DTFLOW: Inference and Visualization of Single-cell Pseudotime Trajectory Using Diffusion Propagation

GCSTI: A Single-Cell Pseudotemporal Trajectory Inference Method Based on Graph Compression

The single-cell pseudotemporal trajectory inference is an important way to explore the process of developmental changes within a cell. Due to the uneven rate of cell growth, changes in gene expression depend less on the time of data collection and more on a cell's "internal clock". To overcome the challenges of gene analysis, and replicate biological developmental processes, several strategies have been put forth. However, due to the size of single-cell datasets, locating relevant signposts usually necessitate clustering analysis or a sizable amount of priori information. To this end, we propose a novel single-cell pseudotemporal trajectory inference technique: GCSTI method, which is based on graph compression and doesn't rely on a priori knowledge or clustering procedures, can handle the trajectory inference problem for a large network in a stable and efficient manner. Additionally, we simultaneously improve the pseudotime defining method currently employed in this study in order to obtain more trustworthy and beneficial outcomes for trajectory inference. Finally, we validate the efficacy and stability of the GCSTI method using datasets from human skeletal muscle myogenic cells and four simulated datasets.

Recent developments in application of single-cell RNA sequencing in the tumour immune microenvironment and cancer therapy

The advent of single-cell RNA sequencing (scRNA-seq) has provided insight into the tumour immune microenvironment (TIME). This review focuses on the application of scRNA-seq in investigation of the TIME. Over time, scRNA-seq methods have evolved, and components of the TIME have been deciphered with high resolution. In this review, we first introduced the principle of scRNA-seq and compared different sequencing approaches. Novel cell types in the TIME, a continuous transitional state, and mutual intercommunication among TIME components present potential targets for prognosis prediction and treatment in cancer. Thus, we concluded novel cell clusters of cancer-associated fibroblasts (CAFs), T cells, tumour-associated macrophages (TAMs) and dendritic cells (DCs) discovered after the application of scRNA-seq in TIME. We also proposed the development of TAMs and exhausted T cells, as well as the possible targets to interrupt the process. In addition, the therapeutic interventions based on cellular interactions in TIME were also summarized. For decades, quantification of the TIME components has been adopted in clinical practice to predict patient survival and response to therapy and is expected to play an important role in the precise treatment of cancer. Summarizing the current findings, we believe that advances in technology and wide application of single-cell analysis can lead to the discovery of novel perspectives on cancer therapy, which can subsequently be implemented in the clinic. Finally, we propose some future directions in the field of TIME studies that can be aided by scRNA-seq technology.