In a recent study published in Nature Communications, a team of researchers at the Carl R. Woese Institute for Genomic Biology reports a new, robust computational toolset to extract biological relationships from large transcriptomics datasets. These efforts will help scientists better investigate cellular processes.

Living organisms are governed by their genome—an instruction manual written in the language of DNA that dictates how an organism grows, survives, and reproduces. By regulating the abundance of different RNA transcripts, cells control their protein expression level, thereby shaping their functions and responses to the environment.

Transcriptomics is the study of gene expression through cataloging the presence and abundance levels of active RNA transcripts generated from the genome under different conditions. Through the lens of RNA, transcriptomics technologies allow scientists to study the complex interactions that enable life and cause disease, as well as assess the biological effects of therapeutics.

Cell–cell alignment and a background of stationary cells together shape the emergence of cellular clusters in a primary tumor.

In a cancer patient, tumor cells that circulate throughout the body in clusters pose a greater threat of metastasis than those that circulate individually. Those clusters are thought to come together while the cells are still within the primary tumor, but researchers still don’t understand the formation mechanism. Quirine Braat at Eindhoven University of Technology in the Netherlands and her colleagues have now used computer simulations to identify some of the factors at play [1].

The team used a computational lattice model of cells and tissues (the cellular Potts model) to examine a 2D layer of two types of cells—one motile (able to move) and one nonmotile. The tendency of the motile cells to migrate was represented in the model by an external force applied to each one. For a given cell, this force could align strongly or weakly with the forces acting on its neighboring cells.

Polarization photodetectors (pol-PDs) have widespread applications in geological remote sensing, machine vision, and biological medicine. However, commercial pol-PDs usually require bulky and complicated optical components and are difficult to miniaturize and integrate.

Chinese researchers have made important progress in this area by developing an on-chip integrated polarization photodetector.

This study, published in Science Advances on Dec. 4, was conducted by Prof. Li Mingzhu’s group from the Technical Institute of Physics and Chemistry of the Chinese Academy of Sciences.