OSCR

A multimodal adaptive optical microscope for in vivo imaging from molecules to organisms.

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 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Image processing › Cell cycle quantification. ↔ pointDetection/src/getCellVolume.m, lines 28–169 · score 0.72 · surface area, cell volume, cell mask, Otsu, smoothing, component
  2. [2] § Methods › Image processing › 3D spine intensity estimation. ↔ pointDetection/CMEAnalysis_104_XR/software/loadTrackSettings.m, lines 24–108 · score 0.53 · frame linking, search radius, tracked, intensity, sigma, filter
  3. [3] § Methods › Image processing › 3D spine intensity estimation. ↔ pointDetection/llsmtools/cmeAnalysis3D/loadTrackSettings.m, the whole file · a weak match · score 0.53 · frame linking, search radius, tracked, intensity, sigma, filter
  4. [4] § Methods › Image processing › Nuclei segmentation. ↔ microscopeDataProcessing/puncta_removal/XR_ExM_PunctaRemovalPointDetection.m, lines 1–101 · score 0.52 · Gaussian smoothed, intensity threshold, filled, segmentation

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 191 lines · 7.9 KB · GPL-3.0 · 1 match

  1. %getCellVolume(data, varargin) calculates cell volume & area based on thresholding
  2. % of the smoothened input data
  3. %
  4. % Inputs:
  5. % data : structure returned by loadConditionData3D
  6. %
  7. % Parameters (specifier/value pairs):
  8. % 'SmoothingSigma' : s.d. for Gaussian smoothing of the data
  9. % 'MinVolume' : minimum accepted size of connected components, in voxels.
  10. % This is used for eliminating spurious detections, for example other
  11. % cells at the borders of the input volume
  12. % 'Mode' : {'Average'}|'Timeseries' specifies whether to calculate volume based
  13. % on an average (max. int. projection) of the data, or for all time points
  14. % 'FillHoles' : {true}|false specifies whether to fill holes at the interior of the
  15. % thresholded volume automatically
  16. % 'Filetype' : 'framePathsDS' | {'framePathsDSR'} selects whether to use
  17. % deskewed (i.e. with objective-scan data) or rotated volumes
  18. %
  19. % Outputs:
  20. % In 'Average' mode, the files 'maxProjRotated.tif' and 'volmaskRotated.tif' are
  21. % written to [data(i).source 'Analysis']
  22. %
  23. % In 'TimeSeries' mode, volume masks are written to [data(i).source 'Analysis/VolMasks']
  24. % and volume/area statistics are saved in [data(i).source 'Analysis/voldata.mat']
  26. % Francois Aguet, 2014
  27. % modified the lowThreshold for OTSU -- Gokul
  28. function getCellVolume(data, varargin)
  30. ip = inputParser;
  31. ip.CaseSensitive = false;
  32. ip.addRequired('data');
  33. ip.addParamValue('SmoothingSigma', 2, @isscalar);
  34. ip.addParamValue('Display', true, @islogical); % for time series only
  35. ip.addParamValue('Overwrite', false, @islogical);
  36. ip.addParamValue('FillHoles', true, @islogical);
  37. ip.addParamValue('ExcludeIndex', [], @isposint);
  38. ip.addParamValue('MinVolume', 200000, @isscalar);
  39. ip.addParamValue('Mode', 'Average', @(x) any(strcmpi(x, {'Average', 'TimeSeries'})));
  40. ip.addParamValue('Filepath', 'framePathsDSR', @(x) any(strcmpi(x, {'framePaths', 'framePathsDS', 'framePathsDSR'})));
  41. ip.addParamValue('ThresholdMode', 'Otsu', @(x) any(strcmpi(x, {'Background', 'Otsu'})));
  42. ip.parse(data, varargin{:});
  44. nd = numel(data);
  46. if strcmpi(ip.Results.Mode, 'TimeSeries')
  47. for i = 1:nd
  48. nf = data(i).movieLength;
  49. fmt = ['%.' num2str(ceil(log10(nf))) 'd'];
  51. mpath = [data(i).source 'Analysis' filesep 'VolMasks' filesep];
  52. rfile = [data(i).source 'Analysis' filesep 'voldata.mat'];
  54. if ~(exist(rfile,'file')==2) || ip.Results.Overwrite
  55. [~,~] = mkdir(mpath);
  57. % define interpolation grids
  58. info = imfinfo(data(i).(ip.Results.Filepath){1}{1}, 'tif');
  59. nz = numel(info);
  60. nx = info(1).Width;
  61. ny = info(1).Height;
  62. [y,x,z] = ndgrid(1:ny,1:nx,1:nz);
  63. [yi,xi,zi] = ndgrid(1:ny,1:nx,1:1/data(i).zAniso:nz);
  65. px = data(i).pixelSize; % [um]
  67. vol = zeros(1,nf);
  68. area = zeros(1,nf);
  69. parfor f = 1:nf
  70. % determine background level from volume borders
  71. stack = readtiff(data(i).(ip.Results.Filepath){1}{f}); %#ok<PFBNS>
  72. gstack = filterGauss3D(stack, ip.Results.SmoothingSigma);
  74. if strcmpi(ip.Results.ThresholdMode, 'Otsu');
  75. % threshold smoothened volume
  76. T = thresholdOtsu(gstack(gstack>0));
  77. else
  78. % quick hack, not an optimal method
  79. b = 10; % distance from border
  80. z0 = 3; % first and last 3 slices have interpolation noise
  81. tmp = stack([1:1+b end-b:end], [1:1+b end-b:end], 1+z0:end-z0);
  82. T = prctile(tmp(:), 99);
  83. end
  84. volMask = gstack>T;
  86. % fill potential holes inside the cell mask (slow)
  87. % this version is saved; not used for actual volume calculation
  88. if ip.Results.FillHoles
  89. volMask = fillHoles2D(volMask);
  90. end
  91. CC = bwconncomp(volMask, 26);
  92. % discard small components (assumed to be noise or debris on glass slide)
  93. csize = cellfun(@numel, CC.PixelIdxList);
  94. idx = csize>=ip.Results.MinVolume;
  95. CC.NumObjects = sum(idx);
  96. CC.PixelIdxList = CC.PixelIdxList(idx);
  97. volMask = labelmatrix(CC)~=0;
  99. % store mask
  100. writetiff(uint8(volMask), [mpath 'volmask_' num2str(f,fmt) '.tif']);
  102. % interpolate for surface area (and volume) calculation
  103. volMask2 = interpn(y, x, z, gstack, yi, xi, zi, 'linear')>T;
  104. if ip.Results.FillHoles
  105. volMask2 = fillHoles2D(volMask2);
  106. end
  107. CC = bwconncomp(volMask2, 26);
  108. % discard small components (assumed to be noise or debris on glass slide)
  109. csize = cellfun(@numel, CC.PixelIdxList);
  110. idx = csize>=2*ip.Results.MinVolume;
  111. CC.NumObjects = sum(idx);
  112. CC.PixelIdxList = CC.PixelIdxList(idx);
  113. volMask2 = labelmatrix(CC)~=0;
  115. % calculate volume & surface area
  116. vol(f) = sum(cellfun(@numel, CC.PixelIdxList));
  117. perim = bwperim(volMask2);
  118. area(f) = sum(perim(:));
  119. end
  120. vol = vol*px^3; %#ok<NASGU>
  121. area = area*px^2; %#ok<NASGU>
  122. save(rfile, 'vol', 'area');
  123. else
  124. load(rfile);
  125. end
  127. if ip.Results.Display
  128. plotMitosisStatistics(data(i), 'ExcludeIndex', ip.Results.ExcludeIndex);
  129. end
  130. end
  131. else
  132. for i = 1:nd
  133. if ~(exist([data(i).source 'Analysis' filesep 'volmaskRotated.tif'], 'file')==2) || ip.Results.Overwrite
  134. % max int. proj. of all frames
  135. maxProj = readtiff(data(i).(ip.Results.Filepath){1}{1});
  136. for f = 2:data(i).movieLength
  137. maxProj = max(maxProj, readtiff(data(i).(ip.Results.Filepath){1}{f}));
  138. end
  139. gstack = filterGauss3D(double(maxProj), ip.Results.SmoothingSigma);
  141. T = thresholdOtsu(gstack(gstack>0));
  142. volMask = gstack>T;
  144. if ip.Results.FillHoles
  145. volMask = fillHoles2D(volMask);
  146. end
  148. CC = bwconncomp(volMask, 26);
  149. % discard small components (assumed to be noise or debris on glass slide)
  150. csize = cellfun(@numel, CC.PixelIdxList);
  151. idx = csize>=ip.Results.MinVolume;
  152. CC.NumObjects = sum(idx);
  153. CC.PixelIdxList = CC.PixelIdxList(idx);
  154. volMask = labelmatrix(CC)~=0;
  156. vol = sum(cellfun(@numel, CC.PixelIdxList));
  157. perim = bwperim(volMask);
  158. area = sum(perim(:));
  159. px = data(i).pixelSize; % [um]
  160. vol = vol*px^3; %#ok<NASGU>
  161. area = area*px^2; %#ok<NASGU>
  162. rfile = [data(i).source 'Analysis' filesep 'voldata.mat'];
  163. save(rfile, 'vol', 'area');
  165. writetiff(single(maxProj), [data(i).source 'Analysis' filesep 'maxProjRotated.tif']);
  166. writetiff(uint8(volMask), [data(i).source 'Analysis' filesep 'volmaskRotated.tif']);
  167. end
  168. end
  169. end
  172. function vol = fillHoles2D(vol)
  174. for z = 1:size(vol,3)
  175. vol(:,:,z) = imfill(vol(:,:,z), 8, 'holes');
  176. end
  177. for x = 1:size(vol,2)
  178. vol(:,x,:) = imfill(squeeze(vol(:,x,:)), 8, 'holes');
  179. end
  180. for y = 1:size(vol,1)
  181. vol(y,:,:) = imfill(squeeze(vol(y,:,:)), 8, 'holes');
  182. end
  183. for z = 1:size(vol,3)
  184. vol(:,:,z) = imfill(vol(:,:,z), 8, 'holes');
  185. end
  186. for x = 1:size(vol,2)
  187. vol(:,x,:) = imfill(squeeze(vol(:,x,:)), 8, 'holes');
  188. end
  189. for y = 1:size(vol,1)
  190. vol(y,:,:) = imfill(squeeze(vol(y,:,:)), 8, 'holes');
  191. end

getCellVolume.m at commit f7dbfd5, under GPL-3.0 · at the source

Overview

Authors: Tian-Ming Fu1,2,3, Gaoxiang Liu4,3, Daniel E Milkie1,3, Xiongtao Ruan4,3, Frederik Görlitz4, Yu Shi5,6, Valentina Ferro4, Nikita S Divekar4, Wei Wang7, Harrison M York8, Velat Kilic14, Matthew Mueller1,4, Yajie Liang9, Timothy A Daugird10,11, Maria Jose Gacha-Garay12, Kathryn A Larkin12, Rebecca C Adikes12,13, Nathanael Harrison4, Cyna Shirazinejad4,14, Samara Williams4
and 29 other authorsJamison L Nourse15, Shu-Hsien Sheu1,16, Liang Gao1, Tongchao Li17,18, Chandrani Mondal19, Kemal Achour4, Wilmene Hercule4, Daniel R Stabley20, Kevin Emmerich21,22, Peng Dong1,23, David G Drubin4, Zhe J Liu1, Jeff S Mumm21,22, Minoru Koyama24, Alison N Killilea4, Jose Javier Bravo-Cordero19, C Dirk Keene25, Liqun Luo17, Tomas Kirchhausen26,27,28, Medha M Pathak15,29, Senthil Arumugam8,30, James K Nuñez4, Ruixuan Gao7,31, David Q Matus12,32, Benjamin L Martin12, Ian A Swinburne4, Eric Betzig1,4,33, Wesley R Legant5,10, Srigokul Upadhyayula4,34,35
ORCID iDs: Tian-Ming Fu, Daniel E Milkie, Xiongtao Ruan, Frederik Görlitz, Yu Shi, Valentina Ferro, Nikita S Divekar, Harrison M York, Velat Kilic1, Matthew Mueller, Timothy A Daugird, Maria Jose Gacha-Garay, Rebecca C Adikes, Shu-Hsien Sheu, Daniel R Stabley, Peng Dong, David G Drubin, Zhe J Liu, Jeff S Mumm, Minoru Koyama, Alison N Killilea, Jose Javier Bravo-Cordero, C Dirk Keene, Liqun Luo, Tomas Kirchhausen, Medha M Pathak, Senthil Arumugam, James K Nuñez, Ruixuan Gao, David Q Matus, Benjamin L Martin, Ian A Swinburne, Eric Betzig, Wesley R Legant, Srigokul Upadhyayula
35 affiliations
  1. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
  2. Present address: Department of Electrical and Computer Engineering, and Omenn Darling Bioengineering Institute, Princeton University, Princeton, NJ, USA
  3. These authors contributed equally: Tian-Ming Fu, Gaoxiang Liu, Daniel E. Milkie, Xiongtao Ruan
  4. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
  5. Lampe Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
  6. Present address: Department of Physics and Astronomy, Western University, London, Ontario, Canada
  7. Department of Chemistry, University of Illinois Chicago, Chicago, IL, USA
  8. Monash Biomedicine Discovery Institute, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton/Melbourne, Victoria, Australia
  9. Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
  10. Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
  11. Present address: Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
  12. Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, NY, USA
  13. Present address: Department of Biology, Siena College, Loudonville, NY, USA
  14. Biophysics Graduate Group, University of California, Berkeley, Berkeley, CA, USA
  15. Department of Physiology and Biophysics, Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, USA
  16. Present address: Chan Zuckerberg Imaging Institute, Redwood City, CA, USA
  17. Department of Biology, Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
  18. Present address: Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China
  19. Department of Medicine, Division of Hematology and Oncology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
  20. Neuroimaging Laboratory, Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN, USA
  21. Wilmer Eye Institute and the Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
  22. McKusick-Nathans Institute and the Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
  23. Present address: Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
  24. Department of Cell and Systems Biology, University of Toronto, Scarborough, Ontario, Canada
  25. University of Washington BioRepository and Integrated Neuropathology (BRaIN) laboratory, Harborview Medical Center, Seattle, WA, USA
  26. Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA, USA
  27. Department of Pediatrics, Harvard Medical School, Boston, MA, USA
  28. Department of Cell Biology, Harvard Medical School, Boston, MA, USA
  29. Department of Biomedical Engineering, and Center for Complex Systems Biology, University of California, Irvine, Irvine, CA, USA
  30. European Molecular Biology Laboratory Australia, Monash University, Clayton/Melbourne, Victoria, Australia
  31. Department of Biological Sciences, University of Illinois Chicago, Chicago, IL, USA
  32. Present address: Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
  33. Department of Physics, Howard Hughes Medical Institute, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
  34. Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
  35. Chan Zuckerberg Biohub, San Francisco, CA, USA
Institutions: Howard Hughes Medical Institute (United States); Janelia Research Campus (United States); University of California, Berkeley (United States); University of North Carolina at Chapel Hill (United States); University of Illinois Chicago (United States); Monash University (Australia); University of Maryland, Baltimore (United States); Stony Brook University (United States); University of California, Irvine (United States); Stanford University (United States); Tisch Cancer Institute; Icahn School of Medicine at Mount Sinai (United States); St. Jude Children's Research Hospital (United States); Johns Hopkins University (United States); Johns Hopkins Medicine (United States); University of Toronto (Canada); University of Toronto Scarborough (Canada); University of Washington (United States); Harborview Medical Center (United States); Boston Children's Hospital (United States); Harvard University (United States); Lawrence Berkeley National Laboratory (United States); Chan Zuckerberg Biohub San Francisco (United States)
Journal: Nature methods, volume 23, issue 6, pages 1184-1195
Dates: published online 22 May 2026; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41592-026-03066-1 · PMID 42174242 · PMCID PMC13310421 · OpenAlex W4411328790
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.06.02.657494
Status: code verified
Categories: histology / microscopy (modality), optical imaging (calcium, voltage, 2-photon) (modality), human (organism), mouse (organism)
Methods: Preprocessing, Connectivity, Statistics, Evoked potentials, fMRI & imaging, Physiology & signal measures, Smoothing, state filtering, decompositions
MeSH: Microscopy*, Multimodal Imaging*, Animals, Humans, Mice, Microscopy, Fluorescence, Multiphoton (* major topic)
Topic: Advanced Fluorescence Microscopy Techniques (Biophysics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: U.S. Department of Health & Human Services | National Institutes of Health (NIH) (1R01DC021710, F32GM133131, R01CA244780, R35GM150290, DP2AT010376, R01NS109810, R01OD020376, R01‑DC005982, T32EY7143‑22, 1DP2GM136653, R01GM121597, R35GM118149); NCI NIH HHS (T32 CA078207); U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) (GM130386); NEI NIH HHS (P30 EY001765, R01 EY033009, F31 EY032790); NIH HHS (R01 OD020376); U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) (P30‑CA196521); NIGMS NIH HHS (R35 GM130386); DOE | LDRD | Lawrence Berkeley National Laboratory (Berkeley Lab) (LDRD 7647437 and 7721359)
Citations: cited by 4 papers (Europe PMC); 79 references in the paper

Abstract

Understanding biological systems requires observing features and processes across vast spatial and temporal scales, spanning nanometers to centimeters and milliseconds to days, often using multiple imaging modalities within complex native microenvironments. Yet, achieving this comprehensive view is challenging because microscopes optimized for specific tasks typically lack versatility due to inherent optical and sample handling tradeoffs, and frequently suffer performance degradation from sample-induced optical aberrations in multicellular contexts. Here, we present Multimodal Optical Scope with Adaptive Imaging Correction (MOSAIC), a reconfigurable microscope that integrates multiple advanced imaging techniques including light-sheet, label-free, super-resolution and multiphoton, all equipped with adaptive optics. MOSAIC enables noninvasive imaging of subcellular dynamics in both cultured cells and live multicellular organisms, nanoscale mapping of molecular architectures across millimeter-scale expanded tissues and structural/functional neural imaging within live mice. MOSAIC facilitates correlative studies across biological scales within the same specimen, providing an integrated platform for broad biological investigation.

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.

scopetools/cudasirecon

License: GPL-3.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: e27c0d9e866fcaad3390e00fe866ec4a4fb14880, 2 October 2024
Languages: C++ (13), C/C++ (10), Shell (2), CUDA (2), Python (1)
Size: 53 files, 28 scripts
Software Heritage: archived
Found in: the text, “SIM reconstruction.”
Holds: README, license file, environment (environment-linux.yml, environment-windows.yml)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
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At the source: github.com/scopetools/cudasirecon

abcucberkeley/PetaKit5D

License: GPL-3.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: f7dbfd5cc5947a32e6028a6c7b895ff0b891c673, 21 August 2026
Languages: MATLAB (678), C++ (32), C (21), C/C++ (16), Python (2), Shell (2)
Size: 1,036 files, 751 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (97 files), Statistics and Machine Learning Toolbox (59 files), Parallel Computing Toolbox (27 files), Optimization Toolbox (13 files), Signal Processing Toolbox (2 files), NumPy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
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753 files
At the source: github.com/abcucberkeley/PetaKit5D

Code availability

Microscope control software is available through a research license agreement with HHMI. PetaKit5D13 data preprocessing software is available on GitHub at https://github.com/abcucberkeley/PetaKit5D.

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

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What the map holds:

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The datasets for this manuscript exceed the size limits of public data repositories, but they will be shared upon reasonable request.

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Version 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio
  • Authors: added Velat Kilic1 (0000-0001-5594-1324); Maria Jose Gacha-Garay (0000-0002-9312-5237); Daniel R Stabley (0000-0001-9187-6099); David G Drubin (0000-0003-3002-6271); David Q Matus (0000-0002-1570-5025); Benjamin L Martin (0000-0001-5474-4492); Ian A Swinburne (0000-0003-4162-0508); removed Velat Kilic1; Maria Jose Gacha-Garay; Daniel R Stabley; David G Drubin; David Q Matus; Benjamin L Martin; Ian A Swinburne

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 49 authors, 6 MeSH terms, 8 funders, 75 references.

Cite

This paper

Fu, T.-M., Liu, G., Milkie, D. E., Ruan, X., Görlitz, F., Shi, Y., Ferro, V., Divekar, N. S., Wang, W., York, H. M., Kilic1, V., Mueller, M., Liang, Y., Daugird, T. A., Gacha-Garay, M. J., Larkin, K. A., Adikes, R. C., Harrison, N., Shirazinejad, C., . . . Upadhyayula, S. (2026). A multimodal adaptive optical microscope for in vivo imaging from molecules to organisms. Nature methods, 23(6), 1184-1195. https://doi.org/10.1038/s41592-026-03066-1

BibTeX

@article{fu2026multimodal,
author = {Fu, Tian-Ming and Liu, Gaoxiang and Milkie, Daniel E and Ruan, Xiongtao and Görlitz, Frederik and Shi, Yu and Ferro, Valentina and Divekar, Nikita S and Wang, Wei and York, Harrison M and Kilic1, Velat and Mueller, Matthew and Liang, Yajie and Daugird, Timothy A and Gacha-Garay, Maria Jose and Larkin, Kathryn A and Adikes, Rebecca C and Harrison, Nathanael and Shirazinejad, Cyna and Williams, Samara and Nourse, Jamison L and Sheu, Shu-Hsien and Gao, Liang and Li, Tongchao and Mondal, Chandrani and Achour, Kemal and Hercule, Wilmene and Stabley, Daniel R and Emmerich, Kevin and Dong, Peng and Drubin, David G and Liu, Zhe J and Mumm, Jeff S and Koyama, Minoru and Killilea, Alison N and Bravo-Cordero, Jose Javier and Keene, C Dirk and Luo, Liqun and Kirchhausen, Tomas and Pathak, Medha M and Arumugam, Senthil and Nuñez, James K and Gao, Ruixuan and Matus, David Q and Martin, Benjamin L and Swinburne, Ian A and Betzig, Eric and Legant, Wesley R and Upadhyayula, Srigokul},
title = {{A multimodal adaptive optical microscope for in vivo imaging from molecules to organisms}},
journal = {Nature methods},
year = {2026},
month = may,
volume = {23},
number = {6},
pages = {1184--1195},
publisher = {Nature Portfolio},
issn = {1548-7091},
doi = {10.1038/s41592-026-03066-1},
url = {https://doi.org/10.1038/s41592-026-03066-1},
pmid = {42174242},
pmcid = {PMC13310421}
}

RIS

TY - JOUR
AU - Fu, Tian-Ming
AU - Liu, Gaoxiang
AU - Milkie, Daniel E
AU - Ruan, Xiongtao
AU - Görlitz, Frederik
AU - Shi, Yu
AU - Ferro, Valentina
AU - Divekar, Nikita S
AU - Wang, Wei
AU - York, Harrison M
AU - Kilic1, Velat
AU - Mueller, Matthew
AU - Liang, Yajie
AU - Daugird, Timothy A
AU - Gacha-Garay, Maria Jose
AU - Larkin, Kathryn A
AU - Adikes, Rebecca C
AU - Harrison, Nathanael
AU - Shirazinejad, Cyna
AU - Williams, Samara
AU - Nourse, Jamison L
AU - Sheu, Shu-Hsien
AU - Gao, Liang
AU - Li, Tongchao
AU - Mondal, Chandrani
AU - Achour, Kemal
AU - Hercule, Wilmene
AU - Stabley, Daniel R
AU - Emmerich, Kevin
AU - Dong, Peng
AU - Drubin, David G
AU - Liu, Zhe J
AU - Mumm, Jeff S
AU - Koyama, Minoru
AU - Killilea, Alison N
AU - Bravo-Cordero, Jose Javier
AU - Keene, C Dirk
AU - Luo, Liqun
AU - Kirchhausen, Tomas
AU - Pathak, Medha M
AU - Arumugam, Senthil
AU - Nuñez, James K
AU - Gao, Ruixuan
AU - Matus, David Q
AU - Martin, Benjamin L
AU - Swinburne, Ian A
AU - Betzig, Eric
AU - Legant, Wesley R
AU - Upadhyayula, Srigokul
TI - A multimodal adaptive optical microscope for in vivo imaging from molecules to organisms
T2 - Nature methods
J2 - Nat Methods
PY - 2026
DA - 2026/05/22
VL - 23
IS - 6
SP - 1184
EP - 1195
SN - 1548-7091
PB - Nature Portfolio
DO - 10.1038/s41592-026-03066-1
UR - https://doi.org/10.1038/s41592-026-03066-1
LA - en
ER -

CSL-JSON

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22
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}

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