OSCR

Behavioral screening defines the molecular Parkinsonism-related subgroups in Drosophila.

Code ↔ Paper

16 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 16 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Experimental model and subject details › Fly behavior monitoring ↔ Figure2_and_related_suppl/extract-sleep-features/library.R, lines 598–665 · score 0.89 · longest bout, sleep architecture, Morning anticipation, bout length, sleep fraction, awake
  2. [2] § Methods › Experimental model and subject details › Correlation, cluster and principal component analysis of behavior screen ↔ Figure2_and_related_suppl/hierarchical clustering and PCA.R, the whole file · a weak match · score 0.86 · variance explained, hierarchically clustered, principal component, corrplot, ggbiplot, prcomp
  3. [3] § Results › High-throughput behavior monitoring reveals Parkinsonism subgroups with distinct sleep and activity patterns ↔ Figure2_and_related_suppl/extract-sleep-features/library.R, lines 598–665 · score 0.77 · sleep architecture, longest bout, morning anticipation, sleep fraction, duration, awake
  4. [4] § Results › Parkinsonism gene expression patterns do not correlate with subgroups ↔ Parkinsonism_gene_expression_flybrain_body/FigureS6.ipynb, lines 35–59 · score 0.73 · Gba1a, gene expression, Parkinsonism genes, iPLA2, Chchd2, Rab39
  5. [5] § Methods › Experimental model and subject details › Parkinsonism gene expression clustering across cell-types of the fly brain and body ↔ Parkinsonism_gene_expression_flybrain_body/FigureS6.ipynb, lines 251–260 · score 0.72 · Parkinsonism gene expression, correlation distance, hierarchical clustering, body, dendrogram, linkage
  6. [6] § Methods › Experimental model and subject details › Identifying behavioral patterns ↔ Figure2_and_related_suppl/nmf_analysis/src/nmf_analysis/nmf.py, lines 128–207 · score 0.69 · explained variance score, reconstruction error, matrix, NMF, component
  7. [7] § Results › Genes in behavioral subgroups show similar genetic interaction profiles ↔ Figure3/Jaccard.R, lines 1–9 · score 0.65 · eIF4G, VAC14, Jaccard, Lrrk, Synj, Tango14
  8. [8] § Methods › Experimental model and subject details › Constructing vectors of temporal behavior ↔ Figure2_and_related_suppl/nmf_analysis/src/nmf_analysis/nmf.py, lines 105–125 · score 0.63 · Gaussian blur, promote smoothness, minute
  9. [9] § Results › High-throughput behavior monitoring reveals Parkinsonism subgroups with distinct sleep and activity patterns ↔ Figure2_and_related_suppl/hierarchical clustering and PCA.R, the whole file · a weak match · score 0.61 · hierarchical clustering, principal component, PCA, PC1, PC2, variance
  10. [10] § Methods › Experimental model and subject details › Genetic interaction analysis ↔ Figure3/bayesian-modelling-interactions/run.py, lines 40–94 · score 0.60 · interacting model, Gene pairs, single mutants, GI, double
  11. [11] § Methods › Experimental model and subject details › Constructing vectors of temporal behavior ↔ Figure2_and_related_suppl/extract-sleep-features/library.R, lines 314–401 · score 0.59 · log transformed, velocity correction, frame, distance, behavior
  12. [12] § Methods › Experimental model and subject details › Quantification and statistical analysis ↔ Figure4/Fishers exact test for Q10 R55.R, the whole file · a weak match · score 0.58 · Fisher, contingency, R55, Q10, B2, treatments
  13. [13] § Results › Genes in behavioral subgroups show similar genetic interaction profiles ↔ Figure3/bayesian-modelling-interactions/run.py, lines 40–94 · score 0.54 · interacting model, gene pair, single mutant, GI, double, traces
  14. [14] § Methods › Experimental model and subject details › Genetic interaction analysis ↔ Figure3/bayesian-modelling-interactions/model.py, lines 34–94 · score 0.54 · interacting model, single mutants, ERG, double, genes
  15. [15] § Results › Genes in behavioral subgroups show similar genetic interaction profiles ↔ Parkinsonism_gene_expression_flybrain_body/FigureS6.ipynb, lines 35–59 · score 0.52 · iPLA2, Lrrk, Synj, Rme, Tango14, anne
  16. [16] § Methods › Experimental model and subject details › Correlation, cluster and principal component analysis of behavior screen ↔ Parkinsonism_gene_expression_flybrain_body/FigureS6.ipynb, lines 88–97 · score 0.52 · correlation distance, hierarchically clustered, linkage

Paper

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The authors' code

Jupyter notebook · 295 lines · 8 KB · no license · 4 matches

  1. # %%
  2. import scanpy as sc
  3. import pandas as pd
  4. import numpy as np
  5. import matplotlib.pyplot as plt
  6. import seaborn as sns
  7. import anndata
  9. # %% [markdown]
  10. # # Brain
  12. # %%
  13. pd_atlas = sc.read_h5ad('/Pech_Janssens_PD_atlas/scRNA_PD_annotated.h5ad')
  15. # %%
  16. ctrl_young_raw = pd_atlas[(pd_atlas.obs.genotype == 'W1118CS') & (pd_atlas.obs.age == 7)]
  18. # %%
  19. df_tmp = ctrl_young_raw.to_df().copy()
  21. df_tmp['Gba1a/b'] = df_tmp['Gba1a'] + df_tmp['Gba1b']
  22. del(df_tmp['Gba1a'])
  23. del(df_tmp['Gba1b'])
  25. df_tmp['Dj-1a/b'] = df_tmp['DJ-1alpha'] + df_tmp['dj-1beta']
  26. del(df_tmp['DJ-1alpha'])
  27. del(df_tmp['dj-1beta'])
  29. # %%
  30. ctrl_young = anndata.AnnData(df_tmp)
  31. ctrl_young.obs = ctrl_young_raw.obs
  32. sc.pp.normalize_total(ctrl_young, target_sum=1e4)
  33. sc.pp.log1p(ctrl_young)
  35. # %%
  36. genes = ['anne',
  37. 'ATP6AP2',
  38. 'Chchd2',
  39. 'Coq2',
  40. 'Dj-1a/b',
  41. 'Rme-8',
  42. 'aux',
  43. 'eIF4G1',
  44. 'HtrA2',
  45. 'Lrrk',
  46. 'park',
  47. 'Pink1',
  48. 'iPLA2-VIA',
  49. 'Rab39',
  50. 'Synj',
  51. 'Vps35',
  52. 'loqs',
  53. 'ntc',
  54. 'CG5608',
  55. 'Vps13',
  56. 'Pu',
  57. 'Gdh',
  58. 'Gba1a/b',
  59. 'Tango14']
  61. # %% [markdown]
  62. # ## Clustering
  64. # %%
  65. from scipy.cluster.hierarchy import dendrogram, linkage
  66. from sklearn.preprocessing import StandardScaler
  68. # %%
  69. df = ctrl_young.to_df()
  70. df['cell_types'] = ctrl_young.obs.final_annotation
  71. df = df.loc[:, genes + ['cell_types']]
  72. df_mean = df.groupby('cell_types').mean()
  74. # %%
  75. from scipy.spatial.distance import pdist
  77. # %%
  78. scaler = StandardScaler()
  79. scaler.fit(df_mean)
  80. df_mean_scaled = scaler.transform(df_mean)
  82. # %%
  83. linked_scaled = linkage(pdist(df_mean_scaled.T, metric='correlation'), method='weighted')
  84. plt.figure(figsize=(15, 2))
  85. dend = dendrogram(linked_scaled, orientation='top', distance_sort='descending', show_leaf_counts=True, labels=df_mean.columns)
  86. plt.show()
  88. # %%
  89. scaler = StandardScaler()
  90. scaler.fit(df_mean)
  91. df_mean_scaled = scaler.transform(df_mean)
  93. linked_scaled = linkage(pdist(df_mean_scaled.T, metric='correlation'), method='weighted')
  94. plt.figure(figsize=(3.5, 1))
  95. plt.axis("off")
  96. dend = dendrogram(linked_scaled, orientation='top', distance_sort='descending', show_leaf_counts=True, labels=df_mean.columns)
  97. plt.savefig('/plots/hierarchical_clustering_correlation_distance_weighted.pdf')
  100. # %%
  101. order = dend['ivl']
  103. # %% [markdown]
  104. # ## Plot expressions
  106. # %%
  107. shorter_names = {'Ensheathing_glia': 'Ensh',
  108. 'dorsal_Fan-shaped_Body_A': 'dFB-A',
  109. 'dorsal_Fan-shaped_Body_B': 'dFB-B',
  110. 'Perineurial_glia': 'PNG'}
  112. # %%
  113. ctrl_young.obs = pd.DataFrame(ctrl_young.obs['final_annotation'].map(shorter_names).fillna(ctrl_young.obs['final_annotation']))
  115. # %%
  116. cts = [ct for ct in ctrl_young.obs.final_annotation[~ctrl_young.obs.final_annotation.str.contains('^[1-9]')].unique()]
  117. cts
  119. # %%
  120. fig, ax = plt.subplots(1,1, figsize=(3.5,9.5), dpi=150)
  121. lw = 0
  122. p = sc.pl.matrixplot(ctrl_young[ctrl_young.obs.final_annotation.isin(cts),],
  123. groupby='final_annotation', var_names=order,
  124. dendrogram=False, ax=ax, return_fig=True, standard_scale='var')
  126. p_axes = p.get_axes()
  127. p_axes['mainplot_ax'].set_xticklabels(p_axes['mainplot_ax'].get_xticklabels(), fontsize=6)
  128. p_axes['mainplot_ax'].set_yticklabels(p_axes['mainplot_ax'].get_yticklabels(), fontsize=6)
  129. p_axes['color_legend_ax'].set_xticklabels(p_axes['color_legend_ax'].get_xticklabels(), fontsize=6)
  130. p_axes['color_legend_ax'].set_title(p_axes['color_legend_ax'].get_title(), fontsize=6)
  132. p_axes['mainplot_ax'].spines['top'].set_linewidth(lw)
  133. p_axes['mainplot_ax'].spines['right'].set_linewidth(lw)
  134. p_axes['mainplot_ax'].spines['bottom'].set_linewidth(lw)
  135. p_axes['mainplot_ax'].spines['left'].set_linewidth(lw)
  136. p_axes['mainplot_ax'].tick_params(axis='x', width=lw, length=2.5, pad=-3)
  137. p_axes['mainplot_ax'].tick_params(axis='y', width=lw, length=2.5, pad=-2)
  140. plt.savefig('/plots/gene_expression_matrix_plot_weighted.pdf')
  142. # %% [markdown]
  143. # ## Plot tSNEs
  145. # %%
  146. ctrl_young.obsm['X_tsne'] = ctrl_young_raw.obsm['X_tsne']
  148. # %%
  149. vmax = 1
  150. fig, ax = plt.subplots(4,2, figsize=(4,8), dpi=300)
  151. plt.subplots_adjust(wspace=0.01, hspace=0.05)
  152. ax = ax.flatten()
  153. for i, mg in enumerate(['Pink1', 'park', 'Chchd2', 'ntc', 'iPLA2-VIA', 'Rab39', 'Gba1a/b', 'Dj-1a/b']):
  154. sc.pl.tsne(ctrl_young, color=mg, size=2, ax=ax[i], sort_order=False, frameon=False, title='', color_map='viridis',
  155. vmin=0, vmax=vmax, show=False, colorbar_loc=None)
  156. ax[i].set_title(mg, fontsize=10, x=0.2, y=0.92, fontname='DejaVu Sans')
  157. plt.savefig('/plots/marker_genes_umap.png')
  159. # %%
  160. sc.set_figure_params(vector_friendly=True, dpi=300)
  161. vmax = 1
  162. fig, ax = plt.subplots(1,1, figsize=(2,2), dpi=300)
  163. sc.pl.tsne(ctrl_young, color='Chchd2', size=2, ax=ax, sort_order=False, frameon=False, title='', color_map='viridis',
  164. vmin=0, vmax=vmax, show=False)
  165. plt.savefig('/plots/colorbar.pdf')
  169. # %%
  172. # %% [markdown]
  173. # # Body
  175. # %%
  176. body = sc.read_h5ad('/home/q019rp/public_data/flycellatlas/s_fca_biohub_body_10x.h5ad')
  178. # %%
  179. body_raw = body.raw.to_adata()
  181. # %%
  182. df_tmp = body_raw.to_df().copy()
  183. df_tmp = np.expm1(df_tmp)
  185. df_tmp['Gba1a/b'] = df_tmp['Gba1a'] + df_tmp['Gba1b']
  186. del(df_tmp['Gba1a'])
  187. del(df_tmp['Gba1b'])
  189. df_tmp['Dj-1a/b'] = df_tmp['DJ-1alpha'] + df_tmp['dj-1beta']
  190. del(df_tmp['DJ-1alpha'])
  191. del(df_tmp['dj-1beta'])
  193. body_comb = anndata.AnnData(df_tmp)
  194. body_comb.obs = body.obs
  195. body_comb.obsm = body.obsm
  196. sc.pp.log1p(body_comb)
  198. # %%
  199. genes = ['anne',
  200. 'ATP6AP2',
  201. 'Chchd2',
  202. 'Coq2',
  203. 'Dj-1a/b',
  204. 'Rme-8',
  205. 'aux',
  206. 'eIF4G1',
  207. 'HtrA2',
  208. 'Lrrk',
  209. 'park',
  210. 'Pink1',
  211. 'iPLA2-VIA',
  212. 'Rab39',
  213. 'Synj',
  214. 'Vps35',
  215. 'loqs',
  216. 'ntc',
  217. 'CG5608',
  218. 'Vps13',
  219. 'Pu',
  220. 'Gdh',
  221. 'Gba1a/b',
  222. 'Tango14']
  224. # %% [markdown]
  225. # ## Clustering
  227. # %%
  228. from scipy.cluster.hierarchy import dendrogram, linkage
  229. from sklearn.preprocessing import StandardScaler
  231. # %%
  232. df = body_comb.to_df()
  233. df['cell_types'] = body_comb.obs.annotation
  234. df = df.loc[:, genes + ['cell_types']]
  235. df_mean = df.groupby('cell_types').mean()
  237. # %%
  238. from scipy.spatial.distance import pdist
  240. # %%
  241. scaler = StandardScaler()
  242. scaler.fit(df_mean)
  243. df_mean_scaled = scaler.transform(df_mean)
  245. # %%
  246. linked_scaled = linkage(pdist(df_mean_scaled.T, metric='correlation'), method='weighted')
  247. plt.figure(figsize=(15, 2))
  248. dend = dendrogram(linked_scaled, orientation='top', distance_sort='descending', show_leaf_counts=True, labels=df_mean.columns)
  249. plt.show()
  251. # %%
  252. scaler = StandardScaler()
  253. scaler.fit(df_mean)
  254. df_mean_scaled = scaler.transform(df_mean)
  256. linked_scaled = linkage(pdist(df_mean_scaled.T, metric='correlation'), method='weighted')
  257. plt.figure(figsize=(3.5, 1))
  258. plt.axis("off")
  259. dend = dendrogram(linked_scaled, orientation='top', distance_sort='descending', show_leaf_counts=True, labels=df_mean.columns)
  260. plt.savefig('/plots/body_hierarchical_clustering_correlation_distance_weighted.pdf')
  262. # %%
  263. order = dend['ivl']
  265. # %% [markdown]
  266. # ## Plot expressions
  268. # %%
  269. fig, ax = plt.subplots(1,1, figsize=(3.5,4), dpi=150)
  270. lw = 0
  271. p = sc.pl.matrixplot(body_comb[body.obs.annotation != 'unannotated',:],
  272. groupby='annotation', var_names=order,
  273. dendrogram=False, ax=ax, return_fig=True, standard_scale='var')
  275. p_axes = p.get_axes()
  276. p_axes['mainplot_ax'].set_xticklabels(p_axes['mainplot_ax'].get_xticklabels(), fontsize=6)
  277. p_axes['mainplot_ax'].set_yticklabels(p_axes['mainplot_ax'].get_yticklabels(), fontsize=6)
  278. p_axes['color_legend_ax'].set_xticklabels(p_axes['color_legend_ax'].get_xticklabels(), fontsize=6)
  279. p_axes['color_legend_ax'].set_title(p_axes['color_legend_ax'].get_title(), fontsize=6)
  281. p_axes['mainplot_ax'].spines['top'].set_linewidth(lw)
  282. p_axes['mainplot_ax'].spines['right'].set_linewidth(lw)
  283. p_axes['mainplot_ax'].spines['bottom'].set_linewidth(lw)
  284. p_axes['mainplot_ax'].spines['left'].set_linewidth(lw)
  285. p_axes['mainplot_ax'].tick_params(axis='x', width=lw, length=2.5, pad=-3)
  286. p_axes['mainplot_ax'].tick_params(axis='y', width=lw, length=2.5, pad=-2)
  289. plt.savefig('/plots/body_gene_expression_matrix_plot_weighted.pdf', bbox_inches='tight')
  291. # %%
  294. # %%

FigureS6.ipynb at commit 82f71c1, no license · at the source

Overview

Authors: Natalie Kaempf1,2,3, Jorge S. Valadas1,2, Pieter Robberechts4,5, Nils Schoovaerts1,2, Roman Praschberger1,2,3,6, Antonio Ortega1,2, Eliana Nachman1,2, Lorenzo Ghezzi1,2, Ayse Kilic1,2, Dries Chabot1,2, Uli Pech1,2, Sabine Kuenen1,2, Sven Vilain1,2,7, El-Sayed Baz1,2,8, Jeevanjot Singh1,2, Jesse Davis4,5, Sha Liu1,2, Patrik Verstreken1,2
ORCID iDs: Natalie Kaempf, Pieter Robberechts, Roman Praschberger, Antonio Ortega, Eliana Nachman, Ayse Kilic, Sabine Kuenen, Sven Vilain, El-Sayed Baz, Jesse Davis, Sha Liu, Patrik Verstreken
  1. VIB-KU Leuven Center for Brain & Disease Research,Leuven, Belgium
  2. KU Leuven, Department of Neurosciences, Leuven Brain Institute,Leuven, Belgium
  3. Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network,Chevy Chase, MD USA
  4. KU Leuven, Department of Computer Science,Leuven, Belgium
  5. Leuven.AI – KU Leuven Institute for AI,Leuven, Belgium
  6. Medical University of Innsbruck, Institute of Human Genetics,Innsbruck, Austria
  7. Present Address: Istanbul Medipol University,Istanbul, Turkey
  8. Zoology Department, Faculty of Science, Suez Canal University,Ismailia, Egypt
Institutions: VIB-KU Leuven Center for Brain & Disease Research (Belgium); KU Leuven (Belgium); Innsbruck Medical University (Austria); Istanbul Medipol University (Türkiye); Suez Canal University (Egypt)
Journal: Nature communications, volume 17, issue 1, article 3761
Dates: received 17 July 2024; accepted 17 February 2026; published online 10 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-70303-8 · PMID 41807392 · PMCID PMC13106710 · OpenAlex W7134890043
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), drosophila (organism), Parkinson's (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
Keywords: Parkinson's disease, Molecular neuroscience
MeSH: Behavior, Animal*, Drosophila melanogaster*, Parkinson Disease*, Parkinsonian Disorders*, Animals, Animals, Genetically Modified, Disease Models, Animal, Dopaminergic Neurons, Drosophila, Drosophila Proteins, Humans, Mitochondria, Mutation (* major topic)
Topic: Parkinson's Disease Mechanisms and Treatments (Neurology, Medicine), according to OpenAlex
Funding: VIB Aligning Science Across Parkinson’s (ASAP-000430) through the Michael J. Fox Foundation for Parkinson's Research (MJFF) Research project, FWO Vlaanderen (G0A5219N, G0B8119N, G031324N) Methusalem project METH/21/05 (3M210778) Opening the Future grant, Leuvens Universiteitsfonds (LUF) EQZ-OPTFUP-O2010 KU Leuven Parkinson Fonds ERC (101054310 – P.V.), Chan Zuckerberg Initiative (DAF 2018 - 191857 (5022) P.V.); European Molecular Biology Organization (EMBO) (299-2019, 980-2019); FWO Vlaanderen (1282123N); Deutsche Forschungsgemeinschaft (DFG) (PE2759/1-1)
Citations: cited by 7 papers (Europe PMC); 129 references in the paper
Research resources: goat α-mouse IgG Alexa Fluor™ 555 RRID:AB_141780, RRID:AB_2576217, RRID:AB_390204, mouse α-DLG RRID:AB_528203

Abstract

Parkinson’s disease (PD) and related familial Parkinsonism are defined by motor dysfunction, but the specific upstream molecular causes of these clinical symptoms can vary widely. We hypothesize that these causes converge onto a limited number of core cellular pathways. To investigate this, we created a collection of 24 genetically well-controlled Drosophila models of familial forms of PD and related mono-genic forms of Parkinsonism. Using unbiased behavioral screening and machine learning we identify clusters of mutants that converge on (1) mitochondrial function; (2) retromer/vesicle trafficking and proteostasis/autophagy. Genes within each cluster have a similar genetic interaction profile and compounds that target specific molecular pathways ameliorate dopaminergic neuron dysfunction in a cluster-specific manner. Together, our data indicate that familial PD and related forms of Parkinsonism may fall into two broad functional groups, and may inform further work toward targeted biomarker discovery and therapeutic development.

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

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Its files are read in the Code ↔ Paper reader above, with 16 matches between paragraphs and lines of code.

verstrekenlab/drosophila-Parkinsonism-subgroups

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Commit: 82f71c1adc356d1819f38e32f97dc0d42afce091, 23 December 2025
Languages: Python (15), R (7), Jupyter (3)
Size: 43 files, 25 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: environment (Figure2_and_related_suppl/nmf_analysis/poetry.lock, Figure2_and_related_suppl/nmf_analysis/pyproject.toml), 3 notebooks
Not found: README, license file, CITATION.cff, tests, continuous integration, documentation
Tools: pandas (9 files), NumPy (8 files), data.table (5 files), Matplotlib (5 files), seaborn (4 files), ggplot2 (3 files), scikit-learn (3 files), ArviZ (2 files), SciPy (2 files), anndata (1 file), patchwork (1 file), pheatmap (1 file), Plotly (1 file), PyMC (1 file), Scanpy (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
25 files
At the source: github.com/verstrekenlab/drosophila-Parkinsonism-subgroups

Zenodo 18032710

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Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (9 files), NumPy (8 files), data.table (5 files), Matplotlib (5 files), seaborn (4 files), ggplot2 (3 files), scikit-learn (3 files), ArviZ (2 files), SciPy (2 files), anndata (1 file), patchwork (1 file), pheatmap (1 file), Plotly (1 file), PyMC (1 file), Scanpy (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
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25 files
At the source:

Code availability

The code for data analysis can be found at https://github.com/verstrekenlab/drosophila-Parkinsonism-subgroups129.

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

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Recorded: type, language, journal, volume, issue, pages, dates, 18 authors, 2 keywords, 13 MeSH terms, 4 funders, 125 references, 4 RRIDs.

Cite

This paper

Kaempf, N., Valadas, J. S., Robberechts, P., Schoovaerts, N., Praschberger, R., Ortega, A., Nachman, E., Ghezzi, L., Kilic, A., Chabot, D., Pech, U., Kuenen, S., Vilain, S., Baz, E.-S., Singh, J., Davis, J., Liu, S., & Verstreken, P. (2026). Behavioral screening defines the molecular Parkinsonism-related subgroups in Drosophila. Nature communications, 17(1), 3761. https://doi.org/10.1038/s41467-026-70303-8

BibTeX

@article{kaempf2026behavioral,
author = {Kaempf, Natalie and Valadas, Jorge S. and Robberechts, Pieter and Schoovaerts, Nils and Praschberger, Roman and Ortega, Antonio and Nachman, Eliana and Ghezzi, Lorenzo and Kilic, Ayse and Chabot, Dries and Pech, Uli and Kuenen, Sabine and Vilain, Sven and Baz, El-Sayed and Singh, Jeevanjot and Davis, Jesse and Liu, Sha and Verstreken, Patrik},
title = {{Behavioral screening defines the molecular Parkinsonism-related subgroups in Drosophila}},
journal = {Nature communications},
year = {2026},
month = mar,
volume = {17},
number = {1},
pages = {3761},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-70303-8},
url = {https://doi.org/10.1038/s41467-026-70303-8},
pmid = {41807392},
pmcid = {PMC13106710}
}

RIS

TY - JOUR
AU - Kaempf, Natalie
AU - Valadas, Jorge S.
AU - Robberechts, Pieter
AU - Schoovaerts, Nils
AU - Praschberger, Roman
AU - Ortega, Antonio
AU - Nachman, Eliana
AU - Ghezzi, Lorenzo
AU - Kilic, Ayse
AU - Chabot, Dries
AU - Pech, Uli
AU - Kuenen, Sabine
AU - Vilain, Sven
AU - Baz, El-Sayed
AU - Singh, Jeevanjot
AU - Davis, Jesse
AU - Liu, Sha
AU - Verstreken, Patrik
TI - Behavioral screening defines the molecular Parkinsonism-related subgroups in Drosophila
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/03/10
VL - 17
IS - 1
SP - 3761
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-70303-8
UR - https://doi.org/10.1038/s41467-026-70303-8
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-70303-8",
"type": "article-journal",
"title": "Behavioral screening defines the molecular Parkinsonism-related subgroups in Drosophila",
"container-title": "Nature communications",
"author": [
{
"family": "Kaempf",
"given": "Natalie"
},
{
"family": "Valadas",
"given": "Jorge S."
},
{
"family": "Robberechts",
"given": "Pieter"
},
{
"family": "Schoovaerts",
"given": "Nils"
},
{
"family": "Praschberger",
"given": "Roman"
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{
"family": "Ortega",
"given": "Antonio"
},
{
"family": "Nachman",
"given": "Eliana"
},
{
"family": "Ghezzi",
"given": "Lorenzo"
},
{
"family": "Kilic",
"given": "Ayse"
},
{
"family": "Chabot",
"given": "Dries"
},
{
"family": "Pech",
"given": "Uli"
},
{
"family": "Kuenen",
"given": "Sabine"
},
{
"family": "Vilain",
"given": "Sven"
},
{
"family": "Baz",
"given": "El-Sayed"
},
{
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"given": "Jeevanjot"
},
{
"family": "Davis",
"given": "Jesse"
},
{
"family": "Liu",
"given": "Sha"
},
{
"family": "Verstreken",
"given": "Patrik"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "3761",
"DOI": "10.1038/s41467-026-70303-8",
"PMID": "41807392",
"PMCID": "PMC13106710",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-70303-8",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
10
]
]
}
}

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