Research

The lab integrates interdisciplinary approaches to develop and apply advanced genetic, genomic, and epigenomic technologies, including methods suitable for small tissue samples and single-cell analysis. Using a range of model organisms, such as fish, chick, mouse, and lamprey, these approaches enable detailed investigation of developmental and evolutionary processes.
By combining experimental and computational strategies, including machine learning and deep learning, the lab seeks to reconstruct gene regulatory networks at high resolution. Ongoing efforts focus on advancing developmental genomics and single-cell epigenomics, incorporating spatial and molecular context to better understand regulatory interactions during developmental transitions and cell fate specification. Through these innovations, the lab aims to provide comprehensive insights into the regulatory logic of development and to establish broadly applicable frameworks for studying complex biological systems.

Discover how the Sauka-Spengler Lab and collaborators developed RegVelo, an AI framework that combines gene regulatory networks with developmental dynamics to predict how cells choose their identities and uncover the genetic programs that drive cell fate.

Postdoctoral Researcher Justin Avila provides an overview of neural crest cells and their unique developmental potential, highlighting why they serve as a powerful model for studying cell fate, gene regulatory networks, and vertebrate evolution.