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Cell Diversity

Cell Diversity

The concept of a cell started with microscopic observations of dead cork tissue by Robert Hooke in 1665. Hooke coined the term "cell" based on the resemblance of the small subdivisions in the cork to the rooms that monks inhabited, called cells. About ten years later, Antonie van Leeuwenhoek became the first person to observe the living and moving cells under a microscope. In the century that followed, the theory that cells represented the basic unit of life developed.
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Related Experiment Video

Updated: Jan 11, 2026

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Published on: March 3, 2023

Multimodal learning enables chat-based exploration of single-cell data.

Moritz Schaefer^1,2, Peter Peneder^3,4, Daniel Malzl^2,5,6

¹Medical University of Vienna, Institute of Artificial Intelligence, Center for Medical Data Science (CEDAS), Vienna, Austria.

Nature Biotechnology

|November 11, 2025

Summary

This summary is machine-generated.

CellWhisperer is an AI tool that uses natural language chats to interpret complex single-cell RNA sequencing data. It makes gene expression analysis more accessible by connecting transcriptomes with textual information.

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Last Updated: Jan 11, 2026

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

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Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing

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Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

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Published on: March 20, 2018

Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Single-cell sequencing generates vast amounts of detailed biological data.
Interpreting this complex gene expression data remains a significant challenge for researchers.

Purpose of the Study:

To develop an AI-powered tool, CellWhisperer, for intuitive, chat-based interrogation of single-cell RNA sequencing data.
To enhance the accessibility and interpretability of single-cell transcriptomic information.

Main Methods:

Constructed a multimodal embedding of transcriptomes and textual annotations using contrastive learning on 1 million RNA sequencing profiles.
Integrated this embedding with a large language model to enable natural-language querying of gene expression data.
Developed a chat interface integrated with the CELLxGENE browser for interactive data exploration.

Main Results:

CellWhisperer demonstrates strong performance in zero-shot prediction of cell types and biological annotations.
The tool facilitates biological discovery, as shown in a meta-analysis of human embryonic development.
Users can interactively explore gene expression through a combined graphical and chat interface.

Conclusions:

CellWhisperer effectively leverages large-scale data repositories to bridge transcriptomes and text.
This AI model significantly enhances the interactive exploration of single-cell RNA sequencing data using natural-language chats.