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The membrane-to-cortex distance regulates mDia1 activity to control cortical mechanics.

Code ↔ Paper

4 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 4 matches
  1. [1] § Methods › Cryo-electron tomography ↔ branch_detection_validation/branch_analysis.ipynb, lines 58–177 · score 0.79 · maximal distance, closest points, actin point, extrapolating, intersection, candidates
  2. [2] § Methods › Cryo-electron tomography ↔ jupyter_based_processing/filament_segmentation_to_coordinates/jupyter/filament_segmentation_to_coordinates.ipynb, lines 42–147 · score 0.73 · Bspline fit, branch points, eroded, equidistant, skeletonized, labelled
  3. [3] § Methods › Cryo-electron tomography ↔ jupyter_based_processing/filament_segmentation_to_coordinates/cluster_python/filament_segmentation_to_coordinates.py, lines 91–153 · score 0.60 · Bspline fit, equidistant, skeletonized, labelled, resampled, orientations
  4. [4] § Methods › Magnetic pincher ↔ Code_Python/TrackAnalyser.py, lines 959–1012 · score 0.55 · cortex thickness, relaxation, tracking, compression, median, field

Paper

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The authors' code

Jupyter notebook · 557 lines · 30 KB · GPL-3.0 · 1 match

  1. # %% [markdown]
  2. # # Branch Analysis
  3. #
  4. # ##### This script identifies branch points on filaments. Credits to Marc Siggel (Kosinski/Mahamid lab) for the initial translation of matlab code to python.
  6. # %% [markdown]
  7. # ## Initialization
  9. # %%
  10. import numpy as np
  11. import pandas as pd
  12. from scipy.spatial.distance import cdist
  13. import os
  15. # %% [markdown]
  16. # ## Helper Functions
  18. # %%
  19. def distance_of_two_lines(e1, e2, r1, r2):
  20. # r1, r2 = Point where the line passes through
  21. # e1, e2 = Direction vector
  23. # Find the unit vector perpendicular to both lines
  24. n = np.cross(e1, e2)
  25. n /= np.linalg.norm(n)
  27. # Calculate distance
  28. d = abs(np.dot(n, r1 - r2))
  29. return d
  31. # normalize to vector
  32. def unit_vector(vector):
  33. return vector / np.linalg.norm(vector)
  35. # find angle between two vectors (undirectionla)
  36. def angle_between(v1, v2):
  37. v1_u = unit_vector(v1)
  38. v2_u = unit_vector(v2)
  39. angle = np.degrees(np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0)))
  40. if angle > 90:
  41. angle = 180 - angle
  43. return angle
  45. # write bild file for chimera visualization
  46. def write_bild_file(list_of_fragments, output_filename, downscale=1.34808, diameter=1, color="gold"):
  47. bild_file = ".color {}\n".format(color)
  48. #print(positions)
  49. for fragment in list_of_fragments:
  50. for i in range(len(fragment)-1):
  51. #print(positions[i])
  52. bild_file += ".cylinder {} {} {} {} {} {} {}\n".format(fragment[i][0]/downscale, fragment[i][1]/downscale, fragment[i][2]/downscale, fragment[i+1][0]/downscale, fragment[i+1][1]/downscale, fragment[i+1][2]/downscale, diameter)
  53. text_file = open(output_filename, "w")
  54. text_file.write(bild_file)
  55. text_file.close()
  56. return
  58. #branch analysis
  59. def branch_analysis(array, cutoff, cutoff_branch, angle_cutoff, voxel_size = 1.34808):
  60. """ cutoff: cutoff of actin points within a tip whihc are considered
  61. cutoff_branch: maximal distance of the two extrapolated line segments at their closest point.
  62. (Note: perfect intersection is unlikely but small error margin is allowed for)
  63. (This avoids any false positives where the actin passes behind another filament)
  64. angle_cutoff: min angle between the two actin segments (max 90 degreess)
  65. Returns: the branch count
  66. """
  67. candidates_per_filament = []
  68. all_results = [] # indicies of all points that belong to a branch for visualization
  69. tip_for_viz = [] # list of points for filament tip visualization
  70. branch_index = 0 # running_index of the branches use because heterogeneous loop
  71. all_angles_list = []
  72. mother_intersect_point = [] # list of all points on mother filaments that is a branch intersection
  73. all_tip_coordinates = [] # list of filament tip coordinates
  75. # generate list of 3 points at the beginning or end of each filament
  76. for fil_idx in array["fil_idx"].unique():
  77. points = array[array["fil_idx"] == fil_idx][["X_coord", "Y_coord", "Z_coord"]] .to_numpy()
  78. end1 = points[0:4]
  79. end2 = points[-3:]
  80. all_tip_coordinates.append(end1)
  81. all_tip_coordinates.append(end2)
  82. all_tip_coordinates = np.vstack(all_tip_coordinates)
  84. # identify branches
  85. for fil_idx in array["fil_idx"].unique():
  86. current_fil = array.index[array["fil_idx"] == fil_idx].to_numpy() #point index on current filament
  87. points = array[array["fil_idx"] == fil_idx][["X_coord", "Y_coord", "Z_coord"]].to_numpy() #coordinates of points on current filament
  88. all_points_coord = array[["X_coord", "Y_coord", "Z_coord"]].to_numpy()
  89. tip1 = np.array([points[0]]) #tip 1 coordinates
  90. tip2 = np.array([points[-1]]) #tip 2 coordinates
  92. # list of points for visualizing each tip
  93. points_for_viz_tip1 = points[0:7]
  94. points_for_viz_tip2 = points[-8:]
  95. tip_for_viz.append(points_for_viz_tip1)
  96. tip_for_viz.append(points_for_viz_tip2)
  98. below_cutoff_tip1 = np.argwhere(cdist(tip1, all_points_coord) < cutoff)[:,1] #index of points of dist below cutoff fom tip 1
  99. below_cutoff_tip2 = np.argwhere(cdist(tip2, all_points_coord) < cutoff)[:,1] #index of points of dist below cutoff from tip 2
  101. not_overlap_tip1 = list(set(below_cutoff_tip1) - set(current_fil)) #excluding points on the same filament
  102. not_overlap_tip2 = list(set(below_cutoff_tip2) - set(current_fil)) #excluding points on the same filament
  104. if len(not_overlap_tip1) != 0:
  105. not_overlap_tip1 = np.sort(not_overlap_tip1)
  106. filament_indices = array.iloc[not_overlap_tip1]["fil_idx"].to_numpy()
  107. # here we analyze all candidates for a specfic tip.
  108. for fil_index in np.unique(filament_indices):
  109. candidate = not_overlap_tip1[filament_indices == fil_index] #all points on potential mother filament
  110. central_index = candidate[int(len(candidate)/2)] #center point index
  111. connection_indices = array.iloc[np.max([central_index-5,0]):central_index+5] #5 points before and after center
  112. connection_indices = connection_indices[connection_indices["fil_idx"] == fil_index] #make sure they're on the potential mother filament
  113. connection_points = connection_indices[["X_coord", "Y_coord", "Z_coord"]].to_numpy() #get coordinates of mother filament points
  115. vector_tip = points_for_viz_tip1[0] - points_for_viz_tip1[-1] #vector of orientation at tip/branch point
  116. vector_connection = connection_points[0] - connection_points[-1] #vector of orientation at connection point of mother filament
  118. line_dist = distance_of_two_lines(vector_tip, vector_connection, points_for_viz_tip1[0], connection_points[0]) # calculate min distance from projected distance from line vec
  119. line_angle = angle_between(vector_tip, vector_connection) #angle between two vectors
  120. closest = connection_points[np.argmin(cdist(tip1,connection_points)[0])] #closest point to tip from potential mother filament
  122. # check if the identified mother-daughter combination is already in the results set. If yes, skip to next.
  123. if len(all_results) == 0:
  124. pass
  125. else:
  126. all_stacked_results = np.vstack(all_results)
  127. if len(np.intersect1d(points_for_viz_tip1, all_stacked_results)) != 0 and len(np.intersect1d(connection_points, all_stacked_results)) != 0:
  128. continue
  130. # if the mother-daughter pair satisfies all criteria on distance and angle, and the mother closest point is not a tip, identify this as a branch
  131. if line_dist <= cutoff_branch and line_angle > angle_cutoff and len(np.intersect1d(closest,all_tip_coordinates)) == 0:
  132. all_results.append(points_for_viz_tip1)
  133. all_results.append(connection_points)
  134. mother_intersect_point.append(array.iloc[central_index][["X_coord", "Y_coord", "Z_coord"]].to_numpy())
  135. branch_index += 1
  137. # repeat above with the other tip
  138. if len(not_overlap_tip2) != 0:
  139. not_overlap_tip2 = np.sort(not_overlap_tip2)
  140. filament_indices = array.iloc[not_overlap_tip2]["fil_idx"].to_numpy()
  141. for fil_index in np.unique(filament_indices):
  142. candidate = not_overlap_tip2[filament_indices == fil_index]
  143. central_index = candidate[int(len(candidate)/2)]
  144. connection_indices = array[np.max([central_index-5,0]):central_index+5]
  145. connection_indices = connection_indices[connection_indices["fil_idx"] == fil_index]
  146. connection_points = connection_indices[["X_coord", "Y_coord", "Z_coord"]].to_numpy()
  148. vector_tip = points_for_viz_tip2[0] - points_for_viz_tip2[-1]
  149. vector_connection = connection_points[0] - connection_points[-1]
  151. line_dist = distance_of_two_lines(vector_tip, vector_connection, points_for_viz_tip2[0], connection_points[0])
  152. line_angle = angle_between(vector_tip, vector_connection)
  153. closest = connection_points[np.argmin(cdist(tip2,connection_points)[0])]
  155. if len(all_results) == 0:
  156. pass
  157. else:
  158. all_stacked_results = np.vstack(all_results)
  159. if len(np.intersect1d(points_for_viz_tip2,all_stacked_results)) != 0 and len(np.intersect1d(connection_points,all_stacked_results)) != 0:
  160. continue
  162. if line_dist <= cutoff_branch and line_angle > angle_cutoff and len(np.intersect1d(closest,all_tip_coordinates)) == 0:
  163. all_results.append(points_for_viz_tip2)
  164. all_results.append(connection_points)
  165. mother_intersect_point.append(array.iloc[central_index][["X_coord", "Y_coord", "Z_coord"]].to_numpy())
  166. branch_index += 1
  168. # add branch information into point csv, a point is only considered a branch point if it is a point on the mother filament at which the branch occurs
  169. is_intersect_point = np.zeros(len(array))
  170. for i in range(len(is_intersect_point)):
  171. if len(mother_intersect_point) == 0:
  172. break
  173. if ((mother_intersect_point == array.iloc[i][["X_coord", "Y_coord", "Z_coord"]].to_numpy()).all(axis = 1)).any(): #check if coordinates match between intesect point array and each point
  174. is_intersect_point[i] = 1
  175. array['branch'] = is_intersect_point
  177. return branch_index, array, all_results
  179. #branch analysis
  180. def branch_analysis_without_endpoint_suppression(array, cutoff, cutoff_branch, angle_cutoff, voxel_size = 1.34808):
  181. """ cutoff: cutoff of actin points within a tip whihc are considered
  182. cutoff_branch: maximal distance of the two extrapolated line segments at their closest point.
  183. (Note: perfect intersection is unlikely but small error margin is allowed for)
  184. (This avoids any false positives where the actin passes behind another filament)
  185. angle_cutoff: min angle between the two actin segments (max 90 degreess)
  186. Returns: the branch count
  187. """
  188. candidates_per_filament = []
  189. all_results = [] # indicies of all points that belong to a branch for visualization
  190. tip_for_viz = [] # list of points for filament tip visualization
  191. branch_index = 0 # running_index of the branches use because heterogeneous loop
  192. all_angles_list = []
  193. mother_intersect_point = [] # list of all points on mother filaments that is a branch intersection
  194. all_tip_coordinates = [] # list of filament tip coordinates
  196. # generate list of 3 points at the beginning or end of each filament
  197. for fil_idx in array["fil_idx"].unique():
  198. points = array[array["fil_idx"] == fil_idx][["X_coord", "Y_coord", "Z_coord"]] .to_numpy()
  199. end1 = points[0:4]
  200. end2 = points[-3:]
  201. all_tip_coordinates.append(end1)
  202. all_tip_coordinates.append(end2)
  203. all_tip_coordinates = np.vstack(all_tip_coordinates)
  205. # identify branches
  206. for fil_idx in array["fil_idx"].unique():
  207. current_fil = array.index[array["fil_idx"] == fil_idx].to_numpy() #point index on current filament
  208. points = array[array["fil_idx"] == fil_idx][["X_coord", "Y_coord", "Z_coord"]].to_numpy() #coordinates of points on current filament
  209. all_points_coord = array[["X_coord", "Y_coord", "Z_coord"]].to_numpy()
  210. tip1 = np.array([points[0]]) #tip 1 coordinates
  211. tip2 = np.array([points[-1]]) #tip 2 coordinates
  213. # list of points for visualizing each tip
  214. points_for_viz_tip1 = points[0:7]
  215. points_for_viz_tip2 = points[-8:]
  216. tip_for_viz.append(points_for_viz_tip1)
  217. tip_for_viz.append(points_for_viz_tip2)
  219. below_cutoff_tip1 = np.argwhere(cdist(tip1, all_points_coord) < cutoff)[:,1] #index of points of dist below cutoff fom tip 1
  220. below_cutoff_tip2 = np.argwhere(cdist(tip2, all_points_coord) < cutoff)[:,1] #index of points of dist below cutoff from tip 2
  222. not_overlap_tip1 = list(set(below_cutoff_tip1) - set(current_fil)) #excluding points on the same filament
  223. not_overlap_tip2 = list(set(below_cutoff_tip2) - set(current_fil)) #excluding points on the same filament
  225. if len(not_overlap_tip1) != 0:
  226. not_overlap_tip1 = np.sort(not_overlap_tip1)
  227. filament_indices = array.iloc[not_overlap_tip1]["fil_idx"].to_numpy()
  228. # here we analyze all candidates for a specfic tip.
  229. for fil_index in np.unique(filament_indices):
  230. candidate = not_overlap_tip1[filament_indices == fil_index] #all points on potential mother filament
  231. central_index = candidate[int(len(candidate)/2)] #center point index
  232. connection_indices = array.iloc[np.max([central_index-5,0]):central_index+5] #5 points before and after center
  233. connection_indices = connection_indices[connection_indices["fil_idx"] == fil_index] #make sure they're on the potential mother filament
  234. connection_points = connection_indices[["X_coord", "Y_coord", "Z_coord"]].to_numpy() #get coordinates of mother filament points
  236. vector_tip = points_for_viz_tip1[0] - points_for_viz_tip1[-1] #vector of orientation at tip/branch point
  237. vector_connection = connection_points[0] - connection_points[-1] #vector of orientation at connection point of mother filament
  239. line_dist = distance_of_two_lines(vector_tip, vector_connection, points_for_viz_tip1[0], connection_points[0]) # calculate min distance from projected distance from line vec
  240. line_angle = angle_between(vector_tip, vector_connection) #angle between two vectors
  241. closest = connection_points[np.argmin(cdist(tip1,connection_points)[0])] #closest point to tip from potential mother filament
  243. # check if the identified mother-daughter combination is already in the results set. If yes, skip to next.
  244. if len(all_results) == 0:
  245. pass
  246. else:
  247. all_stacked_results = np.vstack(all_results)
  248. if len(np.intersect1d(points_for_viz_tip1, all_stacked_results)) != 0 and len(np.intersect1d(connection_points, all_stacked_results)) != 0:
  249. continue
  251. # if the mother-daughter pair satisfies all criteria on distance and angle, and the mother closest point is not a tip, identify this as a branch
  252. if line_dist <= cutoff_branch and line_angle > angle_cutoff:
  253. all_results.append(points_for_viz_tip1)
  254. all_results.append(connection_points)
  255. mother_intersect_point.append(array.iloc[central_index][["X_coord", "Y_coord", "Z_coord"]].to_numpy())
  256. branch_index += 1
  258. # repeat above with the other tip
  259. if len(not_overlap_tip2) != 0:
  260. not_overlap_tip2 = np.sort(not_overlap_tip2)
  261. filament_indices = array.iloc[not_overlap_tip2]["fil_idx"].to_numpy()
  262. for fil_index in np.unique(filament_indices):
  263. candidate = not_overlap_tip2[filament_indices == fil_index]
  264. central_index = candidate[int(len(candidate)/2)]
  265. connection_indices = array[np.max([central_index-5,0]):central_index+5]
  266. connection_indices = connection_indices[connection_indices["fil_idx"] == fil_index]
  267. connection_points = connection_indices[["X_coord", "Y_coord", "Z_coord"]].to_numpy()
  269. vector_tip = points_for_viz_tip2[0] - points_for_viz_tip2[-1]
  270. vector_connection = connection_points[0] - connection_points[-1]
  272. line_dist = distance_of_two_lines(vector_tip, vector_connection, points_for_viz_tip2[0], connection_points[0])
  273. line_angle = angle_between(vector_tip, vector_connection)
  274. closest = connection_points[np.argmin(cdist(tip2,connection_points)[0])]
  276. if len(all_results) == 0:
  277. pass
  278. else:
  279. all_stacked_results = np.vstack(all_results)
  280. if len(np.intersect1d(points_for_viz_tip2,all_stacked_results)) != 0 and len(np.intersect1d(connection_points,all_stacked_results)) != 0:
  281. continue
  283. if line_dist <= cutoff_branch and line_angle > angle_cutoff:
  284. all_results.append(points_for_viz_tip2)
  285. all_results.append(connection_points)
  286. mother_intersect_point.append(array.iloc[central_index][["X_coord", "Y_coord", "Z_coord"]].to_numpy())
  287. branch_index += 1
  289. # add branch information into point csv, a point is only considered a branch point if it is a point on the mother filament at which the branch occurs
  290. is_intersect_point = np.zeros(len(array))
  291. for i in range(len(is_intersect_point)):
  292. if len(mother_intersect_point) == 0:
  293. break
  294. if ((mother_intersect_point == array.iloc[i][["X_coord", "Y_coord", "Z_coord"]].to_numpy()).all(axis = 1)).any(): #check if coordinates match between intesect point array and each point
  295. is_intersect_point[i] = 1
  296. array['branch'] = is_intersect_point
  298. return branch_index, array, all_results
  300. #calculate actin length
  301. def calc_length_all_actin(array, voxel_size = 1.34808):
  302. """calculates the lengtht of all actins (loop over other fxn)"""
  303. length_list = []
  304. positions_bild = []
  305. for fil in array["fil_idx"].unique():
  306. # pick points of single filament
  307. points = array[["X_coord", "Y_coord", "Z_coord"]][array["fil_idx"] == fil].to_numpy()
  308. # vectorized numpy computation of distances
  309. d = np.diff(points, axis=0)
  310. segdists = np.sqrt((d ** 2).sum(axis=1))
  311. # sum parrtial distances between points
  312. full_dist = np.sum(segdists)
  313. length_list.append(full_dist)
  314. positions_bild.append(points)
  315. # sum all filaments
  316. full_length = np.sum(length_list) #total actin length
  317. return full_length, positions_bild
  319. # %% [markdown]
  320. # ## Variable setup
  322. # %%
  323. # list of conditions to compare, if no comparison is needed just enter [""] - the empty quotation within the array is important
  324. conditions = [""]
  325. # list of samples in each condition, each in a separate list; if only one condition is needed it should be [[tomograms]]
  326. tomo_list = [["TS_11", "TS_12", "TS_13", "TS_14"]]
  327. # directory where resampled actin csv files are stored
  328. actin_csv_dir = "/g/scb/mahamid/Dorothy/MCA_Project/Branching_validation/20231016_branch_verficiation_more_actin"
  329. # prefix and suffix of actin file, such as the csv file name is prefix_tomoname_suffix.csv for each file, where tomoname is the name entered in tomo_list
  330. actin_file_prefix = ""
  331. actin_file_suffix = "_coord"
  332. # if separate cell analysis is not needed, enter [""] - the empty quotation within the array is important
  333. cell_suffix = [""]
  334. # directory where membrane csv files are stored
  335. memb_csv_dir = ""
  336. # prefix and suffix of membrane files, such as the csv file name is prefix_tomoname_suffix_memb_1/2.csv for each file, where tomoname is the name entered in tomo_list
  337. memb_file_prefix = ""
  338. memb_file_suffix = ""
  339. # output directory for output files
  340. out_dir = "/g/scb/mahamid/Dorothy/MCA_Project/Branching_validation/20231016_branch_verficiation_more_actin"
  342. # voxel size in sample to be analyzed
  343. voxel_size = 1.3544
  344. # maximum standard distance between mother and daughter filament, in nm
  345. cutoff_std = 35
  346. #cutoff_test = [5, 10, 15, 20, 25]
  347. cutoff_test = [35]
  348. # maximum standard vertical distance at closest point for mother and daughter filament, in nm
  349. cutoff_branch_std = 3
  350. # cutoff_branch_test = [3,5,8,10]
  351. cutoff_branch_test = [3]
  352. # minimum standard angle between mother and daughter filament
  353. angle_cutoff_std = 50
  354. # angle_cutoff_test = [50, 60, 70]
  355. angle_cutoff_test = [50]
  358. # %% [markdown]
  359. # ## Main code
  360. # ##### This code analyzes the number of branches in each tomogram (or in each cell per tomogram, as needed). The actin csv file is updated with a column "branches" which shows 1 if a point is considered a branch point on a mother filament and 0 otherwise. A csv file "branch_analysis" is created to summarize the number of branches and the number of branches per actin length in each tomogrm/cell.
  362. # %%
  363. # set up data frame
  364. df = pd.DataFrame({"condition": [], "cutoff": [], "cutoff_branch": [], "angle_cutoff": [], "cell": [], "num_branch_point": [], "branch_fraction": []})
  366. # create test conditions with different cutoff values (maintaining the same cutoff_branch and angle_cutoff)
  367. list_of_test_conditions = []
  368. for cutoff in cutoff_test:
  369. cutoff_branch = cutoff_branch_std
  370. angle_cutoff = angle_cutoff_std
  372. # check if this condition has already been tested
  373. if (cutoff, cutoff_branch, angle_cutoff) in list_of_test_conditions:
  374. continue
  375. else:
  376. list_of_test_conditions.append((cutoff, cutoff_branch, angle_cutoff))
  378. # create test conditions with different cutoff_branch values (maintaining the same cutoff and angle_cutoff)
  379. for cutoff_branch in cutoff_branch_test:
  380. cutoff = cutoff_std
  381. angle_cutoff = angle_cutoff_std
  383. # check if this condition has already been tested
  384. if (cutoff, cutoff_branch, angle_cutoff) in list_of_test_conditions:
  385. continue
  386. else:
  387. list_of_test_conditions.append((cutoff, cutoff_branch, angle_cutoff))
  389. # create test conditions with different angle_cutoff values (maintaining the same cutoff and cutoff_branch)
  390. for angle_cutoff in angle_cutoff_test:
  391. cutoff = cutoff_std
  392. cutoff_branch = cutoff_branch_std
  394. # check if this condition has already been tested
  395. if (cutoff, cutoff_branch, angle_cutoff) in list_of_test_conditions:
  396. continue
  397. else:
  398. list_of_test_conditions.append((cutoff, cutoff_branch, angle_cutoff))
  400. print(list_of_test_conditions)
  402. for i, condition in enumerate(conditions):
  403. # for storing results
  404. list_of_tomo = []
  405. list_of_num_branch_points = []
  406. list_of_branch_fraction = []
  407. list_of_conditions = []
  408. list_of_cutoff = []
  409. list_of_cutoff_branch = []
  410. list_of_angle_cutoff = []
  412. # run function for all sample files for each test condition
  413. for (cutoff, cutoff_branch, angle_cutoff) in list_of_test_conditions:
  414. for tomo in tomo_list[i]:
  415. for cell in cell_suffix:
  416. suffix = "" + cell
  417. print("Analyzing {}{} with conditions cutoff {}, cutoff_branch {}, angle_cutoff {}".format(tomo, suffix, cutoff, cutoff_branch, angle_cutoff))
  418. list_of_conditions.append(condition)
  419. list_of_tomo.append(tomo + suffix)
  420. list_of_cutoff.append(cutoff)
  421. list_of_cutoff_branch.append(cutoff_branch)
  422. list_of_angle_cutoff.append(angle_cutoff)
  423. # read data
  424. actin = pd.read_csv(os.path.join(actin_csv_dir, actin_file_prefix + tomo + actin_file_suffix + suffix + ".csv"), delimiter = ",")
  426. # calculate full length
  427. full_length, positions = calc_length_all_actin(actin, voxel_size)
  429. # perform branch analysis
  430. branch_index, actin, branch_positions = branch_analysis(actin, cutoff, cutoff_branch, angle_cutoff, voxel_size)
  431. list_of_num_branch_points.append(branch_index)
  432. list_of_branch_fraction.append(float(branch_index)/float(full_length))
  434. # write result summray
  435. df_condition = pd.DataFrame({"condition": list_of_conditions, "cutoff": list_of_cutoff, "cutoff_branch": list_of_cutoff_branch, "angle_cutoff": list_of_angle_cutoff, "cell": list_of_tomo, "num_branch_point": list_of_num_branch_points, "branch_fraction": list_of_branch_fraction})
  436. df = pd.concat([df, df_condition], axis = 0)
  438. df.to_csv(os.path.join(out_dir, "branch_analysis.csv"), sep = ",", index = False)
  440. # re-calculate and save data for the main conditions
  441. for tomo in tomo_list[0]:
  442. # read data
  443. actin = pd.read_csv(os.path.join(actin_csv_dir, actin_file_prefix + tomo + actin_file_suffix + suffix + ".csv"), delimiter = ",")
  445. # calculate full length
  446. full_length, positions = calc_length_all_actin(actin, voxel_size)
  448. # perform branch analysis
  449. branch_index, actin, branch_positions = branch_analysis(actin, cutoff_std, cutoff_branch_std, angle_cutoff_std, voxel_size)
  450. list_of_num_branch_points.append(branch_index)
  451. list_of_branch_fraction.append(float(branch_index)/float(full_length))
  453. # write bild files of branches
  454. write_bild_file(branch_positions, os.path.join(out_dir, "{}_branches.bild".format(tomo + suffix)), downscale=voxel_size, diameter=4, color="gold")
  456. # update actin csv with branch information
  457. actin.to_csv(os.path.join(actin_csv_dir, actin_file_prefix + tomo + actin_file_suffix + suffix + ".csv"), sep = ",", index = False)
  459. # %% [markdown]
  460. # ##### This code performs the same calculation as above, but uses no endpoint suppression in branch detection. That is, it does not exclude detected branch points where the intersection point is at the tip of the mother and daugher filament. Note that this overwrites the coord.csv file so that the branch points are the ones without endpoint suppression. In order to use branch points with endpoint suppression detection for further processing, skip this step (or re-run the previous step after running this step).
  462. # %%
  463. # set up data frame
  464. df = pd.DataFrame({"condition": [], "cutoff": [], "cutoff_branch": [], "angle_cutoff": [], "cell": [], "num_branch_point": [], "branch_fraction": []})
  466. # create test conditions with different cutoff values (maintaining the same cutoff_branch and angle_cutoff)
  467. list_of_test_conditions = []
  468. for cutoff in cutoff_test:
  469. cutoff_branch = cutoff_branch_std
  470. angle_cutoff = angle_cutoff_std
  472. # check if this condition has already been tested
  473. if (cutoff, cutoff_branch, angle_cutoff) in list_of_test_conditions:
  474. continue
  475. else:
  476. list_of_test_conditions.append((cutoff, cutoff_branch, angle_cutoff))
  478. # create test conditions with different cutoff_branch values (maintaining the same cutoff and angle_cutoff)
  479. for cutoff_branch in cutoff_branch_test:
  480. cutoff = cutoff_std
  481. angle_cutoff = angle_cutoff_std
  483. # check if this condition has already been tested
  484. if (cutoff, cutoff_branch, angle_cutoff) in list_of_test_conditions:
  485. continue
  486. else:
  487. list_of_test_conditions.append((cutoff, cutoff_branch, angle_cutoff))
  489. # create test conditions with different angle_cutoff values (maintaining the same cutoff and cutoff_branch)
  490. for angle_cutoff in angle_cutoff_test:
  491. cutoff = cutoff_std
  492. cutoff_branch = cutoff_branch_std
  494. # check if this condition has already been tested
  495. if (cutoff, cutoff_branch, angle_cutoff) in list_of_test_conditions:
  496. continue
  497. else:
  498. list_of_test_conditions.append((cutoff, cutoff_branch, angle_cutoff))
  500. print(list_of_test_conditions)
  502. for i, condition in enumerate(conditions):
  503. # for storing results
  504. list_of_tomo = []
  505. list_of_num_branch_points = []
  506. list_of_branch_fraction = []
  507. list_of_conditions = []
  508. list_of_cutoff = []
  509. list_of_cutoff_branch = []
  510. list_of_angle_cutoff = []
  512. # run function for all sample files for each test condition
  513. for (cutoff, cutoff_branch, angle_cutoff) in list_of_test_conditions:
  514. for tomo in tomo_list[i]:
  515. for cell in cell_suffix:
  516. suffix = "" + cell
  517. print("Analyzing {}{} with conditions cutoff {}, cutoff_branch {}, angle_cutoff {}".format(tomo, suffix, cutoff, cutoff_branch, angle_cutoff))
  518. list_of_conditions.append(condition)
  519. list_of_tomo.append(tomo + suffix)
  520. list_of_cutoff.append(cutoff)
  521. list_of_cutoff_branch.append(cutoff_branch)
  522. list_of_angle_cutoff.append(angle_cutoff)
  523. # read data
  524. actin = pd.read_csv(os.path.join(actin_csv_dir, actin_file_prefix + tomo + actin_file_suffix + suffix + ".csv"), delimiter = ",")
  526. # calculate full length
  527. full_length, positions = calc_length_all_actin(actin, voxel_size)
  529. # perform branch analysis
  530. branch_index, actin, branch_positions = branch_analysis_without_endpoint_suppression(actin, cutoff, cutoff_branch, angle_cutoff, voxel_size)
  531. list_of_num_branch_points.append(branch_index)
  532. list_of_branch_fraction.append(float(branch_index)/float(full_length))
  534. # write result summray
  535. df_condition = pd.DataFrame({"condition": list_of_conditions, "cutoff": list_of_cutoff, "cutoff_branch": list_of_cutoff_branch, "angle_cutoff": list_of_angle_cutoff, "cell": list_of_tomo, "num_branch_point": list_of_num_branch_points, "branch_fraction": list_of_branch_fraction})
  536. df = pd.concat([df, df_condition], axis = 0)
  538. df.to_csv(os.path.join(out_dir, "branch_analysis_without_endpoint_suppression.csv"), sep = ",", index = False)
  540. # re-calculate and save data for the main conditions
  541. for tomo in tomo_list[0]:
  542. # read data
  543. actin = pd.read_csv(os.path.join(actin_csv_dir, actin_file_prefix + tomo + actin_file_suffix + suffix + ".csv"), delimiter = ",")
  545. # calculate full length
  546. full_length, positions = calc_length_all_actin(actin, voxel_size)
  548. # perform branch analysis
  549. branch_index, actin, branch_positions = branch_analysis_without_endpoint_suppression(actin, cutoff_std, cutoff_branch_std, angle_cutoff_std, voxel_size)
  550. list_of_num_branch_points.append(branch_index)
  551. list_of_branch_fraction.append(float(branch_index)/float(full_length))
  553. # write bild files of branches
  554. write_bild_file(branch_positions, os.path.join(out_dir, "{}_branches_without_endpoint_suppression.bild".format(tomo + suffix)), downscale=voxel_size, diameter=4, color="gold")
  556. # update actin csv with branch information
  557. actin.to_csv(os.path.join(actin_csv_dir, actin_file_prefix + tomo + actin_file_suffix + suffix + ".csv"), sep = ",", index = False)

branch_analysis.ipynb at commit 819e80c, under GPL-3.0 · at the source

Overview

Authors: Léanne Strauss1, Sergio Lembo1,2, Samuel F Gérard1,3, Marc Siggel3,4,5, Dorothy Cheng1,3, Martin Bergert1, Sarah K Foster1, Joseph Vermeil6,7,8,9,10, Mauricio Toro-Nahuelpan1,3,11, Lena M Fischer1, Qin Yu1,12, Ewa Sitarska1,13,14, Chii Jou Chan15,16, Jan Kosinski3,4,5, Matthieu Piel6,7, Olivia du Roure8, Julien Heuvingh8, Julia Mahamid1,3, Alba Diz-Muñoz1
ORCID iDs: Martin Bergert, Mauricio Toro-Nahuelpan, Qin Yu, Chii Jou Chan, Matthieu Piel, Olivia du Roure, Julia Mahamid, Alba Diz-Muñoz
16 affiliations
  1. Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
  2. Present Address: Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria
  3. Molecular Systems Biology Unit, EMBL, Heidelberg, Germany
  4. EMBL Hamburg, Hamburg, Germany
  5. Centre for Structural Systems Biology (CSSB), Hamburg, Germany
  6. Institut Curie, PSL University, CNRS, Paris, France
  7. Institut Pierre Gilles de Gennes, PSL University, CNRS, Paris, France
  8. Physique et Mécanique des Milieux Hétérogènes (PMMH), CNRS, ESPCI Paris–Université PSL, Sorbonne Université, Université Paris Cité, Paris, France
  9. Present Address: Laboratoire Jean Perrin, Institut de Biologie Paris-Seine, Sorbonne Université, 11 CNRS UMR, Paris, France
  10. Present Address: Institut Jacques Monod, CNRS UMR, Paris, France
  11. Present Address: Computational Innovation, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
  12. Present Address: The Francis Crick Institute, London, UK
  13. Present Address: Department of Cell Biology, Harvard Medical School, Boston, MA USA
  14. Present Address: Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA USA
  15. Developmental Biology Unit, EMBL, Heidelberg, Germany
  16. Present Address: Mechanobiology Institute, Department of Biological Sciences, National University of Singapore, Singapore, Singapore
Institutions: European Molecular Biology Laboratory (Germany); Institute of Science and Technology Austria (Austria); European Molecular Biology Laboratory (Germany); Centre for Structural Systems Biology (Germany); Centre National de la Recherche Scientifique (France); Université Paris Cité (France); Université Paris Sciences et Lettres (France); Sorbonne Université (France); Institut Pierre-Gilles de Gennes pour la Microfluidique (France); Laboratoire Jean Perrin (France); Institut de Biologie Paris-Seine (France); Institut Jacques Monod (France); Physique et Mécanique des Milieux Hétérogènes (France); Institut Curie (France); ESPCI Paris (France); Boehringer Ingelheim (Germany) (Germany); The Francis Crick Institute (United Kingdom); Boston Children's Hospital (United States); Harvard University (United States); National University of Singapore (Singapore); Mechanobiology Institute (Singapore)
Journal: Nature communications, volume 17, issue 1, article 9501
Dates: received 12 March 2026; accepted 21 April 2026; published online 4 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-72845-3 · PMID 42697882 · PMCID PMC13545285 · OpenAlex W7208712085
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), human (organism), mouse (organism), cellular / molecular (subfield)
Methods: Connectivity, Evoked potentials, Machine learning, fMRI & imaging, Preprocessing
Keywords: Biophysics, Actin
MeSH: Cell Membrane*, Formins*, Actins, Animals, Biomechanical Phenomena, Cryoelectron Microscopy, Humans, Mice (* major topic)
Topic: Cellular Mechanics and Interactions (Cell Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: European Research Council (101124221); Deutsche Forschungsgemeinschaft (DI 2205/3-1)
Citations: not cited yet (Europe PMC); 119 references in the paper

Abstract

The shape of animal cells is controlled by their surface, which comprises the cell cortex, a peripheral actin network, tethered to the plasma membrane by membrane-to-cortex attachment proteins. Changes in cortical components have long been considered to dominate the regulation of forces and mechanical properties at the cell surface and drive morphogenesis. Here, we show that the coupling of the cortex to the membrane is also key for the regulation of its mechanical properties. By combining molecular engineering with biophysical approaches and in-cell cryo-electron tomography we describe the cell surface with nanometer-resolution and link its organization to cell-scale mechanics. We find that membrane-to-cortex attachment proteins can physically draw the cortex closer to the membrane, in a density and length-dependent manner. This reduction of the membrane-to-cortex distance controls the activity of the formin mDia1, leading to a reduction in cortical tension. Our study thus defines a novel mechanism whereby the membrane-to-cortex distance is a functional geometrical parameter that regulates cell surface properties.

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 4 matches between paragraphs and lines of code.

jvermeil-biophys/CortExplore_PublicVersion

License: GPL-3.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: ffddc269c9507e067bf38001d2d0619954a59cf8, 11 April 2023
Languages: Python (17)
Size: 25 files, 17 scripts
Software Heritage: not archived
Found in: the text, “Magnetic pincher”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (15 files), Matplotlib (14 files), pandas (14 files), SciPy (13 files), seaborn (11 files), statsmodels (10 files), scikit-image (5 files), OpenCV (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
19 files
At the source: github.com/jvermeil-biophys/CortExplore_PublicVersion

mahamidlab/actin_cortex_analysis

License: GPL-3.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 819e80c9f6eaf55845c0cea39303e55339f683f0, 30 January 2024
Languages: Jupyter (17), Python (9), R (9), Shell (2), MATLAB (1)
Size: 43 files, 38 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (jupyter_based_processing/setup.py), 17 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (25 files), pandas (25 files), SciPy (22 files), ggpubr (8 files), scikit-image (8 files), Matplotlib (7 files), tidyverse (6 files), ggplot2 (2 files), seaborn (2 files), car (1 file), Image Processing Toolbox (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
40 files
At the source: github.com/MahamidLab/actin_cortex_analysis/tree/revision

Code availability

The code used for cortex analysis from binary segmentations obtained during cryo-ET data processing is available on GitHub with the following link: https://github.com/MahamidLab/actin_cortex_analysis/tree/revision.

The Napari plugin for reviewing and manually correct spot detections used in the filopodia analysis is available at GitHub with the following link: https://github.com/diz-lab/filospot.

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 55 scripts, each with its path and the digest of its content;
  • 4 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 Statement

The dataset for the analysis of cortical actin and p-myosin in fixed cells, as well as actin and myosin−2 in live cells can be found together with the corresponding Fiji macro for immunofluorescence quantification in the Biostudies database (accession number S-BIAD2611).

All raw frames, tilt series, metadata, tomograms and segmentations generated for this work are deposited in the Electron Microscopy Public Image Archive (EMPIAR)118 under accession code EMPIAR-13326. A representative tomogram associated with this entry is available in the Electron Microscopy Data Bank (EMDB)119 under entry EMD-56367.

The experimental imaging data for the filopodia analysis, the used Spotiflow model with corresponding training data and example datasets are available in the BioStudies database (accession number S-BIAD2611).

Raw numbers for plots presented in this paper as well as western blot images are available in the Source Data. All other data and unique reagents that support this study are available from the corresponding authors upon request. Source data are provided in this paper. Source data are provided with this paper.

The code used for cortex analysis from binary segmentations obtained during cryo-ET data processing is available on GitHub with the following link: https://github.com/MahamidLab/actin_cortex_analysis/tree/revision.

The Napari plugin for reviewing and manually correct spot detections used in the filopodia analysis is available at GitHub with the following link: https://github.com/diz-lab/filospot.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 19 authors, 2 keywords, 8 MeSH terms, 2 funders, 116 references.

Cite

This paper

Strauss, L., Lembo, S., Gérard, S. F., Siggel, M., Cheng, D., Bergert, M., Foster, S. K., Vermeil, J., Toro-Nahuelpan, M., Fischer, L. M., Yu, Q., Sitarska, E., Chan, C. J., Kosinski, J., Piel, M., du Roure, O., Heuvingh, J., Mahamid, J., & Diz-Muñoz, A. (2026). The membrane-to-cortex distance regulates mDia1 activity to control cortical mechanics. Nature communications, 17(1), 9501. https://doi.org/10.1038/s41467-026-72845-3

BibTeX

@article{strauss2026membrane,
author = {Strauss, Léanne and Lembo, Sergio and Gérard, Samuel F and Siggel, Marc and Cheng, Dorothy and Bergert, Martin and Foster, Sarah K and Vermeil, Joseph and Toro-Nahuelpan, Mauricio and Fischer, Lena M and Yu, Qin and Sitarska, Ewa and Chan, Chii Jou and Kosinski, Jan and Piel, Matthieu and du Roure, Olivia and Heuvingh, Julien and Mahamid, Julia and Diz-Muñoz, Alba},
title = {{The membrane-to-cortex distance regulates mDia1 activity to control cortical mechanics}},
journal = {Nature communications},
year = {2026},
month = sep,
volume = {17},
number = {1},
pages = {9501},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-72845-3},
url = {https://doi.org/10.1038/s41467-026-72845-3},
pmid = {42697882},
pmcid = {PMC13545285}
}

RIS

TY - JOUR
AU - Strauss, Léanne
AU - Lembo, Sergio
AU - Gérard, Samuel F
AU - Siggel, Marc
AU - Cheng, Dorothy
AU - Bergert, Martin
AU - Foster, Sarah K
AU - Vermeil, Joseph
AU - Toro-Nahuelpan, Mauricio
AU - Fischer, Lena M
AU - Yu, Qin
AU - Sitarska, Ewa
AU - Chan, Chii Jou
AU - Kosinski, Jan
AU - Piel, Matthieu
AU - du Roure, Olivia
AU - Heuvingh, Julien
AU - Mahamid, Julia
AU - Diz-Muñoz, Alba
TI - The membrane-to-cortex distance regulates mDia1 activity to control cortical mechanics
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/09/04
VL - 17
IS - 1
SP - 9501
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-72845-3
UR - https://doi.org/10.1038/s41467-026-72845-3
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-72845-3",
"type": "article-journal",
"title": "The membrane-to-cortex distance regulates mDia1 activity to control cortical mechanics",
"container-title": "Nature communications",
"author": [
{
"family": "Strauss",
"given": "Léanne"
},
{
"family": "Lembo",
"given": "Sergio"
},
{
"family": "Gérard",
"given": "Samuel F"
},
{
"family": "Siggel",
"given": "Marc"
},
{
"family": "Cheng",
"given": "Dorothy"
},
{
"family": "Bergert",
"given": "Martin"
},
{
"family": "Foster",
"given": "Sarah K"
},
{
"family": "Vermeil",
"given": "Joseph"
},
{
"family": "Toro-Nahuelpan",
"given": "Mauricio"
},
{
"family": "Fischer",
"given": "Lena M"
},
{
"family": "Yu",
"given": "Qin"
},
{
"family": "Sitarska",
"given": "Ewa"
},
{
"family": "Chan",
"given": "Chii Jou"
},
{
"family": "Kosinski",
"given": "Jan"
},
{
"family": "Piel",
"given": "Matthieu"
},
{
"family": "du Roure",
"given": "Olivia"
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{
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"given": "Julien"
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{
"family": "Mahamid",
"given": "Julia"
},
{
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"given": "Alba"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "9501",
"DOI": "10.1038/s41467-026-72845-3",
"PMID": "42697882",
"PMCID": "PMC13545285",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-72845-3",
"language": "en",
"issued": {
"date-parts": [
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2026,
9,
4
]
]
}
}

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