Methods for engineering cis-regulatory DNA

Abstract

The present invention relates to methods for generating cell type specific expression cassettes and reporter vectors, and nucleic acid constructs that can be generated by such methods. Cell type specific expression cassettes and reporter vectors are characterized by synthetic cis-regulatory DNA, also referred to as synthetic locus regions (sLCRs). sLCRs allow cell type specific expression of reporter genes or effector genes. The present invention further relates to various uses of reporter vectors, including determining the characteristics of cells in gene and virus therapy, drug discovery or verification, preferably cell type, state or fate transformation.

Description

Technical Field

[0001] The present invention relates to a method for generating a cell type specific expression cassette and a reporter vector, and a nucleic acid construct that can be generated by such a method. The cell type specific expression cassette and the reporter vector are characterized by synthesizing cis-regulatory DNA, also referred to as a synthetic locus region (sLCR). sLCR allows cell type specific expression of reporter genes or effector genes. The present invention further relates to various uses of reporter vectors, including determining the characteristics of cells in gene and virus therapy, drug discovery or verification, preferably cell type, state or fate transition (fate transition). Background Art

[0002] Expression cassettes and reporter vectors have a wide range of applications in basic research, drug screening diagnostics or gene therapy.

[0003] Selective identification of cell type-specific markers is critical to understanding biological processes in which diverse cell types contribute to tissue homeostasis. Ideally, this approach will also be beneficial in disease settings involving alterations in tissue homeostasis including metabolic, immune, neurological or psychiatric disorders as well as inflammation and cancer. In a development setting, this has traditionally been done using lineage tracing. 1 achieved.

[0004] In the best-known example, lineage tracing of Fbx15 expression led to the discovery of defined factors capable of reprogramming fibroblasts into pluripotent cells. 49 , and lineage tracing of Lgr5 expression enables identification of bona fide microcolon and intestinal stem cells 2 , which was later shown to mark many other adult tissue stem cells 3 The parallel development of complex reporter strategies allows single-cell resolution in the analysis of multiple lineages.

[0005] Traditionally, a variety of genetic tracing methods have been used to generate reporter mice for cell type-specific genetic manipulation and cell labeling (e.g., LacZ, mGmT, Brainbow and Confetti systems, dual-labeled chimeric analysis-MADM, etc.). These strategies can reveal complex neuronal connectivity patterns. 4 , and address outstanding questions such as the cell of origin of tumors in living organisms 5 Recently, optogenetics and CRISRP / Cas9-based strategies have added further flexibility in obtaining more quantitative readouts.

[0006] The use of reporter strategies based on adult stem cell biology can simultaneously inform on the origin of a tissue and its abnormal homeostasis 6 7 8Genetic reporters reflecting well-characterized pathways can lead to a deeper understanding of complex signaling dichotomies, such as transforming growth factor counteracting bone morphogenetic protein (BMP) signaling during hair follicle homeostasis. 9 .

[0007] In cancer, this approach rigorously reveals that aberrant homeostasis may underlie treatment resistance 10 , or that regenerative potential and tumor susceptibility may be shared between some organs but distinct in others 11 Quantitative spatiotemporal pattern dynamics can be revealed by designing synthetic reporters based on transcription factor binding sites 47 As inferred from these and many other studies, the choice of genetic reporter is a critical factor in ultimately resolving large and complex biological questions. This is particularly true in developmental or disease settings that are governed by multiple factors and complex interactions. 12 In these settings, the ability to flexibly design synthetic reporters that intercept multiple pathways in a single gene cassette will undoubtedly prove to be an important asset, yet current approaches remain limited.

[0008] For example, currently employed approaches for genetic tracking vectors rely on the use of cell type, pathway specific, or synthetic promoters or enhancers coupled to reporter genes or functional effectors.

[0009] The use of cell type specific promoters is based on placing a reporter gene or functional effector behind a minimal promoter of a signature gene of the cell type of interest. Thus, it allows specific transcriptional activation of a given reporter or effector mediated by the promoter of a given gene. Cell type specific vectors offer the possibility of using a given gene as a proxy for a cell state or developmental stage.

[0010] One example is the use of the Nestin promoter in order to label neural progenitor cells. This approach is widely used and allows researchers to direct the activation of specific reporters or effectors in undifferentiated cells.

[0011] Significant limitations of these methods are the requirement for prior knowledge of the marker gene and the assumption that the regulatory elements of the gene are known and in close proximity to the transcription start site. Furthermore, these methods are limited by the lack of specificity of a single gene to describe a complex regulatory system. A cumbersome solution to this problem requires cell type-specific identification of all specific enhancers for any given cell type of interest, followed by selection of one such element and cloning it upstream of a minimal viral promoter. However, this approach is technically demanding and does rely on supervised selection. 48These two limitations really limit the application of this approach to very selected environments.

[0012] An alternative approach uses pathway-specific promoters to place reporters or effectors behind artificially assembled transcription factor binding sites specific for a given pathway. Thus, specific transcriptional activation can be controlled through the mediation of regulatory elements known to be essential for the pathway.

[0013] One example is the nuclear-active BMP response element (BRE) specific for SMAD1 / 5 / 8, which describes activation of the BMP pathway. Although the BMP response element (BRE) reliably describes canonical pathway activation, it ignores non-canonical activation and provides a reporter system that is not sensitive enough to feedback loops.

[0014] Limitations of using pathway-specific promoters include the need to rely on the assumption that a minimal set of regulatory elements used is sufficient to inform pathway activation. Furthermore, a priori knowledge of such regulatory elements and their extensive characterization and isolation from their natural environment is necessary and hampers their application to complex and poorly characterized cell types.

[0015] As a further approach, synthetic enhancers or promoters have been proposed by placing the reporter of interest behind multiple artificially assembled transcription factor binding sites and in front of a minimal promoter. However, these approaches also rely on prior knowledge of transcription factor binding sites that are known to be associated with cell types or developmental stages.

[0016] All methods are limited by their reliance on prior knowledge or the accurate discovery and validation of regulatory elements specific to the cell type or stage of interest. Furthermore, since in many cases not all regulatory elements are covered, multiple markers have to be used in order to ensure reliable cell type characterization, complicating the construction of reporters and the evaluation of any experimental results.

[0017] Cell characterization based on the expression of cell-specific surface molecules via flow cytometry has also been described in the art. This is a common approach, but is limited in the sense that the corresponding markers must be known in advance and not all cell types have characteristic surface proteins. Furthermore, tracking cell types in vivo is impossible or very challenging using such methods.

[0018] In an attempt to employ multiple transcription factor binding sites to regulate the expression of reporter genes, alternative gene expression reporter vectors have been developed.

[0019] WO2001 / 49868 A1 (Korea Institute of Life Science and Biotechnology) discloses a cancer-specific gene expression vector comprising a promoter having a binding site for an E2F transcription factor expressed in an oncogene (EF2bs) and additional binding sites for other transcription factors (e.g., SP1, AP1, NF1, or C / EFB). However, this approach still relies on prior knowledge of TF binding sites (e.g., EF2bs) previously identified as being associated with a particular type of cancer.

[0020] WO 2015 / 110449 A1 (University of Brussels / Ghent University) discloses a computational method for identifying cardiac and skeletal muscle specific regulatory elements enriched with transcription factor binding sites (TFBS), wherein different regulatory regions (CSk-SH1-6; Sk-SH1) of 300-500 bp in length are disclosed, each containing multiple (3-10) conserved TFBS. However, this technology focuses on the use of evolutionary conserved TFBS, thus relying on the genomic conservation of regulatory sequences in order to enhance expression in muscle.

[0021] WO 2008 / 107725 A1 discloses a computational method for identifying transcription factor regulatory elements (TFREs) active in a cell of interest, wherein the TFREs have a length of at least 6 to 100 bp, wherein 6 or more TFREs can be combined in a promoter element of an expression vector. However, the technique employs the fusion of the same preselected minimal promoter to identify additional TFREs under any given condition, i.e. supervised incorporation of cis-elements with known functions.

[0022] Guo et al. (Trends in Mol. Medicine, 14: 410-418) reviewed several viral vectors and transcriptional regulatory elements. Gargiulo et al. (Mechanisms of Development, 35: 193-203) disclosed the identification of cis-acting elements for cell-specific expression of the vitelline membrane protein gene 32 (VMPE) in the follicular epithelium of Drosophila (Drospholia), wherein the expression vector includes different fragments of the regulatory genomic region.

[0023] Despite these advances in the field, such alternative approaches rely on strategies that are disadvantageous for generating reporter vectors, such as relying on a priori knowledge of relevant promoters, focusing on the genetic / evolutionary conservation of TFBSs, or using a single promoter modified by cis-elements with known functions.

[0024] Therefore, there is a need in the field of synthetic reporters for alternative or improved methods and constructs based on unbiased de novo approaches for decoding and reconstructing regulatory information for any given cell type or state. Summary of the invention

[0025] In view of the prior art, the technical problem underlying the present invention is to provide alternative and / or improved methods for generating genetic tracking cassettes or vectors based on synthetic cis-regulatory DNA, which allow cell type or developmental stage specific expression of reporter genes or functional effectors.

[0026] This problem is solved by the features of the independent claim. Preferred embodiments of the invention are provided by the dependent claims.

[0027] Therefore, the present invention relates to a method for generating a cell type specific expression cassette, said method comprising the following steps:

[0028] a) providing a gene expression profile of a target cell type,

[0029] b) providing genome sequence data of the target cell type,

[0030] c) selecting a set of signature genes from the gene expression profile, the signature genes (i) being differentially regulated compared to a reference cell type, or (ii) being selected based on gene expression levels,

[0031] d) identifying genes encoding transcription factors from the set of signature genes selected in c),

[0032] e) determining a set of genomic regions from the genomic sequence data, wherein each genomic region includes a sequence encoding a signature gene identified in c) and additional genomic sequences adjacent to and flanking the sequence encoding the signature gene,

[0033] f) identifying a plurality of genomic sub-regions of comparable and limited size, preferably of equal size, within said set of genomic regions determined in e), wherein said genomic sub-regions comprise one or more binding sites for one or more transcription factors identified in d),

[0034] g) selecting a minimum set of genomic subregions, preferably between 2 and 10, from the genomic subregions determined in f), wherein the set of genomic subregions is selected to include a predetermined percentage of transcription factor binding sites for all transcription factors identified in d), and

[0035] h) generating a cell type specific expression cassette comprising the set of genomic subregions selected in step g) operably coupled to a reporter gene or an effector gene, wherein the genomic subregions are configured to regulate expression of the reporter gene or the effector gene.

[0036] The methods allow for the generation of expression cassettes that, when introduced into a cell of interest, result in the expression of a reporter gene or effector gene in a manner that is highly specific to a particular entity or state, such as a cell type or state, wherein the reporter has been designed to describe the regulation of gene expression in the entity or state of interest without requiring a priori knowledge.

[0037] In contrast to the prior art, the methods and constructs of the present invention are based on an unbiased de novo approach for decoding and reconstructing regulatory information for any given cell type / state. The present invention represents a completely novel approach based essentially on clustering of cell type / state-specific TFBS on cell type / state-specific tag genes. The present invention is also characterized by the advantage of using quantitative and / or statistical enrichment for the relevant TFBS for any given cell type / state.

[0038] In some embodiments, the method essentially employs a systems biology approach by identifying a collection of endogenously occurring cis-regulatory elements from a given transcriptional signature of a cell type of interest and placing these cis-regulatory elements in front of a reporter gene or effector gene to generate an expression cassette. This method is independent of a priori information on the specific characteristics of the cell type of interest, thereby allowing standardized, unbiased, and straightforward generation of reporter constructs for any given cell type.

[0039] To this end, the method identifies genomic subregions that include transcription factor binding sites characteristic of cell types and assembles them into a collection of genomic subregions that include relevant portions of transcriptional regulatory sequence information within the target cell type. The collection of genomic subregions may also be referred to as "synthetic cis-regulatory DNA," "synthetic regulatory regions," or "synthetic locus control regions (sLCRs)."

[0040] When introduced into a cell, expression of the reporter gene or effector gene will occur because in the cell type, there is a transcription factor corresponding to the characteristic transcription factor binding site and turns on the expression of the reporter gene or effector gene. The expression level is therefore related to the specific cell type. Each cell type will essentially produce a different set of genes based on the set of signature genes, and each cell type will show different reporter expression levels based on the combination of transcription factors present and regulatory regions assembled in the sLCR.

[0041] Advantageously, the method is not limited to certain cell types, but can be applied to virtually any cell type, and even distinguishes cell states or fate transitions within a cell type. To this end, no prior knowledge of gene regulation in the cell type of interest is required.

[0042] Instead, the method relies solely on the provision of gene expression profiles and genomic sequence data for a given cell type, which can be obtained using standard biomolecular techniques or by accessing public databases.

[0043] Gene expression profiles reflect the level of gene expression in the target cell type. For this reason, for example, RNA-SEQ or other sequencing or microarray-based techniques can be used to quantify the level of RNA transcripts in the target cell type. However, proteomics can also be used to potentially derive gene expression profiles, such as by quantifying expressed proteins or peptides present in the target cell type, which can be squared to a gene expression profile.

[0044] From the gene expression profile, select a marker gene that is characteristic of the cell type, cell state, or entity of interest. The selection of marker genes can be adapted to the desired application.

[0045] For example, the label gene can be selected according to its gene expression level, by sorting the genes of the target cell type according to its gene expression level, and selecting genes above or below a certain threshold, or selecting a predetermined number of genes with the highest or lowest expression. For such selection of label genes, the absolute expression level of the gene of the target cell type is used as a reference. The expression cassette thus obtained can faithfully report the presence of the target cell type in various assays independently of the cells to be detected.

[0046] However, for some applications, it may be desirable to generate an expression cassette that distinguishes the target cell type from a reference cell or a reference cell state with specific high specificity. For such applications, the label gene of differential regulation is selected by identifying the gene that is upregulated or downregulated compared with the expression level in the reference cell type. In these embodiments, the gene expression spectrum of the target cell type and the reference cell type is provided. By selecting the gene of differential regulation, the expression cassette can be fine-tuned for the determination that the target cell type (or state or fate) is distinguished from a certain reference type (or state or fate).

[0047] From the selected tag gene, identify all genes of the coding transcription factor in the set of the tag gene.For this reason, the method can rely on publicly accessible annotated databases, such as ENCODE, mENCODE (mouse version of the ENCODE project), JASPAR, Ensemble, Entrez Gene, Genebank etc. Therefore, the set of transcription factors of the target cell type of characteristic expression is identified. Transcription factors can be identified by the technician through the annotation of the function in the common database. In addition, the target sequence of each transcription factor, i.e., the transcription factor binding site is usually known to the technician and / or uses the database such as those obtainable by suitable annotations of above-mentioned ones. Preferably, in some embodiments, the method relates to the use of transcription factors, and the binding sites of these transcription factors (in the form of DNA sequence or sequence motif) are known and / or preferably annotated in public databases.

[0048] In addition, a set of selected genes is used to determine a set of genomic regions from the genomic sequence data of the target cell type, wherein each genomic region includes a sequence encoding a tag gene and an additional genomic sequence adjacent to the sequence encoding the tag gene (preferably located on the flank immediately adjacent to the sequence encoding the tag gene). This genomic sequence, such as non-coding reference DNA (although cis-regulatory elements may be present in the coding region), is intended to encompass regulatory sequences, which may be located upstream, downstream or internal to the coding region, more typically adjacent to the transcription start site but not limited thereto. The size of the additional genomic sequence adjacent to the tag gene can vary because the method is advantageously not overly sensitive to the presence of additional portions of the additional genomic sequence.

[0049] Therefore, the additional genomic sequence should be large enough to cover the cis-regulatory elements (especially transcription factor binding sites, or enhancers or silencers) that regulate the expression of the tag gene. It is well known that such cis-regulatory elements can be very close to the coding region in structure, but - considering the 3D structural distribution of the genome in the nucleolus - with respect to the linear genomic sequence, the cis-regulatory elements can be located at a considerable distance. In a preferred embodiment, the regulatory genomic sequence is selected based on the three-dimensional state of the folding of DNA in the chromatin in the cell type by using the topologically related domain as a boundary. Preferably, in some embodiments, the method assumes that the cell type-specific non-coding CTCF binding site is a representative of the topologically related domain. CTCF binding sites (in the form of DNA sequences or sequence motifs) are generally known to technicians and / or are generally annotated in public databases.

[0050] In a preferred embodiment, after determining the set of genomic regions, the method searches for multiple genomic subregions of similar or comparable size (e.g., equal size), the genomic subregions including one or more, preferably several binding sites of the transcription factor encoded by the tag gene. Therefore, all genomic subregions identified in step f) of the method are included in the DNA binding sites of the transcription factors characteristically expressed in the target cell type. When the genomic subregions are assembled in sLCR and the sLCR is introduced into the target cell, the characteristically expressed tag transcription factors can be combined with the sLCR and regulate the expression of downstream reporter genes or effector genes. Generally, many genomic subregions identified are larger than the genomic subregions that make up the sLCR, and these genomic subregions are redundant with respect to the binding sites of characteristic transcription factors. The assembly of all identified genomic subregions of a limited number is sufficient to represent the entire regulatory complexity, and including all elements does not lead to an increase in specificity, but rather produces unnecessary large expression cassettes.

[0051] Therefore, the method further encompasses the step of selecting a minimal set of genomic subregions comprising a predetermined percentage of transcription factor binding sites for all transcription factors encoded by the selected signature genes.

[0052] For example, it can be assumed that 100 transcription factors can be identified within the set of signature genes, of which 100 transcription factor binding sites are known. However, in some embodiments, the number of transcription factors encoded by the selected signature genes is not necessarily equal to the number of transcription factor binding sites. In some selected embodiments, not all transcription factors may have known binding sites, or multiple transcription factor binding site matrices may be associated with some transcription factors.

[0053] In order to use as few genomic subregions as possible in the assembly of sLCR, for example, in order to keep the resulting regulatory sequence compact, the method preferably ranks the genomic subregions according to the number of transcription factor binding sites in addition to the diversity of the transcription factor binding sites. For example, the highest ranked genomic subregion may contain 35 transcription factor binding sites for the transcription factor of step d), of which 3 of these binding sites are repeated 5 times in the same genomic subregion, while the remaining binding sites appear only once. Then this highest ranked genomic subregion will include 23 different (unique) transcription factor binding sites, which represent the binding sites of 23 transcription factors of the tag gene. Therefore, this highest ranked genomic subregion will cover 23% of the characteristic transcription factors of step d).

[0054] If, for example, the predetermined percentage is set to 50%, a second (and potentially third) genomic subregion will be searched that preferably covers transcription factor binding sites that are not already included in the 23 binding sites of the first genomic subregion, and so on, so that the further genomic subregion or subregions will include at least 7 binding sites for transcription factors that are not already covered by the first highest ranked genomic subregion. Typically, a minimum set of 2-10 genomic subregions will include transcription factor binding sites that are at least 50% of the binding targets of the transcription factors encoded by the signature genes.

[0055] When the expression cassette is introduced into the target cell type, the minimum set of genomic subregions serves as a synthetic cis-regulatory DNA that a characteristic transcription factor can bind to. Therefore, the minimum set of genomic subregions selected in step g) of the method herein is therefore called a synthetic locus control region (sLCR). In some embodiments, the cassette therefore includes a regulatory region (sLCR) enriched with a regulatory sequence bound by a transcription factor (the transcription factor is, for example, expressed or highly expressed in the target cell type). Therefore, this regulatory region is specific / customized for this particular cell type and results in an expression level of a reporter gene specific to this cell type.

[0056] Considering that the total amount of characteristic transcription factors identified in d) reflects the regulatory mechanisms of the cell type of interest, the predetermined percentage of coverage of transcription factors can be regarded as the "percentage of regulatory information" covered by the minimum set of genomic subregions. In theory, the higher the amount of regulatory information covered, the more specific the expression of the reporter gene or effector gene is to the cell type. However, advantageously, a percentage of covering at least 30% of the regulatory information, preferably at least 40% or 50%, produces excellent results in terms of cell type-specific expression profiles, as measured by experimental validation.

[0057] In step h) of the method, a cell type-specific expression cassette is generated by assembling a minimal set of genomic subregions selected in step g) with a reporter or effector such that they are operably coupled, i.e., the genomic subregion comprising a transcription factor binding site as a cis-regulatory element is configured to regulate the expression of a reporter gene or an effector gene.

[0058] The high coverage of regulatory information by means of the assembled genomic subregions does not require prior information, which provides great application potential for the methods and constructs described herein. The expression cassette, as part of a reporter vector, can be used in vitro and in vivo as a reporter of intrinsic cell states for adaptive responses to external signal transduction or chemical input, cell fate transformation, reprogramming, forward and chemical genetic screening. In addition, when cell type-specific sLCR is combined with an endonuclease or suicide gene, the vector can be used in gene therapy or other genetic modification environments to make cell types, developmental stages or disease-specific populations missing. In these other genetic modification settings, sLCR can drive tumor-specific expression of structural components of oncolytic viruses and / or co-stimulatory molecules, aiming to improve the specificity and effectiveness of oncolytic therapy.

[0059] In a preferred embodiment of the present invention, the method is characterized in that the gene expression profile comprises the expression level of the gene in the target cell type, and

[0060] o providing a gene expression profile of a reference cell type according to step c)(i), the gene expression profile comprising the expression levels of genes in the reference cell type, and selecting differentially regulated signature genes by identifying genes that are up-regulated or down-regulated compared to their expression levels in the reference cell type, preferably selecting genes that are up-regulated 3 to 10 times or more in the cell type of interest, or

[0061] o Ordering the genes of the target cell type according to their gene expression levels according to step c)(ii), and selecting signature genes based on a predetermined level or a predetermined number of expression of the signature genes, such as the 100 to 1000 most highly expressed or 100 to 1000 least highly expressed genes in the target cell type.

[0062] The second alternative allows the comparison of the expression level of the gene of the cell type that can be obtained from the gene expression profile to select the label gene. Such an embodiment is particularly well suited to generate an expression cassette that represents the target cell type in different experimental settings. For this reason, selecting a gene that is 3 to 10 times or more higher than the average expression level has produced excellent results.

[0063] The first alternative allows the expression cassette to be customized to distinguish a cell type of interest compared to a reference cell type. For example, the cell type of interest may be a tumor cell, while the reference cell type is a normal cell of a tissue type commonly invaded by the tumor or a cell type from which the tumor cell originates.

[0064] However, reference cell type can also refer to the same type but in different cell states or before or after fate conversion.The gene expression profile of target cell type can refer to the gene expression profile of the cancer cell in mesenchymal state after epithelial to mesenchymal transition (EMT), and the gene expression profile of reference cell type can refer to the same type but in its epithelial state, i.e., the gene expression profile of the cancer cell before epithelial-to-mesenchymal transition (ETM).In this case, expression cassette will be able to distinguish cells that have undergone EMT from cells that have not undergone EMT.

[0065] Expression cassettes obtainable by selecting a signature gene based on its relative regulation compared to a reference cell type are characterized by a particularly high specificity, allowing discrimination between a reference cell type and a cell type of interest without the need for any additional markers.

[0066] In a preferred embodiment of the invention, the method is characterized in that the predetermined percentage of transcription factors covered is 30% or more, preferably 40% or more, most preferably 50% or more.

[0067] In a further preferred embodiment of the present invention, the method is characterized in that the genomic region determined in e) corresponds to a genomic sequence comprising a topologically associated domain of differentially regulated genes, wherein preferably the topologically associated domain corresponds to a genomic sequence between two CTFC binding sites.

[0068] By selecting the size of the genome area based on the topologically related structural domain, the optimal coverage of the potential cis-regulatory elements controlling the transcription of the label gene can be achieved. In the topologically related structural domain, the physical interaction ratio of the DNA sequence is more frequent than the sequence outside the topologically related structural domain, thereby forming a three-dimensional chromosome structure that can be used for transcription mechanism. By selecting the genome sequence between two CTFC binding sites, especially good results can be obtained. Such an embodiment produces optimal balance between the specificity of the gene that they are most likely to regulate and control by computing power resources, non-coding cis-regulatory DNA and the size of the flanking DNA covering the characteristic transcription factor binding site.

[0069] In a preferred embodiment of the method, the identification of genomic sub-regions of comparable, e.g., equal, size in step f) is performed by a sliding window algorithm of the genomic region determined in e), wherein preferably the window has a length of 500 bp to 5000 bp, preferably 700 bp to 2000 bp, more preferably 800 bp to 1200 bp, most preferably 1000 bp, and the sliding step has a length of 100 bp to 1000 bp, preferably 120 bp to 300 bp, more preferably 130 bp to 170 bp, most preferably 150 bp. In one embodiment, the sliding window is fixed to a size of 1000 bp and slides in steps of 150 bp, although the size of the genomic sub-regions generated by the scan may vary in size because it depends on the statistical scores and distribution of the TFBS.

[0070] Further preferably, the sliding window algorithm calculates the statistical enrichment of transcription factor binding site motifs from a relevant database (e.g., JASPAR) limited to the transcription factor binding sites corresponding to the transcription factors identified in step d). Thus, a significantly enriched list of characteristic transcription factor binding sites within a specific region is generated and used to identify comparable, preferably equal-sized genomic subregions that include at least one transcription factor binding site of at least one characteristic transcription factor encoded by a tag gene. Preferably and most likely, dozens (10 to 200, preferably between 20 and 180) TFBS are included in genomic subregions of comparable size.

[0071] According to the present invention, the plurality of genomic sub-regions of comparable and limited size, preferably equal size, within the set of genomic regions determined in e) (according to step f) generally have the same size, but may vary. Comparable in this context refers to a plurality of genomic sub-regions that preferably exhibit any window size of 500 bp to 5000 bp.

[0072] In a further preferred embodiment of the present invention, the length of the genomic subregion is 100bp to 1000bp, preferably 120bp to 300bp, more preferably 130bp to 170bp, and most preferably 150bp. If a sliding window algorithm is used, the length of the genomic subregion will preferably be related to the sliding step size. In other embodiments, the sliding window method can use any given step size, from 1bp to the highest step size specified for the above-mentioned window size. The preferred length has been determined by adopting methods for different cell types and assay systems, and reflects the optimal results in terms of expression specificity and total size of the expression cassette.

[0073] In a further preferred embodiment of the present invention, the method is characterized in that the selection of the set of genomic subregions in g) is performed by calculating for each genomic subregion identified in f):

[0074] - enrichment of binding sites for transcription factors according to d) in genomic sequence data, and

[0075] - a score for the diversity of transcription factors for which binding sites are present,

[0076] - wherein said genomic subregions are ranked according to the cumulative percentage of transcription factors for which binding sites are present, and

[0077] - wherein a minimum set of genomic sub-regions is selected to include a predetermined percentage of binding sites for all transcription factors identified in d).

[0078] For example, after identifying genes encoding transcription factors within the set of tag genes selected in c), the number and type of transcription factor binding sites have been generated. In addition, a list of genomic subregions generated in step f) is provided. With this information, the number of transcription factor binding sites (TFBS) for each genomic subregion (e.g., TFBS=35) can be calculated, which represents the enrichment of the binding sites of the transcription factors according to d) in the genomic sequence data. In addition, preferably, the diversity of transcription factor binding sites for each genomic subregion is calculated. For example, among 35 TFBS, 3 TFBS may exist 5 times, while the remaining TFBS only exists once, resulting in a genomic subregion with a number of 35 TFBSs and a diversity score of 23.

[0079] In a further step, the preferred method sorts the genomic subregions based on the highest number and best diversity score of TFBS. As an example of sorting the first, in the genomic locus chr10:6019558-6019708, the method is associated with the mesenchymal GBM state and there are 20 TFBS, some of which are repeated 2 to 6 times. Once the best sorted genomic subregion is determined, the second best in all remaining genomic subregions can be calculated, wherein the TFBS present in the first genomic subregion is excluded from the sorting. Iteration can be calculated how many different genomic subregions are needed to cover the entire set or predetermined percentage of transcription factor binding sites. When the percentage of all regulatory potentials (TFBSn x TFBSd) is required, two independent LCRs can be generated. Typically, 4-5 elements are sufficient to reach a maximum of 50% of the regulatory potential, and this has been verified to be sufficient to generate two independent sLCRs (see Examples) that respond to the same signal transduction.

[0080] In a further preferred embodiment of the present invention, the method is characterized in that the configuration of the genomic subregions in h) is such that the genomic subregions comprising the transcription start site are assembled adjacent to and upstream of the sequence encoding the reporter gene, and the genomic subregions not comprising the transcription start site are preferably assembled further upstream from the nearest transcription start site. In this case, it is particularly preferred that the method can annotate all genomic subregion elements (e.g., 150 bp elements) that contain the natural transcription start site, as well as those that do not, and will start sorting from the genomic subregions that contain the transcription start site. After selecting the best sorted genomic subregions that contain the transcription start site, sorting of additional genomic subregions can be performed independently of whether those genomic subregions contain the transcription start site.

[0081] According to the present invention, in some embodiments, the term "generating a cell type specific expression cassette" relates to the design and physical production of nucleic acid molecules. In some embodiments, the term "generating a cell type specific expression cassette" relates to designing a cell type specific expression cassette without physically producing the corresponding nucleic acid molecules, for example, the method can be a computer-implemented method or can include one or more computer-implemented steps in the method. In some embodiments, the method is or includes computer-implemented elements and products, as the output of the method, for the computer (insilico) design, product, simulation and / or computer representation of the construct. Therefore, in some embodiments, the "generation" of a box or construct can occur in a computer, i.e., in a computer software, for example, the output can be a nucleic acid sequence, nucleic acid sequence information, i.e., in a computer readable format.

[0082] In some embodiments, the methods of the present invention may also involve a computer program product, such as a software product.

[0083] The software can be configured to execute on a general computing device, and is configured to implement one or more of the steps a) to h) of the method described herein. Therefore, the computer program product of the present invention also encompasses and is directly related to the features described for the method provided herein. The further details of the preferred computer-based method are provided in the examples and related references as described herein. If the method is implemented in a computer program, such as by means of simulation or computer design of the box of the invention, the sequence can be subsequently synthesized by methods known to laboratory technicians in some embodiments and used in desired in vitro or in vivo applications.

[0084] The present invention also relates to a system for implementing the methods described herein, comprising one or more computing devices, data storage devices and / or software as system components, wherein the components are preferably connected to each other in close proximity or via a data connection, such as through the Internet, and are configured to interact with one or more of the components and / or implement the methods described herein. The system may include a computing device, a data storage device and / or appropriate software, such as separate software modules, which interact with each other to implement the methods described herein.

[0085] About computer implementation:

[0086] Step a), concerning providing the gene expression profile of the cell type of interest, may be computer-implemented, ie the information for the gene expression profile of the cell type of interest is preferably presented in a computer readable format, configured to be processed in further steps of the method.

[0087] Step b), concerning providing genomic sequence data of said cell type of interest, may be computer-implemented, ie the information for the genomic sequence data is preferably presented in a computer-readable format, configured for processing in further steps of said method.

[0088] Step c), about selecting a set of tag genes from a gene expression profile, wherein the tag genes (i) are differentially regulated compared to a reference cell type or (ii) are selected based on gene expression levels, is preferably computer-implemented. In a preferred embodiment, the genes and their expression profiles are represented by information in a format configured to be processed by a computing device, so that a specific group of genes can be selected based on this information. Depending on the selection features adopted / required or the skills of the user, this step can be automated or performed manually.

[0089] Step d), about identifying the gene encoding the transcription factor in the set of the tag gene selected in c), is preferably implemented by a computer-implemented method, thereby annotating the gene with function, so that the transcription factor function can be (optionally) automatically queried in any one or more identified tag genes. As mentioned by the examples herein, an appropriate database can be used.

[0090] Step e), with respect to determining a set of genomic regions from the genomic sequence data, wherein each genomic region comprises a sequence encoding a tag gene identified in c) and an additional genomic sequence adjacent to the sequence encoding the tag gene, is preferably implemented in a computer-implemented manner. The skilled person may implement the evaluation and selection of genomic sequences adjacent to the gene of interest based on genomic sequences, i.e., available from a database, by using automatic selection criteria, or by manually evaluating and selecting adjacent sequences.

[0091] Step f), with respect to identifying a plurality of genomic sub-regions of equal size within the set of genomic regions determined in e), wherein the genomic sub-regions include one or more binding sites for one or more transcription factors identified in d), is preferably performed using a computer-implemented method. Identification of binding sites for one or more transcription factors can be performed using methods established in the art, for example, searching and / or interrogating any given sequence for the presence of known binding sites defined by a particular sequence or sequence motif. Software configured to screen sequences for the presence of such known sequences is available to those skilled in the art.

[0092] Step g), with respect to selecting a minimum set of genomic subregions, preferably between 2 and 10, from the genomic subregions determined in f), wherein the set of genomic subregions is selected to include a predetermined percentage of transcription factor binding sites for all transcription factors identified in d), is preferably implemented using (optionally) an automated computer algorithm. Details of determining genomic subregions are provided above. There are a variety of options for software solutions suitable for selecting desired genomic subregions, or the selection can be manually implemented by a skilled user evaluating various subregions and compiling them to include a certain percentage of binding sites for the relevant transcription factors identified in step d).

[0093] A skilled artisan can use established programming, coding, and bioinformatics techniques to design and / or configure software to assess the presence of transcription factor binding sites in genomic subregions, compare these binding sites to transcription factors identified as signature genes, and select compilations of genomic subregions to cover a predetermined percentage of relevant transcription factors.

[0094] According to step h) of the method, a cell type specific expression cassette comprising a set of genomic subregions selected in step g) operably coupled to a reporter gene or an effector gene is generated. As described above, the "generating" may involve computer-implemented generation of nucleic acid sequence information in a computer-readable form and / or involve the synthesis of a physical nucleic acid molecule based on and / or comprising the sequence.

[0095] The present invention therefore further relates to a method for designing and / or making nucleic acid molecules corresponding to, including or based on the product DNA sequence information obtained from step a) to g). Said method preferably comprises implementing the method described herein, and subsequently synthesizing, cloning and / or isolating said nucleic acid molecules.

[0096] In such embodiments, the term "generating cassette" may include any relevant molecular biology or chemistry technique for cloning, mutation, recombination, PCR amplification and / or synthesis for use in generating nucleic acid molecules.

[0097] In a preferred embodiment, the cassette is synthesized using de novo nucleic acid synthesis based on the information obtained by the methods of the invention.

[0098] In a further preferred embodiment, the present invention relates to a cell type specific reporter vector comprising an expression cassette generated by the method described herein.

[0099] In a further aspect, the present invention relates to a cell type specific reporter vector comprising a synthetic regulatory region, wherein the synthetic regulatory region comprises 2 to 10 genomic sub-regions of 100 bp to 1000 bp, the genomic sub-regions being located adjacently, without a linker or with a linker sequence of less than 100 bp located between the sub-regions, wherein the sub-regions are derived from separate (non-adjacent) locations in the same genome of a target cell type, wherein the sub-regions cumulatively comprise at least 5, preferably at least 10, most preferably at least 20 binding sites for transcription factors, and

[0100] Reporter gene or effector gene,

[0101] Wherein, the genomic subregion is operably coupled to a reporter gene or an effector gene to regulate the expression of the reporter gene or the effector gene.

[0102] Particularly preferably, the genomic subregion is selected by the method according to steps a) to g) as described herein. It will be appreciated by those skilled in the art that the preferred embodiments disclosed for the method are equally applicable to the cell type specific reporter vectors described herein. The method of the present invention results in structural features of the vector that are unique in the art.

[0103] A preferred embodiment of the present invention relates to a construct design wherein the transcription factor binding sites from genomic subregions having a length of 100 to 1500 bp or 100 to 1250 bp, preferably 100 to 1000 bp, more preferably 120 bp to 300 bp, more preferably 130 bp to 170 bp, most preferably substantially 150 bp, are combined with the origin of genomic subregions from non-adjacent regions of the same genome. By such a combination, the constructs of the present invention are defined by a new de novo and unbiased construction by bringing together different / separate but highly related regulatory regions reflecting the relevant size of the regulatory information, particularly for a size of preferably 120 bp to 300 bp, more preferably 130 bp to 170 bp, most preferably 150 bp, which approximates the size of the histone particles on which the DNA is wrapped.

[0104] Preferred embodiments of the present invention relate to construct designs in which 5 or more transcription factor binding sites are used, i.e., by bringing together a sufficient number of TFBSs to cover a large regulatory portion of the relevant TFs in any given cell type / state, the higher number of TFBSs reflecting new de novo and unbiased construction.

[0105] The genomic subregions are characterized in that they are derived from separate positions in the same genome of the cell type, and cumulatively include at least 5, preferably at least 10, most preferably at least 20 or more binding sites for transcription factors. In some embodiments, 2-10 (i.e., 2, 3, 4, 5, 6, 7, 8, 9 or 10) genomic subregions are compiled to form sLCRs including at least 5, 10, 15, 20, 25, 30, 35, 40 or more transcription factor binding sites. Therefore, the genomic subregions cover the binding sites of a large number of transcription factors that are usually sufficient to cover the regulatory information of the target cell type. The preferred binding site of the transcription factor refers to the transcription factor characteristically expressed in the target cell type. To determine the transcription factor characteristically expressed in the target cell type, for example, steps a) to d) of the method described herein can be used.

[0106] The use of synthetic regulatory regions comprising 2 to 10 such genomic subregions with a length of 100 bp to 1000 bp has proven to be the optimal approach with respect to minimizing vector size while maintaining the extensive regulatory information represented by the transcription factor binding sites.

[0107] In this regard, positioning the genomic subregions adjacently without linkers or with linker sequences less than 100 bp also ensures compact design of the reporter vector and efficient transduction without including extensive regulatory information.

[0108] In a particularly preferred embodiment of the present invention, the vector is characterized in that each of the genomic subregions has a length of 120bp to 300bp, more preferably 130bp to 170bp, and most preferably 150bp. Such a length of the genomic subregion optimally covers the relevant transcription factor binding sites that are enriched in statistical significance on the background genomic region. The optimal size of 150bp may be due to the fact that histones are wrapped around its core particles with about 146 DNA genome base pairs (bp), preventing the approach of transcription factors. On the contrary, the nucleosome-free region (NFR), which is usually associated with active cis-regulatory DNA, allows transcription factors to approach after DNA is unfolded, so its minimum is 146pb. The average size of cis-regulatory DNA is usually inferred from the average size of NFR-also known as DNAseI hypersensitive site-it is about 1000bp, and usually contains clustering of relevant transcription factor binding sites on these length scales.

[0109] In a further preferred embodiment of the present invention, the vector is characterized in that the genomic subregion adjacent to the reporter gene or the effector gene includes a transcription start site. This ensures that the effector and the reporter are in frame and can be positively regulated by the upstream synthetic regulatory region.

[0110] The unique design of the invention described herein has the advantage that a variety of reporter genes or effector genes can be coupled to the synthetic regulatory region comprising genomic subregions, depending on the desired application.

[0111] In a preferred embodiment of the present invention, the vector is characterized in that the reporter gene or effector gene encodes a protein selected from the group consisting of: fluorescent protein, suicide gene, luciferase, β-galactosidase, chloramphenicol acetyltransferase, surface receptor, protein tag including but not limited to 6XHis tag, V5 tag, GFP tag, self-processing ribozyme cassette, mevalonate kinase and its derivatives, biotin ligase and its derivatives including but not limited to BirA, engineered peroxidase and its derivatives including but not limited to APEX2, endonuclease or site-specific recombinase and its derivatives including but not limited to restriction enzymes, Cre, Flp, Tn5, SpCas9, SaCas9, TALENs, genes correcting monogenic diseases, viral antigens such as E1A and E1B to induce cell type specific vaccination, or adjuvant cytokines / chemokines to enhance immune recognition such as GM-CSF or IL-12.

[0112] Fluorescent proteins may be particularly suitable for any kind of optical measurement of a signal indicative of expression of a reporter gene.To this end, the method may benefit from the use of state of the art microscopic and / or fluorescence activated cell sorting equipment and quantification techniques.

[0113] Furthermore, the present invention can be easily adopted using various types of vector systems and can be easily adapted to target cells.

[0114] In a preferred embodiment of the present invention, the vector is a viral vector, preferably a lentiviral or adeno-associated viral vector.

[0115] In a further preferred embodiment of the present invention, the vector comprises a nucleic acid sequence according to SEQ ID NO 1-6 or a nucleic acid sequence having at least 80%, preferably at least 90% identity to any one of SEQ ID NO 1-6.

[0116] As described herein, the present invention allows providing cell type specific vector constructs that mediate reliable expression of desired reporter genes or effector genes in the target cell type without prior knowledge. In this way, the vector constructs allow a variety of different applications from basic research to clinical research or therapeutic strategies.

[0117] For example, the vector construct can be used to identify cell types or determine the intrinsic cell state or developmental state of a cell. The vector also allows the study of how cells react to external signals or chemicals. In addition, the vector can be used for diagnostics, such as determining the state or type of cancer, such as whether an epithelial or mesenchymal glioblastoma is present, and thereby allowing more effective treatment guidance. In addition, the vector itself can also be used as a pharmaceutical agent, such as in a gene therapy approach.

[0118] In a preferred embodiment, the present invention relates to the use of vectors for transforming cells and / or determining cell properties, preferably cell type, state or fate transitions in gene and viral therapy, drug discovery or validation.

[0119] The presence of a vector or sLCR as described herein within a cell that has been transformed is covered by embodiments of the present invention.

[0120] In one embodiment, the present invention relates to a method for determining a cell property, preferably a cell type, state or fate transition, comprising the following steps

[0121] a. providing a cell type specific reporter vector as described herein,

[0122] b. Provide cells,

[0123] c. transducing cells with the vector,

[0124] d. Measuring a signal indicative of expression of a reporter gene or an effector gene, wherein the amount of said signal is indicative of a property of said cell, preferably a cell type, state or fate transition.

[0125] Any suitable measurement technique may be used. For example, the reporter gene or effector gene may be a fluorescent protein, in which case a microscopic device may be used to quantitatively assess the fluorescent signal and thereby the expression of the reporter gene or effector gene in the probed cells.

[0126] In one embodiment, the present invention relates to a method for determining an intrinsic cell state, comprising the following steps

[0127] a. providing a cell type specific reporter vector as described herein,

[0128] b. providing a cell in which an intrinsic cell state is present or absent or alternatively inducible,

[0129] c. transducing cells with the vector,

[0130] d. optionally inducing the cells,

[0131] e. Measuring a signal indicative of expression of the reporter gene, wherein the amount of the signal is indicative of the intrinsic cellular state of each of the cells.

[0132] In one embodiment, the present invention relates to a method for determining a cell fate transition, comprising the following steps:

[0133] a. providing a cell type specific reporter vector as described herein,

[0134] b. providing cells that undergo fate transitions in response to external signaling and / or chemical perturbations,

[0135] c. transducing cells with the vector,

[0136] d. exposing cells to external signaling and / or chemical perturbations,

[0137] e. Measuring a signal indicative of expression of the reporter gene, wherein the amount of the signal is indicative of a cell fate transition.

[0138] In one embodiment, the present invention relates to a method for determining a cell fate reprogramming factor, comprising the following steps:

[0139] a. providing a cell type specific reporter vector as described herein,

[0140] b. providing cells that undergo fate transition in response to reprogramming factors including transcription factors, external signaling and / or chemical perturbations,

[0141] c. transducing cells with the vector,

[0142] d. exposing cells to transcription factors, external signaling and / or chemical perturbations,

[0143] e. Measuring a signal indicative of expression of the reporter gene, wherein the amount of the signal is indicative of the factor introduced into the cell fate transition.

[0144] In one embodiment, the present invention relates to a method for determining the minimum requirements for in vitro cell proliferation of a desired phenotype, comprising the following steps:

[0145] a. providing a cell type specific reporter vector as described herein,

[0146] b. Providing cells with intrinsic labels in vivo,

[0147] c. transducing cells with said vector reflecting said tag,

[0148] d. expose cells to a range of biological and chemical substances,

[0149] e. Measuring a signal indicative of a desired phenotype, wherein the amount of said signal is indicative of said phenotype.

[0150] In one embodiment, the present invention relates to a method for targeted correction of diseased cells, comprising the steps of:

[0151] a. providing a cell type specific reporter vector as described herein,

[0152] b. Providing cells with an inherent disease state that can be corrected by expression or elimination of a given gene in a given cell

[0153] c. Transducing cells with the vector driving the expression of a gene that corrects the disease, or a suicide gene, or an endonuclease

[0154] d. exposing cells to genes that correct the disease, to drugs that activate suicide genes or endonucleases

[0155] e. Measuring a signal indicative of expression of the reporter gene and a signal indicative of correction of the disease.

[0156] In one embodiment, the present invention relates to a method for oncolytic virus therapy, comprising the following steps:

[0157] a. providing a tumor cell type specific reporter as described herein,

[0158] b. Providing vectors encoding oncolytic viral genomes, including adenovirus, Maraba virus, VSV, HSV-1, Measles virus, Reovirus, retrovirus and vaccinia virus, which can be modified to transgenically express tumor-associated antigens (TAA) and / or molecular adjuvants under the expression of tumor sLCR,

[0159] c. generating viral particles using the vector,

[0160] d. transducing a target organism with the viral particles to infect tumor cells,

[0161] e. Measure viral genetic material within tumor tissue and not in surrounding tissue.

[0162] The methods described herein, such as those for determining a cell identity, preferably a cell type, state or fate transition, can be employed in a variety of biological, biotechnological or pharmaceutical (screening) settings.

[0163] Further embodiments of the invention relate to using DNA methylation and / or ATAC-seq profiles as input for signature gene discovery.

[0164] ATAC-seq (Assay for Transposase Accessible Chromatin Using Sequencing) is a technology for assessing genome-wide chromatin accessibility by probing open chromatin with a hyperactive mutant Tn5 transposase that inserts sequencing adapters into open regions of the genome. The mutant Tn5 transposase will cut any DNA that is long enough in a process called tagmentation, resulting in simultaneous fragmentation and labeling of the DNA by the Tn5 transposase preloaded with sequencing adapters. The labeled DNA fragments are then purified, amplified by PCR, and sent for sequencing. Sequencing reads can then be used to infer regions of increased accessibility, as well as to map regions of transcription factor binding sites and nucleosome positions.

[0165] Chromatin accessibility of several types of cis-regulatory elements is a predictive marker for in vivo DNA bound by transcription factors. The library (repertoire) of all accessible sites in chromatin is the strongest predictor of cell identity (identity). In fact, in cancer, chromatin accessibility is the strongest predictor of cancer type similarity, and can be used to identify the subtype identity in the common two-dimensional space of individual cancer types. In order to study whether the heterogeneity obtained by sLCR is accompanied by changes in genome-wide chromatin accessibility, ATAC-seq can be performed according to the expression level cell sorting of the reporter construct described herein. Therefore, the differential analysis of chromatin accessibility can find many genes that are undergoing reconstruction. These results described in the following embodiments highlight the effectiveness of sLCR in the following: revealing, for example, intratumor heterogeneity, and being able to perform in-depth cell and molecular characterization of tumor models together with major cancer data.

[0166] Further embodiments of the invention relate to target discovery and validation of drug targets in the areas of stress response (e.g. killing of cells with high ER stress or inflammatory signaling) and senolitic (e.g. killing of senescent cells).

[0167] Using the methods of the present invention, specific regulatory profiles can be identified for any given cell state, and reporter constructs can be efficiently generated. In some embodiments, sLCRs can be generated for cell types / states with high ER stress or inflammatory signaling or undergoing aging. Therefore, such reporters can be used to measure whether any given drug candidate, i.e., applied during screening, causes a change in cell state.

[0168] Further embodiments of the present invention relate to target discovery and verification of drug targets in the field of cell identification / fate change. As described in detail herein, specific regulation profiles can be identified for any given cell identification or for the state before and after the identification or fate change, and reporter constructs are effectively generated. In some embodiments, sLCR can be generated for cell types before and after the identification change. Therefore, such reporters can be used to measure whether there is any given drug candidate, i.e., applied during screening, causing cell state changes.

[0169] Further embodiments of the invention relate to target discovery and validation of synthetic peptides using the methods and constructs described herein.

[0170] Further embodiments of the invention relate to target discovery and validation for therapeutic exosomes and antisense oligonucleotides using the methods and constructs described herein.

[0171] Further embodiments of the invention relate to the discovery of the therapeutic potential of drug candidates in immunotherapy, including but not limited to the role of innate immune cells in therapeutic response and resistance, and the use of sLCR to engineer therapeutic adaptive immune cells (T cells, NK) to resist exhaustion and primary target specificity.

[0172] In some embodiments, sLCR can be generated as a readout of immune cell activity and / or target specificity, and candidate molecules can be tested and changes in the sLCR readout measured to assess whether immune cells (T cells, NK) can resist exhaustion when boosted / treated with the candidate compound.

[0173] In a further embodiment, the present invention relates to a computer-implemented method for determining the sequence of a synthetic locus control region (sLCR), comprising steps a) to g) of the method as described herein. Therefore, the present invention also relates to a computer software product capable of and suitable for implementing steps a) to g) of the method as described herein, and a computer program comprising instructions for the method described herein, wherein when a computer executes the program, these instructions cause the computer to implement steps a) to g) of the method described herein. DETAILED DESCRIPTION

[0174] The present invention relates to methods for generating cell type specific expression cassettes, cell type specific vectors using such expression cassettes, and applications of such vectors. Before describing the present invention with respect to embodiments, it should be understood that the terminology used herein is only for the purpose of describing specific embodiments and is not intended to limit the scope of the present invention.

[0175] All cited patent and non-patent literature documents are incorporated herein by reference in their entirety. Unless otherwise described herein, all terms shall have their ordinary technical meanings.

[0176] As used herein, the term "expression cassette" refers to a nucleic acid construct comprising nucleic acid elements sufficient to express a gene product. As described herein, an expression cassette also encompasses an electronic representation of an expression cassette. Typically, an expression cassette includes a nucleic acid (sequence) encoded as a gene product, and a reporter gene or functional effector is operably linked to a selected genomic subregion comprising a transcription binding site that serves as a regulatory element for the expression of the gene product.

[0177] As used herein, the term "synthetic cis-regulatory DNA", "synthetic regulatory region" or "synthetic locus control region (sLCR)" refers to an arrangement of multiple genomic subregions comprising validated and / or potential (hypothetical / predicted) cis-regulatory sequences arranged adjacently (with or without spacers) in a non-naturally occurring order (i.e., not present in such an order or arrangement in a naturally occurring genome). Examples of cis-regulatory sequences are transcription factor binding sites (TFBS), promoters, enhancers, silencers, or other regulatory sequences capable of cis-acting on the expression of a coding region. When these regulatory regions are arranged into synthetic regulatory regions, these regulatory regions are typically characteristic of a cell type. The methods described herein preferably assemble these regulatory regions into a collection of genomic subregions that include relevant portions of the transcriptional regulatory sequence information within the cell type of interest.

[0178] As used herein, the term "reporter vector" refers to a nucleic acid construct comprising an expression cassette and a further nucleic acid element that allows the expression cassette to be introduced into a cell in vitro or in vivo. The terms "reporter vector", "vector" and "effector vector" can be used interchangeably. "Vector" can have one or more restriction endonuclease recognition sites (I, II or IIs type), at which the sequence can be cut in a determinable manner without losing the basic biological function of the vector, and in which a nucleic acid fragment can be spliced ​​or inserted to achieve its replication and cloning. The vector may also include one or more recombination sites that allow the exchange of nucleic acid sequences between two nucleic acid molecules. The vector may further provide primer sites, such as for PCR, transcription and / or translation initiation and / or regulatory sites, recombination signals, replicons, selective markers, etc. The vector may further include one or more selective markers suitable for identifying cells transformed with the vector. Vectors known in the art and commercially available ones (and variants or derivatives thereof) can be used together with expression cassettes described herein. Such vectors are available from, for example, Vector Laboratories Inc., Invitrogen, Promega, Novagen, NEB, Clontech, Boehringer Mannheim, Pharmacia, EpiCenter, OriGenes Technologies Inc., Stratagene, PerkinElmer, Pharmingen, and Research Genetics, or may be freely distributed among scientists through Addgene.

[0179] As used herein, the term "viral vector" refers to a nucleic acid vector construct comprising at least one element of viral origin and having the ability to be packaged into a viral vector particle, encoding at least one exogenous nucleic acid. The vector and / or particle can be used for the purpose of transferring any nucleic acid into a cell in vitro or in vivo. Various forms of viral vectors are known in the art. The term virion is used to refer to a single infectious viral particle. "Viral vector", "viral vector particle" and "virion" also refer to a complete viral particle with its DNA or RNA core and protein coat (because it is present outside the cell).

[0180] The term "transfection" preferably refers to the delivery of DNA into eukaryotic (e.g., mammalian) cells. The term "conversion" preferably refers to the delivery of DNA into prokaryotic (e.g., E. coli) cells. The term "transduction" preferably refers to the infection of cells with viral particles. Nucleic acid molecules can be stably integrated into genomes commonly known in the art. However, the terms "transduction", "transfection" and "conversion" are used interchangeably herein and refer to the process of introducing a vector comprising an expression cassette into a cell.

[0181] As used herein, the term "cell type specificity" relates to the specificity of the expression of a reporter gene or effector gene when an expression cassette as described herein is introduced into a cell of interest compared to other (e.g., reference cells). The term cell type specificity encompasses expression (levels) specific to the cell type of the cell of interest and its cell state or fate. The term cell type specific expression cassette or vector therefore encompasses cell state specificity as well as cell fate specific expression cassettes or vectors.

[0182] As used herein, the term "reporter", "effector" or "reporter or effector gene" refers to a gene product encoded by a nucleic acid included in an expression construct as provided herein, which can be detected by an assay or method known in the art, so the expression of the "reporter" construct and / or the state or fate of the cell in which the "effect" is expressed. Reporters and effectors and nucleic acid sequences encoding reporters are well known in the art. Reporters or effectors include, for example, fluorescent proteins, such as green fluorescent protein (GFP), blue fluorescent protein (BFP), yellow fluorescent protein (YFP), red fluorescent protein (RFP), enhanced fluorescent protein derivatives (e.g., eGFP, eYFP, mVenus, eRFP, mCherry, etc.), enzymes (e.g., enzymes that catalyze the reaction of a detectable product, such as luciferase, β-glucuronidase, chloramphenicol acetyltransferase, aminoglycoside phosphotransferase, aminocyclol phosphotransferase or puromycin N-acetyl-transferase) and surface antigens. Suitable reporters or effectors will be apparent to those skilled in the art. Preferred proteins are selected from the group consisting of fluorescent proteins, suicide genes including but not limited to thymidine kinase, luciferase, β-galactosidase, chloramphenicol acetyltransferase, surface receptors, protein tags including but not limited to 6XHis tags, V5 tags, GFP tags, self-processing ribozyme cassettes, mevalonate kinase and its derivatives, biotin ligases and their derivatives including but not limited to BirA, engineered peroxidases and their derivatives including but not limited to APEX2, endonucleases or site-specific recombinases and their derivatives including but not limited to restriction enzymes, Cre, Flp, Tn5, SpCas9, SaCas9, TALENs, genes correcting monogenic diseases, tumor-associated antigens or genes encoding immunomodulators to promote immunotherapy including but not limited to MAGEA3m GM-CSF, IFNγ, IFNβ, CXCL-9-10-11.

[0183] The term "gene" basically means a coding nucleic acid sequence that is transcribed (DNA) and translated (mRNA) into a polypeptide in vitro or in vivo when operably linked to a suitable regulatory sequence. The gene may or may not include regions before and after the coding region, such as 5' untranslated (5'UTR) or "leader" sequence and 3'UTR or "trailer" sequence, and insertion sequences (introns) between each coding segment (exon).

[0184] As used herein, "gene expression" refers to the absolute or relative expression level and / or expression pattern of a gene. The expression of a gene can be measured at the level of DNA, cDNA, RNA, mRNA, protein, or a combination thereof. Gene expression can also be inferred from protein expression.

[0185] "Gene expression profile" refers to the expression levels of multiple different genes measured for a cell type of interest. Gene expression profiles can be measured in samples, such as samples including various cell types, different tissues, different organs or body fluids (e.g., blood, urine, spinal fluid, sweat, saliva or serum), by various methods including but not limited to RNA-SEQ by massively parallel tag sequencing (MPSS), serial analysis of gene expression (SAGE) technology, microarray technology, microfluidics technology, in situ hybridization methods, quantitative and semi-quantitative RT-PCR technology or mass spectrometry technology.

[0186] Any method in the art that can be used to detect gene expression is contemplated herein."Detecting expression" refers to determining the amount or presence of an RNA transcript or its expression product, for example at the protein level.

[0187] As used herein, the term "expression level" as applied to a gene refers to the normalized level of a gene product, such as a normalized value determined for the RNA expression level of a gene or for the polypeptide expression level of a gene.

[0188] The term "gene product" or "expression product" as used herein refers to the RNA transcription product (transcript) of a gene, including mRNA, and the polypeptide translation product of such RNA transcript. The gene product can be, for example, unspliced ​​RNA, mRNA, splice variant mRNA, microRNA, fragmented RNA, polypeptide, post-translationally modified polypeptide, splice variant polypeptide, etc. As used herein, the term "RNA transcript" refers to the RNA transcription product of a gene, including, for example, mRNA, unspliced ​​RNA, splice variant mRNA, microRNA, and fragmented RNA.

[0189] The method for detecting the expression of gene of the present invention, i.e. gene expression spectrum analysis, comprises the method based on the hybridization analysis of polynucleotide, the method based on the sequencing of polynucleotide, immunohistochemistry and the method based on proteomics.These methods detect the expression product (for example, mRNA) of gene usually.

[0190] Many expression detection methods use isolated RNA. The starting material is typically total RNA isolated from a biological sample such as a cell type of interest and a reference cell type, respectively.

[0191] General methods for RNA extraction are well known in the art and are disclosed in standard textbooks of molecular biology, including Ausubel et al., ed., Current Protocols in Molecular Biology, John Wiley & Sons, New York 1987-1999. Methods for extracting RNA from paraffin-embedded tissues are disclosed, for example, in Rupp and Locker (Lab Invest. 56: A67, 1987) and De Andres et al. (Biotechniques 18: 42-44, 1995). In particular, RNA isolation can be performed using purification kits, buffer sets, and proteases from commercial manufacturers such as Qiagen (Valencia, California) according to the manufacturer's instructions.

[0192] The RNA of separation can be used in hybridization or amplification assays, including but not limited to PCR analysis and probe arrays. A method for detecting RNA levels relates to contacting the RNA of separation with a nucleic acid molecule (probe) that can hybridize to the mRNA encoded by the detected gene. The nucleic acid probe can be, for example, a full-length cDNA or part thereof, such as at least 7,15,30,60,100,250 or 500 nucleotides in length and is enough to specifically hybridize to the oligonucleotides of the intrinsic gene of the present invention under stringent conditions, or any derived DNA or RNA. The hybridization of mRNA with the probe shows that the intrinsic gene in question is being expressed.

[0193] Alternatively, the level of gene expression in the cell type of interest involves a process of nucleic acid amplification, for example, by RT-PCR (U.S. Pat. No. 4,683,202), ligase chain reaction (Barany, Proc. Natl. Acad. Sci. USA 88:189-93, 1991), self-sustained sequence replication (Guatelli et al., Proc. Natl. Acad. Sci. USA 87:1874-78, 1990), transcription amplification system (Kwoh et al., Proc. Natl. Acad. Sci. USA 86:1173-77, 1989), Q-Beta replicase (Lizardi et al., Bio / Technology 6:1197, 1988), rolling circle replication (U.S. Pat. No. 5,854,033), or any other nucleic acid amplification method, followed by detection of the amplified molecules using techniques well known to those skilled in the art. These detection schemes are particularly suitable for the detection of nucleic acid molecules if such molecules are present in very low numbers.

[0194] In particular, gene expression can be assessed by quantitative RT-PCR. Many different PCR or QPCR schemes are known in the art. Generally, in PCR, a target polynucleotide sequence is amplified by reacting with at least one oligonucleotide primer or oligonucleotide primer pair. One or more primers are hybridized with the complementary region of the target nucleic acid, and a DNA polymerase extends one or more primers to amplify the target sequence. Under conditions sufficient to provide a nucleic acid amplification product based on polymerase, a nucleic acid fragment of a size is dominant in the reaction product (target polynucleotide sequence as amplified product). Repeated amplification cycles are performed to increase the concentration of a single target polynucleotide sequence. The reaction can be carried out in any thermal cycler commonly used for PCR. However, preferably a cycler with real-time fluorescence measurement function.

[0195] In some cases, quantitative PCR (QPCR) (also known as real-time PCR) is preferred because it not only provides quantitative measurements, but also reduces time and pollution. As used herein, "quantitative PCR" (or "real-time QPCR") refers to directly monitoring the progress of PCR amplification as it occurs, without the need for repeated sampling of reaction products. In quantitative PCR, the reaction products can be monitored via a signal transduction mechanism (e.g., fluorescence) as the reaction products are generated and tracked after the signal rises above the background level but before the reaction reaches a plateau. At the beginning of the PCR process, the number of cycles required to reach a detectable or "threshold" level of fluorescence varies directly with the concentration of the amplifiable target, so that the signal transduction intensity can be measured to provide a real-time measurement of the amount of target nucleic acid in the sample.

[0196] In addition, microarrays can be used for gene expression profiling. "Microarray" means an ordered arrangement of hybridizable array elements such as, for example, polynucleotide probes on a substrate. The term "probe" refers to any molecule that can selectively bind to a specific target biomolecule, for example, a nucleotide transcript or protein encoded by an intrinsic gene or corresponding to an intrinsic gene. The probe can be synthesized by a person skilled in the art, or derived from an appropriate biological preparation. The probe can be specifically designed to be labeled. Examples of molecules that can be used as probes include, but are not limited to, RNA, DNA, proteins, antibodies, and organic molecules.

[0197] DNA microarray provides a method for measuring the expression level of a large number of genes simultaneously. Each array consists of a repeatable pattern of capture probes attached to a solid support. Labeled RNA or DNA is hybridized with complementary probes on the array and then detected by laser scanning. The hybridization intensity of each probe on the array is determined and converted into a quantitative value representing the relative gene expression level. See, for example, U.S. Patent Nos. 6,040,138, 5,800,992 and 6,020,135, 6,033,860 and 6,344,316. ​​High-density oligonucleotide arrays are particularly useful for determining the gene expression profile of a large number of RNAs in a sample.

[0198] Serial analysis of gene expression (SAGE) is a method that allows simultaneous and quantitative analysis of a large number of gene transcripts without providing a separate hybridization probe for each transcript. First, a short sequence tag (about 10-14bp) containing enough information to uniquely identify the transcript is generated, provided that the tag is obtained from a unique position within each transcript. Then, many transcripts are linked together to form a long series of molecules that can be sequenced, revealing the identification of multiple tags at the same time. By determining the abundance of a single tag and identifying the gene corresponding to each tag, the expression pattern of any transcript population can be quantitatively evaluated. For more detailed information, see Velculescu et al., Science 270: 484-487 (1995); and Velculescu et al., Cell 88: 243-51 (1997).

[0199] Nucleic acid sequencing techniques are suitable methods for analyzing gene expression. The principle underlying these methods is that the number of times a cDNA sequence is detected in a sample is directly related to the relative expression of the mRNA corresponding to that sequence.

[0200] These methods are sometimes referred to by the term digital gene expression (DGE) to reflect the discrete numerical nature of the resulting data. Early methods that applied this principle were serial analysis of gene expression (SAGE) and massively parallel tag sequencing (MPSS). See, e.g., S. Brenner, et al., Nature Biotechnology 18(6): 630-634 (2000).

[0201] The advent of "next generation" sequencing technology has made DGE simpler, more high-throughput, and more affordable. As a result, more laboratories than ever before are able to use DGE to screen the expression of more genes in more cell types of interest. See, for example, J. Marioni, Genome Research 18(9):1509-1517 (2008); R. Morin, Genome Research 18(4):610 621 (2008); A. Mortazavi, Nature Methods 5(7):621-628 (2008); N. Cloonan, Nature Methods 5(7):613-619 (2008).

[0202] Next generation sequencing generally allows significantly higher throughput than the traditional Sanger method. See Schuster, Next-generation sequencing transforms today's biology, Nature Methods 5:16-18 (2008); Metzker, Sequencing technologies the next generation. Nat Rev Genet. 2010 January; 11 (1): 31-46. These platforms can allow sequencing of clonally expanded or unamplified single molecules of nucleic acid fragments. Certain platforms involve, for example, sequencing by ligation (including circular ligation and cleavage) of dye-modified probes, pyrophosphate sequencing, and single molecule sequencing. Nucleotide sequence species, amplified nucleic acid species, and detectable products generated thereby can be analyzed by such sequence analysis platforms. Next generation sequencing can be used in the methods of the present invention, for example, to determine the gene expression profile or genomic sequence data of a target cell type.

[0203] RNA sequencing (RNA-Seq) uses massive parallel sequencing to allow, for example, transcriptome analysis of the genome, with a resolution that is generally much higher than that obtained by methods based on Sanger sequencing and microarrays. In the RNA-Seq method, the complementary DNA (cDNA) generated from the target RNA is directly sequenced using next-generation sequencing technology. RNA-Seq has been successfully used to accurately quantify transcript levels, confirm or modify previously annotated 5' and 3' ends of genes, and map exon / intron boundaries (Eminaga et al., 201 3. Quantification of microRNA Expression with Next-Generation Sequencing. Current Protocols in Molecular Biology. 103: 4.1 7.1-4.17.14).

[0204] As used herein, "sequencing" therefore refers to any technique known in the art that allows identification of consecutive nucleotides of at least a portion of a nucleic acid. Exemplary sequencing techniques include Illumina™ sequencing, direct sequencing, random shotgun sequencing, Sanger dideoxy terminator sequencing, whole genome sequencing, massively parallel tag sequencing (MPSS), RNA-seq (also known as whole transcriptome sequencing), sequencing by hybridization, pyrophosphate sequencing, capillary electrophoresis, gel electrophoresis, duplex sequencing, cycle sequencing, single base extension sequencing, solid phase sequencing, high throughput sequencing, massively parallel tag sequencing, emulsion PCR, sequencing by reversible dye terminator, paired-end sequencing, short-term sequencing, exonuclease sequencing, sequencing by ligation, short read sequencing, single molecule sequencing, sequencing by synthesis, real-time sequencing, reverse terminator sequencing, nanopore sequencing, 454 sequencing, Solexa Genome Analyzer sequencing, SOLiD TM Sequencing, Illumina HiSeq4000, Illumina NextSeq500, Illumina MiSeq and MiniSeq, MS-PET sequencing, mass spectrometry and their combinations.

[0205] Gene expression profiles can also be derived from proteomic information. The term "proteome" is defined herein as the totality of proteins present in a cell type at a certain point in time. Proteomics includes, among other aspects, the study of overall changes in protein expression in a sample (also known as "expression proteomics"). Proteomics generally includes the following steps: (1) separation of individual proteins in a sample by 2-D gel electrophoresis (2-D PAGE); (2) identification of individual proteins recovered from the gel, such as by mass spectrometry or N-terminal sequencing, and (3) analysis of the data using bioinformatics.

[0206] As used herein, the term "genome" generally refers to a complete set of genetic information in the form of one or more nucleic acid sequences, including text or computer representations thereof. A genome may include DNA or RNA, depending on the organism from which it originates. Most organisms have DNA genomes, while some viruses have RNA genomes. As used herein, the term "genome" does not necessarily include a complete set of genetic information. The term may also refer to at least a large portion of a genome, such as at least 50% to 100% of the entire genome, or any integer or fractional percentage therebetween.

[0207] The term "genomic sequence data" refers to data on a genome, including textual or computer representations thereof, wherein genomic sequence data may also relate to preferably a majority of a genome such as at least 50% to 100% of the entire genome or any integer or fractional percentage thereof.

[0208] Providing genome sequence data can include actual sequencing of the genome of the target cell type, or relying on a publicly available database of genome sequence data, such as the annotated genome sequence database (GSDB) operated by the National Center for Genome Resources (NCGR). Providing genome sequence data for a large number of species can be publicly available through the UCSC genome browser created by the UCSC Genome Browser Group of UC Santa Cruz (CA, USA).

[0209] As used herein, the term "genomic region" generally refers to a region of a genome. Typically, a genomic region refers to a continuous stretch of nucleic acid sequence of the genome of a cell type of interest that includes at least one gene.

[0210] The term "genomic subregion" refers to a portion of a genomic region identified as comprising one or more binding sites for one or more transcription factors that have been identified as signature genes based on one or more gene expression profiles as described herein.

[0211] The term "nucleic acid" refers to any nucleic acid molecule, including but not limited to DNA, RNA, and hybrids or modified variants and polymers thereof ("polynucleotides") in single-stranded or double-stranded form. Unless specifically limited, the term encompasses nucleic acids containing known analogs of natural nucleotides, which have similar binding properties to the reference nucleic acid and are metabolized in a manner similar to naturally occurring nucleotides. Unless otherwise indicated, a specific nucleic acid molecule / polynucleotide also implicitly encompasses conservatively modified variants thereof (e.g., degenerate codon substitutions) and complementary sequences as well as the sequences explicitly indicated. Specifically, degenerate codon replacement can be achieved by generating sequences in which the third position of one or more selected (or all) codons is replaced with mixed base and / or deoxyinosine residues (Batzer et al., Nucleic Acid Res. 19:5081 (1991); Ohtsuka et al., J. Biol. Chem. 260:2605-2608 (1985); Rossolini et al., Mol. Cell. Probes 8:91-98 (1994)). Nucleotides are represented by their bases by the following standard abbreviations: adenine (A), cytosine (C), thymine (T), and guanine (G).

[0212] "Exogenous nucleic acid" or "exogenous genetic element" refers to any nucleic acid introduced into a cell that is not a component of the "original" or "native" genome of the cell. The exogenous nucleic acid may be integrated or non-integrated, or refer to a stably transfected nucleic acid.

[0213] "Functional variant" or "functional analogue" preferably refers to a nucleic acid or protein having a nucleotide sequence or amino acid sequence that is "identical", "substantially identical", "substantially identical", "homologous" or "similar" to a reference sequence, respectively, which, by way of non-limiting example, can be the sequence of an isolated nucleic acid or protein, or a consensus sequence obtained by comparing two or more related nucleic acids or proteins, or a group of isoforms of a given nucleic acid or protein. Non-limiting examples of isoform types include isoforms of different molecular weights resulting from, for example, alternative RNA splicing or proteolytic cleavage; and isoforms having different post-translational modifications, such as glycosylation; and the like.

[0214] As used herein, the term "variant" or "analog" refers to a nucleic acid or polypeptide that is different from a reference nucleic acid or polypeptide but retains its essential properties. Typically, variants are very similar overall and identical to the reference nucleic acid or polypeptide in many regions. Thus, "variant" forms of transcription factors are very similar overall and are able to bind to DNA and activate gene transcription.

[0215] As used herein, the term "sense strand" refers to the DNA strand of a gene that is translated or can be translated into a protein. When the gene is oriented in the "sense direction" relative to the promoter in the nucleic acid sequence, the "sense strand" is located at the 5' end downstream of the promoter, the first codon of the protein is located at the proximal end of the promoter, and the last codon is located at the distal end of the promoter. The opposite is called the "antisense" strand.

[0216] As used herein, the term "operably linked" means that the regulatory element in the nucleic acid construct is configured to enable functional coupling between the regulatory element and the gene, thereby resulting in expression of the gene, i.e., the regulatory element is preferably in frame with the nucleic acid encoding the protein or peptide.

[0217] As used herein, the terms "comprising" or "comprises" are used to refer to an expression cassette, a reporter vector, and one or more components of each thereof, which are open ended to include unspecified elements.

[0218] The term "consisting of" refers to the expression cassettes, reporter vectors, and one or more components of each thereof described herein, excluding any elements not recited in the description of that embodiment.

[0219] The term "tag gene" refers to a gene selected from a gene of a target gene cell type, characterized in that the expression profile of the target cell type. Differentially regulated tag genes can be selected, for example, by identifying genes that are upregulated or downregulated compared to expression levels in a reference cell type, or by sorting the gene expression levels of the target cell type and selecting tag genes based on a threshold level or a predetermined number of genes (e.g., the highest or lowest expression).

[0220] As used herein, the term "transcription factor" refers to a protein that binds to a specific DNA sequence and thereby controls the transfer (or transcription) of genetic information from DNA to mRNA. The function of a transcription factor is primarily to regulate gene expression. Transcription factors can play a role alone or in combination with other proteins in a complex by promoting (as an activator) or preventing (as a repressor) RNA polymerase from being recruited to a specific gene. Transcription factors include at least a DNA binding domain that is attached to a specific sequence ("binding site") of DNA that is typically adjacent to the genes they regulate.

[0221] The term "microscopic equipment" refers to equipment including tools for microscopic analysis of cells. Microscopic analysis can be performed by, but is not limited to, optical microscopes, binocular stereo microscopes, bright field microscopes, polarized light microscopes, phase contrast microscopes, differential interference contrast microscopes, automated microscopes, fluorescence microscopes, confocal microscopes, total internal reflection fluorescence microscopes, laser microscopes (laser scanning confocal microscopes), multiphoton excitation microscopes, structured illumination microscopes, transmission electron microscopes (TEM), scanning electron microscopes (SEM), atomic force microscopes (AFM), scanning near-field optical microscopes (SNOM), X-ray microscopes, ultrasonic microscopes. The microscopic equipment can additionally include cameras and / or detectors for recording cell pictures, for example, and a computer system for controlling the microscopic equipment.

[0222] The presence and / or intensity of the signal produced by the reporter gene can be determined with the aid of microscopic equipment, but can also be determined by other equipment that can detect the signal generated by the reporter gene, such as but not limited to a flow cytometer, a luminometer, a spectrometer, a photometer, or a colorimeter.

[0223] As used herein, the term "topologically associated domain" preferably refers to a self-interacting genomic region, meaning that the DNA sequences within the topologically associated domain physically interact with each other more frequently than sequences outside the topologically associated domain, thereby forming a three-dimensional chromosome structure. The size of a topologically associated domain can range from thousands to millions of DNA bases. Many proteins are known to be associated with the formation of topologically associated domains, including protein CTCF and protein complex cohesin. In a preferred embodiment, a topologically associated domain refers to a genomic sequence between two CTFC or cohesin binding sites.

[0224] As used herein, the term "generating a cell type-specific expression cassette" in some embodiments relates to designing a cell type-specific expression cassette without physically producing the corresponding nucleic acid molecule, for example, the method may be a computer-implemented method or may include one or more computer-implemented steps in the method.

[0225] As used herein, the term "generating a cell type specific expression cassette" in some embodiments relates to the design and physical generation of a nucleic acid molecule, preferably by de novo synthesis of the nucleic acid molecule.

[0226] Artificial gene synthesis (or de novo synthesis) is a preferred method for generating the box of the present invention, and relates to a method used in synthetic biology to create any given nucleic acid sequence. In some cases based on solid phase DNA synthesis, artificial synthesis is different from molecular cloning and polymerase chain reaction (PCR) because the user does not have to start from a pre-existing DNA sequence. Therefore, it is possible to manufacture fully synthetic double-stranded DNA molecules without major restrictions on nucleotide sequence or size. Gene synthesis methods can be based on a combination of organic chemistry and molecular biology techniques, and the entire gene can be synthesized "de novo" without the need for precursor template DNA. The method has been used to generate functional bacterial chromosomes containing approximately one million base pairs. Gene synthesis has become an important tool in many fields of recombinant DNA technology, including heterologous gene expression, vaccine development, gene therapy, vector construction, and various forms of molecular engineering. The synthesis of nucleic acid sequences is usually more economical than typical cloning and mutagenesis procedures. A variety of techniques have been well established and are known to technicians.

[0227] The term "gene therapy" preferably refers to transferring DNA into a subject to treat a disease. Those skilled in the art know strategies for gene therapy using gene therapy vectors. Such gene therapy vectors are optimized to deliver exogenous DNA to the host cells of a subject. In a preferred embodiment, the gene therapy vector can be a viral vector. Viruses have a naturally developed strategy to integrate DNA into the genome of a host cell, and can therefore be advantageously used. Preferred viral gene therapy vectors may include, but are not limited to, retroviral vectors such as Moloney murine leukemia virus (MMLV), adenoviral vectors, lentivirus, adenovirus-associated virus (AAV) vectors, poxvirus vectors, herpes simplex virus vectors, or human immunodeficiency virus vectors (HIV-1). However, non-viral vectors may also be preferably used for gene therapy, such as plasmid DNA expression vectors driven by eukaryotic promoters or plasmid DNA sequences containing homology to the host genome, so that the expression cassette is directly integrated into the preferred position in the target genome. DNA transfer may also be performed using liposomes or similar extracellular vesicles. In addition, preferred gene therapy vectors may also refer to methods for transferring DNA, such as electroporation or direct injection of nucleic acid into a subject. Those skilled in the art know how to select a preferred gene therapy vector according to the needs of the application and how to implement a nucleic acid construct such as the expression cassette described herein into a gene therapy vector (P. Seth et al., 2005, N. Koostra et al. 2009., W. Walther et al. 2000, Waehler et al. 2007).

[0228] The methods, systems, or other computer-implemented aspects of the invention may in some embodiments include and / or employ one or more conventional computing devices having: a processor, an input device such as a keyboard or mouse, a memory such as a hard drive and volatile or non-volatile memory, and computer code (software) for running the invention.

[0229] The system may include one or more conventional computing devices pre-installed with required computer code or software, or it may include custom-designed software and / or hardware. The system may include multiple computing devices for carrying out steps of the present invention. In some embodiments, multiple clients such as desktop computers, notebooks or tablet computers may be connected to a server so that, for example, multiple users may provide data or calculate at the different steps of the method. The computer system may also be connected by a local area network (LAN) or connected via the Internet to network with other computers or necessary databases such as a genome database. The system may also include a backup system for retaining a copy of the data obtained by the present invention. Data connections may be required between the various steps of the method, such as being connected by a local area network (LAN) or being connected via the Internet, for example, by any suitable means for data transmission.

[0230] The client or user computer may have its own processor, input tools such as a keyboard, mouse or touch screen, and memory, or it may be a terminal that does not have its own independent processing capabilities but relies on the computing resources of another computer such as a server connected or networked thereto. Depending on the specific implementation of the invention, the client system may contain the necessary computer code to assume control of the system if such a need arises. In one embodiment, the client system is a tablet or laptop computer.

[0231] The components of the computer system used to perform the method can be conventional, although the system can be custom configured for each specific implementation. The computer-implemented method steps or system can be run on any particular architecture, such as a personal / microcomputer, a minicomputer, or a mainframe system. Exemplary operating systems include Apple Mac OS X and iOS, Microsoft Windows and UNIX / Linux; SPARC, POWER, and Itanium-based systems; and z / Architecture. Computer code can be written in any programming language or model-based development environment to perform the present invention, such as, but not limited to, C / C++, C#, Objective-C, Java, Basic / VisualBasic, MATLAB, R, Simulink, StateFlow, Lab View, or an assembler. The computer code may include subroutines written in a proprietary computer language that is exclusive to the manufacturer of the circuit board, controller, or other computer hardware component used in conjunction with the present invention.

[0232] The information processed and / or generated by the present method, i.e., as a digital representation of a nucleic acid sequence, a gene expression profile, a gene list and / or a specific sequence element such as a TF binding site, can adopt any type of file format used in the industry. For example, the digital representation can be stored in a dedicated format, a DXF format, an XML format or other formats used by the present invention. Any suitable computer-readable medium can be utilized. Computer-available or computer-readable medium can be, for example, but not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices, equipment or propagation media. More specific examples (non-exhaustive list) of computer-readable media will include the following: electrical connections with one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disc read-only memories (CD-ROM), optical storage devices, transmission media such as those supporting the Internet or intranet, cloud storage or magnetic storage devices.

[0233] The nucleotide sequences of preferred embodiments of a minimal set of genomic subregions of a cell type specific reporter vector (ie, synthetic locus region) are listed in Table 1 .

[0234] Table 1: Nucleotide sequences of preferred synthetic locus regions of cell type specific reporters:

[0235]

[0236]

[0237]

[0238]

[0239] In one embodiment, the present invention thus encompasses a vector comprising a nucleic acid molecule selected from the group consisting of:

[0240] a) A nucleic acid molecule comprising or consisting of a nucleotide sequence according to SEQ ID NOs 1-6

[0241] b) a nucleic acid molecule complementary to the nucleotide sequence according to a);

[0242] c) nucleic acid molecules comprising nucleotide sequences having sufficient sequence identity to be functionally similar / equivalent to the nucleotide sequences according to a) or b), including preferably at least 70%, 80%, preferably 90%, more preferably 95% sequence identity to the nucleotide sequences according to a) or b).

[0243] d) A nucleic acid molecule of a nucleotide sequence according to a) to c), which is modified by deletion, addition, substitution, translocation, inversion and / or insertion and is functionally similar / equivalent to the nucleotide sequence according to a) to c).

[0244] Functionally similar sequences preferably refer to the ability of the synthetic regulatory region to promote transcription of an operably coupled reporter or effector gene in a cell type of interest.

[0245] In one embodiment, the present invention encompasses a vector for oncolytic virus therapy comprising a nucleic acid molecule selected from the group consisting of:

[0246] a) A nucleic acid molecule comprising or consisting of a nucleotide sequence according to SEQ ID NOs 1-6

[0247] b) a nucleic acid molecule complementary to the nucleotide sequence according to a);

[0248] c) nucleic acid molecules comprising nucleotide sequences having sufficient sequence identity to be functionally similar / equivalent to the nucleotide sequences according to a) or b), including preferably at least 70%, 80%, preferably 90%, more preferably 95% sequence identity to the nucleotide sequences according to a) or b).

[0249] d) A nucleic acid molecule of a nucleotide sequence according to a) to c), which is modified by deletion, addition, substitution, translocation, inversion and / or insertion and is functionally similar / equivalent to the nucleotide sequence according to a) to c).

[0250] e) A nucleic acid molecule produced according to the method.

[0251] Functionally similar sequences preferably refer to the ability of the synthetic regulatory region to promote transcription of viral essential genes and / or effector genes such as co-stimulatory molecules (eg, cytokines / chemokines) in the disease target cells of interest and not in non-disease cells.

[0252] Attached photos

[0253] The present invention is further described by the following drawings. These are not intended to limit the scope of the present invention, but rather to represent preferred embodiments of aspects of the present invention in order to provide a better illustration of the invention described herein.

[0254] BRIEF DESCRIPTION OF THE DRAWINGS

[0255] Figure 1 :Generation and validation of synthetic locus control regions (sLCRs)

[0256] Figure 2 : Intrinsic and adaptive responses in MES- and PN-GICs revealed by sLCR.

[0257] Figure 3 : GBM subtyping and reprogramming using sLCR.

[0258] Figure 4 : Tissue-independent epithelial-mesenchymal homeostasis revealed by sLCR.

[0259] Figure 5 : Heterogeneous mesenchymal transdifferentiation revealed by sLCR in vivo.

[0260] Figure 6 : Selection of subtype-specific genes for MES GBM-subtypes.

[0261] Figure 7 : Automated synthetic locus control region (sLCR) generation.

[0262] Figure 8 : Intrinsic and adaptive responses in MES- and PN-GICs revealed by sLCR.

[0263] Fig. 9 : Transcription factor that binds to MGT#1 cis-regulatory DNA.

[0264] Fig.10 : Homeostatic maintenance of MGT#1 expression in breast cancer cells.

[0265] Fig.11 : MGT#1 reflects the individual and combined contributions of TGFB and GSK126 to EMT.

[0266] Fig.12: MGT#1 is able to screen cell fate transitions driven by external signaling and / or chemical perturbations.

[0267] Fig.13 : Intrinsic and adaptive responses in MES- and PN-GICs revealed by expanded sLCR.

[0268] Fig.14 : Heterogeneous mesenchymal transdifferentiation revealed by in vivo expanded sLCR.

[0269] Fig.15 :sLCR helps to discover the therapeutic significance of non-cell autonomous crosstalk between tumor and immune cells.

[0270] Fig.16 : Extended characterization of the synthetic locus control region (sLCR).

[0271] Fig.17 : Further examples of adaptive responses revealed by sLCR.

[0272] Fig.18 : Induction of the MES-GBM state measured by sLCR in GICs is specific and reversible.

[0273] Fig.19 : MES-sLCR to dissect the role of ionizing radiation and NFkB signaling in MES-GBM.

[0274] Fig. 20 : Further evidence supporting the use of sLCRs in phenotypic CRISPR / Cas9 forward genetic screens.

[0275] Fig.21 : Further evidence supporting hMG cell induction of MGT#1 expression in hGICs and differential sensitivity to therapeutic agents and hMG cells.

[0276] Fig. 22 : Further evidence supporting the use of sLCRs in phenotypic CRISPRi screens.

[0277] Detailed description of the drawings

[0278] Figure 1: Generation and validation of synthetic locus control regions (sLCRs). a) Schematic diagram of sLCR generation starting from differentially regulated genes (DRGs). b) Pairwise correlation heatmaps of significant TFBS motifs detected at genomic GBM subtype-specific loci. The number of TFBSs and DRGs in the analysis is specified above each figure. c) Schematic diagram of sLCRs and experimental steps to generate reported glioma-initiating cells (GICs). d) Left; Confocal imaging of MGT#1-transfected 293T or (right)-lentiviral-transduced cryosectioned MES-hGIC neurospheres. Scale = 10 μM e) Representative mVenus FACS profiles of MES-hGICs and PN-hGICs modified with sLCRs and FACS sorted for H2B-CFP. MES-hGICs express higher levels of MGT#1 (arrows) f) Representative quantification of response to tumor necrosis factor alpha (TNFa) treatment in the indicated GICs. MES-hGICs express higher levels of MGT#1 (arrows). MES = mesenchymal; PN = proneural; CL = classical. MGT#1-2 = MES genetic tracking #1-2. tmd = PDRGFRa transmembrane domain. g) Dual IF and smRNA-FISH. Images of merged channels (left) and separate channels (right) are shown. The yellow overlapping signal and arrows indicate colocalization between MED1 and MGT#1 driven mVenus. h) mVenus FACS profiles of MES-hGICs and PN-hGICs transduced with the indicated sLCRs and FACS sorted for H2B-CFP. Gating and arrows show that MES-hGICs express higher levels of MGT#1 than PN-hGICs.

[0279] Figure 2: Intrinsic and adaptive responses in MES- and PN-GICs revealed by sLCR. a) TFNa is the major signaling contributing to the mesenchymal GBM phenotype. Left, TNFa was identified as the top regulator as an activator of two independently designed MES-GBM reporters (MGT#1-MGT#2) in MES-hGICs by adaptive response screening using the indicated cytokines for up to 48 hours. Data normalized relative to control. MES-hGICs express higher basal levels of MGT#1 compared to PN-hGICs. b) Synergy between IL-6 and microglia in MGT#1 induction. Live cell imaging of MGT#1 expression in MES-hGICs after indicated treatments. c) Immunoblotting of indicated conditions and antibodies. de) Differential MGT#1 activation informs differential adaptive responses to TNFa. Expression changes of genes regulated by TNFa in MES-hGICs or PN-hGICs measured by RNA-seq and stratified sample clustering. f) RT-qPCR validation of the indicated genes in response to tumor necrosis factor alpha (TNFa) treatment in the indicated GICs. n = 3 biologically independent samples, ANOVA test; ****P < 0.0001; g) Synergy between TNFa and treatment-induced mesenchymal commitment revealed by MGT#1 expression. FACS quantification of mesenchymal transdifferentiation after the indicated stimulation. h) Immunoblots for the indicated conditions and antibodies. MES = mesenchymal; PN = proneural; CL = classical. MGT#1-2 = MES genetic tracking #1-2. FBS = fetal bovine serum, CBD = cannabidiol. IRR = ionizing radiation.

[0280] Figure 3 : GBM subtyping and reprogramming using sLCRs. a) Schematic diagram of the use of GBM subtype-specific sLCRs to determine intrinsic GBM subtypes and enhance subtype identification using cell reprogramming or external signaling. b) Enhanced proneural identification in conventional glioma cell lines. T98 cells were transduced with proneural sLCRs or mesenchymal sLCRs driving mCherry as a reporter and PN subtype identification was performed. 50 Representative micrographs (left) and FACS plots (right) of T98 cells showing higher intrinsic and TF-induced expression of PNGT#2 (but not MGT#2) reporter in T98 cells, scale = 100 μM.

[0281] Figure 4: Tissue-independent epithelial-mesenchymal homeostasis revealed by sLCR. a) MGT#1 reveals intrinsic cell fate differences in breast cancer cells. Left, representative expression of MES-GBM reporter MGT#1 transduced into epithelial (top) and mesenchymal (bottom) breast cancer cells. FACS plots show that the reporter in MDA-231 has higher intrinsic expression than that in MCF7 cells. Note that reporter expression is independent of the mesenchymal inducer 10pM TGFβ2. Scale = 100μM. b) MGT#1 reveals adaptive responses to chemicals / morphogenesis in lung cancer cells. Left, representative MGT#1 expression in A549 cells seeded in 96 wells and proliferated for the indicated time. 300,000 cells / plate proliferated in RPMI medium. Supplemented with 10pM TGFβ1+2 and 5μM GSK126 at 0 and 48 hours. Fluorescence was measured, and right, representative micrographs taken by the IncuCyte imaging system (right). Error bars represent the standard deviation of independent wells (n=3). c) CRISPRi and MGT#1 reveal mechanical regulators of lung cancer EMT. Schematic diagram depicting the screening. Dox, doxycycline. d) Immunoblots of representative intermediate time points of CRISRPi screening. MGT#1-fluorescence micrographs were taken before lysis. e) FACS sorting gating strategy for purifying MGT#1 high and low populations. f) MA graph showing the relative enrichment of gRNAs in MGT#1 high MGT#1 CRISPRi screening. Note that two dropout gRNAs identified known and novel regulators of EMT. g) CRISPR-mediated knockout of ARID1A and CNKSR2 was performed using two independent gRNAs, followed by FACS verification of MGT#1 expression. h) Immunoblots of EMT markers in wild-type and ARID1A and CNKSR2 KO cells.

[0282] Figure 5 : Heterogeneous mesenchymal transdifferentiation revealed by sLCR in vivo. a) Representative coronal forebrain images of MES-hGICs; MGT#1-mVenus at humane endpoint in NSG mice (n=10). 微弱 Xenografts. Left, HE staining; right, progressive inset, showing magnification of tissue counterstained for GFP, tubulin, and DAPI. Note that the leading edge of the invasive glioma is homogenous with MGT#1-mVenus 高 b) Representative hybrid MGT#1-mVenus 高 / MGT#1-mVenus 阴性Lesions. cd) Representative H2B-CFP expression (arrows) in MGT#1-positive and -negative lesions, respectively. e) Representative flow cytometry plots showing CD133 and MGT#1-mVenus expression in MES-hGICs; MGT#1-mVenus in NSG mice or in vitro 微弱 Xenograft (left). Individual components are shown on the right. Note the transfer of proprole from in vitro to in vivo. f) Schematic representation of the data shown in ae.

[0283] Figure 6 : MES GBM-subtype selection of subtype-specific genes. a) Heat map showing the fold changes of genes selected from the significance analysis of microarrays (SAM) list of TCGA sorted microarrays for the specified pairwise comparisons. Below, the color code indicates the GBM subtype expression profile associated with the metadata. b) Heat map showing the expression levels of selected genes, illustrating their expression and fold changes in primary biopsies and glioma stem-like cells (GSCs) derived from them. Below, the color code indicates the GBM subtype expression profile associated with the metadata. All genes had an absolute CPM>4, and most genes showed fold changes within GSCs, indicating that their expression also acts in a cell-autonomous manner. Spearman rank correlation was used for samples, and Pearson correlation was used for genes.

[0284] Figure 7 : Automated generation of synthetic locus control regions (sLCRs). a) Top left (I), schematic diagram of the identification of cis-regulatory elements (CREs) associated with a specific gene tag; top right (II), annotation of CREs to genomic locations; bottom (III), iterative selection of 150bp CREs based on TFBS diversity and score [Σ-log10(p-value)+num TFBS)]. sLCR generation involves the assembly of n CREs from the closest native TSS to the farthest distal CRE, up to >50% TFBS diversity (MES-GBM in this example). b) Spearman correlation of individual sLCRs based on TFBS score / diversity. (A) Annotation of sLCRs generated by the automated algorithm.

[0285] Figure 8 : Intrinsic and adaptive responses in MES- and PN-GICs revealed by sLCR. Figure 2 a Representative live cell imaging of MGT#1 expression in GICs.

[0286] Fig. 9: Transcription factors that bind to MGT#1 cis-regulatory DNA. a) Top, schematic diagram of MGT#1 sLCR. Bottom, list of TFs for which ChIP-seq signals could be observed in the ENCODE public database in any of the cell lines used.

[0287] Fig.10 : Homeostatic maintenance of MGT#1 expression in breast cancer cells. a) Schematic representation of the two hyphotes tested: MGT#1 statically reflects the cell state or MGT#1 dynamically reflects cellular homeostasis, and reestablishment of in vitro homeostatic regulation after perturbation (i.e., FACS purification of MGT#1 weak populations). The green dashed circle highlights Figure 4 Results in a, where MCF7 and MDA-231 are shown to have intrinsically low or high MGT#1 expression, respectively, due to their cell identity. b) MCF7 and MDA231 were FACS sorted based on best comparable MGT#1 intensity and proliferated in vitro prior to FACS analysis shown in 4a.

[0288] Fig.11 : MGT#1 reflects the single and combined contributions of TGFB and GSK126 to EMT. a) FACS profile of MGT#1 expression in A549 cells exposed to the indicated treatments for 5 days. A minimum of 10,000 cells were required for each sample. b) FACS profile of cell morphology and MGT#1 expression in A549 cells exposed to the indicated treatments for 5 days. Note the TGFB-dependent changes in cell shape and the synergy between TGFB1+2 and GSK126.

[0289] Fig.12 :MGT#1 is able to screen for cell fate transitions driven by external signaling and / or chemical perturbations. Shown is a principal component analysis (PCA) of the data obtained from the screening. The two components, PC1 and PC2, illustrate the largest changes in the experiment. To generate data, at the end of the program of the cells treated with the initial epithelium A549-MGT#1 and GSK126, the A549-MGT#1 cells were proliferated and cell images were taken. Note that the mesenchymal transition is consistent with previously published data. The normalized fluorescence data from the proliferated A549-MGT#1 cells were hierarchically clustered, and the fluorescence of the bottom readings was scanned using a SPARM 20M TECAN plate reader. Pearson correlation clustering was used. The color code indicates the fluorescence intensity fold change (blue-white-red) and biological replicates (yellow / orange=vehicle, green=GSK126). Live cell imaging was performed, showing the response to LPS in GSK126-treated and control A549-MGT#1 cells.

[0290] Fig.13: Intrinsic and adaptive responses in MES- and PN-GICs revealed by sLCRs. a) Schematic diagram of phenotypic screening using sLCRs (top) and bubble plot visualization of results (bottom). For each GIC and sLCR, the bubble size shows the magnitude of the change for each treatment relative to the control (log2-fold change), and the bubble color indicates the sign of change (red or orange indicates enrichment, light blue indicates loss). b) FACS validation of phenotypic screening. Surface expression of CD133 and PNGT#2 are endogenous markers of cell identity. Note the higher expression of MES-hGIC MGT#1 compared to PN-hGIC. c) Representative FACS quantification of mesenchymal transdifferentiation under specified stimuli. d) Experimental design for functional deconvolution of MGT#1 activation. e) Volcano plot of drug-associated sgRNAs screened in d (red, positive regulators; blue, negative regulators; gray, not significant). Relative to all MGT#1 低 Partial and unsorted controls (n=6), all MGT#1 were calculated 高 Fold changes of sections (n=3, mean of initial, TMZ+IR, TNFa+FBS). Padj was calculated by DeSeq2 (see Methods). Selected sgRNA-compound pairs are highlighted. f) RT-qPCR of the indicated genes under sequential treatment with the indicated treatments and TNFα. Padj are designated for representative comparisons and represent the results of overall two-way ANOVA and Dunnett's multiple comparisons. MES=mesenchymal; PN=proneural; MGT#1-2=MES genetic tracking #1-2. FBS=fetal bovine serum, TNFa=tumor necrosis factor-α. IR=ionizing radiation. TMZ=temozolomide.

[0291] Fig.14 : Heterogeneous mesenchymal transition revealed by in vivo sLCR. a) Scatter plots of ATAC-seq profiles for the indicated conditions, indicated by yellow and blue boxes in 5e). Open chromatin at the TNF receptor superfamily (TNFRS) locus is highlighted. b) UCSC genome browser view of the FADD / TNFRS6 locus. In vitro and in vivo MGT#1 高Changes in accessibility between cells are indicated by arrows and color (red-high, gray-medium). c) Unsupervised t-SNE of ATAC-seq profile analysis for the PanCancer dataset and specified conditions. When available, each point represents a given sample or the merge of all technical replicates. The analysis includes the highest principal component of the 250,000 most variable peaks across all samples. Gray points are all TCGA cancer types, but GBM / LGG are colored together with glioma stem cells from (Park et al., 2017, Cell Stem Cell 21, 209–224 August 3, 2017) and GICs from this study. Circles indicate dimensions occupied by major GBM / LGG and GIC / GSC. d) Unsupervised t-SNE of ATAC-seq profile analysis limited to samples within the glioma size.

[0292] Fig.15 : sLCR helps uncover therapeutic implications of non-cell autonomous crosstalk between tumor and immune cells. a) Representative brightfield views and IF of MES-hGICs with the indicated reporters propagated in spheroids or organoids with immortalized human microglia (hMG) (top and bottom, respectively). Scale bar = 50 um. b) Schematic diagram of contact-free hGIC-hMG co-culture. Left, brightfield images of hGIC and MG in co-culture. c) MES-hGIC-MGT#1 stimulated alone or in co-culture with TNFa or hMG. 高 Representative FACS profiles and gating strategies of . Below is a Venn diagram of NFkB-related genes by Ingenuity Pathway Analysis of DRGs for the indicated conditions. DRGs are enriched compared to control GICs (FC>1, padj<0.05). d) Venn diagram of hMG-driven MES GBM signatures overlapping with those of patients. Note the higher overlap with Neftel et al. compared to others. e) Heatmap of DRGs for the indicated conditions. RNA-seq reads were normalized to transcripts per million, Log2 transformed and Z-scored. Statistical significance was assessed by using the R-package LIMMA (control, n=3, hMG, n=3; TNFα n=2; padj<0.05). f) MA plots of the indicated comparisons. Significant DRGs are highlighted and color-coded. g) Comparison with MES-hGICs-MGT#1 高 Ingenuity upstream regulator analysis of genes regulated by hMG co-culture compared to TNFa in sLCR. h) Left, Schematic diagram of the chemosensitivity profiling assay for sLCR high and low states. Right, FACS sorted MES-hGICs-MGT#1 in response to increasing concentrations of the indicated drugs.高 and-MGT#1 低 LogIC50 values ​​for fractional viability calculations.

[0293] Fig.16 : Extended characterization of the synthetic locus control region (sLCR). Single molecule RNA FISH quantification of MGT#1- and PGK-driven gene expression. Arrows / yellow indicate cytoplasmic colocalization.

[0294] Fig.17 : Further examples of adaptive responses revealed by sLCR. Representative MGT#1 activation under indicated stimuli.

[0295] Fig.18 : Induction of the MES-GBM state measured by sLCR in GICs is specific and reversible. ab) Bar graphs showing individual responses to the indicated factors / sLCRRs 48 hours after induction. cd) Line graphs showing longitudinal expression of the indicated factors / sLCRs.

[0296] Fig.19 : MES-sLCR to dissect the role of ionizing radiation and NFkB signaling in MES-GBM. a) Right, dose response between IR and MGT#1 activation. An example of the experimental setup is shown on the left. b) Representative FACS quantification of mesenchymal transdifferentiation under the indicated stimuli.

[0297] Fig. 20 : Further evidence supporting the use of sLCRs in phenotypic CRISPR / Cas9 forward genetic screens. a) From a genome-wide CRISPR screen, in the MGT#1 高 and MGT#1 低 FACS plots of the indicated conditions before sorting for gRNA amplification. b) Box plots showing data quality assessment by comparing the distribution of highly informative essential and all non-essential or non-targeting gRNAs in the unsorted screening condition (P value = Student's t-test). c) Distribution of sgRNA fold-change values ​​for the indicated gRNA sets between the Brunello library and the unsorted MES-hGICs + Brunello condition (see "Methods"). d) Representative MA plots of sgRNA abundance (X-axis) and fold-change (Y-axis). Initial MES-hGICs carrying the Brunello library were sorted in MGT#1 高 and MGT#1 低 FACS sorting was performed and gRNAs were normalized to the maximum dataset and Log2 transformed (see Methods). 高Compared to the indicated gRNA, the indicated gRNA is missing. e) Ingenuity Pathway Analysis (IPA) top 25 toxicity categories of all hits from the CRISPR / Cas9 KO screen (FC±1.5; padj<0.05). Only "Positive Regulators" exceeded the statistical limit. Categories related to retinoic acid receptor signaling are in bold. IPA upstream regulator analysis of all hits from the CRISPR / Cas9 KO screen (FC±1.5; padj<0.05). Positive and negative regulators of the MES-GBM phenotype are colored in aqua and red, respectively. Gray indicates significant categories with no directional enrichment. f) Volcano plot of the top regulated sgRNAs from the screen in e. Relative to all MGT#1 低 Partial and unsorted controls (n=6) calculated for all MGT#1 高 Fold changes of the groups (n=3, initial, average TMZ+IR, TNFa+FBS). Padj was calculated by DESeq2, and selected sgRNA-FDA-approved compound pairs are highlighted (see Methods).

[0298] Fig.21 : Further evidence supporting hMG cell-induced MGT#1 expression in hGICs and differential sensitivity to therapeutic agents and hMG cells. a) Figure 4 Schematic diagram of the extension of the co-culture experiment; for detailed medium composition, see "Methods". b) MES- or PN-hGICs-MGT#1 alone or co-cultured with human microglia (hMG) or human CD34+-derived myeloid-derived suppressor cells (MDSC) 高 FACS profiles of the indicated. c) Principal component analysis of the indicated RNA-seq profiles. Distances were calculated based on mean expression levels of selected human MG markers obtained from Gosselin et al 2017. d) FACS sorted MES-hGICs -MGT#1 High and -MGT#1 Low fraction viability in response to increasing concentrations of the indicated drugs. e) Scatter plots and gene set enrichment analysis (GSEA) of the indicated gene lists, showing that hMG cells induce MES-GBM and suppress DNA damage transcriptional signature genes.

[0299] Fig. 22: Further evidence supporting the use of sLCR in phenotypic CRISPRi screens. a) Cumulative curve distribution of all samples in the kinome screen (n=42), including technical replicates and biological conditions: plasmid library, A549-H1944 input, A549-H1944+GSK126 high, medium, low – control – A549-H1944+GSK126+dox high, medium, low and A549-H1944+dox high, medium, low – for screens of GSK126-driven EMT and homeostatic EMT, respectively. All gRNAs (n=6615) were normalized by total counts per million reads, log-transformed by percentile normalization (75th percentile), and transformed by conversion to z-scores. bc) Figure 3 Scatter plot of all gRNAs in the screen (n=6615) in cf and GSEA for non-essential sgRNAs (n=483) and essential genes (n=352) respectively. Loss of essential genes is significant by t-test and Kolmogorov-Smirnov test, FC<-1 and padj<0.001. de) Scatter plot of all gRNAs in the pooled A549+H1944+GSK126+dox screen (n=6615) and GSEA for non-essential sgRNAs (n=483) and essential genes (n=352) respectively. Loss of essential genes is significant by t-test and Kolmogorov-Smirnov test, FC<-0.5 and padj<0.001.

[0300] Example

[0301] The present invention is further described by the following examples. These are not intended to limit the scope of the present invention, but rather represent preferred embodiments provided to better illustrate the invention described herein. The examples show that the methods and reporter vectors described herein allow cell type-specific expression of reporter genes and effector genes in various target cell types.

[0302] Materials and methods used in the examples:

[0303] sLCR generation and TFBS discovery: FIMO (PMID: 21330290) with -- output-threshold (pthresh) 1e-4 -- no-q-value (qvalue) was used to identify high-affinity TF binding sites in defined genomic regions (DRG loci; Table X). A database of 1,818 models representing known transcription factor binding preferences (position weight matrices, PWMs) was generated from the literature (Portales-Casamar et al., 2010; Badis et al., 2009; Berger et al., 2008; Bucher, 1990; Jolma et al., 2010). PWMs were pre-selected based on subtype-specific TFs. Regions corresponding to DRGs were retrieved from the UCSC genome browser (hg19; Refseq table downloaded on October 5, 2012) and scanned with a window of 150 bp and a step size of 50 bp (hereafter referred to as cis units). The scanning region around each tag gene is defined by two distal CTCF sites that are >10kb from the TSS or TES. Subtype-specific PWMs are mapped to genomic regions using FIMO. The regions where the PWM is most significantly overexpressed (adj.p value <0.01; multiple backgrounds). For each window, as long as multiple matches of the same PWM are identified, the p-value of the best match is considered to be representative of the affinity of the TF on the region. Given a region, the overall score is calculated based on the sum of the best -log10 (p-value) of each PWM considered. The significantly overexpressed regions (multiple backgrounds) are determined by comparing the motif / background (empirical p value <0.01). Figure 1 The TFBS pairwise correlation heatmap in a uses the top 500 bins with the scores defined above. The genomic coordinates and TFBS correlation heatmaps include Figure 1 The representative graph in a is generated for the top 100 districts with the highest scores.

[0304] Automation of sLCR generation: To focus on cell-intrinsic gene signatures, in the pilot approach we filtered out genes that were lowly expressed in GBM stem-like cells (GSCs) from our previous experiments, whereas the current implementation of this approach involves focusing on a validated glioma-intrinsic signature 20. The highest scoring cis-units were manually selected based on PWM scores and diversity to design the first sLCR. Similarly, the selection of regions containing TSSs was performed manually. Automatic sLCR generation was written in python (URLGitHub / GitLab). The script takes as input a list of TFs, PWMs, and phenotypic gene tags. With these, cis-units can be generated from the defined cis-regulatory regions (default parameters: 150bp window / 50bp step size). A selection of the best cis-units for any given phenotype is generated using an algorithm based on defined selection rules. The algorithm first generates a ranking and selection of the best cis-units by applying the following formula: [sum of scores - log10(p-value)*diversity (number of different TFBSs)]. Iteratively, it removes TFBSs included in the selected cis-units. In order to increase the chance of successful transcriptional triggering, the algorithm also ranks the cis-units based on 5'CAGE data. The ranked list is the output of the algorithm. The automated procedure returns results that overlap with the manual selection ( Figure 7 ). Figure 1 The heatmaps in ab were generated using the heatmap.2 function from the gplots R package.

[0305] RNA-seq generation: RNA was extracted using Trizol (Invitrogen), precipitated using isopropanol, and purified using RNAClean XP beads. The RNA-seq libraries generated for this study were constructed using the TruSeq Chained Total RNA Library Preparation Kit. A bead-based approach was used for rRNA depletion (Ribo-Zero Gold; Illumina), and PCR amplification was performed according to the manufacturer's protocol. The final libraries were analyzed on a Bioanalyzer or TapeStation, and the barcoded libraries were pooled and sequenced on an Illumina HiSeq2500 or HiSeq4000 platform with a single read 51 bp or paired end 100 base protocol. Illumina adapters were trimmed from the raw reads using Cutadapt, and the raw reads were aligned to the human genome (Hg19 or Hg38) using TopHat. HTSeq was used to assess the number of uniquely assigned reads for each gene; expression values ​​were then relative to 10 7 Total reads were normalized and log2 transformed to obtain counts per million (CPM).

[0306] Analysis: For Figure 2Heatmap in d, we used Seqmonk v1.42. In brief, BAM files were aligned to Hg38 using HISAT2, and transcripts were quantified using the RNA-Seq pipeline to correct for characteristic lengths on transcript count reads on exons. Graphical representations were used to quantify, log-transform, and align assumed reverse strand-specific libraries, followed by percentile normalization supplemented with matching distributions.

[0307] exist Fig.15 e, data were analyzed using SeqMonk, and reads were normalized by a standard analysis pipeline, DNA contamination correction was applied, and raw counts were generated for DESeq2 differential analysis. The same pipeline with logarithmic transformation was used for visualization. Significance was determined using standard SeqMonk settings: independent intensity filtering, p<0.05 after Benjamimi and Hochberg correction. Quantification was performed as above. IPA was used to determine NFKB-associated genes in MG vs. GIC and TNFa vs. GIC, MES GBM labels were obtained through respective publications, and graphs were generated using Venny. GSEA significance was determined for MES-GBM FC>0.5-fold with padj=0, for PN FC<-0.4, padj=0, and for SREBP FC>1-fold with padj=0. Fig.15 e Interaction map was generated using the functional Ingenuity upstream regulators from IPA to compare MGT#1 high TNFa and MGT#1 high C20MG co-cultures.

[0308] ATAC-seq: ATAC-seq on FACS sorted populations of 20-50,000 cells from in vivo experiments and 50-100,000 cells in in vitro experiments. The cells were centrifuged in PBS and the pellet was gently resuspended in 50 μl of master mix (25 μL 2×TD buffer, 2.5 μL transposase and 22.5 μL nuclease-free water, Nextera DNA library preparation, Illumina) and incubated at 37°C with moderate shaking for 60 min (500-800 rpm). Transposition was terminated with 5ul proteinase K and 50ul of AL buffer (Quiagen), incubated at 56°C for 10 min, and DNA was purified using 1.8x vol / vol AMPure XP beads and eluted in 18ul. The optimal number of PCR cycles for library amplification of each respective sample was determined using 2ul of template followed by qPCR amplification using heat-activated Kappa Hifi polymerase and EvaGreen 1x. Final amplification was performed in 50ul qPCR volume and 8-12ul template DNA. Primers were previously described (Buenrostro et al. 201). Libraries were individually quantified using Qubit (Life Technologies) and appropriate ladder distribution was determined using high-sensitivity D1000 ScreenTapes on the TapeStation (Agilent). Sequencing (paired-end 75nt) was performed on an Illumina NextSeq 500 using V2 chemistry for 150 cycles. SeqMonk was used for sequencing. Fig.14 ATAC-seq interspersed analysis in a, final annotation of ENSEMBL mRNA using TSS ± ​​5 kb as probes. Quantification was normalized using read counts and reads corrected for total counts in probes only per million reads, log-transformed snfs were further transformed by size factor normalization.

[0309] ATAC-seq analysis Reads were adapter-removed using trim-galore v0.6.2 -- nextera and then mapped using bowtie2 v2.3.5 (reference) default parameters. ATAC-seq analysis was performed using SeqMonk, with final annotation to ENSEMBL mRNA using TSS ± ​​5 kb as probes (2019 assembly). Counts were normalized using the reads technology quantification function, and reads were corrected for total counts in probes per million reads only, log-transformed and further transformed by size factor normalization. Generated according to established protocols Fig.14 c Integration of sLCR ATAC-seq and TCGA ATAC-seq.

[0310] Vector generation: sLCRs were initially synthesized at IDT and later at GenScript. MGT#1-mVenus was cloned into the PacI-BsrGI fragment of the mammalian expression, lentiviral FUGW (a gift from David Baltimore; Addgene #14883). Additional modifications such as exchanging mVenus for mCherry or MGT#1 with all other sLCRs using restriction enzyme digestion or Gibson cloning. The sLCR vector is a 3rd generation lentiviral system and has been used with pCMV-G (Addgene #8454), pRSV-REV (Addgene #12253), and pMDLG / pRRE (Addgene #12251). Sall2 (ccsbBroad304_11117) Pou3f2 (ccsbBroad304_14774) were obtained from the CCSB-Broad lentiviral expression library.

[0311] Cell lines: MES-hGICs and PN-hGICs were generated by our laboratory and are described elsewhere. Briefly, PN-hGICs were generated by transforming human NPCs with the help of pLenti6.2 / V5-IDH1-R132H, TP53R173H and TP53R273H (point mutations introduced into TP53 ccsbBroad304_07088 from the CCSB-Broad lentiviral expression library, and pRS-Puro-sh-PTEN(#1). MES-hGICs were generated by transforming human NPCs with pRSPURO-sh-PTEN(#1), pLKO.1-sh-TP53(TRCN0000003754), and pRS-shNF1. For these lines, comprehensive genetic, transcriptional, and epigenetic characterization, as well as in vivo tumorigenic and phenotypic capabilities, have been performed. In vitro, as described 76 GICs were propagated using a modification. In addition to EGF (20 ng / ml; R&D), bFGF (20 ng / ml; R&D), heparin (1 μg / ml; Sigma), and 5% penicillin and streptomycin, PDGF-AA (20 ng / ml; R&D) was supplemented to RHB-A (Takara). This medium composition will be referred to as RHB-A complete. hGICs were cultured at 37°C in 5% CO 2 , 3% O 2 and 95% humidity in an incubator.

[0312] T98G and U87MG (kindly provided by the van Tellingen laboratory at NKI) were propagated in EMEM medium. Fig.13 In the experiment in a, T98G was replaced by EGF (20 ng ml -1 )、bFGF(20ng ml -1 ), heparin (1 μg ml -1 ) and 5% penicillin and streptomycin and propagated first on standard tissue culture treated plastic and then on ultra-low binding plastic (CORNING).

[0313] MCF7, MDA-231, A549, and H1944 cell lines (kindly provided by Rene Bernards laboratory at NKI) were cultured in RPMI medium. 2 All cell lines were cultured in a 95% CO incubator supplemented with 10% FBS and 5% penicillin and streptomycin.

[0314] The cells were grown in RHB-A medium (Takara) supplemented with 1% FBS, 2.5 mM glutamine (Thermofisher; 35050038), 1 μM dexamethasone (Sigma; D1756), and 1% penicillin and streptomycin at 37°C in the presence of 5% CO. 2 , 19% O 2 Immortalized primary human microglial C20 cells were cultured in an incubator with 95% humidity.

[0315] Donor-derived CD34 cells were propagated in SFEM II (StemCell), SCF, FLT3-L, TPO, IL6 (all 100 ng / ml; easyexperiments.com), UM171 (Selleck, 0.035 μM), SR1 (Selleck, 0.75 μM), 19-deoxy-9-methylene-16,16-dimethyl PGE2 (Cayman, 10 μM).

[0316] Whole-genome CRISPR knockout in vitro screening: For the whole-genome pooled CRISPR knockout screening, we utilized a Brunello library consisting of 77,441 sgRNAs targeting 19,114 genes (average of 4 sgRNAs per gene) and 1000 non-targeting controls. To obtain more than 100x library representation, we transduced a total of 16x10 6 MES-hGICs-MGT#1 低cells and expanded them for 10 days before introducing treatments. On day 10, cells were treated with TNFa (10 ng / ml) and FBS (0.5%); temozolomide (50 μM) and irradiation (20 Gy) or not. Prior to gDNA extraction, we performed FAC sorting for each condition and collected MES-hGICs-MGT#1 低 、MES-hGICs-MGT#1 高 and unsorted populations. Genomic DNA was extracted by lysing cell pellets for 10' at 56°C in AL buffer (Qiagen) supplemented with proteinase K (Invitrogen) and RNAse A (Thermo Scientific), followed by purification with AMPure beads and elution in EB buffer (Qiagen). NGS libraries were constructed in a two-step PCR setup, where PCR1 was used to amplify the sgRNA scaffold and insert staggered sequences to increase library complexity across the flow cell, while PCR2 introduced Illumina-compatible adapters with unique P7 barcodes, allowing sample multiplexing. For PCR1, 5 μg of each gDNA sample was split into 5 parallel reactions, subsequently pooled together and purified using AMPure beads. The optimal number of cycles for PCR2 for 1 μl of each PCR1 was determined individually by qPCR amplification using KAPA HiFi HotStart Ready Mix (Roche) and 1x EvaGreen (Biotium). 10 μl of purified PCR1 of each sample was used as input for the final PCR2. Both PCR1 and PCR2 were performed using KAPA HiFi HotStart Ready Mix. Primers are available upon request. Quantitative control of the final library was performed using the Qubit dsDNAHS kit (Invitrogen) for quantification and the TapeStation high sensitivity D1000ScreenTapes (Agilent) for determining the size of the PCR fragments. The barcode libraries were pooled together in equal moles and sequenced on an Illumina NextSeq500 using 75 cycles of V2 chemistry (1x 75nt single read mode).

[0317] Transwell co-culture: Co-culture of hGIC and immortalized primary human microglia C20 was established using hydrophilic PTFE 6-well cell culture inserts (Merck) with a pore size of 0.4 μm. Human microglia were seeded on 6-well plates in their respective culture media at 1.5×105 cells / well for 24 h. The culture medium was aspirated, and the cells were washed once with PBS, and then 1 ml of RHB-A complete medium was added. The transwell insert was placed in the plate, and 5×105 single hGICs in a total volume of 1 ml of RHB-A complete medium were plated on the insert surface. After 48 h of co-culture, hGIC and C20 human microglia were collected for further analysis.

[0318] Transfection-transduction: Transfection and transduction were described in detail previously. Briefly, 12 μg of DNA mixture (lentiviral vector, pCMV-G, pRSV-REV, pMDLG / pRRE) was incubated with FuGENE-DMEM / F12 mixture at room temperature for 15 min, added to the antibiotic-free medium covering 293T cells, and the first-tap viral supernatant was collected 40 h after transfection. The titer was assessed using the Lenti-X p24 rapid titration kit (Takara) according to the manufacturer's instructions. We applied viral particles to target cells in appropriate complete medium supplemented with 2.5 μg / ml protamine sulfate. After incubation with the viral supernatant for 12-14 h, the medium was updated with appropriate complete medium.

[0319] Preparation of cryosections: Tumor spheroids were sedimented by gravity and fixed in freshly prepared formaldehyde in PBS (1.0%), which was blocked with 140 mM glycine 2M.

[0320] Rinse with 30% sucrose followed by addition of freezing medium (OCT / cryomold). Cryoblocks were obtained by dry ice freezing and stored at -80°C until use. Blocks were cut with a Leica CM 1950.

[0321] Immunohistochemistry: Tissues or tumor spheroids were fixed in 4% PFA for 20'. After fixation, dehydration was performed with EtOH increasing from 70% to 100%, xylene, and overnight paraffin incubation. Paraffin-embedded samples (PES) were cut using a HM 355S microtome (Thermo Scientific). Standard staining with hematoxylin / eosin (HE) was performed, and slide images were obtained using an automated microscope (Keyence).

[0322] Immunofluorescence: Cells grown on coverslips or spheroids spun down onto glass were followed by fixation with 4% paraformaldehyde (PFA, 16005-Sigma Aldrich) in PBS for 10 min at room temperature, washed 5 min (3x) in PBS, permeabilized with 0.5% triton X100 in PBS for 5 min, blocked with 4% BSA (3854.4ROTH) for 15 min, stained with primary and secondary antibodies and 20 μm / ml Hoechst 33258 (16756-50, Cayman), and mounted on glass slides using nail polish and Vectashield (H1000-Linaris). On paraffin-embedded tissues, we performed deparaffinization and citric acid antigen retrieval using standard protocols. Permeabilization was performed with Triton 0,25% in PBS and - when appropriate - with 3% H 2 O 2 Endogenous peroxidase was blocked in water. Typically, we used 5% normal goat serum (NGS) for blocking. Primary antibodies were: anti-GFP (anti-GFP ab6556, 1:000), anti-MED1 (Abcam ab64965 1:500), anti-Tubulin (BD T5168, 1:2000), and secondary antibodies were: A31573, A11055 and A31571 Alexa Fluor647, A21206 Alexa Fluor 488, A31570 Alexa Fluor 555.

[0323] RNA FISH and Dual FISH-IF: Cells were permeabilized in 70% ethanol (RNA FISH only) or with 0.5% tritonX-100 (for Dual IF-RNA FISH), washed in RNase-free PBS (1x (Life Technologies, AM9932), fixed with 10% deionized formamide (EMD Millipore, S4117) in 20% Stellaris RNA FISH Wash Buffer A (Biosearch Technologies, Inc., SMF-WA1-60) and RNase-free PBS for 5 min at room temperature. SMF-1084-5CAL was used at 31.5 μM in 100 μL. Red 635 and SMF-1063-5 570 Customization FISH probes (oligonucleotide sequences available upon request) in 10% deionized formamide 90% Stellaris RNA FISH hybridization buffer (Biosearch Technologies, SMF-HB1-10) were transferred to coverslips and hybridized in the dark at 37°C to detect IgK-MGT#1-mVenus and H2B-CFP. After O / N incubation, slides were washed with RNase-free PBS for 5 min (3x). If primary / secondary staining occurred, proceed as above.

[0324] Imaging: The microscopes used were Zeiss LSM800, Leica SP5-7-8, and Nikon Spinning Disk. The confocal images in Figure S41 were obtained with Leica SP5. mVenus fluorescence was obtained using Ex = 488 nm, Em = 535 nm, and for Figure 1 In d, the images were acquired using a Zeiss LSM800, using Ex = 558 nm, Em = 575 nm for mVenus-QUASAR570, and Ex = 653, Em = 668 for BRD4- or MED1-AF647, respectively. For H2B-CFP-QUASAR670, we used Ex = 631, Em = 670. Images were processed using ImageJ or Photoshop.

[0325] Phenotypic screening: Tumor cells were propagated as described above until screening. We then inoculated at 15'000 / 50 μl / well in a 384-well plate (Corning) in Gibco FluoroBrite DMEM medium supplemented with appropriate growth factors. Cells were dispensed into each well with a 50 μl suspension using a SPARK20M syringe system (50 μl injection volume; 100 μl / s injection speed). For non-adherent cells (e.g. GIC), cells were further centrifuged at 1500 rpm for 1 h 30 min at 37 °C. The SPARM 20M TECAN plate reader was used at 37 °C in 5% CO 2 The bottom reading fluorescence was scanned in a humidified box in -95% air (3% for GIC) with the following settings for mVenus: monochromator, Ex 505nm±20nm, Em 535nm±7.5nm, manual gain: 198, flashes: 35, integration time: 40μs. In independent replicons, cell viability was measured with 0.02% AlamarBlue solution in FluoroBrite medium using the following settings: fluorescence highest reading. Monochromator, Ex 565nm±10nm, Em 592nm±10nm, manual gain: 88, flashes: 30, integration time: 40μs.

[0326] DMSO soluble compounds such as GSK126 were automatically aliquoted using the D300e, while cytokines were automatically aliquoted into each well using the Andrew pipetting robot (Andrew Alliance) using the following concentrations:

[0327] Cytokines Product Code Stock solution Working concentration IL6 206-IL; R&D system 100μg / ml 15ng / ml LPS ALX-581; Enzo 200x 1x TNFα 210-TA; R&D system 100μg / ml 20ng / ml TGF 240-B; R&D system 35μg / ml 5ng / ml IFNg 285-IF; R&D system 100μg / ml 10ng / ml Tenascin C MBS230239; Mybiosource 100μg / ml 100ng / ml HGF 294-HG; R&D system 10μg / ml 10ng / ml IGF 50356.100; Biomol 2μg / ml 2ng / ml FBS 10270106; Gibco 100％ 10％ GSK126 5mM 5μM CBD 10mM 4μM Activin A BV-P1078; Enzo 50μg / ml 50ng / ml NRG1 97642.10; Biomol 16μg / ml 90ng / ml IL1b CYT-094; Biotrend 100μg / ml 10ng / ml

[0328] Data were imported into PRISM7 (GraphPad). Fluorescence intensity from control dead cells was subtracted as background from all values. Each value was normalized to the mean of the control and expressed as fold change.

[0329] Drug dose response screening: Transduced hGICs from permeation chamber co-culture experiments were harvested into single cell suspensions and sorted into mVenus high and low populations using a BD FACSAria III. Cells were counted and 7000 cells / 50 μl / well were seeded into 384-well black-walled plates in RHB-A complete medium using a SPARK20M injector system (50 μl injection volume; 100 μl / s injection speed). Drugs were typically dissolved as 10 mM stocks in DMSO and dispensed using a D300e composite printer (TECAN) for targeted dose responses with plate randomization and DMSO normalization. After 72 h of incubation, cell viability was measured with the following settings after 2-6 h of incubation with 10 μl of Cell-Titer-Blu (Promega) assay reagent: fluorescence highest reading. Monochromator, Ex 565nm ± 10nm, Em 592nm ± 10nm, gain setting: optimal scan, flash: 30, integration time: 40μs. Data were entered into PRISM7 (GraphPad). The fluorescence intensity from the empty wells was subtracted as background from all values. The concentration log10 was converted to log [M] scale, and each value was normalized to the mean of the untreated positive and SDS-treated negative control conditions. Dose response curves and IC50 values ​​were obtained using a nonlinear regression model (log (inhibitor) vs. normalized response - variable slope).

[0330]

[0331] Irradiation of hGICs: Irradiation was delivered using a XenX irradiator platform (XStrahl Life Sciences) equipped with a 225 kV X-ray tube for targeted irradiation. hGICs cultured in 6-well or 96-well plates were placed in the focal plane of the beamline and exposed to radiation for a specific time according to the target dose as calculated using in-house calculation software.

[0332] Generation of Matrigel Organoids: To generate organoids with co-culture of C20 human microglia and hGICs, growth factor-reduced and phenol red-free Matrigel (BD; 734-1101) droplets were used as extracellular matrix carriers. Target cells were harvested and a single cell suspension with 1.5x105 C20 human microglia and 3.5x105 hGICs in a volume of 500 μl was prepared. Using pre-cooled consumables and pipette tips, 30 μl of Matrigel was melted on ice and added to each well of a cold 60-well Minitray (Thermofisher; 439225). 5 μl of the prepared cell suspension was injected into each organoid at 5,000 cells per droplet and mixed by pipetting. The droplets were incubated at 37°C in 5% CO 2 , 3% O 2 The cells were cultured in a 95% humidity incubator for up to 14 days, and RHB-A complete medium was replaced every 2-3 days. Live cell imaging was performed on day 10 using a Leica SP8 confocal microscope.

[0333] RT-qPCR: Using SuperScript in 20 μL TM VILO TM MasterMix RNA (0.5-2.5 μg) was incubated at 25°C for 10', 42°C for 60' and 85°C for 5' to generate cDNA. 10 ul / well was added to 384w ViiA using 1x PowerUpSYBR Green Master Mix (Applied Biosystems). TM RT-qPCR was performed with 10 ng cDNA / well in the 7-well system. Primers are available upon request.

[0334] Tissue dissection and cell surface staining: Brain tumor dissection has been described in detail previously. 77 Briefly, tissues were dissected with a scalpel and digested in Accutase / DNaseI (947 μl Accutase, 50 μl DNase I buffer, 3 μl DNaseI) at 37°C until needed. Cells were first filtered through a 120 μm cell strainer and then through a 40 μm cell strainer, and then RBC lysed (NH 4 Cl, 155 mM; KHCO 3 , 10 mM; EDTA, pH 7.4, 0.1 mM). After washing in cold PBS, viability and cell count were automatically assessed using TECAN SPARK20M with 0.4% trypan blue staining.

[0335] When evaluating surface markers, 200.000 cells / antibody are usually used in 15ml Falcons. The volume of the chromosome is 50 μl in RHB-A culture medium with primary antibodies (e.g. CD133-APC; Miltenyi), on ice in the dark for 30'. Unbound antibodies are removed by washing with PBS twice. Depending on whether to analyze or sort cells, data acquisition is performed on BD LSRFortessa, or cells are sorted using BD Aria II or Astrios Moflo. Suitable laser filter combinations are selected according to the fluorophore analyzed. Typically, in order to remove dead cells, events are first gated according to shape and granularity (FSC-SSC), and we use AnnexinV or live / dead fixable aqueous dead cell staining kit (LIVE / DEAD Fixable AquaDead Cell Stain Kit) as viability dye (depending on the fluorophore analyzed). Analyzed with FlowJo_V10.

[0336] FACS analysis: FlowJo_V10 was used for analysis.

[0337] FACS sorting: Transduced hGICs were harvested into a single cell suspension and resuspended in cold RHB-A complete and filtered into FACS tubes. Sorting was performed using a BD FACSAria III or Fusion. The appropriate laser filter combination was selected depending on the fluorophore to be sorted. Typically, events were first gated based on shape and size (FSC-A vs. SSC-A) and doublets (FSC-A vs. FSC-H) were excluded to remove dead cells. A positive gate was established on PGK-driven and constitutively expressed H2B-CFP as a sorting reporter to sort populations with low to moderate intensity of sLCR-dependent fluorophore expression.

[0338] Immunoblotting: Cell pellets were lysed in RIPA buffer (20 mM Tris-HCl pH 7.5, 150 mM NaCl, 1 mM EDTA, 1 mM EGTA, 1% NP-40) supplemented with 1x protease inhibitor cocktail (Roche), 10 mM NaPPi, 10 mM NaF, and 1 mM sodium orthovanadate. Lysates were sonicated if necessary and electrophoresed in NuPAGE MOPS SDS running buffer (50 mM MOPS, 50 mM Tris base, 0.1% SDS, 1 mM EDTA) using NuPAGE Bis-Tris precast gels (Life Technologies). Proteins were transferred to nitrocellulose membranes at 120 mA for 1 h in transfer buffer (25 mM Tris-HCl pH 7.5, 192 mM glycine, 20% methanol). After washing twice with TBS-T, protein transfer was assessed by staining with Ponceau Red for 5 min. Membranes were blocked with 5% BSA in PBS for 1 h at room temperature. Dilutions of primary antibodies were prepared in PBS + 5% BSA, and the membranes were incubated overnight at 4 ° C. After washing three times with TBS-T for 5 min, dilutions of appropriate HRP-coupled secondary antibodies were prepared in PBS + 5% BSA, and the membranes were incubated for 45 min at room temperature. After washing three times with TBS-T for 5 min, ECL detection reagent (Sigma; RPN2209) was used and the membranes were exposed to ECL Hyperfilm (Sgima; GE28-9068-37) to detect chemiluminescent signals.

[0339] Antibody

[0340] Target Product Code Manufacturer GFP ab6556 Abcam Vinculin p-Stat3 y705 9145L Cell Signaling Stat3 sc-482x Santa Cruz p-NFKB p65 3033P Cell Signaling NFKB p65 86299Abcam p-p38t180d3f9 45115Cell Signaling p-p38 9211s Millipore Neuroepithelial stem cell protein 611658BD Biosciences p-yH2AX Ser 139 05-636 Millipore K27me3 07-449Millipore Total H3 1791Abcam E-cadherin 31950Cell Signaling Vimentin 5741s Cell Signaling Goat anti-mouse IgG(HL)-HRP 626520Invitrogen Goat anti-rabbit IgG(HL)-HRP G21234Invitrogen

[0341] IncuCyte: IncuCyte automated longitudinal imaging was performed in 96-well black-walled plates (Greiner). At the end of the experiment, 300,000 cells were seeded per plate to achieve optimal confluence. GSK126 was aliquoted using a D300e, while TGFB1+2 was aliquoted manually into each well. Both were changed every two days. The final time point was independently verified using a plate reader (BMC Clariostar).

[0342] CRISPRi Screening: For CRISPRi screening, A549-MGT#1±GSK126±Dox cells were sorted on an Astrios Moflo. We aimed for a library representation of 1000x (>6 million cells) in the lowest 10% (faint) and highest 10% (bright) cells within each population. An intermediate population was also sorted and included in the screening analysis as a control. Cells were lysed at 56°C in AL+ProteinaseK buffer (Quiagen) for 10', followed by DNA extraction using AMPure beads (Agencourt) and RNAse A treatment. PCR amplification and barcoding of CRISPRi libraries were performed essentially as described, including the PCR buffer composition 77 For each sample, in PCR1 we used 20ug of DNA divided into 10 parallel reactions including from the input control, while the plasmid library required 0.1ng of DNA in PCR1. The parallel PCR1 reactions were mixed together and 5ul was used as template for PCR2. We used Phusion polymerase (NEB), GC buffer and 3% DMSO in both PCR1 and PCR2. Primers are available upon request.

[0343] Library concentrations were measured, and barcoded libraries were pooled and sequenced on an Illumina HiSeq2500 sequencer. Reads were mapped to the in silico libraries using custom scripts (available upon request) to generate read counts, which were subsequently used as input to Seqmonk. We used custom gene panels for Seqmonk analysis (available upon request), and samples were normalized to RPM and log-transformed to generate MA plots, while DEseq2 with padj < 0.001 was run on raw read counts. We ran 2 independent CRISPRi screens in A549 and an additional screen in H1944.

[0344] CRISPR / Cas9 KO: CNKSR2 and ARID1A were knocked out in A549-MGT#1 using the Cas9 RNP Synthego kit as directed. A549 cells were electroporated using BioRad XCell in PBS and using standard pulses. The optimal gRNA in the kit was first evaluated using T7E1 and TIDE calculations (https: / / tide.nki.nl / ). Afterwards, we performed an overall assessment of MGT#1 fluorescence using flow cytometry as well as low confluence plating and manual colony picking.

[0345] Animal experimentation: All mouse studies were performed according to protocols approved by the Institutional Animal Care and Use Committee and in compliance with European Union regulations. Orthotopic glioma xenograft studies were performed as previously described. 76 NOD-SCID-IL2Rg / (NSG) mice were purchased from The Jackson Laboratory and maintained under specific pathogen-free (SPF) conditions. We used male and female mice between 7 and 12 weeks of age.

[0346] Gene knockout: Gene knockout was performed using the Synthego Knockout Kit. The sgRNA was dissolved in nuclease-free 1X Te buffer to a stock concentration of 30uM. The RNP complex was formed by mixing the Cas9 nuclease-gRNA at a ratio of 6:1. Each RNP complex was electroporated into 250KA549-MGT1#1 in 2mm cuvettes in 1x PBS using a Biorad GenePulser xCell (150 volts, 10ms). After electroporation, cells were cultured in RPMI supplemented with 10% fetal bovine serum and 1% penicillin / streptomycin. Approximately 7 days after electroporation, gDNA was extracted using the Invisorb Spin Tissue Isolation Kit (Stratec), eluted in 50ul elution buffer, and PCR was performed on the target gene of interest using an 800 to 1200bp product centered on the gRNA target locus (primers available on request). Knockout efficiency was calculated using TIDE (NKI) and T7EI assays. Individual clones were established or total KO cells were directly determined by FACS using the BD LSRFortessa and FlowJo program.

[0347] Example 1: Design of expression cassettes comprising subtype-specific synthetic locus control regions (sLCRs) for glioblastoma multiforme (GBM) tumor cells.

[0348] High levels of cellular and molecular heterogeneity are thought to contribute to resistance to standard therapies in solid tumors and present a barrier to the development of targeted approaches. Glioblastoma multiforme (GBM) is the most common primary adult brain tumor that is exceptionally heterogeneous and resistant to therapy. 13 GBM is also one of the most highly genomically and epigenomically characterized cancers. 14-16 GBM tumors were repeatedly classified into three subtypes based on transcriptomes, with mesenchymal and proneural being more frequently cross-validated 52、53、54Several studies have debated the correlation between subtype-specific gene expression signatures and differential responses to treatment and overall survival of patients. This suggests that GBM subtype identity and fate alterations may have therapeutic potential. In GBM tumors, major subtypes and tumor cells with different subtype signatures may coexist. 17,18 Moreover, tumors can change their dominant expression profile upon recurrence. 19,20 .

[0349] Lineage tracing has previously had a major impact on our understanding of GBM biology in mouse models, informing, among other things, the characterization of individual subtypes. 5 The cellular origins of inflammatory cytokines and how abnormal homeostatic regulation affects responses to standard of care in vivo 10 .

[0350] In this example, we describe a systems biology approach to design synthetic systems to genetically mark any cell state or transition in the complex developmental and disease context and test this system to pursue the biological principles underlying the molecular subtypes of human GBM.

[0351] First, we hypothesized that subtype-specific GBM genes would essentially contain regulatory activities (i.e., cis-regulatory elements) required for specific subtype identity. We further hypothesized that transcription factor genes (TFs) expressed in each subtype would be primarily responsible for establishing and maintaining subtype identity.

[0352] To design gene cassettes that would intercept minimal signaling and regulatory information, we identified 16 Subtype-specific GBM genes with the highest fold change compared to all other subtypes in the . Any stringent cutoff (i.e. >6Log2 FC; Figure 6 ) to call MES, CL, and PN subtype-specific genes. Similarly, less stringent cutoffs (i.e., >0Log2 FC) and standard pathway analysis tools (e.g., Ingenuity Pathway Analysis, DAVID, etc.) can be used to identify TFs. Initially, genes with low expression in GBM stem-like cells (GSCs) from our previous experiments (e.g., <4 counts per million, CPM) were discarded as a measure to focus on cell-autonomous regulation ( Figure 6 ). Current implementations of this method use single-cell RNA-seq profiles, such as glioma intrinsic signatures 14 .

[0353] To identify genomic regions with high intrinsic cis-regulatory potential among isoform differentially regulated genes (DGRs), we calculated all pairwise frequencies of the best position weight matrix (PWM) associated with TFs expressed in each isoform ( Figure 1a). Since cis-regulatory DNA is usually nucleosome-free regions (NFRs; >147 bp) and involves an average of ∼1000 bp 21 To precisely locate these elements, we set up a 1 kb sliding window approach with a 150 bp step size. The search for cis-elements that might regulate DRG was performed by the ENCODE consortium. 22,23 The distance from the start / end of the gene was arbitrarily set to >10 kb. These criteria approximate the functional definition of topologically associated domains (TADs), which are believed to contain the vast majority of contact points between cis-regulatory elements at a given locus and use CTCF as a boundary protein. 24 .

[0354] In order to assemble synthetic cis-regulatory elements that drive subtype-specific expression using the TFBS analysis described above, such synthetic locus control regions (sLCRs) should ideally include a minimal set of cis-units with the highest number (i) and diversity (ii). Ideally, at least one of the cis-units that make up an sLCR will also include the natural transcription start site (TSS) and will be placed directly upstream of the reporter element ( Figure 1 a). Using these criteria, we generated sLCRs for genetic tracking MES, CL, and PNGBM (hereafter referred to as MGT, CLGT, and PNGT). Algorithms can be used to minimize decisions and automate sLCR generation ( Figure 7 a). Pairwise correlations of TFBSs that potentially regulate these genes reveal that several TFs cluster together and are distant from other TFBS clusters ( Figure 1 b). This observation is consistent with experimental observations from ChIP-seq experiments, indicating that our program returns results consistent with the functional and structural principles of genome regulation. In addition, ENCODEChIP-seq data in multiple cell lines also support the actual TF binding to a single cis unit ( Fig. 9 Importantly, the distinct MGT#1 and MGT#2 sLCRs were assembled from highly independent single cis-units and measured only 827 pb and 1015 bp in length, respectively, each representing up to 60% of the overall regulatory potential.

[0355] Example 2: Genetic tracking of mesenchymal fate in human glioma-initiating cells using a lentiviral vector including MGT#1 as sLCR

[0356] A typical lentiviral vector carrying an sLCR such as MGT#1 drives the expression of isoforms of the fluorescent reporter mVenus or mCherry. To facilitate in vivo genetic tracking, mVenus was driven to the plasma membrane (tagged with an Igk leader and platelet-derived growth factor receptor (PDGFR) transmembrane sequence; Figure 1 c) and shuttles mCherry to the nucleus via the NLS. To enable fluorescent visualization and sorting of sLCRs independent of reporter expression, we also included a second cassette expressing the H2B-CFP fusion via the ubiquitous PGK promoter ( Figure 1 c).

[0357] As a prototype test, we produced lentiviral particles in HEK293T cells with MGT#1-mVenus sLCR and used the viral particles to infect human glioma-initiating cells (MES-hGIC) with the MES genotype. Membrane mVenus expression was observed in both transient transfections and in stably transduced and cryosectioned tumor spheroids ( Figure 1 d).

[0358] Next, near-isogenic and characterized MES-hGICs and PN-hGICs were transduced with MGT#1 lentiviral particles. PN-hGICs have a combination of IDH1 and TP53 point mutations that are found only in PN GBMs, while MES-hGICs have a triple knockdown of TP53, PTEN, and NF1, characterized in a MES GBM background. Interestingly, we observed a small but measurable increase in basal fluorescence in MES-hGICs, suggesting that MGT#1 reflects a higher basal intrinsic signaling in these cells ( Figure 1 e) Since TNFα is considered to be the dominant MES-GBM signaling pathway and can induce PN to MES transition 20 Next, we used MES-hGICs-MGT#1 低 and PN-hGICs-MGT#1 低 Exposure to TNFα was used to test whether MGT#1 faithfully recapitulated MES GBM signaling. In the presence of TNF, at least two cis-units of the MGT#1 sLCR have been previously shown to directly engage TNF-driven NFkB TFs. Reassuringly, TNFα induced fluorescence enhancement in both cell types compared to each parental control. Interestingly, although the FACS sorting step ensured that there was an equal basal level of MGT#1 expression in both cell types, MES-hGICs-MGT#1 低 Transformation into MES-hGICs-MGT#1 高 PN-hGICs-MGT#1 低 Only PN-hGICs-MGT#1 中 level( Figure 1 ef), validated the MGT#1 reporter for MES GBM subtype-specific expression and used this system to provide evidence that the adaptive responses of hGICs are inscribed in their tumor genotype.

[0359] Human GICs and GSCs continue to proliferate under "NBE" conditions, which stands for serum-free Neurobasal medium supplemented with basal FGF and EGF 25 We further supplemented our GICs with PDGF-AA as this is the signaling pathway most commonly amplified in GBM. 26 To investigate the ground state of MES-GBM signaling using our genetic strategy, we generated a single-cell hybrid in MES-hGICs-MGT#1. 低 and PN-hGICs-MGT#1 低 A mid-throughput cytokine screen was performed in MES-hGICs. GICs were proliferated under standard conditions and they were replated in a 384-well format. Next, GICs were stimulated with individual cytokines in biological and technical replicates, followed by continuous fluorescence bottom readings in predefined time course experiments. In a typical experiment, we acquired MGT#1 fluorescence emission longitudinally from up to 48 hours after stimulation, and we then normalized the fluorescence relative to the initial GIC. Consistent with previous reports and the experiments described above, in the presence of TNFα signaling, MES-hGICs-MGT#1 低 Transformation into MES-hGICs-MGT#1 高 ( Figure 2 a, 8). Thus, MGT#1 informs differential responses to exogenous signaling between tumor cells with different genotypes. In addition, MGT#1 is a screening framework to identify MES-hGICs-MGT#1-supported 低 and PN-hGICs-MGT#1 低 A solid foundation for signaling related to cell growth and subtype identification.

[0360] Example 3: Using MGT#1 and MGT#2 sLCR as a readout to study intrinsic and adaptive responses in GICs

[0361] Under the same experimental conditions, a second independent reporter (MGT#2) showed consistent results ( Figure 2 a), which supports our ability to generate functional sLCRs starting from gene expression profiles. Interestingly, both MGT#1 and MGT#2 reporters indicate that FBS is able to induce mesenchymal differentiation, which, unlike the case of TNFα, is accompanied by GIC differentiation measured by visual inspection and flow cytometry (data not shown). This finding can only be partially explained by the presence of TGFB1, which is indeed a known component of FBS. Indeed, TGFB1 is a mesenchymal inducer, but does not strongly induce MGT#1, which does not promote differentiation when used as a purified cytokine in the same time frame ( Figure 2a) Perhaps more interestingly, this observation on FBS is highly consistent with the TCGA report that the MES GBM signature cannot be found in any mouse brain cells, but only in astrocytes cultured in FBS. 16 .

[0362] The in vivo source of TNFα in mouse models of glioma is believed to be the tumor microenvironment (TME), especially glioblastoma-associated microglia / monocytes (GAMs). 27 TNFα expression was also observed in hGAM 28 Interestingly, IDH1 wild-type GBM infiltration via GAMs has recently been associated with NF1 deficiency and MEG GBM subtype identity. 14 To provide experimental support for the hypothesis that GAMs recruited to GBM drive MES differentiation in NF-deficient GBM cells, we co-cultured IDH1 wild-type and NF1-deficient MES-hGICs-MGT#1 weak cells with MACS-purified CD11b cells purified from patients with GBM in vitro. Surprisingly, in the presence of IL-6 stimulation, co-cultured hGICs-MGT#1 weak cells with CD11b+hGAM induced MGT#1 expression ( Figure 2 b) IL-6 has been previously shown to stimulate GAMs 29 and can be represented by GSCs 30 or mesenchymal stem cells from the TME 31 Notably, both unstimulated and when exposed to the endogenous TLR4 ligand tenascin-C (TNC 32 ), another GSC-derived pro-inflammatory factor 33 , hGAM was not sufficient to drive MGT#1 expression in MES-hGICs. In addition, TNFα drove MGT#1 induction in MES-hGICs regardless of the presence of hGAM ( Figure 2 b). Thus, our data reveal potential cellular crosstalk in the GBM TME involving signaling around IL6 and leading to MES GBM specification. These data also highlight the potential of sLCR to mechanistically dissect non-cell autonomous interactions ex vivo.

[0363] Our data support the use of sLCR as a valid readout for studying intrinsic and adaptive responses in GICs, but do not exclude the possibility that this readout is largely constrained by the unique regulation of the reporter. To understand whether reporter regulation is accompanied by differences in cell identity, we used MES-hGICs-MGT#1 低 and PN-hGICs-MGT#1 低Immunoblotting, spherical gene expression profiling, and targeted mRNA validation were performed in the cells. Although proliferated under the same experimental conditions, MES-hGICs-MGT#1 低 and PN-hGICs-MGT#1 低 Cells consistently show limited but measurable basal differences in signaling pathway activation and gene expression ( Figure 2 cdef). Notably, although TNFα stimulation induced phosphorylation of NFkB-p65, STAT3, and p38-MAPK in both cell types, this resulted in distinctly different gene expression outputs ( Figure 2 cdef). Thus, MGT#1 informs the effects of active signaling (e.g., TNFα), and it reflects similar cell fate transitions even when preexisting context-dependent differences are in place (e.g., mesenchymal signaling amplification or transition). Interestingly, both global and targeted gene expression profiling indicated that TNFα drives PN-hGICs to a state closer to their initial state, MES-hGICs ( Figure 2 cdef).

[0364] Example 4: Using MGT#1 to functionally test whether environmental insults (such as ionizing radiation) can induce mesenchymal transdifferentiation in a GBM cell-autonomous manner

[0365] Mesenchymal differentiation in GBM was originally described as a dominant event in recurrence after radiotherapy 19 , and subsequent activation of NFKB driven by TNF is associated with acquired radioresistance. Repeatedly, relevant evidence supports the connection between inflammatory signaling, EMT and radioresistance. To functionally test whether irradiation may induce mesenchymal transdifferentiation in a cell-autonomous manner, MES-hGICs-MGT#1 低 and PN-hGICs-MGT#1 低 Cells were exposed to ionizing radiation (IR) alone or in combination with TNFα. In this experiment, we focused on delivering a single radiation dose of 10 Gy for two reasons: (i) we experimentally determined that this was sublethal (alone or in combination with other treatments, including TNFα or temozolomide; data not shown), and (ii) 10 Gy is close to experimentally demonstrating priming as a means of intrinsic radioresistance and enhanced repair capacity in multiple human GSCs. 34,35 The residual DNA damage marker H2A phosphorylation twenty-four hours after irradiation confirmed the occurrence of both double-strand breaks and repair. However, only a small fraction of GICs from either genetic background switched to MGT#1. 高 state( Figure 2gh). In contrast, MES-hGICs-MGT#1 低 and PN-hGICs-MGT#1 低 Both cells showed enhanced mesenchymal differentiation in combination with TNFα, indicating that TNF signaling and IR synergistically induce this cell fate specialization. Taken together, these data support the conclusion that sublethal IR cooperates with other mechanisms to drive mesenchymal transformation in GBM. These data also support the speculation that NFKB activation is enhanced by non-classical signaling caused by genotoxic stress. 36 .

[0366] Example 5: GBM subtyping and reprogramming using sLCR.

[0367] Proneural GBMs are thought to represent a common GBM ancestor subtype and also reflect an oligodendrocyte origin 26,37 Previous studies have shown that long-term proliferation in FBS affects the phenotypic identity of individual cell lines. 25,16 To test whether PNsLCRs would reflect the proneural state, we decided to use master TFs under PN identity to induce FBS-driven reprogramming of conventional cell lines into PN-GICs. To this end, we transduced MGT#1 or PNGT#2 into the T98G cell line, which is characterized by a TP53 mutation more likely to be associated with a PN phenotype (https: / / portals.broadinstitute.org / ccle). 16 Consistent with the genotype-driven prediction, T98 cells showed basal expression of PNGT#2, but not MGT#1, when the proliferation conditions were switched from FBS to NBE ( Figure 3 Importantly, transient overexpression of SALL3, SOX2, and POU3F2 further enhanced PNGT#2 activation but was neutral for MGT#1 expression ( Figure 3 b). It is important to note that these sets of experiments were performed with the mCherry fluorescent protein with nuclear localization signaling, thus ruling out that fluorescent protein intensity (mCherry is brighter than mVenus), localization and stability (mVenus is transmembrane and stable) play a major role in the observed phenotypic shifts.

[0368] Overall, these experiments demonstrate that multiple intrinsic and extrinsic triggers known to play key roles in GBM biology can be intercepted by a single sLCR in GBM cells using the systems and synthetic biology approaches described here.

[0369] Example 6: Dissecting epithelial-to-mesenchymal transition in breast and lung cancer cells using MGT#1

[0370] Mesenchymal transdifferentiation is a physiological process hijacked by multiple tumors of epithelial origin 39 To investigate whether our genetic tracing strategy extends beyond GBM homeostasis, we next transduced MGT#1 into well-characterized epithelial and mesenchymal breast cancer cells.

[0371] Tumor subtypes are genetically imprinted in breast cancer cells 40 Consistently, after the first round of lentiviral transduction, epithelial MCF7 cells showed lower MGT#1 expression compared to MDA-231 cells, which are thought to have undergone EMT ( Fig.10 ab). To confirm that MGT#1 expression reflects actual breast cancer subtype identity, we FACS sorted and subcloned cells with the highest MCF7-MGT#1 and intermediate MDA-231-MGT#1 expression. However, further proliferation of the FACS-sorted populations reestablished the pre-sort homeostasis, with MCF7 expressing lower levels of MGT#1 than MDA-231. Such levels appear to be stable, as short-term treatment with the EMT inducer TGFB2 did not strongly modify basal MGT#1 fluorescence in either MCF7 or MDA-231 ( Figure 4 a).

[0372] Ezh2 inhibition can support Kras-driven EMT in several mouse and human lung cancer cells 41 In this context, we tested the use of sLCRs in reflecting cellular and molecular responses to biological and chemical stimuli. In agreement with previous findings, longitudinal measurements in epithelial A549 cells revealed that high MGT#1 fluorescence was synergistically induced by the Ezh2 inhibitor GSK126 and TFGB signaling ( Figure 4 b).

[0373] Epithelial lung cancer cells exposed to TGFB signaling readily changed their morphology and began to express high levels of MGT#1, as measured by flow cytometry ( Fig.11 ab). Interestingly, at early time points, flow cytometry revealed that TFGB signaling and Ezh2 inhibition via GSK126 induced similar degrees of molecular shifts, but GSK126 did not induce cell morphological changes. In a combined setting, TFGB signaling and GSK126 synergistically induced MGT#1 activation, and intermediate morphological changes were also observed ( Fig.11 ab), which raises the intriguing possibility that GSK126 acts on EMT through additional mechanisms in addition to being an amplifier of TGFB signaling.

[0374] Example 7: Use of Ezh2 inhibition and MGT#1 to study the signaling and genetic basis of epithelial-to-mesenchymal transition in NSCLC cells

[0375] To utilize Ezh2 inhibition and MGT#1 as a framework to elucidate the signaling basis of EMT in NSCLC cells, we next investigated the role of EMT in GSK126- and vehicle-treated A549-MGT#1 cells. 低 Cytokine screening was performed in cells. Consistent with the above data and our recently published observations (Serresi et al., J. Exp. Med, 2018, doi: 10.1084 / jem.20180801), TNFα was shown to be the main signal transduction for MGT#1 expression in epithelial lung cancer cells as well, with a moderate additive effect of GSK126 on the overall high fluorescence output measured in the longitudinal medium-throughput microplate reader screening. At the same time, we confirmed that A549 cells responded differently to TLR stimulation via bacterial LPS when GSK126 was present, and also under these experimental conditions, we showed that TGFB1 induced MGT#1 more significantly when combined with GSK126. Systematic analysis of screening with several cytokines and their combinations showed that Ezh2 inhibition enhanced the transcriptional response to external signal transduction of EMT ( Fig.12 Overall, the MGT#1 response suggests that multiple signaling pathways may converge during EMT and implies that transcriptional repression controls cellular metastability.

[0376] Next, we wanted to use Ezh2 inhibition and MGT#1 as a framework for high-throughput screening to elucidate the genetic basis of EMT in NSCLC cells. First, we transduced both A549 and H1944 Kras-driven NSCLC cells with the MGT#1 reporter. Subsequently, we introduced Tet-inducible KRAB-dCas9 and a library of sgRNAs targeting the entire human kinome (543 genes, 5,901 gRNAs total; ~5 gRNAs / gene) in both cell lines. In addition, we also included essential and non-essential genes targeted by the gRNAs to use as controls for the screening process. This system allows for the systematic knockdown of single genes in single cells ( Figure 4 c). By treating with GSK126 as described previously, we FACS purified NSCLC cells that were either improved or impaired in their ability to support expression of fluorescent reporters and displayed either an epithelial or mesenchymal phenotype ( Figure 4 de). Gene set enrichment analysis supported the overall quality of the screen, as measured by essential genes, but not non-essential genes, being significantly depleted in both cell lines in vitro compared to the input population (data not shown). 低 and H1944-MGT#1 低 With its MGT#1 高In contrast, we only retrieved a small fraction of gRNAs that were statistically differentially enriched or depleted in either state in either cell line (14 / 5912, 0.24%), suggesting that most human kinases are dispensable for GSK126-driven EMT. However, both gRNAs were statistically significant and showed similarity to A549-MGT#1. 低 and H1944-MGT#1 低 The high fold change of cell association indicated that their expression could lead to transcriptional repression of kinase-related genes, thus leading to EMT of lung cancer cells after Ezh2 inhibition ( Figure 4 e). Interestingly, one gRNA targeted the ACVR1 receptor that was previously reported to enhance NF-kB-driven EMT. 42 , and one gRNA targets CNKSR2, a scaffold protein involved in RAS-dependent signaling that is a non-obvious candidate for controlling EMT in lung cancer. We validated the results of the screen using conventional CRISPR / Cas9 technology, and two independent clones, CNKSR2 KO clones, showed enhanced epithelial features compared to parental controls and were similar to ARID1A KOs expected to be required for the Ezh2 loss-of-function phenotype ( Figure 4 f) It has been previously shown that RAS-driven EMT occurs via the Hippo pathway 43 Our data generated using potential sLCRs reveal additional mechanisms that may directly promote EMT through RAS / MAPK-dependent signaling.

[0377] Taken together, the results obtained with EMT in three different cancer types highlight the tissue-independent capacity of our sLCR to reveal this homeostasis in tumors.

[0378] Example 6: MGT#1 as a genetic tracking reporter for tumor homeostasis in vivo

[0379] Having demonstrated the utility of sLCR in dissecting cellular and molecular states in vitro, we next wished to test MGT#1 as a reporter for genetic tracking of tumor homeostasis in vivo. 微弱 Cells were transplanted intracranially into NSG mice and tumor formation was monitored longitudinally. At the onset of neurological signs at high-grade disease stages, we sacrificed the animals and performed histochemistry and immunohistochemistry as well as endogenous and surface marker analyses. Histologically, all tumors appeared to be grade IV GBM, with large portions of the mouse brain infiltrated by malignant cells, indicating extensive proliferation and invasion ( Figure 5a). For each animal (n=10), we used imaging-guided tumor resection to generate single-cell preps while preserving infiltrated brain tissue. Immunohistochemical staining revealed that cells expressing MGT#1 were non-randomly distributed in the tumor mass but well confined to the invasion front ( Figure 5 ab).

[0380] Given that both chromatin modifications and gene silencing in response to the virus could potentially influence sLCR expression, we used two approaches to confirm that MGT#1 reflects functional intratumoral heterogeneity and to exclude that cells expressing MGT#1 are merely escapees. First, we examined all dense areas in which MESGBM signaling was absent for expression of other markers as well as MGT#1-independent H2B-CFP. We confirmed that the vast majority of tumor tissue that stained in immunostaining was accessible to the antigen by means of tubulin staining, and we confirmed that several MGT#1 “dark” cells, in which active proliferation could be inferred by chromatin condensation, were indeed H2B-CFP-positive ( Figure 5 cd). Secondly, we performed parallel in vitro / ex vivo surface marker and endogenous analysis by flow cytometry. Consistent with immunohistochemical staining, endogenous mVenus fluorescence expression showed significant heterogeneity in vivo. 微弱 Compared to the cells, xenograft-derived tumor cells showed a small population of bright MES-hGICs-MGT#1 cells, while the vast majority of tumor cells transformed into MGT#1 低 or dark state ( Figure 5 e). The cell surface receptor CD133, which is commonly used to mark tumor-propagating cells in patient-derived xenografts, shows the expression of total CD133 in vitro. 高 Of note, cells expressing CD133 included a significant fraction of both MGT#1-expressing and non-expressing cells, supporting the ability of MGT#1 to describe functional heterogeneity ( Figure 5 e).

[0381] Overall, our experiments highlight the ability of sLCR to elucidate intratumor heterogeneity ( Figure 5 f).

[0382] Further experiments were performed to demonstrate the feasibility and implementation of the present invention:

[0383] Example 7: Further characterization of the synthetic locus control region (sLCR)

[0384] sLCRs are designed to mimic endogenous CREs, such as α-globin LCRs, which show position-independent cell type and developmental stage-specific expression and engage transcription factors. These elements are often defined as super enhancers and are concentrated into coactivator puncta. To test whether sLCRs share features with endogenous LCRs, we measured nascent RNA in MGT#1-transduced cells by RNA-FISH and searched for BRD4 or MED1 concentrates using IF. Dual IF and RNA-FISH identified colocalization between BRD4 or MED1 and nascent RNA of MGT#1 in fixed tumor cells expressing MGT#1 ( Figure 1 g). In addition, both the inducible MGT#1-driven mVenus and the “housekeeping” PGK-driven H2B-CFP mRNA were present in the tumor cell cytoplasm, but only mVenus was detectable in the nucleus ( Fig.16 ), indicating the difference in strength between the two CREs.

[0385] Next, we transduced proneural (PNGT#1-2) and mesenchymal (MGT#1-2) sLCR lentiviral particles into spontaneously immortalized human neural progenitor cells, which had acquired high copy numbers of PDGFRA, c-Myc, and CDK4. To recapitulate the common PN and MES GBM genetic background, we further engineered hGICs to lack PTEN and carry IDH1 R132 and TP53 R273H Point mutations or further deletion of TP53 and NF1 generated PN-hGICs and MES-hGICs, respectively. These cells showed DNA methylation profiles similar to those of GBM patients and acquired subtype-specific gene expression in vivo, and therefore represented two different GBM subtypes. Under in vitro growth factor-defined conditions, PNGT#1-2 showed strong expression in both cell types, while MGT#1-2 showed overall low expression in both genotypes, emphasizing the design specificity for different regulatory networks. Notably, MGT#1 had higher basal expression in MES-hGICs compared to PN-hGICs, indicating a genotype-specific response ( Figure 1 h).

[0386] We therefore designed methods to systematically generate synthetic LCRs that reflect a given cellular identity while retaining key features of the endogenous Cre.

[0387] Example 8: Additional evidence supporting functional reporter activity through sLCR

[0388] In order to investigate the effect of MES-hGICs-MGT#1 低 and PN-hGICs-MGT#1低 To investigate the adaptive response of cells to external signaling, we next performed a phenotypic screen. We stimulated NBE-proliferating hGICs with selected factors (cytokines, growth factors, compounds) and analyzed FACS ( Fig.13 b) Normalized to the initial hGIC, sLCR revealed that MES- and PN-hGICs-MGT#1 低 The results revealed shared and exclusive responses in MES-GBM and highlighted TNFα signaling as well as human serum or FBS and Activin A as regulators in MES-GBM. The results were reproducible and followed up between two independent MES-GBM sLCRs (MGT#1-2). In contrast, the PN phenotype appeared to be less responsive to changes induced by external signaling. Fig.13 bc and 17). MES GBM specialization appears to be complementary to the pre-existing endogenous phenotype, as measured by surface expression of CD133 and PNGT#2. Indeed, TNFα has been previously reported to be an important MES-GBM signaling pathway and an inducer of PN-to-MES transition. Furthermore, NFkB, a known TNF-induced TF, was found to engage at least two CREs included in the MGT#1 sLCR upon TNFα stimulation ( Fig. 9 b) FACS sorted PN-hGICs-MGT#1 低 With MES-hGICs-MGT#1 低 Comparable MGT#1 expression levels, but still not reaching a response similar to TNFα ( Figure 2 g and 8 and 13a). Consistently, despite proliferation under the same signaling conditions, MES-hGICs-MGT#1 低 and PN-hGICs-MGT#1 低 Cells show differences in activation and endogenous expression of selected signaling pathways ( Figure 2 TNFα stimulation induced phosphorylation of NFkB-p65, STAT3, and p38-MAPK in both cell types, but this resulted in distinctly different gene expression outputs ( Figure 2 d). These analyses demonstrate that, whereas TNFα drives the MES GBM signature in MES-hGICs, PN-hGICs commit to a state resembling that of naive MES-hGICs ( Figure 2 Overall, our results suggest that sLCR MGT#1-2 reflects the endogenous mesenchymal GBM gene expression program, capturing both the activation status of signaling pathways (e.g., TNFα) and any pre-existing context-related differences (e.g., MES vs. PN background).

[0389] The observation that pro-differentiation signaling (i.e., human serum or FBS) drives reporter activation is consistent with previous findings showing that the MES-GBM signature may be attributed to FBS-cultured astrocytes rather than any mouse brain cells. Notably, washout experiments showed that the MES-GBM state is reversible within a time frame of several days ( Fig.18 ), indicating that the MESGBM state can be acquired and reversed.

[0390] Mesenchymal transdifferentiation in GBM has been found to be a dominant event in relapse after standard care and has been linked to acquired radiation resistance via TNF-driven NFKB activation. Extensive evidence supports the link between inflammatory signaling, EMT, innate immune cell infiltration, and radiation resistance. To experimentally test whether irradiation can induce mesenchymal transdifferentiation in a cell-autonomous manner, MES-hGICs-MGT#1 were used to treat GBM. 低 and PN-hGICs-MGT#1 低 Cells were exposed to ionizing radiation (IR) alone or in combination with TNFα. MGT#1 activation showed a dose response to increasing IR, whether single or divided doses ( Figure 2 g and 19). MES-hGICs-MGT#1 低 and PN-hGICs-MGT#1 低 Both cells showed enhanced mesenchymal transdifferentiation in combination with TNFα. A single 10 Gy radiation dose is sublethal in a variety of human GSCs. Likewise, our GICs remained healthy, either alone or in combination with other treatments (e.g., TNFα or temozolomide), and showed residual DNA damage marker γH2AX phosphorylation twenty-four hours after irradiation, confirming that double-strand breaks had occurred and were being repaired ( Figure 2 h).

[0391] Classical NFkB activation can occur downstream of TNFα signaling as well as through non-classical genotoxic stress. To provide experimental support for the importance of NFkB in both intrinsic and acquired MES-GBM states, we deleted p65 / RELA using CRISPR / Cas9 in MES-hGICs, which resulted in a significant downregulation of intrinsic MGT#1 expression ( Fig.13 c). Of note, while the ability of TNFα to induce MES-GBM signaling was significantly impaired in both polyclonal and monoclonal RELA KO cells, the IkB kinase (IKK) inhibitor-16 further limited the adaptive response to TNFα. In monoclonal RELA KO GICs, we excluded compensation due to RELA KO escapers, suggesting that other NFkB transcription factors in RELA KO cells can transduce TNF signaling ( Fig.19 b).

[0392] In patients, the GBM stem cell state is the dominant genetic repertoire in maintaining tumor homeostasis. Next, we hope to test whether sLCRs can be used to discover genes that regulate the MES GBM state by performing a genome-wide pooled CRISPR / Cas9 screen. 低 The genetic screens in were performed in their naive state or when the MES-GBM state was induced by external signaling or genotoxic stress (i.e., FBS+TNFα or TNZ+IR, respectively; Fig.13 d). Of the 73,179 gRNAs, the phenotypic screen returned 333 and 1,164 gRNAs associated with the high and low fractions of MGT#1, respectively ( Fig.13 e). Effects of libraries and treatments on MGT#1 expression, average statistical depletion of genes associated with fitness but not controls, and depletion of two sgRNAs targeting RELA in the naive state ( Fig. 20 ad), all suggesting that the screen could uncover functional genes. Interestingly, several clinically relevant drug targets such as PARP1 and EED appear to be key regulators of MGT#1 activation under all conditions but are not essential for proliferation. PARP1 activity has been reported to be required for IR-induced NF-kB activation, and in other contexts, Polycomb repressor complex 2 scaffolds EED inhibition to promote EMT. To test whether this approach could be used to prioritize drug treatments that lead to cell fate changes, we searched for upstream regulators of the hits. Among them, some gRNAs were previously associated with targets downstream of RAR / RXR agonists and MEK1 inhibitors, with statistical trends of enrichment in the MGT#1-low and -high fractions, respectively ( Fig.13 e and 20). To test the prediction that both drugs might have an effect on cell fate determination, we exposed MES-hGICs-MGT#1 to either the MEK1 selective inhibitor TAK-733 or all-trans retinoic acid (ATRA). In both cases, MES-hGICs-MGT#1 responded to short-term TNFα stimulation (4 h) with greater upregulation of both MGT#1 and MES-GBM endogenous markers compared to TNFα alone ( Fig.13 f), indicating that pretreatment sensitized these cells to MES GBM program activation. ATRA and TAK-733 sensitized MGT#1 above the effect of the EED / EZH2 inhibitor GSK126, supporting the specificity of treatment. Thus, sLCR provides a phenotypic layer of pharmacogenomic information compared to previous bulk studies based on fitness alone.

[0393] Overall, these results provide experimental evidence that mesenchymal GBM is a transient and reversible cell state and support the robustness and effectiveness of designed sLCRs in phenotypic screening applications.

[0394] Example 9: sLCR can distinguish molecularly distinct entities

[0395] Primary cancer types can be grouped based on their molecular profiles. Chromatin accessibility is the strongest predictor of cancer type similarity and can be used to identify subtype identities within a common dimensional space of individual cancer types. To investigate whether the acquired heterogeneity described by sLCRs is accompanied by changes in genome-wide chromatin accessibility, we performed in vitro and in vivo chromatin accessibility studies in MES-hGICs-MGT#1. 高 Differential analysis of chromatin accessibility revealed many genes undergoing remodeling, particularly at the driver of the PN-to-MES transition WWTR1 (TAZ) and at several TNF receptor gene loci, indicating that genetic tracking of remodeling events occurs only in the physiologically relevant tumor microenvironment ( Fig.14 ab). Integration of ATAC-seq data from TCGA and glioma stem cells further revealed MES-hGICs-MGT#1 高 Cells represent specific entities within the general glioma space ( Fig.14 c). Importantly, unsupervised chromatin profiling of GIC, divided by high and low expression of MGT#1, grouped those samples into defined clusters ( Fig.14 d), showing that MGT#1 expression emphasizes the acquisition of unique patterns in chromatin accessibility. These results highlight the power of sLCR in revealing intratumor heterogeneity and enable in-depth cellular and molecular characterization of tumor models alongside primary cancer data.

[0396] Example 10: sLCR helps uncover the therapeutic significance of non-cell autonomous crosstalk between tumor and immune cells

[0397] IDH1 wild-type GBM infiltration by glioblastoma-associated microglia / monocytes (GAMs) has recently been linked to NF1 deficiency and MES-GBM subtype identity, but whether there is a causal relationship between GAMs and MES-GBM remains unresolved. To experimentally test the hypothesis that innate immune cells are causal rather than recruited by MES transdifferentiation in NF1-deficient GBM cells, we performed IDH1 wild-type and NF1-deficient MES-hGICs-MGT#1. 低 In vitro co-culture of cells with an immortalized human microglial cell line (hMG; cl.C20).

[0398] First, we compared the expression of both PN- and MES-sLCR by single cells in GBM tumor spheroids and multicellular organoid culture conditions. Spheroid culture supports the expansion of stem and progenitor cells with restricted spontaneous differentiation and cell death 50,51 , while glioma organoids increase the number of phenotypically diverse cell populations. Similar to the in vivo expression pattern ( Fig.14 a), we found that MES-hGICs displayed heterogeneous PN- and MES-sLCR expression patterns under organoid conditions and in the presence of human microglia, in contrast to their homogeneous expression in pure spheroid cultures ( Fig.15 a).

[0399] Next, we used permeable chamber inserts to establish co-cultures between homogenous GBM tumor spheroids and hMG cells. Surprisingly, hMG cells drove MGT#1 induction in MES-hGICs to a comparable extent as TNFα ( Fig.15 bc and 21). Consistent with previous experiments, hMG also activated MGT#1 to a lesser extent in PN-hGICs. In contrast, in vitro, myeloid-derived suppressor cells (MDSCs) derived from human CD34+ only mildly stimulated MGT#1 expression in both cell lines ( Fig.21 ). MES-hGICs-MGT#1 from two conditions 高 Whole-transcriptome analysis of cells revealed common and exclusive NFκB-related gene activation and provided evidence that adaptive immune cells drive specific MES-GBM states, which largely share targets with the patient's signature ( Fig.15 d). Interestingly, we found no evidence for TNFα expression by either cell type. Rather, metabolic transcriptome remodeling characterized by genes in the cholesterol biosynthetic pathway appears to constitute a MES-hGIC signature specific for co-culture with hMG cells ( Fig.15 These data suggest that NFkB activation in tumor cells is primarily due to innate immune cells. Indeed, inflammatory mediators IFNγ and IL-2 from the adaptive immune system, as well as stromal-derived IL-6, did not trigger direct MGT#1 activation to a significant extent ( Fig.17 ), together providing experimental insights into the cascade of events leading to the MES-GBM state in vivo.

[0400] EMT is associated with resistance to chemotherapy but also provides therapeutic opportunities. DNA damage stress is a major therapeutic component of the standard of care in GBM, also known as the Stupp regimen. The TNF-NFkB signature in GBM has previously been associated with a mesenchymal state and radiation resistance in a large number of patients and PDX models. Therefore, we next exploited the ability of sLCR to identify MES homeostasis to explore the therapeutic implications of microglia-driven GBM states.

[0401] To this end, we FACS sorted MGT#1-2 after hMG-driven transformation. 高 and MGT#1-2 低 MES- and PN-hGIC cells were exposed to a selected panel of standard and targeted chemotherapeutic agents. Surprisingly, in contrast to their sLCR-low counterparts, MES-hGICs-MGT#1 高 or -MGT#2 高 The cells also demonstrated increased resistance to both DNA damage-based therapeutics (Olaparib, the ATR inhibitor VE-821, topotecan, mitomycin C) and LXR623, an LXR agonist that regulates cholesterol efflux. Fig.15 h and 21). Importantly, MES-hGICs-MGT#1 高 Cells retain the ability to interact with targeted agents such as BAY11-7085 (IκB), WP1066 (STAT3; Fig.15 h and 21). MES-hGICs-MGT#1 高 The altered chemosensitivity profile was consistent with changes in gene expression driven by hMG cells, including in MES-hGICs-MGT#1 高 The DNA damage gene signature in the cells was impaired and the cell cycle profile was altered along with the overexpression of a cholesterol biosynthesis signature in patient-derived MES-GBM ( Fig.21 ). Similar results were obtained with the proneural genotype, indicating that hMG cells can switch hGICs into two functionally and therapeutically distinct states and supporting the use of sLCRs in target discovery platforms to integrate complex responses associated with tumor heterogeneity.

[0402] Overall, our results serendipitously link innate immune cells to the MES-GBM state and highlight the potential of sLCR to mechanistically dissect relevant non-cell autonomous interactions both in vivo and ex vivo.

[0403] Further advantages and implementations of the present invention:

[0404] Currently, our understanding of complex cellular and molecular mechanisms at the biological level relies primarily on in vivo experiments and is limited by available genetic tracking technologies. We have established a systems biology framework that allows the generation of synthetic reporters capable of intercepting cell-intrinsic and non-cell autonomous signaling. These sLCRs can be used to illustrate genotype-to-molecular and cellular phenotype transitions in vitro and in vivo. Experimentally, sLCRs can be used to characterize molecular mechanisms linking biological, chemical, and environmental stimuli to cell fate transitions, including through chemical and forward genetic screens.

[0405] We have applied this approach to investigate the cellular and molecular features of GBM subtype expression profiles. Identification of proneural and mesenchymal GBM subtypes was consistent across expression platforms (microarray, RNA-seq), readouts (gene expression, DNA methylation), and patient populations (Western and Chinese). Despite such extensive efforts, the significance of GBM subtypes remains elusive when it comes to their origin, location, or spatiotemporal evolution.

[0406] By combining a near-isogenic model and MES sLCR, we show that the most important components of MES-GBM specialization are adaptive in nature. Although genotype-directed intrinsic MES signaling, exemplified by MES-hGICs, showed measurable but modest differences in the expression of MES sLCRs when compared to PN-hGICs, TNF signaling as well as pro-differentiation stimuli (e.g., FBS) are the major triggers of MES signaling. Interestingly, both TNFα and FBS trigger MES transdifferentiation by differentially affecting cell morphology. Both responses appear to be imprinted in vivo, as inferred by the degree of heterogeneity in MGT#1 expression and markers of undifferentiated and self-renewing tumor cells. Our experiments correlated MGT#1 readout in GBM cells to expression of migration-related markers such as CD44, response to a pro-inflammatory microenvironment, and resistance to sublethal doses of genotoxic stress, all of which represent hallmarks of tumor progression, including in GBM at the single-cell level. 18 These findings illustrate the ability of MGT#1 to elucidate cellular and molecular mechanisms in GBM.

[0407] This technology is able to transform cell and molecular profiling into phenotypic maps, which can meet experimental needs related to continuous mapping of cell and molecular features in health and disease (including at the single-cell level). In fact, sLCR improves on in vivo phenotyping, which still represents an essential step for a comprehensive understanding of complex cellular and molecular mechanisms at the biological level. In this way, it provides important ex vivo opportunities.

[0408] We show that sLCRs, which reflect in vivo regulatory networks, accurately intercept cell-intrinsic and non-cell-autonomous signaling and are successfully applied to dissect genotype-to-molecular and cellular phenotype transitions in vivo and in vitro. We demonstrate the utility of this system by studying the cellular and molecular basis of GBM subtype expression profiles. The identification of proneural and mesenchymal GBM subtypes was consistent across expression platforms (microarray, RNA-seq, and single-cell RNA-seq), readouts (gene expression, DNA methylation), and patient ethnicity (Western and Chinese). Despite such extensive efforts, the significance of GBM subtypes remains elusive when it comes to their origin, location, or spatiotemporal evolution, and more importantly, their therapeutic implications.

[0409] The proneural and mesenchymal GBM programs depend on the activity of specific transcription factors. Here, we integrated near-isogenic models and cell lines with sLCRs, and the results are consistent with PN-GBM being the default GBM entity, which is strongly dependent on RTK signaling and is therefore promoted by neural stem cell culture conditions. Instead, we show that the most important components of MES-GBM specialization are adaptive in nature. In the absence of a tumor microenvironment, even in cells with a MEG-GBM genotype (e.g., NF1 deletion), the PN state appears to be hard-wired, but the MES identity is rapidly amplified by acute inflammation and pro-differentiation stimuli (e.g., TNF signaling and bovine or human serum). Interestingly, in different cell types, MES transdifferentiation measured by sLCRs may occur with differentially affecting cell morphology. Our experiments associate MES-sLCR readouts in GBM cells, feedforward responses to pro-inflammatory microenvironments, resistance to sublethal doses of genotoxic stress, and expression of migration-related markers such as CD44, all of which represent hallmarks of human cancer progression, including in GBM at the single-cell level. These features appear to be imprinted in tissue homeostasis, as inferred by clustered cellular expression patterns ("homeostasis") and heterogeneity in in vivo and ex vivo tumor models.

[0410] Genetic tracing of the major components of MES-GBM in three different cancer types underscores the tissue-independent capacity of our sLCRs to reveal tumor homeostasis and provides further evidence that EMT represents a hijacking of developmental cellular processes. These findings illustrate the versatility of sLCRs in elucidating cellular and molecular mechanisms in multifactorial diseases. Furthermore, the use of sLCRs in pharmacogenomics could significantly advance translational medicine by revealing phenotype-specific dependencies and resistances.

[0411] Ultimately, sLCRs can mechanically dissect pathophysiologically relevant non-cell autonomous interactions between innate immune cells and tumor cells. GAMs are thought to constitute the source of TNFα in both glioma mouse models and human tumors. Our results provide experimental support for the clinical association between MES-GBM subtypes and specific immune conditions, and reveal a TNFα-independent pathway for MES-GBM. Importantly, the GAM-driven MES-GBM states identified herein show a degree of overlap with patient signatures that is comparable to the overlap of individual patient signatures themselves.

[0412] In summary, sLCRs have been shown to be able to characterize molecular mechanisms by associating biological, chemical, and environmental stimuli to cell fate transitions, including through chemical and genetic screening. Previous attempts to generate synthetic reporters using massively parallel sequencing or hybrid models have revealed the potential uses of this approach and limitations associated with limited control over design. Our approach essentially solves this problem and represents the basis for future development, from linear improvements to basic design components (e.g., using curated resources of TFBS and cis elements) to systematic generation and verification of a large number of sLCRs, followed by features of successful machine learning. At the same time, robust cell type or state specificity and granularity can be extended by combining sLCRs with DNA barcodes. Adjustable operation can be achieved by coupling sLCR transcriptional inputs with synthetic effector proteins capable of Boolean logic outputs. Therefore, genetic tracking by sLCRs is scalable and can be extended to almost any given system, whether in vitro or in vivo, to dissect cell-intrinsic and non-cell-autonomous mechanisms that control normal and disease homeostasis.

[0413] References

[0414] 1. Kretzschmar, K. & Watt, FMLineage tracing. Cell 148, 33–45 (2012).

[0415] 2. Barker, N. et al. Identification of stem cells in small intestine and colon by marker gene Lgr5. Nature 449, 1003–1007 (2007).

[0416] 3. Barker, N., Tan, S. & Clevers, H. Lgr proteins in epithelial stem cellbiology. Development 140, 2484–2494 (2013).

[0417] 4.Livet,J.et al.Transgenic strategies for combinatorial expression offluorescent proteins in the nervous system.Nature 450,56–62(2007).

[0418] 5.Liu,C.et al.Mosaic analysis with double markers reveals tumor cellof origin in glioma.Cell 146,209–221(2011).

[0419] 6.Schwitalla,S.et al.Intestinal Tumorigenesis Initiated byDedifferentiation and Acquisition of Stem-Cell-like Properties.Cell(2012).doi:10.1016 / j.cell.2012.12.012

[0420] 7.Schepers,A.G.et al.Lineage tracing reveals Lgr5+stem cell activityin mouse intestinal adenomas.337,730–735(2012).

[0421] 8.Driessens,G.,Beck,B.,Caauwe,A.,Simons,B.D.&Blanpain,C.Defining themode of tumour growth by clonal analysis.Nature(2012).doi:10.1038 / nature11344

[0422] 9.Oshimori,N.&Fuchs,E.Paracrine TGF-βSignaling Counterbalances BMP-Mediated Repression in Hair Follicle Stem Cell Activation.Cell Stem Cell 10,63–75(2012).

[0423] 10.Chen,J.et al.A restricted cell population propagates glioblastomagrowth after chemotherapy.Nature(2012).doi:10.1038 / nature11287

[0424] 11.Zhu,L.et al.Multi-organ Mapping of Cancer Risk.Cell 166,1132–1146.e7(2016).

[0425] 12.Church,G.M.,Elowitz,M.B.,Smolke,C.D.,Voigt,C.A.&Weiss,R.Realizingthe potential of synthetic biology.Nat Rev Mol Cell Biol 15,289–294(2014).

[0426] 13.Stupp,R.et al.Effects of radiotherapy with concomitant andadjuvant temozolomide versus radiotherapy alone on survival in glioblastomain a randomised phase III study:5-year analysis of the EORTC-NCICtrial.Lancet Oncol.10,459–466(2009).

[0427] 14.Wang,Q.et al.Tumor Evolution of Glioma-Intrinsic Gene ExpressionSubtypes Associates with Immunological Changes in the Microenvironment.CancerCell 32,42–56.e6(2017).

[0428] 15.Noushmehr,H.et al.Identification of a CpG island methylatorphenotype that defines a distinct subgroup of glioma.Cancer Cell 17,510–522(2010).

[0429] 16.Verhaak,R.G.W.et al.Integrated genomic analysis identifiesclinically relevant subtypes of glioblastoma characterized by abnormalitiesin PDGFRA,IDH1,EGFR,and NF1.Cancer Cell 17,98–110(2010).

[0430] 17.Sottoriva,A.et al.Intratumor heterogeneity in human glioblastomareflects cancer evolutionary dynamics.Proc Natl Acad Sci USA 110,4009–4014(2013).

[0431] 18.Lee,J.-K.et al.Spatiotemporal genomic architecture informsprecision oncology in glioblastoma.Nature Genetics 49,594–599(2017).

[0432] 19.Phillips,H.S.et al.Molecular subclasses of high-grade gliomapredict prognosis,delineate a pattern of disease progression,and resemblestages in neurogenesis.Cancer Cell 9,157–173(2006).

[0433] 20.Bhat,K.P.et al.Mesenchymal Differentiation Mediated by NF-κBPromotes Radiation Resistance in Glioblastoma.Cancer Cell 24,331–346(2013).

[0434] 21.ENCODE Project Consortium et al.Identification and analysis offunctional elements in 1%of the human genome by the ENCODE pilotproject.Nature 447,799–816(2007).

[0435] 22.Thurman,R.E.,Day,N.,Noble,W.S.&Stamatoyannopoulos,J.A.Identification of higher-order functional domains in the human ENCODEregions.Genome Res 17,917–927(2007).

[0436] 23.Kim,T.H.et al.Analysis of the vertebrate insulator protein CTCF-binding sites in the human genome.Cell 128,1231–1245(2007).

[0437] 24.Ong,C.-T.&Corces,V.G.CTCF:an architectural protein bridging genometopology and function.Nat Rev Genet 15,234–246(2014).

[0438] 25.Lee,J.et al.Tumor stem cells derived from glioblastomas culturedin bFGF and EGF more closely mirror the phenotype and genotype of primarytumors than do serum-cultured cell lines.Cancer Cell 9,391–403(2006).

[0439] 26.Ozawa,T.et al.Most Human Non-GCIMP Glioblastoma Subtypes Evolvefrom a Common Proneural-like Precursor Glioma.Cancer Cell 26,288–300(2014).

[0440] 27.Quail,D.F.et al.The tumor microenvironment underlies acquiredresistance to CSF-1R inhibition in gliomas.Science 352,aad3018(2016).

[0441] 28.Szulzewsky,F.et al.Human glioblastoma-associated microglia / monocytes express a distinct RNA profile compared to human control and murinesamples.Glia 64,1416–1436(2016).

[0442] 29.a Dzaye,O.D.et al.Glioma Stem Cells but Not Bulk Glioma CellsUpregulate IL-6 Secretion in Microglia / Brain Macrophages via Toll-likeReceptor 4 Signaling.J.Neuropathol.Exp.Neurol.75,429–440(2016).

[0443] 30.Inda,M.-D.-M.et al.Tumor heterogeneity is an active processmaintained by a mutant EGFR-induced cytokine circuit in glioblastoma.GenesDev 24,1731–1745(2010).

[0444] 31.Hossain,A.et al.Mesenchymal Stem Cells Isolated From Human GliomasIncrease Proliferation and Maintain Stemness of Glioma Stem Cells Through theIL-6 / gp130 / STAT3 Pathway.Stem Cells 33,2400–2415(2015).

[0445] 32.Midwood,K.et al.Tenascin-C is an endogenous activator of Toll-likereceptor 4 that is essential for maintaining inflammation in arthritic jointdisease.Nat Med 15,774–780(2009).

[0446] 33.Jachetti,E.et al.Tenascin-C Protects Cancer Stem-like Cells fromImmune Surveillance by Arresting T-cell Activation.Cancer Res 75,2095–2108(2015).

[0447] 34.Stanzani,E.et al.Radioresistance of mesenchymal glioblastomainitiating cells correlates with patient outcome and is associated withactivation of inflammatory program.Oncotarget 8,73640–73653(2017).

[0448] 35.Bao,S.et al.Glioma stem cells promote radioresistance bypreferential activation of the DNA damage response.Nature 444,756–760(2006).

[0449] 36.Hinz,M.et al.A cytoplasmic ATM-TRAF6-cIAP1 module links nuclearDNA damage signaling to ubiquitin-mediated NF-κB activation.Mol Cell 40,63–74(2010).

[0450] 37.Lei,L.et al.Glioblastoma models reveal the connection betweenadult glial progenitors and the proneural phenotype.PLoS ONE 6,e20041(2011).

[0451] 38.Rheinbay,E.et al.An Aberrant Transcription Factor NetworkEssential for Wnt Signaling and Stem Cell Maintenance in Glioblastoma.CellRep(2013).doi:10.1016 / j.celrep.2013.04.021

[0452] 39.Kalluri,R.&Weinberg,R.A.The basics of epithelial-mesenchymaltransition.Journal of Clinical Investigation 119,1420–1428(2009).

[0453] 40.Baird,R.D.&Caldas,C.Genetic heterogeneity in breast cancer:theroad to personalized medicine?BMC Med 11,151(2013).

[0454] 41.Serresi,M.et al.Polycomb Repressive Complex 2 Is a Barrier toKRAS-Driven Inflammation and Epithelial-Mesenchymal Transition in Non-Small-Cell Lung Cancer.Cancer Cell 29,17–31(2016).

[0455] 42.Wamsley,J.J.et al.Activin upregulation by NF-κB is required tomaintain mesenchymal features of cancer stem-like cells in non-small celllung cancer.Cancer Res 75,426–435(2015).

[0456] 43.Shao,D.D.et al.KRAS and YAP1 converge to regulate EMT and tumorsurvival.Cell 158,171–184(2014).

[0457] 44.Ohinata,Y.,Sano,M.,Shigeta,M.,Yamanaka,K.&Saitou,M.Acomprehensive,non-invasive visualization of primordial germ cell developmentin mice by the Prdm1-mVenus and Dppa3-ECFP double transgenicreporter.Reproduction 136,503–514(2008).

[0458] 45.Gargiulo,G.et al.In vivo RNAi screen for BMI1 targets identifiesTGF-β / BMP-ER stress pathways as key regulators of neural-and malignantglioma-stem cell homeostasis.Cancer Cell 23,660–676(2013).

[0459] 46.Gargiulo,G.,Serresi,M.,Cesaroni,M.,Hulsman,D.&Van Lohuizen,M.Invivo shRNA screens in solid tumors.Nat Protoc 9,2880–2902(2014).

[0460] 47.Li,P.,Markson,J.S.,Wang,S.,Chen,S.,Vachharajani,V.,and Elowitz,M.B.(2018).Morphogen gradient reconstitution reveals Hedgehog pathway designprinciples.Science 360,543–548.

[0461] 48.Blankvoort,S.,Witter,M.P.,Noonan,J.,Cotney,J.,and Kentros,C.(2018).Marked Diversity of Unique Cortical Enhancers Enables Neuron-SpecificTools by Enhancer-Driven Gene Expression.Curr Biol 28,2103–2114.e2105.

[0462] 49.Takahashi,K.,and Yamanaka,S.(2006).Induction of pluripotent stemcells from mouse embryonic and adult fibroblast cultures by definedfactors.Cell 126,663–676.

[0463] 50.Suvà,M.-L.,Rheinbay,E.,Gillespie,S.M.,Patel,A.P.,Wakimoto,H.,Rabkin,S.D.,Riggi,N.,Chi,A.S.,Cahill,D.P.,Nahed,B.V.,et al.(2014).Reconstructing andReprogramming the Tumor-Propagating Potential ofGlioblastoma Stem-like Cells.Cell

[0464] 51.Frith,M.C.,Fu,Y.,Yu,L.,Chen,J.-F.,Hansen,U.,and Weng,Z.(2004).Detection of functional DNA motifs via statistical over-representation.Nucleic Acids Res 32,1372–1381.

[0465] 52.Phillips,H.S.,Kharbanda,S.,Chen,R.,Forrest,W.F.,Soriano,R.H.,Wu,T.D.,Misra,A.,Nigro,J.M.,Colman,H.,Soroceanu,L.,et al.(2006).Molecularsubclasses of high-grade glioma predict prognosis,delineate a pattern ofdisease progression,and resemble stages in neurogenesis.Cancer Cell 9,157–173.

[0466] 53. Verhaak, RGW, Hoadley, KA, Purdom, E., Wang, V., Qi, Y., Wilkerson, MD, Miller, CR, Ding, L., Golub, TR, Mesirov, JP, et al. (2010). Integratedgenomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1.Cancer Cell 17,98–110.

[0467] 54.Sturm,D.,Witt,H.,Hovestadt,V.,Khuong-Quang,D.-A.,Jones,DTW,Konermann,C.,Pfaff,E., M., Sill, M., Bender, S., et al. (2012). HotspotMutations in H3F3A and IDH1 Define Distinct Epigenetic and BiologicalSubgroups of Glioblastoma. Cancer Cell 22, 425–437. Sequence Listing <110> Max-Delbrück-Center for Molecular Medicine Helmholtz Association <120> Methods for engineering cis-regulatory DNA <130> PPI21170396DE <150> EP18192715.3 <151> 2018-09-05 <160> 6 <170> PatentIn version 3.5 <210> 1 <211> 827 <212> DNA <213> Artificial sequence <220> <223> sLCR <400> 1 atatttattt ttaggaccag aaagttaag tgaattggat ttgatccatt ttctgaaagg ctggcaagaa ttcttgacat tgcacagga tttccatgtc agcatgttct cacatgtatg atctaattta gagattattt tggggggcgg gggttgagga aatggcatga ctcagagttt aaaagcccca aatcttagct gtgcctgtgt agctttacca cataaccccat tgataactta 300. gttgtgcaac catcaccacc atctgttttc agaactcttt tcattttgcg aaactgaaac 360. ccgttaagca ctgatttccc actctccctc ctcccagccc atagcaaacc accatcccac cagcactttc atttcgcaaa tggcaaaact gagccgata ttgtggttgt gacttatccc 420 aaagtaatat acacataaac ctctatggat gaggaaaaag acagagga actaaaaatt 540. caaagaaca aatttgactc acagatttgc tgactcatag ttgtgacact tcctggctca ggagttgaa tttcattaag cctttgtggt ttggggctct gctgtgcttt gacagctctg atctcctccc ttccggctgg gctgtctggg gcgctctaaa atgagtgttg atttaatgca 660 ctgccttcgc acccgtgctg gtgcgtcccg gggacagggg tggctgtgcg gtgccgcggc 720 ggccggcggg gctccttccc cagcaggggt ggggacgctg agtcacggat ctgtcaccgc 780 tttgcacctc tccgagccct cgggggccaa agcaaaagcg aaagcga 827 <210> 2 <211> 1015 <212> DNA <213> Artificial Sequence <220> <223> sLCR <400> 2 ctagaacagc agggccacct ccttctctcc cccgcgggca tgggccccca cccccactgc 60 cggcagagtg ctgaggactc gtgcaccatg agaacttctg accatgagaa ctttgacttc 120 cggatttggg ggatctgccc aggtgaacac aatgcaaggg gctgcatgac ctaccaggac 180 agaactttcc ccaattacag ggtgactcac agccgcattg gtgactcact tcaatgtgtc 240 atttccggct gctgtgtgtg agcagtggac acgtgagggg ggggtgggtg agagagacag 300 gccacattgt gcaacagatc tctagagctt tttcatcttg caaaactgaa actgtatacc 360 catggaacaa cagctccctg ctcccctccc cctcagctcc tgggtagtga catttcttga 420 ttctcagtaa actatcacaa gaacaaaaaa ccaaacaccg catattctca ctcataggtg 480 ggaattgaac aatgagatca catggacaca ggaaggggaa tatcacactc tggggactgt 540 ggtggggtgg ggggagtggg gagggatagc actgggatgt cccaagagaa ggggaagagg 600 gggaggtgtt agagaacttg tgtgttcaac cgaaacatga tgaaaacagg gaaagccccc 660 aagatacctg tcattcccga tgatgtcaga ttcagcaaat tcaatgataa caaaacatta 720 tgaaaaaatt agtaattaaa ataatacagc aatgtgtatg aacaaaataa tcaatgaaag 780 tgaaacctaa tagtaattcc acaaacttat tacaaagcta ttaatttaaa gagtagtggc 840 aattgaaaac cacaaccaac accagtgctt acagcagcaa tacttttact cagacttcct 900 gtttctggaa cttgccttct tttttgctgt gtttatactt cccttgtctg tggttagata 960 agtataaagc cctagatcta agcttctctg tcttcctccc tccctccctt cctct 1015 <210> 3 <211> 1050 <212> DNA <213> Artificial Sequence <220> <223> sLCR <400> 3 agagctcctg gccaaggtct ttgtgttcag accagaagag gaaggagggc tccctccccc 60 tggggctgtg gaggctgagg ctcctggggg gttgtccaca tctggaccgt gggagctgtt 120 ggggggaatg ggggcaggtg gagaagagga taagcagctg attgggccca gactactctg 180 ggctggctcc atcttacatg actgccacaa acagctgcag gagtgtgaca gatcacaaca 240 ctagcattgt acctcaaaat atgcttgtac cctaaggcac aagaactggt ttgacttaca 300 accgcagccc ccgtccgggc accccgaggc ccgcgggagc caccctcgaa ccccggccgc 360 gcacgggcgg ggcggcgcgc acctgccggg agcccgtgtt tgtaaacaaa ccgcgcgcct 420 aattagcctg gcgggagcgc gcgcgcgggg cggggggcgg ggcgtcggtg cgcgcgggca 480 ggtcggcccc gcccggggag gagccgcgct ctgccgcgcc ctccgtgtca ccatctcccc 540 cacccgactt ggcggggcgc gggcttgctg gagcctgcgg gacccagagc ccgctccgga 600 gccagccctg ggagtggcca gcttgaaccc gagggccccg cagaccgtta ctccggcccc 660 cgcccggggc ggggcgcgcg ggggcgcggc gcagcccaac ccgcacagcc gcgtccccaa 720 acaccaccga ggagggaaaa cagacggaga ggggtggggc tgcgggcggg gccggcgcct aattgggccg cgggcgcctc gaggtgggcg gggcataagg gggcggggcc gcggagaccc 840 cggggcgggag cgggagg aaaagaaga ctgagtacgc ggagaccgag attcggaat atttctgcct taattgttct tccattgtct ttctcctgtg ggtcccctct caccttctg 960 tatggtcctg gatcaccccc cgaggctttg tctcccccat ccacgggctt attctctcgg 1020 caccccttc ctctcccgtc atcggttgat 1050 <210> 4 <211> 1164 <212> DNA <213> The snowstorm <220> <223> sLCR <400> 4 ttattaatc cctcctctaa tccctccagc gggatcaggg aggaggtgcg ggacctgctg ccccggggctt gcccccatcc cggcctcacg catgggcgcc tgtctcagcc ctctcccagg 120 acgctgcagg tgtggctggg ccagcgctaa ttagtgggcc gcgcggggc cccgctgagc 180 240. ctttgacaga aaaggcggta gggaggtggg ggcagggagg cgctccacca gccagaagtc cggagcgcaa cccaaagtac tccatctcaa aagaaaaaag gcgggggcgg tggggggggg 300 gggtgatttc agtacaaagc ctacagacat tataaaaata ttaagatttt tgttcgtttg 360 ttttttgttt ttgagacaga gtctcactgt cacccccagg ctggagtctg tgccggcgcc 420 cgctgcttcg catctgcgcg cccgcccggt gccgggcccc gccctccgcc tcagccccaa 480 gctcggcccg cgggcccggc cacaggtgcc ccggcggccc cgcctggccc gagggaagag 540 ggcagctggg aggggcccat gagagaacca aaactgtgcc cccaggcttg gaaagaaatc 600 acatgtatgg ccagcaggaa ggttccggaa ggttccggag gacacctgca ggtgggactg 660 agaacagggg tctcggctgg gagtggctga ggccatatga ggacctcgac tgccacaaac 720 agctgcagga gtgtgacaga tcacaacact agcattgtac ctcaaaatat gcttgtaccc 780 taaggcacaa gaactggttt gacttacaaa actgatctca gagttgggat caaagttttt 840 ctaccactct actatgagcc ctcggccggg ccccgccccg ccagctccgc gcggctctgg 900 gctctctagg ggtggggctg cgggcggggc cggcgcctaa ttgggccgcg ggcgcctcga 960 ggtgggcggg gcataagggg gcggggccgc ggagaccccg ggcgggagtt tttttctgca 1020 agcgagaggg ggggtgttgt tggtatcgcc ccctccttct cctcccccca ggggtgaaag 1080 tgcaagagga agtgcagccg ctgccatctt tcctccgctc cgaacacacg gagcccgggg 1140 ccgcacagcc gccgctcctg taca 1164 <210> 5 <211> 1110 <212> DNA <213> Artificial Sequence <220> <223> sLCR <400> 5 atggtctcaa tctcctgacc ttgtgatccg cccacctcgg cctcccaaag tgctgggatt 60 acaggtgtga gccaccacgc ccagccgaca gtcccttatc tggttcatct tcgtacctct 120 aaaagtcagc atggatgctc tattaatgat atatttatac atattagcaa caaacaattg 180 gaaactaaaa ctttaaaaag acattctcac acctgtaatc ccagcatgtt gggaggtcga 240 ggcaggcgaa tcacgaggtc aggagttcga gaccagcctg gccaacatgg tgaaactctg 300 gaagaccgaa actattcagc aagaactaag aaccacaatg ttaagggggt ccattgttta 360 ttttttttc tttagaggat gaaaaccaaa ggtcaggtga tttaatttaa aattaacact 420 cttatttttt gcccgcccgc ctgcctgcct cttacaatt tacagaatgt cttaaggtag 480 ttaagtttca agttttctt tctcagtatc ctaccttcat gcatcaaagt gggtggcctt 540 tatcccatta acggcaatta cgtaagacag atgccctag atgaaatctt acagttcttt 600 tagtcagacc ccccaccccg ccaccgccac cagacaccac catcgctgtg tagtggggt 660 ttttattcgt gttcgtgtgt gtgtgtggac acattttcct ttcggttgc tctgtcctt 720 ggttcgtgct cgcctcgct tttccacact cctgctctct ggctctctgt gtctctcgct 780 cttcgaaaa ttttcctaag tccgggcgcg cgctccctcc cttccgccc accccagccc 840 ctcggcggcg ccccgggag ggggagg cctcgggggc gccgggcgac gcggtccggg 900 gggtggagcg ttggcgtcgt gcgaggggtc gtcactggcg cggagacgcc ccctctcccc 960 cctcggctca gccgggctgc tgcccgagcc cggggggtgg ggggcgtctc cccggcccgt 1020 cccgtccccg gccgggcgcg ggcggaggga cccctcccc gggctcccgg ggggccgcct 1080 ccctccgccg gctcccgccc tcccagccgc 1110 <210> 6 <211> 1112 <212> DNA <213> Artificial Sequence <220> <223> sLCR <400> 6 ttaattaaga atatctggct ggccacgtgt ttgtaaagaa aaaccaagac ggccaggcga 60 ggtggctcac acctgtaatc ccagcacttt gggaggccga ggcgggcgct gcccttcggc 120 cttcaaggag gaattcctac tgtttatgaa gatcgggttt gggtttttgg tttttttttt 180 ctttttcttt tttccgtggt ggtggtgggt gggcttttgt tctttttgtt ttttctgtgg 240 tggtggtggg tgggctttat gaatatacca tattttgcct attgtttttc tatttatcag 300 gtggtgtcat ttgagttgtt ttcaccctct tgtgactatg aataatgata ctataaacaa 360 tcttatacag catcagtgtc aaaaatcact aacattccta tacacagacg tgactaaact 420 tccagcttgg ggtcccgtgg acctgcagcc aggtgcagca ggtcacaggg caaggacacg 480 tgtcattggt gaccttcact attcagtgcc cagatgctca gtgctctgtg caggccacct 540 ggctggtctc aggtaccgct gctctgtctc gctcaccggc cgggctatgt tgattgtccc 600 ctcgcggcgc ccggaagcga ccctcagtaa acaaagccgt gtgtgggcgc agccccagaa 660 gcctggggcg cgcagtccag cccaagagag gcgggggagg aatgttgtga atgaaccccg 720 ggcccgcccc gaaactccgc ataaggcctg ggccgcgggg gtcctcccac tctgattggc 780 ctctggcgcc ccgtgattga cagcgcccct cgctgtgcgc tctggttggg taaacaagaa 840 aagactggca tcgcagtcat cgagtgagca gcgaggcttg gacacgggtc tggcggcgca 900 gccaatggcg ggggagggcc gaggaggccg agggggggcc aatagggaca ggcggtgggg 960 gcgggacgac ggcggagcta aagcggcggc tgaagcagct tcattgttgt gaagagtctt 1020 aaaggggccg catcaccctg ccggcccggc gcgggtcggg ggtgggtgcg gtaggggtcc 1080 cggggcggcc gagcgcagag gacggatgta ca 1112

Claims

1. A method for generating a cell type specific expression cassette, the method comprising the steps of: a) providing a gene expression profile of a target cell type, b) providing genome sequence data of the target cell type, c) selecting a set of signature genes from the gene expression profile, wherein the signature genes are (i) differentially regulated compared to a reference cell type, or (ii) selected based on gene expression levels, d) identifying genes encoding transcription factors from the set of signature genes selected in c), e) determining a set of genomic regions from the genomic sequence data, wherein each genomic region includes a sequence encoding a signature gene identified in c) and an additional genomic sequence adjacent to the sequence encoding the signature gene, Wherein, the adjacent additional genomic sequence comprises a regulatory sequence for the tag gene, wherein the regulatory sequence comprises a transcription factor binding site, f) identifying a plurality of genomic sub-regions within the set of genomic regions determined in e), wherein the genomic sub-regions comprise one or more binding sites for one or more transcription factors identified in d), wherein identifying the genomic subregion is performed by a sliding window algorithm on the genomic region determined in e), g) selecting a minimum set of genomic subregions from the genomic subregions determined in f), wherein the minimum set of genomic subregions is selected to include a predetermined percentage of transcription factor binding sites for all transcription factors identified in d), and h) generating a cell type specific expression cassette comprising a minimal set of said genomic subregions selected in step g) operably coupled to a reporter gene or an effector gene, wherein said genomic subregions of said minimal set are configured to regulate expression of said reporter gene or effector gene.

2. The method for generating an expression cassette according to claim 1, wherein: Step f) comprises identifying a plurality of genomic sub-regions within said set of genomic regions determined in e), wherein the length of each of said genomic sub-regions is equal to the length of each of the other genomic sub-regions.

3. The method for generating an expression cassette according to claim 1, wherein: Step g) comprises selecting a minimum set of between 2 and 10 genomic subregions from the genomic subregions determined in f).

4. The method for generating an expression cassette according to claim 1, wherein: - the gene expression profile includes the expression level of the gene in the cell type of interest, and - providing a gene expression profile of a reference cell type according to step c)(i), said gene expression profile comprising expression levels of genes in said reference cell type, and selecting differentially regulated signature genes by identifying genes that are up-regulated or down-regulated compared to their expression levels in said reference cell type, or - According to step c)(ii), the genes of the target cell type are sorted according to their gene expression levels, and the signature genes are selected based on a predetermined level or a predetermined amount of expression of the signature genes.

5. The method for generating an expression cassette according to claim 4, wherein: Genes that are up-regulated 3 to 10 times in the target cell type are selected as the differentially regulated signature genes.

6. The method for generating an expression cassette according to claim 4, wherein: The 100 to 1000 most highly expressed or 100 to 1000 most minimally expressed genes in the target cell type are selected as the signature genes.

7. The method for generating an expression cassette according to claim 1, wherein: The predetermined percentage of transcription factors covered is 30% or more.

8. The method for generating an expression cassette according to claim 1, wherein: Said genomic region determined in e) corresponds to a genomic sequence comprising topologically associated domains of differentially regulated genes.

9. The method for generating an expression cassette according to claim 8, wherein: The topologically associated domain corresponds to the genomic sequence between the two CTFC binding sites.

10. The method for generating an expression cassette according to claim 9, wherein: The genomic sequence between the two CTFC binding sites is located outside the coding region of the tag gene and includes the tag gene.

11. The method for generating an expression cassette according to claim 1, wherein: The window of the sliding window algorithm has a length of 500 bp to 5000 bp.

12. The method for generating an expression cassette according to claim 1, wherein: The selection of the set of genomic subregions in g) is performed by calculating for each genomic subregion identified in f): - enrichment of binding sites for transcription factors according to d) in genomic sequence data, and - a score for the diversity of transcription factors for which binding sites are present, - wherein said genomic subregions are ranked according to the cumulative percentage of transcription factors for which binding sites are present, and - wherein a minimum set of genomic sub-regions is selected to include a predetermined percentage of binding sites for all transcription factors identified in d).

13. A cell type specific reporter vector, comprising: A synthetic regulatory region comprising 2 to 10 genomic sub-regions of 130 bp to 170 bp, the genomic sub-regions being located adjacently, without a linker or with a linker sequence of less than 100 bp located between the sub-regions, wherein the sub-regions are derived from separate (non-adjacent) locations in the same genome of a cell type, wherein the sub-regions cumulatively comprise at least 5 binding sites for transcription factors, wherein the transcription factor binding sites are bound by a transcription factor that is highly expressed in a cell type of interest compared to a reference cell type, and Reporter gene or effector gene, Wherein, the genomic subregion is operably coupled to the reporter gene or effector gene to regulate the expression of the reporter gene or effector gene.

14. The carrier according to claim 13, wherein Each of the genomic sub-regions has a length of 150 bp.

15. The carrier according to claim 13, wherein The genomic subregion adjacent to the reporter gene or effector gene includes a transcription start site.

16. The carrier according to claim 13, wherein The reporter gene or effector gene encodes a protein selected from the group consisting of: fluorescent protein, suicide gene, luciferase, β-galactosidase, chloramphenicol acetyltransferase, surface receptor, protein tag, including but not limited to 6XHis tag, V5 tag, GFP tag, self-processing ribozyme cassette, mevalonate kinase, biotin ligase, including but not limited to BirA, engineered peroxidase, including but not limited to APEX2, endonuclease or site-specific recombinase including but not limited to restriction enzymes, Cre, Flp, Tn5, SpCas9, SaCas9, TALENs, genes correcting monogenic diseases.

17. The carrier according to claim 13, wherein The vector comprises the nucleic acid sequence according to SEQ ID NO 1-6.

18. Use of a vector according to any one of claims 13 to 17 for transforming cells and / or determining cell properties in vitro in gene and viral therapy, drug discovery or validation.

19. The in vitro use according to claim 18, wherein The cell property is a cell type, state or fate transition.

20. An in vitro method for determining a cell property, said method comprising the steps of: a. providing a carrier according to any one of claims 13 to 17, b. Provide cells, c. transducing cells with the vector, d. measuring a signal indicative of expression of a reporter gene, wherein The amount of the signal is indicative of the properties of the cell.

21. The in vitro method for determining a cell property according to claim 20, wherein: The cell property is a cell type, state or fate transition.

22. A computer-implemented method for determining the sequence of a synthetic locus control region (sLCR), the method comprising steps a) to g) according to claim 1.

Images