Alexander Shakeel Bates

The dataset includes ~160,000 cells, segmented nuclei and mitochondria, synapses and neurotransmitter predictions, and annotations linking the CNS to peripheral sensory, motor, and visceral systems.

Info: banc.community
Data: codex.flywire.ai?dataset=banc
Viewer: ng.banc.community/view

It’s a big project and I’m taking a short holiday now after the preprint! So I’ll make another post later showing all the bits of it, and how we did this as a community !

Flywire collaborators @princetonneuro.bsky.social‬ were essential in building the connectome, and theory collaborators in interpreting it! @jdrugowitsch.bsky.social‬ Art by ‪@amysterling.bsky.social!

A massive community effort, centred on @harvardmed.bsky.social‬
@princetonneuro.bsky.social‬. EM data from @darbly.bsky.social‬ lab, collected by @jasper-tms.bsky.social‬
‪@mindyisminsu.bsky.social‬.

Public access to the first fly connectome that spans the whole CNS - BANC!: codex.flywire.ai?dataset=banc

Different from prior connectomes - it is brain + cord (think spinal cord)

We use it to ‘embody’ the system and find it resembles ‘subsumption architecture’ doi.org/10.1101/2025...

a man with a mustache in a blue suit says tiny celebration in front of a group of people ALT: a man with a mustache in a blue suit says tiny celebration in front of a group of people

Happy Friday! The female adult fly brain-and-nerve-cord (BANC) connectome is now LIVE!

Explore the data here: codex.flywire.ai?dataset=banc

Check out the preprint here: doi.org/10.1101/2025...

The first full central nervous system connectome dataset of an adult fly, with the most comprehensive annotation of sensory and motor neurons to date.

A heroic effort with contributions from many groups, a product of the collaborative spirit of the Drosophila neuroscience community.

If you want a narrative version of this project, check out our blog post on the newly re-minted Funke Lab website!
funkelab.github.io/blog/2024/qu...

Thanks to @bobqubit.bsky.social, @asbates.bsky.social, and @jefferis.bsky.social, & @janfunkey.bsky.social for this 🦆ing awesome collaboration!

6/6

With so many new in blue, I thought I would repost this thread about our neurotransmitter prediction across the entire fly brain connectome! Should prove a very useful hypothesis generator for the insect neuroscience community.

In a fly's brain, there are some 140,000 neurons that connect via 54.5 million synapses. Researchers have now mapped *all of them*. That story and more of the best in Science and science in this edition of #ScienceAdviser: www.science.org/content/arti... 🧪 🧠

Whole-brain annotation and multi-connectome cell typing of Drosophila Nature - A consensus cell type atlas for the fly brain provides both an intellectual framework and open-source toolchains for brain-scale comparative connectomics.

And our contribution to FlyWire, cell typing the connectome with @jefferis.bsky.social and colleagues, is here: www.nature.com/articles/s41...

The FlyWire connectome: neuronal wiring diagram of a complete fly brain Artificial intelligence and human expertise meet to generate a map of all the connections in the fly brain. The resource is already being used by experimentalists and theoreticians to further our unde...

Hard to overstate how pleased I am that the FlyWire consortium converted our FAFB ("Full Adult Fly Brain") EM volume into a connectome, and that the resulting science has been (and will be) so impactful. Thanks to everyone involved. #neuroscience www.nature.com/immersive/d4...

Dataset imaging by @dddavi.bsky.social, Zhihao Zheng, image segmentation by Seung group at Princeton, human proofreading an international collaboration, cell typing and meta data building housed mainly at the the University of Cambridge, Dept. Zoology.

Our fly brain connectome papers are now live (www.nature.com/immersive/d4...), and is getting traction (www.bbc.co.uk/news/article...). With ~139k neurons, >8k cell types, synapses annotated, transmitters predicted, ~134 evo-devo units denoted, so much is now possible in the fly.

Lastly we are interested in cases where our predictions fail and unpublished data linking transmitters, neuropeptides, gap-junctions, post-synaptic receptors, co-transmisison combinations to EM. Please get in touch (Alexander_Bates[at]hms.harvard.edu) with any insights.

Thank you Lou Scheffer, Clare Pilgrim, @vincentcroset.bsky.social and our reviewers at Cell for helpful discussions. And importantly, our funders, NIH BRAIN Initiative, Wellcome Trust, EMBO, MRC LMB, HHMI Janelia @hhmi.bsky.social.

Virtual Fly Brain Welcome to Virtual Fly Brain (VFB) - an interactive tool for neurobiologists to explore the detailed neuroanatomy, neuron connectivity and gene expression of Drosophila melanogaster. Our goal is to ma...

GT built from the FAFB (@dddavi.bsky.social, Zhihao Zheng) and work from 27 labs on manual synapses in FAFB-CATMAID, supported by Tom Kazimiers, @perlman.bsky.social, accessible at VirtualFlyBrain.org.

Nils Eckstein, Andrew Champion, Michelle Du and Jan Funke for the ML, biology w/ Yijie Yin, Philipp Schlegel, Kathi Eichler, @martamcosta2, @gsxej, Volker Hartenstein, data w/  Sven Dorkenwald, Arie Matsilah, Claire McKellar, Amy Sterling, Mala Murthy, Sebastian Seung, the Janelia Annotation Team.

We hope these results will act as an accelerant for connectome-driven/informed hypotheses and neuroscientific investigation. We made these results available to the community as early as we could (#biorxiv @biorxiv-neursci.bsky.social in 2020). Our predictions have been used in >12 studies since.

But we feel our work is a big step forward. Our data  (zenodo.org/records/1059...) and code (github.com/funkelab/syn...) are available. Results also available through Codex (codex.flywire.ai, FlyWire team) and we hope to see them in neuPrint (Janelia Fly EM) and on VirtualFlyBrain.org.

Not quite mission complete. We want more annotations. E.g. receptor localisation delineates excitatory from inhibitory glut connections, moving our +/ - labels to individual connections. Their annotation could enable ML to extract this molecular information from EM at scale.

Which proxy for synaptic signs!... With major assumptions, e.g. no co-transmission, and that glutamate is inhibitory - but, it seems ~80% of brain neurons may express both excitatory and inhibitory GluRs, and co-transmission esp. in monoaminergic neurons is reasonably common.

Which means, we had now ‘coloured in’ the connectome with transmitter labels…

Yet overall, we had predictions which we felt were pretty good, across several data sets that together make up a whole (amalgam) Drosophila melanogaster nervous system! The neurotransmitter proportions turned out similar to those suggested by single-cell RNA-seq studies.

In other cases, we see inconsistent results that look like genuine mispredictions, maybe <10% of our total neuronal predictions. The most obvious Kenyon cells, incorrectly predicted dopaminergic, rather than cholinergic. Our serotonin predictions are the least reliable.

Here, for example, all members of a hemilineages are predicted glutamatergic (these are all tangential cells of the fan-shaped body).

We also noticed higher-level principles. Lacin et al. (2019) had previously shown that neurons that are born together (in a ‘hemilineage’, ~100-200 neurons) express the same fast-acting transmitter in the ventral nerve cord. We found this held true in the ~183 brain hemilineages as well!

Now humans (i.e. Alicia Kun-Yang Lu, Thomson Rhymer, Samantha Finley-May, Tyler Paterson) could look at the images and work out what had been added to cause a ‘misprediction’. This matrix shows the features that will convert a real synapse (rows) into a different prediction by Synister (columns).

But how could it tell from a single synapse, what transmitter was present? Nils and Jan developed an explainable AI method (openreview.net/forum?id=Cn-...) to permute images of real synapses into counterfactuals that fool Synister, e.g. below, adding dark vesicle-like things throws prediction.

We also found that matched across datasets neurons get the same predictions, suggesting our predictions are varying with real biology, and are robust to inter-dataset or biological neuron origin differences.