Our Work

Quantitative Cell Science

The maps we need

graphic background

Diseases are caused by disruptions in the inner workings of cells or in the communication between cells. That’s why we support rigorous, quantitative research in cell biology, showing how cells work in healthy people and, more importantly, what takes place when disease strikes.

Malfunctioning cells are at the root of many human diseases, including cardiovascular disease, neurodegeneration, diabetes, and cancer. Our goal is to map and understand cellular physiology across scales in time and space and make the data we obtain openly available to advance all research in cell biology. Understanding the healthy cell is a prerequisite to diagnosing, understanding, and treating the diseased state.


At the center of CZ Biohub’s Quantitative Cell Science efforts are the Tabula projects led by CZ Biohub Network President Steve Quake and collaborators, who apply single-cell transcriptomics and data sciences to identify the thousands of cell types that comprise the tissues and organs in whole organisms, from fruit flies (Tabula Drosophilae) to mice (Tabula Muris) to humans (Tabula Sapiens).

At the subcellular level, Manuel Leonetti and his group, who are exploring cellular architecture, have endogenously tagged 1,300 proteins, documented their subcellular localization by 3D confocal microscopy, and identified their protein interactions by co-immunoprecipitation and mass spectrometry (in collaboration with Matthias Mann of the Max Planck Institute of Biochemistry). This massive dataset is presented in OpenCell and serves as a valuable resource for cell biology.

The Tabula Projects and OpenCell provide a basis for understanding diverse physiological and pathological states of cells.

Using state-of-the-art, custom-built, light-sheet microscopy coupled with spatial transcriptomics and photoactivation of lineage-specific reporters, Loïc Royer and his group are mapping how tissues form during early development at unprecedented spatial and temporal resolution. These studies are enabled by innovative tools, such as napari (created with Juan Nuñez-Iglesias of Monash University and Nick Sofroniew of the Chan Zuckerberg Initiative), for machine learning, image analysis, refinement, and visualization.

To quantitatively understand cellular dynamics and organization, Greg Huber and his group apply computational mathematics, Monte Carlo simulation, and statistical mechanics to study complex behaviors at multiple scales, including subcellular, developmental, mechanical, epidemiological, and evolutionary.