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Updated: Jan 11, 2026

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

Published on: March 29, 2024

CELLama: Foundation Model for Single Cell and Spatial Transcriptomics by Cell Embedding Leveraging Language Model

Jeongbin Park¹, Sumin Kim¹, Jiwon Kim¹

¹Portrai, Inc., Seoul, 04798, Republic of Korea.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|November 17, 2025

Summary

This summary is machine-generated.

CELLama leverages language models to create universal cell embeddings from single-cell RNA sequencing and spatial transcriptomics data. This framework simplifies cell typing and spatial analysis, transforming cellular research.

Keywords:

artificial intelligence natural language model single cell RNA‐sequencing spatial transcriptomics transformer

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Last Updated: Jan 11, 2026

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

Published on: March 29, 2024

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

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Published on: January 7, 2020

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Area of Science:

Biomedical research
Computational biology
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) generate vast datasets for biological insights.
Analyzing complex scRNA-seq and ST data presents challenges in cell typing, integration, and spatial relationship analysis.

Purpose of the Study:

To develop a novel framework, CELLama, utilizing language models for universal cell embedding.
To address analytical challenges in large-scale single-cell and spatial transcriptomics data.

Main Methods:

CELLama transforms gene expression and metadata into "sentences" for language model processing.
A foundation model approach is employed, leveraging a large cell atlas for broad applicability.
The framework enables cell typing and spatial context analysis independent of dataset-specific workflows.

Main Results:

CELLama provides a universal cell embedding for diverse biological data.
The framework demonstrates effectiveness in cell typing across multi-tissue atlases.
CELLama facilitates the analysis of cell interactions and intricate tissue dynamics.

Conclusions:

CELLama offers a powerful, language model-based approach to cellular analysis.
The framework has significant potential to advance the interpretation of scRNA-seq and ST data.
CELLama can revolutionize the understanding of cellular heterogeneity and tissue organization.