There appear to be some missing data in the supplements? I can only access figs S1-S5.

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?

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.

Did you filter out from Qwen any biomedical corpus contamination (e.g., ClinVar abstracts) to avoid data leakage into downstream evaluation?

The explicit traces are a really cool thing to include. Feels like it can be great for hypothesis generation and wet-lab follow-up.

Stacking them at the token level instead of using adapters is pretty creative!

Nice, clever guard against leakage.

Could this truncation drop functional motifs (e.g., regulatory elements) that lie > 2 kbp from a locus? Are there checks you can do to see whether performance collapses on longer genes?

It would be interesting to design a sequence predicted to be inactive (e.g., by mutating the key catalytic residue or ion binding site) and then confirming its inactivity experimentally, to demonstrate that the model can distinguish functional from non-functional sequences. Similarly, would be interesting to compare ProTrek score (or any of the ranks/scores) against measured enzymatic activity to see if there's a correlation there.

The ADH validation confirming enzyme-dependent activity is a promising proof-of-concept. It will be interesting to see how Pinal performs with proteins with more complex functions. I also think it would be interesting to test a predicted inactive mutant as an additional control.

Easy to use interface, thank you.

This is helpful to know. I'm wondering given the morphological variability of reactive astrocytes across different models (2D, 3D, postmortem) and species, you might consider automated 3D morphometry to supplement manual counting. Or even some kind of inter-rater reliability assessment. This would also help differentiate it from other reactive morphologies like astrocytic clasmatodendrosis.

I think the conclusion that VP astrocytes represent a reactive phenotype rather than a physiological subtype is very well-supported by the data presented in this work.

Sorry, this is just so cool.

If I'm understanding correctly, this might be a typo, should be neurobots.

This is intriguing, would be nice to stain for fibronecting or collagen to see if ECM proteins are actually there. Or WFA labeling.

The comparison to sham NBs helps here, but there are also existing Xenopus developmental transcriptome data (e.g., early neural induction stages vs. later stages) that could provide context for whether activating these genes is a general feature of neural differentiation? Discussing this could help clarify and strengthen this claim. Additionally, some kind of ontology analysis of these genes would be interesting.

I found the upregulation of retinal genes in the neurobots without specific induction to be particularly interesting It makes me wonder if this hints at the activation of highly conserved, intrinsic transcriptional programs for light sensing that are relatively accessible or perhaps even a default pathway for neural precursors to explore during self-organization in such novel contexts? Seems reminiscent of observations in brain organoids, which have also demonstrated an intrinsic ability to self-organize primitive sensory structures, sometimes even with spatial constraints. Though these neurobots are different than typical organoids, the fact that both systems originate neural precursors might suggests these cells might retain a latent capacity to access fundamental sensory development routines when placed in a permissive environment.

This to me is of the major thought-provoking findings of this paper and suggests that our understanding of how early activity shapes circuit development is worth expanding. I agree that one interpretation of the seemingly wave-independent DEGs could be that there may be post-transcriptional modifications or local protein synthesis at synapses. But we also are assuming that major functional changes need to be reflected in transcriptional signatures, and it might be worth exploring the possibility that other mechanisms are at play, such as micro-circuit properties (e.g. synaptic organization), ion channel localization, or something else.

Could you consider for instance, including a table or brief flowchart showing the specific cutoff values (e.g., ddG < -X kcal/mol) and how many designs passed or failed at each step would help readers replicate your pipeline. This more detailed account of each criterion (and its relative weight in eliminating or selecting designs) would be helpful.

Congratulations on this comprehensive work. The referenced GitHub repository, a Zenodo data deposit, and the SURFACE-Bind database as key resources for replicating and extending the findings. However, the GitHub link appears inactive, the Zenodo record is unavailable, and the database link does not load a page. As these resources are essential for reproducibility and broader community use, please ensure that working links are provided or that readers are informed of the timeline for making them accessible.