F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.26888.1 Research Article Articles A new analysis approach for single nephron GFR in intravital microscopy of mice

[version 1; peer review: 1 approved with reservations, 1 not approved]

 Kessel Friederike Conceptualization Formal Analysis Methodology Software Validation Visualization Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0002-1507-8009 1 Kröger Hannah Data Curation Investigation Validation Writing – Review & Editing 1 Gerlach Michael Conceptualization Resources Supervision Writing – Review & Editing 2 Sradnick Jan Supervision Writing – Review & Editing 1 Gembardt Florian Writing – Review & Editing https://orcid.org/0000-0003-2739-345X 1 Todorov Vladimir Supervision Writing – Review & Editing 1 Hugo Christian Funding Acquisition Project Administration Resources Supervision Writing – Review & Editing a 1 Experimental Nephrology and Division of Nephrology, Department of Internal Medicine III, University Hospital Carl Gustav Carus at the Technische Universität Dresden, Fetscherstraße 74, Dresden, 01307, Germany Core Facility Cellular Imaging (CFCI), University Hospital Carl Gustav Carus at the Technische Universität Dresden, Fetscherstraße 74, Dresden, 01307, Germany Christian.Hugo@ukdd.de

Competing interests: The work was partially funded by Boehringer Ingelheim.

 26 11 2020 2020 9 1372 17 11 2020 Copyright: © 2020 Kessel F et al. 2020 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.

Background: 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.

Methods: After intravenous injection of C57BL/6 mice with a freely filtered, non-resorbable, fluorescent dye 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.

Results: Previous methods for this analysis relied on two manually defined measurement points in the PT and the tubular volume was merely estimated in 2D images. We extended the workflow in FIJI by adding continuous measurement of intensity along the PT in every frame of the time series. 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.

Conclusions: These improvements maximize the reliability of a sophisticated intravital microscopy technique for the precise assessment of snGFR, a highly relevant predictor of kidney function.

 Intravital Microscopy 2-Photon Microscopy Kidney Single Nephron GFR ImageJ R Boehringer Ingelheim Deutsche Forschungsgemeinschaft HU600/8-1 European Cooperation in Science and Technology CA15124 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. Introduction

Glomerular filtration rate (GFR) is a key parameter of kidney function and deviations from normal GFR are a hallmark of renal diseases 1, 2 . GFR describes the filtration of substances from blood in the glomerular capillaries, to the primary urine in the tubular system of the kidney. Therefore, changes in GFR serve to monitor disease progression. GFR is also measured in animal models to study effects of pharmacological intervention on kidney function. Advances in intravital imaging and multiphoton microscopy allow repetitive assessment of GFR and morphological changes in the smallest functional unit of the kidney – the nephron. Longitudinal imaging of single nephrons (sn) enable direct correlation of structural and functional data.

After intravenous injection of the freely filtered, non-resorbable, fluorescent dye LuciferYellow (LY), 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 is analyzed to calculate the filtration rate. Translated to an image processing task, this can be generalized as the flow rate in a tube. Previous methods for this analysis 3, 4 relied on two manually annotated measurement points in the PT and stereotypic estimation of PT volume in 2D images. Since results we obtained with this approach were highly variable, we expanded the analysis via 3D modelling with open source software, to increase overall reproducibility and reliability when comparing renal function of different experimental groups.

 Methods Animal experiments

Animal experiments were performed in accordance with the Federation of European Laboratory Animal Science Associations (FELASA) Guidelines for the Care and Use of Laboratory Animals and the Federal Law on the Use of Experimental Animals in Germany and approved by the ethical review committee at the Landesdirektion Sachsen (licence DD-24.1-5131/338/37). For microscopy, male, 10–12 week old C57BL/6 mice were prepared as previously described 5, 6 . In brief, a titanium abdominal imaging window (AIW) covered with a coverslip is surgically implanted above the kidney. The kidney is glued to the coverslip with cyanoacrylate glue before securing the AIW by tightening the skin in the AIW groove. Microscopy was performed one day after AIW implantation.

A custom-built temporary intravenous catheter (polyethylene tubing #587360 by Science Products GmbH with 0.3×12mm needle) was placed in the lateral tail vein. Fluorescent dyes were administered into the tail vein prior (Hoechst, AngioSpark) or during (LuciferYellow) microscopy (detailed information in Table 1).

Dyes.
Dye Order Number Supplier Purpose Application details Channel Exitation Acquisition
AngioSPARK 680 NEV10149 PerkinElmer Vessel dye 30 µl 3 860 nm 685-695 nm
Hoechst 33342 H3570 Thermo Fisher Nuclear dye 50 µl (2 mg/ml) 4 860 nm 415-474nm
Lucifer Yellow CH dilithium salt L0259- 25MG Sigma Aldrich Freely filtered flourescent dye 20 µl via syringe pump in 1 s (5 mg/ml) 2 860 nm 500–550nm

All efforts were made to ameliorate harm to animals. Imaging (including injections of the fluorescent dyes) and the implantation is done under isoflurane narcosis. The image data of the five animals presented for the comparison of the extended workflow with the previous workflow in this manuscript were generated previously as part of an independent experiment (licence DD-24.1-5131/338/37).

 Microscopy

Imaging was performed on an upright Leica SP8 multiphoton laser scanning microscope of the Core Facility Cellular Imaging. Settings for signal acquisition are summarized in Table 2.

Image acquisition settings.
Dye Exitation Objective Resolution Detection
AngioSPARK 680 860 nm, Chameleon II (Coherent) 40x 1.1 NA water immersion objective Pixel size: 0.8513 µm frame rate (time series): 6 fps Voxel depth (z-stack): 1 µm 685-695 nm, HyD detector (Leica)
Hoechst 33342 415-474nm, PMT detector (Leica)
Lucifer Yellow CH dilithium salt 500-550nm, HyD detector (Leica)
Image and data analysis

Image processing and analysis was done in ImageJ 7– 9 (1.53c) with 3D ImageJ Suite 10 and Bio-Formats 11 for the use of 3D image processing plugins and the Bio-Formats Importer. Data analysis was performed in R 12 (4.0.2), with RStudio 13 (1.2.5033) with ggplot2 14 (including dependencies) installed as additional library. The script executed the ImageJ macro from command line and subsequently analyzed and visualized the results. A detailed description of the algorithm is associated with the scripts on GitHub 15 .

The line region of interest (ROI) set for the extended workflow to manually define direction and position of the proximal tubule (PT) was also used to determine the two measuring points (beginning and end of line) for analysis of image material based on the previously described approach 3, 4 . Tubular diameter was calculated as the mean of five manually measured diameters.

 Results

In the time series acquired after application of LuciferYellow (LY), a line region of interest (ROI) was set to manually define the position and direction of the measurement. Along this line ROI, x-y plots measured the dye intensity in the proximal tubule (PT) in every frame ( Figure 1) and numerical results were saved.

Measurement of signal intensity in a time series of the proximale tubule.

Signal intensity of LuciferYellow (LY) was measured along a line region of interest (magenta) in every frame (here only frame 0 - before LY injection, frame 13 and 26).

For the automatic 3D modelling of PT volume the z-stack of the same field of view was acquired. Additional channels (Ch3: AngioSpark - vessels, Ch4: Hoechst - nuclei, Figure 2A) were subtracted from Ch2 (target channel, LuciferYellow intensity) to remove spectral bleed-through artifacts ( Figure 2B). With the 3D watershed, the PT was segmented ( Figure 2C, 3D-model) and saved for visual verification. The cumulative PT volume was measured over the distance along the line ROI and interpolated for every measurement position along the line ROI in the PT ( Figure 3A) in subsequent data analysis. From intensity measurements a threshold intensity was set to the turning point of fluorescence intensity over time at every position (maximum slope, Figure 3B). The position with this intensity was approximated in each frame and used for linear regression ( Figure 3C). The slope of the regression line equals the snGFR. Together with information about PT length, PT volume and R-squared the results were summarized and saved in a data table.

Automatic 3D modelling of tubular volume in a z-stack of the proximal tubule (PT).

A) After applying a 3D median filter, the channel 3 and channel 4 z-stacks were substracted from channel 2 to eliminate spectral bleedthrough artifacts ( B). The proximal tubule (PT) was segmented with the help of a 3D watershed (3D model of the resulting z-stack, C).

 Data analysis and linear regression of signal volume against time for calcuation of glomerular filtration rate (GFR).

A) For every position along the line region of interest (ROI), the cumulative volume was measured. B) Numerical data underlying the x-y plots was saved and used to subsequently plot changes of signal intensity over time for every position along the line ROI. The dashed line represents the threshold value (intensity with maximum slope across all positions), at which position and corresponding volume of the proximal tubule (PT) was approximated for every frame. C) Using linear regression the GFR could be calculated as the volume with the intensity threshold at the frames of interest. Regression line is displayed with 95% confidence interval.

Repeated analysis of 15 indiviudal glomeruli by the same researcher (five times) showed that results obtained with the presented workflow had higher consistency (lower intrasample variance, CV=10.35%) compared to the previous approach (CV=38.75%, Figure 4). Due to the high variance with the previous approach a direct correlation of the workflows was not possible; however, the final result - the mean snGFR - was comparable (previous workflow: 1.71±0.91, extended workflow: 1.70±0.78) and a two-sample Kolmogorov-Smirnof test of both result vectors showed that the distributions were not different (p=0.4662).

Application and comparison of the workflows in image data of healthy mice.

Image data of healthy mice (five animals, 15 glomeruli) was analysed five times by the same researcher using the previous and the extended workflow. Scatter plot of results of the previous (x-axis) and extended workflow (y-axis) with rectangles used to indicate the range of results obtained in one glomerulus. Colours indicate data obtained from individual glomeruli. Intrasample variance with the extended workflow (variance along the y-axis, mean CV=10.35%) was smaller than with the previous workflow (variance along the x-axis, mean CV=38.75%). Both analysis workflows showed similar results (mean snGFR, previous workflow: 1.71±0.91, extended workflow: 1.70±0.78) and a two-sample Kolmogorov-Smirnof test of both result vectors showed that the distributions were not different (p=0.4662).

 Conclusions

The progressive development of microscopy techniques like measurement of snGFR in experimental animals needs to be accompanied by improvements in analysis algorithms to use their full potential. In this manuscript we present a workflow by extending an existing analysis via 3D modelling, for increased reproducibility, accuracy, but also transparency in the measurement of snGFR. By reducing user interaction, intrasample variance was markedly improved.

Additionally, the automatically saved user input and intermediate results (z-stack of watershed of PT as shown in Figure 2C and graphs in Figure 4) for every analyzed dataset provide full possibility to verify every analysis step. These results can be used to objectively evaluate the measurement. Although the snGFR in this manuscript was very low for healthy animals compared to previously published values 3 , the range was comparable in both methods and not an artifact produced by the workflow but more likely caused by the general experimental setup.

Taken together, this workflow extension contributes to an overall improvement of snGFR measurement. Applied to experimental data this can cumulate in a higher power to detect statistically significant differences between experimental groups and even decrease the necessary sample size, thus having an impact on animal welfare.

 Data availability Underlying data

Zenodo: Sample dataset - cont-3D-snGFR. https://doi.org/10.5281/zenodo.4275596 16 .

This project contains the following underlying data:

Sample_Dataset_cont-3D-snGFR.lif (Sample file with time series and z-stack of three different glomeruli after injection of LuciferYellow for the analysis of single nephron GFR)

Results.zip (Sample file for the selection (ROI sets) of the proximal tubulus in the sample dataset, including the resulting measurements (text files) in the time series and 3D modelling of the proximal tubules (tiff files))

Graphs_2020-09-30.zip (Intermediate results and graphs (png files) as obtained from the sample dataset with selections and measurement data in the results file)

2020-09-30-Result_summary.txt (Final summary (text file) of calculated single nephron GFR for the three sample glomeruli based on selections from the results file)

Dataset1.lif (Image data used for the comparison of previous and extended workflow in Figure 4, includes 15 time series and the corresponding z-stacks)

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

 Software availability

Source code available from: https://github.com/NephrologieDresden/cont-3D-snGFR

Archived source code at time of publication: https://doi.org/10.5281/zenodo.4059549 15 .

License: GNU General Public License v3.0

Acknowledgments

The authors acknowlegde the support of the Core Facility Cellular Imaging (CFCI) at the Medical Faculty Carl Gustav Carus, Technical University Dresden. This publication was supported by COST Action NEUBIAS (CA15124), funded by COST (European Cooperation in Science and Technology).

 Trevisan R Dodesini AR : The Hyperfiltering Kidney in Diabetes. Nephron. 2017;136(4):277–280. 27978521 10.1159/000448183 Earle DP Jr : Renal function tests in the diagnosis of glomerular and tubular disease. Bull N Y Acad Med. 1950;26(1):47–65. 15398884 1929902 Kidokoro K Cherney DZI Bozovic A : Evaluation of Glomerular Hemodynamic Function by Empagliflozin in Diabetic Mice Using In Vivo Imaging. Circulation. 2019;140(4):303–315. 30773020 10.1161/CIRCULATIONAHA.118.037418 Kang JJ Toma I Sipos A : Quantitative imaging of basic functions in renal (patho)physiology. Am J Physiol Renal Physiol. 2006;291(2):F495–502. 16609147 10.1152/ajprenal.00521.2005 Hickmann L Steglich A Gerlach M : Persistent and inducible neogenesis repopulates progenitor renin lineage cells in the kidney. Kidney Int. 2017;92(6):1419–1432. 28688581 10.1016/j.kint.2017.04.014 5696031 Schiessl IM Fremter K Burford JL : Long-Term Cell Fate Tracking of Individual Renal Cells Using Serial Intravital Microscopy. Methods Mol Biol. 2020;2150:25–44. 31087287 10.1007/7651_2019_232 6854295 Schindelin J Rueden CT Hiner MC : The ImageJ ecosystem: An open platform for biomedical image analysis. Mol Reprod Dev. 2015;82(7–8):518–529. 26153368 10.1002/mrd.22489 5428984 Schneider CA Rasband WS Eliceiri KW : NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9(7):671–675. 22930834 10.1038/nmeth.2089 5554542 Schindelin J Arganda-Carreras I Frise E : Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9(7):676–682. 22743772 10.1038/nmeth.2019 3855844 Ollion J Cochennec J Loll F : TANGO: a generic tool for high-throughput 3D image analysis for studying nuclear organization. Bioinformatics. 2013;29(14):1840–1841. 23681123 10.1093/bioinformatics/btt276 3702251 Goldberg IG Allan C Burel JM : The Open Microscopy Environment (OME) Data Model and XML file: open tools for informatics and quantitative analysis in biological imaging. Genome Biol. 2005;6(5):R47. 15892875 10.1186/gb-2005-6-5-r47 1175959 R: A Language and Environment for Statistical Computing. (Vienna, Austria, 2017). Reference Source RStudio: Integrated Development Environment for R. (Boston, MA, 2019). Reference Source ggplot2: Elegant Graphics for Data Analysis. (Springer-Verlag New York),2016. Reference Source Kessel F Kröger H Hugo C : Continuous analysis of single nephron GFR.2020. http://www.doi.org/10.5281/zenodo.4059549 Kessel F Kröger H Hugo C : Sample dataset - cont-3D-snGFR.2020. http://www.doi.org/10.5281/zenodo.4275596 10.5256/f1000research.29693.r76548 Reviewer response for version 1 Schmied Christopher 1 Referee https://orcid.org/0000-0003-2058-1124 FMP Berlin, Berlin, Germany

Competing interests: No competing interests were disclosed.

 28 1 2021 Copyright: © 2021 Schmied C 2021 This is an open access peer review report 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. recommendation approve-with-reservations

The authors describe a semi-automated analysis for measuring single nephron glomerular filtration rate (snGFR). An important parameter for assessing renal function. Intravital microscopy was used to record the filtration of a fluorescent dye along glomerular vessels to the proximal tubule. The aim of the analysis is to measure the flow rate. To achieve this the user first sets a line ROI to determine the position and direction for the measurement of the intensity change across the time course. Then the entire 3D volume is segmented in a separate z-stack. The analysis is smart and tries to use all available information in their experimental setup to ensure a robust analysis. The generation of the 3D volume based on a 3D watershed uses the information from different channels to ensure a robust segmentation. Using a regression analysis makes sure that not only 2 points alone will contribute to the final measurement of the snGFR. I think this is a good image and data analysis approach to reduce measurement variability and increase statistical power.

The workflow runs as R script that also calls a Fiji macro. The interaction runs via sequential GUI prompts. This increases the ease of use. 

The article is, in general, well written and contains most of the important information to understand the method and how it compares to previously used methods. The detailed description of the algorithm is sometimes a bit confusing but one can understand the rational of the authors. My main problem is with the lack of documentation that allows one to access and implement the tools. Here are my points for revision: 

MAJOR: The algorithm makes sense. The description in the text and figure legends is however a bit hard to understand.

This sentence is particularly unclear: " The position with this intensity was approximated in each frame and used for linear regression (Figure 3C)". I guess what the authors wanted to express is that the volume was approximated at this position. Then the approximated volume was plotted over time and based on that linear regression was performed?

Figure 3 B&C and their legends are rather confusing: 

Figure 3B mixes in the volume of the segmentation, although this is not the message of the figure (Intensity and computation of threshold on slope). The color code of Figure 3B&C corresponds I guess to the positions? This is not explained anywhere. In Figure 3A first µm³ is used and then in Figure 3C nl?

MAJOR: I downloaded the material and it took me about 4 tries to get the scripts to work correctly. Here are the key impediments: 

Does not execute on ubuntu 20.04: the R script uses functions that only work under Windows. This limitation needs to be explicitly stated. This also abolishes the advantage of cross platform tools such as Fiji and R. 

The 3D watershed is not explicitly stated as dependency of Fiji. It needs to be clearly stated what needs to be installed and how. 

The direction of the ROI is important but this was not clear from the documentation. 

 MAJOR: The Documentation word file provided is not helpful for actually using the scripts. It rather contains a code documentation that has some directions of using the program included. People with little expertise have no clear guidance for the usage and the important settings of the usage are entirely lost in all the detail. 

The usage needs to be documented separately from the code. 

The actual interaction with the program needs to be documented also via screenshots.

The important settings need to be explained clearly and in sufficient detail. 

 MAJOR: That one needs to draw the ROI in the direction of the wave was not really obviously documented or it got lost in the complexity of the code documentation. Please use screenshots or describe with words. 

MAJOR: How the data needs to be acquired and structured for this workflow to function is not explained anywhere. The prompt for selecting a corresponding z-stack made initially zero sense. Since it was not clear that the .lif file must contain the multichannel time series AND the z-stack. Are the channels settings hard coded then? If the analysis is inflexible in its data input (which can be ok), it needs to be mention explicitly as an important prerequisite.

MAJOR: Reproducing the workflow using the provided .lif file resulted mostly in snGFR that were in a similar range. But still off. Maybe drawing the ROIs seems to be still an important source of variability. It would be good if there would be an easy way to load and visualize the ROIs provided by the authors. This shows easily how the authors intend users to set ROIs. One can load them via the ROI Manager during the GUI interactions but this produces an error later on: 

Composite selections cannot be converted to lines. in line 520: 

(called from line 193) 

run ( "Area to Line" <)> 

Maybe it would also be good to document in words along example screenshots how one best should set the ROI.

MAJOR: I am not in the kidney field. Maybe certain statements are common knowledge there and it is practice not to cite them. But the following statements in the introduction would strike me as requiring citations: 

"Therefore, changes in GFR serve to monitor disease progression."

"GFR is also measured in animal models to study effects of pharmacological intervention on kidney function." 

"Advances in intravital imaging and multiphoton microscopy allow repetitive assessment of GFR and morphological changes in the smallest functional unit of the kidney – the nephron."

"Longitudinal imaging of single nephrons (sn) enable direct correlation of structural and functional data."

"Since results we obtained with this approach were highly variable." 

MINOR: All the result files produced by the workflow and how to interpret them and recognize issues are not described anywhere in the documentation. 

MINOR: It would be nice, at least in the documentation, to layout graphically the flow of the workflow.

MINOR: Please include a brief description of the usage in the README. What needs to be installed (also the 3D watershed update site) and how to run the workflow. Also any important prerequisites should be mentioned there as well. 

MINOR: Selecting the "executable Fiji file" needs to be described better and documented with a screenshot. Users that have Fiji preinstalled or rarely use Fiji will not know this. 

MINOR: Figure 4 is hard to interpret and one cannot easily compare own results for reproducing the workflow. The result of the automatic analysis are included as an extra file. It would be nice to have the results of this automatic and manual analysis available as a table for a direct comparison. 

MINOR: I miss an explicit point of contact or means of support for any users such as github, forum or Email.

Is the work clearly and accurately presented and does it cite the current literature?

Partly

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Partly

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

No

Reviewer Expertise:

Bioimage analysis, Computer vision, Data science

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Kessel Friederike University Hospital Carl Gustav Carus Dresden, Germany

Competing interests: No competing interests were disclosed.

 28 3 2021

First of all we thank the reviewer for the extensive report and constructive feedback. We agree that a lack of documentation, guidance and also support information notably impaired the usability of the workflow. In this context, the patience of the reviewer to implement the algorithm is highly appreciated.

Since most of the remarks were directly linked with lack of documentation, we uploaded detailed instructions to the GitHub repository and updated the associated release on Zenodo. This documentation now includes:

A paragraph on the structure of the raw data

System requirements (operating system)

Software requirements (ImageJ, including update sites, R and RStudio with additional libraries)

Instructions on how to run the workflow with screenshots

Explicitly pointing out the importance of the direction of the line ROI

With additional screenshots of example line ROIs for the images included in the sample dataset

Information on all output files (ROI sets, result files, graphs)

Suggestions for data interpretation and troubleshooting

Contact information

 Since this analysis was only recently developed and experiences when applying it to different image data are still limited we are determined to continuously expand the documentation and troubleshooting suggestions. We recognize that there is also room for improvement for the programming itself, regarding the limitation to Windows and hard-coded requirements of the raw data. We plan to support the gradual expansion of the workflow to be more adaptable – and applicable – in the future (as mentioned in the documentation).

Since the reviewer pointed out that some of the descriptions in the manuscript and figure legends were hard to understand, we rephrased some points. We hope it is now more understandable.

Description of Figure 3C: Approximation of the volume for every position and plotting against time for linear regression

Legends for Figures B and C: Colour code

Conversion of units: µm³ to nl in Figure 3C

 We also included the table with the numerical results as shown in Figure 4.

Finally, the statements on GFR and methods in intravital microscopy in the introduction can be supported with references that were already used in other contexts in the manuscript. Therefore, we additionally refer to them in the introduction.

 10.5256/f1000research.29693.r75572 Reviewer response for version 1 Molitoris Bruce A. 1 Referee Department of Medicine, Indiana University School of Medicine, Indianapolis, USA

Competing interests: No competing interests were disclosed.

 16 12 2020 Copyright: © 2020 Molitoris BA 2020 This is an open access peer review report 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. recommendation reject

Measurement of SNGFR is an important undertaking and adding a 3D component to tubular volume is interesting. However, viewing glomeruli in mice of 10-12 weeks is not possible without significant invasive proceedures. Ureteral obstruction has been used by some, but this author uses removal of 1 mm of cortical tissue to get down to cortical glomeruli. They do not say this but reference an existing paper. Since you can only see up to 100 microns with the 2-photon scope with high resolution, it is difficult to imaging tissue injury is not altering function of the glomerulus and tubules. This has to be discussed and if controls are available they should be mentioned. In the referenced paper the sieving Coefficient for albumin was very high and likely due to tissue injury. No glomerulus is shown in the present work and yet the authors indicate they followed from glom and then along the tubule. Were they measuring flow in S1 or S2 segments as flow would vary due to reabsorption?

Also, the subtraction of 3D volumes from each other for background subtraction is not recommended. It is best to subtract each individual plane from the corresponding channels.

It would also be helpful if they put figure 4 data into a table for easier and direct comparison.

Is the work clearly and accurately presented and does it cite the current literature?

Partly

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

No source data required

Is the study design appropriate and is the work technically sound?

Partly

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

No

Reviewer Expertise:

Nephrology, imaging

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

Kessel Friederike University Hospital Carl Gustav Carus Dresden, Germany

Competing interests: No competing interests were disclosed.

 25 1 2021

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) – 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. 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, et al.: Evaluation of Glomerular Hemodynamic Function by Empagliflozin in Diabetic Mice Using In Vivo Imaging. Circulation. 2019; 140(4): 303–315.

Schiessl IM, Fremter K, Burford JL, et al.: Long-Term Cell Fate Tracking of Individual Renal Cells Using Serial Intravital Microscopy. Methods Mol Biol. 2020; 2150: 25–44.