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In situ proteomics unveils specialized domains for extrasynaptic signaling on neuronal cilia.

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  1. [1] § MATERIALS AND METHODS › Analysis of cilia volumes in pan-ExM-t ↔ Grin1-cilia colocalization analysis by centerline dilation.ipynb, lines 60–110 · score 0.73 · intermediate steps, generate centerlines, dilation step, volumes, mask, filtered
  2. [2] § MATERIALS AND METHODS › 3D segmentation of neuronal cilia and synapses in pan-ExM-t ↔ Grin1-cilia colocalization analysis by centerline dilation.ipynb, lines 60–110 · score 0.56 · cilia mask, smoothing, volume, background, filtered, channel

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The authors' code

Jupyter notebook · 373 lines · 14 KB · CC-BY-4.0 · 2 matches

  1. # %%
  2. from skimage import io, morphology, measure, segmentation, filters
  3. from skimage.segmentation import expand_labels
  4. from scipy.ndimage import distance_transform_edt, center_of_mass
  6. import numpy as np
  7. import napari
  8. from skan import Skeleton, summarize #import skan # skeleton/centerline processing
  9. import skan
  10. import glob as glob
  11. import os
  13. # %%
  14. # Change datapath to data pat
  15. basepath = r''
  16. datapath = basepath+r''
  17. files = []
  18. for a in glob.glob(datapath+'/**.tif'):
  19. print(a)
  20. files.append(a)
  22. # %%
  23. ## Sub-Functions
  24. # loading data helper function
  25. def load_data(f):
  26. fname = f.split('\\')[-1]
  27. #print('loading ' + fname)
  28. img = io.imread(f)
  29. #
  30. #print(fname+' loaded.')
  31. return img # ch1, ch2, ch3
  33. # Binary dilation function for dilating centerlines
  34. def binary_dilation_anisotropic(mask, spacing, radius):
  35. # distance to background (invert mask)
  36. dist = distance_transform_edt(~mask, sampling=spacing)
  37. # dilated = points where distance-to-background < radius
  38. return dist <= radius
  40. # calculate centroids of puncta
  41. def centroid_points(labels):
  43. out = np.zeros_like(labels)
  45. label_ids = np.unique(labels)
  46. label_ids = label_ids[label_ids != 0] # remove background
  48. for lab in label_ids:
  49. # Compute centroid (floating point)
  50. cz, cy, cx = center_of_mass(labels == lab)
  52. # Round to nearest voxel index
  53. iz, iy, ix = map(int, np.round([cz, cy, cx]))
  55. # Write the label at that voxel
  56. out[iz, iy, ix] = lab
  58. return out
  60. # %%
  61. ## Main Function
  63. # Note, generating centerlines function is most time consuming
  64. # Saving the intermediate step saves time downstream reloading the data. However, the files can be large.
  65. # Set save_centerlines to True if you want to save them, or False if you don't want to save the intermediate files
  67. def cilia_puncta_colocalization(datapath, save_intermediates, scale_factor, dilation_step_size, dilation_radius_total):
  68. results = []
  69. anisotropy_ref = (scale_factor[0], scale_factor[1], scale_factor[2])
  70. files = []
  71. #print(datapath)
  72. print('Files in path: ')
  73. for a in glob.glob(datapath+'/**.tif'):
  74. print(a.split('\\')[-1])
  75. files.append(a)
  76. for f in files: # Looping through all the imaging data
  77. fname = f.split('\\')[-1].split('.')[0]
  78. print('Filename: ', fname)
  79. print(datapath+'/'+fname+"_centerlines.npy")
  80. if os.path.exists(datapath+'/'+fname+"_centerlines.npy"): # determine whether centerlines have already been calculated
  81. print('Centerlines found: '+datapath+'/'+fname+"_centerlines.npy")
  82. precalc = True
  83. centerlines = np.load(datapath+'/'+fname+"_centerlines.npy")
  84. else:
  85. precalc = False # load the function to create and save the centerline set
  86. print('Centerlines not found')
  87. # Importing data as channels data as channels
  88. img = load_data(f) # img is the 3-c image. c[0] corresponds to the first channel etc.
  89. #print(img.shape) # confirm the shape of the data: z,y,x,c
  90. ch1,ch2,ch3 = img[:,:,:,0], img[:,:,:,1], img[:,:,:,2] # split data into channels
  91. #print(ch1.shape) # confirm the shape of the data: z,y,x
  92. Grin1, Panstain, ARL13b = ch1, ch2, ch3 # rename channels by identity for readability
  94. # segmenting cilia mask from ARL13b signal
  95. print('ARL13B segmentation')
  96. smooth = filters.gaussian(ARL13b, sigma=3)*10000000 # 1 px Gaussian smoothing multiplied by a useful scaling factor
  97. lbl = measure.label(smooth>10) # label connected components of the smoothed ARL13b signal
  98. #print(np.unique(lbl))
  99. labels, counts = np.unique(lbl, return_counts=True)
  100. labels, counts = labels[1:], counts[1:] # drop background (0)
  101. largest = labels[np.argmax(counts)]
  102. result = (lbl == largest)
  103. #filtered_labels = morphology.remove_small_objects(labels, 10000) # cilia should be more than 10000 voxels in volume
  104. cilia = (lbl == largest) # filtered_labels # cilia is labeled with ARL13b, the cilia label is the segmented ARL13b
  106. # Grin1 puncta segmentaion
  107. print('Grin1 segmentation')
  108. Grin1_smooth = filters.gaussian(Grin1, sigma=1)*255
  109. Grin1_seg_rough = measure.label(Grin1_smooth > 0.001)
  110. Grin1_seg = morphology.remove_small_objects(Grin1_seg_rough, 50)
  112. # anywhere cilia is 0 will be equal to 0 when multiplying by Grin1_seg.
  113. # anything else will be a colocalized puncta with unique label number.
  114. Grin1_cilia_labels = Grin1_seg*cilia
  116. # calculate centroids for Grin1_cilia_labels
  117. Grin1_cilia_centroids = centroid_points(Grin1_cilia_labels)
  119. # count number of unique labels, equal to the number of unique Grin1 puncta colocalizing with cilia.
  120. Grin1_cilia_labels_count = len(np.unique(Grin1_cilia_labels))
  121. print('Unique Grin1_cilia_labels: '+str(Grin1_cilia_labels_count))
  125. if precalc == True:
  126. print('Loading precalculated centerlines.')
  127. centerlines = np.load(datapath+'/'+fname+"_centerlines.npy")
  128. centerline = centerlines[0]
  129. else:
  130. # generating the centerline for sequential dilation
  131. print('Cilia dilation and skeletonization')
  132. cilia_dilated = expand_labels(cilia>0, distance=2, spacing=anisotropy_ref) # expand the cilia labels for better skeletonization
  133. cilia_dilated = cilia_dilated>0 # binarize
  134. smoothed_skeleton = morphology.skeletonize(cilia_dilated) # skeletonize
  136. if smoothed_skeleton.sum() < 10:
  137. print('Problem generating skeleton. Skeleton not more than 10 pixels in total.')
  139. ## skeleton/centerline processing using skan
  140. skel = skan.Skeleton(smoothed_skeleton) # generate skan.Skeleton object
  141. skel_image = skel.skeleton_image # skeleton_image for visualization
  143. ## Pruning the excess branches from the skeleton
  144. # adapted from https://gist.github.com/ns-rse/0f67ad2d37a0fad612f634f1fcdf2549
  145. # especially this line: pruned_paths = [pruned.path_coordinates(i) for i in range(pruned.n_paths)]
  146. print('Skeleton pruning')
  147. pruned = skel # create a new variable for the pruned skeleton
  148. branch_data = skan.summarize(pruned, separator='_')
  149. pruned_j2j = pruned.prune_paths(branch_data.loc[branch_data["branch_type"]!=2].index)
  150. paths_j2j_table = skan.summarize(pruned_j2j, separator='_')
  151. pruned_j2j_paths = [pruned_j2j.path_coordinates(i) for i in range(pruned_j2j.n_paths)]
  152. pruned_j2j_skeleton_image = pruned_j2j.skeleton_image
  153. centerline = pruned_j2j_skeleton_image
  155. ## Centerline dilation
  156. # Configure settings to dilate the centerline
  157. # Note, this can take a while. Consider whether to save the intermediate step with save_centerlines variable.
  158. dilation_step_size = dilation_step_size
  159. total_dilation_distance = dilation_radius_total # in um - total radius to grow
  160. dilation_steps = total_dilation_distance/dilation_step_size
  161. centerlines = [] # array for dilated centerlines
  162. d = 0 # dilation counter
  164. # Generate dilated centerlines
  165. print('Dilating centerlines')
  166. print('Centerline pixel sums: ')
  167. while d+1 < dilation_steps:
  168. if not centerlines:
  169. mask_dil = binary_dilation_anisotropic(
  170. mask=pruned_j2j.skeleton_image,
  171. spacing=anisotropy_ref,
  172. radius=dilation_step_size # scaled dilation step size
  173. )
  174. centerlines.append(mask_dil)
  175. else:
  176. mask_dil = binary_dilation_anisotropic(
  177. mask=centerlines[-1],
  178. spacing=anisotropy_ref,
  179. radius=dilation_step_size*d # scaled dilation step size
  180. )
  181. centerlines.append(mask_dil)
  182. d+=1
  183. print(d,str(np.sum(centerlines[-1])), end='')
  185. if save_intermediates == True:
  186. fname = f.split('\\')[-1].split('.')[0]
  187. np.save(datapath+'/'+fname+'_centerlines.npy', centerlines) # "+str(round(scale_factor[0],3))+"-"+str(round(scale_factor[1],3))+"-"+str(round(scale_factor[2],3))+".npy", centerlines)
  189. for d,c in enumerate(centerlines):
  190. n = 'ceneterline'+str(d)
  192. # Calculate colocalizations etc
  193. print('Generating colocalization tables')
  194. Grin1_cilia_centerline_puncta = []
  195. Grin1_counts = []
  196. ARL13b_cilia = []
  197. ARL13b_sums = []
  199. for c in centerlines:
  200. Grin1_counts.append(np.count_nonzero(c.astype(int)*Grin1_cilia_centroids))
  201. ARL13b_conv = np.sum(c*ARL13b)
  202. ARL13b_sums.append(ARL13b_conv)
  204. #result = [ f, Grin1_cilia_labels_count, Grin1_counts, ARL13b_sums, centerline, cilia, Grin1_cilia_labels ]
  205. results.append({
  206. "file": f,
  207. "name": fname,
  208. "n_Grin1_puncta_cilia": Grin1_cilia_labels_count,
  209. "Grin1_counts_curve": Grin1_counts,
  210. "ARL13b_curve": ARL13b_sums,
  211. "centerlines": centerlines,
  212. "cilia": cilia,
  213. "Grin1_cilia_labels": Grin1_cilia_labels
  214. })
  215. print('')
  216. #results.append(result)
  217. print('Done')
  218. return results
  221. # %%
  222. ## Testing the function
  223. # Set parameters
  224. scaleref = [0.362939,0.0822064,0.0822064] # scale in microns: z, y, x
  225. dilation_step_size=0.05
  226. dilation_radius_total=1.5
  228. # %%
  229. ## Testing the function
  230. # Run the function
  231. # def cilia_puncta_colocalization(datapath, save_intermediates, scale_factor, dilation_step_size, dilation_radius_total):
  232. results = cilia_puncta_colocalization(datapath,
  233. save_intermediates=True,
  234. scale_factor=scaleref,
  235. dilation_step_size=dilation_step_size,
  236. dilation_radius_total=dilation_radius_total)
  238. # %%
  241. # %%
  242. ## View data and interemediate steps in Napari
  243. import napari
  244. viewer = napari.Viewer()
  246. # %%
  247. # choose which dataset and analysis result to visualize
  248. n = 0
  249. print(results[0].keys())
  251. # %%
  252. # Add data to napari
  253. f = results[n]['file']
  254. #print(f)
  255. img = load_data(f)
  256. ch1, ch2, ch3 = img[:, :, :, 0], img[:, :, :, 1], img[:, :, :, 2]
  257. Grin1, Panstain, ARL13b = ch1, ch2, ch3
  258. viewer.add_image(Grin1, name='Grin1', colormap='yellow', blending='additive')
  259. viewer.layers['Grin1'].scale = scaleref
  260. viewer.add_image(Panstain, name='Panstain', blending='additive')
  261. viewer.layers['Panstain'].scale = scaleref
  262. viewer.add_image(ARL13b, name='ARL13b', colormap='cyan', blending='additive')
  263. viewer.layers['ARL13b'].scale = scaleref
  265. # %%
  266. # Load segmentation and colocalization results
  267. #cilia = results[n]['cilia']
  268. Grin1_cilia_labels = results[n]['Grin1_cilia_labels']
  269. centerlines = results[n]['centerlines']
  271. # %%
  272. viewer.add_labels(results[n]['cilia'], name='cilia', blending='additive', opacity=0.7)
  273. viewer.layers['cilia'].scale = scaleref
  275. # %%
  276. viewer.add_labels(Grin1_cilia_labels, name='Grin1_cilia_labels', blending='additive', opacity=0.7)
  277. viewer.layers['Grin1_cilia_labels'].scale = scaleref
  279. # %%
  280. viewer.add_labels(Grin1_cilia_labels, name='Grin1_cilia_labels', blending='additive', opacity=0.7)
  281. viewer.layers['Grin1_cilia_labels'].scale = scaleref
  283. # %%
  284. viewer.add_labels(Grin1_cilia_labels, name='Grin1_cilia_labels', blending='additive', opacity=0.7)
  285. viewer.layers['Grin1_cilia_labels'].scale = scaleref
  287. # %%
  288. print(np.asarray(centerlines).shape) # number of centerlines calculated
  289. cn = 0 # centerline number
  290. viewer.add_labels(centerlines[cn], name='centerline'+str(cn), blending='additive', opacity=0.7)
  291. viewer.layers['centerline'+str(cn)].scale = scaleref
  293. # %%
  296. # %%
  297. ## Plot the colocalization results per centerline dilation
  298. ARL13b_curve = results[n]['ARL13b_curve']
  299. ARL13b_diffs = np.diff(ARL13b_curve)
  300. Grin1_counts_curve = results[n]['Grin1_counts_curve']
  301. Grin1_diffs = np.diff(Grin1_counts_curve)
  302. ARL13b_sums = []
  304. # %%
  307. # %%
  308. # Single ARL13b and Grin1 plots
  309. from matplotlib import pyplot as plt
  310. x = np.arange(0, dilation_radius_total, dilation_step_size)
  311. #print(ARL13b_curve)
  312. plt.plot(x[0:-2], ARL13b_diffs)
  313. plt.title('ARL13b pixel count by radius of distance from centerline (um)')
  314. plt.show()
  316. plt.plot(x[0:-2], Grin1_diffs)
  317. plt.title('Grin1 puncta colocalization by radius of dilation from centerline (um)')
  318. plt.show()
  320. # %%
  323. # %%
  324. # Combined plots
  326. gdata = [] # group Grin1 dat
  327. cdata = [] # group Cilia data
  329. for r in results:
  330. # normalizing: a - a.min()) / (a.max() - a.min()
  331. g = np.diff(r['Grin1_counts_curve']/np.asarray(r['Grin1_counts_curve']).max())
  332. c = np.diff(r['ARL13b_curve']/np.asarray(r['ARL13b_curve']).max())
  333. #g = r['Grin1_counts_curve']
  334. #c = r['ARL13b_curve']
  335. gdata.append(g)
  336. cdata.append(c)
  338. # %%
  339. gdata = np.array(gdata)
  340. cdata = np.array(cdata)
  342. gmean_vals = gdata.mean(axis=0)
  343. gstd_vals = gdata.std(axis=0)
  345. cmean_vals = cdata.mean(axis=0)
  346. cstd_vals = cdata.std(axis=0)
  348. x = np.arange(0, dilation_radius_total, dilation_step_size)
  349. x = x[0:-2]
  351. # plot
  352. plt.figure(figsize=(8, 5))
  353. plt.plot(x, gmean_vals, marker='o', label='Grin1 coloc Mean')
  354. plt.fill_between(x, gmean_vals - gstd_vals, gmean_vals + gstd_vals, alpha=0.2, label='Grin1 coloc Std Dev')
  356. plt.plot(x, cmean_vals, marker='o', label='ARL13b Mean', c='orange')
  357. plt.fill_between(x, cmean_vals - cstd_vals, cmean_vals + cstd_vals, alpha=0.2, label='ARL13b Std Dev', color='orange')
  361. plt.xlabel("Radius of dilation from centerline (um)")
  362. plt.ylabel("Normalized Grin1 puncta")
  363. plt.title("")
  364. plt.legend()
  365. plt.grid(True)
  366. plt.tight_layout()
  368. # plt.savefig('plot.pdf')
  370. plt.show()
  372. # %%

Grin1-cilia colocalization analysis by centerline dilation.ipynb, under CC-BY-4.0 · at the source

Overview

Authors: Chia-Hsiang Chang1,2, Van Ngu Trinh3, Sammy Weiser Novak4, Nidhi Rani Lokesh2,5, Catalina Kretschmar Montecinos1,2, Daniela Boassa4, Mark E Pownall2,5, Marian Kalocsay3, Maxence V Nachury1,2
ORCID iDs: Chia-Hsiang Chang, Van Ngu Trinh, Sammy Weiser Novak, Nidhi Rani Lokesh, Catalina Kretschmar Montecinos, Daniela Boassa, Mark E Pownall, Marian Kalocsay, Maxence V Nachury
  1. Department of Ophthalmology, University of California, San Francisco, San Francisco, CA 94143, USA
  2. Smith Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA 94143, USA
  3. Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
  4. Waitt Advanced Biophotonics Core, Salk Institute, La Jolla, CA 92093, USA
  5. Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94143, USA
Institutions: University of California, San Francisco (United States); The University of Texas MD Anderson Cancer Center (United States)
Journal: Science advances, volume 12, issue 23, article eaed5548
Dates: received 3 November 2025; accepted 23 April 2026; published online 3 June 2026; in print June 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1126/sciadv.aed5548 · PMID 42234764 · PMCID PMC13232615 · OpenAlex W4414261192
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.09.10.675452
Status: code verified
Categories: genetics / omics (modality), mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Machine learning, Evoked potentials, fMRI & imaging
MeSH: Cilia*, Neurons*, Proteomics*, Signal Transduction*, Synapses*, ADP-Ribosylation Factors, Animals, Mice, Receptors, N-Methyl-D-Aspartate (* major topic)
Topic: Microtubule and mitosis dynamics (Cell Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: American Diabetes Association (1-20-VSN-03); Sandler Program for Breakthrough Biomedical Research; UCSF Valhalla Fellows Program; National Institutes of Health (EY002162, EY031462, GM089933); Cancer Prevention and Research Institute of Texas (RR220032); National Science and Technology Council (MOST-110-2917-I-564-010); Research to Prevent Blindness
Citations: not cited yet (Europe PMC); 121 references in the paper

Abstract

Neuronal cilia have emerged as crucial signaling hubs, yet their molecular composition and integration with synaptic communication remain poorly understood. Using a newly developed Arl13b-TurboID mouse model, we achieved robust cilia-specific biotinylation and proteomic profiling across diverse tissues and cell types. Comparative proteomics revealed notable tissue-specific specialization, with neuronal cilia uniquely enriched in synaptic proteins, adhesion molecules, and neurotransmitter receptors. Unexpectedly, several signaling and adhesion molecules localize to neuronal cilia in discrete nanodomains maintained by active retrieval mechanisms. In the mouse cortex, expansion microscopy revealed that the NMDA receptor subunit GluN1 is organized in nanodomains on neuronal ciliary membranes, which are precisely positioned to sample neurotransmitter efflux from neighboring glutamatergic synapses. These findings establish neuronal cilia as specialized extrasynaptic signaling platforms, with nanoscale organization enabling them to integrate local synaptic cues and modulate neuronal connectivity.

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

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Zenodo 18001606

License: CC-BY-4.0
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Found in: “Data, code, and materials availability:”
Holds: 1 notebook
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (1 file), napari (1 file), NumPy (1 file), scikit-image (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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At the source: zenodo.org/records/18001606

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

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Data

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

Data, code, and materials availability

The MS proteomic data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE (105) partner repository with the dataset identifiers PXD067626 and PXD067892. The code for the analysis of cilia volume is available at https://zenodo.org/records/18001606 (DOI:10.5281/zenodo.18001606 (http://dx.doi.org/10.5281/zenodo.18001606)). All materials generated in this paper (Arl13b-GFP-TurboID knock-in mice, plasmids for ciliary biotinylation, and Daoy stable cell lines) are available from M.V.N. upon request, and all other necessary materials for replication are available from commercial suppliers listed in the method. All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 9 MeSH terms, 7 funders, 118 references.

Cite

This paper

Chang, C.-H., Trinh, V. N., Novak, S. W., Lokesh, N. R., Montecinos, C. K., Boassa, D., Pownall, M. E., Kalocsay, M., & Nachury, M. V. (2026). In situ proteomics unveils specialized domains for extrasynaptic signaling on neuronal cilia. Science advances, 12(23), eaed5548. https://doi.org/10.1126/sciadv.aed5548

BibTeX

@article{chang2026situ,
author = {Chang, Chia-Hsiang and Trinh, Van Ngu and Novak, Sammy Weiser and Lokesh, Nidhi Rani and Montecinos, Catalina Kretschmar and Boassa, Daniela and Pownall, Mark E and Kalocsay, Marian and Nachury, Maxence V},
title = {{In situ proteomics unveils specialized domains for extrasynaptic signaling on neuronal cilia}},
journal = {Science advances},
year = {2026},
month = jun,
volume = {12},
number = {23},
pages = {eaed5548},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aed5548},
url = {https://doi.org/10.1126/sciadv.aed5548},
pmid = {42234764},
pmcid = {PMC13232615}
}

RIS

TY - JOUR
AU - Chang, Chia-Hsiang
AU - Trinh, Van Ngu
AU - Novak, Sammy Weiser
AU - Lokesh, Nidhi Rani
AU - Montecinos, Catalina Kretschmar
AU - Boassa, Daniela
AU - Pownall, Mark E
AU - Kalocsay, Marian
AU - Nachury, Maxence V
TI - In situ proteomics unveils specialized domains for extrasynaptic signaling on neuronal cilia
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/06/03
VL - 12
IS - 23
SP - eaed5548
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aed5548
UR - https://doi.org/10.1126/sciadv.aed5548
LA - en
ER -

CSL-JSON

{
"id": "10.1126/sciadv.aed5548",
"type": "article-journal",
"title": "In situ proteomics unveils specialized domains for extrasynaptic signaling on neuronal cilia",
"container-title": "Science advances",
"author": [
{
"family": "Chang",
"given": "Chia-Hsiang"
},
{
"family": "Trinh",
"given": "Van Ngu"
},
{
"family": "Novak",
"given": "Sammy Weiser"
},
{
"family": "Lokesh",
"given": "Nidhi Rani"
},
{
"family": "Montecinos",
"given": "Catalina Kretschmar"
},
{
"family": "Boassa",
"given": "Daniela"
},
{
"family": "Pownall",
"given": "Mark E"
},
{
"family": "Kalocsay",
"given": "Marian"
},
{
"family": "Nachury",
"given": "Maxence V"
}
],
"container-title-short": "Sci Adv",
"volume": "12",
"issue": "23",
"page": "eaed5548",
"DOI": "10.1126/sciadv.aed5548",
"PMID": "42234764",
"PMCID": "PMC13232615",
"ISSN": "2375-2548",
"publisher": "American Association for the Advancement of Science",
"URL": "https://doi.org/10.1126/sciadv.aed5548",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
3
]
]
}
}

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