Bridging Large Language Models and Single-Cell Transcriptomics in Dissecting Selective Motor Neuron Vulnerability
Douglas Jiang, Zilin Dai, Luxuan Zhang, Qiyi Yu, Haoqi Sun, Feng Tian·May 12, 2025
Summary
A novel framework combines gene-specific textual annotations and large language models to generate biologically contextualized cell embeddings. This multimodal approach ranks genes, retrieves descriptions, and transforms them into vector representations, facilitating applications like cell type clustering, vulnerability dissection, and trajectory inference. It addresses challenges in predicting neuronal identity and susceptibility in neurodegenerative diseases, offering a compact, semantically rich representation to mitigate high dimensionality and sparsity in biological datasets.
Introduction
Background
Overview of gene expression analysis
Challenges in biological data representation
Objective
To introduce a novel framework that integrates gene-specific textual annotations and large language models for generating biologically contextualized cell embeddings
Method
Data Collection
Gathering gene expression data from various sources
Incorporating gene-specific textual annotations
Data Preprocessing
Cleaning and standardizing gene expression data
Processing textual annotations for semantic understanding
Model Integration
Utilizing large language models for embedding generation
Aligning gene expression data with textual annotations
Training and Validation
Training the multimodal model on annotated gene expression datasets
Evaluating model performance on various biological tasks
Applications
Cell Type Clustering
Enhancing the accuracy of cell type identification
Improving the resolution of cell subpopulations
Vulnerability Dissection
Identifying genes associated with disease susceptibility
Analyzing the impact of genetic variations on cell function
Trajectory Inference
Mapping the developmental or disease progression pathways
Predicting cellular states and transitions
Case Studies
Predicting Neuronal Identity
Application in neuroscience for understanding neural cell types
Insights into the molecular basis of neuronal diversity
Susceptibility in Neurodegenerative Diseases
Analysis of genetic factors contributing to disease progression
Identification of potential therapeutic targets
Challenges and Solutions
High Dimensionality and Sparsity
Strategies for dimensionality reduction
Techniques to handle sparse biological data
Semantic Rich Representation
Enhancing the interpretability of cell embeddings
Utilizing semantic information for more accurate predictions
Conclusion
Summary of Contributions
Future Directions
Expanding the framework to include additional biological data types
Integrating with other AI-driven biological research tools
Basic info
papers
genomics
artificial intelligence
Advanced features
Insights
What are the key steps involved in transforming gene descriptions into vector representations?
In what ways does the framework address challenges in predicting neuronal identity and susceptibility in neurodegenerative diseases?
How does the novel framework integrate gene-specific textual annotations with large language models to generate cell embeddings?
What are the innovative aspects of using a multimodal approach for cell type clustering and trajectory inference?