{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T05:09:04Z","timestamp":1772168944231,"version":"3.50.1"},"update-to":[{"DOI":"10.12688\/f1000research.26888.1","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T00:00:00Z","timestamp":1623283200000}},{"DOI":"10.12688\/f1000research.26888.2","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T00:00:00Z","timestamp":1623283200000}}],"reference-count":16,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T00:00:00Z","timestamp":1623283200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008349","name":"Boehringer Ingelheim","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008349","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000921","name":"European Cooperation in Science and Technology","doi-asserted-by":"publisher","award":["CA15124"],"award-info":[{"award-number":["CA15124"]}],"id":[{"id":"10.13039\/501100000921","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["HU600\/8-1"],"award-info":[{"award-number":["HU600\/8-1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["f1000research.com"],"crossmark-restriction":false},"short-container-title":["F1000Res"],"abstract":"<ns3:p>\n                    <ns3:bold>Background:<\/ns3:bold>\n                    Intravital microscopy is an emerging technique in life science with applications in kidney research. Longitudinal observation of (patho-)physiological processes in living mice is possible in the smallest functional unit of the kidney, a single nephron (sn). In particular, effects on glomerular filtration rate (GFR) - a key parameter of renal function - can be assessed.\n                  <\/ns3:p>\n                  <ns3:p>\n                    <ns3:bold>Methods:<\/ns3:bold>\n                    After intravenous injection of a freely filtered, non-resorbable, fluorescent dye in C57BL\/6 mice, a time series was captured by multiphoton microsopy. Filtration was observed from the glomerular capillaries to the proximal tubule (PT) and the tubular signal intensity shift was analyzed to calculate the snGFR.\n                  <\/ns3:p>\n                  <ns3:p>\n                    <ns3:bold>Results:<\/ns3:bold>\n                    Previously described methods for snGFR analysis relied on two manually defined measurement points in the PT and the tubular volume was merely estimated in 2D images. We present an extended image processing workflow by adding continuous measurement of intensity along the PT in every frame of the time series using ImageJ. Automatic modelling of actual PT volume in a 3D dataset replaced 2D volume estimation. Subsequent data analysis in R, with a calculation of intensity shifts in every frame and normalization against tubular volume, allowed exact assessment of snGFR by linear regression. Repeated analysis of image data obtained in healthy mice showed a striking increase of reproducibility by reduction of user interaction.\n                  <\/ns3:p>\n                  <ns3:p>\n                    <ns3:bold>Conclusions:<\/ns3:bold>\n                    These improvements in image processing and data analysis maximize the reliability of a sophisticated intravital microscopy technique for the precise assessment of snGFR, a highly relevant predictor of kidney function.\n                  <\/ns3:p>","DOI":"10.12688\/f1000research.26888.3","type":"journal-article","created":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T03:53:16Z","timestamp":1623297196000},"page":"1372","update-policy":"https:\/\/doi.org\/10.12688\/f1000research.crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A new analysis approach for single nephron GFR in intravital microscopy of mice"],"prefix":"10.12688","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1507-8009","authenticated-orcid":false,"given":"Friederike","family":"Kessel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hannah","family":"Kr\u00f6ger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Gerlach","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Sradnick","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2739-345X","authenticated-orcid":false,"given":"Florian","family":"Gembardt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vladimir","family":"Todorov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Hugo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2560","published-online":{"date-parts":[[2021,6,10]]},"reference":[{"key":"ref-1","doi-asserted-by":"publisher","first-page":"277-280","DOI":"10.1159\/000448183","article-title":"The Hyperfiltering Kidney in Diabetes.","volume":"136","author":"R Trevisan","year":"2017","journal-title":"Nephron."},{"key":"ref-2","first-page":"47-65","article-title":"Renal function tests in the diagnosis of glomerular and tubular disease.","volume":"26","author":"D Earle","year":"1950","journal-title":"Bull N Y Acad Med."},{"key":"ref-3","doi-asserted-by":"publisher","first-page":"303-315","DOI":"10.1161\/CIRCULATIONAHA.118.037418","article-title":"Evaluation of Glomerular Hemodynamic Function by Empagliflozin in Diabetic Mice Using In Vivo Imaging.","volume":"140","author":"K Kidokoro","year":"2019","journal-title":"Circulation."},{"key":"ref-4","doi-asserted-by":"publisher","first-page":"F495-502","DOI":"10.1152\/ajprenal.00521.2005","article-title":"Quantitative imaging of basic functions in renal (patho)physiology.","volume":"291","author":"J Kang","year":"2006","journal-title":"Am J Physiol Renal Physiol."},{"key":"ref-5","doi-asserted-by":"publisher","first-page":"25-44","DOI":"10.1007\/7651_2019_232","article-title":"Long-Term Cell Fate Tracking of Individual Renal Cells Using Serial Intravital Microscopy.","volume":"2150","author":"I Schiessl","year":"2020","journal-title":"Methods Mol Biol."},{"key":"ref-6","doi-asserted-by":"publisher","first-page":"1419-1432","DOI":"10.1016\/j.kint.2017.04.014","article-title":"Persistent and inducible neogenesis repopulates progenitor renin lineage cells in the kidney.","volume":"92","author":"L Hickmann","year":"2017","journal-title":"Kidney Int."},{"key":"ref-7","doi-asserted-by":"publisher","first-page":"518-529","DOI":"10.1002\/mrd.22489","article-title":"The ImageJ ecosystem: An open platform for biomedical image analysis.","volume":"82","author":"J Schindelin","year":"2015","journal-title":"Mol Reprod Dev."},{"key":"ref-8","doi-asserted-by":"publisher","first-page":"671-675","DOI":"10.1038\/nmeth.2089","article-title":"NIH Image to ImageJ: 25 years of image analysis.","volume":"9","author":"C Schneider","year":"2012","journal-title":"Nat Methods."},{"key":"ref-9","doi-asserted-by":"publisher","first-page":"676-682","DOI":"10.1038\/nmeth.2019","article-title":"Fiji: an open-source platform for biological-image analysis.","volume":"9","author":"J Schindelin","year":"2012","journal-title":"Nat Methods."},{"key":"ref-10","doi-asserted-by":"publisher","first-page":"1840-1841","DOI":"10.1093\/bioinformatics\/btt276","article-title":"TANGO: a generic tool for high-throughput 3D image analysis for studying nuclear organization.","volume":"29","author":"J Ollion","year":"2013","journal-title":"Bioinformatics."},{"key":"ref-11","doi-asserted-by":"publisher","first-page":"R47","DOI":"10.1186\/gb-2005-6-5-r47","article-title":"The Open Microscopy Environment (OME) Data Model and XML file: open tools for informatics and quantitative analysis in biological imaging.","volume":"6","author":"I Goldberg","year":"2005","journal-title":"Genome Biol."},{"key":"ref-12","article-title":"R: A Language and Environment for Statistical Computing","year":"2017"},{"key":"ref-13","article-title":"RStudio: Integrated Development Environment for R","year":"2019"},{"key":"ref-14","article-title":"ggplot2: Elegant Graphics for Data Analysis","year":"2016"},{"key":"ref-15","first-page":"2020","article-title":"NephrologieDresden\/cont-3D-snGFR: Continuous analysis of single nephron GFR","author":"F Kessel"},{"key":"ref-16","article-title":"Sample dataset - cont-3D-snGFR.","author":"F Kessel"}],"container-title":["F1000Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/f1000research.com\/articles\/9-1372\/v3\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/f1000research.com\/articles\/9-1372\/v3\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/f1000research.com\/articles\/9-1372\/v3\/iparadigms","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,16]],"date-time":"2021-06-16T07:20:12Z","timestamp":1623828012000},"score":1,"resource":{"primary":{"URL":"https:\/\/f1000research.com\/articles\/9-1372\/v3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,10]]},"references-count":16,"URL":"https:\/\/doi.org\/10.12688\/f1000research.26888.3","relation":{"has-review":[{"id-type":"doi","id":"10.5256\/f1000research.57648.r87230","asserted-by":"subject"},{"id-type":"doi","id":"10.5256\/f1000research.57648.r87230","asserted-by":"object"}]},"ISSN":["2046-1402"],"issn-type":[{"value":"2046-1402","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,10]]},"assertion":[{"value":"Indexed","URL":"https:\/\/f1000research.com\/articles\/9-1372\/v3#article-reports","order":0,"name":"referee-status","label":"Referee status","group":{"name":"current-referee-status","label":"Current Referee Status"}},{"value":"10.5256\/f1000research.29693.r75572, Bruce Molitoris, Department of Medicine, Indiana University School of Medicine, Indianapolis, USA, 16 Dec 2020, version 1, 1 approved with reservations, 1 not approved","URL":"https:\/\/f1000research.com\/articles\/9-1372\/v1#referee-response-75572","order":0,"name":"referee-response-75572","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Friederike Kessel<\/b>; \n<i>Posted: 25 Jan 2021<\/i>; We thank Prof. Molitoris for his time and the detailed review. However there seems to be a misunderstanding of the intentions of the submitted manuscript.  We present and validate a workflow and an algorithm to process image data obtained from single nephron GFR measurements by intravital microscopy. The measurement itself is not the focus of the manuscript. A detailed protocol on animal preparation and image data acquisition was merely cited (1), however the experimental setup is reproducible using these protocols. In our setup, superficial glomeruli can be imaged in the intact kidney of 10-12 week old animals with 2-photon microscopy with sufficient quality. We want to specifically point out that removing parts of the kidney cortex prior imaging is not included in the cited protocols and we did not perform this in our study either. Prof. Molitoris is possibly referring to the paper of Kidokoro et al. (2) which was cited by us in the context of illustrating an existing analysis approach for image data obtained from the snGFR measurement.  With the focus of the manuscript in mind: We compare results obtained from the analysis of the same image datasets with two different workflows (but using the same segments of the proximal tubule) \u2013 and not different experimental groups of differently treated animals. With all raw image data (openly accessible at Zenodo), the source code (openly accessible at GitHub) and open source software (ImageJ and R) the data we present are completely reproducible.  We agree with Prof. Molitoris that presenting more image data in the manuscript might be beneficial. The challenges of depicting time series and complex 3D image data in a 2D representation led us to the decision to upload all raw image data (time series and 3D datasets) to Zenodo, where it can be freely accessed.&nbsp;Lastly we rephrase the description of one of the image processing steps: As Prof. Molitoris pointed out correctly, the subtraction of one z-stack from the other is performed plane by plane, and not with 3D volumes.  Clarifying changes in the manuscript on the emphasis of the intention (presenting an image analysis workflow) will be made in an upcoming version. We would gladly invite Prof. Molitoris to review the updated manuscript again with this perspective.  Kidokoro K, Cherney DZI, Bozovic A, \n<i>et al.<\/i>: Evaluation of Glomerular Hemodynamic Function by Empagliflozin in Diabetic Mice Using In Vivo Imaging. \n<i>Circulation.<\/i> 2019; \n<b>140<\/b>(4): 303\u2013315. Schiessl IM, Fremter K, Burford JL, \n<i>et al.<\/i>: Long-Term Cell Fate Tracking of Individual Renal Cells Using Serial Intravital Microscopy. \n<i>Methods Mol Biol.<\/i> 2020; \n<b>2150<\/b>: 25\u201344.","URL":"https:\/\/f1000research.com\/articles\/9-1372\/v3#referee-comment-6299","order":1,"name":"referee-comment-6299","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.55759.r83125, Christopher Schmied, FMP Berlin, Berlin, Germany, 23 Apr 2021, version 2, 1 approved, 1 approved with reservations, 2 not approved","URL":"https:\/\/f1000research.com\/articles\/9-1372\/v2#referee-response-83125","order":2,"name":"referee-response-83125","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.55759.r84810, Georgina Gyarmati, Departments of Physiology and Neuroscience, and Medicine, Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, Los Angeles, CA, USA, 18 May 2021, version 2, 1 approved, 1 approved with reservations, 2 not approved","URL":"https:\/\/f1000research.com\/articles\/9-1372\/v2#referee-response-84810","order":3,"name":"referee-response-84810","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.57648.r87230, Anna Schueth, Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, Maastricht, The Netherlands, 16 Jun 2021, version 3, indexed","URL":"https:\/\/f1000research.com\/articles\/9-1372\/v3#referee-response-87230","order":4,"name":"referee-response-87230","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"Article Publication Charges were funded by COST Action NEUBIAS (CA15124). The work was supported by DFG grant No. HU600\/8-1 as well as research grant from Boehringer Ingelheim. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","order":5,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}