OSCR

Intravital calcium imaging of meningeal macrophages reveals niche-specific dynamics and aberrant responses to brain hyperexcitability.

Code ↔ Paper

20 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 20 matches · 8 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Quantification and statistical analysis › General linear models ↔ GLM_LocoFluorescence.m, lines 47–170 · score 0.79 · GLMnet, temporal shift, frame rate, locomotion state, fluorescence, Gaussian
  2. [2] § Methods › Quantification and statistical analysis › Clustering of Ca2+ dynamics ↔ clusterCa2Signals.m, lines 43–87 · score 0.78 · peak prominence, Fourier Transform, dominant frequency, Elbow, smooth, optimal
  3. [3] § Methods › Quantification and statistical analysis › General linear models ↔ GLM_2_DiamFluorescence.m, lines 48–183 · score 0.77 · GLMnet, temporal shift, frame rate, fluorescence, Gaussian, trained
  4. [4] § Results › Characterizing macrophage Ca2+ signaling features in homeostatic brain meninges ↔ clusterCa2Signals.m, lines 43–87 · score 0.71 · Power spectrum density, frequency domain features, peak detection, PSD, Clustering, square
  5. [5] § Results › An acute aberrant pro-inflammatory brain hyperexcitability event drives diverse Ca2+ dynamics in meningeal macrophages ↔ chiSquare_residuals.m, the whole file · a weak match · score 0.70 · Chi square, persistent increase, persistent decrease, acute increase, PreCSD, post CSD
  6. [6] § Methods › Quantification and statistical analysis › Analysis of locomotion ↔ TrainLocoHMM.m, lines 1–56 · score 0.69 · Hidden Markov Model, locomotion state, cm, trained, velocity, speed
  7. [7] § Methods › Quantification and statistical analysis › Analysis of locomotion ↔ GetLocoState.m, the whole file · a weak match · score 0.69 · Hidden Markov Model, locomotion state, hmmviterbi, trained, velocity, speed
  8. [8] § Methods › Quantification and statistical analysis › Analysis of CSD-related changes in macrophage Ca2+ dynamics ↔ clusterParams_CSD.m, the whole file · a weak match · score 0.66 · duringCSD, postCSD, preCSD, event rate, phases, acute
  9. [9] § Methods › Quantification and statistical analysis › Analysis of CSD-related changes in macrophage Ca2+ dynamics ↔ classifyEventRatesCSD2.m, the whole file · a weak match · score 0.66 · duringCSD, postCSD, preCSD, event rate, phases, cell
  10. [10] § Results › An acute aberrant pro-inflammatory brain hyperexcitability event drives diverse Ca2+ dynamics in meningeal macrophages ↔ AQuA2_6_Waves_CSD.m, lines 28–79 · score 0.66 · postCSD, preCSD, CSD event, wave, event rate, acute
  11. [11] § Results › An acute aberrant pro-inflammatory brain hyperexcitability event drives diverse Ca2+ dynamics in meningeal macrophages ↔ clusterWaves_eventRate6_CSD.m, the whole file · a weak match · score 0.65 · postCSD, preCSD, CSD event, wave, event rate, acute
  12. [12] § Methods › Quantification and statistical analysis › Vascular signals ↔ GetVesselProfile.m, the whole file · a weak match · score 0.64 · vessel profile, Radon transform, polygons, pixels, ROI
  13. [13] § Results › Dural perivascular macrophage Ca2+ activity is tuned to behaviorally driven dural vasomotion ↔ plotGLM_locoDiamFluor.m, lines 7–128 · score 0.56 · fluorescence signal, deviance explained, locomotion state, GLM, model, zero
  14. [14] § Results › An acute aberrant pro-inflammatory brain hyperexcitability event drives diverse Ca2+ dynamics in meningeal macrophages ↔ AQuA2_6_Waves_CSD.m, lines 28–79 · score 0.55 · Chi square, acute response, preCSD, post CSD, NP, bar
  15. [15] § Results › CGRP receptor signaling mediates CSD-evoked persistent increase in meningeal macrophage Ca2+ activity ↔ plotRasterByCluster.m, the whole file · a weak match · score 0.54 · acute increase, preCSD, post CSD, raster, event rate, BIBN
  16. [16] § Results › Characterizing macrophage Ca2+ signaling features in homeostatic brain meninges ↔ AQuA2_5_cellNetwork_multiExpt_CSD.m, lines 487–526 · score 0.52 · concurrent events, simultaneous event, networks, distances, delay, duration
  17. [17] § Results › Characterizing macrophage Ca2+ signaling features in homeostatic brain meninges ↔ plotFFT.m, the whole file · a weak match · score 0.52 · frequency spectra, dominant frequency, magnitude, noise, signal
  18. [18] § Methods › Quantification and statistical analysis › Ca2+ signal detection pipeline ↔ AQuA2_5_cellNetwork_multiExpt_baseline.m, lines 373–420 · score 0.51 · spatial resolution, AQuA2, pixel, propagation, event, cell
  19. [19] § Methods › Quantification and statistical analysis › Statistical analysis ↔ AQuA2_6_Waves.m, lines 93–177 · score 0.51 · Mann Whitney, Chi square, sum
  20. [20] § Methods › Quantification and statistical analysis › Statistical analysis ↔ AQuA2_6_Waves_CSD.m, lines 569–650 · score 0.51 · Mann Whitney, Chi square, sum

Paper

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The authors' code

MATLAB · 689 lines · 27 KB · GPL-3.0 · 3 matches

  1. %% create clusters based on preCSD x duringCSD and preCSD x postCSD response
  2. clear all;
  4. experiment = input('CSD or BIBN-CSD?: ', 's'); % 's' means input as string
  5. durationCSDmin = input('Enter duration of duringCSD in minutes (1 or 2): ');
  7. if strcmp(experiment, 'CSD')
  8. load('D:\2photon\Simone\Simone_Macrophages\AQuA2_Results\fullCraniotomy\CSD\corrected_for_pinprick\0.49resolution_correct\AQuA2_data_fullCraniotomy_CSD.mat');
  9. if durationCSDmin == 1 %due to mismatch of combinedTable and eventHz_byCell, which filters out events that happen after 61min
  10. toRemove = [22,23,40,80,81,90,100,129,143,144,145,214,215,216,233,248,249,250];
  11. combinedTable_complete(toRemove, :) = [];
  12. elseif durationCSDmin == 2 %due to mismatch of combinedTable and eventHz_byCell, which filters out events that happen after 62min
  13. toRemove = [22,23,40,80,81,100,129,144,145,214,215,216,233,248,249,250];
  14. combinedTable_complete(toRemove, :) = [];
  15. end
  16. elseif strcmp(experiment, 'BIBN-CSD')
  17. if durationCSDmin == 1
  18. load('D:\2photon\Simone\Simone_Macrophages\AQuA2_Results\fullCraniotomy\BIBN\AQuA2_data_fullCraniotomy_BIBN_CSD-1min-duringCSD.mat');
  19. toRemove = 43;
  20. combinedTable_complete(toRemove, :) = [];
  21. elseif durationCSDmin == 2
  22. load('D:\2photon\Simone\Simone_Macrophages\AQuA2_Results\fullCraniotomy\BIBN\AQuA2_data_fullCraniotomy_BIBN-CSD_2min-duringCSD.mat');
  23. toRemove = 43; %same as 1min-duringCSD
  24. combinedTable_complete(toRemove, :) = [];
  25. end
  26. end
  28. %% CLUSTER BY EVENT RATE (9)
  29. %
  30. % % Extract cell types and cluster CSD events
  31. % cellTypes = combinedTable_complete{:, 13};
  32. % cells = ["cells"; "perivascular"; "non-perivascular"];
  33. %
  34. % % 9 clusters
  35. % [eventRate_directions, eventRate_directionCounts, eventRate_directionLabels, eventRate_clusterID, rates_mHz, rates_mHz_clusterID] = clusterWaves_eventRate9_CSD(eventHz_byCell, cellTypes);
  36. % eventRate_clusters = [eventRate_directions; eventRate_directionCounts];
  37. % eventRate_clusters = [eventRate_clusters, cells];
  38. %
  39. % combinedTable_clusters = addvars(combinedTable_complete, eventRate_clusterID, 'NewVariableNames', 'eventRate_clusterID');
  40. %
  41. % % Assuming rates_mHz is Nx3 (columns: preCSD, duringCSD, postCSD)
  42. % combinedTable_clusters = addvars(combinedTable_clusters, ...
  43. % rates_mHz(:,1), rates_mHz(:,2), rates_mHz(:,3), ...
  44. % 'NewVariableNames', {'eventRate_preCSD', 'eventRate_duringCSD', 'eventRate_postCSD'});
  45. %
  46. % rates_mHz_clusterID_sorted = sortrows(rates_mHz_clusterID, 4); % descending sort by column 4
  47. %
  48. % % Chi-square - comparing the distribution between perivascular vs non-perivascular for each cluster
  49. % [p_values_byCluster, stats_byCluster] = chi_square_plot(eventRate_directionCounts);
  50. %
  51. % % Run Chi-square test of independence - testing whether cluster and cell type are independent
  52. % [~, p_values_All, stats_All] = chi2gof_from_table(eventRate_directionCounts);
  53. %
  54. % % Chi-square test by ACUTE response
  55. % [p_values_Acute, stats_Acute] = chi_square_by_acuteResponse(eventRate_directionCounts, 3);
  56. %
  57. % % Chi-square test by CHRONIC response
  58. % [p_values_Chronic, stats_Chronic] = chi_square_by_chronicResponse(eventRate_directionCounts);
  59. %
  60. % % Plot heatmap of conditional probabilities (3x3)
  61. % [All_matrix, P_matrix, NP_matrix] = plotConditionalProbabilities(eventRate_directionCounts, outputDir);
  62. %
  63. % Distributions
  64. % Plot distribution Cell type x Cluster
  65. [clustersPercentage, roundedData] = plotClusterDistributionByCellType(eventRate_clusters, eventRate_directions, cells, outputDir);
  67. % Plot distribution clusters x FOV
  68. plotFOVDistributionByCluster(combinedTable_clusters, outputDir);
  70. % Plot distribution FOV x cluster
  71. plotClusterDistributionByFOV(combinedTable_clusters, outputDir);
  72. %
  73. % %create heatmap
  74. % plotClusterHeatmap_FOVnormalized(combinedTable_clusters, [1:9], sortedFileNames, outputDir); %[1,2, 5:9] BIBN by FOV
  75. % plotClusterHeatmap_zscore(combinedTable_clusters, [2,4,9], outputDir); %[1,2,3,1,4,7,3,6,9]
  76. %
  77. % % stacked bars
  78. % probs = compute_Clusterwise_probabilities(eventRate_directionCounts);
  79. % plotStackedDirectionGroups(eventRate_directionCounts, outputDir)
  81. %% CLUSTER by EVENT RATE (6)
  83. % Extract cell types and cluster CSD events
  84. cellTypes = combinedTable_complete{:, 13};
  85. cells = ["cells"; "perivascular"; "non-perivascular"];
  87. % 6 cluster
  88. [eventRate_directions, eventRate_directionCounts, eventRate_directionLabels, eventRate_clusterID, rates_mHz, rates_mHz_clusterID] = clusterWaves_eventRate6_CSD(eventHz_byCell, cellTypes);
  89. eventRate_clusters = [eventRate_directions; eventRate_directionCounts];
  90. eventRate_clusters = [eventRate_clusters, cells];
  92. combinedTable_clusters = addvars(combinedTable_complete, eventRate_clusterID, 'NewVariableNames', 'eventRate_clusterID');
  93. %
  94. % cluster6 = [];
  95. % for cell = 1:size(combinedTable_clusters,2)
  96. % if combinedTable_clusters.eventRate_clusterID(cell) == 6
  97. % cellID = [combinedTable_clusters.("Cell ID")(cell); combinedTable_clusters.("Number of Events")(cell); combinedTable_clusters.fileNameColumn(cell); combinedTable_clusters.("eventRate_clusterID")(cell)];
  98. % cluster6 = [cluster6; cellID];
  99. % end
  100. % end
  102. % Find rows where eventRate_clusterID == 6
  103. mask = combinedTable_clusters.eventRate_clusterID == 6;
  105. % Extract relevant columns into a new table
  106. cluster6 = combinedTable_clusters(mask, ...
  107. {'Cell ID','Number of Events','fileNameColumn','eventRate_clusterID'});
  110. % % Example 1: Acute Increase
  111. % AcuteIncrease = [7 7; 3 4];
  112. % plotChi2Residuals(AcuteIncrease, {'P','NP'}, {'Persistent Increase','Persistent Decrease'});
  113. %
  114. % % Example 2: Persistent Increase
  115. % PersistentIncrease = [7 11; 3 34];
  116. % plotChi2Residuals(PersistentIncrease, {'P','NP'}, {'Acute Increase','Acute No Change'});
  117. %
  118. % % Example 3: Persistent Decrease
  119. % PersistentDecrease = [7 25; 4 110];
  120. % plotChi2Residuals(PersistentDecrease, {'P','NP'}, {'Acute Increase','Acute No Change'});
  121. %
  123. % max dFF during CSD
  124. preCSD_maxDFF = table2cell(paramTables_allPhases.Max_dFF(:,2));
  125. duringCSD_maxDFF = table2cell(paramTables_allPhases.Max_dFF(:,3));
  126. postCSD_maxDFF = table2cell(paramTables_allPhases.Max_dFF(:,4));
  128. % Assuming rates_mHz is Nx3 (columns: preCSD, duringCSD, postCSD)
  129. combinedTable_clusters = addvars(combinedTable_clusters, ...
  130. rates_mHz(:,1), rates_mHz(:,2), rates_mHz(:,3), preCSD_maxDFF, duringCSD_maxDFF, postCSD_maxDFF,...
  131. 'NewVariableNames', {'eventRate_preCSD', 'eventRate_duringCSD', 'eventRate_postCSD', 'preCSD_maxDFF', 'duringCSD_maxDFF', 'postCSD_maxDFF'});
  133. preCSD_maxDFF_all = [];
  134. for x = 1:size(combinedTable_clusters,1)
  135. if ismember(combinedTable_clusters.eventRate_clusterID(x), [3 6])
  136. preCSD_maxDFF_x = combinedTable_clusters.preCSD_maxDFF(x);
  137. preCSD_maxDFF_all = [preCSD_maxDFF_all; preCSD_maxDFF_x];
  138. end
  139. end
  141. duringCSD_maxDFF_all = [];
  142. for x = 1:size(combinedTable_clusters,1)
  143. if ismember(combinedTable_clusters.eventRate_clusterID(x), [1 2 3])
  144. duringCSD_maxDFF_x = combinedTable_clusters.duringCSD_maxDFF(x);
  145. duringCSD_maxDFF_all = [duringCSD_maxDFF_all; duringCSD_maxDFF_x];
  146. end
  147. end
  149. postCSD_maxDFF_all = [];
  150. for x = 1:size(combinedTable_clusters,1)
  151. if ismember(combinedTable_clusters.eventRate_clusterID(x), [3 6])
  152. postCSD_maxDFF_x = combinedTable_clusters.postCSD_maxDFF(x);
  153. postCSD_maxDFF_all = [postCSD_maxDFF_all; postCSD_maxDFF_x];
  154. end
  155. end
  160. % % without separating by cell type
  161. % rate_cluster_acuteIncrease = []; %2
  162. % rate_cluster_chronicIncrease = []; %4
  163. % rate_cluster_chronicDecrease = []; %6
  164. %
  165. % rate_cluster_AllacuteIncrease = []; %1,2,3
  166. % rate_cluster_AllchronicIncrease = []; %1,4
  167. % rate_cluster_AllchronicDecrease = []; %3,6
  168. %
  169. % for cellCluster = 1:size(rates_mHz, 1)
  170. % clusterID = combinedTable_clusters.eventRate_clusterID(cellCluster);
  171. % currentCell_rate = rates_mHz(cellCluster, :); % row vector
  172. % cellType = cellTypes(cellCluster);
  173. %
  174. % % Individual clusters
  175. % if clusterID == 2
  176. % rate_cluster_acuteIncrease = [rate_cluster_acuteIncrease; [currentCell_rate, cellType]];
  177. % end
  178. % if clusterID == 4
  179. % rate_cluster_chronicIncrease = [rate_cluster_chronicIncrease; [currentCell_rate, cellType]];
  180. % end
  181. % if clusterID == 6
  182. % rate_cluster_chronicDecrease = [rate_cluster_chronicDecrease; [currentCell_rate, cellType]];
  183. % end
  184. %
  185. % % Combined clusters
  186. % if ismember(clusterID, [1,2,3])
  187. % rate_cluster_AllacuteIncrease = [rate_cluster_AllacuteIncrease; [currentCell_rate, cellType]];
  188. % end
  189. % if ismember(clusterID, [1, 4])
  190. % rate_cluster_AllchronicIncrease = [rate_cluster_AllchronicIncrease; [currentCell_rate, cellType]];
  191. % end
  192. % if ismember(clusterID, [3, 6])
  193. % rate_cluster_AllchronicDecrease = [rate_cluster_AllchronicDecrease; [currentCell_rate, cellType]];
  194. % end
  195. % end
  197. % === Initialize ===
  198. rate_cluster_acuteIncrease_peri = [];
  199. rate_cluster_acuteIncrease_nonperi = [];
  200. rate_cluster_chronicIncrease_peri = [];
  201. rate_cluster_chronicIncrease_nonperi = [];
  202. rate_cluster_chronicDecrease_peri = [];
  203. rate_cluster_chronicDecrease_nonperi = [];
  205. rate_cluster_AllacuteIncrease_peri = [];
  206. rate_cluster_AllacuteIncrease_nonperi = [];
  207. rate_cluster_AllchronicIncrease_peri = [];
  208. rate_cluster_AllchronicIncrease_nonperi = [];
  209. rate_cluster_AllchronicDecrease_peri = [];
  210. rate_cluster_AllchronicDecrease_nonperi = [];
  212. % === Loop through each cell ===
  213. for cellCluster = 1:size(rates_mHz, 1)
  214. clusterID = combinedTable_clusters.eventRate_clusterID(cellCluster);
  215. currentCell_rate = rates_mHz(cellCluster, :); % row vector
  216. cellType = cellTypes(cellCluster); % 0 = peri, 2 = non-peri
  218. % --- Individual clusters ---
  219. if clusterID == 2
  220. if cellType == 0
  221. rate_cluster_acuteIncrease_peri = [rate_cluster_acuteIncrease_peri; currentCell_rate];
  222. elseif cellType == 2
  223. rate_cluster_acuteIncrease_nonperi = [rate_cluster_acuteIncrease_nonperi; currentCell_rate];
  224. end
  225. elseif clusterID == 4
  226. if cellType == 0
  227. rate_cluster_chronicIncrease_peri = [rate_cluster_chronicIncrease_peri; currentCell_rate];
  228. elseif cellType == 2
  229. rate_cluster_chronicIncrease_nonperi = [rate_cluster_chronicIncrease_nonperi; currentCell_rate];
  230. end
  231. elseif clusterID == 6
  232. if cellType == 0
  233. rate_cluster_chronicDecrease_peri = [rate_cluster_chronicDecrease_peri; currentCell_rate];
  234. elseif cellType == 2
  235. rate_cluster_chronicDecrease_nonperi = [rate_cluster_chronicDecrease_nonperi; currentCell_rate];
  236. end
  237. end
  239. % --- Combined clusters ---
  240. if ismember(clusterID, [1, 2, 3]) % All acute increase
  241. if cellType == 0
  242. rate_cluster_AllacuteIncrease_peri = [rate_cluster_AllacuteIncrease_peri; currentCell_rate];
  243. elseif cellType == 2
  244. rate_cluster_AllacuteIncrease_nonperi = [rate_cluster_AllacuteIncrease_nonperi; currentCell_rate];
  245. end
  246. end
  248. if ismember(clusterID, [1, 4]) % All chronic increase
  249. if cellType == 0
  250. rate_cluster_AllchronicIncrease_peri = [rate_cluster_AllchronicIncrease_peri; currentCell_rate];
  251. elseif cellType == 2
  252. rate_cluster_AllchronicIncrease_nonperi = [rate_cluster_AllchronicIncrease_nonperi; currentCell_rate];
  253. end
  254. end
  256. if ismember(clusterID, [3, 6]) % All chronic decrease
  257. if cellType == 0
  258. rate_cluster_AllchronicDecrease_peri = [rate_cluster_AllchronicDecrease_peri; currentCell_rate];
  259. elseif cellType == 2
  260. rate_cluster_AllchronicDecrease_nonperi = [rate_cluster_AllchronicDecrease_nonperi; currentCell_rate];
  261. end
  262. end
  263. end
  265. % Initialize group labels
  266. eventRateGroup = strings(height(combinedTable_clusters), 1);
  267. % Assign group based on event rate
  268. eventRateGroup(combinedTable_clusters.eventRate_preCSD == 0) = "0";
  269. eventRateGroup(combinedTable_clusters.eventRate_preCSD > 0 & combinedTable_clusters.eventRate_preCSD <= 1) = "0-1";
  270. eventRateGroup(combinedTable_clusters.eventRate_preCSD > 1) = ">1";
  271. % Convert to categorical
  272. combinedTable_clusters.eventRateGroup = categorical(eventRateGroup, {'0', '0-1', '>1'});
  273. % Use groupcounts on the new categorical variable
  274. groupCounts = groupcounts(combinedTable_clusters.eventRateGroup);
  275. total = sum(groupCounts);
  276. groupPercents = 100 * groupCounts / total;
  278. % Plot heatmap of conditional probabilities (3x3)
  279. [All_matrix, P_matrix, NP_matrix] = plotCluster6Distribution(eventRate_directionCounts, outputDir);
  281. %% CLUSTER by EVENT COUNT (6)
  283. % cellTypes = combinedTable_complete{:, 13};
  284. % cells = ["cells"; "perivascular"; "non-perivascular"];
  285. % [eventCounts_directions, eventCounts_directionCounts, eventCounts_directionLabels, counts_clusterID, eventCounts, eventCounts_clusterID] = clusterWaves_eventCount6_CSD(events_byCell_all, cellTypes);
  286. % eventCounts_clusters = [eventCounts_directions; eventCounts_directionCounts];
  287. % eventCounts_clusters = [eventCounts_clusters, cells];
  288. %
  289. % combinedTable_clusters = addvars(combinedTable_complete, counts_clusterID, 'NewVariableNames', 'counts_clusterID');
  290. %
  291. % % Plot heatmap of conditional probabilities (3x3)
  292. % [All_matrix, P_matrix, NP_matrix] = plotCluster6Distribution(eventCounts_directionCounts, outputDir);
  294. %% CLUSTER BY dFF (9)
  296. % cellTypes = combinedTable_complete{:, 13};
  297. % cells = ["cells"; "perivascular"; "non-perivascular"];
  298. % [dFF_directions, dFF_directionCounts, dFF_directionLabels, dFF_clusterID, phaseParams_dFF] = clusterParams_CSD(paramTables_allPhases, cellTypes);
  299. % dFF_clusters = [dFF_directions; dFF_directionCounts];
  300. % dFF_clusters = [dFF_clusters, cells];
  301. %
  302. % combinedTable_clusters = addvars(combinedTable_clusters, dFF_clusterID, 'NewVariableNames', 'dFF_clusterID');
  303. %
  304. % % Convert vectors to tables
  305. % T_eventRate = table(eventRate_clusterID, 'VariableNames', {'eventRate_clusterID'});
  306. % T_dFF = table(dFF_clusterID, 'VariableNames', {'dFF_clusterID'});
  307. %
  308. % dFF_clustersTable = [T_eventRate, T_dFF, phaseParams_dFF];
  309. %
  310. %
  311. % dFFcluster_acuteIncrease = []; %2
  312. % dFFcluster_chronicIncrease = []; %4
  313. % dFFcluster_chronicDecrease = []; %6
  314. %
  315. % dFFcluster_AllchronicIncrease = []; %1,4
  316. % dFFcluster_AllchronicDecrease = []; %3,6
  317. %
  318. %
  319. % for cellCluster = 1:size(phaseParams_dFF, 1)
  320. % clusterID = dFF_clustersTable.eventRate_clusterID(cellCluster);
  321. % currentCell_dFF = phaseParams_dFF(cellCluster, :); % row vector
  322. %
  323. % % Individual clusters
  324. % if clusterID == 2
  325. % dFFcluster_acuteIncrease = [dFFcluster_acuteIncrease; currentCell_dFF];
  326. % end
  327. % if clusterID == 4
  328. % dFFcluster_chronicIncrease = [dFFcluster_chronicIncrease; currentCell_dFF];
  329. % end
  330. % if clusterID == 6
  331. % dFFcluster_chronicDecrease = [dFFcluster_chronicDecrease; currentCell_dFF];
  332. % end
  333. %
  334. % % Combined clusters
  335. % if ismember(clusterID, [1, 4])
  336. % dFFcluster_AllchronicIncrease = [dFFcluster_AllchronicIncrease; currentCell_dFF];
  337. % end
  338. % if ismember(clusterID, [3, 6])
  339. % dFFcluster_AllchronicDecrease = [dFFcluster_AllchronicDecrease; currentCell_dFF];
  340. % end
  341. % end
  344. %% Cluster 3x2 (up, none, down - acute & chronic)
  346. % cellTypes = combinedTable_complete{:, 13};
  347. % cells = ["cells"; "P"; "NP"];
  348. % [directions, acuteLabels, chronicLabels, acuteIDs, chronicIDs, acuteCounts, chronicCounts, rates_mHz] = clusterWaves_CSD_acute_chronic(eventHz_byCell, cellTypes);
  349. %
  350. % clustersAcute = [directions; acuteCounts];
  351. % clustersAcute = [clustersAcute, cells];
  352. % clustersChronic = [directions; chronicCounts];
  353. % clustersChronic = [clustersChronic, cells];
  354. %
  355. % combinedTable_clustersAcute = addvars(combinedTable_complete, acuteIDs, 'NewVariableNames', 'acuteIDs');
  356. % combinedTable_clustersChronic = addvars(combinedTable_complete, chronicIDs, 'NewVariableNames', 'chronicIDs');
  357. %
  358. % % Chi-square - comparing the distribution between perivascular vs non-perivascular for each cluster
  359. % [p_values_byCluster_acute, stats_byCluster_acute] = chi_square_plot(acuteCounts);
  360. % [p_values_byCluster_chronic, stats_byCluster_chronic] = chi_square_plot(chronicCounts);
  361. %
  362. % % Run Chi-square test of independence - testing whether cluster and cell type are independent
  363. % [~, p_values_All_acute, stats_All_acute] = chi2gof_from_table(acuteCounts);
  364. % [~, p_values_All_chronic, stats_All_chronic] = chi2gof_from_table(chronicCounts);
  365. %
  366. % % Distribution Cell type x Cluster
  367. % [clustersPercentage_acute, roundedData_acute] = plotClusterDistributionByCellType(clustersAcute, directions, cells, outputDir);
  368. % figure; bar(roundedData_acute,'stacked','DisplayName','roundedData_acute')
  369. % legend(directions); xticklabels(cells(2:3)); title('Acute response by cell type')
  370. %
  371. % [clustersPercentage_chronic, roundedData_chronic] = plotClusterDistributionByCellType(clustersChronic, directions, cells, outputDir);
  372. % figure; bar(roundedData_chronic,'stacked','DisplayName','roundedData_chronic')
  373. % legend(directions); xticklabels(cells(2:3)); title('Chronic response by cell type')
  374. %
  375. % % Heatmaps ACUTE vs CHRONIC
  376. % plotClusterHeatmap_zscore(combinedTable_clustersAcute, 1:2);
  377. % plotClusterHeatmap_zscore(combinedTable_clustersChronic, 1:2);
  378. %
  379. % % Compare baseline between chronic clusters %1,4,7x3,6,9
  380. % preHz = str2double(eventHz_byCell(:, 2));
  381. % preHz_cluster = [preHz, combinedTable_clusters.eventRate_clusterID];
  382. % compareBaselineRates(preHz, acuteIDs, chronicIDs)
  384. %% EVENT raster plot
  386. % Process startingFrames for raster plot
  387. allEmpty = all(cellfun(@isempty, startingFrames_byCell_all(:, 2:5)), 2);
  388. startingFrames_byCell_all(allEmpty, :) = []; %% Remove rows where all event phases are empty
  390. % Step 1: Flatten nested cells inside columns 2 to 5
  391. flattenedData = startingFrames_byCell_all;
  393. for i = 1:size(flattenedData, 1)
  394. for j = 2:5
  395. val = flattenedData{i, j};
  396. if iscell(val)
  397. % If it's a cell, convert to numeric vector
  398. try
  399. flattenedData{i, j} = cell2mat(val);
  400. catch
  401. flattenedData{i, j} = [];
  402. end
  403. end
  404. end
  405. end
  407. % Step 2: Convert to table
  408. startingFrames_table = cell2table(flattenedData, ...
  409. 'VariableNames', {'cellID', 'preCSD', 'duringCSD', 'postCSD', 'baseline_preCSD'});
  411. % Convert clusterID column to a table
  412. clusterID_table = table(combinedTable_clusters.eventRate_clusterID(:), 'VariableNames', {'eventRate_clusterID'});
  413. dFF_table = table(combinedTable_clusters.dFF(:), 'VariableNames', {'dFF'});
  415. % Horizontally concatenate the tables
  416. rasterTable = [startingFrames_table, clusterID_table, dFF_table];
  417. rasterTable_sorted = plotRasterByCluster(rasterTable, [2,4,6], experiment);
  419. %%
  420. load('D:\2photon\Simone\Simone_Macrophages\AQuA2_Results\fullCraniotomy\CSD\corrected_for_pinprick\0.49resolution_correct\AQuA2_data_fullCraniotomy_CSD.mat')
  421. cellLocation_indices = combinedTable{:,13};
  422. redLabel_indices = combinedTable{:,14};
  424. % By cell type
  425. perivascular_indices = cellLocation_indices == 0;
  426. nonPerivascular_indices = cellLocation_indices == 2;
  427. combinedTable_perivascular = combinedTable(perivascular_indices,:);
  428. combinedTable_nonPerivascular = combinedTable(nonPerivascular_indices,:);
  430. % dFF
  431. dFF_all = cell2mat(combinedTable.dFF); % Convert to matrix
  432. dFF_perivascular = cell2mat(combinedTable_perivascular.dFF);
  433. dFF_nonPerivascular = cell2mat(combinedTable_nonPerivascular.dFF);
  435. Fs = 1.03; % Sampling frequency
  437. %%
  438. combinedTable_sorted = sortrows(combinedTable, 'Max dFF', 'descend');
  439. dFF_all_sorted = cell2mat(combinedTable_sorted.dFF); % Convert to matrix
  441. dFF_all_sorted_preCSD = dFF_all_sorted(:, 1:1854);
  442. dFF_all_sorted_duringCSD = dFF_all_sorted(:, 1855:1917); %60sec
  443. dFF_all_sorted_postCSD = dFF_all_sorted(:, 1918:3772);
  444. %dFF_all_sorted(38,:) = [];
  445. %dFF_check = dFF_all_sorted(:, 1700:2000);
  447. combinedTable_perivascular_sorted = sortrows(combinedTable_perivascular, 'Max dFF', 'descend');
  448. dFF_perivascular_sorted = cell2mat(combinedTable_perivascular_sorted.dFF);
  450. combinedTable_nonPerivascular_sorted = sortrows(combinedTable_nonPerivascular, 'Max dFF', 'descend');
  451. dFF_nonPerivascular_sorted = cell2mat(combinedTable_nonPerivascular_sorted.dFF);
  453. sortedMatrix = sortrows(dFF_all_sorted, 1855, "descend");
  455. %% plot heatmap for quick visualization
  457. minData = min(sortedMatrix(:));
  458. maxData = max(sortedMatrix(:));
  460. % all
  461. figure;
  462. imagesc(sortedMatrix);
  463. caxis([minData, maxData]);
  464. colormap(flipud(gray));
  465. colorbar;
  466. % sorte
  467. figure;
  468. imagesc(dFF_perivascular_sorted);
  469. caxis([minData, maxData]);
  470. colormap(flipud(gray));
  471. colorbar;
  473. % perivascular
  474. figure;
  475. imagesc(dFF_perivascular);
  476. caxis([minData, maxData]);
  477. colormap(flipud(gray));
  478. colorbar;
  479. % sorte
  480. figure;
  481. imagesc(dFF_perivascular_sorted);
  482. caxis([minData, maxData]);
  483. colormap(flipud(gray));
  484. colorbar;
  486. % non-perivascular
  487. figure;
  488. imagesc(dFF_nonPerivascular);
  489. caxis([minData, maxData]);
  490. colormap(flipud(gray));
  491. colorbar;
  492. % sorted
  493. figure;
  494. imagesc(dFF_nonPerivascular_sorted(1:65,:));
  495. caxis([minData, maxData]);
  496. colormap(flipud(gray));
  497. colorbar;
  500. %% Plot selected waves
  501. plotSelectedWaves_together(combinedTable, [88, 265, 330, 451], 'my_figure.eps');
  503. %% Categorize waves based on frequency, duration and amplitude of events
  504. cellTable = categorize_waves(combinedTable);
  506. %% Plot all curves
  507. %saveFolder = 'D:\2photon\Simone\Simone_Macrophages\AQuA2_Results\fullCraniotomy\baseline\dFF_waves';
  508. plot_dFF_waves(combinedTable, saveFolder, false);
  510. %% Visualize FFT - Fast Fourier Transformation
  512. % for each cell
  513. for w = 1:2 %size(dFF_all, 1)
  514. plotFFT(dFF_all(w, :), Fs);
  515. end
  517. for w = 1:5 %size(dFF_all, 1)
  518. normalized_signal = zscore(dFF_all(w,:));
  519. plotFFT(normalized_signal, Fs);
  520. end
  522. % for all cells
  523. plotFFT_all(dFF_all, Fs); % dFF_all is your matrix of signals for all cells
  525. %% 1.Cluster based on wave shape
  527. maxClusters = 10; % Test up to 10 clusters
  528. optimalK = findOptimalClusters(dFF_all, maxClusters);
  529. [idx, count_k1, count_k2] = clusterWaves(dFF_all, optimalK);
  531. %% 2. Cluster based on SNR
  533. % Compute SNR
  534. snr_ma = computeSNR(dFF_all_sorted, 'ma', 5); % Using a window size of 5
  535. snr_fft = computeSNR(dFF_all_sorted, 'fft', 0.2); % Using 20% of frequencies as the cutoff
  537. maxClusters = 10; % Test up to 10 clusters
  538. [idx, clustered_cells, optimalK, count_k1, count_k2] = clusterSNR(snr_ma, maxClusters, dFF_all_sorted); % Use snr_ma or snr_fft
  540. %% 3. Cluster based on detrend, sgolayfilt, kmeans
  541. % https://www-science-org.ezp-prod1.hul.harvard.edu/doi/pdf/10.1126/scisignal.abe6909
  543. % 1. Apply Savitzky-Golay filtering only for visualization, not for clustering.
  544. % Optimize Sgolay params - takes very long time to optimize both params
  545. poly_orders = 1:6;
  546. frame_sizes = 5:2:927;
  547. [best_poly_order, best_frame_size, best_snr, best_filtered_signals] = optimizeSgolayParams(dFF_all, poly_orders, frame_sizes);
  549. % Check best frame size
  550. unique_vals_frame = unique(best_frame_size);
  551. counts = histc(best_frame_size, unique_vals_frame);
  552. figure; bar(unique_vals_frame, counts, 'FaceColor', 'b');
  554. % Check best poly order
  555. unique_vals_order = unique(best_poly_order);
  556. counts = histc(best_poly_order, unique_vals_order);
  557. figure; bar(unique_vals_order, counts, 'FaceColor', 'b');
  559. % 2. Cluster signals
  560. % Define your inputs
  561. maxClusters = 10; % Test up to 10 clusters
  562. poly_order = 6; % Range of polynomial orders for Savitzky-Golay filter
  563. frame_size = 7; % Frame sizes (odd numbers)
  564. % Assuming 'dFF_all' is your matrix of Ca2+ signals (cells x time points)
  565. [optimal_k, idx, features, cluster_means, count_k1, count_k2] = clusterCa2Signals(dFF_all_sorted, Fs, poly_order, frame_size, maxClusters);
  569. %% check differences between clusters
  570. darkPurple = [0.5, 0, 0.5]; % Dark purple
  571. darkGreen = [0, 0.5, 0];
  572. % SNR differences
  573. snr_1 = computeSNR(dFF_all_sorted(idx == 1,:), 'ma', 5); %'ma', 5 OR 'fft', 0.2
  574. snr_2 = computeSNR(dFF_all_sorted(idx == 2,:), 'ma', 5);
  575. % Median and IQR
  576. med1 = median(snr_1);
  577. med2 = median(snr_2);
  578. iqr1 = iqr(snr_1);
  579. iqr2 = iqr(snr_2);
  580. % Mann-Whitney U test
  581. [p, h, stats] = ranksum(snr_1, snr_2);
  582. fprintf('Mann-Whitney p = %.4f\n', p);
  583. % Plot
  584. figure;
  585. bar(1, med1, 'FaceColor', darkPurple); hold on;
  586. bar(2, med2, 'FaceColor', darkGreen);
  587. %bar([med1, med2], 'FaceColor', [0.2 0.6 0.8]); %'Color', colors
  588. % Set x-axis labels
  589. set(gca, 'XTick', [1 2], 'XTickLabel', {'Cluster 1', 'Cluster 2'});
  590. ylabel('Median SNR');
  591. % Error bars (IQR)
  592. hold on;
  593. errorbar([1, 2], [med1, med2], [iqr1, iqr2], '.k', 'LineWidth', 1.5);
  594. % Annotate p-value
  595. y_max = max([med1 + iqr1, med2 + iqr2]);
  596. text(1.5, y_max * 1.05, sprintf('Mann-Whitney p = %.4f', p),'HorizontalAlignment', 'center', 'FontSize', 12);
  597. hold off; box off;
  599. % cell location
  600. wave_location = zeros(size(idx,1), 2); % Preallocate for efficiency
  601. for cellIdx = 1:size(idx,1)
  602. wave_location(cellIdx, :) = [idx(cellIdx), combinedTable_sorted{cellIdx, "Cell location (0,perivascular;1,adjacent;2,none)"}];
  603. end
  604. tbl = chiSquaredTestForAssociation(wave_location);
  606. % Calculate the column sums
  607. colSums = sum(tbl);
  608. % Convert each value to a percentage of its respective column
  609. percentageDataColumn = (tbl ./ colSums) * 100;
  611. % Calculate the row sums
  612. rowSums = sum(tbl,2);
  613. % Convert each value to a percentage of its respective column
  614. percentageDataRow = (tbl ./ rowSums) * 100;
  616. % other features
  617. %combinedTable_NM(105,:) = [];
  618. clusterFeatures = table(idx, ...
  619. combinedTable_sorted.("Area(um2)"), ...
  620. combinedTable_sorted.Perimeter, ...
  621. combinedTable_sorted.Circularity, ...
  622. combinedTable_sorted.("Max dFF"), ...
  623. combinedTable_sorted.("dFF AUC"),...
  624. combinedTable_sorted.("Duration 10% to 10%"), ...
  625. combinedTable_sorted.("Number of Events"), ...
  626. 'VariableNames', {'Cluster', 'Area_um2', 'Perimeter', 'Circularity', 'Max_dFF', 'dFF AUC', 'Duration_10to10', 'Num_Events'});
  628. % Split data into two tables based on cluster assignment
  629. clusterFeatures_cluster1 = clusterFeatures(clusterFeatures.Cluster == 1, :);
  630. clusterFeatures_cluster2 = clusterFeatures(clusterFeatures.Cluster == 2, :);
  632. compareClusterFeatures(clusterFeatures, clusterFeatures_cluster1, clusterFeatures_cluster2);
  634. %number of events
  635. numberOfEvents_Cluster1 = clusterFeatures_cluster1(:,"Num_Events");
  636. totalEvents_Cluster1 = sum(numberOfEvents_Cluster1{:,:});
  637. %number of events in Hz
  638. numberOfEvents_Cluster1_Hz = table2cell(numberOfEvents_Cluster1);
  639. numberOfEvents_Cluster1_Hz = cell2mat(numberOfEvents_Cluster1_Hz);
  640. numberOfEvents_Cluster1_Hz = numberOfEvents_Cluster1_Hz / 900;
  641. numberOfEvents_Cluster1_Hz_new = numberOfEvents_Cluster1_Hz * 1000;
  643. %number of events
  644. numberOfEvents_Cluster2 = clusterFeatures_cluster2(:,"Num_Events");
  645. totalEvents_Cluster2 = sum(numberOfEvents_Cluster2{:,:});
  646. %number of events in Hz
  647. numberOfEvents_Cluster2_Hz = table2cell(numberOfEvents_Cluster2);
  648. numberOfEvents_Cluster2_Hz = cell2mat(numberOfEvents_Cluster2_Hz);
  649. numberOfEvents_Cluster2_Hz = numberOfEvents_Cluster2_Hz / 900;
  650. numberOfEvents_Cluster2_Hz_new = numberOfEvents_Cluster2_Hz * 1000;
  652. %%
  653. % Plot waves based on SNR (visual)
  654. figure;
  655. histogram(snr_ma);
  656. Q2 = prctile(snr_ma, 75);
  658. % Define threshold for high SNR
  659. snr_threshold = 22;
  661. % Find high SNR cells
  662. highSNRcells = snr_ma > snr_threshold;
  663. lowSNRcells = snr_ma < snr_threshold;
  665. % Get logical indices
  666. selectedCells_indices = find(highSNRcells);
  668. % Plot selected dFF traces
  669. set(0, 'DefaultFigureWindowStyle', 'docked');
  670. plot_selected_dFF_waves(combinedTable_NM, '', false, selectedCells_indices);
  672. % correlation SNR and event duration
  673. snr_duration = zeros(size(snr_ma,1), 2);
  674. for cellIdx = 1:size(snr_ma,1)
  675. snr_duration(cellIdx, :) = [snr_ma(cellIdx), combinedTable_NM{cellIdx, "Duration 10% to 10%"}];
  676. end
  678. %% Apply Savitzky-Golay filtering only for visualization, not for clustering.
  680. % Optimize Sgolay params - takes very long time to optimize both params
  681. poly_orders = 1:6;
  682. frame_sizes = 5:2:927;
  683. [best_poly_order, best_frame_size, best_snr, best_filtered_signals] = optimizeSgolayParams(dFF_all, poly_orders, frame_sizes);
  685. % Optimize only frame range
  686. poly_order = 3;
  687. frame_range = 5:2:927; % Must be odd values
  688. best_frame_sizes = optimizeFrameSize(dFF_all, poly_order, frame_range);

AQuA2_6_Waves_CSD.m at commit 1b99674, under GPL-3.0 · at the source

Overview

Authors: Simone Carneiro-Nascimento1, Chao Wei1, Anna Gutterman1, Dan Levy1
ORCID iDs: Simone Carneiro-Nascimento, Dan Levy
  1. Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School Boston United States
Institutions: Beth Israel Deaconess Medical Center (United States)
Journal: eLife, volume 15, article RP109888
Dates: published online 13 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.109888 · PMID 42126091 · PMCID PMC13171100 · OpenAlex W7128806177
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), optical imaging (calcium, voltage, 2-photon) (modality), mouse (organism), pain (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Machine learning, Preprocessing, Spectral & time-frequency, fMRI & imaging, Single-unit activity, calcium imaging, Smoothing, state filtering, decompositions
Keywords: meninges, macrophages, calcium imaging, cortical spreading depolarization, migraine, calcitonin gene-related peptide, CGRP, Mouse
MeSH: Brain*, Calcium*, Calcium Signaling*, Macrophages*, Meninges*, Animals, Cortical Spreading Depression, Intravital Microscopy, Mice (* major topic)
Journal subjects: Immunology and Inflammation, Neuroscience
Topic: Neuroscience of respiration and sleep (Endocrine and Autonomic Systems, Neuroscience), according to OpenAlex
Funding: National Institutes of Health (R21NS130561, R01NS115972, R01NS133625)
Citations: cited by 1 paper (Europe PMC); 76 references in the paper

Abstract

The meninges, which envelop and protect the brain, host a dense network of resident macrophages with diverse roles in regulating homeostasis and neuroinflammation. Despite their importance, we have a limited understanding of their behavior in vivo. Many dynamic cellular functions of macrophages involve intracellular Ca2+ signaling. However, virtually nothing is known about the spatiotemporal Ca2+ dynamics of meningeal macrophages in vivo. We developed a chronic intravital two-photon imaging approach and related computational analysis tools to interrogate meningeal macrophage Ca2+ dynamics, at subcellular resolution, in a novel Pf4-Cre:Ai162 conditional GCaMP6s reporter mouse model. Using imaging in awake mice, we characterized Ca2+ activity in meningeal macrophages at steady state and in response to cortical spreading depolarization (CSD), an aberrant pro-inflammatory brain hyperexcitability event implicated in migraine, traumatic brain injury, and stroke. In homeostatic meninges, macrophages in the dural perivascular niche exhibited several Ca2+ dynamic features, including event duration and signal frequency spectrum, distinct from those localized to the interstitial, non-perivascular niche. Simultaneous tracking of macrophage Ca2+ dynamics and local vasomotion revealed a subset of dural perivascular macrophages whose activity was coupled to locomotion-driven diameter fluctuations of their associated vessels. Most perivascular and non-perivascular meningeal macrophages displayed propagating intracellular Ca2+ activity and synchronized intercellular Ca2+ elevations, potentially driven by extrinsic factors. In response to CSD, the majority of perivascular and non-perivascular meningeal macrophages showed a persistent decrease in Ca2+ activity, while a smaller subset displayed Ca2+ elevations. Mechanistically, calcitonin gene-related peptide receptor signaling mediated the increase but not the decrease in CSD-mediated Ca2+ signaling. Collectively, our results highlight a previously unknown diversity of Ca2+ dynamics in meningeal macrophages at steady state and in response to an aberrant brain hyperexcitability event linked to neuroinflammation.

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

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levylabheadache/movieprocessing

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levylabheadache/locomotion

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levylabheadache/Aqua2Processing

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At the source: github.com/levylabheadache/Aqua2Processing

levylabheadache/vasculature

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levylabheadache/GeneralLinearModel_Macrophages

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At the source: github.com/levylabheadache/GeneralLinearModel_Macrophages

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Data availability

All data needed to evaluate the conclusions in the paper are present in the manuscript. The code used for analyzing the data in this study was deposited in the Levy Lab GitHub account. Code for movie processing is available at GitHub (https://github.com/levylabheadache/MovieProcessing/tree/SCN) (copy archived at Levy Lab_Headache, 2026a). Code for locomotion analysis is available GitHub (https://github.com/levylabheadache/Locomotion/tree/SCN) (copy archived at Levy Lab_Headache, 2026b). Code for post Aqua2-processing is available at GitHub (https://github.com/levylabheadache/Aqua2Processing) (copy archived at Levy Lab_Headache, 2026c). Code for vascular segmentation is available at GitHub (https://github.com/levylabheadache/Vasculature/tree/SCN) (copy archived at Levy Lab_Headache, 2026d). Code for GLM is available at GitHub (https://github.com/levylabheadache/GeneralLinearModel_Macrophages) (copy archived at Levy Lab_Headache, 2026e).

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Recorded: type, language, journal, volume, pages, dates, 4 authors, 8 keywords, 9 MeSH terms, 1 funder, 71 references.

Cite

This paper

Carneiro-Nascimento, S., Wei, C., Gutterman, A., & Levy, D. (2026). Intravital calcium imaging of meningeal macrophages reveals niche-specific dynamics and aberrant responses to brain hyperexcitability. eLife, 15, RP109888. https://doi.org/10.7554/elife.109888

BibTeX

@article{carneironascimento2026intravital,
author = {Carneiro-Nascimento, Simone and Wei, Chao and Gutterman, Anna and Levy, Dan},
title = {{Intravital calcium imaging of meningeal macrophages reveals niche-specific dynamics and aberrant responses to brain hyperexcitability}},
journal = {eLife},
year = {2026},
month = may,
volume = {15},
pages = {RP109888},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.109888},
url = {https://doi.org/10.7554/elife.109888},
pmid = {42126091},
pmcid = {PMC13171100}
}

RIS

TY - JOUR
AU - Carneiro-Nascimento, Simone
AU - Wei, Chao
AU - Gutterman, Anna
AU - Levy, Dan
TI - Intravital calcium imaging of meningeal macrophages reveals niche-specific dynamics and aberrant responses to brain hyperexcitability
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/05/13
VL - 15
SP - RP109888
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.109888
UR - https://doi.org/10.7554/elife.109888
LA - en
ER -

CSL-JSON

{
"id": "10.7554/elife.109888",
"type": "article-journal",
"title": "Intravital calcium imaging of meningeal macrophages reveals niche-specific dynamics and aberrant responses to brain hyperexcitability",
"container-title": "eLife",
"author": [
{
"family": "Carneiro-Nascimento",
"given": "Simone"
},
{
"family": "Wei",
"given": "Chao"
},
{
"family": "Gutterman",
"given": "Anna"
},
{
"family": "Levy",
"given": "Dan"
}
],
"container-title-short": "Elife",
"volume": "15",
"page": "RP109888",
"DOI": "10.7554/elife.109888",
"PMID": "42126091",
"PMCID": "PMC13171100",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.109888",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
13
]
]
}
}

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