Our Work

Cell Atlas

The map we need

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Diseases are caused by disruptions in the inner workings of cells or in the communication between cells. That’s why we support fundamental science focused on 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 the Cell Atlas Initiative are the Tabula projects led by CZ Biohub Co-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 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). This massive dataset is presented in OpenCell and serves as a valuable resource for cell biology. These tools provide the 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 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.