INNOVATIVE ADVANCEMENTS IN SINGLE-CELL SEQUENCING TECHNOLOGIES FOR COMPREHENSIVE ANALYSIS OF CELLULAR HETEROGENEITY AND ITS ROLE IN CANCER PROGRESSION AND THERAPEUTICS

Abstract

Cancer is a group of rather diverse diseases that develop from normal to malignant state by means of functional capacity generation. Understanding the mechanisms of emergence, progression, metastases, and treatment resistance remains challenging despite such robust experimental and computational approaches. But since big scale RNA sequencing technology only shows an average gene expression of the sample, they cannot grasp the heterogeneity of the tumor microenvironment. The chance to grasp the minor tumor biology differences has come from the recent arrival and development of single cell RNA sequencing (scRNA-seq) technology that can identify different cell subpopulations, evaluate the tumor microenvironment, and characterize cellular genetic changes. The cancer research progressively started using more and more scRNA-seq technology to investigate the tumor microenvironment and heterogeneity in recent years in order to improved knowledge of carcinogenesis and evolution of cancer. The basics, development and increasing use of scRNA-seq technology in clinical practice and cancer research are described in this work. Because of high resolution transcript sequencing, ScRNA-seq has been shown to be a useful technique as it offers heretofore unheard mapping data for cancer study. New cell subpopulations and markers, gap intra, and inter-tumor heterogeneity, tumor microenvironment, intercellular communication, and lineage trajectories may all be defined using it. Our knowledge of cancer development, diagnosis, prognosis, therapy and medication resistance has been much improved by such investigations. Still, there are several restrictions that prevent the scRNA-seq from finding general use. First, while it does not data cancer biology at the most understandable level (in the level of proteomics, epigenomics, genomics, etc.), scRNA-seq is the most developed and extensively utilized single cell omics in cancer research. Second, scRNAseq only uses living cells as detection object and has great sample demand, hence there will be significant batch effects on samples examined at various times. Thirdly, RNAs could be broken down and single cell suspension produced from enzymatic digestion of cells could be killed. Furthermore producing this effect is the loss of morphological and spatial information.