Tabula Muris is a comprehensive atlas of cells within a mouse. This project details the unique characteristics of individual cells, acting as a detailed map of cellular composition. It systematically catalogs what makes each cell distinct across various tissues and organs. The creation of this atlas marks a substantial step forward in understanding biological systems.
The Foundation of Single-Cell Biology
Scientists have long recognized that tissues are complex structures, composed of distinct cell types. Analyzing these tissues as a whole, through what is called “bulk analysis,” often averages out important differences between individual cells. This approach can obscure the unique roles and states of specific cell populations within a tissue. For example, a bulk sample of the liver provides an average gene expression profile, making it difficult to discern the activities of specific cell types like hepatocytes versus immune cells.
Single-cell biology emerged to overcome these limitations by focusing on individual cells. This approach allows researchers to uncover the diversity within tissues, revealing how different cells contribute to the overall function of an organ. By analyzing cells one by one, scientists can identify rare cell types, understand their specific gene expression patterns, and observe how their functions might vary even within seemingly uniform tissues.
Building the Tabula Muris Map
The Tabula Muris dataset involved analyzing gene activity in individual cells from a wide array of mouse tissues and organs. The Chan Zuckerberg Biohub led this effort, compiling single-cell transcriptome data from nearly 100,000 cells sourced from 20 different organs and tissues. This project aimed to provide a detailed catalog of cell types and their gene expression profiles across the mouse body.
Two distinct technical approaches were employed to generate this dataset. Microfluidic droplet-based 3′-end counting allowed for surveying thousands of cells per organ, providing broad coverage. Simultaneously, FACS-based full-length transcript analysis offered higher sensitivity and comprehensive gene expression data for individual cells. The combination of these methods provided a robust catalog of cell types and their various states within the mouse.
Unlocking Biological Insights
Analysis of Tabula Muris data has led to significant discoveries, providing insights into biological systems. A primary benefit has been the identification of previously unknown or poorly characterized cell types within various organs. Researchers can now precisely map the cellular composition of organs and observe how gene expression changes across different cell populations. This level of detail allows for a deeper understanding of cellular identity and function.
The dataset also enables a direct comparison of gene expression in cell types shared between different tissues. Tabula Muris has been extended to study aging, with the Tabula Muris Senis project investigating age-related changes across 18 mouse tissues and organs. This expanded atlas has revealed cell-specific changes occurring across multiple cell types and organs during the aging process. Researchers can now assess how aging manifests at the single-cell level.
The Impact on Research
Tabula Muris serves as a foundational reference and valuable resource for the scientific community, accelerating various research endeavors. It provides a standardized benchmark for identifying and classifying cell types, useful for validating findings in future targeted single-cell studies. This comprehensive atlas helps researchers understand disease mechanisms by pinpointing specific cell types involved in disease progression or susceptibility.
The data also aids in the development of new therapies by identifying potential target cells. Understanding how gene expression changes in specific cells during aging can inform strategies for healthy aging. Tabula Muris fosters collaboration among scientists and advances fields such as aging research, immunology, and regenerative medicine by providing openly accessible data.