Global Journal of Science Frontier Research, G: Bio-Tech & Genetics, Volume 22 Issue 2

examine the intersection of SCS and liposarcoma clinical care. Where liposarcoma-specific data are limited, we will extrapolate lessons learned from the cancer field and other sarcomas. The democratization and commercialization of SCS have led to stable platforms for cancer research. The most widely used modality – transcriptomics or single-cell RNA-sequencing (scRNA-seq) –can profile gene expression for thousands of cells within a single experiment. The gene expression profile for each cell can be used to characterize and catalogue the cellular taxonomy of the tumor as well as define novel states or subtypes in cancer cells. Importantly, scRNA-seq has been used to detect rare subpopulations of cells including cancer stem cells and circulating tumor cells. For epigenomics, the most popular method is single-cell ATAC (assay for transposase-accessible chromatin) sequencing (scATAC-seq), which is used to measure the chromatin accessibility in single cells. Lastly, for genomics, single-cell DNA-sequencing (scDNA-seq) can be used for copy number alteration profiling, mutations, and clonal evolution. Additional layers of information can be studied through single-cell multiomics, where subsequent technologies can be used on cells of the same specimen followed by computational integration methods to combine the data or within the same cell where cellular barcodes link different -omics data. While somatic hallmarks can be detected with techniques such as WES, SCS enables deeper exploration of mutations in the subpopulations within the tumor. Since both WDLPS and DDLPS contain amplifications within chr12q regions, SCS could be used to detect copy number alterations (CNAs) to separate malignant cells apart from normal cells and determine the clonal substructure of the malignant cells. This could enable understanding the cell of origin and how degrees of adipocytic could affect tumor burden. Technologies like Tapestri (Mission Bio) and Single-Cell CNV (10x Genomics) can directly detect CNAs by scDNA-seq. Recent work by the Navin group demonstrated that CNAs between scDNA-seq from single cells when merged together and bulk whole-exome-sequencing (WES) had high concordance for patients with triple negative breast cancer (TNBC). Pearson correlation showed a mean of 0.871 across five different matched patient data sets 148 . A key limitation with this approach is that possible mutations for TNBC patients must be well- known in advance. This data feeds into a custom targeted panel of all known mutation sites for scDNA- seq, which greatly reduces the cost when compared to an unbiased panel. Advantages to using this approach, aside from the cost reduction, is enabling high- throughput single-cell analysis of clonal diversity within patients and understanding of possible clonal substructures. Key questions this could answer for WDLPS and DDLPS would be to understand the clonal evolution or transition, if it occurs, between WDLPS and DDLPS. As an alternative to scDNA-seq, there are multiple software packages that can infer CNAs from scRNA-seq data. This has the advantage of utilizing the more popular scRNA-seq with the addition of evaluating gene expression 149,150 . The currently available software packages used to infer CNAs from scRNA-seq data are: InferCNV, CaSpER, and CopyKAT 149,151,152 . They operate under the assumption that CNAs are correlated with increasing or decreasing gene expression and that by fitting a mixture model to the data set, confounding factors from normal gene expression fluctuation could be removed. Work on synovial sarcoma, an aggressive neoplasm driven by the SS18–SSX fusion, demonstrated that CNAs could be detected using infer CNV on the scRNA-seq data and that the inferred CNAs matched the data from WES 153 . The limitations with using software to infer CNAs from scRNA-seq is dependent on the model used with each software varying in detection of CNAs. In addition, normal reference cells may be required as an input and, in some cases, malignant and normal reference cells may not be easily distinguishable from the gene expression data alone. Nonetheless, these inference methods could determine tumor heterogeneity and enable identification of patient- specific features that are not found in the gene expression data. Importantly for MLPS, which is driven by FUS- DDIT3, and in some cases, EWSR1-DDIT3 fusion, SCS can detect and quantitate fusions or structural rearrangements. However, this depends on the sequencing chemistry. There are four popular chemistries for generating sequencing reads – full- length, 3’, 5’, and tagmentation. 3’ and 5’ sequencing have been popularized by 10x Genomics, since these chemistries can easily enable profiling of up to 10,000 cells. However, these chemistries have high bias for 3’ or 5’ read coverage. This hinders the ability to detect mutations such as SNPs, indels, and rearrangements that may not exist at either 3’ or 5’ ends. In that regard, full-length mRNA profiling does enable in-depth sequencing capable of genotyping and detecting mutations. One such method that uses full-length mRNA sequencing is the SMART-seqwork flow (Takara Bio). However, SMART-seq has much lower throughput compared to 3’ and 5’ SCS. It requires fluorescent- activated cell sorting (FACS) to sort single-cells into wells of a 96-well plate. This does have an added benefit of cell typing the cells prior to sequencing if the cell type specific surface markers are well-expressed. Recently, SMART-seq was employed to detect the SS18-SSX fusion transcripts in synovial sarcoma 153 . A common problem in SCS is annotating malignant cells v. normal cells. In this case, the presence of the fusion transcript was used to delineate malignant cells from normal cells. As for MLPS, since there are at least 10 known variants © 2022 Global Journals 1 Year 2022 24 Global Journal of Science Frontier Research Volume XXII Issue ersion I VII ( G ) The Genomics of Liposarcoma: A Review and Commentary