Constructive Connectomics: how neuronal axons get from here to there using gene-expression maps derived from their family trees

Abstract

During brain development, billions of axons must navigate over multiple spatial scales to reach specific neuronal targets, and so build the processing circuits that generate the intelligent behavior of animals. However, the limited information capacity of the zygotic genome puts a strong constraint on how, and which, axonal routes can be encoded. We propose and validate a mechanism of development that can provide an efficient encoding of this global wiring task. The key principle, confirmed through simulation, is that basic constraints on mitoses of neural stem cells—that mitotic daughters have similar gene expression to their parent and do not stray far from one another—induce a global hierarchical map of nested regions, each marked by the expression profile of its common progenitor population. Thus, a traversal of the lineal hierarchy generates a systematic sequence of expression profiles that traces a staged route, which growth cones can follow to their remote targets. We have analyzed gene expression data of developing and adult mouse brains published by the Allen Institute for Brain Science, and found them consistent with our simulations: gene expression indeed partitions the brain into a global spatial hierarchy of nested contiguous regions that is stable at least from embryonic day 11.5 to postnatal day 56. We use this experimental data to demonstrate that our axonal guidance algorithm is able to robustly extend arbors over long distances to specific targets, and that these connections result in a qualitatively plausible connectome. We conclude that, paradoxically, cell division may be the key to uniting the neurons of the brain.

Cell lineage predicts neural connectivity beyond cell type

“…For now, C. elegans remains the only model organism for which cellular lineage and chemical synapse-resolution connectomes are available. Parallel efforts have demonstrated a similar effect in aggregate in rodent Thompson et al (2014); Kerstjens et al (2022), and we believe there is still much more to be learned by achieving cell-resolution data. Beyond simply predicting the connectome, cellular lineage may explain organization of other complex anatomical structures, or carry more information in its network structure than can be efficiently encoded in the limited information storage of the genome Koulakov et al (2021).…”

“…Techniques like electron microscopy connectomics now yield comprehensive, synapse-resolution connectivity datasets Glasser et al (2016); Huang et al (2021); Shapson-Coe et al (2021); Witvliet et al (2021). In combination with these connectomes, we can now ask how much of the connectivity of a brain is affected by cell types, and how much by developmental lineage Kerstjens et al (2022).…”