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In ExM, samples are embedded in a swellable polymer gel to physically expand the sample and isotropically increase resolution in x, y, and z. The maximum resolution increase is limited by the expansion factor of the gel, which is four-fold for the original ExM protocol. Variations on the original ExM method have been reported that allow for greater expansion factors but at the cost of ease of adoption or versatility. Here, we systematically explore the ExM recipe space and present a novel method termed Ten-fold Robust Expansion Microscopy (TREx) that, like the original ExM method, requires no specialized equipment or procedures. We demonstrate that TREx gels expand 10-fold, can be handled easily, and can be applied to both thick mouse brain tissue sections and cultured human cells enabling high-resolution subcellular imaging with a single expansion step. Furthermore, we show that TREx can provide ultrastructural context to subcellular protein localization by combining antibody-stained samples with off-the-shelf small-molecule stains for both total protein and membranes.<\/jats:p>","DOI":"10.7554\/elife.73775","type":"journal-article","created":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T08:15:48Z","timestamp":1643616948000},"update-policy":"https:\/\/doi.org\/10.7554\/elife.73775","source":"Crossref","is-referenced-by-count":202,"title":["Visualizing cellular and tissue ultrastructure using Ten-fold Robust Expansion Microscopy (TREx)"],"prefix":"10.7554","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0847-609X","authenticated-orcid":true,"given":"Hugo GJ","family":"Damstra","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04pp8hn57","id-type":"ROR","asserted-by":"publisher"}],"name":"Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University","place":["Utrecht, Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8613-2869","authenticated-orcid":true,"given":"Boaz","family":"Mohar","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/013sk6x84","id-type":"ROR","asserted-by":"publisher"}],"name":"Janelia Research Campus, HHMI","place":["Ashburn, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Eddison","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/013sk6x84","id-type":"ROR","asserted-by":"publisher"}],"name":"Janelia Research Campus, HHMI","place":["Ashburn, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9048-8614","authenticated-orcid":true,"given":"Anna","family":"Akhmanova","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04pp8hn57","id-type":"ROR","asserted-by":"publisher"}],"name":"Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University","place":["Utrecht, Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9418-6739","authenticated-orcid":true,"given":"Lukas C","family":"Kapitein","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04pp8hn57","id-type":"ROR","asserted-by":"publisher"}],"name":"Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University","place":["Utrecht, Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2568-2365","authenticated-orcid":true,"given":"Paul W","family":"Tillberg","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/013sk6x84","id-type":"ROR","asserted-by":"publisher"}],"name":"Janelia Research Campus, HHMI","place":["Ashburn, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4374","published-online":{"date-parts":[[2022,2,18]]},"reference":[{"key":"bib1","doi-asserted-by":"publisher","first-page":"2424","DOI":"10.1093\/bioinformatics\/btx180","article-title":"Trainable weka segmentation: a machine learning tool for microscopy pixel classification","volume":"33","author":"Arganda-Carreras","year":"2017","journal-title":"Bioinformatics"},{"key":"bib2","doi-asserted-by":"publisher","first-page":"1749","DOI":"10.1126\/science.1146598","article-title":"Multicolor super-resolution imaging with photo-switchable fluorescent probes","volume":"317","author":"Bates","year":"2007","journal-title":"Science"},{"key":"bib3","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1038\/s41592-019-0582-9","article-title":"ilastik: interactive machine learning for (bio)image analysis","volume":"16","author":"Berg","year":"2019","journal-title":"Nature Methods"},{"key":"bib4","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2016.7493463","article-title":"2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016)","author":"Bogovic","year":"2016","unstructured":"Bogovic JA, Hanslovsky P, Wong A, Saalfeld S. 2016. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016). 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