AI in Single-Cell Biology

The field of single-cell biology has revolutionized our understanding of complex biological systems by enabling researchers to uncover cellular heterogeneity within tissues at an unprecedented resolution. This transformative approach allows for the dissection of cellular states and phenotypes, which are crucial in understanding developmental processes, disease progression, and treatment responses. Simultaneously, artificial intelligence (AI) has seen exponential advancements, providing powerful tools capable of analyzing high-dimensional data, discovering patterns, and making predictions that were previously unattainable. Combining AI with single-cell biology represents a promising frontier in biomedical research, offering opportunities to enhance data analysis, interpretation, and ultimately, the precision of biological insights.

Despite its potential, the integration of AI technologies in single-cell studies is not without challenges. These include handling the complexity and volume of single-cell data, extracting biologically meaningful information, dealing with noise and variability inherent in biological datasets, and ensuring computational methods are interpretable and clinically relevant. Addressing these issues requires interdisciplinary efforts involving AI, bioinformatics, and life sciences, thereby fostering collaborations that transcend traditional boundaries.

The aim of this research topic is to explore the application of AI in single-cell biology, from developing methods and tools that can handle the complexity of single-cell data and uncover novel insights into cellular function and disease to highlighting applications of AI in single-cell biology. This includes enhancing data preprocessing, improving clustering and classification methods, and discovering dynamic cellular processes.

We encourage submissions in the form of original research, reviews, methodologies, technology and code, perspectives and case studies that demonstrate the use of AI in single-cell biology. Submissions can explore but are not limited to:

1. AI-Driven Data Preprocessing and Normalization Techniques: Approaches that improve data quality and reduce technical variability, enabling more accurate downstream analyses.
2. Advanced Clustering and Classification Algorithms: Development of novel AI algorithms to better classify single-cell populations and uncover rare cell types within complex tissues.
3. Integration and Analysis of Multi-Omics Single-Cell Data: Strategies for combining transcriptomics, proteomics, epigenomics, and other single-cell data types, utilizing AI to derive comprehensive biological insights.
4. AI-Assisted Dynamic Cellular Process Modeling: AI applications for modeling temporal or spatial dynamics at the single-cell level, aiding in the understanding of developmental processes and disease progression.
5. AI Applications in Personalized Medicine: Use of AI in single-cell analyses to tailor medical treatments based on individual cellular profiles, improving prognosis and therapeutic outcomes.
6. Interpretable and Transparent AI Models: Development of AI models that provide explanations for predictions, ensuring that derived insights can be understood and trusted by biologists and clinicians.

By addressing these themes, this Research Topic seeks to catalyze the development of AI methodologies in single-cell research, enabling deeper biological insights and fostering translational applications in medicine.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: Artificial Intelligence (AI), Single-Cell, Multi-Omics Integration, Dynamic Cellular Processes, AI Models, Preprocessing, Normalization, Clustering, Classification

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.