypridiana luciferase (Cluc) on a separate transcript to provide ratiometric control over transfection variation (Figure 54A). With this RADARSv1 design and co-transfection with exogenous ADAR1p150, we observed approximately 5-fold activation (Figure 54A), quantified by an increase in the Gluc / Cluc ratio in the presence of expression of the exogenous target IL6 (Figure 54B; elsewhere in this paper, the change in the Gluc / Cluc ratio between conditions is defined as the RADARS activation fold). Because ADAR1p150 prefers long double-stranded RNA as a substrate, we titrated the guide region over lengths from 51 nucleotides to 279 nucleotides, resulting in a slight increase in activation at 81 nucleotides, but with increasing lengths, activation was reduced due to increased background payload expression in the absence of target RNA (Figure 54A, Figure 54B).

[0126] Three strategies were used to prevent the formation of dsRNA in the absence of target, which is due in part to translational read-through and self-folding. First, we introduced binding sites interspersed with multiple MS2 hairpin loops into the guide region to create secondary structure to prevent self-folding and allow multivalent binding. We optimized these engineered guides, termed assembled guide RNAs (engineered guide RNAs), by varying the number of MS2 loops and binding sites on the guide. RADARS activation was highest with an engineered guide RNA containing five binding sites interspersed with MS2 hairpin loops, which reduced background payload expression in the absence of target compared to uninterrupted guide designs and achieved approximately 20-fold activation (Figure 54A, Figure 54B).

[0127] Next, we increased the length of the translatable open reading frame (ORF) in front of the engineered guide RNA to promote termination and prevent ribosome re-triggering, which is known to be dependent on the upstream ORF length. We investigated 5' peptide lengths at ORF lengths of 0, 100 and 200 residues, and found that at 200 residues, we were able to substantially reduce background translational read-through (Figure 55A) and achieve >100-fold activation.

[0128] Finally, we engineered stop codons in the +1 and +2 frames following the engineered guide RNA region to capture translating ribosomes across all frames. These out-of-frame stop codon designs synergized with the long 5' peptide to significantly reduce background and result in approximately 200-fold activation. We selected this sensor design, called RADARSv2, incorporating a structured guide, upstream peptide, and out-of-frame stop codon as a unifying structure for future sensors (Figure 54A, Figure 55B).

[0129] We assessed our RADARSv2 design across exogenously expressed targets IL6, EGFP, and neuropeptide Y (NPY) by tiling engineered guide RNAs across 14 CCA sites distributed across the transcripts. We found that while RADARS activation depends on the hybridization site chosen for a given target, the majority of sensors resulted in substantial payload activation in the presence of their targets, with activation less than 1,000-fold, demonstrating the generalizability of the RADARSv2 design (Figure 56A). To confirm that payload expression resulted from RNA editing, we harvested RNA from cells transfected with a panel of 14 IL6-targeting engineered guide RNAs and quantified editing by next-generation sequencing. In the presence of the target transcript, all 14 engineered guide RNAs resulted in over 15% editing, with an average of 35.1% ± 11.4%. In the absence of target transcripts, 13 of 14 engineered guide RNAs resulted in minimal editing (0.32%±0.34%). We also observed minimal editing by non-targeting sensors, reaffirming that RNA editing by RADARS sensors requires recognition of the target RNA by a specific engineered guide (Figure 56B).

[0130] [Example 7] Quantitative / correlation analysis of gene sensors To determine whether the above gene sensor can be used as a "dose-sensitive" sensor, an inducible EGFP transcript was introduced into HEK293FT cells. The EGFP transcript was placed under the control of a doxycycline-inducible promoter, and the cells were then exposed to 0 ng / mL, 8 ng / mL, 40 ng / mL, or 200 ng / mL doxycycline to vary the expression of the EGFP transcript. In HEK293FT cells that were not transfected with exogenous ADAR (Figure 6A), none of the guide strands showed significant differences in luciferase activity. However, guide strand 3 showed a trend toward dose sensitivity (Figure 33A, white bar).

[0131] When full-length ADAR2 was introduced into cells simultaneously with the guide strands (Figure 33B), guide strand 3 displayed a clear dose sensitivity, whereas guide 1 displayed some dose sensitivity, but only to a lesser extent (Figures 33B-33C, white bars). Cells treated with a dose of 200 ng / mL doxycycline showed similar luciferase activity as cells constitutively expressing EGFP transcripts. As the dose of doxycycline was reduced, there was a corresponding reduction in the luciferase activity identified in the cells. This same trend was seen in cells exposed simultaneously to full-length ADAR2 and guide strand 1 (Figure 33B, blue bars). Figure 34 depicts dose-dependent reporter expression when guide strand 1 (Figure 34A) and guide strand 3 (Figure 34B) targeting EGFP were introduced into cells. The luciferase fold activation also follows this dose-dependent trajectory (Figure 33C). Figure 35 depicts the level of luciferase activity as a function of GFP fluorescence. These results indicate that these gene sensors may be useful as quantitative sensors for transcript levels and not just as "on / off" sensors.

[0132] We compared the expression of the target with the highest transcript per million (TPM) gene (RPS5) and the lowest expression gene (KRAS) by siRNA perturbation experiments to compare how the performance of RADARSv2 varies between exogenous ADAR1p150 and endogenous ADAR. We observed that both sensors detect siRNA knockdown without supplementation of exogenous ADARp150, but the RPS5 sensor benefited more from exogenous ADAR (Figure 57), suggesting that large expression changes may benefit more from exogenous ADAR.

[0133] To further explore the quantitative value of the ADAR sensor, we measured the luciferase response of a 7-site avidity-linked guide with a double stop codon, which resulted in a wide range of expression levels, both by transfected and virally integrated forms of our inducible IL-6 expression system (Figure 36A). We found that the activation of luciferase by the ADAR sensor was linearly correlated with the concentration of the target transgene confirmed by qPCR (Figure 37, Figure 38, R2 = 0.96). Thus, RNA editing of the first stop codon in the ADAR sensor guide resulted in a strong correlation with gene expression levels (Figure 39), indicating that the ADAR sensor can quantitatively sense transcripts at both the RNA editing level and the payload level.

[0134] To enable single-cell measurements with RADARSv2, we engineered a fluorescent payload for microscopy and flow cytometry-based readout (Figure 58A). We designed a fluorescent sensor as a single transcript containing a self-cleaving peptide sequence, T2A, in front of the mNeon payload, followed by an upstream mCherry normalization control separated by a self-cleaving peptide sequence, P2A, from the best EGFP-targeted engineered guide RNA. We transfected HEK293FT cells with EGFP-targeted RADARS with or without a combination of exogenous ADAR1p150 or a frameshifted non-fluorescent EGFP target transcript. We observed mNeon fluorescent signal by microscopy in the presence of the target transcript and negligible background in the absence of the target transcript (Figure 58A). Quantification of the fluorescent signal by flow cytometry revealed a shift in the distribution of mNeon / mCherry ratios from 1.00% mNeon / mCherry positive cells in the absence of the target transcript to 56.1% mNeon / mCherry positive cells in the presence of the target transcript, resulting in a 38-fold increase in the geometric mean ratio (Figure 58B, Figures 59A-59C).

[0135] To further explore the quantitative accuracy of RADARS, we used transfected and virally integrated versions of the tetracycline-inducible IL-6 expression system to provide a wide range of expression levels and measure luciferase responses with the best IL-6 sensing engineered guide RNAs. Luciferase activation by RADARS was quantitative and showed a linear correlation with the concentration of the target transgene as confirmed by qPCR (Figure 60A, Figure 60B, R2 = 0.95). Furthermore, RADARS activation was invariant to the amount of transfected sensor, and activation rates were robust across large titers of sensor loading, allowing fine tuning of the total sensor output independent of total sensor activation (Figure 60C).

[0136] For validation, we further designed sensors for a panel of 10 different transcripts with TPMs ranging from about 10,000 to about 10 in HEK293FT cells. For each transcript, we compared 8 different targeted engineered guide RNAs with 8 non-targeted engineered guide RNAs. After siRNA transfection, we verified the knockdown of these 10 genes by qPCR (Figure 61) and observed a significant reduction in RADARS signal for each transcript compared to the non-targeted sensor control. The robustness of engineered guide RNAs was related to expression: for highly expressed genes, the majority of the 8 different targeted engineered guide RNAs detected knockdown of the target transcript, whereas for genes with low expression levels, only a few engineered guide RNAs were successful in detecting knockdown (Figure 63A). As TPM decreased, the sensitivity of RADARS also decreased, but at least one of the eight engineered guide RNAs tested was able to detect transcript knockdown significantly (Figure 62A). These data suggest that RADARS is sensitive to relative changes in gene expression across a wide range of expression levels. By measuring the editing rate of the stop codon UAG for the best performing sensor for each of the 10 target transcripts (Figure 62B), we found that the overall editing rate was also low for all 10 genes, but there was a statistically significant reduction in editing rate when the target was knocked down. Because RADARS is overexpressed compared to endogenous targets with low turnover rates, the editing rate of the stop codon becomes less sensitive to perturbations in the copy number of the target.

[0137] We next attempted to determine the sensitivity of RADARS by measuring changes in gene expression of endogenous targets. To study RADARS across a range of expression levels of endogenous transcripts, we applied sensors to measure siRNA-mediated transcriptional downregulation (Figure 63). We utilized a commercially available validated siRNA pool targeting six endogenous genes, divided into highly expressed genes RPL41, GAPDH, and ACTB, and moderately to low expressed genes HSP90AA1, PPIB, and KRAS. For each transcript, we first compared eight different engineered guide RNAs for the highest sensitivity to knockdown (Figure 63A). We then titrated the amount of siRNA that resulted in a range of expression levels confirmed by qPCR, and tracked changes in expression levels using the best engineered guide RNA supplemented with exogenous ADAR1p150. We observed that for all six genes, RADARS tracked transcript levels measured by qPCR with a high degree of Pearson correlation (R>0.86, FIG. 61B). We found that for KRAS expressed at 13 transcripts per million (TPM) in HEK293FT cells (Karlsson et al., 2021), the raw activation fold for RADARS activation deviated from the fold change measured by qPCR, likely due to loss of sensitivity at such low expression levels. However, the RADARS response was still highly correlated with KRAS levels determined by qPCR (R=0.93, FIG. 61B).

[0138] Next, we explored whether RADARS could sense the upregulation of endogenous transcripts using a cellular heat shock model that results in the upregulation of heat shock family genes. We designed RADARSv2 engineered guide RNAs targeting HSP70, a dynamic heat shock response protein, and transfected them into HeLa cells with exogenous ADAR1p150 before exposing the cells to heat shock at 42°C (Figure 64A). RADARS showed strong agreement with qPCR, with the best HSP70-targeted engineered guide RNA resulting in a 5.9-fold activation in response to heat shock, compared to a 7.2-fold increase in HSP70 transcript expression levels measured by qPCR (Figure 64B). These results suggest that RADARS is sensitive to the upregulation of endogenous transcripts and can detect relative gene expression changes with high fidelity.

[0139] [Example 8] Logic gate We also attempted to determine whether the ADAR sensor of the present disclosure could be multiplexed into a logic system that could include AND and OR gates. These AND / OR methods are shown in schematic form in FIG. 40A (AND) and FIG. 40B (OR). The AND gate can deliver the payload completely only if both target strands are present. However, the OR gate can deliver the payload in the presence of one of the target strands, but not in the presence of both target strands. To create a rudimentary AND gate, we connected two single guides of 51 nucleotides each, targeting EGFP and IL6, respectively, in tandem with an MS2 hairpin loop. However, this design performed poorly due to a combination of low signal and background read-through (FIG. 40A).

[0140] To improve the signal of the AND gate, we used the RADARSv2 design and found that the resulting AND gate sensor behaved target-specifically, requiring both targets to reach full activation, with negligible leakage in single-target conditions (Figure 65A, Figure 65B). The AND logic chain exhibited 36-fold activation in the presence of both target transcripts, and only 1.3-1.5-fold activation in the presence of only one target RNA (Figure 65B).

[0141] To engineer an OR logic gate, we co-transfected two 5-binding site-type avidity sensors targeting EGFP and IL6. These sensors responded to the target EGFP or IL6 transcripts in a manner consistent with the OR gate (Figure 41B). The OR logic sensor exhibited a significant increase in fold change in the presence of each gene individually, but not in the absence of both genes. Overall, these results suggest that the modularity of ADAR sensors allows logic operations to be performed on mRNAs in living cells.

[0142] To improve the OR logic gate (Figure 66A), we co-transfected two engineered guide RNAs, RADARSv2 (upstream ORF, out-of-frame stop codon), targeting EGFP and IL6 transcripts, and found that the sensor responded to the targeted EGFP or IL6 transcripts in a manner consistent with the OR gate (Figure 66B).

[0143] Example 9: Use of ADAR sensors to induce apoptosis in target cells To determine whether the ADAR sensor of the present disclosure can be used for payloads other than reporters, we determined whether the ADAR sensor can induce apoptotic cell death in target cell populations. To apply the ADAR sensor to cell state specific killing, we engineered a payload with therapeutically relevant iCaspase-9 (Straathof et al., 2005) (Figure 42A). The payload iCaspase was incorporated into a sensor chain that can target human IL6. Mammalian cells were transfected with ADAR sensor for caspases, target and MCP-ADAR2dd. 24 hours after transfection, cells were split 1:5 into fresh medium and samples with drugs were supplemented with 10 nM AP20187 (Sigma Aldrich). After 24 hours of further growth, cells were assayed for viability by CellTiter-Glo Luminescent Cell Viability Assay (Promega). The control caspase was a sensor strand with a scrambled sensor region (i.e., the sensor strand does not specifically target IL6) and a caspase without an intervening stop codon. CellTiter-Glo Assay (Promega) was used to measure cell death as the fold change in luminescence value of cell lysates of the target group over the non-target group. We found that fusion of an IL6 sensor using a double stop codon 7 avidity guide in front of a caspase selectively kills IL-6 expressing cells with minimal toxicity in the absence of IL-6 induction (Figure 42B, 42C). IL6-responsive caspases exhibited a significant increase in the induction of apoptotic cell death, indicating that ADAR sensors can be used to induce cell death in target cell groups. See Figure 42B. The percent cell survival in cells treated with IL6-responsive caspases and caspases without stop codons was also analyzed in cells with and without the target transcript (Figure 42C).

[0144] Next, we used the highly specific SERPINA1-targeting engineered guide RNA for cell-specific killing by combining the engineered guide RNA with the payload iCaspase-9 (Figure 67A) (Straathof et al., 2005). We co-transfected A549, HeLa and HepG2 cells with RADARS against SERPINA1-iCaspase9 together with ADARp150 and assayed cell viability 48 hours after transfection. We found that SERPINA1-targeting RADARS-iCaspase selectively killed HepG2 cells with minimal toxicity in other cell types, and the non-targeting negative control showed no differential death (Figure 67B, Figure 67C).

[0145] Example 10: Use of ADAR sensors to track cell state and cell type To determine whether the developed ADAR sensor can be used to track cell status, we first examined the heat shock response of HeLa cells. Two sets of HeLa cells were transfected with ADAR sensors whose guides were designed to target heat shock family genes, including HSP70 and HSP40. HSP70 and HSP40 can be upregulated in an in vitro heat shock model (Figures 43A, 43B). ADAR sensors with 5-site avidity-linked guide designs or 7-site avidity-linked guide designs detected the upregulation of both HSP70 and HSP40 in cells exposed to heat shock (Figure 43A). HeLa cells (ATCC CCL-2) were transfected with ADAR sensors for HSP40 or HSP70. 24 hours after transfection, a portion of the cells were shifted to 42 degrees Celsius (5% CO2) for 24 hours. At the end of 24 hours of heat shock, the medium was collected and subjected to luciferase measurement.To control for non-specific changes to translation as a result of heat shock, we transfected scrambled non-targeted guide.By normalizing to non-targeted guide, we found that the activation of ADAR sensor in response to heat shock is 3-fold or less (Figure 43C).

[0146] We repeated the heat shock experiment with the RADARSv2 design, delivering only the RADARSv2 sensor, without supplementing ADAR and designed sensors (Figure 64). We found that the best HSP70 sensor (CCA42) exploits endogenous ADARs in Hela cells to track HSP70 upregulation upon heat shock (Figure 68), thus supporting the feasibility of a single-component RADARSv2 system deployed with endogenous ADARs.

[0147] Cell type differences represent major variations in gene expression within tissues. Thus, we attempted to determine whether ADAR sensors could accurately track cell type differences. First, to identify marker transcripts for significant cell type differences, we performed differential gene analysis between HEK293, Hela, and HepG2 cells (Figure 44A), and selected SERPINA1, a hepatic serine protease inhibitor with therapeutically relevant pathogenic variants (Boelle et al., 2019), as a marker expressed only in HepG2 cells and not in other cell lines (Figure 44B). We designed a panel of ADAR sensors with guides targeting SERPINA1 and investigated their ability to discriminate between HepG2 and Hela cells, and found that the CCA30 guide design produced the greatest activation fold change between HepG2 and Hela cells (Figure 44C). We transfected three different cell types with a SERPINA1 (CCA30) targeted sensor together with a non-targeted scrambled sensor designed to control for background ADAR editing, transfection variability, and protein production and secretion differences between the three cell types. Each cell type was transfected with a CCA30 SERPINA1 sensor with five avidity regions connected by MS2 hairpins, with or without MCP-ADAR2dd. The fold change (Figure 44D) contributing to protein production / secretion and background ADAR kinetic differences between cell types was calculated via raw luciferase values ​​from the SERPINA1 sensor normalized by the scrambled non-targeted sensor, followed by normalization to the ratio in HEK cells for comparison between cell types.

[0148] The normalized fold change in editing rates in all three cell types in the presence of endogenous ADARs, supplemented ADARs, and controls was also analyzed (Figure 45). Various CCA sites on the SERPINA1 transcript were also used as targets.

[0149] To model liver-specific cell targeting in vitro, we expressed human SERPINA1 transcripts in Hepa-1-6 cells, synthesized top CCA SERPINA1 sensors as mRNA in vitro, and transfected Hepa-1-6 cells with the mRNA sensors alone. We found that both SERPINA1 sensors targeting CCA30 and CCA35 were able to recruit endogenous ADARs to sense the induction of SERPINA1 transcripts in Hepa-1-6 cells (Figure 46).

[0150] To assess RADARSv2 for cell type discrimination, we first leveraged the modular nature of RADARS to design a system for permanent genetic labeling of cell populations. We designed a dual loxP system for conditional / permanent labeling of cells with EGFP upon Cre expression and examined this reporter in combination with ADAR1p150 and IL6-targeted engineered guide RNA with Cre payload in HEK293FT cells. Upon IL6 induction, we observed significant production of EGFP protein with minimal signal in the absence of target RNA (Figure 69A, Figure 69B).

[0151] Using the RADARSv2 design, we then identified SERPINA1 as a differentially expressed marker gene in the liver-derived cell line, HepG2, compared to two non-liver cell lines, A549 and HeLa (Karlsson et al., 2021), which do not express SERPINA1. Using a SERPINA1-targeting engineered guide RNA to selectively activate Cre in HepG2 cells (Figure 70A), we co-transfected this sensor with ADAR1p150 and Cre loxP reporters into HepG2, Hela and A549 cells and assessed activation against a non-targeting RADARS construct. Whereas non-targeting engineered guide RNAs showed no reporter activation in either cell type, targeted engineered guide RNAs showed significant activation of the EGFP reporter only in HepG2 cells (Figure 70B, Figure 70C). These results establish that the RADARS system can distinguish cell types based on specific markers, and that engineered guide RNAs and payloads can be combined in a modular manner for cell-type specific expression of diverse transgenes.

[0152] Example 11: Use of ADAR sensors in vivo Next, to determine whether ADAR sensors can be used in vivo, we investigated SERPINA1 sensors in mice. We synthesized ADAR sensors targeting the CCA30 and CCA35 sites of human SERPINA1 in constructs expressing Akaluciferase (Akaluc) (Yeh et al., 2019), which allows for easy non-invasive luminescence imaging to confirm cell-specific ADAR sensor activation (Figure 47). Prior to bioluminescence imaging, 8-10 week old Albino B6 and NSG-PiZ mice were anesthetized with 3% isoflurane and injected with 5 μg of synthetic mRNA via retro-orbital injection using in vivo-jetRNA transfection reagent (Polyplus). 18 hours after injection, mice were re-anesthetized with 3% isoflurane and 100 μl of 15 mM AkaLumine-HCL (Sigma Aldrich) was rapidly administered for imaging. Ventral bioluminescence images were obtained using IVIS Spectrum In Vivo Imaging System (PerkinElmer). The following conditions were used for image collection: exposure time=60 seconds, binning=medium:4, field of view=12.5×12.5 cm, and f / stop=1. Bioluminescence images were analyzed using Living Image 4.3 software (PerkinElmer). Albino B6 mice do not express human SERPINA1, so they represent a negative control (no sites for binding to CCA30 or CCA35). To determine whether endogenous ADAR alone can edit administered ADAR sensors, mice were not administered with an additional enzyme, ADAR. The SERPINA1-sensing mRNA RADARS design resulted in significant activation of Akaluc expression in NSG-PiZ mice compared to NSG-WT mice (p=0.007, N=3 mice, one-way ANOVA), and we observed no significant differences between the two strains under substrate-only background luciferase and constitutive Akaluc mRNA RADARS conditions (Figures 71A-71C).

[0153] In addition to the ADAR sensor targeting the CCA30 and CCA35 sites of SERPINA1, we also designed Akaluc payloads with constitutive ADAR sensor or scrambled non-targeting guides that lack stop codons and express Akaluciferase (see FIG. 47B). Three sensor systems were examined. The SERPINA1-sensing ADAR sensor design resulted in significant activation of Akaluc expression in NSG-PiZ mice compared to wild-type mice (p=0.04, N=2 mice, unpaired two-tailed t-test), and the control guide did not result in significant differences between the two strains (FIG. 47B, FIG. 48). This activation supports that the ADAR sensor can be delivered as synthetic mRNA to sense cellular conditions in vivo by endogenous ADARs.

[0154] Further considerations Analysis of public tissue gene expression data (GTEx Consortium, 2013) shows that 34 of 37 tissues were identified by the sensor with a 3-fold sensitivity involving a single gene (Figure 49A), and an additional 3 tissues were classified by a combination of genes, supporting the straightforward application of the ADAR sensor to both sensitivity and logical input (Figure 49).

[0155] [Example 12] Examination of off-target effects and disruption of conventional cellular processes Since the RADARSv2 mechanism involves the formation of a long hybridization region between the sensor-driven guide RNA and the target transcript, we explored whether this duplex perturbs target transcript levels via Dicer knockdown or transcript stabilization. We compared target expression between the top-driven guide RNA condition and the non-targeting driven guide RNA sensor condition for each endogenous transcript knocked down via siRNA (Figure 63A) but found no significant change in target transcript expression (Figure 72A). In addition, to confirm that the resulting driven guide RNA-target hybridization did not interfere with endogenous translation, we co-transfected ADAR1p150 with ACTB-targeting or PPIB-targeting driven guide RNA into HEK293FT cells and quantified target protein levels by Western blot. Similar to mRNA levels, we observed that ACTB and PPIB protein levels were unchanged under targeted engineered guide RNA conditions compared to non-targeted engineered guide RNA conditions, supporting the lack of appreciable effects on RADARS-mediated target expression (Figure 72B, Figures 72C-72D).

[0156] Since the formation of dsRNA in cells can activate immune response pathways, we next investigated the RADARS-induced upregulation of the major endogenous innate immune signaling pathways involved in dsRNA response (IFNB1, MDA5, OAS1, and RIG-1) by qPCR using both ACTB and GAPDH as normalization genes (Figure 73A, Figure 73B). To compare with the positive control, we examined RADARS constructs targeting ACTB, PPIB, RPS5, and exogenously introduced IL6 transgenes together with high molecular weight poly(I:C), which acts as an analog dsRNA and activates these four pathways. We found that RADARS constructs did not significantly upregulate any of the four dsRNA-responsive transcripts, whereas poly(I:C) caused significant activation of all four pathways (Figure 73A). To generalize our findings across cell lines, we also examined the same set of RADARS constructs in the HepG2 human hepatocellular carcinoma cell line and observed that the RADARS sensor did not induce a dsRNA response (Figure 73C).

[0157] We then explored whether overexpression of ADAR1p150 leads to off-target editing in the transcriptome as observed with ADAR-based therapeutic RNA editing methods (Cox et al., 2017; Qu et al., 2019; Reautschnig et al., 2022). First, we profiled the regions surrounding the hybridization duplexes of PPIB and ACTB transcripts with RADARS-operated guide RNAs, but found no significant off-target editing due to sensor hybridization or overexpression of ADAR1p150 (Figure 74A). Next, to unbiasedly examine potential off-targets, we performed polyA mRNA sequencing of cells expressing PPIB sensor and ADAR1p150. We found that overexpression of ADAR1p150 in combination with PPIB-targeted RADARS resulted in only 23 detectable sites in the transcriptome, all of which showed less than 10% editing (Figure 74B). Moreover, in the absence of overexpression of ADARp150, we found no significant site editing with non-targeted engineered guide RNAs. In contrast, when we analyzed published RNA-seq data with overexpression of MCP-ADAR2(E488Q) deaminase domain, we detected >10,000 sites with significant A→I RNA editing, highlighting the impact of deaminase construct selection on off-target profiles (Figure 74C).

[0158] We did not find any significant homology between the engineered guide RNA and the sequence surrounding the off-target editing site (Figure 75A). Running the same sequencing and analysis on a different engineered guide RNA targeting the exogenous target IL6 in the presence of ADARp150 overexpression shows a low significant off-target editing rate (all 42 sites show less than 10% editing, Figure 74B). It is important to note that 22 of the 23 PPIB off-target sites were shared between the two different engineered guide RNA samples. Finally, we found that the sequence motif of the bases surrounding the editing site closely resembled the preferred substrate of ADAR1 (Eggington et al., 2011) (Figure 75B). Overall, these observations imply that RADARS, when used with ADARp150 overexpression, results in a relatively small amount of non-specific RNA editing within the transcriptome.

Claims

1. a) (i) A sensor domain comprising a first hybridization region and a stop codon editable by ADAR, and (ii) a synthetic single-stranded RNA (ssRNA) construct comprising a payload, and b) An adenosine deaminase acting on RNA (ADAR deaminase) An RNA sensor system comprising: The first hybridization region of the synthetic ssRNA sensor construct has a certain degree of sequence complementarity to a second ssRNA, and the second ssRNA is an endogenous ssRNA target in human cells. The certain degree of sequence complementarity enables hybridization of the first hybridization region to the endogenous ssRNA target, and is sufficient to form a bimolecular double-stranded RNA (dsRNA) duplex of the synthetic ssRNA and the second ssRNA, including a mispairing within the stop codon of the synthetic ssRNA sensor domain. The dsRNA duplex is a substrate for the ADAR deaminase, The mispairing is editable by the ADAR deaminase, and the editing can effectively remove the editable stop codon so as to enable translation of the payload from the synthetic single-stranded RNA construct. RNA sensor system.

2. The RNA sensor system according to claim 1, wherein the mispairing within the stop codon of the synthetic ssRNA sensor domain includes a mispairing of adenosine and cytosine, wherein the adenosine in the synthetic ssRNA sensor domain is opposite to cytosine in the endogenous ssRNA target in the dsRNA duplex.

3. The RNA sensor system according to claim 2, wherein the adenosine in the synthetic ssRNA sensor domain in the mispairing within the stop codon is edited to inosine by the ADAR deaminase.

4. The RNA sensor system according to claim 1, comprising two or more mispairings within the bimolecular dsRNA duplex.

5. The RNA sensor system according to claim 1, wherein the payload comprises a reporter protein, a transcription factor, an enzyme, a transgene protein or a therapeutic protein.

6. The RNA sensor system according to claim 5, wherein the payload comprises a therapeutic protein.

7. The RNA sensor system according to claim 1, wherein the payload comprises a fluorescent reporter.

8. The RNA sensor system according to claim 7, wherein the payload comprises an eGFP reporter or a luciferase reporter.

9. The RNA sensor system according to claim 1, wherein the payload comprises a caspase.

10. The RNA sensor system according to claim 1, wherein the ADAR is an endogenous ADAR or an exogenous ADAR.

11. The RNA sensor system according to claim 1, wherein the ADAR deaminase comprises a programmable A to I (G) substitution RNA editing (REPAIR) molecule, a Cas13b-ADAR fusion molecule, a Cas13d-ADAR fusion molecule, a Cas7-11-ADAR fusion molecule, and an MS2-ADAR fusion molecule, the deaminase domain of ADAR2, full-length ADAR2, or truncated ADAR2.

12. The RNA sensor system according to claim 1, wherein the synthetic ssRNA sensor domain further comprises a normalization gene.

13. The RNA sensor system according to claim 1, wherein the synthetic ssRNA sensor is a circular RNA.

14. a) An AND gate, or b) An OR gate A cell logic system comprising, wherein the a) AND gate (i) A synthetic single-stranded RNA (ssRNA) sensor construct, and (ii) An adenosine deaminase (ADAR deaminase) that acts on RNA Comprising, The (i) synthetic single-stranded RNA (ssRNA) sensor construct comprises (1) one or more payloads, and (2) a sensor domain comprising a plurality of ADAR-editable stop codons, at least a first hybridization region, and a second hybridization region, wherein the first hybridization region and the second hybridization region have a certain degree of sequence complementarity to different ssRNA targets. The first hybridization region of the synthetic ssRNA sensor domain enables hybridization of the first hybridization region to a first endogenous ssRNA target, comprises one of a plurality of ADAR-editable stop codons, and has a degree of sequence complementarity sufficient to form an RNA complex of the first hybridization region and the first endogenous ssRNA that is a substrate for the ADAR deaminase. The second hybridization region of the synthetic ssRNA sensor enables hybridization of the second hybridization region to the second endogenous ssRNA target, includes one of the plurality of ADAR-variable stop codons, and has a degree of sequence identity sufficient to form an RNA complex of the second hybridization region and the second endogenous ssRNA target that is a substrate for the ADAR deaminase. The substrate includes a mispairing within the ADAR-editable stop codon. The editing can effectively remove the stop codon to enable translation of one or more payloads. The b) OR gate includes (i) a plurality of independent synthetic ssRNA sensor constructs, and (ii) an adenosine deaminase acting on RNA (ADAR deaminase). Each of the (i) independent synthetic ssRNA sensor constructs includes (1) one or more payloads, (2) an ADAR-variable stop codon, and (3) at least one first hybridization region having a degree of sequence complementarity to at least one endogenous ssRNA target. The first hybridization region of each synthetic ssRNA sensor construct enables hybridization of the first hybridization region to the at least one endogenous ssRNA target and has a degree of sequence complementarity sufficient to form a double-stranded RNA (dsRNA) duplex of the synthetic ssRNA and the endogenous ssRNA target that is a substrate for the ADAR deaminase and includes a mispairing within at least one ADAR-editable stop codon of the synthetic sensor domain. The substrate includes a mispairing within the ADAR-editable stop codon. The editing can effectively remove the stop codon to enable translation of one or more payloads. A cell logic system. ​

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