Whole-Brain Co-Mapping of Gene Expression and NeuronalActivity at Cellular Resolution in Behaving Zebrafish
Marquez Legorreta, E.; Fleishman, G. M.; Hesselink, L. W.; Eddison, M.; Smeets, K.; Stringer, C.; Keller, P. J.; Narayan, S.; Chen, A. B.; Mensh, B. D.; Sternson, S. M.; Englitz, B.; Tillberg, P. W.; Ahrens, M. B.
Show abstract
The brains capabilities rely on both the molecular properties of individual cells and their interactions across brain-wide networks. However, relating gene expression to activity in individual neurons across the entire brain remains elusive. Here we developed an experimental-computational platform, WARP, for whole-brain imaging of neuronal activity during behavior, expansion-assisted spatial transcriptomics, and cellular-level registration of these two modalities. Through joint analysis of whole-brain neuronal activity during multiple behaviors, cellular gene expression, and anatomy, we identified functions of molecularly defined populations--including luminance coding in a cckb-pou4f2 midbrain population and task-structured activity in pvalb7-eomesa hippocampal-like neurons--and defined over 2,000 other function-gene-anatomy subpopulations. Analysis of this unprecedented multimodal dataset also revealed that most gene-matched neurons showed stronger activity correlations, highlighting a brain-wide role for gene expression in functional organization. WARP establishes a foundational platform and open-access dataset for cross-experiment discovery, high-throughput function-to-gene mapping, unification of cell biology and systems neuroscience, and scalable circuit modeling at the whole-brain scale.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging data 98%
- Recurrent pattern completion drives the neocortical representation of sensory inference 97%
- A deep learning framework for inference of single-trial neural population dynamics from calcium imaging with sub-frame temporal resolution 97%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.