A data-driven framework linking the connectome to spatial gene expression gradients inspired by chemoaffinity theory

TL;DR

This paper introduces SPERRFY, a framework that uses gene expression and brain connectivity data to explore how molecular gradients guide brain wiring, extending Sperry's chemoaffinity theory to the whole brain.

Contribution

SPERRFY is a novel data-driven framework that applies chemoaffinity theory to whole-brain connectomics using spatial gene expression data.

Findings

SPERRFY identifies gradient patterns that capture major aspects of mouse brain connectome wiring.

Gradient-based connectivity reconstruction shows strong predictive performance and biological relevance.

The framework screens candidate genes potentially involved in positional wiring of neural circuits.

Abstract

Understanding how gene expression patterns determine the wiring of the brain is a fundamental question in neuroscience. SPERRFY offers a data-driven framework to decode spatial encoding of whole-brain connectivity by applying multivariate analysis to connectomic data and spatial gene expression patterns, grounded in Sperry’s chemoaffinity theory. Using mouse brain data, we identified specific gradient patterns that capture major aspects of the connectome’s wiring pattern. These findings place connectome-scale wiring patterns in line with key principles of the chemoaffinity theory, and also provide a versatile tool for mapping genetic determinants of brain-wide neural circuits. By integrating transcriptomics with connectomics, our approach potentially opens avenues for exploring the design principles underlying brain architecture.

Understanding how brain-wide neural circuits are genetically wired remains a fundamental question in neuroscience. While Sperry’s chemoaffinity theory [Sperry, Proc. Natl. Acad. Sci. U.S.A.
50, 703–710 (1963)] posits that molecular gradients provide positional cues for axonal projections, its application has been largely limited to localized sensory systems. Here, we present SPERRFY (Spatial Positional Encoding for Reconstructing Rules of axonal Fiber connectivitY), a data-driven framework that operationalizes Sperry’s theory at the whole-brain scale. By integrating connectomic data with spatial transcriptomic profiles from the Allen Mouse Brain Atlas, SPERRFY infers latent positional gradients that underlie axonal wiring. Using canonical correlation analysis (CCA), we extract top gradient pairs that align with observed neural connectivity patterns, capturing both global (interregional) and local (intraregional) organizational principles. Connectivity reconstruction based on these gradients shows strong predictive performance, and permutation-based null models confirm the biological relevance of the inferred structures. Furthermore, SPERRFY can screen for candidate genes that may contribute to positional wiring information, providing molecular insight into the developmental logic of brain-wide circuitry. Our results extend Sperry’s foundational theory beyond the sensory domain, offering a unified, data-driven framework for understanding genetically encoded connectivity across the entire brain.

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