Gathering satellite imagery, street-level photographs, aerial views, and user-contributed data.

Capture detailed representations of Earth's surface from various perspectives to create a comprehensive and up-to-date map.

Creating a cell atlas starts with generating comprehensive single-cell datasets: researchers isolate individual cells from various tissues such as the brain, heart, or immune system and profile their molecular characteristics. This extensive data collection mirrors how Google Maps gathers geospatial information to form a detailed global map. To fully represent human cellular diversity, cell atlases must incorporate a diversity of sources, including samples from multiple donors, developmental stages, and conditions. However, managing data quality and volume poses significant challenges, as high-throughput technologies produce massive datasets that must be accurate, reliable, and carefully curated.

Combining single-cell datasets from different experiments, laboratories, and technologies.

Build a unified and comprehensive cell atlas that accurately reflects biological reality.

Just as Google Maps integrates diverse geospatial data into a coherent map, building a cell atlas involves merging datasets from multiple experiments to ensure a unified representation of cellular diversity. Technical variations, or batch effects, must be addressed using methods like Nygen, Harmony or Seurat’s integration tools, ensuring that the data accurately reflects biological differences rather than experimental artifacts. As the number of datasets grows, scalability becomes essential, requiring efficient computational strategies to maintain data integrity and preserve meaningful biological signals.

Classifying cells based on gene expression profiles, surface markers, and functional characteristics.

Enable researchers to understand the roles of different cells in health and disease by providing detailed annotations.

In cell atlases, annotation means identifying and labeling various cell types and states, turning raw data into a powerful resource for understanding human biology. Researchers rely on computational algorithms, expert input, and automated annotation tools like those offered by Nygen to classify cells efficiently and accurately. Much like how users contribute to Google Maps, community collaboration enhances cell atlases as scientists share data, refine annotations, and enrich the collective understanding of cellular diversity.

Applying computational models, artificial intelligence, and machine learning to interpret the data.

Predict cellular behaviors, understand disease mechanisms, and identify potential therapeutic targets.

In building cell atlases, integrating functional data such as signaling pathways and responses to stimuli transforms static maps of cellular states into dynamic models of behavior. By applying computational models, AI, and machine learning, researchers can predict how cells will act under different conditions, informing drug discovery and personalized medicine. Tools like Nygen Insights enhance this process, using advanced analytics to identify therapeutic targets, while simulation of cellular processes helps researchers understand diseases at a systems level.

Making cell atlases available to researchers, clinicians, pharmaceutical companies, and educators.

Facilitate collaboration, accelerate drug discovery, advance personalized medicine, and enhance biomedical education.

Making cell atlases broadly accessible transforms them into a shared resource that researchers, clinicians, and pharmaceutical companies can use to accelerate discoveries and advance personalized medicine. By using platforms like Nygen Analytics, which does not require extensive bioinformatics expertise, cell atlas data becomes readily available to a wider audience. This approach fosters collaboration and sharing, allowing the community to collectively leverage these detailed cellular maps for understanding human biology, identifying drug targets, and developing more effective therapies.