Enhanced Selection of Effective Targeted Genome Manipulators

Abstract

For enhanced selection of effective targeting of genomic manipulators (310), the device (102) includes a first chip-based biosensor and a second chip-based biosensor (104a, 104b) having one or more sensing surfaces (308a), the sensing surfaces (308a) being configured to detect biomolecular binding interactions between a nucleic acid sample (306) and a targeting genomic manipulator (310) with a capture surface (126) within a sensing range (328) functionalized to the sensing surface (308a). The first chip-based biosensor (104a) uses a nucleic acid sample (306) incubated with a blocker (318), and the second chip-based biosensor (104b) holds a nucleic acid sample (306) that omits the blocker (318). The measurement device (102) measures a first set of response signals and a second set of response signals (342a, 342b) indicative of the respective biomolecular binding interactions. The analysis module (116) determines a genomic manipulation efficiency parameter of the targeting genomic manipulator (310). The system (100) and method (300) perform the functions of the device (102).

Description

[0001] Cross - reference to related applications

[0002] This application claims priority to U.S. Provisional Patent Application No. 62 / 801,555, filed on February 5, 2019, entitled "Systems and Methods for Chip - Assisted CRISPR", U.S. Provisional Patent Application No. 62 / 866,312, filed on June 25, 2019, entitled "Systems and Methods for Electronic Detection of Cleavage and Collateral Activity of CRISPR - Associated Endonucleases", and U.S. Provisional Patent Application No. 62 / 883,887, filed on August 7, 2019, entitled "Devices and Methods for Label - free Detection of Analytes", the entire contents of which are incorporated herein by reference in their entirety to the extent permitted by law. Technical field

[0003] The subject matter disclosed herein relates to biosensor systems and assays, and more particularly to devices, methods, computer program products, and systems for enhanced selection of agents for effective targeting of genome manipulation. Background art

[0004] Targeted genome manipulation has become a very powerful tool in biology and medicine. For example, some targeted genome manipulation techniques perform gene editing through the cell's DNA repair mechanism to create double - strand breaks ("DSBs") at precise locations in the genome, and knock out specific genes by introducing indels at the DSBs. When co - transfected with a vector that produces a copy of a specific DNA sequence, targeted genome manipulation techniques can introduce new DNA sequences at the DSBs, thus allowing, for example, the replacement of an altered or dysfunctional gene with a working copy. Various screening and validation tools for genome manipulation targeting components include in vitro methods, in vivo methods, and computer (e.g., computer simulation) methods. Summary of the invention

[0005] One general aspect includes: a first chip-based biosensor and a second chip-based biosensor, the first chip-based biosensor and the second chip-based biosensor each may include one or more sensing surfaces configured to detect biomolecular binding interactions between a nucleic acid sample and one or more capture surfaces functionalized with a targeted genome manipulator, the targeted genome manipulator having a genome manipulation component and a targeting component, wherein the one or more capture surfaces are within the sensing range of the one or more sensing surfaces, and wherein the first chip-based biosensor is configured to hold a first aliquot of the nucleic acid sample optionally incubated with a blocker configured to bind to a sequence overlapping the target sequence of the nucleic acid sample, and the second chip-based biosensor is configured to hold a second aliquot of the nucleic acid sample that omits the blocker. The device further includes a measurement controller configured to measure one or more first response signals and second response signals generated in response to biomolecular binding interactions occurring between the nucleic acid samples in the first and second aliquots and the targeted genome manipulator on the functionalized capture surfaces of the first and second chip-based biosensors. The device further includes an analysis module configured to determine one or more genome manipulation efficiency parameters associated with the targeted genome manipulator based on performing a comparison of the first and second response signals. Other embodiments of this aspect include corresponding computer systems, devices, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.

[0006] One general aspect includes: preparing first and second aliquots that can each include a nucleic acid sample, wherein: the nucleic acid sample is measured to detect biomolecular binding interactions between the nucleic acid sample dispensed to one or more sensing surfaces and a targeted genome manipulator that has a genome manipulation component and a targeting component and is functionalized as a capture surface within the sensing range of the one or more sensing surfaces; optionally incubating the first aliquot with a blocker that is configured to bind to a sequence that overlaps the target sequence of the nucleic acid sample; and the second aliquot omitting the blocker. The method further includes: measuring one or more first response signals and second response signals generated in response to biomolecular binding interactions occurring between the nucleic acid samples in the first and second aliquots and the targeted genome manipulator on the functionalized capture surfaces of a first chip-based biosensor and a second chip-based biosensor; and determining an efficiency parameter of the targeted genome manipulator based on comparing the one or more first response signals with the one or more second response signals. Other embodiments of this aspect include corresponding computer systems, devices, and computer programs recorded on one or more computer storage devices, each of which is configured to perform the actions of the method.

[0007] One general aspect includes a computer program product that can include a computer-readable storage medium having program instructions implemented as the computer program product, the program instructions controlling the measurement of one or more first response signals and second response signals generated by a first chip-based biosensor and a second chip-based biosensor in response to biomolecular binding interactions occurring between a nucleic acid sample and a targeted genome manipulator that has a manipulation component and a targeting component and is functionalized as a capture surface within the sensing range of one or more respective sensing surfaces of the first chip-based biosensor and the second chip-based biosensor, wherein: the first chip-based biosensor is configured to hold a first aliquot of the nucleic acid sample optionally incubated with a blocker that is configured to bind to a sequence that overlaps the target sequence of the nucleic acid sample; and the second chip-based biosensor is configured to hold a second aliquot of the nucleic acid sample that omits the blocker. The product can further cause a processor to determine one or more genome manipulation efficiency parameters associated with the targeted genome manipulator based on performing a comparative analysis of the first response signals and the second response signals. Other embodiments of this aspect include corresponding computer systems, devices, and computer programs recorded on one or more computer storage devices, each of which is configured to perform the actions of the method. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] A more specific description of the embodiments briefly described above will be presented by reference to specific embodiments shown in the accompanying drawings. It should be understood that these drawings depict only some embodiments and should not be considered as limiting the scope. The embodiments will be described and illustrated with additional distinctiveness and detail by using the drawings, in which:

[0009] Figure 1 is a schematic block diagram showing a system for enhanced selection for effective targeting of genome editing agents according to one or more aspects of the present disclosure;

[0010] Figure 2 is a schematic block diagram showing a device for enhanced selection for effective targeting of genome editing agents according to one or more aspects of the present disclosure;

[0011] Figure 3A is a diagram showing a method for enhanced selection for effective targeting of genome editing agents according to one or more aspects of the present disclosure;

[0012] Figure 3B is an enlarged detail diagram showing an exemplary implementation of a blocking agent according to one or more aspects of the present disclosure;

[0013] Figure 4 is a diagram showing an exemplary implementation of using a chip-based biosensor with a biological gated transistor for enhanced selection for effective targeting of genome editing agents according to one or more aspects of the present disclosure;

[0014] Figure 5A shows an implementation of using a capture surface functionalized with a targeting genome editing agent according to one or more examples of the present disclosure;

[0015] Figure 5B shows an implementation of using a capture surface functionalized with a targeting genome editing agent according to one or more examples of the present disclosure;

[0016] Figure 5C shows an implementation of a sensing surface for detecting one or more capture surfaces functionalized with a targeting genome editing agent according to one or more examples of the present disclosure;

[0017] Figure 6 shows a method for determining binding efficiency parameters regarding a targeting genome editing agent immobilized on a sensing surface according to one or more examples of the present disclosure;

[0018] Figure 7A method for determining binding efficiency parameters of a targeted genome manipulator with respect to double-stranded nucleic acids immobilized on a sensing surface according to one or more examples of the present disclosure is shown;

[0019] Figure 8 Fragmentation and adapter ligation of a nucleic acid sample for sequencing after measurement of genome manipulation parameters according to one or more examples of the present disclosure are shown;

[0020] Figure 9 Fragmentation and adapter ligation of a nucleic acid sample for sequencing after measurement of genome manipulation parameters according to one or more examples of the present disclosure are shown;

[0021] Figure 10 Labeling of a nucleic acid sample for sequencing after measurement of genome manipulation efficiency parameters according to one or more examples of the present disclosure is shown;

[0022] Figure 11 Using a selected targeted genome manipulator for preparing a nucleic acid sample for sequencing according to one or more examples of the present disclosure is shown; and

[0023] Figure 12 Is a schematic flowchart showing a method for enhanced selection of an effective targeted genome manipulator according to one or more examples of the present disclosure. Detailed Description

[0024] As will be understood by those skilled in the art, aspects of the present disclosure may be implemented as a system, method, or program product. Accordingly, aspects or implementations may take the form of an entirely hardware implementation, an entirely software implementation (including firmware, resident software, microcode, etc.), or an implementation combining software aspects and hardware aspects, which forms are generally referred to herein as "circuitry", "module", "controller", or "system". In addition, aspects of the disclosed subject matter may take the form of a program product implemented in one or more computer-readable storage devices storing machine-readable code, computer-readable code, and / or program code (hereinafter referred to as code). The storage device may be tangible, non-transitory, and / or non-transmissive. The storage device may not include a signal. In one implementation, the storage device only employs a signal for accessing the code.

[0025] The specific functional units described in this specification have been labeled as modules or controllers to more specifically emphasize the implementation options available. For example, some functions of a module or controller can be implemented as hardware circuits including custom VLSI circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. A module or controller can also be implemented in programmable hardware devices such as field-programmable gate arrays, programmable array logic, programmable logic devices, etc.

[0026] Various modules or controllers can also be implemented in part or in whole with code and / or software for execution by various types of processors. The identified controllers or modules of code can include, for example, one or more physical or logical blocks of executable code that can be organized, for example, as objects, procedures, or functions. However, the executable portions of the identified controllers or modules need not be physically located together, but can include different instructions stored in different locations that, when logically combined, include the module and implement the stated purpose for the controller or module.

[0027] In fact, a controller or module of code can be a single instruction or many instructions, and can even be distributed over several different code segments, distributed in different programs, and spread across several memory devices. Similarly, operational data can be identified and illustrated herein as being within a module, and can be implemented in any suitable form and organized within any suitable type of data structure. The operational data can be collected as a single data set, or can be distributed over different locations, including distributed over different computer-readable storage devices. In cases where a controller, module, or a portion thereof is implemented in software, the software portion is stored on one or more computer-readable storage devices.

[0028] Any combination of one or more computer-readable media can be utilized. A computer-readable medium can be a computer-readable storage medium. A computer-readable storage medium can be a storage device that stores code. The storage device can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, holographic, micro-mechanical, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.

[0029] More specific examples (non-exhaustive list) of storage devices will include the following: electrical connections with one or more wirings, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. In the context of this document, a computer-readable storage medium can be any tangible medium that can include or store a program used by or in conjunction with an instruction execution system, apparatus, or device.

[0030] Code for performing the operations for some implementations can be written in any combination of one or more programming languages, which include object-oriented programming languages such as Python, Ruby, Java, Smalltalk, C++, etc. and conventional procedural programming languages such as the "C" programming language, etc. and / or machine language such as assembly language. The code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0031] References throughout this specification to "one aspect", "aspect", or similar language mean that a particular feature, structure, or characteristic described in connection with that aspect is included in at least one implementation. Thus, unless otherwise explicitly stated, the appearances of the phrases "in one implementation", "in an implementation", and similar language throughout this specification may, but do not necessarily, all refer to the same implementation, but rather mean "one or more but not all implementations". Unless otherwise explicitly stated, the terms "comprise", "comprising", "have", and their variants mean "including but not limited to". Unless otherwise explicitly stated, a list of recited items does not imply that any or all of those items are mutually exclusive. Unless otherwise explicitly stated, the terms "a", "an", and "the" also refer to "one or more".

[0032] In addition, the described features, structures, or characteristics of these aspects or implementations can be combined in any suitable manner. In the following description, numerous specific details are provided, such as examples of programming, software modules, user selections, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, etc., to provide a thorough understanding of the aspects and implementations. However, those skilled in the relevant art will recognize that the implementations can be practiced without one or more of the specific details or with other methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the implementations.

[0033] Aspects of the disclosed implementations are described below with reference to the schematic flowcharts and / or schematic block diagrams of methods, apparatuses, systems, and program products according to examples. It will be understood that some blocks in the schematic flowcharts and / or schematic block diagrams, as well as combinations of some blocks in the schematic flowcharts and / or schematic block diagrams, can be implemented by code. The code can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to generate a machine, such that the instructions executed via the processor of the computer or other programmable data processing device create means for implementing the functions / actions specified in one or more blocks of the schematic flowchart and / or schematic block diagram.

[0034] The schematic flowcharts and / or schematic block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatuses, systems, methods, and program products according to various embodiments. In this regard, each block in the schematic flowcharts and / or schematic block diagrams can represent a code module, a code segment, or a portion of code that includes one or more executable instructions for implementing the specified logical function.

[0035] It should also be noted that in some alternative implementations, the functions marked in the blocks can occur in a different order than marked in the figures. For example, two blocks shown consecutively can actually be executed substantially simultaneously, or the blocks can sometimes be executed in the reverse order, depending on the functions involved. Other steps and methods can be envisioned that are equivalent in function, logic, or effect to one or more of the blocks or portions of the blocks shown in the figures.

[0036] Although various arrow types and line types may be employed in flowcharts and / or block diagrams, they are to be understood as not limiting the scope of the corresponding aspects or implementations. In fact, some arrows or other connectors may be used to indicate only the logical flow of the depicted exemplary aspects. For example, an arrow may indicate a waiting or monitoring period of unspecified duration between the recited steps of the depicted exemplary implementation. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a system based on dedicated hardware that performs a particular function or action, or a combination of dedicated hardware and code.

[0037] The description of an element in each figure may refer to the elements of the preceding figures. Unless explicitly stated or otherwise clear from the context, the same reference numerals refer to the same elements in all figures, including alternative implementations involving the same elements.

[0038] As used herein, a list using the conjunction "and / or" includes any single item in the list or a combination of items in the list. For example, the list of A, B, and / or C includes only A, only B, only C, the combination of A and B, the combination of B and C, the combination of A and C, or the combination of A, B, and C. As used herein, a list using the term "one or more of" includes any single item in the list or a combination of items in the list. For example, one or more of A, B, and C includes only A, only B, only C, the combination of A and B, the combination of B and C, the combination of A and C, or the combination of A, B, and C. As used herein, a list using the term "one of" includes one and only one of any single item in the list. For example, "one of A, B, and C" includes only A, only B, or only C and does not include the combination of A, B, and C. As used herein, "a component selected from the group consisting of A, B, and C" includes one and only one of A, B, or C and does not include the combination of A, B, and C. As used herein, "a component selected from the group consisting of A, B, and C and their combinations" includes only A, only B, only C, the combination of A and B, the combination of B and C, the combination of A and C, or the combination of A, B, and C.

[0039] This disclosure describes various aspects and implementations of methods, systems, and devices for enhanced selection of effective targeting genome manipulators. Various examples of the described aspects address many drawbacks associated with existing methods for selecting targeting genome manipulators.

[0040] Definitions. As used herein, the term "bead" refers to a particle having a diameter in the range of from about 1 nm to 10 μm and having a functionalized surface configured to bind to a corresponding component of a molecule in solution. Some beads are magnetic while other beads are non-magnetic. Non-limiting examples of beads include particles functionalized with a streptavidin coating configured to bind to biotinylated molecules in solution. Other non-limiting examples of materials for functionalizing the surface of beads include antibodies, streptavidin, neutravidin, avidin, captavidin, zinc finger proteins, CRISPR Cas family enzymes, nucleic acids, and synthetic nucleic acid analogs such as peptide nucleic acids, xeno nucleic acids, and the like.

[0041] As used herein, the term "binding" refers to an electrostatic interaction between a genome manipulator and its nucleic acid target. Non-limiting examples include interactions facilitated by protein-nucleic acid interactions such as those using TALEN or ZFN genome manipulation techniques or by ribonucleoprotein complexes such as CRISPR Cas9 and targetrons.

[0042] As used herein, the term "bio-gated transistor" refers to a transistor gated by a change in surface potential caused by molecular binding.

[0043] As used herein, the term "biomolecule" refers to any molecule involved in being produced by a biological organism, said molecules including large polymeric molecules such as proteins, polysaccharides, lipids, and nucleic acids (DNA and RNA), as well as small molecules such as primary metabolites, secondary metabolites, and other natural products.

[0044] As used herein, the term "chip-based biosensor" refers to a device including one or more solid two-dimensional sensor elements disposed on a solid support, the solid two-dimensional sensor elements directly or indirectly responsive to the presence of a similar biochemical or biomolecular analyte or interaction or both in a sample regarding or sufficiently close to generating an electrical or electromagnetic response signal suitable for amplification, filtering, digitization, and other analog and digital signal processing operations. Some "chip-based biosensors" include a plurality of transistors and a plurality of detection portions, wherein at least one of the transistors is a liquid-gated transistor.

[0045] As used herein, the terms "cleavage" or "cutting" of a nucleic acid refer to the breakage of the covalent backbone of a nucleic acid molecule. Cleavage can be initiated by a variety of methods, including but not limited to enzymatic or chemical hydrolysis of phosphodiester bonds. With respect to DNA, the terms "cleavage" or "cutting" as used herein refer to double-stranded cleavage that occurs as a result of two different single-strand break events. DNA cleavage may result in the production of blunt ends or staggered "sticky" ends.

[0046] As used herein, the term "DNA recognition complex" in the context of genome engineering technologies can refer to a protein, a naturally occurring or artificially developed nucleic acid, or a complex of nucleic acid and protein that is used to target a specific region, sequence, or locus of a genome. Non-limiting examples of DNA recognition complexes can include those in the context of CRISPR, guide RNAs, and Cas nucleases such as Cas9, Cas13, or another engineered Cas nuclease that is active or mutated to prevent DSBs. With respect to transcription activator-like effector nucleases (TALENs) or other genome editing technologies that use TALs (transcription activator-like effectors) such as TAL-deaminases, the DNA recognition complex is a combination of a transcription activator-like effector and an active nuclease such as Fok1 or a deaminase or is a TAL alone. Another example of a DNA recognition complex with respect to zinc finger polymerases (Zing FingerPolymerase) or (ZFP) is a complex formed by the combination of a small zinc finger domain Cys2His2 with the IIS-type non-specific DNA cleavage domain of the Fok1 restriction enzyme. Another example of a DNA recognition complex includes a targetron, which is a ribonucleoprotein particle (RNP) having an engineered group II intron RNA lariat molecule and a multi-domain group II intron-encoded protein. Recognition and cleavage are facilitated by both the RNA lariat molecule and the intron-encoded protein, the RNA lariat molecule having ribozyme activity and the intron-encoded protein contributing to the recognition of the target sequence by stabilizing the RNA at its specific sequence and contributing to the cleavage process and the insertion of new DNA sequences through its reverse transcriptase activity.

[0047] As used herein, the term "endonuclease" refers to any wild-type or variant enzyme capable of catalyzing the hydrolysis (cleavage) of the bond between nucleic acids within a nucleic acid molecule such as DNA and / or RNA. Non-limiting examples of endonucleases include type II restriction endonucleases such as Fold, Hhal, HindIII, Notl, BbvCl, EcoRI, Bglll, and AlwI. Non-limiting examples of endonucleases also include rare-cutting endonucleases when having a polynucleotide recognition site typically having a length of about 12 to 45 base pairs (bp), more preferably 14 to 45 bp. Rare-cutting endonucleases induce DNA double-strand breaks (DSBs) at defined loci. For example, rare-cutting endonucleases can be homing endonucleases, meganucleases, chimeric zinc finger nucleases (ZFNs) or transcription activator-like effector nucleases (TALENs) derived from engineered zinc finger domains or TAL effector domains fused to a catalytic domain of a restriction enzyme such as Fokl, other nucleases, or chemical endonucleases. Endonucleases can also be part of the Cas family such as Cas9, Cas12, Cas13, etc.

[0048] As used herein, the term "genome manipulation" refers to something capable of modifying components or behaviors of nucleic acids, genes, exons, nucleic acid sequences, genomes, and / or similar nucleotide combinations. Genome manipulation is not limited to manipulation of the entire genome, or even manipulation of nucleic acid sequences found in a naturally occurring genome, but can include manipulation of any of the above components whether artificially derived or naturally occurring. Non-limiting examples of genome manipulation include genome editing, chromatin engineering, chromatin imaging, epigenetic editing, gene activation, gene silencing, etc.

[0049] As used herein, the term "off-target" refers to regions in a nucleic acid sample other than the intended or desired pre-determined site (e.g., target sequence) of the nucleic acid with respect to, for example, binding, cleavage, editing, manipulation, and / or other biomolecular interactions of the nucleic acid sample. As used herein, the term "on-target" refers to regions in a nucleic acid sample corresponding to the intended or desired pre-determined site (e.g., target sequence) of the nucleic acid with respect to, for example, binding, cleavage, editing, manipulation, and / or other biomolecular interactions of the nucleic acid sample. The term "on-plus-off-target" refers to the combination of both on-target and off-target biomolecular interactions.

[0050] As used herein, the term "targeted genome manipulator" refers to a biomolecular reagent expected or desired to modify components or behaviors of nucleic acids, genes, exons, nucleic acid sequences, genomes, and / or similar nucleotide combinations at a pre-determined site (e.g., target sequence) of an expected or desired region, locus, or sequence.

[0051] Despite various genome manipulation techniques, systems, devices, methods, and computer programs for enhanced selection of effective targeted genome manipulators allow for significant improvements over the prior art in relation to specific regions, genes, loci, sequences, etc. The benefits extend not only to the discovery of effective targeting agents but also to the improved use of targeted genome manipulators. For example, with respect to genome editing, targeting non-mutated genes into the genome at very precise locations presents several advantages. First, it enables the induction of added DNA sequences in regions of chromatin programmed to precisely regulate the expression of the gene. Second, targeted genome editing also helps prevent the dangerous random insertions that can lead to cancer found in non-targeted gene therapy. Thus, by practicing various aspects and implementations of the present disclosure, the efficiency of downstream technologies such as amplification, sequencing, and therapeutic use of targeted genome manipulators can also be increased by enabling them to be more effectively utilized.

[0052] Other genome manipulation techniques do not produce double-strand breaks (DSBs), but rather correct single nucleotide polymorphisms that cause harmful genes. Such techniques typically use deaminases or other DNA repair enzymes to reverse the mutation to the wild-type nucleotide base. Another class of targeted genome manipulation techniques does not alter the DNA but rather targets the epigenetic code that drives the expression of important genes.

[0053] Aspects of targeted genome manipulation techniques differ in the way they repair the genetic code or modify the epigenetic code. However, various genome manipulation techniques all use systems that target the region or gene to be modified or affected. Systems, devices, methods, and computer programs for enhanced selection of effective targeted genome manipulators utilize relatively fast and inexpensive electronic biosensing systems, thereby improving targeted genome manipulation techniques, for example, by increasing targeting precision and reducing the cost and time for such screening and validation.

[0054] Aspects of the systems, devices, methods, and computer program products described herein can be used with different targeted genome manipulation systems. For example, for a targeted genome editing system, a first group of specific DNA sequence targeting systems achieve specific DNA sequence targeting via protein-DNA interactions. Systems in the first group can include, for example, meganucleases ("MN"), zinc finger nucleases (ZFN), and transcription activator-like effector nucleases ("TALEN").

[0055] The second set of specific DNA sequence targeting systems achieve specific DNA sequence targeting via interactions between nucleic acids. Systems in the second set include, for example, target peptide nucleotide triple helix forming oligonucleotides (“TFOs”), structure-guided nucleases (“SGNs”), and clustered regularly interspaced short palindromic repeats (“CRISPRs”).

[0056] While aspects of the present disclosure can be used with any of the above targeting techniques, applying these aspects to the second set—and especially CRISPR-Cas9—facilitates easy targeting of desired sequences in the genome by simply changing the nucleic acid sequence that guides the modifying enzyme or nuclease to its target.

[0057] Using the prior art, it is not easy to design guide nucleic acids that provide effective target recognition without off-target site recognition, especially for complex genomes such as the human genome. Additionally, the presence of off-target binding of specific nucleic acid sites to targeted genome manipulators can create problems beyond the inefficient use of time and resources. For example, if the guide RNA (“gRNA”) is not carefully designed and tested, CRISPR-Cas9, one of the most successful genome editing techniques, may exhibit a significant likelihood of editing at off-target sites. Conversely, editing at off-target sites by CRISPR-Cas9 can lead to non-specific and / or unintended genetic modifications such as harmful mutations and chromosomal aberrations.

[0058] Some existing tools and / or methods have evaluated on-target site efficiency and putative off-target sites of CRISPR-associated gRNAs, but have deficiencies that can be addressed by aspects of the present disclosure. Such existing tools and / or methods can be divided into three groups: computational methods, in vivo methods, and in vitro methods.

[0059] Computational methods (e.g., computer simulations) can be used for a first level of high-throughput screening to eliminate gRNAs with a high likelihood of not working effectively with the on-target site. However, existing computational methods have not been able to demonstrate the ability to determine that a gRNA will function as predicted in real-world applications. Additionally, existing computational methods can be used to predict putative off-target recognition sites for a specific gRNA, but lack the sensitivity and specificity required to make a reliable tool because computational methods may miss some important putative off-target sites (e.g., false positive determination of an effective guide RNA), and may give a very high background of putative off-target sites not found in vivo (e.g., false negative or rejection of an in vivo effective guide RNA).

[0060] In vitro methods, e.g., have been developed to assess gRNA cleavage efficiency by decomposition tracking indels ("TIDE"), amplicon analysis indel detection ("IDAA"), and mismatch cleavage assays such as T7 endonuclease I ("T7E1") or Surveyor nuclease. However, such methods require a cell transfection step, which makes them more user-unfriendly and more cumbersome than the various systems, devices, methods, and computer program products described herein that do not require a cell transfection step.

[0061] In vivo methods can be used for off-target putative site discovery. For example, some in vivo methods such as Guide-Seq or Digenome-Seq are considered by some to be good predictive tools for assessing off-target sites for gRNAs. However, such methods have drawbacks. For example, Guide-Seq requires efficient delivery of double-stranded oligonucleotides, which can be toxic to certain cell types at certain doses and has not been demonstrated in in vivo models. In vivo methods are cumbersome, time-consuming, expensive, and more difficult to reproduce. In vivo methods may also be affected by cell fitness (e.g., may be sensitive to the time of cell fixation) and may require the use of relatively large numbers of cells and / or transfected or transduced cells.

[0062] Accordingly, certain in vitro methods have been developed that are more convenient to overcome some of the limitations of in vivo methods. Some examples of in vitro methods include Circle-Seq and Site-Seq, which enrich putative CRISPR-Cas9 off-target sites found in the whole genome and then sequence said off-target sites by next-generation sequencing ("NGS"). Although significant progress has been made in reducing the average cost of NGS per human genome over the past 20 years, in the past 5 years, the sharp decline in NGS costs has significantly leveled off, and this method remains rather expensive for screening gRNA efficiency against various genomic sensing systems and methods relative to the present disclosure. In addition, many in vitro methods do not perform well on chromatin, which is a determinant in assessing Cas9 activity in vivo. A method for directly assessing CRISPR-Cas9 off-target sites on chromatin is described in the abstract of WO2018097657(A1). However, this method does not provide enrichment of off-target putative sites and requires whole-genome sequencing to assess off-target putative sites.

[0063] Accordingly, aspects of the present disclosure can also be used to enhance off-target site discovery using the chip-based biosensor systems and methods described below to provide much lower cost, faster screening and validation, and better precision for off-target site discovery. Certain chip-based biosensors (e.g., biosensors that utilize field-effect biosen-sing) provide label-free measurements that are faster, can be manufactured at lower cost, have higher reproducibility, and lower complexity than non-chip-based or other measurement techniques that involve expensive fluids and / or precision optical measurement devices.

[0064] Figure 1 FIG. 4 is a schematic block diagram of a system 100 for enhanced selection of effective targeted genome agents in accordance with one or more aspects of the present disclosure. System 100 in various implementations includes one or more of a sample preparation device 112, a biomolecule measurement device 102, a computing device 114, an enrichment device 118, a sequencing device 120, and a data network 122.

[0065] In at least one implementation, the biomolecule measurement device 102 includes a first chip-based biosensor 104a and a second chip-based biosensor 104b. In various implementations, the biomolecule measurement device further includes a measurement controller 124 configured to measure one or more first response signals and second response signals generated in response to biomolecular binding interactions that occur between nucleic acid samples in a first aliquot and a second aliquot and targeted genome agents on the functionalized capture surfaces of the first chip-based biosensor 104a and the second chip-based biosensor 104b. The first chip-based biosensor 104a and the second chip-based biosensor 104b. In certain implementations, each chip-based biosensor 104a, 104b has one or more sensing surfaces 106a, 106b configured to detect biomolecular binding interactions between a nucleic acid sample 108 and one or more capture surfaces 126 within the sensing range of the one or more sensing surfaces 106a, 106b.

[0066] In Figure 1In it, the chip-based biosensors 104a, 104b are depicted as being separate from each other and removable from the chassis of the biomolecule measurement device 102. In some implementations, the first chip-based biosensor 104a and the second chip-based biosensor 104b can be implemented such that, for example, the first biosensor, the second biosensor, and / or more biosensors are supported by the same substrate. Similarly, one or more sensing surfaces 106a, 106b can be arranged in various array configurations and can be configured to be the same or different. Additionally, the first chip-based biosensor 104a and the second chip-based biosensor 104b can be implemented in a non-removable configuration. In various implementations, the biomolecule measurement device 102 includes a measurement controller 124 that is configured to measure one or more first response signals and second response signals generated in response to biomolecular binding interactions occurring between nucleic acid samples in the first aliquot and the second aliquot and targeted genomic manipulators on the functionalized capture surfaces of the first chip-based biosensor and the second chip-based biosensor.

[0067] In certain implementations, the first chip-based biosensor 104a is configured to hold a first aliquot 108a of a nucleic acid sample 108 that has been optionally incubated with a blocker 110, which is configured to bind to a sequence that overlaps a target sequence of the nucleic acid sample 108. In various implementations, incubation with the blocker 110 is useful because it enables both the first chip-based biosensor 104a and the second chip-based biosensor 104b to utilize the same functionalized capture surface. In such an implementation, the second chip-based biosensor 104b is configured to hold a second aliquot 108b of the nucleic acid sample 108 that omits the blocker 110.

[0068] Instead of using the same functionalized capture surface on both the first chip-based biosensor 104a and the second chip-based biosensor 104b, one of the chip-based biosensors can be functionalized with a binding moiety other than the targeted genome editing agent being tested. For example, in some aspects of the present disclosure, the lysis efficiency parameters can be compared by functionalizing the first chip-based biosensor with a version of the targeted genome editing agent, where the blocker is omitted from the first aliquot and the first chip-based biosensor and the second chip-based biosensor use the same targeting components (e.g., the same gRNA), where the genome editing component functionalized to the capture surface associated with the first chip-based biosensor is configured not to lyse the nucleic acid sample, while the genome editing component functionalized to the capture surface associated with the second chip-based biosensor is configured to lyse the nucleic acid sample. With this arrangement, the lysis efficiency parameters can be compared and analyzed. For example, the lysis parameters can be compared using the same nucleic acid sample aliquot and the same RNA by selecting an inactivated editing component such as CRISPR-dCas9 for culturing the nucleic acid sample on the first chip-based biosensor and a purported lytic editing component such as CRISPR-Cas9.

[0069] In some implementations, the first aliquot 108a and the second aliquot 108b are manually prepared in a sample container such as a PCR tube or other selected container. In other implementations, the first aliquot 108a and the second aliquot 108b are automatically or semi-automatically prepared by a sample preparation device 112, which includes, for example, automated dispensing performed by a dispensing robot and / or a fluid system. In such an implementation, the sample preparation device 112 can include its own controller and a user interface for setting the time, temperature, etc. of the culturing. In other implementations, the sample preparation device 112 can receive commands from another device such as a computing device 114 or even a biomolecular measurement device 102 via a data network 122.

[0070] Certain implementations of the biomolecular measurement device 102 can vary according to the technique used to sense the biomolecular interaction between the nucleic acid sample and the targeted genome editing agent. For example, in implementations where the chip-based biosensors 104a, 104b use field-effect biosensing (e.g., Figure 4(depicted in device 400), the biomolecular binding and / or cleavage interactions of a label-free nucleic acid sample can be measured without a flow cell or fluid propulsion member for performing the measurement. In other implementations, the biomolecular measurement device 102 uses a chip-based biosensor 104 that includes a flow cell. Thus, various implementations of the devices, systems, and methods described herein can be used in accordance with one or more implementations of the present disclosure. The following is more detailed in the description of device 200 depicted in Figure 2 and device 400 depicted in Figure 4 of the biomolecular measurement device 102. In certain implementations, the biosensor 104 can include (but not by way of limitation) various types of chip-based biosensors that use terahertz spectroscopy, surface-enhanced spectroscopy, quartz crystal microbalance, grating-coupled interferometry, etc.

[0071] System 100 includes an analysis module 116. In some implementations, the analysis module 116 is implemented using a computing device 114. In various implementations, the analysis module 116 is configured to determine one or more genome manipulation efficiency parameters associated with a targeted genome manipulator based on performing a comparative analysis of a first response signal and a second response signal, where the first response signal and the second response signal can be a first set of response signals and a second set of response signals generated by a measurement controller 124 of the biomolecular measurement device 102.

[0072] In certain implementations, the analysis module 116 can be programmed to perform a comparative analysis between the genome manipulation efficiency parameters determined using a chip-based biosensor and the corresponding genome manipulation efficiency parameters determined using one or more other methods such as the various computer methods, in vitro methods, and in vivo methods described above. For example, after fragmentation and adapter ligation are performed according to one or more of the methods 800, 900, 1000, 1100, and 1200 described below, the analysis results of the chip-based biosensor can be compared and analyzed with one or more of the in vivo, in vitro, and / or computer binding and / or cleavage efficiency results obtained using any of the above techniques for the same or similar targeted genome manipulators. Thus, the various systems, devices, and methods of the present disclosure improve such in vivo, in vitro, and / or computer binding and / or cleavage efficiency determination techniques by enhancing the selection of effective targeted genome manipulators, and further improve such techniques by providing independently derived data for comparative analysis or verification using or exploiting such techniques.

[0073] In some implementations, the analysis module may be implemented on a device separate from the biomolecular measurement device 102. For example, in some implementations, the analysis module 116 is implemented on the computing device 114. The computing device 114 may be a laptop computer, a desktop computer, a smart phone, a handheld computing device, a tablet computing device, a virtual computer, or an embedded computing device integrated into an instrument. The computing device includes a processor 218, a memory 220, a communication interface 222, and a keyboard display or similar visual output. In some implementations, the analysis module 116 is fully implemented within the computing device, while in other implementations, the analysis module 116 is at least partially implemented within the biomolecular measurement device 102.

[0074] In an implementation where the analysis module 116 is implemented on the computing device 114, the computing device 114 may communicate with the measurement controller 124 via the data network 122. Similarly, the analysis module 116 may transmit data to other components of the system, such as the enrichment device 118, the sequencing device 120, and / or the sample preparation device 112.

[0075] In some implementations, the computing device 114 is part of the biomolecular measurement device 102 and may utilize the processor, memory, and communication interface of the biomolecular measurement device 102 to measure the first response signal and the second response signal or the first set of response signals and the second set of response signals respectively generated in response to biomolecular binding interactions occurring between nucleic acid samples in the first aliquot and the second aliquot and the targeted genomic manipulator on the functionalized capture surfaces of the first chip-based biosensor and the second chip-based biosensor. In certain implementations, the analysis module 116 may be implemented as an embedded processor system or other integrated circuit forming part of the chip-based biosensors 104a, 104b.

[0076] In one exemplary implementation, the analysis module 116 may be configured to perform a comparative analysis of the first set of response signals and the second set of response signals from identically prepared biosensors exposed to different solutions, for example, when the first chip-based biosensor 104a is exposed to the first aliquot 108a of the nucleic acid sample 108 that has been incubated with the blocker 110, and the second chip-based biosensor 104b is exposed to the second aliquot 108b of the same nucleic acid sample 108 that omits the blocker 110. In this case, the analysis module 116 may be configured to determine the probability distribution of the detected nucleic acid concentration based on an empirical model determined from calibration measurements of identically prepared biosensors exposed to known concentrations of the target nucleic acid.

[0077] Then, in some implementations, the analysis module 116 can determine the probability distribution of the concentration of off-target DNA by subtracting the calculated probability distribution of the concentration of DNA from the first aliquot 108a (measured using the first chip-based biosensor 104a) in the presence of the blocker 110 from the probability distribution of the concentration of DNA (measured using the second chip-based biosensor 104b) from the second aliquot 108b in the absence of the blocker 110.

[0078] In some implementations, the analysis module 116 can be configured to, for example, compare and analyze the time-dependence of the first set of response signals and the second set of response signals when preparing (e.g., functionalizing) the first chip-based biosensor 104a with dcas9 and the second chip-based biosensor 104b with cas9 having the same gRNA as the first chip-based biosensor 104b, and expose both the first chip-based biosensor 104a and the second chip-based biosensor 104b to the same analyte (e.g., nucleic acid sample 108) to determine the cleavage rate of cas9 exposed to the nucleic acid sample 108 at a known concentration. Statistical analysis of the changes in the observed first and second response signals or the first and second sets of response signals can be performed, for example, by calculating histograms of the first and second signals to determine the amount of time that the nucleic acid sample (e.g., DNA) binds to the chip-based biosensors 104a, 104b, where the response signals are, for example, drain current, capacitance, etc.

[0079] The analysis module 116 can be configured to convert the first set of response signal values and the second set of response signal values into the frequency space using an algorithm such as the fast Fourier transform to determine the frequency of cleavage. These examples are non-limiting, and both types of analysis are commonly used in biosensors prepared in the same or different ways and in the measurement of the first aliquot 108a and the second aliquot 108b prepared in the same or different ways.

[0080] In various implementations, the system 100 can include only Figure 1 some of the items depicted, such as the biomolecule measurement device 102, the measurement controller 124, and the analysis module 116, where the analysis module 116 can be implemented on the computing device 114 in some implementations or on the biomolecule measurement device 102.

[0081] Figure 2FIG. 0 is a schematic block diagram of a device 200 for enhanced selection of a genome manipulator for effective targeting according to one or more aspects of the present disclosure. In one implementation, the device 200 includes an example of a biomolecular measurement device 102. In various implementations, the biomolecular measurement device 102 includes a biosensor 104 such as a first chip-based biosensor 104a and a second chip-based biosensor 104b. In various implementations, the biomolecular measurement device 102 further includes one or more of the following: a signal conditioning circuit 204, a digitizing circuit 206, a processor 208, a memory 210, and a communication interface 212. In certain implementations, the biomolecular measurement device 102 further includes one or more sample excitation devices 214 and one or more fluid devices 216.

[0082] In some implementations, the fluid device 216 can be used to drive a sample through a flow cell or other fluid or microfluidic channels. If needed, Figure 4 the biogated transistor implementation depicted in FIG. 5 can also use a flow cell, but due to the high sensitivity of the biogated transistor, a flow cell is not required to perform high-sensitivity measurements.

[0083] In various implementations, the biosensor 104 includes a first chip-based biosensor 104a and a second chip-based biosensor 104b configured to perform label-free sensing of biomolecular interactions between a nucleic acid sample 108 and a functionalized capture surface 126 of the biosensor 104. Chip-based biosensors 104a, 104b of various types and technologies can be used according to one or more aspects of the present disclosure. For example, Figure 4 device 400 depicted in FIG. 10 depicts an implementation of the use of a chip-based biosensor using field-effect biosensing technology for label-free detection of biomolecular binding interactions.

[0084] In this implementation, various biosensor parameters such as drain current, electrochemical current (e.g., gate current), gate capacitance, drain impedance, gate impedance, transconductance, gate curve non-linearity, gate curve hysteresis, Hall effect voltage, magnetoresistance, etc. can be measured to generate a first response signal and a second response signal that can be a first set of response signals and a second set of response signals.

[0085] In some implementations, one or more sample excitation devices 214 are configured to subject nucleic acid samples to more types of excitation, e.g., magnetic excitation, electromagnetic excitation (e.g., light, radio waves, ionizing electromagnetic or other radiation (e.g., ultraviolet light, X-rays, gamma rays, electron beams, etc.)) within a predetermined range of the electromagnetic spectrum, physical excitation (e.g., ultrasonic waves or agitation), electrical excitation (e.g., modulating a gate bias voltage), temperature excitation (e.g., a Peltier device for controlling heating and cooling of a chip-based biosensor), etc. In certain implementations, these sample excitation devices 214 may be controlled by a measurement controller 124.

[0086] In certain implementations, as described above, the analysis module 116 may be implemented using the processor 208, the memory 210, and / or the communication interface 212. In other implementations, the analysis module 116 may be implemented using the computing device 114. In certain implementations, the analysis module 116 may be configured to perform a comparative analysis of a first set of response signals and a second set of response signals from identically prepared biosensors exposed to different solutions, e.g., when a first chip-based biosensor 104a is exposed to a first aliquot 108a of a nucleic acid sample 108 that has been incubated with a blocker 110 and a second chip-based biosensor 104b is exposed to a second aliquot 108b of the same nucleic acid sample 108 that omits the blocker 110. In such a case, the analysis module 116 may be configured to determine a probability distribution of the detected nucleic acid concentration based on an empirical model determined from calibration measurements of identically prepared biosensors exposed to a target nucleic acid of a known concentration.

[0087] Then, in certain implementations, the analysis module 116 may determine a probability distribution of the concentration of off-target binding nucleic acid (e.g., DNA) by subtracting the calculated probability distribution of the DNA from the first aliquot 108a (measured using the first chip-based biosensor 104a) in the presence of the blocker 110 from the probability distribution of the concentration of DNA from the second aliquot 108b (measured using the second chip-based biosensor 104b) in the absence of the blocker 110.

[0088] In some implementations, the analysis module 116 can be configured to, for example, compare and analyze the time-dependence of a first set of response signals and a second set of response signals when preparing (e.g., functionalizing) a first chip-based biosensor 104a with dcas9 and a second chip-based biosensor 104b with cas9 having the same gRNA as the first chip-based biosensor 104b, and expose both the first chip-based biosensor 104a and the second chip-based biosensor 104b to the same analyte (e.g., nucleic acid sample 108) to determine the cleavage rate of cas9 exposed to a nucleic acid sample 108 of known concentration. Statistical analysis of the observed changes in the first and second response signals or the first and second sets of response signals can be performed, for example, by calculating histograms of the first and second signals to determine the amount of time a nucleic acid sample (e.g., DNA) binds to the chip-based biosensors 104a, 104b, where the response signals are, for example, drain current, capacitance, etc.

[0089] The analysis module 116 can be configured to transform the first set of response signal values and the second set of response signal values into the frequency space by using an algorithm such as the fast Fourier transform to determine the frequency of cleavage. These examples are non-limiting, and both types of analysis are commonly used in biosensors of the same or different preparations and in the measurement of the first aliquot 108a and the second aliquot 108b of the same or different preparations. Although the systems, methods, and devices described herein can utilize various chip-based biosensors, as Figure 4 shown, implementations using field-effect biosensing technology offer significant advantages in terms of instrument cost, biosensor cost, accuracy, sampling time, etc., because no precision optics or fluidics are required for field-effect biosensing.

[0090] Figure 3A is a diagram showing a method 300 for enhanced selection of an effective targeting genome manipulator 310 according to one or more aspects of the present disclosure. Figure 3B is an enlarged detail diagram showing an exemplary implementation of a blocker according to one or more aspects of the present disclosure.

[0091] In one embodiment, method 300 begins and includes preparing 302 a first aliquot 304a and a second aliquot 304b, both including a nucleic acid sample 306, which is measured to detect biomolecular binding interactions between the nucleic acid sample 306 dispensed to one or more sensing surfaces 308a, 308b and a targeted genome manipulator 310. The targeted genome manipulator has a genome manipulation component 312 and a targeting component 314 and is functionalized into one or more capture surfaces 316 within the sensing range of the one or more sensing surfaces 308a, 308b.

[0092] In various implementations, the first aliquot 304a may be optionally incubated with a blocker 318, which is configured to bind to an overlapping sequence 320 that overlaps with the on-target sequence 322 of the nucleic acid sample 306, while the second aliquot 304b omits the blocker 318. As Figure 3B shown in the expanded details in, incubating the first aliquot 304a with the blocker 318 effectively blocks the binding between the on-target sequences 322 of the nucleic acid sample 306. For example, in certain implementations, the blocker 318 minimizes on-target binding between the nucleic acid sample with the blocker and the targeted genome manipulator 310 by steric hindrance caused by materials near the on-target sequence or by causing the DNA to adopt a shape incompatible with binding or by directly covering at least a portion of the on-target sequence. Due to these blocking mechanisms, the measurable binding that occurs between the targeted genome manipulator 310 and the nucleic acid sample 306 will bind at off-target sites 324.

[0093] In certain implementations, the targeting component 314 of the targeted genome manipulator 310 includes a guide RNA having a guide sequence 326 configured to bind complementarily to the on-target sequence 322, and the genome manipulation component 312 includes a CRISPR-associated protein molecule such as Cas9, Cas12, Cas13, or a similar CRISPR Cas complex. In Figure 3A and Figure 3B the implementation shown, the genome manipulation component 312 is depicted as CRISPR-Cas9. However, in some implementations, a non-cleaving genome manipulation component 312 such as a dCas molecule may be used.

[0094] In various implementations, the sensing surfaces 308a, 308b include one or more functionalized capture surfaces 316. In certain implementations, one or more of the capture surfaces 316 are the surfaces of beads 305 functionalized with the targeted genome manipulator 310 within the sensing range 328 of the one or more sensing surfaces 308a, 308b, as Figure 3B depicted in. The following with respect to Figure 5A, 5B The description of 5C provides more details regarding the functionalization of the capture surface.

[0095] In various implementations, method 300 continues and includes measuring 340 one or more first response signals 342a and second response signals 342b resulting from biomolecular binding interactions occurring between a nucleic acid sample 306 in a first aliquot 304a and a second aliquot 304b and a targeted genomic agent 310 on one or more capture surfaces 316 functionalized with a first chip-based biosensor 104a and a second chip-based biosensor 104b. In some implementations, method 300 includes calibrating 334 the chip-based biosensors 104a, 104b prior to incubating 336 the first aliquot 108a (blocked) and the second aliquot 108b (unblocked). Calibrating 334 the chip-based biosensors 104a, 104b provides a normalized baseline for the first response signals 342a and the second response signals 342b.

[0096] In various implementations, method 300 also includes washing 338 unbound portions of the nucleic acid sample 306. In the case of the first aliquot 304a (blocked), DNA having an on-target segment 332 is washed 338 away, and portions of the nucleic acid sample 306 exhibiting off-target binding are retained. Thus, one or more first response signals 342a indicate binding parameters associated with off-target binding between the nucleic acid sample 306 incubated with a blocker 318 and a targeted genomic agent 310 on one or more capture surfaces 316 within the sensing range of one or more sensing surfaces 308a functionalized.

[0097] For example, in an implementation using a biogated transistor as the first and second biosensors, the first response signal 342a, such as the drain current or other parameter discussed in

[0078] of the biogated transistor of the first chip-based biosensor, can be a monotonic function of the off-target binding concentration present, while the corresponding second response signal 342b of the biogated transistor of the second chip-based biosensor can be a monotonic function of the on-target binding plus off-target binding concentration present. In certain implementations, various relationships between biogated transistor parameters and target concentrations (e.g., the concentration of binding molecules, whether on-target or off-target or both on-target and off-target) can be pre-calibrated using representative samples of identically prepared biosensor chips. In some desired implementations, the biogated transistor response can be proportional to the concentration of DNA.

[0098] In certain implementations, for example, the following with respect to Figure 4In the implementation of using field-effect biosensing as shown by the device 400 depicted, a sampling rate that meets a predetermined Nyquist criterion is used to optionally measure one or more first response signals 342a and second response signals 342b for measuring at least one parameter of the biomolecular binding interaction between a nucleic acid sample and a targeted genome manipulator during a predetermined time period associated with the biomolecular binding interaction. In some implementations where the first response signals 342a and the second response signals 342b involve measurements using a biogated transistor, the sampling rate can be programmable (e.g., using the measurement controller 124 of the device 200 described above with respect to Figure 2 . In certain implementations, the predetermined Nyquist criterion can be at least partially based on the frequency-dependent characteristics (e.g., bandwidth) of the biomolecular binding interaction and the components involved in the biomolecular binding interaction.

[0099] In some implementations, the predetermined Nyquist criterion can be at least partially based on the frequency-dependent characteristics (e.g., bandwidth) of the measurement circuit. For example, in various implementations, the sampling rate is higher than the measurement bandwidth of the measurement circuit to minimize artifacts such as aliasing. In certain implementations, when the sampling rate that meets the predetermined Nyquist criterion is high enough, the step of washing 338 can be omitted based on the additional accuracy and information obtained by measuring 340 at the sampling rate that complies with the predetermined Nyquist criterion.

[0100] In some embodiments, the method 300 continues and includes determining 344 an efficiency parameter 346 of the targeted genome manipulator based on comparing 345 one or more first response signals 342a with one or more second response signals 342b. For example, the response signals can be used to determine the concentration of off-target DNA and the concentration of on-target plus off-target DNA, in which case a more accurate measurement of on-target binding can be derived by subtracting the off-target binding value from the on-target plus off-target binding value.

[0101] Figure 4 is a schematic block diagram of a device 400 showing one implementation of a biosensor 202 including one or more examples according to the present disclosure. In one implementation, the biosensor 202 is a chip-based biosensor using a biogated transistor 402 - also known as a liquid-gated field-effect transistor. In certain implementations, the biogated transistor 402 includes a source electrode 404, a drain electrode 406, and a sensing surface 408 on a portion of a channel 410 extending between the source electrode 404 and the drain electrode 406.

[0102] Unlike gate electrodes similar to those found in conventional field effect transistors, the bio-gated transistor 402 has a liquid gate electrode 412 that enables a drain-source current Ids to flow through a channel 410 between a drain electrode 406 and a source electrode 404 based at least in part on the amount of charge in a liquid within a sensing range 418 of a sensing surface 408. In various implementations, a partially functionalized capture surface 420 is used. In certain implementations, for the purpose of enhancing the selection of effective targeted genome manipulators, the capture surface 420 is functionalized with a targeted genome manipulator 416 of interest. In other implementations, the capture surface 420 is functionalized with a nucleic acid sample 424 having a target of interest. In additional implementations, the capture surface 420 includes functionalized beads that capture a target of interest of the nucleic acid sample 424 within the sensing range 418 of the sensing surface 408.

[0103] In response to a biomolecular binding interaction occurring between the nucleic acid sample 424 and the targeted genome manipulator 416, the sensing surface 408 is configured to detect fairly slight changes in charge or other transistor parameters caused by the biomolecular binding interaction within the sensing range 418. Those detected changes in charge or other transistor parameters produce a measurable response signal, e.g., changes in drain current, gate current, drain impedance, gate impedance, transconductance, gate hysteresis, gate curve non-linearity, gate curve hysteresis, Hall effect voltage, and magnetoresistance.

[0104] In some implementations, the device 400 includes a reference electrode 413 for detecting the potential of the liquid gate electrode 412. In certain exemplary implementations, the biosensor 202 includes a counter electrode 414 for adjusting the potential of the liquid gate electrode 412. In certain implementations, the measurement controller 124 is configured to modulate the counter electrode 414 at a rate that can be incrementally and programmatically adjusted to determine how a biomolecular interaction between the nucleic acid sample 424 and the targeted genome manipulator 416 proceeds.

[0105] In various implementations, the channel 410 can be laminated with a support layer 430 such as a silica layer. In certain implementations, the channel 410 is made of a highly sensitive conductive material such as graphene. In some implementations, the channel 410 uses other two-dimensional materials (sometimes also referred to as van der Waals materials, e.g., materials with strong in-plane covalent bonding and weak interlayer interactions), such as, for example, graphene nanoribbons (GNRs), bilayer graphene, phosphorene, tin, graphene oxide, reduced graphene, fluorographene, molybdenum disulfide, topological insulators, etc. A variety of materials that are conductive and exhibit field-effect characteristics and are stable at room temperature when directly exposed to various solutions can be used for bio-gated transistors (e.g., as the sensing surface or a portion thereof). In various implementations, compared to one-dimensional alternatives (e.g., carbon nanotubes), using bio-gated transistors that utilize planar two-dimensional van der Waals materials improves manufacturability and reduces costs.

[0106] In some implementations, Figure 1 the measurement controller 124 depicted in is configured to measure the drain current Ids and / or other bio-gated transistor parameters and is configured to generate one or more response signals that can be further conditioned, for example, using the signal conditioning circuit 204. Figure 2 the digitization circuit 206 depicted in is configured to convert one or more response signals into digital signals that can be stored, analyzed, and processed together with other response signals. In some implementations, various other parameters of the bio-gated transistor are also measurable and / or convertible into response signals that can be measured, recorded, conditioned, digitized, and compared and analyzed.

[0107] The targeted genome manipulator 416 includes a manipulation component 426 and a targeting component 422, and the targeted genome manipulator 416 is configured to manipulate (e.g., cleave, block) a nucleic acid sample 424 at a site of a predetermined sequence complementary to the targeting component 422. In the case where the target site on the nucleic acid sample 424 is not blocked by a blocker, the binding of the nucleic acid sample 424 can occur at the "on-target" site of the genome; or in the case where the target site on the nucleic acid sample 424 is blocked by a blocker, the binding of the nucleic acid sample 424 can occur at an off-target site.

[0108] In certain implementations, the device 400 includes a measurement controller 124 configured to control various devices, electrodes, signal conditioning, amplifiers, etc. of the biosensor 202. For example, in addition to controlling the gate voltage Vg for incremental adjustment and measurement, in some embodiments, e.g., affected by the Debye layer described in more detail below, the measurement controller 124 can apply and / or incrementally adjust a modulated liquid gate bias voltage for adjusting the sensing range of the biosensor.

[0109] In other implementations, the measurement controller 124 can control one or more sample excitation devices 214, such as a resistive heater, which is used to raise the temperature of a biological sample to a predetermined temperature to determine how biomolecular interactions occur at a predetermined body temperature. This can also be done on-chip using an integrated device, such as a resistive wire that acts as a Joule heater and a thermistor.

[0110] In certain implementations, the analysis module 116 can be programmed to perform a comparative analysis between certain genome manipulation efficiency parameters determined using the chip-based biosensors described in the present disclosure and the corresponding genome manipulation efficiency parameters determined using one or more other methods, such as the various computer methods, in vitro methods, and in vivo methods described above (e.g., Guide-Seq, Site-Seq, etc.). For example, after performing a comparative analysis of a first set of response signals and a second set of response signals measured under conditions (e.g., body temperature, pH, etc.) configured to align with the corresponding conditions for another efficiency determination technique (e.g., one of the in vivo systems described above) to select one or more targeted genome manipulation agents, and after performing fragmentation and adapter ligation according to one or more of the methods 800, 900, 1000, 1100, and 1200 described below, the analysis module 116 can perform a comparative analysis of the results using the chip-based biosensors 104a, 104b and the efficiency parameters determined for one or more of the in vivo, in vitro, and / or computer binding and / or cleavage efficiency results obtained for the same or similar targeted genome manipulation agents using any of the techniques described above.

[0111] Thus, the various systems, devices, and methods of the present disclosure improve such in vivo, in vitro, and / or computer binding and / or cleavage efficiency determination techniques by enhancing the selection of effective targeted genome manipulation agents and further improve such techniques by providing independently derived data for comparative analysis using such techniques or for validation using such techniques.

[0112] As another non-limiting example, the measurement controller 124 can control the sample excitation device 214, such as a Peltier device, to cool the temperature of the sensing surface 408 and the nucleic acid sample to more precisely analyze the response of biomolecules to the cooling effect of the sample excitation device 214 through interactions.

[0113] Other sample excitation devices 214, such as light emitters of any desired wavelength, can be used to measure the effect of excitation on biomolecular binding interactions.

[0114] In some implementations where the capture surface is functionalized magnetic beads, the measurement controller 124 can control one or more electromagnets or mechanically positionable magnets to affect the position of the beads within the sensing range. The beads can be positioned to contact the biosensor surface for sensing purposes and away from the surface for target capture purposes. These movements can cause the beads to move outside the bilayer and thus become undetectable by the sensor.

[0115] Figure 5A , Figure 5B and Figure 5C illustrate various implementations of the capture surface 511 in accordance with one or more examples of the present disclosure. In some implementations, the targeted genome manipulator 510 is functionalized into one or more capture surfaces 511. In certain implementations, the capture surface 511 is part of the sensing surface 504. In one or more implementations, the capture surface 511 is only a capture surface and the evaluation of binding efficiency and lysis efficiency is performed separately. In another embodiment, the capture surface 511 is a flat surface made of a biocompatible material having low nucleic acid binding adsorption and low protein binding adsorption and known for being functionalized with proteins or DNA. Various examples of biocompatible materials include, but are not limited to, glass, plastic, silicon, metal, or hydrogel functionalized with the targeted genome manipulator 510. In another embodiment, the capture surface 511 is a column made of the targeted genome manipulator 510 bound to a resin.

[0116] In some implementations, the targeted genome manipulator 510 includes a genome manipulation component 512 (e.g., a Cas protein such as dCas9 or Cas9) and a targeting component 514 (e.g., a guide RNA), where the targeting component 514 is configured to bind to a target locus of a nucleic acid sample. In some implementations, the genome manipulation component 512 is active (e.g., Cas9) to perform cleavage of the nucleic acid at the target locus of the nucleic acid sample. In certain implementations, the genome manipulation component 512 is inactive (e.g., dCas9) to perform on-target binding to the target site of the nucleic acid sample without cleavage.

[0117] Figure 5A illustrates an implementation 500 of a capture surface 511 functionalized with a targeted genome manipulator 510 in accordance with one or more examples of the present disclosure. In various implementations, the targeted genome manipulator 510 is functionalized into the capture surface 511 via the genome manipulation component 512 (e.g., a Cas protein). In certain implementations, the capture surface 511 is such as described above with respect to Figure 4A portion of the sensing surface 504 of the bio-gated transistor of the depicted bio-gated transistor 402. In other implementations, the sensing surface 504 is the surface of a surface plasmon resonance (“SPR”) sensor chip, a terahertz spectroscopy sensor chip, a surface enhanced spectroscopy sensor chip, a quartz crystal microbalance sensor chip, a grating-coupled interferometry sensor chip, etc.

[0118] In some implementations, the sensing surface 504 includes graphene, and the targeting genome manipulator 510 is functionalized into the capture surface 511 using an amine bond between the graphene-modified COOH surface of the sensing surface 504 and one or more amine (NH2) groups in the genome manipulation component 512 such as Cas9.

[0119] Figure 5B An implementation 525 is shown that utilizes the capture surface 511 functionalized with the targeting genome manipulator 510 according to one or more examples of the present disclosure. In one implementation, the targeting genome manipulator 510 is functionalized into the sensing surface 504 via the targeting component 514 (e.g., the gRNA portion of the Cas-gRNA complex). In the implementation 525 where the sensing surface 504 is part of a bio-gated transistor such as Figure 4 depicted, the targeting genome manipulator 503b (e.g., the Cas-gRNA complex) is tethered to the sensing surface 504 (e.g., the graphene surface) via the targeting component 502b (e.g., the gRNA).

[0120] In a first gRNA tethering implementation, the targeting component 502b is a gRNA that is synthesized with an amino group at one end and is immobilized to a COOH chemical that decorates the sensing surface 504 (e.g., the graphene channel). In a second gRNA tethering implementation, the targeting component 502b is a gRNA that is synthesized with biotin at one end and is immobilized (e.g., tethered) to a streptavidin coating at the sensing surface 504. In a third gRNA tethering implementation, the targeting component 502b is a gRNA that is functionalized into the sensing surface 504 using an oligonucleotide that binds to the sensing surface 504 via Watson-Crick base pairing.

[0121] Figure 5CIllustrated is an implementation 530 of a sensing surface 504 for detecting biomolecular binding interactions between one or more capture surfaces 511 functionalized with a targeted genome manipulator 510, according to one or more examples of the present disclosure. In certain implementations, the one or more capture surfaces 511 are beads 520 having dimensions from about 1 nanometer (nanoparticle) to 1000 micrometers. In some implementations, the beads 520 (at least on their outer surfaces) are composed of a biocompatible material that provides low nucleic acid and protein binding adsorption and is known to be functionalized with protein or DNA.

[0122] Such biocompatible materials include, but are not limited to, glass, plastic, silicon, metal, or hydrogel functionalized with a targeted genome manipulator 510. In various implementations, the beads 520 can be non-magnetic, magnetic, or paramagnetic. In certain implementations, a first magnet 516 and a second magnet 518 can be disposed above and below the sensing surface. The first magnet 516 can be activated to attract the magnetic beads 520 towards the sensing surface 504, and the second magnet 518 can be activated to direct the magnetic beads 520 away from the sensing surface 504. Thus, by controlling the activation of the first magnet 516 and the second magnet 518, the functionalized targeted genome manipulator 510 can be moved up or down or otherwise agitated, including entering or leaving the Debye layer.

[0123] In implementation 530, the capture surface 511 is part of one or more functionalized beads 520 such that binding sensed by a chip-based biosensor when a biomolecular binding interaction occurs between a nucleic acid sample and the targeted genome manipulator 510 is within the sensing range 508 of the sensing surface 504, the targeted genome manipulator 510 being functionalized to the capture surface 511 of the beads 520.

[0124] In various implementations, the sensing surface 504 is part of a chip-based biosensor configured to perform label-free detection of one or more components of a nucleic acid sample. Figure 4 An exemplary implementation of a biosensor 202 is depicted as being chip-based and performing field-effect biosensing using a bio-gated transistor 402.

[0125] In certain implementations, the one or more capture surfaces 511 can both detect and capture a target nucleic acid sequence of interest. In some implementations, the nucleic acid sample captured by the capture surface 511 can be recovered to provide enrichment of the sequence targeted by the targeted genome manipulator 510.

[0126] Figure 6Method 600 for determining binding efficiency parameters of a targeted genome manipulator fixed to a sensing surface in accordance with one or more examples of the present disclosure is shown. In one implementation, method 600 begins and includes calibrating 632 a chip-based biosensor 605 using a reference buffer. Based on the response signal generated by the chip-based biosensor 605, the method displays a calibration baseline 540a, which is then used as a reference for changes in the charge of the liquid gate brought about, for example, by one or more biomolecular binding interactions within a sensing range 618 sensed by the chip-based biosensor 605. In various implementations, method 600 includes increasing the sensitivity of the chip-based biosensor 605 by reducing the length or thickness of the Debye layer using a low-salt reference buffer.

[0127] In one example, method 600 for determining on-target binding parameters includes incubating 634 a nucleic acid sample 608, such as DNA, using a chip-based biosensor 605 that has a functionalized capture surface 611 associated with a sensing surface 604 of the chip-based biosensor 605. In some examples, the targeted genome manipulator 603 is a Cas / gRNA complex that is functionalized onto a chip-based biosensor 104 that utilizes a biological gated transistor, such as a graphene FET (“gFET”).

[0128] In certain implementations, the chip-based biosensor 605 is a removable or non-removable chip connected to an external or integrated electronic reader 616 that is configured to measure different transistor parameters affected by the binding of a targeted genome manipulator 603, such as a Cas9 / gRNA complex, and a target sequence 607 of a nucleic acid sample 608. In various implementations, the sensing surface 604 of the chip-based biosensor 605 detects negative and / or positive charges brought into the sensing range 618 by the capture of the nucleic acid sample 608 by the functionalized capture surface 611. In some implementations, the amount and polarity of the charge can be controlled within biologically necessary bounds by changing the pH and ionic concentration of the buffer solution.

[0129] In some implementations, various parameters, including parameters other than charge, are affected by the presence of captured nucleic acid molecules or fragments near the sensing surface 604. Some such parameters include, for example, gate capacitance (e.g., Cgs, Cgd), leakage current (e.g., “Ids”), and gate voltage (e.g., “Vgs”). For example, using a response signal indicative of a change in capacitance can enable the detection of uncharged molecules.

[0130] In certain implementations, the reference buffer includes from about 1 mM to 20 mM of NaCl and from 0 mM to 20 mM of EDTA. In some implementations, the reference buffer can be replaced by pure water. Method 600 continues and includes removing the reference buffer and incubating the nucleic acid sample 608 (e.g., a DNA molecule) in a binding buffer that facilitates identification of the target site of the nucleic acid sample 608 and facilitates binding of the target site of the nucleic acid sample 608 to the capture surface 611 functionalized with the targeted genome manipulator 603. In certain implementations, the binding buffer is selected to minimize cleavage of the captured nucleic acid at the target sequence 607. For example, in some implementations, the method includes adding a saturating amount of quencher molecules such as EDTA to the binding buffer to quench divalent cations, e.g., Mg2+, Mn2+, Fe2+, Co2+, Ni2+, or Zn2+ (if present in the solution). In one example, the binding buffer with quencher molecules contains from about 1 mM to 500 mM of NaCl, from 0 mM to 100 mM of HEPES, from 100 mM to 1 M of EDTA, and the pH of the binding buffer is between 5 and 8.5.

[0131] In certain implementations, method 600 continues and includes a binding step 636 for incubating the sample nucleic acid in the chip-based biosensor at room temperature for about 1 minute to about 16 hours. Method 600 continues and includes discarding the supernatant after incubation and washing the chip-based biosensor 605 with a wash buffer from 1 to 10 times. In various examples, the wash buffer includes from 1 mM to 500 mM of NaCl and from 1 mM to 500 mM of EDTA. In certain examples, method 600 further includes incubating the sensing surface 604 of the field effect biosensor again with the reference buffer and measuring a new value for the selected parameter.

[0132] In one aspect, when a targeted genome manipulator 603 such as a Cas9 / gRNA complex identifies a predetermined target sequence 607 in the absence of magnesium, it binds tightly to the target sequence 607 without cleaving the target sequence 607. Any charged molecule tethered to the graphene surface - e.g., the nucleic acid sample 608 (e.g., DNA) in this example - will cause a change in the parameters listed above. Method 600 continues and includes displaying, recording, and / or comparing the intensity differences of the parameters recorded before (e.g., during the calibration step 634 using the reference buffer) and after incubation of the nucleic acid sample 608 with the targeted genome manipulator. In various implementations, method 600 includes determining an efficiency parameter of the targeted genome manipulator 603 based on comparing one or more first response signals measured in the calibration step 634 with one or more second response signals measured throughout the binding step 636.

[0133] Since DNA is a charged molecule, DNA molecules located within the Debye layer may affect one or more of the parameters listed above. Thus, method 600 determines 640 an efficiency parameter of the targeted genome manipulator 603 based on comparing the difference in response signals proportional to the captured nucleic acid from the cultivation period to the calibration period.

[0134] In various implementations, method 600 continues and includes identifying the targeted genome manipulator 603 as having a suitable targeting component 602 in response to determining that the difference between one or more response signals measured during the binding step 636 and the corresponding response signals measured during the calibration step 634 meets a predetermined binding efficiency condition. In other words, the greater the response measured during the binding step, the more of the targeting component 602 (e.g., gRNA) that will capture its targeted DNA, thus giving the user an indication to further use that particular gRNA or, in the case where the difference in response signals does not meet the predetermined binding efficiency condition, indicating to the user to design a recommended new targeting component 602.

[0135] In various implementations, method 600 continues and includes applying 638 a lysis buffer to the chip-based biosensor after recording the binding ability of the targeted genome manipulator 603. In one example, the lysis buffer includes 1 mM to 500 mM of NaCl, 5 mM to 20 mM of MgCl2, and 0 mM to 100 mM of HEPES, and the pH of the lysis buffer is between 5 and 8.5. In the presence of Mg2+ or other divalent cations such as Mn2+, Fe2+, Co2+, Ni2+, or Zn2+, Cas9 cuts the DNA at the target sequence 607 (e.g., its recognition site).

[0136] In response to the lysis buffer applied to the chip-based biosensor 605 after measurement in the binding step 636, the nucleic acid sample 608 such as DNA bound to the capture surface by the effective targeted genome manipulator 603 is efficiently cleaved. The cleavage causes a portion of the nucleic acid sample 608 to flow away from the sensing surface 604.

[0137] To determine semi - quantitative or quantitative results, method 600 includes incubating a targeted genome manipulator 603 (e.g., a Cas9 / gRNA immobilized complex) with a lysis buffer at room temperature for about 30 seconds to about 60 minutes and replacing the lysis buffer with a reference buffer. Depending on the efficiency of the targeted genome manipulator being tested, the response signal of one or several of the parameters listed above will reach a lysis level 638 between the level of the first measurement taken during the calibration step and the response signal during the binding step 636. Since genome manipulation components such as Cas9 remain bound to one end 610 of its substrate even after effective lysis, the response signal should not reach the first reference measurement taken during the calibration step 634.

[0138] In some implementations, method 600 continues and determines the overall efficiency of the targeted genome manipulator 603 based on a comparison of the differences in the calibration step relative to the measured response signals during binding and lysis. The greater the difference between the corresponding binding response signal and lysis response signal and the calibration response signal, the more indication to the user that the selected targeted genome manipulator 603 is effective and can be further tested to determine if it induces putative off - target binding on the genome of interest. In various examples, if an appropriate genome manipulation component 601 such as Cas13 is used with an appropriate targeting component 602, the nucleic acid sample 608 can be an amplicon, a genomic DNA strand, chromatin, or other types of nucleic acids such as RNA.

[0139] Figure 7 Shown is a method 700 for determining binding efficiency parameters regarding a targeted genome manipulator 703 using double - stranded DNA 708 immobilized to a sensing surface 704, according to one or more examples of the present disclosure. In one implementation, method 700 begins and includes providing 702 a chip - based biosensor including double - stranded DNA 708 immobilized to a sensing surface 704. The immobilized double - stranded DNA 708 contains an on - target sequence 707 to which the targeted genome manipulator 703 is configured to bind. Relative to Figure 6The method 600 depicted uses a similar calibration step 702, a binding step 705, and a cleavage step 711 in which a targeted genomic manipulator 703 cleaves nucleic acid fragments 709 as described above to perform method 700. However, in method 600, the charge monitored by the biogated transistor sensor is not the DNA charge, but rather the charge of the targeted genomic manipulator 703 in response to its binding to dsDNA immobilized to the sensing surface 704. This method 700 is particularly useful when testing genomic manipulation components such as engineered Cas9 or similar nucleases for cleavage efficiency because the same functionalized sensing surface 704 can be used until a satisfactory targeted genomic manipulator 703 such as a Cas9 / gRNA complex is identified. Method 700 continues and displays 712 a response signal regarding the calibration step 702, the binding step 705, and the cleavage step 711. The response signal varies with changes in the measured parameters described above (e.g., the concentration of the binding molecule).

[0140] Detection of putative off-target binding / cleavage activity. In response to determining that a targeted genomic manipulator meets one or more predetermined binding efficiency parameters and cleavage efficiency parameters (e.g., where the tested Cas9 / gRNA complex exhibits good binding / cleavage activity), it is beneficial to examine whether the targeted genomic manipulator targets off-target regions or sites of the genome for which it was designed. The above provides various ways to comparatively analyze on-target binding and off-target binding with respect to Figures 1 to 4 the systems 100, devices 200, 400, and methods 300 described.

[0141] Figure 8 An implementation of a method 800 for fragmenting DNA 812 and ligating adapters 813 of a nucleic acid sample 811 for sequencing after measurement of genomic manipulation efficiency parameters according to one or more examples of the present disclosure is shown. Method 800 begins and includes fragmenting 802 a nucleic acid sample 811 comprising whole DNA that has been purified using known DNA purification techniques. Fragmentation 802 is performed using any suitable fragmentation technique such as sonication, acoustic shearing, hydrodynamic shearing, endonuclease digestion, etc. In various implementations, the parameters of physical shearing and enzymatic shearing are selected to produce DNA fragments comprising between 50 bp and 10,000 bp, more preferably between 100 bp and 1,000 bp.

[0142] Method 800 continues and uses an enzyme selected based on the fragmentation pattern used to repair 804 the fragmented DNA 812. For example, if sonication is used to fragment the DNA 812, the fragmented DNA 812 is repaired using, for example, T4 DNA polymerase in the presence of dNTPs to generate "blunt" ends by filling in 5' overhangs via its 5'→3' activity and filling in recessed 3' overhangs via its 3'→5' exonuclease activity.

[0143] The method continues and includes ligating 812 the fragmented DNA 812 to an adaptor 813 designed to be used with a user-selected NGS technology. Each adaptor 813 is designed to generate an overhang at one end of its terminus and is modified with blocking moieties at both ends of the other terminus to avoid self-ligation and subsequent ligation when ligated to the DNA fragment 812 in place.

[0144] Figure 9 Another method 900 for preparing a fragmented and adaptor-ligated nucleic acid sample for sequencing after measurement using one or more chip-based biosensors using genomic manipulation parameters is shown according to one or more examples of the present disclosure. Method 900 begins and includes incubating 902 a nucleic acid sample such as adaptor-ligated native chromatin 912 that has been fragmented and labeled with adaptors 913. One or more instances of the adaptor-ligated native chromatin 912 are incubated at a capture surface 920, which in some embodiments is the sensing surface of a chip-based biosensor. The capture surface 920 is functionalized with a targeted genomic manipulator 903. In one implementation, the buffer for this incubation includes a divalent cation quencher molecule (e.g., EDTA) to prevent cleavage (e.g., Cas9 cleavage). Non-limiting examples of such buffers contain between 1 mM and 500 mM NaCl, between 0 mM and 100 mM HEPES, and between 100 mM and 1 M EDTA, and the buffer has a pH between 5 and 8.5.

[0145] In certain implementations, the incubation is performed at room temperature for a period ranging from 1 minute to 24 hours. Method 900 continues and includes capturing 904 during incubation a DNA or chromatin genomic fragment containing a target sequence 907 recognized by the targeted genomic manipulator 903, which is functionalized to the capture surface 920. Method 900 continues and includes washing 906 the capture surface 920 with a wash buffer to keep the target DNA / chromatin fragment bound to the targeted genomic manipulator 903 and to wash away any unbound DNA or chromatin fragments. In various implementations, the washing 906 is performed one to five times at room temperature.

[0146] Method 900 continues and triggers by applying a buffer containing a certain amount of Mg2+ or divalent cations such as Mn2+, Fe2+, Co2+, Ni2+ or Zn2+ to release one or more chromatin segments 918 corresponding to sequences in the targeting region close to the genome into the supernatant

[0147] →

[0148] Cas9 cleavage. Additionally, method 900 continues and includes removing DNA 909 from the cleaved chromatin 918 to be repaired and polyadenylated using any suitable enzyme mixture known in the art.

[0149] In some implementations, method 900 continues and (after purification and quantification) includes ligating 908 the cleaved DNA to an adaptor 921 using T4 DNA ligase. In various implementations, method 900 continues and includes amplifying 914 the ligated DNA sample after purification. Amplification 914 can be performed using PCR with universal primers 922, 923 and targeting adaptors 921 and 923. In certain implementations, method 900 continues and includes sequencing 916 the purified amplicons using, for example, next-generation sequencing. In some implementations, adaptors 913 and 921 have the same sequence. In other implementations, adaptors 913, 921 have different sequences.

[0150] In one implementation, adaptor 913 is configured to generate an overhang at one of its ends and is modified with a blocking moiety at the other end to avoid self-ligation and subsequent ligation when ligating to a DNA fragment in place. In various implementations, method 900 includes reducing the likelihood of self-ligation of adaptor 913 by configuring the 3’ end of the first strand of adaptor 913 to include an overhang thymine linked to the rest of the sequence by a phosphorothioate bond using the 5’ end of the same strand lacking a phosphate moiety, and configuring the second strand of adaptor 913 to have a 5’ end including a phosphate group and further configuring the 3’ end of the second strand to include a moiety configured to prevent any ligation such as a fluorophore molecule, a click chemistry moiety or a reverse dT (reverse ligation).

[0151] Figure 10Method 1000 for performing tagging on a nucleic acid sample for sequencing after measurement of targeted genome manipulation efficiency parameters is shown, in accordance with one or more examples of the present disclosure. In at least one embodiment, method 1000 begins and includes tagging 1002 (e.g., performing one-step tagging and fragmentation) of nucleic acid sample 1011 (e.g., naked DNA). Tagging 1002 uses a transposon 1015 that includes a transposase (e.g., Tn5) and two adaptors 1013. The transposon 1015 fragments and transposes the two adaptors 1013 into the nucleic acid sample 1011 (e.g., genomic DNA). In certain implementations, the steps of tagging 1002 include optimizing time 1004 and optimizing transposase concentration 1006 to produce one or more tagged fragments 1012 that are about 150 base pairs to about 1000 base pairs.

[0152] Figure 11 Method 1100 for preparing a nucleic acid sample 1111 for sequencing using a selected targeted genome manipulation agent is shown, in accordance with one or more examples of the present disclosure. In certain implementations of the systems, devices, and methods disclosed herein, it may be useful to avoid forming double-strand breaks on nucleic acid samples such as DNA.

[0153] Thus, in some implementations, method 1100 begins and includes preparing 1130 a chip-based biosensor having a capture surface 1104 functionalized with a targeted genome manipulation agent 1126 that includes a non-cleaving manipulation component 1124. For example, in various implementations, the manipulation component 1124 can be an inactivated Cas protein (also referred to as dCas or dead Cas) that has been mutated in one or two catalytic cleavage sites.

[0154] In certain implementations, preparing 1130 the functionalized capture surface 1104 with the targeted genome manipulation agent 1126 includes fusing the targeted genome manipulation agent 1126 with a selected enzyme 1125 such as a deaminase or histone deacetylase for targeting specific alleles and / or specific regions of the targeted genome to modify single nucleotide polymorphisms (“SNPs”) and / or alter the methylation of targeted nucleotides of the nucleic acid sample 1111.

[0155] In various implementations, method 1100 includes measuring 1138 one or more chip-based biosensor parameters to evaluate the binding regions of a non-cleaving targeting component of a targeted genome manipulation agent such as dCas9 across the genome and to evaluate one or more off-target sites of an adenine base editor.

[0156] In some implementations, the targeted genomic manipulator 1126 includes a non-cleaving manipulation component 1124 such as an inactive Cas protein. In certain implementations, a non-limiting example of such a manipulation component 1124 is dCas9. In various implementations, the manipulation component 1124 is fused and combined with a selected enzyme 1125 (e.g., a deaminase) and a targeting component such as gRNA 1102 to form the targeted genomic manipulator 1126. In various implementations, according to one or more aspects of the present disclosure, the capture surface 1120 functionalized with the targeted genomic manipulator 1126 is the sensing surface of a chip-based biosensor.

[0157] In certain implementations, method 1100 continues and includes fragmenting 1132 a nucleic acid sample 1111 (e.g., genomic DNA) using (by way of non-limiting example) sonication, acoustic shearing, hydrodynamic shearing, and / or endonuclease digestion to produce a fragmented nucleic acid sample 1112 (e.g., fragmented DNA). In certain implementations, the fragmenting 1132 step of method 1100 further includes incubating the nucleic acid sample 1112 using the functionalized capture surface 1104 of a chip-based biosensor 1105 - such as the biogated transistor 402 described above with respect to Figure 4 In some implementations, the chip-based biosensor 1105 is read by a reader 1106. Method 1100 continues and includes measuring 1138 one or more binding parameters associated with a biomolecular binding interaction between the targeted genomic manipulator 1126 and the fragmented nucleic acid sample 1112 that occurs within the sensing range of the sensing surface of the chip-based biosensor 1105.

[0158] For certain implementations, e.g., implementations of a chip-based biosensor using field-effect biosenzing described above with respect to Figure 4 one or more binding parameters indicating the rate and / or magnitude of a biochemical and / or biomolecular interaction may include the average change, rate of change, or characteristic shape of any one of gate capacitance, source-drain current, gate-related current, and / or gate voltage. In various implementations, the method includes incubating the functionalized capture surface 1120 (e.g., functionalized with a non-cleaving targeted genomic manipulator 1126 (e.g., a dCas complex)) with a reference buffer. In some implementations, method 1100 includes minimizing the Debye layer length of the field-effect biosensor by selecting a reference buffer with a low salt content to improve sensitivity. In some implementations, the reference buffer includes from 1 mM to 20 mM of NaCl and from 0 mM to 20 mM of EDTA.

[0159] In some implementations, method 1100 continues and includes, as part of measurement 1138, first measuring one or more chip-based biosensor parameters in the presence of a reference buffer, such as those listed above. In some implementations, method 1100 includes removing the reference buffer and incubating nucleic acid sample 1112 (e.g., fragmented DNA) resuspended in a binding enhancement buffer, which is configured to enhance the targeting function of non-lytic targeting genome manipulator 1126 (e.g., dCas9 / deaminase / gRNA complex) and enhance the binding of targeting genome manipulator 1126 to a predetermined target sequence 1107. By way of example, the binding enhancement buffer in various implementations includes NaCl from 1 mM to 500 mM, HEPES from 0 mM to 100 mM, EDTA from 100 mM to 1 M, and the pH of the binding enhancement buffer is between 5 and 8.5.

[0160] In various implementations, method 1100 includes incubating nucleic acid sample 1112, such as fragmented DNA, in the binding enhancement buffer at room temperature for from about 1 minute to about 16 hours. In certain implementations, after incubation, method 1100 includes discarding the supernatant and washing the chip-based biosensor 1105 with a wash buffer from 1 to 10 times. In one example, the wash buffer includes NaCl from 1 mM to 500 mM and EDTA from 1 mM to 500 mM.

[0161] In various implementations, method 1100 includes incubating the chip-based biosensor again with the reference buffer and measuring the parameters again using, for example, measurement module 124 as described above with respect to Figure 1 , Figure 2 and / or Figure 4 described.

[0162] In certain implementations, method 1100 includes determining whether the nucleic acid sample (e.g., fragmented DNA) includes targeting SMP 1127 by measuring the biomolecular interaction between predetermined target sequence 1107 and a selected enzyme 1125 (e.g., deaminase) via measurement 1138. In some implementations, measurement 38 includes measuring whether any charged molecule, such as nucleic acid sample 1112 in this example, causes a change in any of the above-described chip-based biosensor parameters. In various implementations, method 1100 includes comparing the intensity differences between one or more pre-binding incubation parameters recorded before incubation of the capture surface of chip-based biosensor 1105 and nucleic acid sample 112 to determine the efficiency parameter of targeting genome manipulator 1126 based on using, for example, analysis module 116 as described above with respect to Figure 1 and Figure 2 described.

[0163] In various implementations, after determining the efficiency parameters of the targeted genome manipulator, method 1100 includes replacing the reference buffer used to perform the measurement with a deamination buffer that favors the deaminase activity of the selected enzyme 1125 (e.g., a deaminase), where the selected enzyme 1125 is fused to a non-lytic manipulation component 1124 (e.g., inactive Cas9).

[0164] In certain implementations, method 1100 includes performing deamination 1134 of adenine to produce inosine 1128 that is read as guanine by DNA polymerase. In some implementations, method 1100 includes replacing 1136 the deamination buffer with a solution that includes Tn5 transposase 1115 and two adapters 1129 in a buffer that favors transposition. In various implementations, the adapter 1129 includes a standard universal adapter for NGS. In some implementations, method 1100 continues and includes amplifying 1140 the deaminated nucleic acid sample 1109 having the adapter 1129 using the universal primers 922 and 923 depicted in step 910 as Figure 9 depicted.

[0165] In various implementations, method 1100 includes capturing a portion of the nucleic acid sample that includes target and off-target sites bound to the capture surface of a second chip-based biosensor and releasing the captured sample portion. For example, in certain implementations, method 1100 continues and includes using proteinase K digestion to release a portion of the captured nucleic acid sample 1109 (e.g., labeled fragmented DNA) recovered from the chip-based biosensor 1105 into the supernatant. It can be noted that various types of release of the captured nucleic acid sample can be performed at other points within method 1100 or within methods 300, 600, 700, 800, 900, 1000, and / or 1200. In at least one embodiment, method 1100 includes amplifying 1140 the released nucleic acid sample 1109 using, for example, PCR purification. In some examples, each inosine added by the deaminase is changed to guanine at this point.

[0166] It can be noted that one or more of the steps in methods 300, 600, 700, 800, 900, 1000, 1100, and / or 1200 can be performed in any combination, in whole or in part, with other steps of the methods mentioned above. Similarly, one or more of the steps of the methods mentioned above can be used in any combination with any component or the whole of system 100 and / or any one of devices 200, 400, or portions thereof.

[0167] Figure 12FIG. 1200 is a schematic flow chart showing a method 1200 for enhanced selection of a genome editing agent for effective targeting according to one or more examples of the present disclosure. In one embodiment, method 1200 begins and includes preparing 1202 a first aliquot and a second aliquot, each aliquot including a nucleic acid sample that is measured to detect a biomolecular binding interaction between the nucleic acid sample allocated to one or more sensing surfaces and a genome editing agent, the genome editing agent having a genome editing component and a targeting component and being functionalized as a capture surface within the sensing range of one or more sensing surfaces. In some implementations, the targeting component is a guide RNA and the genome editing component is a CRISPR-associated protein.

[0168] If the intention is to capture sequences for amplification and sequencing based on positive efficiency determination, method 1200 may optionally include fragmenting 1202 the nucleic acid sample and labeling the fragments for sequencing prior to determining the efficiency parameter. Fragmentation and labeling may be performed separately in certain implementations, or in other implementations, fragmentation and labeling are performed simultaneously for sequencing prior to applying the first aliquot and the second aliquot to the first surface and the second surface, respectively.

[0169] For reproducibility, in various implementations, method 1200 includes calibrating 1204 each chip-based biosensor to establish a baseline against which a comparative analysis of a first set of response signals and a second set of response signals generated by binding is performed. This may be particularly useful when the first set of response signals and the second set of response signals are relatively weak during the binding step.

[0170] After calibration 1204, method 1200 continues and includes using 1206 a blocker and the first aliquot to minimize on-target binding to the genome editing agent on the first chip-based biosensor, and omitting 1208 the blocker in the second aliquot used with the second chip-based biosensor to enable on-target binding and off-target binding to occur between the nucleic acid sample and the functionalized capture surface of the second chip-based biosensor. In some implementations, the blocker is an inactivated Cas complexed with a blocking RNA, and the blocking RNA is configured to bind to a sequence overlapping the guide sequence of the gRNA. In other implementations, the blocker is a synthetic nucleic acid analogue configured to bind to a sequence substantially overlapping the guide sequence of the gRNA.

[0171] After incubating 1206 the first aliquot with the blocker sufficiently, method 1200 continues and includes incubating 1210 the first aliquot blocked against on-target binding on the first chip-based biosensor and incubating the second aliquot not blocked against either on-target binding or off-target binding.

[0172] Method 1200 continues and optionally includes washing 1212 unbound sample from the first chip-based biosensor and the second chip-based biosensor. In some implementations, if the measurement bandwidth is high enough and the noise is low enough, the need for a washing step can be reduced.

[0173] Method 1200 continues and includes measuring 1214 one or more first response signals and second response signals generated in response to biomolecular binding interactions occurring between nucleic acid samples in the first aliquot and the second aliquot and targeted genomic manipulators on the functionalized capture surfaces of the first chip-based biosensor and the second chip-based biosensor. More than one response signal can be measured using a field effect biosensor for each chip. For example, response signals regarding drain current, gate capacitance, gate current, etc. can all generate response signals and can all be monitored for comparison with a second set of response signals from the second chip-based biosensor.

[0174] In some implementations, the one or more first response signals and second response signals are optionally measured using a sampling rate that satisfies a predetermined Nyquist criterion for measuring at least one parameter of the biomolecular binding interaction between the nucleic acid sample and the targeted genomic manipulator during a predetermined time period associated with the biomolecular binding interaction.

[0175] Measuring using a sampling rate that satisfies the Nyquist criterion for a given parameter enables a better understanding of the dynamics of the parameter, especially in cases where multiple binding interactions occur simultaneously. One or more first response signals indicate binding parameters associated with off-target binding between a nucleic acid sample incubated with a blocker and targeted genomic manipulators functionalized as capture surfaces within the sensing range of one or more sensing surfaces of the first chip-based biosensor, and one or more second response signals indicate binding parameters associated with on-target plus off-target binding between a nucleic acid sample without the blocker and targeted genomic manipulators functionalized as capture surfaces within the sensing range of one or more sensing surfaces of the second chip-based biosensor.

[0176] Method 1200 continues and includes determining 1216 the binding efficiency and / or cleavage efficiency of a targeted genome manipulator. For example, in certain implementations, a predetermined calibration process is derived for a representative population of sample biosensors based on comparing the concentration of response signals generated by a first chip-based biosensor with the concentration of response signals generated by a second chip-based biosensor. Since the response signals from the two chip-based biosensors include off-target binding, the difference between the concentrations represents on-target binding of the targeted genome manipulator. At this point, the values of both on-target binding and off-target binding are known and can be compared as a ratio or as a difference.

[0177] In certain implementations, method 1200 continues and includes capturing 1222 a portion of a nucleic acid sample that includes on-target and off-target sites bound to the capture surface of a second chip-based biosensor from the unblocked second aliquot, and releasing the captured sample portion. Thus, the chip-based biosensor can be used to enrich fragments for performing PCR and the like. In some implementations, method 1200 continues and includes sequencing 1224 one or more labeled fragments of a target sample in response to determining that the efficiency of the targeted genome manipulator meets a predetermined efficiency criterion, and method 1200 ends.

[0178] A computer program product including a computer-readable storage medium having program instructions implemented as a computer program product, the program instructions executable by a processor to cause the processor to control the measurement of one or more first response signals and second response signals generated by a first chip-based biosensor and a second chip-based biosensor in response to a biomolecular binding interaction occurring between a nucleic acid sample and a targeted genome manipulator, the targeted genome manipulator having a manipulation component and a targeting component and being functionalized to a capture surface within the sensing range of one or more respective sensing surfaces of the first chip-based biosensor and the second chip-based biosensor, wherein the first chip-based biosensor is configured to hold a first aliquot of a nucleic acid sample that is optionally incubated with a blocker configured to bind to a sequence overlapping the on-target sequence of the nucleic acid sample, and the second chip-based biosensor is configured to hold a second aliquot of the nucleic acid sample that omits the blocker, and to determine one or more genome manipulation efficiency parameters associated with the targeted genome manipulator based on performing a comparative analysis of the first response signals and the second response signals.

[0179] In some implementations, program instructions may be executed to cause a processor to perform a comparative analysis between genomic manipulation efficiency parameters determined using a chip-based biosensor and corresponding genomic manipulation efficiency parameters determined using one or more other methods such as the various computer methods, in vitro methods, and in vivo methods described above. For example, after fragmentation and adapter ligation are performed according to one or more of methods 800, 900, 1000, 1100, and 1200, the analysis results of the chip-based biosensor may be compared with one or more of the in vivo, in vitro, and / or computer binding and / or cleavage efficiency results obtained for the same or similar targeted genomic manipulators.

[0180] The implementations may be practiced in other specific forms. The described implementations are to be considered illustrative rather than restrictive in all respects. Accordingly, the scope of the invention is indicated by the appended claims rather than by the foregoing description. All changes that come within the meaning and range of equivalency of the claims are to be embraced within their scope.

Claims

1. An apparatus for enhanced selection of effective targeting of genome manipulators, comprising: A first chip-based biosensor and a second chip-based biosensor, wherein the first chip-based biosensor and the second chip-based biosensor each comprise one or more sensing surfaces on a channel utilizing planar two-dimensional van der Waals materials, wherein the channel extends between a source electrode and a drain electrode of a liquid-gated field-effect transistor configured to detect biomolecular binding interactions between a nucleic acid sample and one or more capture surfaces functionalized with a targeting genome manipulator having a genome manipulation component and a targeting component, wherein the one or more sensing surfaces corresponding to the first chip-based biosensor and the second chip-based biosensor comprise one or more capture surfaces, and wherein: The first chip-based biosensor receives a first aliquot of the nucleic acid sample incubated with a blocker selected to bind to a sequence overlapping the target sequence of the nucleic acid sample; and The second chip-based biosensor receives a second aliquot of the nucleic acid sample omitting the blocker; A measurement controller that measures one or more first field-effect transistor response signals and one or more second field-effect transistor response signals, the first and second field-effect transistor response signals comprising characteristic shapes of source-drain currents in response to incrementally adjusted potentials of the liquid gates of the field-effect transistors on the first chip-based biosensor and the second chip-based biosensor, the first and second field-effect transistor response signals being generated in response to biomolecular binding interactions occurring between the nucleic acid sample in the first and second aliquots and the targeting genome manipulator on the functionalized capture surfaces of the channels of the liquid-gated field-effect transistors of the first and second chip-based biosensors; and An analysis module that determines one or more genome manipulation efficiency parameters associated with the targeting genome manipulator based on performing a comparison of the first field-effect transistor response signals and the second field-effect transistor response signals.

2. The apparatus according to claim 1, wherein, The targeting component comprises a guide RNA, i.e., gRNA.

3. The apparatus according to claim 2, wherein, The genome manipulation component comprises a CRISPR-associated protein molecule, and the CRISPR-associated protein molecule is a Cas molecule.

4. The apparatus according to claim 3, wherein, The Cas molecule is selected from Cas9, Cas12, and Cas13.

5. The apparatus according to claim 3, wherein, The targeting genome manipulator is attached to the one or more capture surfaces via the Cas molecule.

6. The device according to claim 2, wherein, the targeted genome manipulator attaches to the one or more capture surfaces via the gRNA.

7. The device according to claim 2, wherein, the blocker is selected from: an inactivated Cas molecule complexed with a blocking RNA, the blocking RNA being configured to bind to a sequence overlapping with the on-target sequence of the nucleic acid sample; and a synthetic nucleic acid analogue, the synthetic nucleic acid analogue being configured to bind to a sequence overlapping with the on-target sequence of the nucleic acid sample.

8. The device according to claim 2, wherein: the first chip-based biosensor and the second chip-based biosensor are configured to detect the biomolecular binding interaction when the nucleic acid sample is in a fluid-undriven state.

9. The device according to claim 2, wherein, the measurement controller is configured to measure the one or more first field-effect transistor response signals and the one or more second field-effect transistor response signals after any unbound components of the first aliquot and the second aliquot respectively held and incubated on the first chip-based biosensor and the second chip-based biosensor are washed away.

10. The device according to claim 9, wherein: the one or more first field-effect transistor response signals change in response to a change in a binding parameter associated with off-target binding between the nucleic acid sample and the targeted genome manipulator; and the one or more second field-effect transistor response signals change in response to a change in a binding parameter associated with on-target plus off-target binding between the nucleic acid sample and the targeted genome manipulator.

11. The device according to claim 2, wherein: a part of the one or more capture surfaces that is part of the sensing surface on a portion of the channel of the liquid-gated field-effect transistor serving as the first chip-based biosensor is functionalized with a first instance of the targeted genome manipulator, wherein the genome manipulation component is configured not to lyse the nucleic acid sample; a part of the one or more capture surfaces that is part of the sensing surface of the channel of the liquid-gated field-effect transistor serving as the second chip-based biosensor is functionalized with a second instance of the targeted genome manipulator, wherein the genome manipulation component is configured to lyse the nucleic acid sample; wherein the first instance and the second instance of the targeted genome manipulator include the same targeting component; the first aliquot omits the incubation with the blocker; and the one or more first field-effect transistor response signals and the one or more second field-effect transistor response signals indicate lysis parameters associated with binding between the nucleic acid sample and the corresponding functionalized capture surfaces of the one or more sensing surfaces of the first chip-based biosensor and the second chip-based biosensor.

12. A method for enhanced selection of an effective targeted genome manipulator, comprising: Preparing a first aliquot and a second aliquot each including a nucleic acid sample, wherein: the nucleic acid sample is measured to detect a biomolecular binding interaction between the nucleic acid sample and a targeted genome manipulator, the nucleic acid sample is functionalized as one or more capture surfaces that are part of the sensing surfaces of a first chip-based biosensor and a second chip-based biosensor that utilize channels of planar two-dimensional van der Waals materials, wherein the channels extend between a source electrode and a drain electrode of a liquid-gated field effect transistor, and the targeted genome manipulator has a genome manipulation component and a targeting component and is functionalized as the one or more capture surfaces; culturing the first aliquot with a blocker, the blocker being configured to bind to a sequence that overlaps a target sequence of the nucleic acid sample; and the second aliquot omits the blocker; measuring one or more first field effect transistor response signals and one or more second field effect transistor response signals generated in response to the biomolecular binding interaction that occurs between the nucleic acid sample in the first aliquot and the second aliquot and the targeted genome manipulator on the functionalized capture surfaces of the first chip-based biosensor and the second chip-based biosensor; and determining an efficiency parameter of the targeted genome manipulator based on a comparative analysis of the one or more first field effect transistor response signals and the one or more second field effect transistor response signals.

13. The method according to claim 12, wherein the targeting component includes a guide RNA, i.e., gRNA; and the genome manipulation component includes a CRISPR-associated protein molecule, and the CRISPR-associated protein molecule is a Cas molecule.

14. The method according to claim 13, wherein, the Cas molecule is selected from Cas9, Cas12, and Cas13.

15. The method according to claim 13, wherein, the blocker is selected from: an inactivated Cas molecule complexed with a blocking RNA, the blocking RNA being configured to bind to a sequence that overlaps a target sequence of the nucleic acid sample; and a synthetic nucleic acid analogue, the synthetic nucleic acid analogue being configured to bind to a sequence that overlaps the guide sequence of the gRNA.

16. The method according to claim 12, wherein the one or more first field effect transistor response signals change in response to a change in a binding parameter associated with off-target binding between the nucleic acid sample cultured with the blocker and the targeted genome manipulator, the targeted genome manipulator being functionalized as the one or more capture surfaces of the channel of the liquid-gated field effect transistor of the first chip-based biosensor ； and The one or more second field effect transistors change in response to a signal in response to a change in a binding parameter associated with on-target binding plus off-target binding between the nucleic acid sample lacking the blocker and the targeted genome manipulator, the targeted genome manipulator being functionalized to the one or more capture surfaces of the channel of the second chip-based biosensor; and Measure the one or more first field effect transistor response signals and the one or more second field effect transistor response signals using a sampling rate that meets a predetermined Nyquist criterion for measuring at least one parameter of the biomolecular binding interaction between the nucleic acid sample and the targeted genome manipulator over a predetermined time period associated with the biomolecular binding interaction.

17. The method according to claim 12, further comprising: Capturing a portion of the nucleic acid sample from the second aliquot, the portion including on-target and off-target sites that bind to the one or more capture surfaces of the second chip-based biosensor; and Releasing the captured sample portion.

18. The method according to claim 12, further comprising fragmenting and labeling the nucleic acid sample for sequencing simultaneously before applying the first aliquot and the second aliquot to a first capture surface and a second capture surface in the one or more capture surfaces, respectively.

19. The method according to claim 12, further comprising sequencing one or more labeled fragments of the nucleic acid sample in response to determining that an efficiency parameter of the targeted genome manipulator meets a predetermined efficiency criterion.

20. A computer program product comprising a computer-readable storage medium having program instructions implemented by the computer program product, the program instructions being executable by a processor to cause the processor to: Control the measurement of one or more first field effect transistor response signals and one or more second field effect transistor response signals generated by a first chip-based biosensor and a second chip-based biosensor in response to a biomolecular binding interaction occurring between a nucleic acid sample and a targeted genome manipulator, wherein The first chip-based biosensor and the second chip-based biosensor each include one or more sensing surfaces on a channel utilizing a planar two-dimensional van der Waals material, wherein the channel extends between a source electrode and a drain electrode of a liquid-gated field effect transistor configured to detect a biomolecular binding interaction between a nucleic acid sample and one or more capture surfaces functionalized with a targeted genome manipulator, the targeted genome manipulator having a genome manipulation component and a targeting component, wherein the one or more sensing surfaces corresponding to the first chip-based biosensor and the second chip-based biosensor include one or more capture surfaces; and wherein The first chip-based biosensor receives a first aliquot of the nucleic acid sample cultivated with a blocker, the blocker being selected to bind to a sequence overlapping with the target sequence of the nucleic acid sample; and The second chip-based biosensor receives a second aliquot of the nucleic acid sample omitting the blocker; and One or more genome manipulation efficiency parameters associated with the targeted genome manipulator are determined based on performing a comparative analysis of the first field effect transistor response signal and the second field effect transistor response signal.

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