Assessing a single-cell multi-omic analytic platform to characterize -engineered T-cell therapy products.

Genetically engineered CD8 T cells are being explored for the treatment of various cancers. Analytical characterization represents a major challenge in the development of genetically engineered cell therapies, especially assessing the potential off-target editing and product heterogeneity. As conventional sequencing techniques only provide information at the bulk level, they are unable to detect off-target CRISPR translocation or editing events occurring in minor cell subpopulations.

Personalized Single-Cell Proteogenomics to Distinguish Acute Myeloid Leukemia from Non-Malignant Clonal Hematopoiesis.

Genetic mutations associated with acute myeloid leukemia (AML) also occur in age-related clonal hematopoiesis, often in the same individual. This makes confident assignment of detected variants to malignancy challenging. The issue is particularly crucial for AML post-treatment measurable residual disease monitoring, where results can be discordant between genetic sequencing and flow cytometry.

An atlas of transposable element-derived alternative splicing in cancer.

Transposable element (TE)-derived sequences comprise more than half of the human genome, and their presence has been documented to alter gene expression in a number of different ways, including the generation of alternatively spliced transcript isoforms. Alternative splicing has been associated with tumorigenesis for a number of different cancers. The objective of this study was to broadly characterize the role of human TEs in generating alternatively spliced transcript isoforms in cancer.

Graph-Based Hub Gene Selection Technique Using Protein Interaction Information: Application to Sample Classification.

Classification of samples of gene expression profile plays a significant role in prediction and diagnosis of diseases. In the task of sample classification, a robust feature selection algorithm is very much essential to identify the important genes from the high dimensional gene expression data. This paper explores the information of protein-protein interaction with a graph mining technique for finding a proper subset of features (genes), which further takes part in sample classification.