Andrew Payne

Unlocking the brain’s secrets: E11 Bio’s brain circuit mapping dataset now on AWS Open Data | Amazon Web Services Today, we are excited to announce the release of E11 Bio’s brain tissue dataset on AWS as part of the Registry of Open Data on AWS (E11bio PRISM). Read this post to learn more.

Thanks to AWS for hosting our data sets as described in their blog post:

aws.amazon.com/blogs/public...

Wonderful to collaborate with, and to support as an advisor, E11 Bio - and to announce PRISM, a technology for mapping brains in a self-correcting way, by barcoding neurons followed by expansion microscopy! Thread below by E11 Bio CEO Andrew Payne, with preprint at, www.biorxiv.org/content/10.1...

Combinatorial protein barcodes enable self-correcting neuron tracing with nanoscale molecular context Mapping nanoscale neuronal morphology with molecular annotations is critical for understanding healthy and dysfunctional brain circuits. Current methods are constrained by image segmentation errors an...

Preprint: www.biorxiv.org/content/10.1...

Portal and gallery: www.e11.bio/tech

Addgene constructs: www.addgene.org/depositor-co...

Volara: github.com/e11bio/volara

Open data: github.com/e11bio/e11-o...

In our first study, we acquired a unique dataset in mouse hippocampus. Barcodes improved the accuracy of tracing genetically labelled neurons by 8x – with a clear path to 100x or more. They also permit tracing across spatial gaps – essential for mitigating tissue section loss in whole-brain scaling.

We discovered a new cell barcoding approach exceeding comparable methods by more than 750x. This is the heart of PRISM. We integrated this capability with microscopy and AI image analysis to automatically trace neurons at high resolution and annotate them with molecular features.

E11 Bio is excited to unveil PRISM technology for mapping brain wiring with simple light microscopes. Today, brain mapping in humans and other mammals is bottlenecked by accurate neuron tracing. PRISM uses molecular ID codes and AI to help neurons trace themselves.

Read more: e11.bio/blog/prism

Combinatorial protein barcodes enable self-correcting neuron tracing with nanoscale molecular context Mapping nanoscale neuronal morphology with molecular annotations is critical for understanding healthy and dysfunctional brain circuits. Current methods are constrained by image segmentation errors an...

Preprint: www.biorxiv.org/content/10.1...

Portal and gallery: www.e11.bio/tech

Addgene constructs: www.addgene.org/depositor-co...

Volara: github.com/e11bio/volara

Open data: github.com/e11bio/e11-o...

In our first study, we acquired a unique dataset in mouse hippocampus. Barcodes improved the accuracy of tracing genetically labelled neurons by 8x–with a clear path to 100x or more. They also permit tracing across spatial gaps–essential for mitigating tissue section loss in whole-brain scaling.

PRISM uses molecular ID codes and AI to help neurons trace themselves. We discovered a new cell barcoding approach exceeding comparable methods by more than 750x. We integrated this with microscopy and AI image analysis to automatically trace neurons at high resolution with molecular features.

Congrats to Arlo Sheridan and Will Patton for releasing Volara, our first open-source library for scalable image processing, and also for a great technical writeup!

@e11bio.bsky.social needs help to scale optical connectomics to humans and other mammals! Huge opportunity to have an impact on brain science as part of a world-class, mission-driven team. #hiring computational scientists at all levels to build data pipelines for brain microscopy images! 🧪

🧪 E11 Bio is excited to share a major step towards brain mapping at 100x lower cost, making whole-brain connectomics at human & mouse scale feasible (🧠→🔬→💻). Critical for curing brain disorders, building human-like AI systems, and even simulating human brains.

Read more: e11.bio/news/roadmap

Hi Monica, I'm working on a non-profit open science project for brain connectome mapping. Would love to be added to the science feed. scholar.google.com/citations?us...