Cell Science

The CZI Cell Science program (formerly the Single-Cell Biology program) supports and enables the generation and application of technologies for the engineering and research of complex cellular systems. Despite an ever-increasing knowledge of cell types and states, the vast majority of primary cells remain remarkably difficult to culture and experiment with. The Cell Science program seeks to change this by building on their established interdisciplinary collaborations to derive molecular definitions of cells and dig deeper into their biology. At its core, the program is focused on scalable technology that enables deeper insight into cells in health and disease states via experimentation and modeling.

An immunofluorescence image of the first part of the small intestine, called the duodenum (CZI Single-Cell Biology).

An immunofluorescence image of the first part of the small intestine, called the duodenum. Photo provided by Emma Lundberg, KTH Royal Institute of Technology, Sweden.

The Cell Science program has funded the following RFAs:

Measuring Metabolism Across Scales, $8.6 million
Single-Cell Biology Data Insights (Cycle 1, $5.2 million and Cycle 2, $4.9 million)
Patient-Partnered Single-Cell Analysis of Rare Pediatric Disease, $10 million

Ancestry Networks for the Human Cell Atlas, $27.9 million
Pediatric Networks for the Human Cell Atlas, $34.2 million
Single-Cell Analysis of Inflammation, $14 million
Seed Networks for the Human Cell Atlas, $67.7 million
Collaborative Computational Tools for a Human Cell Atlas, $14.8 million
Pilot Projects for a Human Cell Atlas, $14.9 million

Single-Cell Technology

We build tools to support and accelerate the work of biologists who are uncovering new insights about healthy and diseased cells using single-cell methods. Our platform, Chan Zuckerberg CELL by GENE (CZ CELLxGENE), allows scientists to access, analyze, and annotate high-dimensional single-cell data at scale, so they can inform their experiments or quickly surface new insights that could lead to discoveries in treating disease. Learn more at czcellxgene.org and read our pre-print.

CZ CELL×GENE Discover

CZ CELLxGENE Discover provides scientists with fast, easy access to the largest collection of standardized single-cell data — 50+ million cells and growing. Scientists can access the data in different ways based on their research needs:

Census

Access any custom slice of standardized cell data available on CZ CELLxGENE Discover via R and Python.

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CellGuide

Explore an interactive encyclopedia of 700+ cell types that provides detailed definitions, marker genes, lineage, and relevant datasets in one place.

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Gene Expression

Uncover expression levels of any gene across datasets of cell types and tissues with advanced visualization tools.

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CZ CELL×GENE Annotate

CZ CELLxGENE Annotate is a self-hosting interactive data explorer for single-cell datasets. Our desktop tool is a powerful interface that allows you to annotate, analyze, and explore your own single-cell data. Learn more here.

Cellular Measurement

Advancements in technology offer an unprecedented potential to profile and visualize cells, RNA, proteins, and metabolites. This work aims to generate comprehensive data references spanning modalities, ancestries, and developmental stages that can be used to rapidly understand the molecular mechanisms that define cells.

Prediction Engines

There remain many opportunities to use and reuse single-cell data to more deeply interrogate cellular mechanisms and disease. This work aims to equip leading-edge computational scientists with large data resources and unlock untapped power to describe cellular function.

Perturbation and Validation

Perturbation and manipulation of complex cellular systems are critical to extending and validating inferences made by current cell atlases. This work focuses on the development of new technologies that enable scientists to perturb cells and systems at scale across organisms, modalities, and diseases.

Cell Engineering

This work moves beyond functional interrogation and extends into engineering cell identity, fate, or behavior using synthetic biology, with the goal of validating our molecular knowledge of cells.

Closed

This grant program seeks to support computational experts to advance tools and resources that make it possible to gain greater insights into health and disease from single-cell biology datasets.

Single-Cell Biology Data Insights

- $5.2 Million
Total Grant Resources Cycle 2 - $4.9 Million

Closed

This RFA aims to accelerate innovative discoveries in the field of metabolism and metabolic physiology in health and disease.

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This RFA aims to support the application of single-cell biology methods to rare inflammatory pediatric diseases that will clarify cellular mechanisms of disease, understand disease heterogeneity, and improve biomarker identification or diagnostics.

Closed

This RFA supports researchers to contribute healthy, single-cell reference data from ancestrally diverse tissue samples to the Human Cell Atlas, with the aim of creating a more globally representative resource to understand disease.

Closed

This grant program supports researchers to contribute pediatric tissue samples to the global Human Cell Atlas as a foundation for understanding how cells and organs progress and relate to disease onset in children.

Closed

This RFA supports interdisciplinary scientists as they apply new tools and work together in accelerating inflammation research. With these research grants, we aim to further our understanding of inflammation’s role in maintaining health and triggering disease.

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This RFA supports the continued development of the Human Cell Atlas, a global effort to map all cells in the human body.

Closed

This program awards grants to spur the creation of new computational tools, algorithms, visualizations, and benchmark datasets to analyze, interpret, and disseminate a large variety of molecular and imaging data generated for the global Human Cell Atlas.

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This program awards scientific grants to support the Human Cell Atlas community’s convergence around standards, protocols, and best practices for the creation of a freely and openly available reference database of healthy human cells and tissues.