Integrated STRaM characteristic gene marker spectrum evaluation system for human cell product

Abstract

The invention discloses an integrated STRaM characteristic gene marker spectrum evaluation system for a human cell product, and relates to the field of cytobiology and genomics, the integrated STRaM characteristic gene marker spectrum evaluation system comprises a first evaluation assembly, a second evaluation assembly and a third evaluation assembly, the first evaluation assembly is an original sample purity algorithm and is used for constructing a sample purity test method; the second evaluation assembly is used for carrying out various tests, including a traceability similarity test, a sample purity test and a gene editing and mutation test; the second evaluation component is also used for detecting microbial contamination of integrated bacteria, fungi and mycoplasmas and detecting integration sites of exogenous genes; and the third evaluation component expands and simplifies evaluation indexes on the basis of a test result of the second evaluation component. According to the method, traceability similarity evaluation, sample purity evaluation and cell gene modification evaluation are preliminarily integrated, the method is combined with the production process of cell treatment products, and informatization and simplification management monitoring can be carried out on the human cell products.

Description

Technical Field

[0001] This invention relates to the fields of cell biology and genomics, and specifically to an integrated STRaM characteristic gene marker profiling evaluation system for human cell products. Background Technology

[0002] Short tandem repeats (STRs) have a high mutation probability and genetic diversity, thus STR genotyping is species-specific. Studies have confirmed that STR genotyping is an important method for mapping genetic loci in the human genome, commonly used for individual sample identification, kinship identification, and disease diagnosis analysis such as cancer and genetic diseases. In cell biology research, cell line identification uses capillary electrophoresis (CE) to construct STR genotyping profiles, which are then compared with the feature loci profiles in a reference database for similarity. Traditional STR genotyping similarity analysis can effectively identify and detect contamination in in vitro cultured cell lines. However, traditional similarity analysis based on STR genotyping has limited monitoring capabilities for cross-contamination. Monitoring contamination based on the abnormal distribution of low-signal stutter products is more sensitive. Furthermore, with the rapid development of gene editing technology, similarity analysis has become limited in distinguishing between clones from the same source. In addition, CE detection cannot be integrated with other relevant detection methods, especially for targeted sequence analysis, such as cell gene modification monitoring tests.

[0003] Cell therapy, utilizing functional and active cells as novel drugs, is an emerging treatment approach, primarily including stem cells and immune cells. Cell therapy has been rapidly applied to the treatment of human diseases; however, the greatest challenge in the current drug production process lies in the development, optimization, and production of cell technology. Quality control of cell therapy products, including purity, activity, and safety during preparation, significantly impacts their subsequent efficacy. The cell product manufacturing process involves multiple steps such as cell culture, activation, transduction, purification, and enrichment, posing significant difficulties for large-scale production. Therefore, monitoring gene modification of therapeutic cells, product preparation, storage, and administration are essential for quality control. Strict standardization and stability of the production process are crucial to ensure production efficiency and cell quality. Simultaneously, biosafety controls are necessary to prevent cross-contamination of cell products from different individuals, which could lead to immune rejection.

[0004] Therefore, in the production and treatment processes of therapeutic cell products, simple similarity analysis is insufficient to meet the multi-level and multi-faceted quality control requirements. Thus, innovative designs and replacements are needed for the STR gene marker profiling data acquisition process and systems to address existing problems. Summary of the Invention

[0005] This invention addresses the problems existing in the prior art by providing an integrated STRaM characteristic gene marker profiling evaluation system for human cell products.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] An integrated STRaM characteristic gene marker profiling evaluation system for human cell products includes a first evaluation component, a second evaluation component, and a third evaluation component. The first evaluation component is an original sample purity algorithm used to construct a sample purity testing method. The second evaluation component performs multiple testing operations, including traceability similarity testing, sample purity testing, and gene editing and mutation testing. The sample purity testing uses a sample purity algorithm. The second evaluation component also integrates detection of microbial contamination such as bacteria, fungi, and mycoplasma, as well as detection of integration sites of exogenous genes. The third evaluation component expands and simplifies the evaluation indicators based on the test results of the second evaluation component. The evaluation indicators include traceability similarity index SI, purity index PI, and gene editing and mutation index EMI.

[0008] Based on the above technical solution, further, the sample purity algorithm process is as follows: Establish a deep sequencing signal distribution model of the STR alleles and their stutter products at each STR locus in the STRaM gene marker spectrum, and compare the ratio S of the stutter products at the STR loci in the STRaM gene marker spectrum of a cell sample with the deep sequencing signal distribution model. r Does it meet the empirical threshold S? rθ The final result is less than the empirical threshold S. rθ The number of qualified STR loci and their proportion of the total number of valid STR loci.

[0009] Based on the above technical solution, further, in the sample purity algorithm, the established deep sequencing signal distribution model includes the salience ratio P of STR alleles. r Empirical threshold P rθ The ratio S of the stutter products r and empirical threshold S rθ .

[0010] Based on the above technical solution, further, in the sample purity algorithm, the ratio P is highlighted. r This is the ratio of the statistic of each STR sequence reading at a given STR locus to the highest statistic of that locus's sequence; the expression is:

[0011]

[0012] The ratio S of stutter products rThis is the ratio of the sequencing read count of the stutter product to the sequencing read count of the neighboring STR alleles; the expression is:

[0013]

[0014] Based on the above technical solution, further, in the sample purity algorithm, the number of qualified STR sites refers to the number of S sites in the site set that conform to the STR site. r The ratio is less than the empirical threshold S rθ The number of sites, where the empirical threshold S rθ S represents the ratio S of adjacent and non-adjacent stutter products at all STR loci in the STRaM gene marker profile of uncontaminated cell samples. r The maximum value.

[0015] Based on the above technical solution, further, in the sample purity algorithm process, the effective total number of STR sites is the total number of STR sites in the STRaM gene marker spectrum after excluding the number of sites that are unqualified in both the test sample and the control source sample.

[0016] Based on the above technical solution, the source similarity test is used to calculate the source similarity index SI; the sample purity test is used to calculate the purity index PI; and the gene editing and mutation test is used to calculate the gene editing and mutation index EMI.

[0017] Based on the above technical solution, the formula for calculating the purity index PI is further as follows:

[0018]

[0019] Based on the above technical solution, the purity index PI is further evaluated based on all STR sites in the STRaM gene marker profile and is used to more sensitively monitor cross-contamination between different cells. Based on the deep sequencing signal distribution model of the STR alleles and their stutter products at each site in the STRaM gene marker profile, the purity index PI reflects the degree to which the STR allele signals of contaminated cells alter the stutter signal distribution of the host cell.

[0020] The PI formula is based on the STRaM gene marker profile. Commonly abnormal or undetected STR loci are excluded during sample collection. When no original maternal parent is available as a control, the number of common non-compliant loci is "0". Some STRaM loci in cross-contaminated cells have alleles different from those in the test cells, leading to abnormal signal changes in the stutter products of the test cells. This results in a decrease in the purity index (PI). Therefore, combinatorial stutters are excluded when calculating the purity index (PI).

[0021] Based on the above technical solution, the formula for calculating the gene editing and mutation index EMI is further as follows:

[0022]

[0023] Based on the above technical solutions, further, the gene editing and mutation indicator EMI is used to report the percentage of target gene editing or mutation or the relative percentage of exogenous gene integration. Furthermore, the gene editing and mutation indicator EMI can also be used for genotyping, distinguishing clones from different genotypes of the same origin. It is noteworthy that the gene editing and mutation indicator EMI is associated with a specific detected DNA sequence; EMI represents the percentage of cells with different DNA sequences or different DNA mutations. In other words, cell clones carrying different targeted gene editing or gene mutations can be distinguished by the EMI indicator.

[0024] Based on the above technical solutions, further, microbial contamination detection includes the detection of bacteria, fungi, and mycoplasma.

[0025] Furthermore, the evaluation system is applied to the evaluation of human samples, wherein the human samples include one or more of the following: human tissues, cells, genomic DNA, human cell preparations, human cell lines, in vitro human primary cultured cells and tissues, exogenous human genetically engineered cells, human gene-edited cells, xenotransplanted human tissue PDX, and organoids PDO derived from healthy human bodies and patients.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] This invention designs an integrated STRaM characteristic gene marker profiling evaluation system for human cell products. This system integrates traceability similarity assessment, sample purity assessment, and cell gene modification assessment, enabling it to align with the manufacturing processes of cell therapy products. Furthermore, the system is highly scalable, capable of further integrating bacterial and mycoplasma contamination detection, as well as the detection of integration sites for exogenous genes. Through a single data acquisition, it achieves integrated multi-dimensional and multi-faceted quality control testing, thereby simplifying and digitizing the management and monitoring of cell products, and improving the process of product translation into clinical treatment. Attached Figure Description

[0028] Figure 1This is a flowchart illustrating the integrated multi-quality control process for cell samples in this invention. This process includes a STRaM technology analysis and evaluation system. The first step involves data analysis of the cell samples using STRaM technology, including genomic DNA extraction, amplification and deep sequencing of STRaM marker sets and specific DNA sequences, and STRaM analysis (STR analysis, STR flanking analysis, EMS analysis, microbial analysis, and integration point analysis). The second step involves STRaM analysis to obtain STRaM marker profiles, stutter signal distribution, and EMS information related to gene editing (or mutation, microbial contamination, etc.). The third step involves conducting source similarity tests, sample purity tests, and gene editing degree tests to obtain SI, PI, and EMI evaluation indicators for the cell samples.

[0029] Figure 2 P represents the salience ratio of all STR sites in the STRaM marker set in this invention. r Statistical and empirical threshold P rθ The distribution graph; where the vertical axis is Log. 10 (P r The horizontal axis represents the number of STR repeating units (NRU) at each STR locus, which is dimensionless. The highest sequencing read at each STR locus is set to a relative NRU of "0". Moving left (relatively backward) from this data point results in a negative value, while moving right (relatively forward) results in a positive value. Light gray dots mark allele sequencing reads, while black and dark gray dots represent stutter product sequencing reads. The dashed line represents the empirical threshold P. rθ .

[0030] Figure 3 S is the ratio of stutter products of all STR sites in the STRaM marker set in this invention. r Statistical and empirical threshold S rθ Distribution map; Figure 3 The part corresponding to A in the diagram is a definition diagram of the three subtypes of stutter products (forward stutter, backward stutter, and combined stutter); Figure 3 The part corresponding to B in the middle is the S of the stutter products of the backward and forward shift types. r Distribution plot, the dashed line represents the corresponding empirical threshold S rθ ; Figure 3 The part corresponding to C in the middle is the S of the combinatorial stutter product. r Distribution plot, the dashed line represents the empirical threshold S of the combined stutter product. rθ .

[0031] Figure 4S represents the ratio of each STR site in the STRaM marker set in this invention. r Statistical and empirical threshold S rθ Distribution map; Figure 4 The part corresponding to A in the middle is the S of all STR sites in T47D cells. r Distribution diagram (n = 3 independent experiments); Figure 4 The part corresponding to B is the S of all STR sites in 16 cell lines. r Distribution plot (n = 16 independent experiments); dashed lines represent the empirical threshold S of STR loci. rθ .

[0032] Figure 5 This is a schematic diagram showing the results of the qualification principle and purity index PI calculation for STR loci in this invention. Figure 5 The section corresponding to A in the diagram represents qualified and unqualified STR loci, as well as unqualified STR loci exhibiting abnormal stutter products (stutter products that do not conform to the normal stutter product distribution model). The dashed and solid horizontal lines in the diagram represent the empirical threshold S in the normal stutter product distribution model. rθ . Figure 5 Part B illustrates the principle by which cell contamination causes an STR locus to change from a qualified to an unqualified locus. When cell B contaminates cell A, the sequencing read of allele B of cell B at a STR locus will cause a significant increase in the sequencing read of the stutter product of cell A, which is abnormal compared to the sequencing read of the stutter product of uncontaminated cell A. This results in the STR locus changing from a qualified to an unqualified locus. Figure 5 Part C represents the S of all STR sites in the STRaM marker set in uncontaminated T47D cells. r Value heatmap, site qualification determination, and PI index calculation. Figure 5 Part D represents the St values ​​at all STR loci in a STRaM marker set from T47D cell samples contaminated with HS578T cells at a 100:1 ratio. r Value heatmap, site qualification determination, and PI index calculation.

[0033] Figure 6 This is a quality control assessment analysis of STRaM in T47D and HCC827 cells used in this invention (n = 3 independent experiments). The figure shows the SI and PI of uncontaminated T47D and HCC827 cells.

[0034] Figure 7This invention relates to the discovery and quality control assessment of cross-contamination of T47D cells. To test the sensitivity of the PI indicator, T47D cells were artificially mixed with 1% and 2% HS578T cells, and then analyzed and evaluated using STRaM technology. SI and PI indicators were plotted. SI indicates homologous cells of T47D, but PI indicates a significant decrease in the purity of T47D cells artificially mixed with 1% and 2% HS578T cells.

[0035] Figure 8 This figure shows the STRaM quality control assessment analysis of the 293 cell clone carrying the gene-editing mutation and the parent cell in this invention. The 293 cells underwent CRISPR / Cas9 editing of the PTEN gene. Then, source similarity testing, sample purity testing, and gene editing and mutation testing were performed on three different cell clones. The corresponding SI, PI, and EMI indicators are summarized in this figure. EMI was obtained based on the specific mutated DNA sequence. Compared with the parent cell, all three cell clones carried the corresponding mutated DNA sequence with near 100% accuracy.

[0036] Figure 9 This is a schematic diagram of sampling before and after autologous CAR-T cell therapy in this invention, and a STRaM quality control assessment analysis. Figure 9 The section corresponding to A in the diagram is a schematic of the autologous CAR-T cell therapy protocol. Before CAR-T treatment, immune T cells are extracted from the patient's peripheral blood, expanded, and infused with an exogenous CAR gene. Simultaneously, before and after CAR-T treatment, chromosomal DNA is isolated from the patient's peripheral blood after extraction, and STRaM analysis and quality control monitoring at multiple time points are performed. SI, PI, and EMI assessments are then compiled into a single graph.

[0037] Figure 9 The part corresponding to B in the diagram represents the SI (Sequential Similarity Index) for monitoring CAR-T cells. An SI of 100 ± 0.0% indicates that all samples are homologous. Figure 9 The part corresponding to C is the PI for monitoring the purity of CAR-T cells. The evaluation index PI is 97.7±0.9%, indicating that the purity of the CAR-T sample is very high. Figure 9 The part corresponding to D in the table represents the EMI assessment indicators during CAR-T therapy. The EMI indicators show a significant increase in peripheral blood EMI indicators (CAR-T cells) from 0 before CAR-T therapy to 15 days after treatment, indicating relatively successful CAR-T therapy. PBMCs from 5 days prior to infusion (-5) were used as maternal controls for SI and PI. PBMCs: peripheral blood mononuclear cells. Data were analyzed using Student's t-test. Error bars are expressed as mean ± SEM. Detailed Implementation

[0038] The present invention will be further described and illustrated below with reference to the accompanying drawings and specific embodiments. The technical features of each embodiment of the present invention can be combined accordingly, provided that there is no mutual conflict.

[0039] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below. Technical features in the various embodiments of the present invention can be combined accordingly without mutual conflict.

[0040] In the description of this invention, it should be understood that when an element is considered to be "connected" to another element, it can be a direct connection to the other element or an indirect connection, i.e., there is an intermediate element. Conversely, when an element is said to be "directly" connected to another element, there is no intermediate element.

[0041] In the description of this invention, it should be understood that the terms "first" and "second" are used only for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first" and "second" may explicitly or implicitly include at least one of those features.

[0042] Terminology and Declarations of this Invention:

[0043] In this invention, the term "STR" refers to the abundance of short tandem repeats (STRs) in the genomes of prokaryotes and eukaryotes.

[0044] In this invention, the term "STRaM gene marker spectrum" refers to a set of STR marker sites and gene target sites selected for human cell samples in the STRaM technology; the STRaM gene marker spectrum includes the STR marker set, the corresponding total number of STR repeat units, and the gene target sites. Specifically, the STRaM technology can be found in the description in Chinese invention patent application number 202310347891.7.

[0045] In this invention, the term "deep sequencing" refers to a method for obtaining DNA sequences in high throughput using second-generation sequencing technology.

[0046] In this invention, the term "deep sequencing read" refers to short DNA sequence information generated by determining the DNA sequence through second-generation sequencing technology.

[0047] In this invention, the term "deep sequencing reads" refers to the number of high-throughput reads of a DNA sequence obtained using second-generation sequencing technology.

[0048] In this invention, the term "STRaM analysis" refers to an automated and precise bioinformatics analysis method for STR sequences, X / Y chromosome characteristic sequences, and specific DNA sequences following deep sequencing data.

[0049] In this invention, the term "allele" refers to a gene located at the same position on a pair of homologous chromosomes that controls the same trait but with different morphologies. The alleles in this invention are represented by the same or different number of STR repeat units expressed at the same locus.

[0050] In this invention, the term "stutter product" refers to an amplicon that has one or more repeating units deleted / added compared to the true allele product, and is an artificial byproduct caused by strand slippage during PCR. Each STR locus's depth sequencing read contains both the STR allele and the stutter product.

[0051] In this invention, the term "source similarity index (SI)" refers to comparing the STR locus spectrum of the test sample with the STR locus spectrum of the original sample (database). The comparison uses the Tanabe formula: the number of identical STR loci in the test cell sample and the source cell sample is multiplied by 2, and the product is then divided by the total number of STR loci in both samples. When calculating the source similarity index SI, STR loci not detected in either sample are excluded. The calculation formula is as follows:

[0052]

[0053] Combination Figures 1-9 As shown in all embodiments of this application, all cell samples underwent genomic DNA collection and processing, PCR amplification, and deep sequencing data acquisition and processing using STRaM technology, as described in "Method and Application for Identification and Tracking of Human Samples Based on Genomic Genetic Markers" (refer to Chinese Invention Patent Application No. 202310347891.7). The data after preliminary STRaM processing was then subjected to data quality control assessment using an integrated STRaM characteristic gene marker profiling evaluation system for human cell products.

[0054] Specifically, such as Figure 1As shown, it includes a first evaluation component, a second evaluation component, and a third evaluation component. The first evaluation component is an original sample purity algorithm used to construct a sample purity testing method. The second evaluation component performs various testing operations, including source similarity testing, sample purity testing, and gene editing and mutation testing. The sample purity testing uses a sample purity algorithm. It also performs integrated detection of microbial contamination such as bacteria, fungi, and mycoplasma, and detection of integration sites of exogenous genes. The third evaluation component expands and simplifies the evaluation indicators based on the test results of the second evaluation component. In this embodiment, it is expanded to three evaluation indicators, including the source similarity index SI (Similarity index), the purity index PI (Purity index), and the gene editing and mutation index EMI (Editing / mutation index).

[0055] In this embodiment, the sample purity algorithm in the first evaluation component is as follows: 1) Establish a deep sequencing signal distribution model of the STR alleles and their stutter products at each STR locus in the STRaM gene marker spectrum; 2) Based on this deep sequencing signal distribution model, compare the ratio S of the stutter products at the STR locus in the STRaM gene marker spectrum of a cell sample. r Does it meet the empirical threshold S? rθ 3) The final statistical result is less than the empirical threshold S. rθ The number of qualified STR loci and their proportion of the total number of valid STR loci were determined. The established deep sequencing signal distribution model included the salience ratio P of STR alleles. r Empirical threshold P rθ The ratio S of the stutter products r and empirical threshold S rθ ;

[0056] Furthermore, highlight the ratio P r The ratio of the statistic of each STR sequence reading at a given STR locus to the highest statistic of the sequence at that locus; the stutter product ratio S. r The ratio of the sequencing read count of the stutter product to the sequencing read count of the neighboring STR alleles.

[0057] Furthermore, in the cell sample purity algorithm, a deep sequencing signal distribution model of STR alleles and their stutter products is established to calculate abnormal changes in stutter products for sensitive monitoring of cell cross-contamination. Stutter products generated during STR sequence amplification are often discarded as noise in existing methods.

[0058] It should be noted that stutter products are non-specific products generated during PCR amplification and deep sequencing of STR loci. The stutter length is defined as an integer fold increase or decrease in length compared to the corresponding STR locus length by the number of STR repeat units. A stutter adjacent to an STR locus is defined as a stutter product length that is one STR repeat unit longer or shorter than the allele length of the corresponding STR locus. A stutter not adjacent to an STR locus is defined as a stutter product length that is two or more STR repeat units longer or shorter than the allele length of the corresponding STR locus.

[0059] The greater the variation in the length of forward-shifted or backward-shifted stutters, the smaller the proportion of stutter product (readings from depth sequencing) relative to the number of alleles at neighboring STR loci. This ratio is defined as the stutter ratio S. r Specifically, it is the ratio of the number of sequencing reads of the stutter product to the number of sequencing reads of the corresponding STR locus allele.

[0060] Further explanation is needed regarding the ratio S. r The empirical threshold refers to the ratio S of adjacent to non-adjacent stutter products obtained when analyzing a large number of uncontaminated cell samples using STRaM technology. r The maximum threshold.

[0061] Among them, when the S of a STR locus r The ratio change is less than the empirical threshold S rθ At that time, the STR locus was defined as a valid STR locus. The total number of valid STR loci is the total number of loci in the STRaM gene marker profile minus the number of loci that are invalid in both the test sample and the control sample.

[0062] In this embodiment, the second evaluation component employs a 3-in-1 integrated test, which integrates source similarity testing, sample purity testing, and gene editing and mutation testing. The test results are expressed using a third evaluation component (three simplified evaluation metrics).

[0063] Specifically, the source similarity test uses the Tanabe formula: the number of STR sites identical to the original source reference sample in the test sample is multiplied by 2, and the product is then divided by the total number of STR sites in both samples. The result of the source similarity test is expressed as the SI index. STR sites that were not detected in either sample are not included when calculating the SI index. The SI index shows the similarity between the test cell sample and the original sample, and its expression is as follows:

[0064]

[0065] Specifically, the sample purity test employs the aforementioned original sample purity algorithm. This algorithm can detect abnormal signal changes in stutter products caused by cell cross-contamination. The test results are expressed as a purity index (PI). The PI index indicates the purity of the tested cell sample.

[0066] Specifically, the gene editing and mutation test monitors the percentage of cells with endogenous target gene mutations or the percentage of cells carrying genetically engineered genes. The results of this test are expressed as the gene editing and mutation index EMI. EMI reports the percentage of cells with the target gene subtype or the percentage of cells with integrated exogenous genes; it can also be used to distinguish between clones of the same origin but with different gene subtypes, such as gene-edited cell clones, CAR-T cells, etc.

[0067] More specifically, when detecting exogenous genes, the Amelogenin gene on the sex chromosome is used as an internal reference gene to calculate the relative percentage of cells in which the exogenous gene has been introduced. Examples include gene-edited cell clones and CAR-T cells. When the sex chromosome is identified as XX, only one allele is used (i.e., the statistical number is divided by 2).

[0068] It should be noted that this integrated quality control technology achieves a multi-dimensional and low-cost organic combination, eliminating the need to complete the three tests mentioned above separately, thus fully meeting the multi-node and multi-dimensional quality inspection needs of clinical cell products.

[0069] In this embodiment, STRaM can be used to analyze targeted SNPs and the relative level changes of exogenous genes. It should be noted that, in order to meet higher quality control requirements for clinical cell products, more functional tests can be integrated to meet the needs of cell therapy and other applications, such as microbial contamination tests for bacteria, fungi, and mycoplasma.

[0070] Furthermore, by integrating sample purity testing and gene editing and mutation testing into the evaluation system, the system calculates new purity indicators (PI) and gene editing and mutation indicators (EMI), simplifying the evaluation report and making information management more convenient.

[0071] Specifically, the formula for calculating the purity index PI is as follows:

[0072]

[0073] The PI formula is based on the STRaM gene marker profile. Commonly abnormal or undetected STR loci are excluded during sample collection. When no original maternal parent is available as a control, the number of commonly unqualified loci is "0". If the alleles of some STR loci in the test cells differ from those in cross-contaminated cells, it can cause abnormal signal changes in the stutter products of the test cells, leading to the determination that the locus is unqualified and resulting in a lower PI value. Combinatorial stutters are excluded when calculating PI.

[0074] Specifically, the formula for calculating gene editing and the mutation index EMI is as follows:

[0075]

[0076] The EMI formula can calculate the percentage of cells carrying targeted gene editing or gene mutations, or the percentage of cells carrying exogenous genes. Amelogenin on the sex chromosome is used as an internal reference gene in calculating the relative percentage of cells carrying exogenous genes. It is noteworthy that the gene editing and mutation indicator EMI is associated with specific detection DNA sequences, and cell clones carrying different targeted gene editing or gene mutations can be distinguished by the gene editing and mutation indicator EMI.

[0077] It should be noted that the entire evaluation system can be broken down into separate tests for the quality of human samples (tissues, cells, and DNA, etc.), or it can be used for a comprehensive evaluation of cell product quality control, information management, and gene monitoring.

[0078] Example 1

[0079] Establish a cell sample purity algorithm.

[0080] In this embodiment, the data is directly processed using STRaM technology for analysis and evaluation.

[0081] Using STRaM data from 16 human cell lines, a deep sequencing signal distribution model was established for the STR alleles and their stutter products at each locus in the STRaM gene marker profile (including the allele salience ratio Pr and the empirical threshold P). rθ stutter ratio Sr and empirical threshold S rθ ), used to establish a cell purity algorithm.

[0082] 1.1 Classification of Stutter products and deep sequencing signal distribution model.

[0083] Cell samples were analyzed using targeted sequencing (TAS) in the STRaM technology, which involves extracting genomic DNA from cells, performing PCR amplification, and then performing amplicon targeted sequencing. Each STR locus in these TAS data generates a corresponding stutter product. Stutter products are technically nonspecific products generated during STR sequence amplification and sequencing. Compared to the STR template, stutter products contain one or more deletions or insertions of repeat units. In traditional analysis, changes in stutter peak height ratios or proportions are used to exclude stutter products that produce unwanted signals. Therefore, identifying and distinguishing allelic signals from stutter signals is crucial for constructing STR characteristic locus profiles.

[0084] In this embodiment, the salience ratio (P) of the sequencing read statistics is... r The prominence ratio (PRR) is the ratio of each STR sequence reading at a given STR locus to the highest statistical count of that locus's sequence. It is used to distinguish between alleles and stutter products. Figure 2 As shown, P was summarized based on 16 human cell lines. r Operational model. The P-value of the repeating sequence adjacent to the repeating sequence with the highest statistic. r Greater than 0.3, i.e., Log 10 (P r )>-0.5; or P of repeating sequences at non-adjacent positions r A value greater than 0.1, i.e., Log 10 (P r )>-1 were all identified as STR alleles, while the remaining repetitive sequences were Stutter products.

[0085] Further, an empirical model of stutter products is established. Stutter products are classified into three types: backward stutter, forward stutter, and combined stutter. For example... Figure 3 As shown in Figure A, backward-shifted stutters of a STR allele contain deletions of one or more repeat units (i.e., n-1, n-2, etc.); while forward-shifted stutters contain insertions of one or more repeat units (i.e., n+1, n+2, etc.). Furthermore, when there are two distinct alleles at a STR locus, combinatorial stutters are the stutter products between the two alleles. For example, the n of a STR allele... a +1 and n of another allele b The stutter product at position -1.

[0086] Stutter ratio S rEmpirical models used to build backward and forward stutter products. For example... Figure 3 As shown in B, through S r Analysis shows that, compared with the nearest allele, the stutter product exhibits an S-shape at adjacent positions (such as n-1 and n+1). r ≤0.12, i.e., Log 10 (S r The value is ≤-0.9; while the stutter product exhibits an S-shape at non-adjacent positions (such as n-2 and n+2). r ≤0.01, i.e., Log 10 (S r )≤-2; such as Figure 3 As shown in C, combinatorial stutters present S r ≤0.3, i.e., Log 10 (S r )≤-0.5. Overall P r and S r The analysis can yield the empirical threshold S. rθ The definition is shown in Table 1, and then the identification and analysis of alleles and stutters products are carried out.

[0087] Table 1. Empirical thresholds and classifications of alleles and stutter products in STRaM sequencing data.

[0088]

[0089] To investigate the distribution of adjacent and non-adjacent stutters at each site in the STRaM locus set, stutter sequencing reads from human cell lines were used for feature analysis. Based on the empirical thresholds in Table 1, three independent experimental analyses were first performed on the T47D breast cancer cell line, revealing highly consistent stutter readings at each site (e.g., ...). Figure 4 As shown in Part A of the diagram, for each site adjacent to Stutters (n-1 and n+1, S... r <0.12) and non-adjacent stutters (n-2, n+2, n-3 and n+3, S r <0.01) are all in S r Within the range of variation. Then, statistical analysis of the stutters at each point in the 16 cell lines was performed, showing the S values ​​of adjacent stutters. r <0.12 (100.0±0.0% for n-1 and n+1); S for non-adjacent stutters r <0.01 (n-2 and n+2 are 96.9±1.0%, n-3 and n+3 are 98.6±0.7%) (e.g. Figure 4(As shown in Part B of the results). The results show that the distribution of stutters at each site in the STRaM site set exhibits a fixed range of fluctuation, and the signal of non-adjacent stutters is lower than that of adjacent stutters.

[0090] 1.2 Cell sample purity algorithm and PI index.

[0091] The cell sample purity algorithm is based on the stutter product classification and deep sequencing signal distribution model in section 1.1. It assesses the eligibility of each STR locus in the STRaM marker set and ultimately calculates the cell purity PI index. First, the distribution model of stutters is used to determine whether a STR locus is eligible. It should be noted that combined stutters need to be excluded when calculating STR locus eligibility and the PI index. For example... Figure 5 As shown in Part A of the diagram, in the sample purity algorithm, the qualified STR site refers to the ratio S of a single STR site. r Less than the empirical threshold S rθ That is, adjacent stutters (i.e., n-1 and n+1) require S r Stutters <0.12 and not adjacent (n-2, n+2, n-3, n+3, etc.) require S r If the value is less than 0.01, the locus is considered a valid locus; conversely, if any one condition is not met, the locus is considered invalid. For example... Figure 5 As shown in Part B, in the stutter product signal model of uncontaminated cell samples, the stutter signal of a STR locus is much lower than the allele signal at that locus. When exogenous cell sample contamination occurs, at a STR locus, the number of STR repeat units in the STR alleles of the exogenous sample differs from that of the STR alleles of the test sample. Therefore, the allele signal from the exogenous cell leads to an abnormally high stutter product signal distribution in the test cell, causing that STR locus to change from a qualified locus to an unqualified locus.

[0092] Based on the sample purity algorithm, the pass / fail test of all STR loci in the STRaM marker set was completed. Finally, the number of pass / fail STR loci was summarized and the PI index was calculated. If there is no reference system for the tested sample, the number of common non-pass / fail STR loci in the PI calculation formula is zero. The PI index calculation of a cell sample can be repeatedly verified through multiple independent experiments. Through sample purity testing and PI index calculation of 16 cell lines, the PI index of uncontaminated samples should be maintained between 80% and 100%. The PI index of cross-contaminated cell samples is usually below 80%.

[0093] like Figure 5As shown in section CD, artificially simulating cross-contamination of cell samples demonstrates how to calculate the PI index. T47D cells and HS578T cells were mixed at a ratio of 100:1 to create T47D cell samples cross-contaminated with HS578T cells (T47D cells containing 1% HS578T cells), with uncontaminated T47D cell samples serving as a control. Genomic DNA was extracted from these contaminated cell samples using STRaM technology for TAS sequencing analysis. The TAS sequencing data was processed using STRaM analysis, and then the purity of the cross-contaminated cell samples was tested. Then, based on the sample purity algorithm, the adequacy of all STR loci in the STRaM marker set was tested. In the figure, adequacy STR loci are marked "yes," and adequacy STR loci are marked "no." The PI index was calculated by summing the adequacy PI loci. The PI index of the uncontaminated T47D cell sample was 100%; the PI index of the T47D cells contaminated with 1% HS578T cells was 50%, far below the 80% threshold.

[0094] Example 2

[0095] STRaM technology and evaluation systems are used for traceability similarity testing and cross-contamination testing of cell products.

[0096] In cell experiments and cell genetic engineering procedures, errors in cell sample recording or cross-contamination frequently occur, leading to experimental, production, and therapeutic failures. Therefore, it is essential to enhance quality control measures such as cell sample traceability similarity testing and cross-contamination testing.

[0097] like Figure 6 The results showed that two uncontaminated tumor cell lines, T47D and HCC827, were repeatedly validated through independent experiments. Figure 6 As shown, the STRaM gene marker profiles of T47D and HCC827 tumor cell lines were analyzed using an evaluation system in three independent STRaM assays. The SI index of both T47D and HCC827 tumor cell lines was 100.0±0.0%, and the PI indexes were 100.0±0.0% and 97.0±3.0%, respectively. These two indices indicate that the STRaM gene marker profiles of these two test cell lines are consistent with the known T47D and HCC827 cell lines and have not been contaminated by other cells.

[0098] like Figure 7As shown, an additional sample was added, mixed at a 50:1 ratio, to simulate cross-contamination of T47D cells containing 2% HS578T cells. This 50:1 mixed sample, along with the aforementioned 100:1 mixed sample, was used in multiple independent experiments for traceability similarity testing and cross-contamination testing of cell products. After data analysis, the SI indices for T47D cells contaminated with 2% and 1% HS578T cells were 98.8±1.2% and 98.4±1.1%, respectively, while the PI indices were 42.0±7.0% and 46.6±8.6%, respectively. This indicates that the contaminated T47D cells are homologous to pure T47D cells (SI index reaches over 80%), but these two samples with trace amounts of contaminated T47D cells sensitively detected the contamination (PI index less than 80%). In other words, the PI index can very sensitively detect trace amounts of cell cross-contamination (as low as 1% contamination).

[0099] Example 3

[0100] Gene editing and mutation testing and EMI indicators.

[0101] Gene editing and mutation assays can be used to monitor the percentage of cells carrying endogenous gene editing or mutations, as well as the percentage of cells carrying exogenous genes. The test results are presented as the gene editing and mutation index EMI. It should be noted that gene editing and mutation assays and the gene editing and mutation index EMI can also monitor and differentiate between different gene-edited clones from the same origin.

[0102] like Figure 8 As shown, to detect three HEK293 cell clones carrying PTEN gene mutations caused by CRISPR / Cas9 editing, STRaM technology was first used for detection, and then EMI was calculated to show the genetic differences between the edited cell clones and the parent cells. The SI index of the STRaM gene marker profiles of the edited cell clones and the parent cells was 96.3±0.6%, and the PI index was 97.7±1.9%, indicating that these two indicators identified these cell samples as homologous and uncontaminated, but could not distinguish these three homologous edited cell lines from the parent cells. However, EMI analysis of gene editing and mutation indicators targeting specific gene DNA editing regions revealed that the EMI index of the edited cell clones was 98.7±0.2%, while that of the parent cells was 0%. This indicates that the EMI index can be used to monitor and distinguish cell clones from the same source but with different gene editing.

[0103] Example 4

[0104] STRaM technology and evaluation system use three evaluation indicators to monitor the quality control and clinical response of genetically engineered CAR-T immune cells.

[0105] CAR-T cells are derived from the genes of a patient's own normal immune T cells. The exogenous CAR gene is introduced into the expanded patient's immune T cells, thereby enabling the modified CAR-T cells to recognize and destroy target cells (e.g., CD19-positive leukemia cells).

[0106] like Figure 9 As shown in the example, according to clinical evaluation, the therapeutic CAR-T immune cells will be used to treat diffuse large B-cell lymphoma (DLBCL). To evaluate the quality control and efficacy of clinical CAR-T immune cells, peripheral blood mononuclear cells (PBMCs) were collected from DLBCL patients before treatment and PBMCs on days 5, 10, and 15 after CAR-T treatment (e.g., ...). Figure 9 (A is indicated in the diagram). Genomic DNA was extracted from CAR-T cells and PBMCs for subsequent STRaM analysis and three evaluation tests of the samples (origin similarity test, sample purity test, and gene editing and mutation test).

[0107] Figure 9 The STRaM analysis and three evaluation tests in sections B and C showed that the genetically engineered CAR-T cells and their corresponding maternal T cells had the same STR locus profile, with an SI index of 100 ± 0.0%, confirming this. The PI index was 97.7 ± 0.9%, indicating that the CAR-T cells were not cross-contaminated. Figure 9 The D section of the study detected changes in the relative levels of the CAR gene, and further tracked the integration of the CAR gene into CAR-T cells (gene editing and mutation marker EMI). The tests revealed that patients before clinical CAR-T therapy did not have T cells carrying the exogenous CAR gene in their peripheral blood, while 5-15 days after CAR-T therapy, the number of CAR-T cells carrying the exogenous CAR gene significantly increased in the peripheral blood. This demonstrates that the STRaM technology and evaluation system are an effective and comprehensive solution for quality and biosafety control of cell therapy before and during clinical treatment.

[0108] In summary, the STRaM technology and evaluation system can provide a novel quality control technology and evaluation system with high stability, high scalability, high integration, and multiple detection parameters for specific cell products in scenarios such as cell product manufacturing and clinical treatment.

[0109] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions made by those skilled in the art to the technical solution of the present invention do not depart from the essence and scope of the technical solution of the present invention.

Claims

1. An integrated STRaM signature gene marker profile evaluation system for human cell products, characterized in that, The evaluation system comprises a first evaluation component, a second evaluation component and a third evaluation component, The first evaluation component is an original sample purity algorithm used for constructing a sample purity test method; The second evaluation component performs a plurality of test operations, including a traceability similarity test, a sample purity test and a gene editing and mutation test, wherein the sample purity test is tested by using the sample purity algorithm; and the second evaluation component also performs microbial contamination detection of integrated bacteria, fungi and mycoplasma and integrated site detection of exogenous genes; The third evaluation component expands and simplifies the evaluation index based on the test results of the second evaluation component, wherein the evaluation index includes a traceability similarity index SI, a purity index PI and a gene editing and mutation index EMI.

2. The integrated STRaM signature gene marker profile evaluation system for human cell products according to claim 1, characterized in that, The process of the sample purity algorithm is: establishing a deep sequencing signal distribution model of each STR allele and its stutter product of each STR locus in the STRaM genetic marker spectrum; and comparing the ratio S of the stutter product of each STR locus in the STRaM genetic marker spectrum of a cell sample based on the deep sequencing signal distribution model r whether it meets the empirical threshold S rθ ; finally, the number of qualified STR loci less than the empirical threshold S rθ , and the proportion of the number in the total number of effective STR loci.

3. The integrated STRaM signature gene marker profile evaluation system for human cell products according to claim 2, characterized in that, In the sample purity algorithm, a deep sequencing signal distribution model is established including a stutter ratio P r , an empirical threshold P rθ , a stutter product ratio S r , and an empirical threshold Sr θ .

4. The integrated STRaM signature gene marker profile evaluation system for human cell products according to claim 3, characterized in that, In the sample purity algorithm, the prominence ratio P r is the ratio of the number of statistical counts for each STR sequence read in a STR locus to the highest number of statistical counts for the sequence of the locus; the stutter product ratio S r is the ratio of the number of statistical counts for a sequencing read of a stutter product to the number of statistical counts for a sequencing read of a neighboring STR allele.

5. The integrated STRaM signature gene marker profiling system for human cell products according to claim 3, wherein, In the sample purity algorithm, the number of qualified STR loci refers to the ratio S of loci in the locus set that meet the STR loci r The number of loci less than the empirical threshold S rθ , wherein the empirical threshold S rθ is: the maximum value of the ratio S of adjacent and non-adjacent stutter products of all STR loci in the STRaM genetic marker spectrum of the uncontaminated cell sample. r ​ 6. The integrated STRaM signature gene marker profiling system for human cell products according to claim 2, wherein, In the process of the sample purity algorithm, the effective total STR site number is the total STR site number after excluding the site number that is not qualified for both the test sample and the control source sample in the STRaM genetic marker spectrum.

7. The integrated STRaM signature gene marker profile evaluation system for human cell products according to claim 2, characterized in that, The traceability similarity test is used to calculate the traceability similarity index SI; the sample purity test is used to calculate the purity index PI; and the gene editing and mutation test is used to calculate the gene editing and mutation index EMI.

8. The integrated STRaM signature gene marker profile evaluation system for human cell products according to claim 7, characterized in that, The formula for calculating the purity index PI is:

9. The integrated STRaM signature gene marker profiling system for human cell products according to claim 7, wherein, The formula for calculating the gene editing and mutation index EMI is:

10. The integrated STRaM signature gene marker profile evaluation system for human cell products according to claim 1, characterized in that, The evaluation system is applied to the evaluation of human samples, wherein the human samples include one or more of human tissues, cells, genomic DNA, human cell preparations, human cell lines, in vitro human primary culture cells and tissues, exogenous human genetic engineering cells, human gene editing cells, xenotransplant human tissues PDX, and organoids PDO derived from healthy human bodies and patients.

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