Performing a Quantitative Assessment of 3D Refractive Index Maps

The 3D Cell Explorer from Nanolive allows excellent 3D imaging of living cells with high content, and excellent spatial as well as temporal resolution at x, y: 180 nm; z: 400 nm; t: 1.7 sec. The ability to derive quantitative data which describes various objects visualized in these images, such as cell populations, cells and cell organelles) is the fundamental driver in today’s cutting-edge research in biology.

This article continues to discuss the extraction of meaningful numbers from segmented objects with the use of the FIJI software. Other features of the cell are also considered in this light to help carry out well-designed quantitative analyses using the 3D Cell Explorer.

Introduction

The 3D Cell Explorer is meant to render 3D maps of the refractive index (RI) of objects under study (Cotte et al., 2013). The device is set up with such sophistication as to allow it to distinguish RI differences down to 0.001, which is without doubt far ahead of other comparable devices in terms of sensitivity. This allows different components of a sample to be viewed, like the various cell compartments, for instance. Thus the lack of expertise need not hamper the identification of a mammalian cell nucleus in an RI map, for instance.

However, in today’s age when cell biology is being studied quantitatively (Langen et al., 2015; Li et al., 2007), it is no longer the standard to see, feel or manually analyze an image. Instead, computerized analysis to derive meaningful features of the object organized in space, time and object identity is required (Frechin et al., 2015).

The aim of this article is to show how a set of numerical attributes can be obtained from the objects of study following their segmentation. A couple of instances are used here to show the utility of FIJI in this process, using sets of 3D segmented objects. This requires two softwares, FIJI (Schindelin et al., 2012; Schmid, Schindelin, Cardona, Longair, & Heisenberg, 2010; Schneider, Rasband, & Eliceiri, 2012) and STEVE.

It shows how to extract a complete array of features from an object and how to visualize simple objects, as well as segmentation of 3D objects at a more advanced level, and feature plots targeting the calculation of dry mass using the RI values. The procedures and experimental parameters described here are not hard and fast, and should be tested fully before use in any particular experiment.

Prerequisites

The first step is to acquire the most recent version of STEVE, which is the powerful software designed to control the 3D Cell Explorer, as well as of FIJI, along with the 3D ImageJ suite.

Setup of the 3D manager options

Figure 1. Setup of the 3D manager options

The latest FIJI version should be started up and the stack of segmented HeLa cell nuclei must be loaded. The procedure shown in Figure 1 is followed to set up the 3D Manager options. These make sure that the initial calculations are rapidly processed. Later in the course of the calculation, only a few measurements are required additionally, such as the volume and the surface of the individual objects in voxels, and the compactness attribute, which describes the spherical nature of the object from 0 to 1.

How to Use the 3D Manager to Analyze Segmented Objects

Using the 3D manager

Figure 2. Using the 3D manager

The 3D manager is undoubtedly the best tool for 3D object analysis, and is easy to use. To acquire 3D images of the stacked segmented nuclei, the following steps are advised:

Go to Plugins>3D>3D manager (shown in Figure 2, step 2).

As the window opens, click on the segmentation stack and then on the AddImage button in Figure 2, step 3. This causes the objects in the segmented image to load.

Now click the Distances button to create a table with the distances between the individual objects, such as centroid to centroid or edge to edge (see Figure 2, step 4). Figure 3 displays some of the features shown by the Distances and Measure 3D functions of the 3D manager plugin. It measures in voxels, converting them to µm2 or µm3 (the x,y,z dimensions of one voxel are  0.186, 0.186 and 0.372 µm).

Even more features are returned by the 3D manager by changing the 3D manager options, as shown in Figure 1.

Extraction of HeLa nuclei features

Figure 3. Extraction of HeLa nuclei features

Advanced Segmentation with the MorphoLibJ

The procedure described here is slightly modified to execute a little more advanced segmentation of mouse embryonic stem cells. However, entire cells rather than nuclei are segmented here. A simple thresholding approach could be used for a simple segmentation of the nuclei (Plugins>3D>3D Simple Segmentation), but here the MorphoLibJ plugin is used. The installation procedure is followed: Help>Update>Manage update sites and then checking IJPB-plugins, followed by Close and Apply Changes.

MorphoLibJ: morphological segmentation allows for a more advanced segmentation

Figure 4. MorphoLibJ: morphological segmentation allows for a more advanced segmentation

The procedure displayed in Figure 4 opens the morphological segmentation tool shown in Figure 4 steps 1-4. To start with, click object image unless there is a border image already obtained from the original image. This will leave the gradient radius (which is the parameter that is used for object border detection) on 1, as well as the tolerance (the minimal distance required to detect successive maxima in the image) on 10. Now click Run and observe. Overall, this is a wonderful place to start for 3D Cell Explorer images, as shown in Figure 4, step 5 and 6. This is followed by creating an image from Catchment basins, as in Figure 4, step 7. At this point it must be changed to grayscale so that it can be used again as a normal segmented image. This is done by clicking on Image>Type>8bit after selection of the colored image that was produced. To use this type of tool properly, a lot of time is required to find the right experimental parameters that will yield the kind of results that are desired.

Example of Quantitative Analysis of Mouse Embryonic Stem Cells Division

Analysis of cell volume and dry mass of two dividing mESCs

Figure 5. Analysis of cell volume and dry mass of two dividing mESCs

Information can be extracted very conveniently from another image like a 3D RI map provided specific 3D segmentation has already been carried out. The 3D segmentation is loaded in the 3D manager as in paragraph 3, Figure 2. Then the image from which information is to be extracted, that is, the 3D RI map, is opened. This must be identical in size to the segmented image. Click it to select it as the active window and then go to the 3D manager window, to click on Quantif 3D. This opens a table containing all the necessary measures, at one line per object in the segmentation table that was first loaded.

The Quantif 3D returns the features that are correlated to the gray values in the 3D manager options panel. Playing around with various values will allow the experimenter to understand how it works and fine tune it to obtain the desired results.

Figure 5 shows an analysis which displays how images were acquired from mouse Embryonic Stem Cells (mESCs) over a period of 48 minutes using the 3D Cell Explorer. The imaged cells have undergone segmentation as already described. This enables the extraction of the volume as well as the RI of each object. The following formula is then used to calculate the dry mass of each object (Friebel & Meinke, 2006; Phillips, Jacques, & McCarty, 2012):

These values can be plotted against time in the case of two cells that are undergoing mitosis in consecutive order. It can be observed that cellular dry mass increases in concentration as mitosis proceeds, and decreases thereafter. The cell volume is still quite small when the dry mass concentration begins to go down. This means possibly that the cellular material starts to go down actively once cytokinesis is over.

General Hardware and Software Requirements

3D Cell Explorer models:

  • 3D Cell Explorer
  • 3D Cell Explorer-fluo

Software:

  • STEVE – all versions
  • FIJI with the 3D ImageJ suite

References

Cotte, Y., Toy, F., Jourdain, P., Pavillon, N., Boss, D., Magistretti, P., … Depeursinge, C. (2013). Marker-free phase nanoscopy. Nature Photonics, 7(2), 113–117.

Frechin, M., Stoeger, T., Daetwyler, S., Gehin, C., Battich, N., Damm, E.-M., … Pelkmans, L. (2015). Cell-intrinsic adaptation of lipid composition to local crowding drives social behaviour. Nature, 523(7558), 88–91. https://doi.org/10.1038/nature14429

Friebel, M., & Meinke, M. (2006). Model function to calculate the refractive index of native hemoglobin in the wavelength range of 250-1100 nm dependent on concentration. Applied Optics, 45(12), 2838. https://doi.org/10.1364/AO.45.002838

Langen, M., Agi, E., Altschuler, D. J., Wu, L. F., Altschuler, S. J., & Hiesinger, P. R. (2015). The Developmental Rules of Neural Superposition in Drosophila. Cell, 162(1), 120–133. https://doi.org/10.1016/j.cell.2015.05.055

Li, G., Liu, T., Tarokh, A., Nie, J., Guo, L., Mara, A., … Wong, S. T. (2007). 3D cell nuclei segmentation based on gradient flow tracking. BMC Cell Biology, 8(1), 40. https://doi.org/10.1186/1471-2121-8-40

Phillips, K. G., Jacques, S. L., & McCarty, O. J. T. (2012). Measurement of Single Cell Refractive Index, Dry Mass, Volume, and Density Using a Transillumination Microscope. Physical Review Letters, 109(11). https://doi.org/10.1103/PhysRevLett.109.118105

Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., … Cardona, A. (2012). Fiji: an open-source platform for biological-image analysis. Nature Methods, 9(7), 676–682. https://doi.org/10.1038/nmeth.2019

Schmid, B., Schindelin, J., Cardona, A., Longair, M., & Heisenberg, M. (2010). A high-level 3D visualization API for Java and ImageJ. BMC Bioinformatics, 11(1), 274. https://doi.org/10.1186/1471-2105-11-274

Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012). NIH Image to ImageJ: 25 years of image analysis. Nature Methods, 9(7), 671–675. https://doi.org/10.1038/nmeth.2089

About Nanolive SA

Nanolive SA are scientists, working for scientists.

Their belief is that each and every Biologist, Researcher and Physician should be able to explore and interact instantly with living cells without damaging them.

Nanolive want to support the study of how living cells and bacteria work, evolve and react, thus building a solid base for new drugs and therapies, in order to enable breakthrough researches.

This is the reason why they have developed the 3D Cell Explorer.


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Last updated: Mar 12, 2019 at 4:01 AM

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