New Cell Developmental Analysis Tool - Single-cell topological RNA-seq analysis (scTDA)

Organic tissue originates from a single cell, and its copy is differentiated into specialized cells while cells division, such as the heart, bones or brain cells. In order to understand the internal and external clues of cell transformation in different directions, scientists usually carry out RNA sequencing techniques for these cells because RNA is a molecular messenger that converts DNA into proteins and other products. However, the sequencing results of RNA extraction from a batch of cells are not accurate enough due to the different cells developmental conditions. To solve this problem, scientists have developed single cell RNA sequencing. "A new technology will always provide us a new perspective," said Dr. Raul Rabadan, associate professor of biomedical information science and systems biology at Columbia University. "But we still have cognitive blind spots on the relationship between different states of cells that have also contributed to the development of the process.” Scientists analyzed massive sequencing data with mathematics tools for cells developmental study. But current analysis methods rely on the potential assumptions excessively that limited the search scope."Considering the complexity of cell development, model assumptions limit your ability to detect," said Abbas Rizvi, a postdoctoral researcher at the University of Columbia, Department of Biochemistry and Molecular Biophysics at the University of Columbia. In order to ascertain the potential connections between different cell states and active genes, Dr. Rizvi turned attention to topology with his theory of physical physics postgraduate researcher Pablo G. Camara.Topology is a branch of mathematical research for the spatial relationship between shape and surface. They have collaborated to develop an algorithm called "single-cell topological data analysis (scTDA)". scTDA is a nonlinear, model-independent, unsupervised statistical framework that can characterize transient cellular states. The algorithm can reconstruct the basic development trajectories by analyzing the RNA sequences of individual cells and capture the process of different transcription programs in time. Researchers applied scTDA to the analysis of murine embryonic stem cell (mESC) differentiation in vitro in response to inducers of motor neuron differentiation.The path of the technology output correctly shows the possible trajectories of the cells. By observing the active genes in the vicinity of the pathway, the researchers identified various proteins that lead to cell development as shown. And scTDA also being used to study the mouse lung, human embryos, and mouse brain stem cell development pathways. "We expect more discoveries and progress mined from these data by more scientists," said Dr. Tom Maniatis, co-author of this paper, director of the Department of Biochemistry and Molecular Biophysics at Columbia University. "It facilitates the deep analysis of the possibility of a single cell development period." "This approach provides insight into the potential fate of cells, offering us with closer access to key regulatory molecules and molecular changes that confer cell identity, exposing the negative effects of manipulating cell development from normal orbits." Dr. Rizvi Said. scTDA can be applied to study asynchronous cellular responses to either developmental cues or environmental perturbations.At present, it has been applied in American research institutions to reveal the dynamic processes of various complex biological processes, including cancer.