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Optical genome mapping (OGM) is a novel non-sequencing genetic analysis technology that enables high-precision analysis of structural variations across the entire genome. It possesses unique technical advantages, and its procedural simplicity makes it easy to implement. In recent years, the application efficacy of OGM technology in the analysis of genomic structural variations in hematologic malignancies has been widely validated and recognized. Increasing evidence indicates that the application of OGM technology can help improve the genetic diagnosis, prognostic stratification and treatment guidance of hematologic malignancies. This article draws upon pertinent reports from the 65th American Society of Hematology Annual Meeting to provide an overview of the progress in applying OGM technology for the precise diagnosis and treatment of hematologic malignancies.
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Objective:To evaluate the feasibility of 8E5 cells and CD19-CAR-Jurkat cells used as reference cells in the detection of lentiviral vector integration sites with different methods.Methods:Single clones of 8E5 cells and CD19-CAR-Jurkat cells were selected using limiting dilution method. Digital PCR was established to detect the copy number of HIV-1 in 8E5 cells and the copy number of CAR in CD19-CAR-Jurkat cells. High-throughput sequencing techniques (whole-genome resequencing, modified genome sequencing and probe hybridization capture) were used to detect integration sites in 8E5 cells and CD19-CAR-Jurkat cells, and optical genome mapping (OGM) technology was used for further confirmation.Results:Three clones of 8E5-D8 cells and six clones of CD19-CAR-Jurkat 2-6 cells were selected using the limiting dilution method. 8E5-D8 and CD19-CAR-Jurkat 2-6 were chosen as candidate cells based on their gene copy numbers detected by digital PCR and flow cytometry. These cells were then expanded and cryopreserved. Digital PCR showed that 8E5-D8 cells contained approximately 1 copy per cell, while CD19-CAR-Jurkat 2-6 cells contained approximately 13 copies per cell. High-throughput sequencing revealed one integration site in 8E5 cells and 13 integration sites in CD19-CAR-Jurkat cells, which matched the copy number detection results. All these integration sites were further confirmed at the submicroscopic level of chromosomes using OGM.Conclusions:Based on the insertion copy numbers and integration sites, 8E5-D8 cells and CD19-CAR-Jurkat 2-6 cells could be used as reference cells in further development of methods for detecting integration sites in CAR-T cell lentiviral vectors.
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ObjectiveTo investigate the application of optical genome mapping （OGM） technology in detecting complex chromosomal rearrangement. MethodsWe recruited five patients who were diagnosed as complex chromosomal rearrangement at the Reproductive Medicine Center of the Sixth Affiliated Hospital of Sun Yat-sen University from January 2022 to June 2023. They underwent OGM， nanopore sequencing and pre-implantation genetic testing （PGT）. The results were compared with the results of karyotype and chromosomal microarray analysis （CMA）/ copy number variation sequencing （CNV-Seq）. ResultsOGM could detect translocation， invert inversion， and triplet translocation， which were consistent with the results of OGM and CMA/ CNV-Seq. But OGM could not detect Robertsonian translocation. ConclusionBecause of its ultra-long reads， OGM realizes the detection across repetitive regions， and it has great advantages when applied in patients with complex chromosome rearrangement or uncertain karyotype analysis. It can accurately locate breakpoints.
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In this article we describe and demonstrate the versatility of a computer program, GENOME MAPPING, that uses interactive graphics and runs on an IRIS workstation. The program helps to visualize as well as analyse global and local patterns of genomic DNA sequences. It was developed keeping in mind the requirements of the human genome sequencing programme, which requires rapid analysis of the data. Using GENOME MAPPING one can discern signature patterns of different kinds of sequences and analyse such patterns for repetitive as well as rare sequence strings. Further, one can visualize the extent of global homology between different genomic sequences. An application of our method to the published yeast mitochondrial genome data shows similar sequence organizations in the entire sequence and in smaller subsequences.