Combinatorial protein barcodes enable self-correcting neuron tracing with nanoscale molecular context

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Abstract

Mapping nanoscale neuronal morphology with molecular annotations is critical for understanding healthy and dysfunctional brain circuits. Current methods are constrained by image segmentation errors and by sample defects (e.g., signal gaps, section loss). Genetic strategies promise to overcome these challenges by using easily distinguishable cell identity labels. However, multicolor approaches are spectrally limited in diversity, whereas nucleic acid barcoding lacks a cellfilling morphology signal for segmentation. Here, we introduce PRISM (Protein-barcode Reconstruction via Iterative Staining with Molecular annotations), a platform that integrates combinatorial delivery of antigenically distinct, cell-filling proteins with tissue expansion, multi-cycle imaging, barcode-augmented reconstruction, and molecular annotation. Protein barcodes increase label diversity by > 750-fold over multicolor labeling and enable morphology reconstruction with intrinsic error correction. We acquired a ∼10 million µm 3 volume of mouse hippocampal area CA2/3, multiplexed across 23 barcode antigen and synaptic marker channels. By combining barcodes with shape information, we achieve an 8x increase in automatic tracing accuracy of genetically labelled neurons. We demonstrate PRISM supports automatic proofreading across micron-scale spatial gaps and reconnects neurites across discontinuities spanning hundreds of microns. Using PRISM’s molecular annotation capability, we map the distribution of synapses onto traced neural morphology, characterizing challenging synaptic structures such as thorny excrescences (TEs), and discovering a size correlation among spatially proximal TEs on the same dendrite. PRISM thus supports selfcorrecting neuron reconstruction with molecular context.

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  1. The accuracy is increased with the number of barcodes usedfor matching.

    The steep drop is quite noticeable from 3->6 bits but it seems to reach some diminishing returns by 12-18. Do you have ideas as to why this plateau occurs? It suggests we're no longer diversity limited. My guess is it's either residual merge errors contaminating the segment-averaged barcode or the KDTree matcher's local radius might obscure some true pairs?

  2. Crucially, we showed automatic proofreading canbridge spatial gaps to reconnect neurite segments bothlocally and even across hundreds of microns, a major steptowards addressing signal discontinuity challenges

    The barcode proofreading approach in Fig 4 is fantastic. I can't, however, find empirical reconnections across hundreds of microns, apologies if I missed it. As you pointed out, though, ~10–30 µm is quite local. Block-wise execution would still be local, it doesn't impose a global consistency.