Method Article

Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo

DOI:

10.3791/57214

⸱

May 4th, 2018

 ,  ,  ,  ,  ,  ,  ,  ,  , 
1Department of Radiology, University of California, San Diego, 2Central European Institute of Technology, Brno University of Technology, 3Department of Neurosciences, University of California, San Diego, 4Department of Physics, John Carroll University, 5Department of Biomedical Engineering, Boston University, 6Bioengineering Undergraduate Program, University of California, San Diego, 7Institute of Physical Engineering, Faculty of Mechanical Engineering, Brno University of Technology, 8Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School

In This Article

Summary

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A graphical user interface for exploring and sharing a database of optogenetically-induced vascular responses in mouse somatosensory cortex in vivo measured by 2-photon microscopy is presented. It allows browsing the data, criteria-based selection, averaging, localization of measurements within a 3D volume of vasculature and exporting the data.

Abstract

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The importance of sharing experimental data in neuroscience grows with the amount and complexity of data acquired and various techniques used to obtain and process these data. However, the majority of experimental data, especially from individual studies of regular-sized laboratories never reach wider research community. A graphical user interface (GUI) engine called Neurovascular Network Explorer 2.0 (NNE 2.0) has been created as a tool for simple and low-cost sharing and exploring of vascular imaging data. NNE 2.0 interacts with a database containing optogenetically-evoked dilation/constriction time-courses of individual vessels measured in mice somatosensory cortex in vivo by 2-photon microscopy. NNE 2.0 enables selection and display of the time-courses based on different criteria (subject, branching order, cortical depth, vessel diameter, arteriolar tree) as well as simple mathematical manipulation (e.g. averaging, peak-normalization) and data export. It supports visualization of the vascular network in 3D and enables localization of the individual functional vessel diameter measurements within vascular trees.

NNE 2.0, its source code, and the corresponding database are freely downloadable from UCSD Neurovascular Imaging Laboratory website1. The source code can be utilized by the users to explore the associated database or as a template for databasing and sharing their own experimental results provided the appropriate format.

Introduction

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The brain is considered one of the most intricate organs and the desire to untangle its complex function is unflagging. It is being studied at different scales from the molecular to the behavioral level using a wide palette of tools2,3,4,5,6,7,8. The amount of non-homogeneous experimental data grows with unprecedented speed. The awareness of the need for experimental data sharing, organization and standardization grows with the amount of acquired data. It has become evident that neuroinformatics will play a critical role in integrating experimental data across scales into models of brain function and dysfunction9,10.

To this end some studies, especially larger-scale studies, were able to earmark resources to make their results available via extensive databases11,12,13,14,15. However, a vast amount of experimental data from individual studies and regular-sized laboratories never reached the wider research community. This is mainly for two reasons: first, more dedicated time is needed to build a database and create tools that would enable the user to interact with the database; and second, more money is needed to support these tasks. Motivated by these challenges, a MATLAB based graphical user interface (GUI) engine called the Neurovascular Network Explorer 2.0 (NNE 2.0)16 was developed as a simple and low-cost tool for databasing, sharing and exploring of vascular imaging data. This manuscript provides a manual for operation of NNE 2.0 and the associated database of experimental data.

NNE 2.0 is already a second-generation software engine. The first generation, called Neurovascular Network Explorer 1.0 (NNE 1.0)17 was built to interact with a database of sensory-evoked vasodilation in rat primary somatosensory cortex (SI) in vivo measured by 2-photon microscopy18. NNE 1.0, its source code as well as the associated database are freely downloadable as a zipped file called ‘NNE 1 Tian’ from UCSD Neurovascular Imaging Laboratory website1. More information about NNE 1.0 and the associated database can be found in17.

The second generation, the NNE 2.0, interacts with a database of optogenetically-evoked dilation of individual vessels in mice SI in vivo measured by 2-photon microscopy20. The user can browse, select and visualize data based on selection categories such as cortical depth, branching order, vessel diameter, animal subject or a particular arteriolar tree. The GUI further performs simple mathematical operations such as averaging and peak-normalization in selected categories. NNE 2.0 enables to view and browse through images capturing 3D volumes of vasculature as well as identify the location of the functional measurement within the vascular trees. This feature can be used to reconstruct vascular morphologies in 3D and populate them with real single-vessel vaso-motion measurements. These reconstructions can in turn be incorporated into computational models of brain function21,22. NNE 2.0, its source code and the associated database are freely downloadable as a zipped file called ‘NNE 2.0 HDbase v1.0’ from UCSD Neurovascular Imaging Laboratory website1.

NNE 2.0 works with a database called 'vdb.mat'. This database is a matrix containing temporal profiles (time-courses) of single vessel diameter changes evoked by an optogenetic stimulus and measured at different locations of arteriolar trees. Each time-course was computed using custom-written software. It calculates the relative change of a vessel diameter from expansion of a fluorescent intensity profile acquired by scanning across the vessel. The fluorescent contrast was presented by intravascular injection of fluorescein isothiocyanate (FITC)-labeled dextran. For more information about the data and analysis procedures, please see20,23. The database has 305 time-courses (i.e. database entries) in total. In addition to the diameter change, each entry to the database holds an array of additional metadata which (1) quantify the time-course (2) describe the measured vessel and (3) identify the measurement location within a 3D volume of cortical vasculature. The metadata include the onset time, peak amplitude, peak amplitude time, cortical depth, branching order, vessel diameter at baseline, path to original reference images and 3D image stacks for each measurement and low-magnification maps of brain surface vasculature. Please see all parameters in the metadata listed and described in detail previously in Table 116.

NNE 2.0 interacts with reference images that are X-Y scans of a plane where the diameter measurement occurred. Each database entry has one corresponding reference image with a reference name displayed in the GUI. Each database entry also has an associated stack of images (3D stack) capturing a 3D volume of the vascular tree within which the measurement occurred. The GUI enables to choose a particular database entry and display the corresponding reference image as well as the 3D stack. It also guides the user to find the matching reference image and frame in the 3D stack (the same features can be found in both images). All stack and reference images in their full resolution (1024 pix x 1024 pix) are included in folders hana_stk and hana_refs, respectively. Low-magnification maps of brain vasculature are included in folder 'maps'. All three folders as well as the database matrix ‘vdb.mat’ are downloaded in the zipped file ‘NNE 2.0 HDbase v1.0’ from the UCSD Neurovascular Imaging Laboratory website1 and saved to the root folder of NNE 2.0 during the installation process.

The GUI has been designed as a set of four panels (Panel 1 (Main Panel) – Panel 4) which open sequentially as the user explores the database and selects specific data based on selection categories. Each panel is divided into two main parts: (1) the right column provides the possibility to interact with the database by selecting parameters and categories of the data and displays important information from the metadata; (2) the left column displays the data in the form of time-courses (diameter change in time) and scatter-plots. There are four types of scatter plots displaying (1) dilation onset time (2) time of the dilation peak (3) maximum diameter change (peak amplitude) and (4) baseline diameter (diameter before stimulation) as function of cortical depth. The user has the possibility to display average time-courses and values for selected data grouped either by cortical depth or branching order. This is to highlight the feature of gradient diameter-change behavior with increasing depth and branching order20. NNE 2.0 allows the user to export the selected subset of data in the format of '.xls', '.csv' or '.mat'.

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Protocol

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1. Installation of NNE 2.0

  1. Go to UCSD Neurovascular Imaging Laboratory website1 and left-click at ‘NNE 2.0 HDbase v1.0’ to download the zipped program files to the desired location on your PC.
    NOTE: NNE 2.0 requires a Windows-operating system of versions 7-10, at least 2.8 GB of free space to download the zipped file and 6.9 GB to install the program.
  2. Unzip ‘NNE2_HDbase_v1.0.zip’.
    NOTE: The unzipped folder NNE2 contains 10 files: ‘hana_refs.tar.gz’, ‘hana_stk.tar.gz’, ‘maps.tgz’, ‘MCRInstaller.exe’, ‘NNE2.exe’, ’NNE2.zip’,  ‘NNE2_README.txt’, ’source.zip’, ’users_guide.pdf’ and ‘vdb.mat’.
  3. Install NNE 2.0 by following instructions in ‘NNE2_README.txt’.

2. Running NNE 2.0

  1. Start the NNE 2.0 with 'NNE2.exe'.
  2. Panel 1 (Main Panel): Select a subset of data (Figure 1). Images in the left column of the Main Panel show graphs of time-courses and parameters of all entries to the 'vdb.mat' (Figure 1).
    1. Select the range for Cortical Depth in the right column of the panel. Type in the range of depth in the format [dmin dmax], where dmin is the minimum depth and dmax is the maximum depth.
      NOTE: The data was measured at depth from 30-560 µm.
    2. Select Branching Order in the right column of the panel. Left-click on the arrow and choose one of the options from the list (Surface | Diving Trunk | First Order Branches | Higher Order Branches).
    3. Select the range of Baseline Diameter in the right column of the panel. Type it in the format [diamin diamax], where diamin is the minimum diameter and diamax is the maximum diameter.
    4. Select subjects (animals according to the date of acquisition) in the right column of the panel. Left-click on the arrow and choose from the available options. Alternatively, choose data from all subjects by left-clicking All in the blue rectangle.
    5. Press SUBMIT to display and explore the selected data in Panel 2.
  3. Panel 2: Explore the selected subset of data and continue further data refinement (Figure 2).
    1. Select the type for group-averaging of the data by left-clicking the appropriate button in the right column on the top. Select: Avg By Cortical Depth or Avg By Branching Order.
      NOTE: The actual choice is highlighted in green below (Figure 2).
    2. Select data based on vessel morphology or subject. Left-click Select all data for tree (a single diving arteriole and its branches) or Select all data for Subj (animal subject).
    3. Left-click Submit to display selected data on the left in graphs of (1) individual time-courses (2) group-averaged time-courses and scatter plots of (3) onset times (4) time-to-peaks (5) peak amplitudes and (6) baseline diameters
    4. Left-click on a trace in the graph of Individual timecourses in the left column to select a time-course.
      NOTE: The selected time-course gets highlighted in the graph (magenta) and its onset time, time-to-peak, peak amplitude and baseline diameter will be marked by red circles in the graphs below. Red points in the scatter plots are average values.
    5. Note the identifiers of the subject (Subject ID) and tree (Tree ID) for the selected time-course in the right column at the bottom.
    6. If desired, change the type of group-averaging by left-clicking the appropriate choice on the top of the right column followed by the Submit button and repeat steps from 2.3.4.
    7. Right-click anywhere in Panel 2 with a cross cursor to view and explore all traces for the selected subject (Subject ID) or tree (Tree ID) in Panel 3.
  4. Panel 3: Explore the final subset of data and export them (Figure 3).
    1. Select a time-course in the top graph of the left column by left-clicking on a trace: the selected trace will be highlighted in the graph (magenta) and descriptive parameters of the database entry will be displayed on top of the graph.
      NOTE: The average time-course is displayed in thick black (Figure 3).
    2. Note corresponding onset time, time-to-peak, peak amplitude and baseline diameter in the graphs below.
    3. Left-click the EXPORT SET button in the right column to save traces displayed in the top graph into the folder where NNE 2.0 is running from.
      NOTE: This action saves three files: 'vdb_subset.xls', 'vdb_subset.csv' and 'vdb_subset.mat' containing vectors of diameter change and time vectors; 'vdb_subset.mat' contains also descriptive parameters and information from 'vdb.mat'.
    4. To inspect all data for 'subject' instead of 'tree' close Panel 3 by pressing [x], restart NNE 2.0, repeat the selection of categories in Panel 1 (steps 2.2.1-2.2.5) and select all data for subject in Panel 2 (step 2.3.2).
    5. Right click anywhere in Panel 3 with a cross cursor to go to Panel 4 to explore reference images and 3D stacks for all traces in the top graph of Panel 3.
      NOTE: Panel 4 will open if option all data for 'tree' was selected in Panel 2. If all data for 'subject' were selected instead, the user will be prompted to change his selection and directed to Panel 1.
  5. Panel 4: Localize the functional measurement within a reference image and within a 3D image stack of vasculature (Figure 4).
    1. Select a time-course by left-clicking on it in the graph on top of the left column.
      NOTE: The selected trace will be highlighted in the graph (magenta) and its descriptive information from metadata will be displayed on top.
    2. Explore the corresponding reference image which is loaded automatically from 'hana_refs' folder at the bottom right of the left column.
    3. Explore the corresponding 3D image stack loaded automatically from 'hana_stk' folder at the bottom left of the left column. Scroll through stack using the arrows or slider below the figure.
      NOTE: When the stack image reaches the level of the reference image – i.e. the diameter measurement level ('Stack index' = 'Ref'), the stack image is highlighted and indicated as 'Frame level'.
    4. Click EXPORT SET in the right column to export the highlighted time-course into a file 'ref_stacks_trace.xls' which is saved to the folder where NNE 2.0 is running from.
      NOTE: The file contains the time vector, diameter change vector, subject ID, entry index, location of reference image, location of 3D stack and the stack image number for the frame level.
    5. Close Panel 4 by [x] to go back to Panel 1.

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Results

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NNE 2.0 and the associate database serve to browse and view the data of the database, sort out the data based on selection criteria, download the selected data, and find the vascular measurements within the corresponding vascular tree.

Panel 1 features selection of data based on categories: 'Cortical Depth', 'Branching Order', 'Baseline Diameter' and 'Subjects' – Figure 1). Please note that in this ...

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Discussion

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NNE 2.0 was written in order to share the vascular imaging data of a specific study20 but with the intention of developing a simple tool for sharing and exploring data of similar kind by other users. Researchers interested in inspecting the associated database of vascular data may use the GUI to browse the data, select subsets of data, compare them to their own experimental results or process them further using their own computational procedures. Users familiar with MATLAB can utilize directly the...

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Disclosures

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The authors have nothing to disclose.

Acknowledgements

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We gratefully acknowledge support from the NIH (NS057198, EB00790, MH111359, and S10RR029050) and the Ministry of Education, Youth and Sports of the Czech Republic (CEITEC 2020, LQ1601). KK was supported by postdoctoral fellowships from the International Headache Society in 2014 and The Scientific and Technological Research Council of Turkey in 2015. MT was supported by postdoctoral fellowship from the German Research Foundation (DFG TH 2031/1).

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Materials

List of materials used in this article
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MATLABMathWorksprogram
WinrarRarlabsprogram

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Tags

Neurovascular Network ExplorerOptogenetic VasomotionTwo Photon MicroscopyVascular Network VisualizationMATLAB GUI ToolCortical Depth AnalysisBranching Order SelectionVessel Diameter MeasurementData Export Function3D Image Stack

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