BIFROST: A method for registering diverse imaging datasets of the Drosophila brain

• 成果类型: Article
• 署名作者: Brezovec, Bella E.; Berger, Andrew B.; Hao, Yukun A.; Lin, Albert; Ahmed, Osama M.; Pacheco, Diego A.; Thiberge, Stephan Y.; Murthy, Mala; Clandinin, Thomas R.
• 署名单位: Stanford University; Princeton University; University of Colorado System; University of Colorado Boulder; Stanford University; Princeton University; University of Washington; University of Washington Seattle; Harvard University; Harvard Medical School
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-9711
• DOI: 10.1073/pnas.2322687121
• 发表日期: 2024-11-19
• 关键词: registration; connectome; Similarity; images; atlas

摘要

Imaging methods that span both functional measures in living tissue and anatomical measures in fixed tissue have played critical roles in advancing our understanding of the brain. However, making direct comparisons between different imaging modalities, particularly spanning living and fixed tissue, has remained challenging. For example, comparing brain-wide neural dynamics across experiments and aligning such data to anatomical resources, such as gene expression patterns or connectomes, requires precise alignment to a common set of anatomical coordinates. However, reaching this goal is difficult because registering in vivo functional imaging data to ex vivo reference atlases requires accommodating differences in imaging modality, microscope specification, and sample preparation. We overcome these challenges in Drosophila by building an in vivo reference atlas from multiphoton-imaged brains, called the Functional Drosophila Atlas. We then develop a registration pipeline, BrIdge For Registering Over Statistical Templates (BIFROST), for transforming neural imaging data into this common space and for importing ex vivo resources such as connectomes. Using genetically labeled cell types as ground truth, we demonstrate registration with a precision of less than 10 microns. Overall, BIFROST provides a pipeline for registering functional imaging datasets in the fly, both within and across experiments.