Sub-diffraction Limit Localization of Proteins in Volumetric Space Using Bayesian Restoration of Fluorescence Images from Ultrathin Specimens
Open accessAbstract
Photon diffraction limits the resolution of conventional light microscopy at the lateral focal plane to 0.61λ/NA (λ = wavelength of light, NA = numerical aperture of the objective) and at the axial plane to 1.4nλ/NA(2) (n = refractive index of the imaging medium, 1.51 for oil immersion), which with visible wavelengths and a 1.4NA oil immersion objective is -220 nm and -600 nm in the lateral plane and axial plane respectively. This volumetric resolution is too large for the proper localization of protein clustering in subcellular structures. Here we combine the newly developed proteomic imaging technique, Array Tomography (AT), with its native 50-100 nm axial resolution achieved by physical sectioning of resin embedded tissue, and a 2D maximum likelihood deconvolution method, based on Bayes' rule, which significantly improves the resolution of protein puncta in the lateral plane to allow accurate and fast computational segmentation and analysis of labeled proteins. The physical sectioning of AT allows tissue specimens to be imaged at the physical optimum of modern high NA plan-apochormatic objectives. This translates to images that have little out of focus light, minimal aberrations and wave-front distortions. Thus, AT is able to provide images with truly invariant point spread functions (PSF), a property critical for accurate deconvolution. We show that AT with deconvolution increases the volumetric analytical fidelity of protein localization by significantly improving the modulation of high spatial frequencies up to and potentially beyond the spatial frequency cut-off of the objective. Moreover, we are able to achieve this improvement with no noticeable introduction of noise or artifacts and arrive at object segmentation and localization accuracies on par with image volumes captured using commercial implementations of super-resolution microscopes.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Astrocytes engulf synaptic material immunolabeled for presynaptic and postsynaptic proteins.
“Using Array Tomography (AT), a super-resolution imaging technique visualizing synaptic protein expression with sub-100nm volumetric resolution20, we found that astrocytes engulfed much of the CTB-labeled debris as well as synaptic material immunolabeled for both presynaptic (Bassoon) and postsynaptic (PSD-95 and GluR1) proteins (Extended Data Fig. 5).”
From Astrocytes mediate synapse elimination through MEGF10 and MERTK pathways (2013) · cites this paper as [20]
Citation 20 is attached to the description of array tomography, not to the citing authors’ finding that astrocytes engulf synaptic material. The Introduction identifies array tomography as a technique for immunolabeling proteins in ultrathin sections, but specifies 50–100 nm axial resolution while noting that lateral resolution remains diffraction-limited at approximately 200 nm. It therefore does not establish sub-100 nm resolution throughout the volume. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Bayesian-Based Iterative Method of Image Restoration*
Richardson, W. H. (1972) · Journal of the Optical Society of America · doi:10.1364/josa.62.000055 · Cited by 4,160 · see citing works
An iterative technique for the rectification of observed distributions
Lucy, L. B. (1974) · The Astronomical Journal · doi:10.1086/111605 · Cited by 3,866 · see citing works
Gustafsson, M. G. L. (2000) · Journal of Microscopy · doi:10.1046/j.1365-2818.2000.00710.x · Cited by 3,786 · see citing works
Handbook Of Biological Confocal Microscopy
Pawley, J. B. (2006) · Springer eBooks · doi:10.1007/978-0-387-45524-2 · Cited by 3,685 · see citing works
Gustafsson, M. G. L., Shao, L., Carlton, P. M., Wang, R., Golubovskaya, I. N., Cande, W. Z., Agard, D. A., & Sedat, J. W. (2008) · Biophysical Journal · doi:10.1529/biophysj.107.120345 · Cited by 1,560 · see citing works
STED microscopy reveals that synaptotagmin remains clustered after synaptic vesicle exocytosis
Willig, K. I., Rizzoli, S. O., Westphal, V., Jahn, R., & Hell, S. W. (2006) · Nature · doi:10.1038/nature04592 · Cited by 1,227 · see citing works
Stillman, B. (1995) · Molecular Medicine · doi:10.1007/bf03401885 · Cited by 1,031 · see citing works
Micheva, K. D., & Smith, S. J. (2007) · Neuron · doi:10.1016/j.neuron.2007.06.014 · Cited by 787 · see citing works
Self-citationChapter 13 Fluorescence Microscopy in Three Dimensions
Agard, D. A., Hiraoka, Y., Shaw, P., & Sedat, J. W. (1989) · Methods in cell biology · doi:10.1016/s0091-679x(08)60986-3 · Cited by 649 · see citing works
STED microscopy with continuous wave beams
Willig, K. I., Harke, B., Medda, R., & Hell, S. W. (2007) · Nature Methods · doi:10.1038/nmeth1108 · Cited by 525 · see citing works
Three-dimensional architecture of a polytene nucleus
Agard, D. A., & Sedat, J. W. (1983) · Nature · doi:10.1038/302676a0 · Cited by 443 · see citing works
Nanoscale resolution in GFP-based microscopy
Willig, K. I., Kellner, R. R., Medda, R., Hein, B., Jakobs, S., & Hell, S. W. (2006) · Nature Methods · doi:10.1038/nmeth922 · Cited by 373 · see citing works
Single-Synapse Analysis of a Diverse Synapse Population: Proteomic Imaging Methods and Markers
Micheva, K. D., Busse, B. L., Weiler, N. C., O’Rourke, N. A., & Smith, S. J. (2010) · Neuron · doi:10.1016/j.neuron.2010.09.024 · Cited by 336 · see citing works
Self-citationImage deblurring with Poisson data: from cells to galaxies
Bertero, M., Boccacci, P., Desiderà, G., & Vicidomini, G. (2009) · Inverse Problems · doi:10.1088/0266-5611/25/12/123006 · Cited by 308 · see citing works
Information capacity and resolution in an optical system
Cox, I. J., & Sheppard, C. J. R. (1986) · Journal of the Optical Society of America A · doi:10.1364/josaa.3.001152 · Cited by 251 · see citing works
WILSON, T. (2011) · Journal of Microscopy · doi:10.1111/j.1365-2818.2011.03549.x · Cited by 237 · see citing works
Restoration, Resolution, and Noise
Rushforth, C. K., & Harris, R. W. (1968) · Journal of the Optical Society of America · doi:10.1364/josa.58.000539 · Cited by 184 · see citing works
Maximum-likelihood image restoration adapted for noncoherent optical imaging
Holmes, T. J. (1988) · Journal of the Optical Society of America A · doi:10.1364/josaa.5.000666 · Cited by 130 · see citing works
On super-resolution in astronomical imaging
Puschmann, K. G., & Kneer, F. (2005) · Astronomy and Astrophysics · doi:10.1051/0004-6361:20042320 · Cited by 96 · see citing works
A New Approach for Increasing the Resolving Power by Data Processing
Biraud, Y. (1969) · Cited by 92 · see citing works
Analysis of the limit to superresolution in incoherent imaging
Sementilli, P. J., Hunt, B. R., & Nadar, M. S. (1993) · Journal of the Optical Society of America A · doi:10.1364/josaa.10.002265 · Cited by 85 · see citing works
Comparison of Widefield/Deconvolution and Confocal Microscopy for Three-Dimensional Imaging
Shaw, P. (2006) · Springer eBooks · doi:10.1007/978-0-387-45524-2_23 · Cited by 73 · see citing works
Nanguneri, S., Flottmann, B., Horstmann, H., Heilemann, M., & Kuner, T. (2012) · PLoS ONE · doi:10.1371/journal.pone.0038098 · Cited by 70 · see citing works
Masters, B. R. (2010) · Cited by 66 · see citing works
Iterative multiframe superresolution algorithms for atmospheric-turbulence-degraded imagery
Sheppard, D. G., Hunt, B. R., & Marcellin, M. W. (1998) · Journal of the Optical Society of America A · doi:10.1364/josaa.15.000978 · Cited by 64 · see citing works
Chapter 1 Image Fidelity: Characterizing the Imaging Transfer Function
Young, I. T. (1989) · Methods in cell biology · doi:10.1016/s0091-679x(08)60974-7 · Cited by 57 · see citing works
Holmes, T. J., & Liu, Y.-H. (1989) · Applied Optics · doi:10.1364/ao.28.004930 · Cited by 53 · see citing works
Micheva, K. D., & Smith, S. J. (2007) · Neuron · doi:10.1016/j.neuron.2007.08.007 · Cited by 53 · see citing works
Self-citationResolution limits for deconvolved images
Lucy, L. B. (1992) · The Astronomical Journal · doi:10.1086/116315 · Cited by 45 · see citing works
Monachesi, A., Trager, S. C., Lauer, T. R., Freedman, W. L., Dressler, A., Grillmair, C. J., & Mighell, K. J. (2011) · The Astrophysical Journal · doi:10.1088/0004-637x/727/1/55 · Cited by 43 · see citing works
Kimura, S., & Munakata, C. (1990) · Applied Optics · doi:10.1364/ao.29.003007 · Cited by 36 · see citing works
Statistical Limits to Super Resolution
Lucy, L. B. (1992) · Cited by 32 · see citing works
Hubble Space Telescope image restoration in its fourth year
Adorf, H. M. (1995) · Inverse Problems · doi:10.1088/0266-5611/11/4/003 · Cited by 26 · see citing works
Hubble Space Telescope Wide-Field/Planetary Camera images of Saturn
Westphal, J. A., Baum, W. A., Lauer, T. R., Danielson, G. E., Currie, D. G., Ewald, S. P., Faber, S. M., Groth, E. J., Hester, J. J., Light, R. M., et al. (1991) · The Astrophysical Journal · doi:10.1086/185956 · Cited by 14 · see citing works
Iterative multiframe super-resolution algorithms for atmospheric turbulence-degraded imagery
Sheppard, D. G., Hunt, B. R., & Marcellin, M. W. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1998.678121 · Cited by 13 · see citing works
When Does the Richardson-Lucy Deconvolution Converge ?Preprint
Bi, H., & Boêrner, G. (1994) · arXiv (Cornell University) · doi:10.48550/arxiv.astro-ph/9409093 · Cited by 9 · see citing works
Holographic method of deconvolution and analytic continuation
Peřina, J. (1971) · Czechoslovak Journal of Physics · doi:10.1007/bf01702982 · Cited by 7 · see citing works
Multiframe Poisson map deconvolution of astronomical images
Hunt, B. R., Sheppard, D. G., & Nadar, M. S. (2002) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.1996.560381 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [6]Agard Da, Hiraoka Y, Shaw P, Sedat JW (1989) Fluorescence microscopy in three dimensions. Methods Cell Biol 30: 353– 77. 2494418
- [9]Wilson T (2011) Resolution and optical sectioning in the confocal microscope. J Microsc 244: 113– 121. 22004276
Cited by
Sorted by citations
Astrocytes mediate synapse elimination through MEGF10 and MERTK pathways
Chung, W.-S., Clarke, L. E., Wang, G. X., Stafford, B. K., Sher, A., Chakraborty, C., Joung, J., Foo, L. C., Thompson, A., Chen, C., et al. (2013) · Nature · doi:10.1038/nature12776 · Cited by 1,377 · see citing works
“The aligned and registered image stacks were imported into MATLAB and deconvolved using the native implementation of Richardson-Lucy deconvolution with empirical or theoretical PSFs with 10 iterations 20 .”
Self-citationAdvances in fluorescence labeling strategies for dynamic cellular imaging
Dean, K. M., & Palmer, A. E. (2014) · Nature Chemical Biology · doi:10.1038/nchembio.1556 · Cited by 511 · see citing works
Hyperexcitable arousal circuits drive sleep instability during aging
Li, S., Damonte, V. M., Chen, C., Wang, G. X., Kebschull, J. M., Yamaguchi, H., Bian, W.-J., Purmann, C., Pattni, R., Urban, A. E., et al. (2022) · Science · doi:10.1126/science.abh3021 · Cited by 196 · see citing works
Self-citationMapping Synapses by Conjugate Light-Electron Array Tomography
Collman, F., Buchanan, J., Phend, K. D., Micheva, K. D., Weinberg, R. J., & Smith, S. J. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.4274-14.2015 · Cited by 116 · see citing works
Self-citationSmith, S. J. (2018) · BMC Biology · doi:10.1186/s12915-018-0560-1 · Cited by 88 · see citing works
Self-citationHoogendoorn, E., Crosby, K. C., Leyton-Puig, D., Breedijk, R. M. P., Jalink, K., Gadella, T. W. J., & Postma, M. (2014) · Scientific Reports · doi:10.1038/srep03854 · Cited by 80 · see citing works
Schoonover, C. E., Tapia, J.-C., Schilling, V. C., Wimmer, V. C., Blazeski, R., Zhang, W., Mason, C. A., & Bruno, R. M. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.0305-14.2014 · Cited by 79 · see citing works
Imaging zebrafish neural circuitry from whole brain to synapse
Leung, L. C., Wang, G. X., & Mourrain, P. (2013) · Frontiers in Neural Circuits · doi:10.3389/fncir.2013.00076 · Cited by 63 · see citing works
Self-citationZahrai, A., Vahid-Ansari, F., Daigle, M., & Albert, P. R. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-01008-9 · Cited by 54 · see citing works
Presynaptic gating of excitation in the dorsal raphe nucleus by GABA
Soiza‐Reilly, M., Anderson, W. B., Vaughan, C. W., & Commons, K. G. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1304505110 · Cited by 51 · see citing works
Unraveling the architecture of the dorsal raphe synaptic neuropil using high-resolution neuroanatomy
Soiza‐Reilly, M., & Commons, K. G. (2014) · Frontiers in Neural Circuits · doi:10.3389/fncir.2014.00105 · Cited by 48 · see citing works
Belmer, A., Klenowski, P. M., Patkar, O. L., & Bartlett, S. E. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1278-x · Cited by 47 · see citing works
Fmr1 KO and Fenobam Treatment Differentially Impact Distinct Synapse Populations of Mouse Neocortex
Wang, G. X., Smith, S. J., & Mourrain, P. (2014) · Neuron · doi:10.1016/j.neuron.2014.11.016 · Cited by 35 · see citing works
Self-citationKnowing a synapse when you see one
Burette, A. C., Collman, F., Micheva, K. D., Smith, S. J., & Weinberg, R. J. (2015) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2015.00100 · Cited by 30 · see citing works
Self-citationMiller, G. W., Chandrasekaran, V., Yaghoobi, B., & Lein, P. J. (2018) · NeuroToxicology · doi:10.1016/j.neuro.2018.04.016 · Cited by 26 · see citing works
Automated Analysis of a Diverse Synapse Population
Busse, B. L., & Smith, S. J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002976 · Cited by 24 · see citing works
Self-citationProbabilistic fluorescence-based synapse detection
Simhal, A. K., Aguerrebere, C., Collman, F., Vogelstein, J. T., Micheva, K. D., Weinberg, R. J., Smith, S. J., & Sapiro, G. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005493 · Cited by 13 · see citing works
Self-citationWeiler, N. C., Collman, F., Vogelstein, J. T., Burns, R. C., & Smith, S. J. (2014) · Scientific Data · doi:10.1038/sdata.2014.46 · Cited by 13 · see citing works
Self-citationWang, G. X., Smith, S. J., & Mourrain, P. (2016) · eLife · doi:10.7554/elife.20560 · Cited by 10 · see citing works
Self-citationArray tomography: trails to discovery
Micheva, K. D., Burden, J. J., & Schifferer, M. (2024) · Methods in microscopy · doi:10.1515/mim-2024-0001 · Cited by 9 · see citing works
Vahid-Ansari, F., Zahrai, A., Daigle, M., & Albert, P. R. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.1147-23.2023 · Cited by 5 · see citing works
Conjugate Immunofluorescence—SEM Array Tomography for Studying Mammalian Synapses and Axons
Micheva, K. D., & Phend, K. D. (2017) · Biological and medical physics, biomedical engineering · doi:10.1007/978-3-319-68997-5_6 · Cited by 4 · see citing works
Array Tomography: A Novel High-Resolution Immunofluorescence Technique
Soiza‐Reilly, M. (2015) · Neuromethods · doi:10.1007/978-1-4939-2313-7_20 · Cited by 2 · see citing works
Eisenstein, M. S. (2012) · Nature Methods · doi:10.1038/nmeth.2239
Moser, E. I., & Rowland, D. C. (2014) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.718185619.793489234
Supplementary Methods from BLIMP1 Induces Transient Metastatic Heterogeneity in Pancreatic Cancer
Chiou, S., Risca, V. I., Wang, G. X., Yang, D., Grüner, B. M., Kathiria, A. S., Ma, R. K., Vaka, D., Chu, P., Kozak, M. M., et al. (2023) · doi:10.1158/2159-8290.22531568
Self-citation
- Authors
- Wang, G., & Smith, S. J.
- Affiliations
- Stanford Medicine (US)
- Corresponding author
- Wang, Gordon
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-08-30
- Volume
- 8
- Issue
- 8
- Pages
- e1002671
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC3431294
- OpenAlex
- W2070385263
- Cited by
- 27
- References
- 38
- Citations by year
- 2024: 1 · 2023: 3 · 2022: 1 · 2020: 1 · 2018: 2 · 2017: 2 · 2016: 2 · 2015: 3 · 2014: 7 · 2013: 4 · 2012: 1
- FWCI
- 2.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Howard Hughes Medical Institute (43667)
- FRAXA Research Foundation
- Grass Foundation
- G. Harold and Leila Y. Mathers Foundation
- National Institute of Neurological Disorders and Stroke (R01NS062798, R01NS075252, R21NS063210, R01NS077601)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Advanced Fluorescence Microscopy Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Keywords
- protein localization, array tomography, maximum-likelihood deconvolution, Bayesian image restoration, point spread function, protein clustering, image segmentation, super-resolution microscopy, ultrathin sections, subcellular structures
- MeSH terms
- Bayes Theorem, Microscopy, Fluorescence, Tomography, Proteomics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
