Cell Populations

A Graph-Based Clustering Approach to Identify Cell Populations in Single-Cell RNA Sequencing Data

Introduction: The emergence of single-cell RNA-sequencing (scRNA-seq) technology has provided new information about the structure of cells, and provided data with very high resolution of the expression of different genes for each cell at a single time. One of the main uses of scRNA-seq is data clustering based on expressed genes, which sometimes leads to the detection of rare cell populations. ...

A Graph-Based Clustering Approach to Identify Cell Populations in Single-Cell RNA Sequencing Data

Introduction: The emergence of single-cell RNA-sequencing (scRNA-seq) technology has provided new information about the structure of cells, and provided data with very high resolution of the expression of different genes for each cell at a single time. One of the main uses of scRNA-seq is data clustering based on expressed genes, which sometimes leads to the detection of rare cell populations. ...

Sequencing rare T-cell populations

High-throughput sequencing of T cell (TCR) receptor repertoires is a promising approach that can be used to characterize the state and dynamics of adaptive immunity and, potentially, to deduce the antigen specificity of the immune response [1]. While the number of applications of TCR profiling is constantly growing, basic protocols have several drawbacks that are making it difficult to utilize ...

Simulating Heterogeneous Tumor Cell Populations

Certain tumor phenomena, like metabolic heterogeneity and local stable regions of chronic hypoxia, signify a tumor's resistance to therapy. Although recent research has shed light on the intracellular mechanisms of cancer metabolic reprogramming, little is known about how tumors become metabolically heterogeneous or chronically hypoxic, namely the initial conditions and spatiotemporal dynamics ...

Interference with Dendritic Cell Populations