OSCR

Peripheral anatomy and central connectivity of proprioceptive sensory neurons in the <i>Drosophila</i> wing.

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 › Reconstructed axon morphology clusters ↔ similarity.ipynb, lines 69–80 · score 0.64 · AgglomerativeClustering, cosine_similarity, dendrogram, threshold
  2. [2] § Results › Comprehensive reconstruction of wing axons in the FANC connectome ↔ heatmap.ipynb, lines 66–112 · score 0.52 · Postsynaptic neurons, descending, efferent, unproofread, sensory neurons, classified

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Jupyter notebook · 198 lines · 5.6 KB · MIT · 1 match

  1. # %%
  2. # using dataframes stored in /dfs, compute cosine similarity between each left ADMN sensory neuron
  4. # %%
  5. # import packages
  6. import pandas as pd
  7. import numpy as np
  9. from matplotlib import pyplot,patches
  10. import matplotlib.pyplot as plt
  11. import seaborn as sns
  12. import cmocean
  14. from sklearn.metrics.pairwise import cosine_similarity
  15. from scipy.cluster.hierarchy import dendrogram
  16. from sklearn.cluster import AgglomerativeClustering
  18. # %%
  19. # call in sn_connectivity dataframe
  20. full_df = pd.read_pickle("dfs/sn_connectivity.pkl")
  22. # %%
  23. # limit to connections with five synapses
  24. # long - 2 min 12 sec
  26. # function from tuthill-lab/Lesser_Azevedo_2023
  27. def group_and_count_inputs(df, thresh):
  29. # count the number of synapses between pairs of pre and post synaptic inputs
  30. syn_in_conn=df.groupby(['pre_pt_root_id','post_pt_root_id']).transform(len)['id']
  31. # save this result in a new column and reorder the index
  32. df['syn_in_conn']=syn_in_conn
  33. df = df[['id', 'pre_pt_root_id','post_pt_root_id','score','syn_in_conn']].sort_values('syn_in_conn', ascending=False).reset_index()
  35. # Filter out small synapses between pairs of neurons and now print the shape
  36. df = df[df['syn_in_conn']>=thresh]
  37. # print(df.shape)
  38. return df
  40. df = group_and_count_inputs(full_df,thresh=5)
  42. # %%
  43. def plot_dendrogram(model, **kwargs):
  44. # create the counts of samples under each node
  45. counts = np.zeros(model.children_.shape[0])
  46. n_samples = len(model.labels_)
  47. for i, merge in enumerate(model.children_):
  48. current_count = 0
  49. for child_idx in merge:
  50. if child_idx < n_samples:
  51. current_count += 1 # leaf node
  52. else:
  53. current_count += counts[child_idx - n_samples]
  54. counts[i] = current_count
  56. linkage_matrix = np.column_stack(
  57. [model.children_, model.distances_, counts]
  58. ).astype(float)
  60. # Plot the corresponding dendrogram
  61. dendrogram(linkage_matrix, **kwargs)
  62. dend_dict = dendrogram(linkage_matrix, **kwargs)
  64. # sorted order of indices found through clustering
  65. clustered_order = dend_dict['ivl']
  67. return clustered_order
  69. # %%
  70. def organize_by_cos_long(map_df):
  71. adj = pd.crosstab(map_df['pre_pt_root_id'],map_df['post_pt_root_id'])
  72. sim_mat_temp = cosine_similarity(adj.to_numpy())
  73. model = AgglomerativeClustering(distance_threshold=0, n_clusters=None).fit(sim_mat_temp)
  74. clustered_order = plot_dendrogram(model)#, truncate_mode="level", p=12) # p truncate mode
  75. clustered_order = np.array(clustered_order).astype(int) # convert strins into integers
  76. reordered_df = adj.iloc[clustered_order,:]
  77. sim_mat = cosine_similarity(reordered_df.to_numpy())
  80. return reordered_df
  82. # %%
  83. # ordered adjacency matrix
  84. adj = pd.crosstab(df.pre_pt_root_id,df.post_pt_root_id)
  85. adj_ordered = organize_by_cos_long(df)
  87. # %%
  88. # visualize
  90. sim_mat = cosine_similarity(adj_ordered.to_numpy())
  92. fig = plt.figure(1, figsize = [6,5])
  93. cmap = cmocean.cm.gray_r
  94. ax = sns.heatmap(sim_mat, cmap = cmap)# xticklabels=mn_ids, cmap = cmap)
  95. cbar = ax.collections[0].colorbar
  96. ax.xaxis.set_ticks_position('bottom')
  98. plt.xlabel('', fontsize =16)
  99. plt.title('SN sim', fontsize = 18)
  101. # plt.show()
  103. # plt.savefig('../SN_simmat_0725.svg', format='svg', bbox_inches='tight')
  105. # %%
  106. # plot in-group out-group cosine similarity
  107. sn_table = pd.read_pickle('dfs/sn_table.pkl')
  108. dict_root_cluster = dict(zip(sn_table.pt_root_id,sn_table.classification_system))
  110. adj_ordered['cluster'] = adj_ordered.index.map(dict_root_cluster)
  111. adj_ordered
  114. # %%
  115. def plot_similarity_distributions(similarity, labels):
  116. labels = np.array(labels)
  117. within = []
  118. between = []
  120. for i in range(len(labels)):
  121. for j in range(i + 1, len(labels)):
  122. if labels[i] == labels[j]:
  123. within.append(similarity[i, j])
  124. else:
  125. between.append(similarity[i, j])
  127. # plt.hist(between, bins=30, alpha=0.6, label="Between-cluster")
  128. # plt.hist(within, bins=30, alpha=0.6, label="Within-cluster")
  129. # plt.legend()
  130. # plt.xlabel("Similarity")
  131. # plt.ylabel("Count")
  132. # plt.title("Within vs Between Cluster Similarities")
  133. # plt.show()
  134. return within, between
  136. # %%
  137. within, between = plot_similarity_distributions(sim_mat,adj_ordered['cluster'])
  138. df = pd.DataFrame({
  139. "similarity": np.concatenate([within, between]),
  140. "group": (["Within"] * len(within)) + (["Between"] * len(between))
  141. })
  143. # %%
  144. # Violin plot
  145. sns.violinplot(
  146. data=df,
  147. x="group", y="similarity",
  148. density_norm="count",
  149. inner=None, # remove inner bars, we'll add dots instead
  150. cut=0 # don't extend beyond data range
  151. )
  153. # Add jittered individual points
  154. sns.stripplot(
  155. data=df,
  156. x="group", y="similarity",
  157. color="black", alpha=0.5, jitter=0.2, size=2,
  158. )
  160. plt.title("Within vs Between Cluster Similarities")
  161. plt.xlabel("")
  162. plt.ylabel("Similarity")
  163. plt.tight_layout()
  165. plt.savefig('../within_sim_1023.svg', format='svg', bbox_inches='tight')
  166. plt.show()
  169. # %%
  170. # permutation test to compare within- and between- cluster pairwise values
  172. from scipy.stats import permutation_test
  173. np.random.seed(0)
  175. within = np.array([df[df.group.isin(['Within'])].similarity])
  176. between = np.array([df[df.group.isin(['Between'])].similarity])
  178. within = np.asarray(within).ravel()
  179. between = np.asarray(between).ravel()
  181. def stat_func(a, b):
  182. return np.mean(a) - np.mean(b)
  184. res = permutation_test(
  185. (within, between),
  186. statistic=stat_func,
  187. permutation_type='independent',
  188. n_resamples=10000,
  189. alternative='greater',
  190. vectorized=False
  191. )
  193. print("Observed statistic:", res.statistic)
  194. print("p-value:", res.pvalue)
  197. # %%

similarity.ipynb at commit 5b84c07, under MIT · at the source

Overview

Authors: Ellen Lesser1, Anthony J Moussa1, John C Tuthill1
ORCID iDs: Ellen Lesser, Anthony J Moussa, John C Tuthill
  1. Department of Neurobiology and Biophysics, University of Washington, Seattle, United States
Institutions: University of Washington (United States)
Journal: eLife, volume 14, article RP107867
Dates: published online 10 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.107867 · PMID 41805047 · PMCID PMC12975126 · OpenAlex W4413687817
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), drosophila (organism), cellular / molecular (subfield)
Methods: Statistics, Machine learning, Evoked potentials
Keywords: D. melanogaster
MeSH: Drosophila*, Drosophila melanogaster*, Proprioception*, Sensory Receptor Cells*, Wings, Animal*, Animals, Axons, Connectome, Female, Microscopy, Electron, Motor Neurons (* major topic)
Topic: Neurobiology and Insect Physiology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: NINDS NIH HHS (U19NS104655, R01NS102333); New York Stem Cell Foundation (Robertson Neuroscience Investigator Award); NIH HHS (T90DA032436, T32NS099578); McKnight Foundation (Pecot Fellowship)
Citations: cited by 6 papers (Europe PMC); 69 references in the paper
Research resources: nompC-GAL4 RRID:BDSC_36361, 49F11-GAL4 RRID:BDSC_38701, 60B12-GAL4 RRID:BDSC_39239, 60D12-GAL4 RRID:BDSC_39249, 60G04-GAL4 RRID:BDSC_39258, 64C04-GAL4 RRID:BDSC_39296, 70G12-GAL4 RRID:BDSC_39552, 73F02-GAL4 RRID:BDSC_39824, 75B09-GAL4 RRID:BDSC_39883, 79G12-GAL4 RRID:BDSC_40051, 44H11-GAL4 RRID:BDSC_41268, 83B04-GAL4 RRID:BDSC_41309, 13B12-GAL4 RRID:BDSC_45796, 57F03-GAL4 RRID:BDSC_46386, 72C01-GAL4 RRID:BDSC_47729, 76E12-GAL4 RRID:BDSC_47753, 44G12-GAL4 RRID:BDSC_47933, 54H12-GAL4 RRID:BDSC_48205, 10F07-GAL4 RRID:BDSC_48266, 10G03-GAL4 RRID:BDSC_48271, 10A07-GAL4 RRID:BDSC_48435, 12C07-GAL4 RRID:BDSC_48496, 16C09-GAL4 RRID:BDSC_48720, 21C09-GAL4 RRID:BDSC_48936, 24C04-GAL4 RRID:BDSC_49072, 26B11-GAL4 RRID:BDSC_49164, 26D04-GAL4 RRID:BDSC_49175, 26F04-GAL4 RRID:BDSC_49191, 15F10-GAL4 RRID:BDSC_49266, 37D11-GAL4 RRID:BDSC_49536, 45D07-GAL4 RRID:BDSC_49562, 35B08-GAL4 RRID:BDSC_49818, 21A01-GAL4 RRID:BDSC_49853, 36C09-GAL4 RRID:BDSC_49933, 38H01-GAL4 RRID:BDSC_50025, 39 F05-GAL4 RRID:BDSC_50056, 42G08-GAL4 RRID:BDSC_50166, 48H11-GAL4 RRID:BDSC_50396

Abstract

Recent advances in electron microscopy (EM) and automated image segmentation have produced synaptic wiring diagrams of the Drosophila central nervous system. A limitation of existing fly connectome datasets is that most sensory neurons are excised during sample preparation, creating a gap between the central and peripheral nervous systems. Here, we bridge this gap by reconstructing wing sensory axons from the Female Adult Nerve Cord (FANC) EM dataset and mapping them to peripheral sensory structures using genetic tools and light microscopy. We confirm the location and identity of known wing mechanosensory neurons and identify previously uncharacterized axons, including a novel population of putative proprioceptors that make monosynaptic connections onto wing steering motor neurons. We also find that adjacent campaniform sensilla on the wing have distinct axon morphologies and postsynaptic partners, suggesting a high degree of specialization in axon pathfinding and synaptic partner matching. The peripheral location and central projections of wing sensory neurons are stereotyped across flies, allowing this wing proprioceptor atlas and genetic toolkit to guide analysis of other fly connectome datasets.

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

Repository

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

ellenlesser/lesser_elife_2025

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 5b84c07798969d7f93a8b658e23994f3f6629b18, 26 February 2026
Languages: Jupyter (2)
Size: 11 files, 2 scripts
Software Heritage: not archived
Found in: the references
Holds: README, license file, 2 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (2 files), NumPy (2 files), pandas (2 files), seaborn (2 files), scikit-learn (1 file), SciPy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
4 files
At the source: archive.softwareheritage.org/swh:1:dir:9feb4776b68cdeaa24262cc23b1c8718d81e0427;origin=https://github.com/EllenLesser/Lesser_eLife_2025;visit=swh:1:snp:f4d8fe0227669971ea0e3cd1551eddcb8616bcc6;anchor=swh:1:rev:6c9f8e76abcfe4534e7b9c7490e0e5a449959ef5

The paper's code and data availability statement is in the Data section.

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2 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

No dataset and no data link were found in the paper.

Data availability

VNC images are publicly available via FlyLight (https://www.janelia.org/project-team/flylight). Confocal stacks of the genetic expression in the wing for each driver line are available for download from Dryad (https://doi.org/10.5061/dryad.mgqnk99b5). An annotation table that includes the FANC cell ID and peripheral identification of each segment in detail is available to the FANC community, as well as on Dryad as a CSV. Analyses and a connectivity table are stored at https://github.com/EllenLesser/Lesser_eLife_2025 (copy archived at Lesser, 2026).

The following dataset was generated:

Tuthill JC. 2025. Peripheral anatomy and central connectivity of proprioceptive sensory neurons in the Drosophila wing. Dryad Digital Repository.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 3 authors, 1 keyword, 11 MeSH terms, 4 funders, 63 references, 38 RRIDs.

Cite

This paper

Lesser, E., Moussa, A. J., & Tuthill, J. C. (2026). Peripheral anatomy and central connectivity of proprioceptive sensory neurons in the &lt;i&gt;Drosophila&lt;/i&gt; wing. eLife, 14, RP107867. https://doi.org/10.7554/elife.107867

BibTeX

@article{lesser2026peripheral,
author = {Lesser, Ellen and Moussa, Anthony J and Tuthill, John C},
title = {{Peripheral anatomy and central connectivity of proprioceptive sensory neurons in the \&lt;i\&gt;Drosophila\&lt;/i\&gt; wing}},
journal = {eLife},
year = {2026},
month = mar,
volume = {14},
pages = {RP107867},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.107867},
url = {https://doi.org/10.7554/elife.107867},
pmid = {41805047},
pmcid = {PMC12975126}
}

RIS

TY - JOUR
AU - Lesser, Ellen
AU - Moussa, Anthony J
AU - Tuthill, John C
TI - Peripheral anatomy and central connectivity of proprioceptive sensory neurons in the &lt;i&gt;Drosophila&lt;/i&gt; wing
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/03/10
VL - 14
SP - RP107867
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.107867
UR - https://doi.org/10.7554/elife.107867
LA - en
ER -

CSL-JSON

{
"id": "10.7554/elife.107867",
"type": "article-journal",
"title": "Peripheral anatomy and central connectivity of proprioceptive sensory neurons in the &lt;i&gt;Drosophila&lt;/i&gt; wing",
"container-title": "eLife",
"author": [
{
"family": "Lesser",
"given": "Ellen"
},
{
"family": "Moussa",
"given": "Anthony J"
},
{
"family": "Tuthill",
"given": "John C"
}
],
"container-title-short": "Elife",
"volume": "14",
"page": "RP107867",
"DOI": "10.7554/elife.107867",
"PMID": "41805047",
"PMCID": "PMC12975126",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.107867",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
10
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41586-026-10735-w [code]
Distributed control circuits across a brain-and-cord connectome.
Journal: Nature
In common: seaborn, scikit-learn, pandas, 3 other tools, drosophila, 8 references, author John C Tuthill
[2] doi:10.7554/elife.96084 [code]
Organization of circuits linking descending input to motor output in the &lt;i&gt;Drosophila&lt;/i&gt; Male Adult Nerve Cord connectome.
Journal: eLife
In common: drosophila, 13 references
[3] doi:10.1038/s41467-026-72152-x [code]
Centralized brain networks controlling antennal grooming coordination.
Journal: Nature communications
In common: seaborn, scikit-learn, pandas, 3 other tools, drosophila, 9 references
[4] doi:10.1038/s41593-026-02388-9 [code]
Hippocampal CA3 connectomics reveals a gradient of mossy fiber inputs and selective feedforward inhibition onto pyramidal cells.
Journal: Nature neuroscience
In common: seaborn, scikit-learn, pandas, 3 other tools, histology / microscopy, 2 references
[5] doi:10.1093/g3journal/jkag037 [code]
Visual recognition of the anteroposterior female body axis drives spatial elements of male courtship in Drosophila.
Journal: G3 (Bethesda, Md.)
In common: scikit-learn, pandas, SciPy, 2 other tools, drosophila, 2 references
[6] doi:10.1371/journal.pbio.3003959 [code]
Recurrent synapses between CO2-sensitive olfactory sensory neurons enable robust CO2 detection in Aedes aegypti mosquitoes.
Journal: PLoS biology
In common: seaborn, scikit-learn, pandas, 3 other tools, drosophila, histology / microscopy, cellular / molecular, 1 reference
[7] doi:10.7554/elife.107276 [code]
Deployment of endocytic machinery to periactive zones of nerve terminals is independent of active zone assembly and evoked release.
Journal: eLife
In common: seaborn, scikit-learn, pandas, 3 other tools, drosophila, cellular / molecular, 1 reference
[8] doi:10.1038/s41598-026-48613-0 [code]
An snRNA-seq aging clock for the fruit fly head sheds light on sex-biased aging.
Journal: Scientific reports
In common: seaborn, scikit-learn, pandas, 3 other tools, drosophila, cellular / molecular, 1 reference
[9] doi:10.1016/j.cub.2026.07.041 [code]
Notch-mediated lateral inhibition is shaped by morphological differences to reinforce bias toward signal-sending or -receiving roles.
Journal: Current biology : CB
In common: seaborn, scikit-learn, pandas, 3 other tools, drosophila, cellular / molecular, 1 reference
[10] doi:10.1038/s41467-026-70303-8 [code]
Behavioral screening defines the molecular Parkinsonism-related subgroups in Drosophila.
Journal: Nature communications
In common: seaborn, scikit-learn, pandas, 3 other tools, drosophila, cellular / molecular, 1 reference

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.