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A global molecular code for birth order and neuronal identity in Drosophila.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches
  1. [1] § Methods › Atlas annotation › Hemilineage ↔ vignettes/articles/accessing-graphene-server.Rmd, lines 20–91 · score 0.53 · electron microscopy, machine learning, components, transferring, neuronal, mapping
  2. [2] § Methods › Connectomics data analysis › Identification of 14A types matching light-level stainings ↔ R/annotations.R, lines 186–314 · score 0.52 · soma_side, RHS, Clio, IDs, hemilineage, match

Paper

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The authors' code

R Markdown · 116 lines · 6.2 KB · GPL-3.0 · 1 match

  1. ---
  2. title: "Accessing the chunked graph server"
  3. ---
  4. ```{r, include = FALSE}
  5. knitr::opts_chunk$set(
  6. collapse = TRUE,
  7. comment = "#>"
  8. )
  9. ```
  10. ## tl;dr
  11. To set up programmatic access to FlyWire, use `flywire_set_token()` as follows:
  12. ```{r, eval=FALSE}
  13. library(fafbseg)
  14. flywire_set_token()
  15. ```
  16. and follow the on-screen prompts.
  17. ## Background
  18. The main goal of connectomics is to trace (segment) the branching morphology of
  19. neurons in any 3D volume (typically based on electron microscopy) and to
  20. identify the connections (synapses) between them that together define a
  21. connectome.
  22. Neuron segmentation can be either skeleton-based
  23. (in which only the centre line defining each part of the neuronal arbour is traced out)
  24. or voxel-based,
  25. where a filled 3D structure is defined by labelling every voxel with an integer id,
  26. representing its membership of particular object;
  27. this is typically then displayed as a 3D mesh surface. Although skeleton-based
  28. segmentation is more efficient for manual segmentation by humans,
  29. voxel-wise segmentation is more natural for machine learning algorithms.
  30. Furthermore, it generates a richer representation of the neuron
  31. (capturing more of its structural features) and, crucially,
  32. also resolves ambiguities about object identity.
  33. For example when two users manually trace a neuron skeleton,
  34. they will place nodes in different positions and it is not trivial
  35. computationally to determine if they are part of the same object or how to join tracing of independent pieces together.
  36. In contrast in a voxel-wise segmentation every xyz location in the image is
  37. explicitly defined to be part of a specified object.
  38. In a voxel-wise segmentation, each neuron can be considered as a collection of voxels grouped together as connected components. Machine learning based algorithms try to identify such connected components (e.g. using flood filling networks). However machines are not perfect and many segments are wrongly connected. Hence we need a step where humans proofread the segments and alters the connections. These connected components can be readily represented as vertices and the connections between them as edges. The human observer essentially modifies these edges in the graph structure. Note that this graph representation of object segmentation is distinct from the binary tree that represents the neuronal morphology or the connection graph of neurons within a
  39. neural network.
  40. This graph based representation of segmentation also has advantages for synchronous
  41. editing, which can be a major technical issue if many users are to generate or
  42. proof-read segmentation at the same time.
  43. The `CATMAID` web application (for skeleton based tracing) approaches this by
  44. allowing multiple users to edit all neurons using a shared database.
  45. This implementation reduces conflicts by the simple expedient of allowing users
  46. to see each other's work in real time.
  47. However an explicit merge step is needed when conflicting annotations are created
  48. (usually because editing has happened in different database) and this can be computationally complex. The [Seung Lab](https://seunglab.org/) has developed a data structure system called `chunked graph` that lets users modify segmentations (3D voxel-wise) in real-time and allows access to earlier versions of the segmentation. A brief overview of the system is published in [medium](https://medium.com/princeton-systems-course/building-a-multi-user-platform-for-real-time-very-large-graph-editing-ee37268025ad).
  49. In practice an initial *over-segmentation* is generated, consisting of
  50. many "supervoxels". These supervoxels, which are small collections of perhaps
  51. tens to thousands of individual voxels, are effectively immutable but are almost
  52. certain to belong to the same object. It is these supervoxels which are the leaves
  53. on the graph database representation of segmented objects.
  54. The use of supervoxels reduces the size of the graph database compared with the
  55. situation in which individual voxel has to be represented.
  56. So long as human proof-reading is based on the same base segmentation, there are
  57. strategies to merge proof-reading even if this happens asynchronously.
  58. However merging proof-reading based on different *base segmentations* defined by
  59. different underlying supervoxels is still complex;
  60. a similar challenge exists if it is necessary to map proof-reading based on one
  61. base segmentation to a newer (presumably improved) base segmentation.
  62. ## Goal
  63. One of the goals in the **fafbseg** package is to provide access between different 3D segmentations of the `FAFB` dataset. Currently available segmentations include `FlyWire` (based on 3D CNNs : [U-Net](https://arxiv.org/abs/1709.02974)) and `GoogleBrain` (based on recursive CNNs : [flood-filling networks](https://www.nature.com/articles/s41592-018-0049-4)). Furthermore to enable location transfer and comparison with `CATMAID` (skeleton based) manual reconstruction. In order to achieve that one needs access to the 3D segmentations which are stored in a graph database [Graphene/PyChunkedGraph](https://github.com/seung-lab/PyChunkedGraph).
  64. They also provide a Python tool called
  65. [CloudVolume](https://github.com/seung-lab/cloud-volume) which provides
  66. programmatic access to the graph database.
  67. ## ChunkedGraph token
  68. ### Simple Instructions
  69. You gain access to the `Graphene/Chunkedgraph` server by using a *token* which is
  70. granted to you after you authenticate via a Google account. The simplest way to
  71. do this is by doing
  72. ```{r, eval=FALSE}
  73. library(fafbseg)
  74. flywire_set_token()
  75. ```
  76. This will offer to open your browser, generate a new token and save it in
  77. a standard location. **Note that this will invalidate your previous token**!
  78. ### Details
  79. The manual steps that `flywire_set_token()` tries to automate are are as follows
  80. 1. Visit https://globalv1.flywire-daf.com/auth/api/v1/refresh_token,
  81. 2. Copy the token present there, let's say it was `"xxyyzz"`
  82. 3. create a file named `cave-secret.json` at location `~/.cloudvolume/secrets/cave-secret.json`. For example, in Terminal:
  83. ```bash
  84. mkdir -p .cloudvolume/secrets/
  85. touch ~/.cloudvolume/secrets/cave-secret.json
  86. open ~/.cloudvolume/secrets/cave-secret.json -e
  87. ```
  88. 4. Paste in the following contents
  89. ```json
  90. {
  91. "token": "xxyyzz"
  92. }
  93. ```
  94. 5. Save the file
  95. 6. Now you have access to the chunked graph server

accessing-graphene-server.Rmd at commit 7dad86d, under GPL-3.0 · at the source

Overview

Authors: Sebastian Cachero1, Myrto Mitletton1, Isabella R. Beckett1, Elizabeth C. Marin2, Laia Serratosa Capdevila3, Marina Gkantia2, Jelly H. M. Soffers4, Haluk Lacin4, Gregory S. X. E. Jefferis1,2, Erika Donà1,5
  1. Neurobiology Division, MRC Laboratory of Molecular Biology,Cambridge, UK
  2. Department of Zoology, University of Cambridge,Cambridge, UK
  3. Aelysia, Bristol, UK
  4. Division of Biological and Biomedical Systems, School of Science and Engineering, University of Missouri-Kansas City,Kansas City, MO USA
  5. Institute of Neuroscience, Consiglio Nazionale delle Ricerche (CNR),Vedano al Lambro, Italy
Journal: Nature, volume 657, issue 8130, pages 202-212
Dates: received 19 August 2025; accepted 11 June 2026; published online 22 July 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41586-026-10797-w · PMID 42486976 · PMCID PMC13538039 · OpenAlex W7170051355
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), drosophila (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Evoked potentials, Machine learning
Keywords: Cellular neuroscience, Molecular neuroscience, Neural patterning, Cell type diversity, Sexual dimorphism
MeSH: Drosophila melanogaster*, Neurons*, Animals, Apoptosis, Atlases as Topic, Cell Lineage, Connectome, Female, Gene Expression Regulation, Developmental, Larva, Male, Neurogenesis, Sex Characteristics, Transcription Factors, Transcriptome (* major topic)
Topic: Neurobiology and Insect Physiology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (649111)
Citations: cited by 3 papers (Europe PMC); 117 references in the paper

Abstract

The assembly of functional neural circuits relies on the generation of diverse neural types with precise molecular identity and connectivity. Unlocking general principles of neuronal specification and wiring across the nervous system requires a systematic and high-resolution characterization of its diversity, recently enabled by advances in single-cell transcriptomics and connectomics. However, linking the molecular identity of neurons to circuit architecture remains a key challenge. Here we present a high-resolution developmental transcriptional atlas for the Drosophila melanogaster nerve cord, the central hub for sensory–motor circuits. With a considerable 38× aggregate coverage relative to its reference connectome1,2, our atlas captures extensive molecular diversity and enables robust alignment to the adult connectome. We identified three developmental principles underlying neuronal diversity in the nerve cord. First, the timing of neurogenesis shapes diversification of molecular identity: embryonic-born neurons diverge faster than larval-born neurons, as also observed in the adult connectome. Second, 17 transcription factors common to neurons from all lineages provide a global molecular identity code for birth order. Lastly, by mapping sex-specific transcriptional profiles to the connectome, we identified female-specific apoptosis and transcriptional divergence as key global drivers of sex specification. By revealing key organizational axes of molecular identity, this atlas opens avenues to dissect the molecular mechanisms underpinning the development and evolution of neural circuits.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

natverse/malevnc

License: GPL-3.0
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Evidence: files inventoried
Commit: d24c79554a58f7cf985e71d2578ad86423667413, 4 June 2026
Languages: R (41)
Size: 123 files, 41 scripts
Software Heritage: not archived
Found in: the text, “Connectomics data analysis”
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flyconnectome/fancr

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Languages: R (24)
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natverse/fafbseg

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Holds: README, license file, environment (DESCRIPTION), tests, continuous integration, documentation, 6 notebooks
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Availability: 1 check, the latest on 27 September 2026: the link answers
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91 files

google/neuroglancer

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Languages: TypeScript (702), Python (133), C++ (23), JavaScript (20), Shell (11), C/C++ (6), Go (3), NEURON (1), Jupyter (1), Rust (1), C (1)
Size: 2,060 files, 902 scripts
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Found in: the text, “Identification of neurons belonging to fru MARCM”
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904 files

FlyNeuroAtlas/DevSeqVNC

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Commit: bed37b484f2ef27fa52bec22e947f5770b4e3a38, 20 July 2026
Languages: R (1)
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Software Heritage: not archived
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2 files

Zenodo 17185431

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  • 27 September 2026: the link is dead (HTTP 410)
At the source:

Code availability

Code with instructions to download and inspect data from GEO and SCope is available at GitHub (https://github.com/FlyNeuroAtlas/DevSeqVNC). Additional code is available on request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 6 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1,057 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

10x scRNA-seq data collected in this study (FASTQ files, CellRanger outputs and processed and annotated Seurat objects) are available at the Gene Expression Omnibus (GEO) under accession number GSE304221 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE304221), and at BioProject under accession number PRJNA1297747 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1297747). Annotated loom files are also accessible at Zenodo (full dataset115: https://zenodo.org/records/17183028; neurons116: https://zenodo.org/records/17184089; glia117: https://zenodo.org/records/17185432) and at SCope (https://scope.aertslab.org/#/HundredDrills/*/welcome), an interactive web platform for exploring single-cell datasets. The SCope interface allows rapid inspection of marker genes and cell-level information, making it straightforward to interact with the data. Data presented here, including links between the atlas and the connectome, can also be browsed on a dedicated webpage (https://flyem.mrc-lmb.cam.ac.uk/VNCatlas). Previously published 10x scRNA-seq data used in this study26 are available at GEO under accession number GSE141807 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE141807). Additional imaging data are available on request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 5 keywords, 15 MeSH terms, 1 funder, 116 references.

Cite

This paper

Cachero, S., Mitletton, M., Beckett, I. R., Marin, E. C., Capdevila, L. S., Gkantia, M., Soffers, J. H. M., Lacin, H., Jefferis, G. S. X. E., & Donà, E. (2026). A global molecular code for birth order and neuronal identity in Drosophila. Nature, 657(8130), 202-212. https://doi.org/10.1038/s41586-026-10797-w

BibTeX

@article{cachero2026global,
author = {Cachero, Sebastian and Mitletton, Myrto and Beckett, Isabella R. and Marin, Elizabeth C. and Capdevila, Laia Serratosa and Gkantia, Marina and Soffers, Jelly H. M. and Lacin, Haluk and Jefferis, Gregory S. X. E. and Donà, Erika},
title = {{A global molecular code for birth order and neuronal identity in Drosophila}},
journal = {Nature},
year = {2026},
month = jul,
volume = {657},
number = {8130},
pages = {202--212},
publisher = {Nature Portfolio},
issn = {0028-0836},
doi = {10.1038/s41586-026-10797-w},
url = {https://doi.org/10.1038/s41586-026-10797-w},
pmid = {42486976},
pmcid = {PMC13538039}
}

RIS

TY - JOUR
AU - Cachero, Sebastian
AU - Mitletton, Myrto
AU - Beckett, Isabella R.
AU - Marin, Elizabeth C.
AU - Capdevila, Laia Serratosa
AU - Gkantia, Marina
AU - Soffers, Jelly H. M.
AU - Lacin, Haluk
AU - Jefferis, Gregory S. X. E.
AU - Donà, Erika
TI - A global molecular code for birth order and neuronal identity in Drosophila
T2 - Nature
J2 - Nature
PY - 2026
DA - 2026/07/22
VL - 657
IS - 8130
SP - 202
EP - 212
SN - 0028-0836
PB - Nature Portfolio
DO - 10.1038/s41586-026-10797-w
UR - https://doi.org/10.1038/s41586-026-10797-w
LA - en
ER -

CSL-JSON

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