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Energetic diversity in retinal ganglion cells is modulated by neuronal activity and correlates with resilience to degeneration.

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  1. [1] § Methods › In vivo image processing and quantification ↔ ProcessRaw.ipynb, lines 180 to 229 · score 0.79 · ScanImageTiffReader, interleaved, accomplished, subtracted, Background, clipped · Report a mismatch

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The authors' code

Jupyter notebook · 302 lines · 11 KB · no license · 1 match

  1. # %%
  2. %%capture systemMsg
  4. !pip install suite2p
  5. !pip install cellpose
  7. import numpy as np
  8. from skimage import io
  9. import os, requests
  10. from pathlib import Path
  11. import suite2p
  12. import matplotlib.pyplot as plt
  13. from ScanImageTiffReader import ScanImageTiffReader
  14. from PIL import Image
  17. # %%
  18. #Concatenate scanimage raw files
  19. filelist = []
  20. for file in os.listdir():
  21. if file.endswith(".tif"):
  22. filelist.append(file)
  23. filelist.sort(key=os.path.getmtime)
  24. selection_tuple = tuple(filelist)
  27. # %%
  28. #create `/registered` output folder
  29. i = 0
  30. while os.path.exists(os.getcwd() + '\\registered%s' % i):
  31. i += 1
  32. os.makedirs(os.getcwd() + '\\registered%s' % i)
  33. newdir = os.getcwd() + '\\registered%s' % i
  35. # %%
  36. #for visual inspection of pixels in clipped image region used to measure background intensity
  37. def drawTriangles(concatenated_input):
  39. plotTriangles = concatenated_input.copy()
  41. #upper left
  42. plotTriangles[:,:,
  43. np.triu_indices(25)[0] + 5,
  44. 25 - np.triu_indices(25)[1] + 5
  45. ] = 1000
  46. #lower left
  47. plotTriangles[:,:,
  48. 511 - np.triu_indices(16)[0] - 5,
  49. 16 - np.triu_indices(16)[1] + 5
  50. ] = 1000
  51. #upper right
  52. plotTriangles[:,:,
  53. np.triu_indices(25)[0] + 5,
  54. 511 - (25 - np.triu_indices(25)[1]) - 5
  55. ] = 1000
  56. #lower right
  57. plotTriangles[:,:,
  58. 511 - np.triu_indices(25)[0] - 5,
  59. 511 - (25 - np.triu_indices(25)[1]) - 5
  60. ] = 1000
  62. plt.figure(figsize = (36,36))
  63. plt.subplot(2,2,1)
  64. plt.gca().set_title('ch0 image')
  65. plt.imshow(np.average(concatenated[:,0,:,:], axis = 0), vmin=-10, vmax=10)
  66. plt.subplot(2,2,2)
  67. plt.gca().set_title('ch0 triangles')
  68. plt.imshow(np.average(plotTriangles[:,0,:,:], axis = 0), vmin=-10, vmax=10)
  69. plt.subplot(2,2,3)
  70. plt.gca().set_title('ch1 image')
  71. plt.imshow(np.average(concatenated[:,1,:,:], axis = 0), vmin=-10, vmax=10)
  72. plt.subplot(2,2,4)
  73. plt.gca().set_title('ch1 triangles')
  74. plt.imshow(np.average(plotTriangles[:,1,:,:], axis = 0), vmin=-10, vmax=10)
  76. # %%
  77. #measures background intensity in pixels at corner regions of image clipped by objective lens
  79. def measureTriangles(concatenated_input):
  81. #channel 0
  82. #upper left
  83. a1 = concatenated_input[:,0,
  84. np.triu_indices(25)[0] + 5,
  85. 25 - np.triu_indices(25)[1] + 5
  86. ]
  87. #lower left
  88. a2 = concatenated_input[:,0,
  89. 511 - np.triu_indices(16)[0] - 5,
  90. 16 - np.triu_indices(16)[1] + 5
  91. ]
  92. #upper right
  93. a3 = concatenated_input[:,0,
  94. np.triu_indices(25)[0] + 5,
  95. 511 - (25 - np.triu_indices(25)[1]) - 5
  96. ]
  97. #lower right
  98. a4 = concatenated_input[:,0,
  99. 511 - np.triu_indices(25)[0] - 5,
  100. 511 - (25 - np.triu_indices(25)[1]) - 5
  101. ]
  102. global acat
  103. acat = np.concatenate((a1.flatten(),a2.flatten(),a3.flatten(),a4.flatten()))
  105. #channel 1
  106. #upper left
  107. b1 = concatenated_input[:,1,
  108. np.triu_indices(25)[0] + 5,
  109. 25 - np.triu_indices(25)[1] + 5
  110. ]
  111. #lower left
  112. b2 = concatenated_input[:,1,
  113. 511 - np.triu_indices(16)[0] - 5,
  114. 16 - np.triu_indices(16)[1] + 5
  115. ]
  116. #upper right
  117. b3 = concatenated_input[:,1,
  118. np.triu_indices(25)[0] + 5,
  119. 511 - (25 - np.triu_indices(25)[1]) - 5
  120. ]
  121. #lower right
  122. b4 = concatenated_input[:,1,
  123. 511 - np.triu_indices(25)[0] - 5,
  124. 511 - (25 - np.triu_indices(25)[1]) - 5
  125. ]
  126. global bcat
  127. bcat = np.concatenate((b1.flatten(),b2.flatten(),b3.flatten(),b4.flatten()))
  129. print('SI_Ch2_Offset: ' + str(SI_Ch2_Offset) )
  130. print('SI_Ch3_Offset: ' + str(SI_Ch3_Offset) )
  132. print('channel 0 total pixel median: ' + str(np.median(acat)) )
  133. print('channel 0 upper left pixel median: ' + str(np.median( a1.flatten() )) )
  134. print('channel 0 lower left pixel median: ' + str(np.median( a2.flatten() )) )
  135. print('channel 0 upper right pixel median: ' + str(np.median( a3.flatten() )) )
  136. print('channel 0 lower right pixel median: ' + str(np.median( a4.flatten() )) )
  138. print('channel 1 total pixel median: ' + str(np.median(bcat)) )
  139. print('channel 1 upper left pixel median: ' + str(np.median( b1.flatten() )) )
  140. print('channel 1 lower left pixel median: ' + str(np.median( b2.flatten() )) )
  141. print('channel 1 upper right pixel median: ' + str(np.median( b3.flatten() )) )
  142. print('channel 1 lower right pixel median: ' + str(np.median( b4.flatten() )) )
  144. plt.figure(figsize=(16, 6))
  146. plt.subplot(2, 5, 1)
  147. plt.gca().set_title('ch0 total')
  148. plt.hist(acat, bins = 50, range = ( np.percentile(acat, 5)-10, np.percentile(acat, 95)+10 ))
  149. plt.subplot(2, 5, 2)
  150. plt.gca().set_title('ch0 upper left')
  151. plt.hist(a1.flatten(), bins = 50, range = ( np.percentile(a1.flatten(), 5)-10, np.percentile(a1.flatten(), 95)+10 ))
  152. plt.subplot(2, 5, 3)
  153. plt.gca().set_title('ch0 lower left')
  154. plt.hist(a2.flatten(), bins = 50, range = ( np.percentile(a2.flatten(), 5)-10, np.percentile(a2.flatten(), 95)+10 ))
  155. plt.subplot(2, 5, 4)
  156. plt.gca().set_title('ch0 upper right')
  157. plt.hist(a3.flatten(), bins = 50, range = ( np.percentile(a3.flatten(), 5)-10, np.percentile(a3.flatten(), 95)+10 ))
  158. plt.subplot(2, 5, 5)
  159. plt.gca().set_title('ch0 lower right')
  160. plt.hist(a4.flatten(), bins = 50, range = ( np.percentile(a4.flatten(), 5)-10, np.percentile(a4.flatten(), 95)+10 ))
  162. plt.subplot(2, 5, 6)
  163. plt.gca().set_title('ch1 total')
  164. plt.hist(bcat, bins = 50, range = ( np.percentile(bcat, 5)-10, np.percentile(bcat, 95)+10 ))
  165. plt.subplot(2, 5, 7)
  166. plt.gca().set_title('ch1 upper left')
  167. plt.hist(b1.flatten(), bins = 50, range = ( np.percentile(b1.flatten(), 5)-10, np.percentile(b1.flatten(), 95)+10 ))
  168. plt.subplot(2, 5, 8)
  169. plt.gca().set_title('ch1 lower left')
  170. plt.hist(b2.flatten(), bins = 50, range = ( np.percentile(b2.flatten(), 5)-10, np.percentile(b2.flatten(), 95)+10 ))
  171. plt.subplot(2, 5, 9)
  172. plt.gca().set_title('ch1 upper right')
  173. plt.hist(b3.flatten(), bins = 50, range = ( np.percentile(b3.flatten(), 5)-10, np.percentile(b3.flatten(), 95)+10 ))
  174. plt.subplot(2, 5, 10)
  175. plt.gca().set_title('ch1 lower right')
  176. plt.hist(b4.flatten(), bins = 50, range = ( np.percentile(b4.flatten(), 5)-10, np.percentile(b4.flatten(), 95)+10 ))
  178. plt.show()
  180. # %%
  181. #read tifs, perform background subtraction, concatenate tifs
  183. #first tif
  184. read_img = ScanImageTiffReader(selection_tuple[0])
  185. concatenated = np.reshape(read_img.data(), (int(read_img.data().shape[0]/2),2,512,512) ) #ScanImageTiffReader reads all images as (interleaved_frames, x, y). Reshape to (frames, ch, x, y)
  187. #By default ScanImage performs Offset Correction using measured PMT voltages. See https://docs.scanimage.org/Configuration/PMT/index.html#pmt-calibration
  188. #This correction is reversed by adding the ‘channelOffsets’ value from the .tif metadata to each image pixel
  189. #Background subtraction is then accomplished using objective lens clipped pixels as described above
  190. SI_Ch2_Offset = int((read_img.metadata()).splitlines()[36][30:-1].split()[1]) #read ScanImage .tif metadata ‘channelOffsets’
  191. SI_Ch3_Offset = int((read_img.metadata()).splitlines()[36][30:-1].split()[2])
  193. concatenated[:,0,:,:] = concatenated[:,0,:,:] + SI_Ch2_Offset
  194. concatenated[:,1,:,:] = concatenated[:,1,:,:] + SI_Ch3_Offset
  195. measureTriangles(concatenated)
  196. concatenated[:,0,:,:] = concatenated[:,0,:,:] - int(np.median(acat))
  197. concatenated[:,1,:,:] = concatenated[:,1,:,:] - int(np.median(bcat))
  198. measureTriangles(concatenated)
  201. #subsequent tifs
  202. for i in range(1, len(selection_tuple)):
  203. read_img = ScanImageTiffReader(selection_tuple[i])
  204. reshaped = np.reshape(read_img.data(), (int(read_img.data().shape[0]/2),2,512,512))
  206. SI_Ch2_Offset = int((read_img.metadata()).splitlines()[36][30:-1].split()[1])
  207. SI_Ch3_Offset = int((read_img.metadata()).splitlines()[36][30:-1].split()[2])
  209. #subsequent tifs undo scanimage auto offset subtraction
  210. reshaped[:,0,:,:] = reshaped[:,0,:,:] + SI_Ch2_Offset
  211. reshaped[:,1,:,:] = reshaped[:,1,:,:] + SI_Ch3_Offset
  212. measureTriangles(reshaped)
  213. #subsequent tifs subtract clipped pixels measured offsets
  214. reshaped[:,0,:,:] = reshaped[:,0,:,:] - int(np.median(acat))
  215. reshaped[:,1,:,:] = reshaped[:,1,:,:] - int(np.median(bcat))
  216. measureTriangles(reshaped)
  218. concatenated = np.concatenate((concatenated, reshaped))
  220. drawTriangles(concatenated)
  222. read_img.close() # Need to close the ScanImageTiffReader process
  225. os.chdir(newdir)
  226. text_file = open("raw_concat_inputs.txt", "w")
  227. text_file.write('\n'.join(map(str, selection_tuple)))
  228. text_file.close()
  229. io.imsave('raw_concat.tif', concatenated)
  231. # %%
  232. #set Suite2p parameters. See https://suite2p.readthedocs.io/en/latest/settings.html
  234. ops = suite2p.default_ops()
  235. ops['fs'] = 1.07
  236. ops['nchannels'] = 2
  237. ops['reg_tif']=1 #see https://github.com/MouseLand/suite2p/issues/89
  238. ops['reg_tif_chan2'] = True
  239. ops['two_step_registration'] = True
  240. ops['keep_movie_raw'] = True
  241. ops['max_overlap'] = 1
  242. ops['roidetect'] = False
  243. print(ops)
  245. db = {
  246. 'data_path': [os.getcwd()],
  247. }
  248. print(db)
  250. output_ops = suite2p.run_s2p(ops=ops, db=db) #run
  251. output_ops_file = np.load(Path(output_ops['save_path']).joinpath('ops.npy'), allow_pickle=True).item()
  252. output_ops_file.keys() == output_ops.keys()
  254. # %%
  255. #Concatenate outputs of Suite2p registration
  256. def read_tiff(path):
  257. """
  258. path - Path to the multipage-tiff file
  259. """
  260. img = Image.open(path) #from PIL
  261. images = []
  262. for i in range(img.n_frames):
  263. img.seek(i)
  264. images.append(np.array(img, dtype=np.int16))
  265. return np.array(images)
  268. os.chdir(newdir + '\\suite2p\\plane0\\reg_tif')
  269. regtif = sorted(os.listdir(), key=os.path.getmtime)
  270. regtif_concat = read_tiff(regtif[0])
  271. for i in range(1, len(regtif)):
  272. regtif_concat = np.concatenate((regtif_concat, read_tiff(regtif[i])))
  273. os.chdir(newdir)
  274. io.imsave('reg_concat_ch0.tif', regtif_concat)
  276. os.chdir(newdir + '\\suite2p\\plane0\\reg_tif_chan2')
  277. regtif_chan2 = sorted(os.listdir(), key=os.path.getmtime) #naming ch1 as `chan2` in code follwowing Suite2p convention. Output name as ch1 (ie FRET = ch1/ch0)
  278. regtif_chan2_concat = read_tiff(regtif_chan2[0])
  279. for i in range(1, len(regtif_chan2)):
  280. regtif_chan2_concat = np.concatenate((regtif_chan2_concat, read_tiff(regtif_chan2[i])))
  281. os.chdir(newdir)
  282. io.imsave('reg_concat_ch1.tif', regtif_chan2_concat)
  284. #save an avg project across slices and channels
  285. fixed_sliceAvg_ch0 = np.average(regtif_concat, axis = 0).astype(np.float32)
  286. fixed_sliceAvg_ch1 = np.average(regtif_chan2_concat, axis = 0).astype(np.float32)
  287. fixed_sliceAvg_chAvg = ( (fixed_sliceAvg_ch0 + fixed_sliceAvg_ch1)/2 )
  288. io.imsave('reg_concat_chAvg_sliceAvg.tif',fixed_sliceAvg_chAvg )
  290. # %%
  291. #remove intermediate Suite2p files
  293. os.remove('raw_concat.tif')
  294. import shutil
  295. shutil.rmtree('suite2p')
  297. os.chdir("..") #move up one directory to folder containing input .tifs
  298. #remove input files
  299. for i in selection_tuple:
  300. if i in os.listdir(".."):
  301. os.remove(i)

ProcessRaw.ipynb at commit cd53dc9, no license · at the source

Overview

Authors: Zelun Wang1,2,3, Christopher Zhao1, Shelly Xu1, Minglei Zhao1, Sean McCracken1,2, Rajendra S Apte1,4,5, Philip R Williams1,6,7
ORCID iDs: Zelun Wang, Rajendra S Apte, Philip R Williams
  1. John F. Hardesty, MD, Department of Ophthalmology and Visual Sciences, Washington University School of Medicine, St. Louis, MO USA
  2. Graduate Program in Neuroscience, Washington University School of Medicine, St. Louis, MO USA
  3. Medical Scientist Training Program, Washington University School of Medicine, St. Louis, MO USA
  4. Department of Developmental Biology, Washington University in St. Louis School of Medicine, St. Louis, MO USA
  5. Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO USA
  6. Department of Neuroscience, Washington University School of Medicine, St. Louis, MO USA
  7. Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO USA
Institutions: Washington University in St. Louis (United States); Hope Center for Neurological Disorders (United States)
Journal: Nature communications, volume 17, issue 1, article 4531
Dates: received 26 February 2025; accepted 10 March 2026; published online 27 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-71106-7 · PMID 41896245 · PMCID PMC13194979 · OpenAlex W7141203511
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Preprocessing, fMRI & imaging, Smoothing, state filtering, decompositions, Physiology & signal measures
Keywords: Molecular neuroscience, Stress and resilience
MeSH: Adenosine Triphosphate*, Energy Metabolism*, Retinal Ganglion Cells*, Animals, Axons, Axotomy, Cell Survival, Homeostasis, Male, Mice, Mice, Inbred C57BL, Mitochondria, Neurons, Oxidative Phosphorylation (* major topic)
Topic: Retinal Development and Disorders (Ophthalmology, Medicine), according to OpenAlex
Funding: NEI NIH HHS (R01 EY012543, R01 EY032908, T32 EY013360, P30 EY002687, R01 EY036111, R01 EY035684); Research to Prevent Blindness (RPB) (Young Investigator Award, Unrestricted grant to Department of Ophthalmology); U.S. Department of Health & Human Services | NIH | National Eye Institute (P30 EY002687, T32 EY013360, R01 EY035684, R01 EY012543, R01 EY036111, R01 EY032908); Novartis | Alcon | Alcon Research Institute (ARI) (Young Investigator Award); BrightFocus Foundation (National Glaucoma Award)
Citations: not cited yet (Europe PMC); 102 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

zelunw/RGC-ATP

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: cd53dc996fb5d3616cabd2a9cdbb3a034cdfd30d, 14 March 2025
Languages: Jupyter (3)
Size: 6 files, 3 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 3 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (3 files), NumPy (3 files), scikit-image (3 files), Cellpose (2 files), pandas (1 file), Pillow (1 file), SciPy (1 file), seaborn (1 file), Suite2p (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: github.com/zelunw/RGC-ATP

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-71106-7.

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;
  • 3 scripts, each with its path and the digest of its content;
  • 1 match 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 statement

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  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s41467-026-71106-7.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 2 keywords, 14 MeSH terms, 5 funders, 99 references.

Cite

This paper

Wang, Z., Zhao, C., Xu, S., Zhao, M., McCracken, S., Apte, R. S., & Williams, P. R. (2026). Energetic diversity in retinal ganglion cells is modulated by neuronal activity and correlates with resilience to degeneration. Nature communications, 17(1), 4531. https://doi.org/10.1038/s41467-026-71106-7

BibTeX

@article{wang2026energetic,
author = {Wang, Zelun and Zhao, Christopher and Xu, Shelly and Zhao, Minglei and McCracken, Sean and Apte, Rajendra S and Williams, Philip R},
title = {{Energetic diversity in retinal ganglion cells is modulated by neuronal activity and correlates with resilience to degeneration}},
journal = {Nature communications},
year = {2026},
month = mar,
volume = {17},
number = {1},
pages = {4531},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71106-7},
url = {https://doi.org/10.1038/s41467-026-71106-7},
pmid = {41896245},
pmcid = {PMC13194979}
}

RIS

TY - JOUR
AU - Wang, Zelun
AU - Zhao, Christopher
AU - Xu, Shelly
AU - Zhao, Minglei
AU - McCracken, Sean
AU - Apte, Rajendra S
AU - Williams, Philip R
TI - Energetic diversity in retinal ganglion cells is modulated by neuronal activity and correlates with resilience to degeneration
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/03/27
VL - 17
IS - 1
SP - 4531
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71106-7
UR - https://doi.org/10.1038/s41467-026-71106-7
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-71106-7",
"type": "article-journal",
"title": "Energetic diversity in retinal ganglion cells is modulated by neuronal activity and correlates with resilience to degeneration",
"container-title": "Nature communications",
"author": [
{
"family": "Wang",
"given": "Zelun"
},
{
"family": "Zhao",
"given": "Christopher"
},
{
"family": "Xu",
"given": "Shelly"
},
{
"family": "Zhao",
"given": "Minglei"
},
{
"family": "McCracken",
"given": "Sean"
},
{
"family": "Apte",
"given": "Rajendra S"
},
{
"family": "Williams",
"given": "Philip R"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "4531",
"DOI": "10.1038/s41467-026-71106-7",
"PMID": "41896245",
"PMCID": "PMC13194979",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-71106-7",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
27
]
]
}
}

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

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