The human body is comprised of trillions of specialized cells, each interacting with their neighbors and the environment to keep the body functioning as it should. NIH-funded researchers have developed a powerful new tool called Mixscale that helps scientists quickly analyze how genetic changes affect these cells. A new article introduces this improved method to help researchers better understand the impact of genetic modifications on cellular processes. 

The research is part of the NIH Common Fund’s Human BioMolecular Atlas Program, a consortium of over 300 researchers using innovative and revolutionary techniques to study the spatial organization of cells within tissues to better understand how cells function in health and disease. These cutting-edge methods can map the locations of molecules such as RNA and proteins inside cells and show where those cells reside in tissues. These techniques provide mountains of data, so scientists need computational workflows to understand and analyze it. 

One of the most powerful methods researchers use to study the workings of cells is called Perturb-seq, which changes the actions of specific genes and then examines how those changes impact the work of the cell. Perturb-seq creates large amounts of data, which can be difficult to analyze. Dr. Rahul Satija’s team at the New York Genome Center developed a new way to study the Perturb-seq data much more quickly. This method, called Mixscale, uses improved statistical methods to pinpoint important gene changes and their effects on cellular pathways. 

To test Mixscale, the Satija group used Perturb-seq on six different types of cancer cells, including cells associated with lung, breast, colon, pancreas, and bone marrow cancer. They then developed a computational pipeline to identify which genes changed due to the genetic modifications, group these changes into key biological pathways, and interpret the single-cell RNA sequencing results. 

The results showed that Mixscale provides clear and reliable insights into how genes and cells respond to changes in their environment. 

Because it works across different types of cells and diseases, it could be a valuable tool for many areas of medical research. 

Reference: Jiang L, Dalgarno C, Papalexi E, Mascio I, Wessels HH, Yun H, Iremadze N, Lithwick-Yanai G, Lipson D, Satija R. Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens. Nat Cell Biol. 2025 Mar;27(3):505-517.