Deconwolf enables high-performance deconvolution of widefield fluorescence microscopy images.
Journal
Nature methods
ISSN: 1548-7105
Titre abrégé: Nat Methods
Pays: United States
ID NLM: 101215604
Informations de publication
Date de publication:
06 Jun 2024
06 Jun 2024
Historique:
received:
16
11
2023
accepted:
25
04
2024
medline:
7
6
2024
pubmed:
7
6
2024
entrez:
6
6
2024
Statut:
aheadofprint
Résumé
Microscopy-based spatially resolved omic methods are transforming the life sciences. However, these methods rely on high numerical aperture objectives and cannot resolve crowded molecular targets, limiting the amount of extractable biological information. To overcome these limitations, here we develop Deconwolf, an open-source, user-friendly software for high-performance deconvolution of widefield fluorescence microscopy images, which efficiently runs on laptop computers. Deconwolf enables accurate quantification of crowded diffraction limited fluorescence dots in DNA and RNA fluorescence in situ hybridization images and allows robust detection of individual transcripts in tissue sections imaged with ×20 air objectives. Deconvolution of in situ spatial transcriptomics images with Deconwolf increased the number of transcripts identified more than threefold, while the application of Deconwolf to images obtained by fluorescence in situ sequencing of barcoded Oligopaint probes drastically improved chromosome tracing. Deconwolf greatly facilitates the use of deconvolution in many bioimaging applications.
Identifiants
pubmed: 38844629
doi: 10.1038/s41592-024-02294-7
pii: 10.1038/s41592-024-02294-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)
ID : StG-2016_GENOMIS_715727
Organisme : EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)
ID : EPIC-XS_823839
Organisme : Cancerfonden (Swedish Cancer Society)
ID : 19 0130 Pj 03 H
Organisme : Cancerfonden (Swedish Cancer Society)
ID : CAN 2018/604
Organisme : Cancerfonden (Swedish Cancer Society)
ID : 21 1785 Pj
Organisme : Human Frontier Science Program (HFSP)
ID : CDA
Organisme : Knut och Alice Wallenbergs Stiftelse (Knut and Alice Wallenberg Foundation)
ID : KAW 2018.0172
Organisme : Vetenskapsrådet (Swedish Research Council)
ID : 2019-01238
Organisme : U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)
ID : RM1HG011016
Organisme : U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)
ID : RM1HG011016
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : R01GM123289
Organisme : Stiftelsen för Strategisk Forskning (Swedish Foundation for Strategic Research)
ID : BD15_0095
Informations de copyright
© 2024. The Author(s).
Références
Goodwin, P. C. Quantitative deconvolution microscopy. Methods Cell Biol. 123, 177–192 (2014).
doi: 10.1016/B978-0-12-420138-5.00010-0
pubmed: 24974028
Richardson, W. H. Bayesian-based iterative method of image restoration. J. Opt. Soc. Am. 62, 55–59 (1972).
doi: 10.1364/JOSA.62.000055
Lucy, L. B. An iterative technique for the rectification of observed distributions. Astron. J. 79, 745 (1974).
doi: 10.1086/111605
Guo, M. et al. Rapid image deconvolution and multiview fusion for optical microscopy. Nat. Biotechnol. 38, 1337–1346 (2020).
doi: 10.1038/s41587-020-0560-x
pubmed: 32601431
pmcid: 7642198
Bruce, M. A. & Butte, M. J. Real-time GPU-based 3D deconvolution. Opt. Express 21, 4766–4773 (2013).
doi: 10.1364/OE.21.004766
pubmed: 23482010
pmcid: 3601650
Zhao, W. et al. Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy. Nat. Biotechnol. 40, 606–617 (2022).
doi: 10.1038/s41587-021-01092-2
pubmed: 34782739
Li, Y. et al. Incorporating the image formation process into deep learning improves network performance. Nat. Methods 19, 1427–1437 (2022).
doi: 10.1038/s41592-022-01652-7
pubmed: 36316563
pmcid: 9636023
Gyllborg, D. et al. Hybridization-based in situ sequencing (HybISS) for spatially resolved transcriptomics in human and mouse brain tissue. Nucleic Acids Res. 48, e112 (2020).
doi: 10.1093/nar/gkaa792
pubmed: 32990747
pmcid: 7641728
Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).
doi: 10.1126/science.1250212
pubmed: 24578530
pmcid: 4140943
Wassie, A. T., Zhao, Y. & Boyden, E. S. Expansion microscopy: principles and uses in biological research. Nat. Methods 16, 33–41 (2019).
doi: 10.1038/s41592-018-0219-4
pubmed: 30573813
Sahl, S. J., Hell, S. W. & Jakobs, S. Fluorescence nanoscopy in cell biology. Nat. Rev. Mol. Cell Biol. 18, 685–701 (2017).
doi: 10.1038/nrm.2017.71
pubmed: 28875992
Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A. & Tyagi, S. Imaging individual mRNA molecules using multiple singly labeled probes. Nat. Methods 5, 877–879 (2008).
doi: 10.1038/nmeth.1253
pubmed: 18806792
pmcid: 3126653
Gelali, E. et al. iFISH is a publically available resource enabling versatile DNA FISH to study genome architecture. Nat. Commun. 10, 1636 (2019).
doi: 10.1038/s41467-019-09616-w
pubmed: 30967549
pmcid: 6456570
Nguyen, H. Q. et al. 3D mapping and accelerated super-resolution imaging of the human genome using in situ sequencing. Nat. Methods 17, 822–832 (2020).
doi: 10.1038/s41592-020-0890-0
pubmed: 32719531
pmcid: 7537785
Wang, H. & Miller, P. C. Scaled heavy-ball acceleration of the Richardson-Lucy algorithm for 3D microscopy image restoration. IEEE Trans. Image Process. 23, 848–854 (2014).
doi: 10.1109/TIP.2013.2291324
pubmed: 26270922
Sage, D. et al. DeconvolutionLab2: an open-source software for deconvolution microscopy. Methods 115, 28–41 (2017).
doi: 10.1016/j.ymeth.2016.12.015
pubmed: 28057586
Frigo, M. & Johnson, S. G. The design and implementation of FFTW3. Proc. IEEE 93, 216–231 (2005).
doi: 10.1109/JPROC.2004.840301
Perdigao, L. M. A. et al. Computing for optimized biological microscopy data processing and analysis at The Rosalind Franklin Institute. Microsc. Microanal. 28, 1462–1464 (2022).
doi: 10.1017/S1431927622005931
Born, M., Wolf, E. & Bhatia, A. B. Principles of Optics: Electromagnetic Theory of Propagation, Interference, and Diffraction of Light (Cambridge Univ. Press, 2019).
Sage, D. PSF generator. Icy http://icy.bioimageanalysis.org/plugin/psf-generator/ (2014).
Bertero, M. & Boccacci, P. A simple method for the reduction of boundary effects in the Richardson-Lucy approach to image deconvolution. Astron. Astrophys. 437, 369–374 (2005).
doi: 10.1051/0004-6361:20052717
Rao, S. S. P. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).
doi: 10.1016/j.cell.2014.11.021
pubmed: 25497547
pmcid: 5635824
Langseth, C. M. et al. Comprehensive in situ mapping of human cortical transcriptomic cell types. Commun. Biol. 4, 998 (2021).
doi: 10.1038/s42003-021-02517-z
pubmed: 34429496
pmcid: 8384853
Lieberman-Aiden, E. et al. Comprehensive mapping of long range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).
doi: 10.1126/science.1181369
pubmed: 19815776
pmcid: 2858594
Wang, G., Moffitt, J. R. & Zhuang, X. Multiplexed imaging of high-density libraries of RNAs with MERFISH and expansion microscopy. Sci. Rep. 8, 4847 (2018).
doi: 10.1038/s41598-018-22297-7
pubmed: 29555914
pmcid: 5859009
Chen, F. et al. Nanoscale imaging of RNA with expansion microscopy. Nat. Methods 13, 679–684 (2016).
doi: 10.1038/nmeth.3899
pubmed: 27376770
pmcid: 4965288
Tsanov, N. et al. smiFISH and FISH-quant: a flexible single RNA detection approach with super-resolution capability. Nucleic Acids Res. 44, e165 (2016).
doi: 10.1093/nar/gkw784
pubmed: 27599845
pmcid: 5159540
Castells-Garcia, A. et al. Super resolution microscopy reveals how elongating RNA polymerase II and nascent RNA interact with nucleosome clutches. Nucleic Acids Res. 50, 175–190 (2022).
doi: 10.1093/nar/gkab1215
pubmed: 34929735
Hodge, R. D. et al. Conserved cell types with divergent features in human versus mouse cortex. Nature 573, 61–68 (2019).
doi: 10.1038/s41586-019-1506-7
pubmed: 31435019
pmcid: 6919571
Semrau, S. et al. FuseFISH: robust detection of transcribed gene fusions in single cells. Cell Rep. 6, 18–23 (2014).
doi: 10.1016/j.celrep.2013.12.002
pubmed: 24373969
Lyubimova, A. et al. Single-molecule mRNA detection and counting in mammalian tissue. Nat. Protoc. 8, 1743–1758 (2013).
doi: 10.1038/nprot.2013.109
pubmed: 23949380
Aguet, F. Super-Resolution Fluorescence Microscopy Based on Physical Models https://doi.org/10.5075/epfl-thesis-4418 (EPFL, 2009).
Axelrod, S. et al. starfish: scalable pipelines for image-based transcriptomics. J. Open Source Softw. 6, 2440 (2021).
doi: 10.21105/joss.02440
Chalfoun, J. et al. MIST: accurate and scalable microscopy image stitching tool with stage modeling and error minimization. Sci. Rep. 7, 4988 (2017).
doi: 10.1038/s41598-017-04567-y
pubmed: 28694478
pmcid: 5504007
Qian, X. et al. Probabilistic cell typing enables fine mapping of closely related cell types in situ. Nat. Methods 17, 101–106 (2020).
doi: 10.1038/s41592-019-0631-4
pubmed: 31740815