Methods and systems for target screening

Abstract

The present disclosure provides a method for identifying nucleic acids, which may include incubating cells that have been transfected or transduced, or are suspected of being transfected or transduced, with an exogenous ribonucleic acid (RNA) molecule or exogenous deoxyribonucleic acid (DNA) molecule. Morphological changes in the cells may then be identified. The contents of the cells may then be processed to identify the nucleic acid sequence or peptide, polypeptide, or protein or sequence of the peptide, polypeptide, or protein. The nucleic acid sequence or peptide, polypeptide, or protein or sequence of the peptide, polypeptide, or protein may then be analyzed to determine the exogenous sequence of the exogenous RNA molecule or exogenous DNA molecule. The exogenous sequence of the exogenous RNA molecule or exogenous DNA molecule may then be identified as affecting the morphological changes in the cells. The exogenous RNA molecule or exogenous DNA molecule may encode a gene or peptide, polypeptide, or protein that inhibits, activates, or regulates a biochemical pathway within the cell, thereby resulting in the morphological changes in the cell.

Description

[Technical field]

[0001] cross reference This application claims the benefit of U.S. Provisional Patent Application No. 62 / 747,620, filed October 18, 2018, and U.S. Provisional Patent Application No. 62 / 825,524, filed March 28, 2019, each of which is incorporated by reference in its entirety for all purposes. [Background technology]

[0002] Pooled genetic screening is a very powerful tool to identify genes involved in pathways of interest. So far, selection methods for pooled genetic screening mainly rely on positive screening, negative screening, or screening based on fluorescence intensity by fluorescence activated cell sorting (FACS).

[0003] Image-based screening is also a very useful screening method for identifying genes that may have important roles in pathways of interest. Most image-based screening methods can only be performed by microscopy and may not be applicable for pooled format screening. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost Cytometry”, Science 360(6394), pp. 1246-1251(2018), doi: 10.1126 / science.aan0096 [Non-Patent Document 2] Shalem O., Sanjana NE., Hartenian E., Shi X., Scott DA., Mikkelson T., Heckl D., Ebert BL., Root DE., Doench JG., Zhang F., Science. 2014, 343 (6166): 84 - 87. doi: 10.1126 / science.1247005. Genome-scale CRISPR-Cas9 knockout screening in human cells Non-Patent Document 3 Joung J., Konermann S., Gootenberg JS., Abudayyeh OO., Platt RJ., Brigham MD., Sanjana NE., Zhang F., Nat Protoc. 2017, 12(4): 828 - 863. doi: 10.1038 / nprot.2017.016. Genome-scale CRISPR-Cas9 knockout and transcriptional activation screening Non-Patent Document 4 N. Nitta, T. Sugimura, A. Isozaki, H. Mikami, S. Sakuma, T. Iino, F. Arai, T. Endo, Y. Fujiwaki, H. Fukuzawa, M. Hase, T. Hayakawa, K. Hiramatsu, Y. Hoshino, M. Inaba, T. Ito, H. Karakawa, Y. Kasai, K. Koizumi, S. Lee, C. Lei, M. Li, T. Maeno, S. Matsusaka, D. Murakami, A. Nakagawa, Y. Oguchi, M. Oikawa, T. Ota, K. Shiba, H. Shintaku, Y. Shirasaki, K. Suga, Y. Suzuki, N. Suzuki, Y. Tanaka, H. Tezuka, C. Toyokawa, Y. Yalikun, M. Yamada, M. Yamagishi, T. Yamano, A. Yasumoto, Y. Yatomi, M. Yazawa, D. Di Carlo, Y. Hosokawa, S. Uemura, Y. Ozeki, and K. Goda, “Intelligent Image-Activated Cell Sorting", Cell 175(1), 266~276 pages (2018), doi: 10.1016 / j.cell.2018.08.028 [Non-Patent Document 5] Krstenansky JL, Owen TJ., Hagaman KA., McLean LR., FEBS Lett., 1989, 242(2):409~13 pages. doi.org / 10.1016 / 0014-5793(89)80512-5. Short model peptides having a high α-helical tendency: Design and solution properties [Summary of the Invention] [Problems to be Solved by the Invention]

[0005] Presented herein are systems and methods for rapid image-based pooled gene / peptide screening (RIPGS). Such systems and methods may represent a novel, high-throughput hit identification platform for undruggable targets such as transcription factors, RAS, and phosphatases. Similarly, RIPGS may be applied to identify one or more genes involved in transcription factor nuclear translocation, protein aggregation, organelle dysfunction, and the like. One or more peptide expression libraries, ribonucleic acid (RNA) libraries, small hairpin RNA (shRNA) libraries, small interfering RNA (siRNA) libraries, microRNA (miRNA) libraries, antisense RNA (asRNA) libraries, or clustered regularly interspaced short palindromic repeats (CRISPR) guide RNA (gRNA) libraries may be transduced into one or more cells. The guide RNA library may be a single guide RNA (sgRNA) library. Once the cells are transfected or transduced with the library and ready to be analyzed, the cells of interest may be sorted based on fluorescent signal patterns indicative of behaviors such as protein localization. The sorted cells can be analyzed by nucleic acid sequencing, such as next generation sequencing (NGS), to determine which plasmids were transfected or transduced into the cells, a first for a drug discovery class platform, benefiting not only industry but also patients. [Means for solving the problem]

[0006] In various aspects, the disclosure provides a method for the detection of a cell comprising the steps of: (a) providing a cell that has been or is suspected to have been transfected or transduced with at least one exogenous ribonucleic acid (RNA) molecule or at least one exogenous deoxyribonucleic acid (DNA) molecule, said cell being in a population of cells; (b) subsequent to (a), identifying a morphological change in said cell; (c) processing the contents of said cell to identify (i) a nucleic acid sequence or (ii) a peptide, polypeptide or protein or (iii) the sequence of said peptide, polypeptide or protein; (d) analyzing (i) the nucleic acid sequence or (ii) the peptide, polypeptide or protein or (iii) the sequence of said peptide, polypeptide or protein to determine the exogenous sequence of said exogenous RNA molecule or exogenous DNA molecule; and (e) identifying the exogenous sequence of said exogenous RNA molecule or exogenous DNA molecule as influencing the morphological change of said cell. The present invention provides a method for identifying a nucleic acid molecule, comprising:

[0007] In some embodiments, the foreign sequence of the foreign RNA molecule or the foreign DNA molecule is unknown. In some embodiments, the foreign sequence of the foreign RNA molecule or the foreign DNA molecule is known. In some embodiments, the foreign RNA molecule or the foreign DNA molecule encodes a gene, a peptide, a polypeptide, or a protein.

[0008] In some embodiments, the morphological change comprises one or more elements selected from the group consisting of a change in protein-protein interactions within the cell, a change in protein localization within the cell, a change in the shape of the cell, a change in the shape of one or more components of the cell, and a change in the shape of one or more organelles of the cell. In some embodiments, the cell is transfected with a plasmid comprising the exogenous RNA molecule or the exogenous DNA molecule.

[0009] In some aspects, (c) comprises sequencing at least one nucleic acid molecule or at least one protein molecule from said cell. In some aspects, said sequencing comprises massively parallel array sequencing. In some aspects, said sequencing comprises sequencing by synthesis. In some aspects, said sequencing comprises next generation sequencing.

[0010] In some aspects, the morphological changes are identified using light microscopy, hi some aspects, the light microscopy comprises confocal microscopy.

[0011] In some aspects, in (b), the morphological change in the cell is identified while the cell is flowing in a flow cell or flow channel. In some aspects, the morphological change is identified in (b) and the cell is not immobilized or fixed. In some aspects, the cell is fixed when the morphological change is identified in (b).

[0012] In some embodiments, the population of cells is heterogeneous. In some embodiments, (b) is repeated for each cell of the plurality of cells in the population of cells. In some embodiments, (b) is repeated at a rate of at least about 1500 cells per second (cells / sec). In some embodiments, (b) is repeated at a rate of at least about 2000 cells / sec, at least about 3000 cells / sec, at least about 4000 cells / sec, at least about 5000 cells / sec, at least about 6000 cells / sec, at least about 7000 cells / sec, at least about 8000 cells / sec, at least about 9000 cells / sec, or at least about 10,000 cells / sec.

[0013] In some embodiments, the cells are isolated from the population of cells based on the morphological change. In some embodiments, the population of cells is not immobilized or fixed. In some embodiments, the population of cells is fixed. In some embodiments, the morphological change is identified in (b) and the population of cells is flowing through a flow cell or flow channel. In some embodiments, each cell in the population of cells comprises at least one randomly inserted exogenous RNA molecule or at least one randomly inserted exogenous DNA molecule.

[0014] In some embodiments, (b) comprises imaging the cells. In some embodiments, (b) comprises obtaining a temporal signal containing image information. In some embodiments, the temporal signal is converted to image information that is not time dependent. In some embodiments, (b) comprises obtaining a temporal signal containing spatial information. In some embodiments, the temporal signal is converted to spatial information that is not time dependent.

[0015] In some aspects, the method further comprises isolating the cell after identifying the morphological change in the cell, and subsequently obtaining the sequence information from the cell. In some aspects, the cell is isolated from a plurality of cells.

[0016] The method of claim 1 further comprises the step of using at least said morphological change to determine that said exogenous DNA molecule or said RNA molecule inhibits or activates a biochemical pathway within said cell.

[0017] In some embodiments, (b) comprises using a machine learning algorithm to identify the morphological change. In some embodiments, the machine learning algorithm comprises one or more elements selected from the group consisting of support vector machines, random forests, artificial neural networks, convolutional neural networks, deep learning, very deep learning, gradient boosting, AdaBoosting, decision trees, linear regression analysis, and logistic regression analysis. In some embodiments, the machine learning algorithm is trained using a training set comprising image information of a cell population positive for the morphological change and image information of a cell population negative for the morphological change. In some embodiments, the machine learning algorithm is trained using a training set comprising spatial information of a cell population positive for the morphological change and spatial information of a cell population negative for the morphological change.

[0018] In some embodiments, the method further comprises, prior to (b), labeling the peptides, polypeptides, or proteins in the cells, hi some embodiments, the labeling comprises labeling the one or more peptides, polypeptides, or proteins with one or more fluorescent labels, Forster Resonance Energy Transfer (FRET) labels, dyes, fluorophores, or quantum dots.

[0019] In various aspects, the disclosure provides methods for screening cells for a target nucleic acid, peptide, polypeptide, or protein, comprising the steps of: (a) providing a cell that has been, or is suspected of being, transfected or transduced with an exogenous ribonucleic acid (RNA) molecule or an exogenous deoxyribonucleic acid (DNA) molecule, wherein the cell is within a population of cells, and the exogenous RNA or DNA causes the cell to express a population of macromolecules within the cell; (b) identifying a morphological change in the cell in response to the population of macromolecules expressed by the cell; (c) subjecting the genome of the cell to sequencing to identify the exogenous RNA or DNA; and (d) determining that the exogenous RNA or DNA induced the morphological change.

[0020] In some embodiments, identifying the morphological change comprises identifying a macromolecular localization profile or pattern of the population of macromolecules. In some embodiments, identifying the morphological change comprises identifying a shape of the cell or one or more subcellular components of the cell. In some embodiments, the one or more subcellular components comprise an organelle of the cell. In some embodiments, the population of macromolecules comprises a population of one or more members selected from the group consisting of peptides, polypeptides, proteins, and nucleic acid molecules.

[0021] In various aspects, the present disclosure provides a system for identifying nucleic acid molecules, comprising one or more computer processors individually or collectively programmed to: (a) identify a morphological change in a cell that has been or is suspected to have been transfected or transduced with at least one exogenous ribonucleic acid (RNA) molecule or at least one exogenous deoxyribonucleic acid (DNA) molecule, wherein the cell is within a population of cells; (b) process the contents of the cell to identify (i) a nucleic acid sequence or (ii) a peptide, polypeptide or protein or the sequence of the peptide, polypeptide or protein; (c) analyze (i) the nucleic acid sequence or (ii) the peptide, polypeptide or protein or the sequence of the peptide, polypeptide or protein to determine the exogenous sequence of the exogenous RNA molecule or the exogenous DNA molecule; and (d) identify the exogenous sequence of the exogenous RNA molecule or the exogenous DNA molecule as influencing the morphological change of the cell.

[0022] In various aspects, the disclosure provides a system for screening cells for a target nucleic acid, peptide, polypeptide, or protein, comprising one or more computer processors individually or collectively programmed to: (a) identify a morphological change in a cell in response to a population of macromolecules expressed by the cell, wherein the cell has been or is suspected to have been transfected or transduced with an exogenous ribonucleic acid (RNA) molecule or an exogenous deoxyribonucleic acid (DNA) molecule, the cell being within a population of cells, and the exogenous RNA or DNA causes the cell to express the population of macromolecules within the cell; (b) subjecting the genome of the cell to sequencing to identify the exogenous RNA or DNA; and (c) determining that the exogenous RNA or DNA induced the morphological change.

[0023] Aspects of the present disclosure provide a non-transitory computer-readable medium containing machine-executable code that, after execution by one or more computer processors, implements any of the methods described above or elsewhere herein.

[0024] Another aspect of the present disclosure provides a system including one or more computer processors and a computer memory coupled thereto, the computer memory including machine executable code that, upon execution by the one or more computer processors, implements any of the methods described above or elsewhere herein.

[0025] Additional aspects and advantages of the present disclosure will become readily apparent to those skilled in the art from the following detailed description, in which merely illustrative embodiments of the present disclosure are shown and described. As will be understood, the present disclosure is capable of other and different embodiments, and its several details are capable of modification in various obvious respects, all without departing from the present disclosure. Accordingly, the drawings and descriptions are to be regarded as illustrative in nature, and not as restrictive.

[0026] Incorporation by Reference All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, and patent application was specifically and individually indicated to be incorporated by reference. To the extent that the publications and patents or patent applications incorporated by reference conflict with the disclosure contained in the specification, it is intended that the specification supersede and / or supersede any such conflicting material.

[0027] Brief description of the drawings The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

[0028] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings (also referred to herein as "Figure" and "FIG"), in which: [Brief description of the drawings]

[0029] [Figure 1] FIG. 1 illustrates a computer system that can be programmed or otherwise configured to perform the methods provided herein. [Figure 2A] FIG. 1 shows an example of a method for pooled genetic screening. [Figure 2B] FIG. 1 shows an example for image-based genetic screening. [Diagram 3] FIG. 1 shows an example for image-based pooled genetic screening. [Figure 4A] Fluorescence images of cells containing fluorescently labeled mitochondria and cells containing fluorescently labeled lysosomes. [Figure 4B] FIG. 1 illustrates sorting of cells containing fluorescently labeled mitochondria and cells containing fluorescently labeled lysosomes using the systems and methods of the present disclosure. [Figure 4C] Fluorescence images of STAT3 protein localized inside the nucleus (nuclear localization) and outside the nucleus (cytoplasm). [Figure 4D] FIG. 1 shows classification of STAT3 protein localized within the nucleus or outside the nucleus (cytoplasm) using the systems and methods of the present disclosure. [Figure 4E] Fluorescence images of non-aggregated and aggregated protein complexes in cells. [Figure 4F] FIG. 1 illustrates classification of proteins as non-aggregated proteins or aggregated protein complexes using the systems and methods of the present disclosure. [Figure 4G]Fluorescence images of cells containing nuclear-localized nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) protein and cells containing cytoplasmic NF-κB protein. [Figure 4H] FIG. 1 shows the classification of cells containing nuclear-localized NF-κB protein and cells containing cytoplasmic NF-κB protein using the systems and methods of the present disclosure. [Diagram 5] 1 is a flow chart of an example method for identifying a nucleic acid molecule. [Figure 6] FIG. 1 shows examples of methods for identifying nucleic acid molecules or peptides, polypeptides or proteins based on protein-protein interactions, protein aggregation or protein localization. [Figure 7] FIG. 1 shows an example of a method for target discovery using image-based pooled genetic screening. [Figure 8A] Fluorescence images of cells containing nuclear-localized p65 protein and cells containing cytoplasmic p65 protein. [Figure 8B] FIG. 1 shows the classification of cells containing nuclear-localized p65 protein and cells containing cytoplasmic p65 protein using the systems and methods of the present disclosure. [Figure 9A] FIG. 1 shows the sorting of cells containing nuclear-localized fluorescent proteins and cells containing cytoplasmic fluorescent proteins using an image-based system prior to sorting. [Figure 9B] FIG. 1 shows the classification of cells containing nuclear-localized fluorescent proteins and cells containing cytoplasmic fluorescent proteins using an image-based system followed by sorting using the systems and methods of the present disclosure. [Figure 10A] Fluorescence images of cells containing nuclear-localized nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) protein and cells containing cytoplasmic NF-κB protein. [Figure 10B] FIG. 1 shows the classification of cells containing nuclear-localized NF-κB protein and cells containing cytoplasmic NF-κB protein using the systems and methods of the present disclosure. [Figure 10C]Fluorescence images of cells containing p53 bound to MDM2 and cells containing p53 not bound to MDM2. [Figure 10D] FIG. 1 shows the classification of cells containing p53 bound to MDM2 and cells containing p53 not bound to MDM2 using the systems and methods of the present disclosure. [Figure 10E] Fluorescence images of cells containing fluorescently labeled mitochondria and cells containing fluorescently labeled lysosomes. [Figure 10F] FIG. 1 illustrates sorting of cells containing fluorescently labeled mitochondria and cells containing fluorescently labeled lysosomes using the systems and methods of the present disclosure. [Figure 11] FIG. 1 shows sorting of cells containing nuclear-localized NF-κB or cytoplasmic NF-κB using the systems and methods of the present disclosure, followed by validation using an image-based system and fluorescence activated cell sorting (FACS). DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0030] Detailed Description While various embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes and substitutions will occur to those skilled in the art without departing from the invention. It is understood that various alternatives to the embodiments of the present invention described herein may be used.

[0031] Unless otherwise defined, all technical terms used herein have the same meaning as understood by a person skilled in the art to which this invention belongs. As used in this specification and the appended claims, the singular forms "a", "an" and "the" include plural references unless the context clearly indicates otherwise. References to "or" in this specification are intended to include "and / or" unless stated otherwise.

[0032] Whenever the terms "at least," "greater than," or "greater than or equal to" are associated with the first number in a series of two or more numbers, the terms "at least," "greater than," or "greater than or equal to" apply to every number in the series. For example, 1, 2, or 3 or more is equivalent to 1 or more, 2 or more, or 3 or more.

[0033] Whenever the terms "not greater than," "less than," "less than or equal to," or "maximum" are associated with the first number in a series of two or more numbers, the terms "not greater than," "less than," "less than or equal to," or "maximum" apply to each number in the series. For example, 3, 2, or 1 or less is equivalent to 3 or less, 2 or less, or 1 or less.

[0034] When values ​​are described as ranges, it is understood that the disclosure includes all possible subranges within that range, as well as specific numerical values ​​within that range, whether or not a specific numerical value or specific subrange is expressly stated.

[0035] In one aspect, the present disclosure provides a method for identifying a nucleic acid molecule, which may include providing a cell that has been or is suspected to have been transfected or transduced with at least one exogenous ribonucleic acid (RNA) molecule or at least one exogenous deoxyribonucleic acid (DNA) molecule. The nucleic acid molecule may confer a characteristic, such as a morphological change, to the cell. The morphological change of the cell may then be identified. The contents of the cell may then be treated to identify (i) the nucleic acid sequence or (ii) the peptide, polypeptide or protein or the sequence of the peptide, polypeptide or protein. The (i) nucleic acid sequence or (ii) the peptide, polypeptide or protein or the sequence of the peptide, polypeptide or protein may then be analyzed to determine the exogenous sequence of the exogenous RNA molecule or exogenous DNA molecule. The exogenous sequence of the exogenous RNA molecule or exogenous DNA molecule may then be identified as affecting the morphological change of the cell.

[0036] The foreign sequence of the foreign RNA molecule or foreign DNA molecule may be unknown. The foreign sequence of the foreign RNA molecule or foreign DNA molecule may be known. The foreign sequence of the foreign RNA molecule or foreign DNA molecule may code for a gene, peptide, polypeptide or protein.

[0037] The morphological change may include one or more elements selected from the group consisting of a change in protein-protein interactions within the cell, a change in protein localization within the cell, a change in the shape of the cell, a change in the shape of one or more components of the cell, and a change in the shape of one or more organelles of the cell.

[0038] The morphological change of a cell may be directly or indirectly influenced by an exogenous RNA molecule or an exogenous DNA molecule. In some cases, the exogenous RNA molecule or the exogenous DNA molecule may directly influence the morphological change by directly interacting with one or more elements selected from the group consisting of proteins, small molecules, ions, cofactors, organelles, phospholipids, or nucleic acids. In some cases, the exogenous RNA molecule or the exogenous DNA molecule may indirectly influence the morphological change by encoding a protein or nucleic acid that directly influences the morphological change. The protein or nucleic acid that directly influences the morphological change may influence the morphological change by directly interacting with one or more elements selected from the group consisting of proteins, small molecules, ions, cofactors, organelles, phospholipids, or nucleic acids. In some cases, the exogenous RNA molecule or the exogenous DNA molecule may indirectly influence the morphological change by encoding a protein or nucleic acid that indirectly influences the morphological change. A protein or nucleic acid that indirectly affects a morphological change may indirectly affect the morphological change by interacting with or modifying an interaction with one or more elements selected from the group consisting of proteins, small molecules, ions, cofactors, organelles, phospholipids, or nucleic acids, where modifying the interaction affects the morphological change downstream of the interaction.

[0039] In some cases, imaging may be performed using the methods and systems disclosed in S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost Cytometry”, Science 360(6394), pp. 1246-1251 (2018), doi: 10.1126 / science.aan0096, which is incorporated by reference in its entirety (referred to herein as “ghost cytometry”).

[0040] Cells can be transfected or transduced using a plasmid containing the exogenous RNA molecule or the exogenous DNA molecule.

[0041] Sequence information can be obtained from a cell by sequencing at least one DNA molecule and / or at least one RNA from the cell (e.g., a transduced exogenous RNA or DNA molecule, the genome and / or the transcriptome of the cell).

[0042] The sequencing may be massively parallel array sequencing. The sequencing may include sequencing by synthesis (e.g., Illumina, Pacific Biosciences of California, or Ion Torrent). The sequencing may be single molecule sequencing (e.g., Oxford Nanopore). The sequencing may include next generation sequencing.

[0043] In some cases, the foreign RNA or DNA molecule is designed to code for a peptide, polypeptide, or protein with a stable alpha-helical structure, which may be an important aspect of peptide-protein interactions. The RNA or DNA encoding the peptide can be cloned into a plasmid vector for transfection or transduction into cells. The cells can express the encoded peptide, polypeptide, or protein.

[0044] Shalem O., Sanjana NE., Hartenian E., Shi X., Scott DA., Mikkelson T., Heckl D., Ebert BL., Root DE., Doench JG., Zhang F., Science. 2014, 343 (6166):84-87. doi: 10.1126 / science.1247005. Genome-scale CRISPR-Cas9 knockout screening in human cells and Joung J., Konermann S., Gootenberg JS., Abudayyeh OO., Platt RJ., Brigham MD., Sanjana NE., Zhang F., Nat Protoc. 2017, 12(4):828-863. doi: 10.1038 / nprot.2017.016, each of which is incorporated by reference in its entirety in some cases. As described in Genome-scale CRISPR-Cas9 knockout and transcriptional activation screening, exogenous RNA molecules or exogenous DNA molecules are transfected into cells using clustered regularly interspaced short palindromic repeats (CRISPR), such as CRISPR-associated 9 (Cas9), Cas13d, CasX or other variants, or other gene editing technologies, such as zinc finger nucleases (ZFNs) or TAL effector nucleases (TALENs).

[0045] In some cases, the genome-scale CRISPR library or peptide expression library is amplified.

[0046] In some cases, the morphological changes are identified using optical microscopy. In some cases, the morphological changes are identified using fluorescence microscopy. In some cases, the morphological changes are identified using confocal microscopy. In some cases, the morphological changes are identified using super-resolution microscopy. In some cases, the morphological changes are identified while the cells are flowing in a flow cell.

[0047] The cells may be unimmobilized or fixed when the morphological change is identified. The cells may be immobilized or fixed when the morphological change is identified. The cells may be in a population of cells. The population of cells may be unimmobilized or fixed. The population of cells may be immobilized or fixed. The population of cells may be flowing through a flow channel when the morphological change is identified.

[0048] Identifying the morphological change may include imaging the cell. Identifying the morphological change may include obtaining a temporal signal containing image information. The temporal signal may be converted into non-time-dependent image information. Identifying the morphological change may include obtaining a temporal signal containing structural information of the cell. The temporal signal may be converted into non-time-dependent structural information of the cell.

[0049] The method may further comprise isolating the cell after identifying the morphological change, and subsequently obtaining said sequence information from the cell. The cell may be isolated from a plurality of cells.

[0050] The method may further comprise using at least the morphological change to determine that the exogenous DNA or RNA inhibits, activates or alters a biochemical pathway within the cell. The biochemical pathway may comprise one or more elements selected from the group consisting of protein-protein interactions, protein conformational changes, localization, translocation, transport, endocytosis, exocytosis, chemical reactions, enzymatic reactions, phosphorylation, dephosphorylation, hydrolysis, hydration, cleavage or fusion.

[0051] Identifying the morphological changes may include using a machine learning algorithm to identify the morphological changes. The machine learning algorithm may include one or more members selected from the group consisting of support vector machines, random forests, artificial neural networks, convolutional neural networks, deep learning, very deep learning, gradient boosting, adaboost, decision trees, linear regression and logistic regression, or any other machine learning algorithm.

[0052] The presence or absence of a morphological change can be identified in each of a plurality of cells in the population of cells. The presence or absence of a morphological change can be identified in each cell in the population of cells at a rate of at least about 10 cells per second (cells / second), at least about 50 cells / second, at least about 100 cells / second, at least about 500 cells / second, at least about 1000 cells / second, at least about 1500 cells / second, at least about 2000 cells / second, at least about 3000 cells / second, at least about 4000 cells / second, at least about 5000 cells / second, at least about 6000 cells / second, at least about 7000 cells / second, at least about 8000 cells / second, at least about 900 cells / second, at least about 1000 cells / second, at least about 1500 cells / second, at least about 2000 cells / second, at least about 3000 cells / second, at least about 4000 cells / second, at least about 5000 cells / second, at least about 6000 cells / second, at least about 7000 cells / second, at least about 80 ...1000 cells / second, at least about 1500 cells / second, at least about 1500 cells / second, at least about 1000 cells / second, at least about 15 In some embodiments, the cells may be identified at a rate of at least about 9000 cells / second, at least about 10,000 cells / second, at least about 11,000 cells / second, at least about 12,000 cells / second, at least about 13,000 cells / second, at least about 14,000 cells / second, at least about 15,000 cells / second, at least about 20,000 cells / second, at least about 25,000 cells / second, at least about 30,000 cells / second, at least about 35,000 cells / second, at least about 40,000 cells / second, at least about 50,000 cells / second or more. The presence or absence of morphological changes may be detected in each cell in the population of cells at a rate of up to about 50,000 cells / sec, up to about 40,000 cells / sec, up to about 35,000 cells / sec, up to about 30,000 cells / sec, up to about 25,000 cells / sec, up to about 20,000 cells / sec, up to about 15,000 cells / sec, up to about 14,000 cells / sec, up to about 13,000 cells / sec, up to about 12,000 cells / sec, up to about 11,000 cells / sec, up to about 10,000 cells / sec, In some embodiments, the cells may be identified at a rate of about 9,000 cells / second, about 8,000 cells / second, about 7,000 cells / second, about 6,000 cells / second, about 5,000 cells / second, about 4,000 cells / second, about 3,000 cells / second, about 2,000 cells / second, about 1,500 cells / second, about 1,000 cells / second, about 500 cells / second, about 100 cells / second, about 50 cells / second, about 10 cells / second or slower.

[0053] The presence or absence of morphological changes is assessed for each cell in the population of cells at 10 cells / sec to 500 cells / sec, 100 cells / sec to 1000 cells / sec, 500 cells / sec to 1500 cells / sec, 1000 cells / sec to 2000 cells / sec, 1500 cells / sec to 2000 cells / sec, 1500 cells / sec to 10,000 cells / sec, 1500 cells / sec to 15 ... 20,000 cells / sec, 1500 cells / sec to 40,000 cells / sec, 1500 cells / sec to 50,000 cells / sec, 2000 cells / sec to 10,000 cells / sec, 2000 cells / sec to 15,000 cells / sec, 2000 cells / sec to 20,000 cells / sec, 2000 cells / sec to 40,000 cells / sec, 2000 cells / sec to 50,000 cells / sec, 5000 cells / sec to 10 ,000 cells / sec, 5000 cells / sec to 15,000 cells / sec, 5000 cells / sec to 20,000 cells / sec, 5000 cells / sec to 40,000 cells / sec, 5000 cells / sec to 50,000 cells / sec, 9000 cells / sec to 10,000 cells / sec, 9000 cells / sec to 15,000 cells / sec, 9000 cells / sec to 20,000 cells / sec, 9000 cells / sec to 40,000 cells In some embodiments, the rate may be determined from the range of 9000 cells / sec to 50,000 cells / sec, 10,000 cells / sec to 15,000 cells / sec, 10,000 cells / sec to 20,000 cells / sec, 10,000 cells / sec to 40,000 cells / sec, 10,000 cells / sec to 50,000 cells / sec, 20,000 cells / sec to 40,000 cells / sec, or 20,000 cells / sec to 50,000 cells / sec.

[0054] The method may further comprise labeling one or more peptides, polypeptides and / or proteins in the cell prior to identifying a morphological change in the cell in response to the population of macromolecules expressed by the cell, wherein the labeling comprises labeling the one or more peptides, polypeptides and / or proteins with one or more fluorescent labels, Forster Resonance Energy Transfer (FRET) labels, dyes, fluorophores or quantum dots.

[0055] Labeling steps include SYBR green, SYBR blue, 4',6-diamidino-2-phenylindole (DAPI), propidium iodine, Hoechst, SYBR gold, ethidium bromide, acridine, proflavine, acridine orange, acriflavine, fluorcoumanin, ellipticine, daunomycin, chloroquine, distamycin D, chromomycin, homidium, mithramycin, ruthenium polypyridyl, anthramycin, phenanthridine and acridine, ethidium bromide, propidium iodide, hexidium iodide, dihydroethidium, ethidium homodimer 1 and 2, ethidium monoazide, and 9-amino-6-chloro-2-methoxyacridine (ACMA), Hoechst 33258, Hoechst 33342, Hoechst 34580, Acridine Orange, 7-AAD, Actinomycin D, LDS751, Hydroxystilbamidine, SYTOX Blue, SYTOX Green, SYTOX Orange, POPO-1, POPO-3, YOYO-1, YOYO-3, TOTO-1, TOTO-3, JOJO-1, LOLO-1, BOBO-1, BOBO-3, PO-PRO-1, PO-PRO-3, BO-PRO-1, BO-PRO-3, TO-PRO-1, TO-PRO-3, TO-PRO-5, JO-PRO-1, LO-PRO-1, YO-PRO-1, YO-PRO-3, PicoGreen, OligoGreen, RiboGreen, SYBR Gold, SYBR Green I, SYBR Green II, SYBR DX, SYTO-40, -41, -42, -43, -44 and -45 (blue), SYTO-13, -16, -24, -21, -23, -12, -11, -20, -22, -15, -14 and -25 (green), SYTO-81, -80, -82, -83, -84 and -85 (orange), SYTO-64, -17, -59, -61, -62, -60 and -63(Red), Fluorescein, Fluorescein isothiocyanate (FITC), Tetramethylrhodamine isothiocyanate (TRITC), Rhodamine, Tetramethylrhodamine, R-Phycoerythrin, Cy-2, Cy-3, Cy-3.5, Cy-5, Cy5.5, Cy-7, Texas Red, Phar-Red, Allophycocyanin (APC), SYBR Green I, SYBR Green II, SYBR Gold, CellTracker Green, 7-Aminoactinomycin D (7-AAD), ethidium homodimer I, ethidium homodimer II, ethidium homodimer III, ethidium bromide, umbelliferone, eosin, green fluorescent protein, erythrosine, coumarin, methylcoumarin, pyrene, malachite green, stilbene, lucifer yellow, cascade blue, dichlorotriazinylamine fluorescein, dansyl chloride, fluorescent lanthanide complexes such as those containing europium and terbium, carboxytetrachlorofluorescein, 5 and / or 6-carboxyfluorescein (FAM), VIC , 5-(or 6-)iodoacetamidofluorescein, 5-{[2(and 3)-5-(acetylmercapto)-succinyl]amino}fluorescein (SAMSA-fluorescein), Lissamine rhodamine B sulfonyl chloride, 5 and / or 6 carboxyrhodamine (ROX), 7-amino-methyl-coumarin, 7-amino-4-methylcoumarin-3-acetic acid (AMCA), BODIPY fluorophores, 8-methoxypyrene-1,3,6-trisulfonic acid trisodium salt, 3,6-disulfonate-4-amino-naphthalimide, phycobiliproteins, Atto 390, 425, 465, 488, 495, 532, 565, 594, 633, 647, 647N, 665, 680, and 700 dyes, AlexaFluor 350, 405, 430, 488, 532, 546, 555, 568, 594, 610, 633, 635, 647, 660, 680, 700, 750, and 790 dyes, DyLight 350, 405, 488, 550, 594, 633, 650, 680, 755, and 800 dyes, or other fluorophores, Black Hole Quencher Dyes (BiosearchLabelling one or more peptides, polypeptides and / or proteins with one or more members selected from the group consisting of: QSY Dye fluorescent quenchers (from Molecular Probes / Invitrogen), such as BH1-0, BHQ-1, BHQ-3 and BHQ-10, QSY Dye fluorescent quenchers (from Molecular Probes / Invitrogen), such as QSY7, QSY9, QSY21 and QSY35, and other quenchers, such as Dabcyl and Dabsyl, Cy5Q and Cy7Q and Dark Cyanine dyes (GE Healthcare), Dy-Quenchers (Dyomics), such as DYQ-660 and DYQ-661, ATTO fluorescent quenchers (ATTO-TEC GmbH), such as ATTO 540Q, 580Q and 612Q, green fluorescent protein (GFP), yellow fluorescent protein (YFP), blue fluorescent protein (BFP), cyan fluorescent protein (CYP) and red fluorescent protein (RFP).

[0056] FIG. 2A shows an example of a method for pooled genetic screening. In pooled genetic screening, cell seeding and transduction (such as through the use of lentivirus) can produce a pool of genetically modified cells, each of which can contain an exogenous nucleic acid molecule (such as an exogenous DNA molecule or an exogenous RNA molecule) from a library. The pool of genetically modified cells can be heterogeneous. A selection procedure can then be performed, and nucleic acids from the cells of interest are sequenced by next-generation nucleic acid sequencing. Pooled genetic screening can be a very powerful tool in some cases, such as in identifying genes involved in pathways of interest. To date, selection methods have mainly relied on positive screening, negative screening, or screening based on fluorescence intensity by fluorescence-activated cell sorting (FACS).

[0057] FIG. 2B shows an example method of image-based genetic screening. In image-based screening, cell seeding and transduction produce genetically modified cells. However, in contrast to pooled genetic screening, in image-based screening, the resulting genetically modified cells are analyzed using image processing techniques, such as fluorescent imaging. The genetically modified cells can be screened using image-based screening to identify cells with properties of interest. Such image-based screening can provide detailed structural information about the cells of interest, but can be time-consuming or expensive.

[0058] Figure 3 shows an example method of image-based pooled genetic screening. Image-based pooled genetic screening procedures may utilize cell seeding and transduction (such as through the use of lentivirus) to produce a pool of genetically modified cells, each of which may contain an exogenous nucleic acid molecule (such as an exogenous DNA molecule or an exogenous RNA molecule) from a library. The pool of genetically modified cells may be heterogeneous. Imaging procedures (e.g., one or more of the imaging procedures disclosed in S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost Cytometry”, Science 360(6394), pp. 1246-1251 (2018), doi: 10.1126 / science.aan0096, or N. Nitta, T. Sugimura, A. Isozaki, H. Mikami, S. Sakuma, T. Iino, F. Arai, T. Endo, Y. Fujiwaki, H. Fukuzawa, M. Hase, T. Hayakawa, K. Hiramatsu, Y. Hoshino, M. Inaba, T. Ito, H. Karakawa, Y. Kasai, K. Koizumi, S. Lee, C. Lei, M. Li, T. Maeno, S. Matsusaka, D. Murakami, A. Nakagawa, Y. Oguchi, M. Oikawa, T. Ota, K. Shiba, H. Shintaku, Y. Shirasaki, K. Suga, Y. Suzuki, N. Suzuki, Y. Tanaka, H. Tezuka, C. Toyokawa, Y. Yalikun, M. Yamada, M. Yamagishi, T. Yamano, A. Yasumoto, Y. Yatomi, M. Yazawa, D. Di Carlo, Y. Hosokawa, S. Uemura, Y.One or more imaging procedures disclosed in Ozeki, K., and K. Goda, “Intelligent Image-Activated Cell Sorting”, Cell 175(1), pp. 266-276 (2018), doi: 10.1016 / j.cell.2018.08.028, the entire references of which are incorporated herein by reference, can then be performed to select cells having a property of interest (e.g., protein localization or protein aggregation within the cells of interest) based on the information contained in the image. Cells having the property of interest can be sorted, and nucleic acids of interest can be extracted from the cells having the property of interest. The nucleic acids of interest can then be sequenced by next-generation nucleic acid sequencing methods. Image-based pool screening can provide a number of advantages over the pool screening and image-based screening methods described above.

[0059] FIG. 7 shows an example method of target discovery using rapid image-based pooled genetic screening (RIPGS). In some cases, morphological changes can be identified in cells containing exogenous RNA or DNA molecules. In some cases, morphological changes can be identified as caused by exogenous RNA or DNA molecules. Identification of morphological changes can provide information about the exogenous RNA or DNA molecules, which can be identified as target molecules. In some cases, the peptides or proteins encoded by the exogenous RNA or DNA molecules can be peptides or proteins with therapeutic properties or can be therapeutic drugs. In some cases, the target can be a peptide or nucleic acid that can affect morphological changes of cells. According to the present invention, the morphological changes can include one or more elements selected from the group consisting of changes in protein-protein interactions within the cell, changes in protein localization within the cell, changes in the shape of the cell, changes in the shape of one or more components of the cell, and changes in the shape of one or more organelles of the cell. In a first operation exemplified in panel 1, the method may include transfecting a pooled plasmid library into cells to produce a pool of cells that includes genetically modified cells. For example, the pooled plasmid library may include plasmids encoding one or more elements selected from the group consisting of peptides, proteins, polypeptides, non-translated RNA, gRNA, sgRNA, shRNA, siRNA, miRNA, or asRNA. The pool of cells may be heterogeneous. In some cases, the plasmids in the pooled plasmid library may encode potential target candidates.

[0060] In a second operation illustrated in panel 2 of FIG. 7, the method includes isolating cells with a target phenotype (e.g., based on whether an image of the cells shows protein localization, protein-protein interaction, translocation, colocalization, puncta, or other properties). Isolating the cells may include sorting the cells. In some cases, isolating the cells may include cell sorting based on the images. The target phenotype may include a morphological change in the cells. The cells may be isolated based on a morphological change in the cells. The target phenotype may be affected by a plasmid from a pooled plasmid library, or a product encoded by the plasmid.

[0061] In a third operation illustrated in panel 3 of FIG. 7, the method may include identifying the inserted plasmid by nucleic acid sequencing. The nucleic acid sequencing may include DNA sequencing or RNA sequencing. The nucleic acid sequencing may include reverse transcription followed by DNA sequencing. The nucleic acid sequencing may include massively parallel array sequencing. The nucleic acid sequencing may include sequencing by synthesis. The nucleic acid sequencing may include next generation sequencing. The method may be applied to a number of applications including target identification or screening for hit molecules for functionality in cells. In general the method may be considered as a method for high throughput, high content screening.

[0062] For example, the isolation of cells having a target phenotype, performed in the second operation of FIG. 7, may include sorting cells using rapid image-based pooled gene / peptide screening (RIPGS). RIPGS may include training a machine learning algorithm to distinguish cells based on the presence or absence of the target phenotype. The machine learning algorithm may be trained using a training set, as illustrated in FIG. 8A and FIG. 8B. In some cases, the training set may include a positive control dataset and a negative control dataset. The positive control dataset may include cells having the target phenotype. The negative control dataset may include cells lacking the target phenotype. For example, the positive control dataset may include cells treated with a reagent that induces the target phenotype. The positive training set and the negative training set may be imaged, for example, using fluorescence microscopy, as shown in FIG. 8A. Waveforms for individual cells from each of the positive control dataset and the negative control dataset may be measured, as shown in the top panel of FIG. 8B. The waveforms may be used to train a machine learning algorithm and assign machine learning (ML) scores to the measured cell waveforms, as shown in the bottom panel of FIG. 8B. The ML score determined from the cell waveform may indicate the similarity of the cell to the target phenotype. The trained machine learning algorithm may be used to assign ML scores to individual cells from a pool of cells, such as a pool of cells that includes genetically modified cells. The individual cells forming the pool of cells may be sorted based on the assigned ML scores. In some cases, the isolation of cells may be performed using ghost cytometry, as described in S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost Cytometry”, Science 360(6394), pp. 1246-1251 (2018), doi: 10.1126 / science.aan0096, which is incorporated herein by reference in its entirety.

[0063] FIG. 5 shows a flow chart of an example method 600 for identifying a nucleic acid molecule.

[0064] In a first operation 610, the method 600 may include providing a cell that has been or is suspected of being transfected or transduced with at least one exogenous RNA or at least one exogenous DNA molecule. The exogenous sequence of the exogenous RNA or DNA molecule may be unknown. The exogenous sequence of the exogenous RNA or DNA molecule may be known. The exogenous RNA or DNA molecule may code for a gene, a peptide, a polypeptide, or a protein.

[0065] The cells may be from any source. For example, the cells may be human cells, animal cells, non-human primate cells, horse cells, porcine cells, canine cells, feline cells, murine cells, plant cells, bacterial cells, or any other cells. The cells may be from a tissue or from an organ. For example, the cells may be from the heart, arteries, veins, brain, nerves, lungs, spine, spinal cord, bone, connective tissue, trachea, esophagus, stomach, small or large intestine, bladder, liver, kidneys, spleen, urethra, ureters, prostate, vas deferens, penis, ovaries, uterus, endometrium, fallopian tubes, or vagina, or any tissue associated with any of the foregoing. The cells may be or may be suspected to be cancerous cells (such as tumor cells). For example, the cells may be from tumor tissue. The cells may comprise natural or non-natural components. For example, the cells may comprise nucleic acids (such as DNA or RNA), peptides, polypeptides, proteins, lipids, or carbohydrates. A cell may contain one or more optically detectable elements, such as one or more fluorophores. The fluorophores may be native or non-native to the cell. For example, the fluorophores may be non-native fluorophores that have been introduced into the cell, such as by one or more cell staining or labeling techniques.

[0066] The cells may be in a population of cells. For example, the cells may be at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1,000, 2,000, 3,000, 4,000, 5,000, 6,000, 7,000 cells. , 8,000 pieces, 9,000 pieces, 10,000 pieces, 20,000 pieces, 30,000 pieces, 40,000 pieces, 50,000 pieces, 60,000 pieces, 70,000 pieces, 80,000 pieces, 90,000 pieces , 100,000 pieces, 200,000 pieces, 300,000 pieces, 400,000 pieces, 500,000 pieces, 600,000 pieces, 700,000 pieces, 800,000 pieces, 900,000 pieces, 1,000 pieces ,000 pieces, 2,000,000 pieces, 3,000,000 pieces, 4,000,000 pieces, 5,000,000 pieces, 6,000,000 pieces, 7,000,000 pieces, 8,000,000 pieces, 9,0 00,000 pieces, 10,000,000 pieces, 20,000,000 pieces, 30,000,000 pieces, 40,000,000 pieces, 50,000,000 pieces, 60,000,000 pieces, 70,000, The nucleic acid may be in a population of 000, 80,000,000, 90,000,000, 100,000,000, 200,000,000, 300,000,000, 400,000,000, 500,000,000, 600,000,000, 700,000,000, 800,000,000, 900,000,000, 1,000,000 or more. The cells are available in sizes up to approximately 1,000,000,000, 900,000,000, 800,000,000, 700,000,000, 600,000,000, 500,000,000, 400,000,000, 300,000,000, 200,000,000, 100,000,000, 90,0 00,000 pieces, 80,000,000 pieces, 70,000,000 pieces, 60,000,000 pieces, 50,000,000 pieces, 40,000,000 pieces, 30,00 0,000 pieces, 20,000,000 pieces, 10,000,000 pieces, 9,000,000 pieces, 8,000,000 pieces, 7,000,000 pieces, 6,000,000 pieces pcs, 5,000,000 pcs, 4,000,000 pcs, 3,000,000 pcs, 2,000,000 pcs, 1,000,000 pcs, 900,000 pcs, 800,000 pcs, 700,000 pcs, 600,000 pcs, 500,00 0 pieces, 400,000 pieces, 300,000 pieces, 200,000 pieces, 100,000 pieces, 90,000 pieces, 80,000 pieces, 70,000 pieces, 60,000 pieces, 50,000 pieces, 40,000 pieces, 30,000 pieces, 20,00 pieces The cell may be in a population of 0, 10,000, 9,000, 8,000, 7,000, 6,000, 5,000, 4,000, 3,000, 2,000, 1,000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 90, 80, 70, 60, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 or fewer. The cell may be in a population of cells that is within a range defined by any two of the above values. Cells are: 1 to 1,000,000 cells, 10 to 1,000,000 cells, 100 to 1,000,000 cells, 1,000 to 1,000,000 cells, 10,000 to 1,000,000 cells, 100,000 to 1,000,000 cells, 1 to 100,000 cells, 100 to 100,000 cells, 1,00 The population may be 0 to 100,000 cells, 10,000 to 100,000 cells, 1 to 10,000 cells, 10 to 10,000 cells, 100 to 10,000 cells, 1,000 to 10,000 cells, 1 to 1,000 cells, 10 to 1,000 cells, 100 to 1,000 cells, 1 to 100 cells, 10 to 100 cells, 10 to 100 cells, or 1 to 10 cells. The cell may be isolated from a population of cells. The cell may be isolated from a plurality of cells.

[0067] The cell or population of cells may be immobilized or fixed. The cell or population of cells may be unimmobilized or fixed. The cell or population of cells may be stationary. The cell or population of cells may be moving, e.g., the cell or population of cells may be flowing (such as through or in a flow channel or flow cell).

[0068] The at least one exogenous RNA molecule or the at least one exogenous DNA molecule can comprise at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1,000 or more exogenous RNA molecules or exogenous DNA molecules. The at least one foreign RNA molecule or the at least one foreign DNA molecule may comprise up to about 1,000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 90, 80, 70, 60, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 or fewer foreign RNA molecules or foreign DNA molecules. The at least one foreign RNA molecule or the at least one foreign DNA molecule may comprise a number of foreign RNA molecules or foreign DNA molecules that is within a range defined by any two of the preceding values. The at least one foreign RNA molecule or the at least one foreign DNA molecule can comprise 1 to 1,000, 10 to 1,000, 100 to 1,000, 1 to 100, 10 to 100, 1 to 10, 1 to 5, 5 to 10, 10 to 20, or 1 to 20 foreign RNA molecules or foreign DNA molecules.

[0069] The cells may be transfected using a plasmid, which may contain an exogenous RNA molecule or an exogenous DNA molecule.

[0070] In a second operation 620, the method 600 may include identifying a morphological change in the cell. The morphological change may include one or more elements selected from the group consisting of a change in protein-protein interactions within the cell, a change in protein localization within the cell, a change in the shape of the cell, a change in the shape of one or more components of the cell (such as a change in the shape of the cytoplasm or a change in the shape or arrangement of the cytoskeleton within the cell), and a change in the shape of one or more organelles of the cell (such as a change in the shape of the nucleolus, nucleus, ribosomes, vesicles, rough endoplasmic reticulum, Golgi apparatus, cytoskeleton, smooth endoplasmic reticulum, mitochondria, vacuoles, lysosomes, centrosomes, or cell membrane of the cell).

[0071] The morphological changes may be identified using one or more optical techniques, such as optical imaging, optical microscopy, or optical spectroscopy. Morphological changes can be observed using a number of techniques including bright-field microscopy, cross-polarized microscopy, dark-field microscopy, phase contrast microscopy, differential interference contrast (DIC) microscopy, reflected interference microscopy, Sarfus microscopy, fluorescence microscopy, epifluorescence microscopy, confocal microscopy, light-sheet microscopy, multiphoton microscopy, super-resolution microscopy, near-field optical scanning microscopy, near-field optical random mapping (NORM) microscopy, structured illumination microscopy (SIM), spatially-modulated illumination (SMI) microscopy, 4-pi microscopy, spectral precision distance microscopy, stimulated emission depletion (STED) microscopy, ground-state depletion (GSD) microscopy, and reversible saturable optical linear fluorescence transition. The proteins may be identified using one or more of: (RESOLFT) microscopy, binding-activated localization (BALM) microscopy, photo-activated localization (PALM) microscopy, stochastic optical reconstruction (STORM) microscopy, direct stochastic optical reconstruction microscopy (dSTORM), super-resolution optical fluctuation (SOFI) microscopy or omnipresent localization microscopy (OLM).

[0072] The morphological changes may be identified by obtaining a temporal signal containing image information. The temporal signal may be converted into image information that is not time-dependent. The morphological changes may be identified without reconstructing the image. For example, the morphological changes may be identified without obtaining a full image of the cell. Identifying the morphological changes may include obtaining a temporal signal containing structural information. The temporal signal may be converted into structural information that is not time-dependent. Identifying the morphological changes may include processing a temporal waveform signal obtained by effectively compressing the spatial information of the cell in optical imaging instead of the two-dimensional information of the cell image. The morphological changes may be identified using a trained machine learning algorithm. The trained machine learning algorithm may be trained using training data. For example, the training data may include image, structural or temporal data of cells having known morphological characteristics. For example, morphological changes may be identified using one or more of the imaging methods and systems disclosed in S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost Cytometry”, Science 360(6394), pp. 1246-1251 (2018), doi: 10.1126 / science.aan0096, which is incorporated by reference in its entirety.

[0073] The morphological change may be identified while the cells are immobilized or fixed. The morphological change may be identified while the cells are not immobilized or fixed. The morphological change may be identified while the cells are stationary. The morphological change may be identified while the cells are moving. For example, the morphological change may be identified while the cells are flowing (such as through or in a flow channel or flow cell).

[0074] Machine learning algorithms can be used to identify morphological changes. For example, machine learning algorithms can be trained using a training set that includes labeled or unlabeled images of cell populations that focus on cells that exhibit one or more characteristics of interest (such as one or more specific protein localization patterns, protein-protein interactions, migration, colocalization, puncta, or other characteristics described herein). The machine learning algorithm can then be applied to test images to classify cells in the population of cells as positive or negative for one or more characteristics of interest.

[0075] The machine learning algorithm may include one or more supervised, semi-supervised or unsupervised machine learning techniques. The machine learning algorithm may include one or more of regression analysis, regularization, classification, dimensionality reduction, ensemble learning, meta-learning, reinforcement learning, association rule learning, cluster analysis, anomaly detection, deep learning or very deep learning.Machine learning algorithms include, but are not limited to, k-means, k-means clustering, k-nearest neighbors, learning vector quantization, linear regression, nonlinear regression, least squares regression, partial least squares regression, logistic regression, stepwise regression, multivariate adaptive regression splines, ridge regression, principal component regression, least absolute shrinkage and selection operation, least angle regression, canonical correlation analysis, factor analysis, independent component analysis, linear discriminant analysis, multidimensional scaling, nonnegative matrix factorization, principal component analysis, principal coordinate analysis, projection pursuit, Sammon mapping, t-distributed stochastic neighbor embedding, AdaBoosting, boosting, gradient boosting, bootstrap aggregation, ensemble averaging, decision trees, conditional decision trees, boosted decision trees, gradient boosted decision trees, random forests, stacked generalization, and the like. generalization), a Bayesian network, a Bayesian belief network, naive Bayes, Gaussian naive Bayes, polynomial naive Bayes, hidden Markov model, hierarchical hidden Markov model, support vector machine, encoder, decoder, autoencoder, stacked auto-encoder, perceptron, multi-layer perceptron, artificial neural network, feedforward neural network, convolutional neural network, regression neural network, long short-term memory, deep belief network, deep Boltzmann machine, deep convolutional neural network, deep recurrent neural network, or generative adversarial network.

[0076] The machine learning algorithm can classify cells as positive or negative for one or more characteristics, including one or more of a sensitivity, specificity, and accuracy of at least about 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or more. The machine learning algorithm can classify cells as positive or negative for one or more characteristics, including one or more of a sensitivity, specificity, and accuracy that are within a range defined by any two of the preceding values.

[0077] The cells can be isolated after or following identification of the morphological changes. For example, the cells can be isolated using flow cytometry or one or more of the "ghost cytometry" methods and systems disclosed in S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, "Ghost Cytometry", Science 360(6394), pp. 1246-1251 (2018), doi: 10.1126 / science.aan0096, which is incorporated herein by reference in its entirety.

[0078] Prior to the second operation 620, the method may further include labeling the peptides, polypeptides and / or proteins within the cells. The labeling may include labeling the peptides, polypeptides and / or proteins within the cells with one or more fluorescent labels, Forster Resonance Energy Transfer (FRET) labels, dyes, fluorophores, quantum dots, or any other labels. For example, the step of labeling can include labeling the peptides, polypeptides and / or proteins in the cells with SYBR green, SYBR blue, 4',6-diamidino-2-phenylindole (DAPI), propidium iodine, Hoechst, SYBR gold, ethidium bromide, acridine, proflavine, acridine orange, acriflavine, fluorescent couminine, ellipticine, daunomycin, chloroquine, distamycin D, chromomycin, homidium, mithramycin, ruthenium polypyridyl, anthramycin, phenanthridine and acridine, ethidium bromide, propidium iodide, hexidium iodide, dihydroethidium, ethidium homodimer 1 and 2, ethidium monoazide, and 9-amino-6-chloro-2-methoxyacridine (ACMA), Hoechst 33258, Hoechst 33342, Hoechst 34580, Acridine Orange, 7-AAD, Actinomycin D, LDS751, Hydroxystilbamidine, SYTOX Blue, SYTOX Green, SYTOX Orange, POPO-1, POPO-3, YOYO-1, YOYO-3, TOTO-1, TOTO-3, JOJO-1, LOLO-1, BOBO-1, BOBO-3, PO-PRO-1, PO-PRO-3, BO-PRO-1, BO-PRO-3, TO-PRO-1, TO-PRO-3, TO-PRO-5, JO-PRO-1, LO-PRO-1, YO-PRO-1, YO-PRO-3, PicoGreen, OligoGreen, RiboGreen, SYBR Gold, SYBR Green I, SYBR Green II, SYBR DX, SYTO-40, -41, -42, -43, -44 and -45 (blue), SYTO-13, -16, -24, -21, -23, -12, -11, -20, -22, -15, -14 and -25 (green), SYTO-81, -80, -82, -83, -84 and -85(orange), SYTO-64, -17, -59, -61, -62, -60 and -63 (red), fluorescein, fluorescein isothiocyanate (FITC), tetramethylrhodamine isothiocyanate (TRITC), rhodamine, tetramethylrhodamine, R-phycoerythrin, Cy-2, Cy-3, Cy-3.5, Cy-5, Cy5.5, Cy-7, Texas Red, Phar-Red, allophycocyanin (APC), SYBR Green I, SYBR Green II, SYBR Gold, CellTracker Green, 7-aminoactinomycin D (7-AAD), ethidium homodimer I, ethidium homodimer II, ethidium homodimer III, ethidium bromide, umbelliferone, eosin, green fluorescent protein, erythrosine, coumarin, methylcoumarin, pyrene, malachite green, stilbene, lucifer yellow, cascade blue, dichlorotriazinylamine fluorescein, dansyl chloride, fluorescent lanthanide complexes such as those containing europium and terbium, carboxytetrachlorofluorescein, 5 and / or 6-carboxyfluorescein (FAM), VIC , 5-(or 6-)iodoacetamidofluorescein, 5-{[2(and 3)-5-(acetylmercapto)-succinyl]amino}fluorescein (SAMSA-fluorescein), Lissamine rhodamine B sulfonyl chloride, 5 and / or 6 carboxyrhodamine (ROX), 7-amino-methyl-coumarin, 7-amino-4-methylcoumarin-3-acetic acid (AMCA), BODIPY fluorophores, 8-methoxypyrene-1,3,6-trisulfonic acid trisodium salt, 3,6-disulfonate-4-amino-naphthalimide, phycobiliproteins, Atto 390, 425, 465, 488, 495, 532, 565, 594, 633, 647, 647N, 665, 680 and 700 dyes, AlexaFluor 350, 405, 430, 488, 532, 546, 555, 568, 594, 610, 633, 635, 647, 660, 680, 700, 750 and 790 dyes, DyLight 350, 405, 488, 550, 594, 633, 650, 680, 755 and 800 dyes, or other fluorophores, Black Hole QuencherThe labeling step may include labeling with one or more of the following: Dyes (Biosearch Technologies), such as BH1-0, BHQ-1, BHQ-3 and BHQ-10, QSY Dye fluorescent quenchers (from Molecular Probes / Invitrogen), such as QSY7, QSY9, QSY21 and QSY35, and other quenchers, such as Dabcyl and Dabsyl, Cy5Q and Cy7Q, and Dark Cyanine dyes (GE Healthcare), Dy-Quenchers (Dyomics), such as DYQ-660 and DYQ-661, ATTO fluorescent quenchers (ATTO-TEC GmbH), such as ATTO 540Q, 580Q and 612Q, green fluorescent protein (GFP), yellow fluorescent protein (YFP), blue fluorescent protein (BFP), cyan fluorescent protein (CYP) and red fluorescent protein (RFP). In some cases, the label may include one or more linkers. For example, the label may have a disulfide linker attached to the label. Non-limiting examples of such labels include Cy5-azide, Cy-2-azide, Cy-3-azide, Cy-3.5-azide, Cy5.5-azide, and Cy-7-azide. In some cases, the linker may include a cleavable linker. In some cases, the label may be configured such that the label is not self-quenching or does not exhibit proximity quenching. Non-limiting examples of types of labels that are not self-quenching or do not exhibit proximity quenching include bimane derivatives such as monobromobimane. Alternatively, the label may be configured such that the label is self-quenching or exhibits proximity quenching. Non-limiting examples of such labels include Cy5-azide, Cy-2-azide, Cy-3-azide, Cy-3.5-azide, Cy5.5-azide and Cy-7-azide.

[0079] In a third operation 630, the method 600 may include processing the contents of the cell to identify a nucleic acid sequence or a peptide, polypeptide or protein or a sequence of a peptide, polypeptide or protein. Operation 630 may include sequencing at least one nucleic acid molecule and / or at least one peptide, polypeptide or protein molecule from the cell. The method includes the step of: generating at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1,000, 2,000, 3,000, 4,000, 5,000, 6,000, 7,000, 8,000, 9,000, 10,000, 15,000, 16,000, 17,000, 18,000, 19,000, 21,000, 22,000, 23,000, 24,000, 25,000, 26,000, 27,000, 28,000, 29,000, 30,000, 31,000, 32,000, 33,000, 34,000, 35,000, 36,000, 37,000, 38,000, 39,000, 40,000, 41,000, 42,000, 43,000, 44,000, 45,000, 46,000, 47,000, 48,000, 49,000, 50,000, 51,000, 52,000, 53,000, 54,000, 55,000, 00 pieces, 10,000 pieces, 20,000 pieces, 30,000 pieces, 40,000 pieces, 50,000 pieces, 60,000 pieces, 70,000 pieces, 80,000 pieces, 90,000 pieces, 100,000 pieces, 200,00 pieces 0 pieces, 300,000 pieces, 400,000 pieces, 500,000 pieces, 600,000 pieces, 700,000 pieces, 800,000 pieces, 900,000 pieces, 1,000,000 pieces, 2,000,000 pieces, 3,0 00,000 pieces, 4,000,000 pieces, 5,000,000 pieces, 6,000,000 pieces, 7,000,000 pieces, 8,000,000 pieces, 9,000,000 pieces, 10,000,000 pieces, 20,00 0,000 pieces, 30,000,000 pieces, 40,000,000 pieces, 50,000,000 pieces, 60,000,000 pieces, 70,000,000 pieces, 80,000,000 pieces, 90,000,000 pieces, The method may comprise sequencing 100,000,000, 200,000,000, 300,000,000, 400,000,000, 500,000,000, 600,000,000, 700,000,000, 800,000,000, 900,000,000, 1,000,000,000 or more nucleic acid molecules or peptide, polypeptide or protein molecules.The method can be used to generate up to about 1,000,000,000, 900,000,000, 800,000,000, 700,000,000, 600,000,000, 500,000,000, 400,000,000, 300,000,000, 200,000,000, 100,000,000, 90,000,000, 80,000,000, 70,000,000 , 60,000,000 pieces, 50,000,000 pieces, 40,000,000 pieces, 30,000,000 pieces, 20,000,000 pieces, 10,000,000 pieces, 9,000,000 pieces, 8,000, 000 pieces, 7,000,000 pieces, 6,000,000 pieces, 5,000,000 pieces, 4,000,000 pieces, 3,000,000 pieces, 2,000,000 pieces, 1,000,000 pieces, 900,000 pieces , 800,000 pieces, 700,000 pieces, 600,000 pieces, 500,000 pieces, 400,000 pieces, 300,000 pieces, 200,000 pieces, 100,000 pieces, 90,000 pieces, 80,000 pieces, 7 0,000 pieces, 60,000 pieces, 50,000 pieces, 40,000 pieces, 30,000 pieces, 20,000 pieces, 10,000 pieces, 9,000 pieces, 8,000 pieces, 7,000 pieces, 6,000 pieces, 5,000 pieces, The method may comprise sequencing 4,000, 3,000, 2,000, 1,000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 90, 80, 70, 60, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2 or 1 nucleic acid molecules or peptide, polypeptide or protein molecules. The method may comprise sequencing a number of nucleic acid molecules or peptide, polypeptide or protein molecules from a cell that are within a range defined by any two of the preceding values.The method includes: generating a 1 to 1,000,000,000, 10 to 1,000,000,000, 100 to 1,000,000,000, 1,000 to 1,000,000,000, 10,000 to 1,000,000,000, 100,000 to 1,000,000,000, 1,000,000 to 1,000,000,000, 10,000,000 to 1,000,000,000 0,000 pcs, 1 to 100,000,000 pcs, 10 to 100,000,000 pcs, 100 to 100,000,000 pcs, 1,000 to 100,000,000 pcs, 10,000 to 100,000,000 pcs, 100,000 to 100,000,000 pcs, 1,000,000 to 100,000,000 pcs, 10,000,000 to 100,000,000 pcs, 1 to 10,000,000 pcs, 10 to 10,000,000 pcs, 100 to 10, 000,000 pieces, 1,000 to 10,000,000 pieces, 10,000 to 10,000,000 pieces, 100,000 to 10,000,000 pieces, 1,000,000 to 10,000,000 pieces, 1 to 1,000,000 pieces, 10 to 1,000,000 pieces, 100 to 1,000,000 pieces, 1,000 to 1,000,000 pieces, 10,000 to 1,000,000 pieces, 100,000 to 1,000,000 pieces, 1 to 100,000 pieces, 10 to In some embodiments, the method may comprise sequencing from 1 to 100,000, 100 to 100,000, 1,000 to 100,000, 10,000 to 100,000, 1 to 10,000, 10 to 10,000, 100 to 10,000, 1,000 to 10,000, 1 to 1,000, 10 to 1,000, 100 to 1,000, 1 to 100, 10 to 100 or 1 to 10 nucleic acid molecules or peptide, polypeptide or protein molecules.

[0080] Sequencing may include any nucleic acid sequencing such as any DNA sequencing or RNA sequencing. Sequencing may include polymerase chain reaction (PCR), digital PCR, real-time PCR, quantitative PCR (qPCR), reverse transcription, reverse transcription PCR (RT-PCR), Sanger sequencing, high throughput sequencing, sequencing by synthesis, single molecule sequencing, sequencing by ligation, RNA-Seq (Illumina), next generation sequencing, Digital Gene Expression (Helicos), array hybridization, Clonal Single MicroArray (Solexa), shotgun sequencing, Maxim-Gilbert sequencing, massively parallel sequencing, or massively parallel array sequencing.

[0081] In a fourth operation 640, the method 600 may include analyzing a nucleic acid sequence or a peptide, polypeptide or protein or a sequence of a peptide, polypeptide or protein to determine the exogenous sequence of the exogenous RNA or exogenous DNA.

[0082] In a fifth operation 650, the method 600 can include identifying an exogenous sequence of the exogenous RNA or exogenous DNA molecule as affecting a morphological change in the cell.

[0083] Method 600 may further include using at least the morphological change to determine that the at least one exogenous DNA molecule or the at least one exogenous RNA molecule inhibits or activates a biochemical pathway in the cell.

[0084] In one aspect, the disclosure provides a method for screening cells for a target nucleic acid, peptide, polypeptide, or protein, comprising: (a) providing a cell transfected or transduced with an exogenous ribonucleic acid (RNA) molecule or an exogenous deoxyribonucleic acid (DNA) molecule, the exogenous RNA or DNA encoding a population of macromolecules to be expressed in the cell; (b) identifying a morphological change in the cell in response to the population of macromolecules expressed by the cell; (c) subjecting the genome of the cell to sequencing to identify the exogenous RNA or DNA; and (d) determining that the exogenous RNA or DNA induced a morphological change. Identifying a morphological change may include identifying a macromolecular localization profile or pattern of the population of macromolecules. Identifying a morphological change may include identifying a shape of the cell or one or more intracellular components of the cell. The one or more intracellular components may include an organelle of the cell. The population of macromolecules may include a population of one or more members selected from the group consisting of peptides, polypeptides, proteins, and nucleic acid molecules.

[0085] FIG. 6 shows an example of a method for identifying nucleic acid molecules or peptides, polypeptides or proteins based on protein-protein interactions, protein aggregation or protein localization. As shown in panel 1 of FIG. 6, cells can be transfected with a plasmid library prepared and containing exogenous DNA or RNA molecules as described herein. As described herein, the exogenous DNA or RNA molecules can code for peptides, polypeptides, proteins or nucleic acids such as RNA, DNA, small hairpin RNA (shRNA), guide RNA (gRNA), small interfering RNA (siRNA), microRNA (miRNA), antisense RNA (asRNA) or guide DNA (gDNA). Cells exhibiting a target phenotype can be isolated based on the optical spatial information described herein. Exogenous DNA or RNA molecules associated with cells exhibiting a target phenotype can then be decoded using nucleic acid sequencing such as next generation sequencing (NGS) as described herein.

[0086] As shown in panel 2 of FIG. 6, protein-protein interaction (PPI) between a first protein A and a second protein B can result in a morphological change that can be detected. Proteins A and B can be fluorescently tagged. Proteins A and B can form a fluorescent foci when proteins A and B interact. When proteins A and B do not interact, they do not form such a fluorescent foci. The presence or absence of such a fluorescent foci can be detected using the optical detection systems and methods described herein. One or more exogenous DNA or RNA molecules (or peptides, polypeptides, or proteins expressed from the exogenous DNA or RNA molecules) described herein can inhibit the formation of fluorescent foci, while other exogenous DNA or RNA molecules (or peptides, polypeptides, or proteins expressed from the exogenous DNA or RNA molecules) can not inhibit the formation of fluorescent foci. Thus, cells transfected with an exogenous DNA or RNA molecule that inhibits the formation of fluorescent foci (e.g., an exogenous DNA or RNA molecule that inhibits a PPI, or a peptide, polypeptide, or protein expressed from an exogenous DNA or RNA molecule that inhibits a PPI) can be distinguished from cells transfected with an exogenous DNA or RNA molecule that does not inhibit the formation of fluorescent foci (e.g., an exogenous DNA or RNA molecule that does not inhibit a PPI, or a peptide, polypeptide, or protein expressed from an exogenous DNA or RNA molecule that does not inhibit a PPI). The exogenous DNA or RNA molecule that inhibits a PPI (or a peptide, polypeptide, or protein expressed from the exogenous DNA or RNA molecule) can then be isolated and subjected to nucleic acid sequencing to identify peptides, polypeptides, proteins, or nucleic acids that inhibit a PPI between proteins A and B.

[0087] In some cases, one or more of the exogenous DNA or RNA molecules (or peptides, polypeptides, or proteins expressed from the exogenous DNA or RNA molecules) described herein may promote the formation of fluorescent foci, while other exogenous DNA or RNA molecules (or peptides, polypeptides, or proteins expressed from the exogenous DNA or RNA molecules) do not promote the formation of fluorescent foci. Thus, cells transfected with an exogenous DNA or RNA molecule that promotes the formation of fluorescent foci (e.g., an exogenous DNA or RNA molecule that promotes PPI, or a peptide, polypeptide, or protein expressed from an exogenous DNA or RNA molecule that promotes PPI) can be distinguished from cells transfected with an exogenous DNA or RNA molecule that does not promote the formation of fluorescent foci (e.g., an exogenous DNA or RNA molecule that does not promote PPI, or a peptide, polypeptide, or protein expressed from an exogenous DNA or RNA molecule that does not promote PPI). The exogenous DNA or RNA molecule that promotes PPI (or a peptide, polypeptide, or protein expressed from the exogenous DNA or RNA molecule) can then be isolated and subjected to nucleic acid sequencing to identify the peptide, polypeptide, protein, or nucleic acid that promotes PPI between proteins A and B.

[0088] As shown in panel 3 of Figure 6, protein localization may result in detectable morphological changes. Protein A may be fluorescently tagged. In some cases, protein A may be detected in the nucleus of the cell.

[0089] In some cases, one or more of the exogenous DNA or RNA molecules described herein (or peptides, polypeptides, or proteins expressed by a cell in response to the exogenous DNA or RNA molecules) may cause protein A to localize to the cytoplasm of the cell, while other exogenous DNA or RNA molecules (or peptides, polypeptides, or proteins expressed from the exogenous DNA or RNA molecules) may not cause such localization. Thus, cells transfected with exogenous DNA or RNA molecules that cause such protein localization (e.g., exogenous DNA or RNA that cause protein localization, or peptides, polypeptides, or proteins expressed from exogenous DNA or RNA molecules that cause protein localization) can be distinguished from cells transfected with exogenous DNA or RNA molecules that do not cause such protein localization (e.g., exogenous DNA or RNA molecules that do not cause protein localization, or peptides, polypeptides, or proteins expressed from exogenous DNA or RNA molecules that do not cause protein localization). The exogenous DNA or RNA molecule that causes protein localization (or a peptide, polypeptide, or protein expressed from the exogenous DNA or RNA molecule) can then be isolated and subjected to nucleic acid sequencing to identify the peptide, polypeptide, protein, or nucleic acid that causes protein localization.

[0090] In some cases, one or more exogenous DNA or RNA molecules described herein (or peptides, polypeptides, or proteins expressed by a cell in response to the exogenous DNA or RNA molecules) can cause a change in the localization of protein A. For example, one or more exogenous DNA or RNA molecules described herein (or peptides, polypeptides, or proteins expressed by a cell in response to the exogenous DNA or RNA molecules) can cause a change in the localization of protein A from a first organelle or subcellular region or compartment to a second organelle or subcellular region or compartment. The first organelle or subcellular region or compartment can include a mitochondrion, a nucleus, a cytoplasm, a cytoskeleton, a cell membrane, a nuclear membrane, a lysosome, an endosome, an endoplasmic reticulum, a Golgi apparatus, a vacuole, a lysosome, a centrosome, a nucleolus, a ribosome, or a vesicle. The second organelle or subcellular region or compartment may comprise a mitochondrion, nucleus, cytoplasm, cytoskeleton, cell membrane, nuclear membrane, lysosome, endosome, endoplasmic reticulum, Golgi apparatus, vacuole, lysosome, centrosome, nucleolus, ribosome, or vesicle.

[0091] Computer Systems The present disclosure provides a computer system programmed to implement the methods of the present disclosure. Figure 1 shows a computer system 101 programmed or otherwise configured to screen a biological sample for one or more targets, such as peptides, polypeptides, proteins or organelles, or one or more target phenotypes, such as protein localization, protein-protein interactions or cell morphology.

[0092] The computer system 101 includes a central processing unit (CPU, also referred to herein as "processor" and "computer processor") 105, which may be a single-core or multi-core processor, or multiple processors for parallel processing. The computer system 101 also includes memory or memory locations 110 (e.g., random access memory, read-only memory, flash memory), electronic storage devices 115 (e.g., hard disks), communication interfaces 120 (e.g., network adapters) for communication with one or more other systems, and peripherals 125, such as cache, other memory, data storage, and / or electronic display adapters. The memory 110, storage devices 115, interfaces 120, and peripherals 125 communicate with the CPU 105 through a communication bus (solid line), such as a motherboard. The storage device 115 may be a data storage device (or data repository) for storing data. The computer system 101 may be operatively coupled to a computer network ("network") 130 with the aid of the communication interface 120. Network 130 may be the Internet, an internet and / or an extranet, or an intranet and / or an extranet in communication with the Internet. In some cases network 130 is a telecommunications and / or data network. Network 130 may include one or more computer servers that may enable distributed computing such as cloud computing. Network 130 may implement a peer-to-peer network that may enable devices to be coupled to computer system 101 to act as clients or servers, in some cases with the assistance of computer system 101.

[0093] CPU 105 can execute a sequence of machine-readable instructions, which may be embodied in a program or software. The instructions may be stored in a memory location, such as memory 110. The instructions may be directed to CPU 105, which can then be programmed or otherwise configured to implement the methods of the present disclosure. Examples of operations performed by CPU 105 may include fetch, decode, execute, and writeback.

[0094] The CPU 105 may be part of a circuit, such as an integrated circuit. One or more other components of the system 101 may be included in the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC).

[0095] Electronic storage device 115 can store files such as drivers, libraries, and saved programs. Electronic storage device 115 can store user data, such as user preferences and user programs. In some cases, computer system 101 may include one or more additional data storage devices external to computer system 101, such as located on a remote server that communicates with computer system 101 over an intranet or the Internet.

[0096] Computer system 101 can communicate with one or more remote computer systems through network 130. For example, computer system 101 can communicate with a remote computer system of a user. Examples of remote computer systems include a personal computer (e.g., a portable PC), a slate or tablet PC (e.g., an Apple® iPad, a Samsung® Galaxy Tab), a phone, a smart phone (e.g., an Apple® iPhone, an Android® compatible device, a Blackberry®), or a personal digital assistant. A user can connect to computer system 101 through network 130.

[0097] The methods described herein may be implemented by machine (e.g., computer processor) executable code stored in an electronic storage location of computer system 101, such as memory 110 or electronic storage device 115. Machine executable or machine readable code may be provided in the form of software. In use, the code may be executed by processor 105. In some cases, the code may be read from storage device 115 and stored in memory 110 for easy connection by processor 105. In some circumstances, electronic storage device 115 may be excluded and machine executable instructions may be stored in memory 110.

[0098] The code may be precompiled and configured for use with a machine having a processor adapted to execute the code, or it may be compiled at run time. The code may be provided in a programming language that may be selected to allow the code to be executed in a precompiled or compiled fashion.

[0099] Aspects of the systems and methods provided herein, such as the computer system 101, may be embodied in programming. Various aspects of the technology are typically considered a "product" or "article" in the form of machine (or processor) executable code and / or associated data maintained or embodied in a type of machine-readable medium. The machine-executable code may be stored in memory (e.g., read-only memory, random access memory, flash memory) or an electronic storage device such as a hard disk. A "storage" type medium may include any or all tangible memory of a computer, processor, etc., or its associated modules, such as various semiconductor memories, tape drives, disk drives, etc., which may provide non-transitory storage at any time for software programming. All or a portion of the software may be communicated at any time over the Internet or various other telecommunications networks. Such communication may, for example, allow the software to be loaded from one computer or processor to another, for example, from a management server or host computer to a computer platform of an application server. Thus, other types of media that may bear software elements include optical, electrical, and electromagnetic waves, such as those used between local devices, across physical interfaces, through wired and optical landline networks, and across various air links. Physical elements that convey such waves, such as wired or wireless links, optical links, etc., may also be considered media that bear software. As used herein, unless limited to non-transitory, tangible "storage" media, terms such as computer or machine "readable medium" refer to any medium that participates in providing instructions to a processor for execution.

[0100] Thus, a machine-readable medium, such as a computer-executable code, may take many forms, including but not limited to, a tangible storage medium, a carrier wave medium, or a physical transmission medium. Non-volatile storage media include optical or magnetic disks, such as any storage device in any computer, such as may be used to implement a database, for example, as shown in the figures. Volatile storage media include dynamic memory, such as the main memory of a computer platform. Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that comprise a bus within a computer system. Carrier wave transmission media may take the form of electronic or electromagnetic signals, or acoustic or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Thus, common forms of computer readable media include, for example: a floppy disk, a flexible disk, a hard disk, a magnetic tape, any other magnetic medium, a CD-ROM, a DVD or a DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium using a pattern of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave carrying data or instructions, a cable or link carrying such a carrier wave, or any other medium from which a computer can read programming code and / or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

[0101] The computer system 101 may include or be in communication with an electronic display 135 that includes a user interface (UI) 140 for, for example, providing images of cells containing peptides, polypeptides and / or proteins (e.g., protein distribution) and / or sequence information from genomes (deoxyribonucleic acid and / or ribonucleic acid) from the cells. Non-limiting examples of UIs include graphical user interfaces (GUIs) and web-based user interfaces.

[0102] The methods and systems of the present disclosure may be implemented as one or more algorithms. The algorithms may be implemented by software following execution by the central processing unit 105. EXAMPLES

[0103] Example 1 Experimental Protocol In some cases, the foreign RNA or DNA molecule is designed to express a peptide, polypeptide or protein. The peptide, polypeptide or protein may have a stable alpha-helical structure, which may be an important aspect of peptide-protein interactions. The peptide-expressing RNA or DNA may be cloned into a plasmid vector for transfection or transduction into cells.

[0104] Two peptide expression libraries were constructed using tailored randomization of 30 residues. The scaffold peptide was designed to have a stable secondary structure with eight helical turns. An extensive network of salt bridges between lysine (K) and glutamic acid (E) residues on their surfaces could stabilize the helical structure and may also help to increase peptide solubility as reported. Alanine residues were positioned to promote the formation of (internal) helical structures. Libraries were constructed by randomizing (for each library) seven positions of alanine residues located on the same planar face of the helical structure, as detailed in Krstenansky JL, Owen TJ., Hagaman KA., McLean LR., FEBS Lett., 1989, 242(2):409-13. doi.org / 10.1016 / 0014-5793(89)80512-5. Short model peptides having a high α-helical tendency: Design and solution properties, which is incorporated herein by reference in its entirety.

[0105] Shalem O., Sanjana NE., Hartenian E., Shi X., Scott DA., Mikkelson T., Heckl D., Ebert BL., Root DE., Doench JG., Zhang F., Science. 2014, 343 (6166):84-87. doi: 10.1126 / science.1247005. Genome-scale CRISPR-Cas9 knockout screening in human cells and Joung J., Konermann S., Gootenberg JS., Abudayyeh OO., Platt RJ., Brigham MD., Sanjana NE., Zhang F., Nat Protoc. 2017, 12(4):828-863. doi: 10.1038 / nprot.2017.016, each of which is incorporated by reference in its entirety in some cases. As described in Genome-scale CRISPR-Cas9 knockout and transcriptional activation screening, exogenous RNA molecules or exogenous DNA molecules can be transfected into cells using clustered regularly interspaced short palindromic repeats (CRISPR), such as CRISPR-associated 9 (Cas9), Cas13d, CasX or other variants, or other gene editing techniques such as zinc finger nucleases (ZFNs) or TAL effector nucleases (TALENs).

[0106] In some cases, genome-scale CRISPR libraries or peptide expression libraries are amplified. Briefly, an appropriate amount of plasmid is electroporated into electrocompetent cells. After checking the transformation efficiency, the amplified single guide RNA (sgRNA) library or peptide expression library is harvested and purified using Macherey-Nagel NucleoBond Xtra Maxi EF Kit. The sequences containing the peptide coding region within the sgRNA or plasmid are then PCR amplified and barcoded before being subjected to high-throughput sequencing analysis prior to knockout experiments.

[0107] In some cases, oligos of the peptide library were cloned into viral or non-viral vectors such as the pLVX-Puro lentiviral expression vector. These vectors can provide high-level expression of peptides in virtually any type of mammalian cell. Lentiviral particles for peptide libraries and sgRNAs were produced using HEK 293FT cells with the Lenti-X Packaging Single Shots kit (TaKara). To ensure that the majority of cells received genetic perturbation only once, lentiviral particles (containing sgRNA / peptide library) were transduced into cell lines at an MOI (multiplicity of infection) < 0.05 for screening setups.

[0108] The cellular plasmid sequences were PCR amplified, purified and subjected to de novo sequencing. A deep sequencing approach using a next-generation sequencing platform was used to decode the inserted sgRNA and plasmid distribution in the sorted cells.

[0109] Example 2 Sorting of fluorescently tagged mitochondria or lysosomes Figure 4A and Figure 10E show fluorescent images of HEK293T cells containing fluorescently tagged mitochondria or fluorescently tagged lysosomes. The left panels of Figure 4A and Figure 10E show cells containing mitochondria stained with Mito Tracker. The right panels of Figure 4A and Figure 10E show cells containing lysosomes stained with Lyso Tracker. Cells stained with Mito Tracker or Lyso Tracker were used as training sets. Cells stained with Mito Tracker and Lyso Tracker were distinguished by visual analysis using fluorescent imaging.

[0110] Figure 4B shows sorting of HEK293T cells containing fluorescently tagged mitochondria (left peak, "mitochondria") or fluorescently tagged lysosomes (right peak, "lysosomes") using the systems and methods of the present disclosure. Figure 10F shows sorting of HEK293T cells containing fluorescently tagged mitochondria (right peak, "mitochondria") or fluorescently tagged lysosomes (left peak, "lysosomes") using the systems and methods of the present disclosure. Cells stained with Mito Tracker and Lyso Tracker were sorted based on the morphology of the fluorescently tagged organelles, thereby distinguishing between cells containing fluorescently tagged mitochondria and cells containing fluorescently tagged lysosomes. Proteins were classified using the imaging procedure disclosed in S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost Cytometry”, Science 360(6394), pp. 1246-1251 (2018), doi: 10.1126 / science.aan0096, which is incorporated herein by reference in its entirety. The two peaks shown in FIG. 4B and FIG. 10F, respectively, illustrate the machine learning (ML) score distributions of the two cell populations. The classification method utilized a support vector machine (SVM). The ML score is also referred to as the SVM score. SVM yielded a receiver operating characteristic-area under the curve (ROC-AUC) of 0.993 for the samples shown in FIG. 4B and a ROC-AUC of greater than 0.999 for the samples shown in FIG. 10F.

[0111] Example 3 Classification of STAT3 cytoplasmic or nuclear localization FIG. 4C shows fluorescent images of HepG2 cells containing fluorescently tagged STAT3 protein localized within the nucleus (nucleus) and outside the nucleus (cytoplasm). Cells were labeled using immunofluorescence (IF) for STAT3. Wild-type (WT) HepG2 cells with or without interleukin 6 (IL-6) stimulation served as the training set. Cells stained for STAT3 in the presence or absence of IL-6 were differentiated by visual analysis using fluorescent imaging. The right panel of FIG. 4C illustrates HepG2 cells stained for STAT3 in the absence of IL-6, and the left panel of FIG. 4C illustrates HepG2 cells stained for STAT3 in the presence of IL-6. IL-6 activates STAT3 and promotes its localization to the nucleus as seen by the accumulation of fluorescence near the center of the cell in the presence of IL-6.

[0112] For the screening set, pooled sgRNAs were transduced into Cas9-expressing HepG2 cells, and pooled peptide expression libraries were transduced into HepG2 cells. STAT3 was activated by IL-6 to screen for functional genes or peptides that inhibit STAT3 nuclear translocation. For the training set, WT HepG2 cells were trypsinized and centrifuged at low speed for 5 minutes. After removal of the supernatant, 1 mL of fresh RPMI 10% FBS medium was added and aliquoted into two Eppendorf tubes. One was used for IL-6 stimulation and the other for unstimulated control. For STAT3 activation, HepG2 cells and the screening set were treated with 100 micrograms per milliliter (μg / mL) IL-6 and incubated at 37° C. for 15 minutes. After centrifugation and removal of the supernatant, the cells were fixed with 500 microliters (μL) 4% formaldehyde for 15 minutes at room temperature. Then, several washes with PBS buffer were performed. The cells were then permeabilized with 500 μL of permeabilization buffer (methanol, acetone [1:2]) for 30 min at 4°C. After washing with PBS, the cells were blocked with 500 μL of 5% BSA (in PBS) and incubated for 1 h at room temperature. The cells were then washed with PBS and incubated with 125 μL of primary STAT3 antibody (50x dilution in PBS) and incubated overnight at 4°C. The cells were then washed several times with PBS to remove non-specific binding, and the pellet was resuspended with 300 μL of secondary rabbit antibody (goat anti-rabbit IgG-488- 100x dilution in PBS). After incubation for 60 min at room temperature (in the dark), the cells were washed three times with PBS, and the pellet was finally resuspended in 300 μL of PBS. The number of cells was counted, and 1 x 10 cells were counted. 6 The concentration was adjusted to 1 / mL.

[0113] FIG. 4D shows the classification of HepG2 cells with nuclear-localized STAT3 (right peak, "IL6_plus") and cells with cytoplasmic STAT3 (left peak, "IL6_minus") protein using the disclosed system and method. Cells were stained using immunofluorescence for STAT3, and cells treated with IL-6 or untreated were classified based on STAT3 localization. The two peaks shown in FIG. 4D each illustrate the SVM score distribution of two cell populations based on similarity to the phenotype of nuclear-localized STAT3. The images were classified using the imaging procedure disclosed in S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost Cytometry”, Science 360(6394), pp. 1246-1251 (2018), doi: 10.1126 / science.aan0096, which is incorporated herein by reference in its entirety. The classification method utilized SVM. The SVM obtained a ROC-AUC of 0.962.

[0114] Example 4 Classification of NF-κB cytoplasmic or nuclear localization FIG. 10A shows fluorescent images of THP-1 cells containing fluorescently tagged NF-κB proteins localized in the nucleus or cytoplasm. Following stimulation with lipopolysaccharide (LPS), NF-κB subunits translocate to the nucleus. Cells were labeled using immunofluorescence for NF-κB. LPS-stimulated or unstimulated THP-1 cells served as the training set. Cells stained for NF-κB in the presence or absence of LPS stimulation were differentiated by visual analysis using fluorescent imaging. The left panel of FIG. 10A illustrates THP-1 cells stained for NF-κB in the absence of LPS stimulation. The right panel of FIG. 10A illustrates THP-1 cells stained for NF-κB in the presence of LPS stimulation. The addition of LPS promotes the translocation of NF-κB subunits to the nucleus.

[0115] FIG. 10B shows classification of THP-1 cells with nuclear and cytoplasmic NF-κB using the disclosed system and method. Cells were stained using immunofluorescence for NF-κB, and cells pre-treated with LPS and untreated cells were classified based on nuclear (right peak, "LPS (+)") or cytoplasmic localization (left peak, "LPS (-)") of NF-κB. The two peaks shown in FIG. 10B each illustrate the machine learning (ML) score distribution of two cell populations based on similarity to phenotypes with NF-κB nuclear localization. The images were classified using the imaging procedure disclosed in S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost Cytometry”, Science 360(6394), pp. 1246-1251 (2018), doi: 10.1126 / science.aan0096, which is incorporated herein by reference in its entirety. The classification method utilized SVM. The SVM obtained a ROC-AUC of 0.997.

[0116] 8A and 8B illustrate how to train a machine learning algorithm to classify cells based on the localization of fluorescently tagged proteins. The training set shown in FIG. 8A was generated using fluorescent images of THP-1 cells stained by immunofluorescence for the p65 subunit of NF-κB ("anti-p65"). After stimulation with LPS, p65 localizes to the nucleus. Cells were co-stained with a nuclear stain (e.g., DAPI or Hoechst). Labeled cells stimulated with LPS were used as the training set for cells positive for nuclear localization (FIG. 8A, right column), and unstimulated labeled cells were used as the training set for cells negative for nuclear localization (FIG. 8A, left column). The waveforms shown in the upper panel of FIG. 8B were generated from stimulated and unstimulated data sets. The waveforms of the stimulated and unstimulated data sets, respectively, were used to develop a machine learning model to classify cells positive for nuclear localization or negative for nuclear localization. As shown in the lower panel of Figure 8B, the classification model utilized SVM to discriminate between cells with nuclear-localized NF-κB (right peak, "stimulated") and cells with cytoplasmic NF-κB (left peak, "unstimulated"). The trained model was able to discriminate between the waveforms of stimulated and unstimulated cells.

[0117] Example 5: Sorting cells based on cytoplasmic or nuclear localization of target proteins Figures 9A and 9B show cells sorted based on cytoplasmic or nuclear localization of target proteins. Cells were stained by immunofluorescence for the p65 subunit of NF-κB ("anti-p65"). Stained cells were stimulated with LPS to promote nuclear localization of p65 and pooled with cells stained in the absence of stimulation (Figure 9A, upper panel). Pooled cells were classified using an image-based system (FIG. 9A, middle and lower panels) or sorted using ghost cytometry image-based cell sorting (FIG. 9B, upper panel) as disclosed in S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost Cytometry”, Science 360(6394), pp. 1246-1251 (2018), doi: 10.1126 / science.aan0096, which is incorporated herein by reference in its entirety. Ghost cytometry sorting was based on fluorescence intensity and morphology as determined by a previously obtained training set. The training set was obtained as described in Examples 4 and 5. Cells were sorted into “stimulated” and “unstimulated” subpopulations based on the localization of fluorescently tagged p65 protein. The subpopulation of cells classified as unstimulated was further subjected to analysis using an image-based system to confirm sorting (FIG. 9B, middle and lower panels). Before sorting, the pooled cells contained two distinct cell populations (FIG. 9A). After sorting, the selected cell subpopulation contained a single population (FIG. 9B), indicating that ghost cytometry could effectively sort cells based on the nuclear localization of fluorescently labeled p65.

[0118] Example 6 Classification of intracellular unaggregated and aggregated proteins FIG. 4E shows the fluorescence images of unaggregated or aggregated proteins in HEK293T cells. Cells were stained using the Fluoppi system to detect protein-protein interactions between induced myeloid leukemia cell differentiation protein (MCL-1) and Bcl-2 homologous antagonist / killer protein (BAK). The Fluoppi system facilitates the detection of protein-protein interactions by promoting the formation of fluorescent spots or bright spots following interaction between target proteins. Cells stained with Fluoppi for MCL-1 / BAK interaction in the presence or absence of MCL-1 inhibitor A-1210477 were used as the training set. A-1210477 inhibits MCL-1 and disrupts the interaction between MCL-1 and BAK. Cells stained for MCL-1 / BAK interaction in the presence or absence of A-1210477 were distinguished by visual analysis using fluorescence imaging. The right panel of FIG. 4F illustrates HEK293T cells in the absence of A-1210477. In the absence of A-1210477, MCL-1 and BAK stained with the Fluoppi system form visible fluorescent foci. The left panel of Figure 4F illustrates HEK293T cells in the presence of A-1210477. Addition of A-1210477 disrupts the interaction between MCL-1 and BAK.

[0119] FIG. 4F shows the classification of cells containing unaggregated or aggregated proteins using the disclosed system and method. The Fluoppi protein-protein interaction visualization system was used to detect protein interactions through the detectable formation of aggregates. Briefly, Fluoppi works by attaching a multimeric fluorescent protein tag to a first labeled protein and an assembly helper tag to a second labeled protein. When the first and second labeled proteins interact, the interaction between the fluorescent protein tag and the assembly helper tag forms an optically detectable oligomeric protein complex. The complex can be detected as a bright spot containing protein aggregates or oligomers. Here, the interaction between MCL-1 and BAK was explored using the Fluoppi system. The interaction between MCL-1 and BAK was disrupted using the MCL-1 inhibitor A-1210477. The cells were classified based on the presence of detectable protein aggregates (left peak, "positive") or the absence of detectable protein aggregates (right peak, "negative"). The two peaks shown in Figure 4F respectively illustrate the SVM score distribution of two cell populations based on the similarity of protein-protein interactions to phenotypes. The images were classified using the imaging procedure disclosed in S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, "Ghost Cytometry", Science 360(6394), pp. 1246-1251 (2018), doi: 10.1126 / science.aan0096, which is incorporated herein by reference in its entirety. The classification method utilized SVM.

[0120] Example 7 Classification of protein-protein interactions in cells Ghost cytometry was performed to obtain the fluorescent signal waveforms containing image information (such as protein localization information) from both protein-protein interaction (PPI)-positive and PPI-negative cells. Each cell type was then set as a training set to develop a machine learning model to classify PPI-positive and PPI-negative cells. The classification model utilized SVM to discriminate between PPI-positive and PPI-negative cells.

[0121] FIG. 10C shows a fluorescent image of CHO-K1 cells labeled for protein-protein interaction between p53 and mouse double minute 2 homolog (MDM2). Proteins p53 and MDM2 were labeled using the Fluoppi system as described in Example 6. The left panel of FIG. 10C shows untreated cells, and the right panel of FIG. 10C shows cells treated with Nutulin-3. Nutulin-3 disrupts the interaction between p53 and MDM2, as seen by the dispersion of fluorescence after treatment with Nutulin-3 compared to untreated samples. Cells were labeled for protein-protein interaction between p53 and MDM2, and either treated with Nutulin-3 or untreated were used as training set. Cells with or without Nutulin-3 were distinguished by visual analysis using fluorescent imaging.

[0122] FIG. 10D shows the classification of cells positive (right peak, "untreated") or negative (left peak, "nutulin-3") for protein-protein interaction between p53 and MDM2 using the system and method of the present disclosure. Nutulin-3 was used to disrupt the interaction between p53 and MDM2. Cells were stained for the interaction between p53 and MDM2, and cells treated with nutulin-3 or untreated cells were classified based on the interaction between p53 and MDM2. The two peaks shown in FIG. 10D respectively illustrate the ML score distribution of two cell populations based on the similarity to the phenotype of the interaction between p53 and MDM2. The images were classified using the imaging procedure disclosed in S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost Cytometry”, Science 360(6394), pp. 1246-1251 (2018), doi: 10.1126 / science.aan0096, which is incorporated herein by reference in its entirety. The classification method utilized SVM. The SVM obtained a ROC-AUC of 0.958.

[0123] Figure 4G shows the fluorescence images of THP cells with nuclear-localized NF-κB protein and cells with cytoplasmic NF-κB protein. To identify NF-κB nuclear translocation inhibitors, cytosol-localized NF-κB (Figure 4G, right panel) and NF-κB stimulated with lipopolysaccharide (LPS) to induce nuclear translocation (Figure 4G, left panel) were stained as positive and negative training sets, respectively. LPS stimulation promotes the dissociation of p65 from the NF-κB complex and the translocation of p65 to the nucleus. Image information of the cells was acquired and classified using the imaging procedure disclosed in S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost Cytometry”, Science 360(6394), pp. 1246-1251 (2018), doi: 10.1126 / science.aan0096, which is incorporated herein by reference in its entirety. For the screening set, the pooled sgRNA library was transduced into Cas9-expressing THP-1 cells, or the pooled peptide-expressing library was transduced into WT THP-1 cells. After stimulation with LPS, the classification model was applied to the screening set to select cells without NF-κB nuclear translocation. Plasmids were isolated from the selected cells and subjected to next-generation sequencing analysis to identify genes or peptides that inhibit the nuclear localization of NF-κB.

[0124] 4H shows the classification of THP-1 cells based on the nuclear-localized p65 subunit of NF-κB and cells with the cytoplasmic p65 subunit of NF-κB protein using the disclosed systems and methods. LPS was used to stimulate p65 localization to the nucleus. Cells were classified based on morphology and localization of fluorescently labeled p65 in the presence (right peak, "stimulated") or absence (left peak, "unstimulated") of LPS stimulation. The images were classified using the imaging procedure disclosed in S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost Cytometry”, Science 360(6394), pp. 1246-1251 (2018), doi: 10.1126 / science.aan0096, which is incorporated herein by reference in its entirety. The classification yielded a ROC-AUC of 0.972.

[0125] Example 8 Validation of rapid image-based pooled gene / peptide screening (RIPGS) Figure 11 shows the validation of the rapid image-based pooled gene / peptide screening method using the image-based system and FACS. Cells were stained by immunofluorescence for the p65 subunit of NF-κB ("anti-p65"). Stained cells were stimulated with LPS to promote nuclear localization of p65. Stimulated cells were pooled with stained cells that were not subjected to LPS stimulation. Pooled cells were subjected to ghost cytometry sorting according to their fluorescence intensity together with the ghost signals previously obtained in the training set. The training set was obtained as described in Example 4 and Example 5. Sorted cells were collected, concentrated, and checked for purity using the image-based system and conventional FACS. Cell sorting based on ghost cytometry images (Figure 11, top left panel) was performed as disclosed in S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, "Ghost Cytometry", Science 360(6394), pp. 1246-1251 (2018), doi: 10.1126 / science.aan0096, which is incorporated herein by reference in its entirety. Cells were sorted into "stimulated" and "unstimulated" subpopulations based on the localization of fluorescently tagged p65 protein. The subpopulation of cells classified as unstimulated was further subjected to analysis using FACS (Figure 11, top center panel) or an image-based system to confirm the sorting (Figure 11, top right panel and bottom panel). The receiver operating characteristic curve (Roc-AUC) and accuracy (Acc) obtained from ghost cytometry sorting were 0.959 and 0.912, respectively, indicating high purity. Results were compared with those calculated using FACS and image-based systems (purity 83.6% and 86.7%, respectively), indicating that ghost cytometry could effectively sort cells based on the nuclear localization of fluorescently labeled p65.

[0126] Example 9 Image-based genetic screening The disclosed systems and methods can be used to perform rapid image-based pooled gene and / or peptide screening for a variety of purposes. For example, the systems and methods can be used to identify genes or peptides that alter the fluorescent image of a cell (e.g., nuclear translocation, protein aggregation, etc.). Alternatively or in combination, the systems and methods can be used to identify genetic sequences that alter the structure and / or function of one or more peptides, polypeptides and / or proteins expressed by a cell.

[0127] The alterations may be detected by the imaging systems and methods described herein For example, the alterations may be detected based on whether the images show protein localization, protein-protein interactions, translocation, co-localization, puncta, or other properties.

[0128] The systems and methods described herein can be used to identify one or more peptides, polypeptides and / or proteins that block nuclear translocation of transcription factors, dissociate protein aggregation, restore organelle function, etc.

[0129] The systems and methods described herein can be utilized to identify non-druggable targets, such as transcription factors, phosphatases RAS, or protein sites, such as PPI sites. For example, sites involved in PPIs may often include relatively large, relatively flat surfaces that are not generally inhibited by low molecular weight drug-like molecules. However, such sites may be inhibited by large molecules, such as peptides, polypeptides, and / or proteins. As another example, transcription factors may lack mobility, ordered structure, or may be structurally heterogeneous. Transcription factors may only assume their native three-dimensional structure within a cell. Thus, screening for transcription factors may require screening within the transcription factor's native cellular environment. The methods described herein can be used to screen non-druggable targets (e.g., transcription factors, phosphatases RAS, or protein sites) in their native cellular environment.

[0130] While preferred embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the present invention be limited by the specific examples provided within the specification. The present invention has been described with reference to the foregoing specification, but the description and illustration of the embodiments herein are not meant to be construed in a limiting sense. Numerous variations, changes and substitutions will occur to those skilled in the art without departing from the present invention. It is further understood that all aspects of the present invention are not limited to the specific depictions, arrangements or relative proportions described herein which depend upon a variety of conditions and variables. It should be understood that various alternatives to the embodiments of the present invention described herein may be used in the practice of the present invention. It is therefore anticipated that the present invention will cover any such alternatives, modifications, variations or equivalents. It is intended that the following claims define the scope of the present invention, and that methods and structures within the scope of these claims and their equivalents are covered thereby. [Explanation of symbols]

[0131] 101 Computer Systems, Systems 105 Central Processing Unit, CPU, Processor 110 Memory Location, Memory 115 Electronic storage devices, storage devices 120 Communication interface, interface 125 Peripherals 130 Computer Networks, Networks 135 Electronic Display 140 User Interface 600 ways 610 First Operation 620 Second Operation 630 Third Operation 640 Fourth Operation 650 Fifth Operation

Claims

1. The method of claim 1, comprising the steps of: (a) providing a first cell population, the first cell population comprising one or more nucleic acids comprising one or more targets; (b) transfecting or transducing the first cell population with a library of nucleic acids comprising a plurality of exogenous ribonucleic acid (RNA) molecules or a plurality of exogenous deoxyribonucleic acid (DNA) molecules, thereby generating a second cell population, wherein the second cell population comprises cells transfected or transduced with at least one exogenous RNA molecule derived from the plurality of exogenous RNA molecules or at least one exogenous DNA molecule derived from the plurality of exogenous DNA molecules; (c) identifying morphological changes in the cells in the second cell population using a machine learning algorithm without reconstructing images of the cells, wherein identifying the morphological changes in the cells is performed by processing one or more temporal waveform signals comprising spatial information of the cells; (d) isolating the cell from the second cell population based on identification of a morphological change in the cell, thereby generating an isolated cell; (e) sequencing at least a portion of the at least one exogenous RNA molecule or at least a portion of the at least one exogenous DNA molecule derived from the isolated cell, thereby generating sequence information about the isolated cell; and (f) using said sequence information to identify one or more targets that cause said morphological change in said isolated cells; Including, method.

2. 2. The method of claim 1, wherein when transfecting the first population of cells, the cells are transfected using gene editing techniques such as clustered regularly interspaced short palindromic repeats (CRISPR), zinc finger nucleases (ZFN), TAL effector nucleases (TALEN), or any combination thereof.

3. The method of claim 1, wherein at least one foreign RNA molecule derived from the plurality of foreign RNA molecules or at least one foreign DNA molecule derived from the plurality of foreign DNA molecules encodes a peptide, polypeptide or protein.

4. 2. The method of claim 1, wherein the morphological change comprises a change in protein-protein interactions within the cell, a change in protein localization within the cell, a change in the shape of the cell, a change in the shape of one or more components of the cell, a change in the shape of one or more organelles of the cell, or any combination thereof.

5. 2. The method of claim 1, wherein the first population of cells is transfected using a plasmid or a lentivirus comprising at least one exogenous RNA molecule derived from the plurality of exogenous RNA molecules or at least one exogenous DNA molecule derived from the plurality of exogenous DNA molecules.

6. 2. The method of claim 1, wherein the sequencing comprises massively parallel array sequencing.

7. 2. The method of claim 1, wherein said sequencing comprises sequencing by synthesis.

8. The method of claim 1, wherein the sequencing comprises next generation sequencing.

9. 2. The method of claim 1, wherein (c) is repeated for each cell of the second cell population.

10. 10. The method of claim 9, wherein (c) is repeated at a rate of at least 1500 cells per second (cells / sec).

11. The method of claim 1, wherein the cell comprises at least one randomly inserted exogenous RNA molecule or at least one randomly inserted exogenous DNA molecule.

12. 2. The method of claim 1, further comprising using at least said morphological change to determine that said at least one exogenous DNA molecule or said at least one RNA molecule inhibits or activates a biochemical pathway in said isolated cells.

13. 2. The method of claim 1, wherein the machine learning algorithm is generated using a support vector machine, a random forest, an artificial neural network, a convolutional neural network, deep learning, very deep learning, gradient boosting, adapive boosting, decision trees, linear regression and logistic regression, or any combination thereof.

14. 2. The method of claim 1, wherein the machine learning algorithm is trained using a training set comprising spatial information of cell populations positive for the morphological change and spatial information of cell populations negative for the morphological change.

15. 2. The method of claim 1, further comprising, prior to (c), labeling one or more peptides, polypeptides, proteins, or any combination thereof, in a plurality of cells contained in the second cell population.

16. 16. The method of claim 15, wherein the labeling step comprises labeling the one or more peptides, polypeptides or proteins with one or more fluorescent labels, Forster Resonance Energy Transfer (FRET) labels, dyes, fluorophores or quantum dots or combinations thereof.

17. (a) using a machine learning algorithm to identify a morphological change in a cell without reconstructing an image of the cell, wherein identifying the morphological change in the cell is performed by processing one or more temporal waveform signals comprising spatial information of the cell, the cell being transfected or transduced with a library of nucleic acids comprising at least one exogenous RNA molecule derived from a plurality of exogenous ribonucleic acid (RNA) molecules or at least one exogenous DNA molecule derived from a plurality of exogenous deoxyribonucleic acid (DNA) molecules, and the cell being included in a cell population; (b) isolating said cell from said cell population based on identification of a morphological change in said cell, thereby generating an isolated cell; (c) sequencing at least a portion of the at least one exogenous RNA molecule or at least a portion of the at least one exogenous DNA molecule derived from the isolated cell, thereby generating sequence information about the isolated cell; and (d) using said sequence information to identify one or more targets that cause said morphological change in said isolated cells. A system comprising one or more computer processors individually or collectively programmed to perform the steps of:

18. The method of claim 1, wherein in step (c), the morphological changes in the cells are identified while the cells are flowing in a flow cell or flow channel.

19. 2. The method of claim 1, wherein the library comprises one or more of a single guide RNA (sgRNA) library, a small hairpin RNA (shRNA) library, a small interfering RNA (siRNA) library, a microRNA (miRNA) library, an antisense RNA (asRNA) library or a clustered regularly interspaced short palindromic repeats (CRISPR) guide RNA (gRNA) library, a plasmid library, a genome-scale CRISPR library, or any combination thereof.

20. The method of claim 1, wherein the at least one exogenous RNA molecule or the at least one exogenous DNA molecule comprises an unknown sequence.

21. The method of claim 1 , wherein the machine learning algorithm comprises one or more supervised machine learning techniques, unsupervised machine learning techniques, or a combination thereof.

22. The method of claim 1 , wherein the machine learning algorithm comprises one or more semi-supervised machine learning techniques.

23. The method of claim 1 , wherein the one or more temporal waveform signals are obtained using one or more optical techniques.

24. 24. The method of claim 23, wherein the one or more optical techniques comprise optical imaging, optical microscopy, optical spectrometry, ghost cytometry, or any combination thereof.

25. The method of claim 1, wherein the one or more temporal waveform signals are converted into non-time-dependent spatial information of the cells.

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