DEGREE PROJECT IN MEDICAL ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2020

The Influence of 3D Cell Organization in Tumor Spheroid on Natural Killer Cell Infiltration and Migration

LUIGI MORRONE

KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ENGINEERING SCIENCES IN CHEMISTRY, BIOTECHNOLOGY AND HEALTH

The Influence of 3D Cell Organization in Tumor Spheroid on Natural Killer Cell Infiltration and Migration

Inverkan av 3D-cellorganisation i tumorsfäroid på naturlig mördarcellinfiltration och migration

Luigi Morrone

Degree Project in Medical Engineering Supervisor: Quentin Verron Reviewer: Carsten Mim KTH Royal Institute of Technology, Stockholm, Sweden School of Engineering Sciences in Chemistry, Biotechnology and Health Abstract

Natural Killer cells are a type of lymphocyte belonging to the innate immune system and they operate cell-mediated cytotoxicity and release of pro-inflammatory cytokines against cancerous cells. However, in vivo testings have shown a reduced activity of NK cells against solid tumors probably due to the negative influence of the immunosuppressive tumor microenvironment. Multicellular tumor spheroids may constitute an advantageous model in cancer biology for studying the mechanisms behind cancer immune editing since it more closely mimics the complexity of the human body compared with the 2D model counterpart. This study investigated the interaction between NK cells isolated from blood and tumor spheroids obtained from A498 renal carcinoma cells, using light-sheet microscopy imaging which allows satisfactory cell tracking in the inner layers of the spheroids. NK cells not only indeed interact with tumor spheroids, but many of them were able to penetrate the spheroids inducing some changes in the structure of the latter. NK cells were also tracked over time, displaying the migration path and calculating the speed. The fluorescence intensity of the NK cells was found reduced as soon as they penetrate the spheroid but, conversely, the speed seems to increase inside the spheroid, a possible sign of the fallibility of the tracking algorithm in this specific case. We propose solutions for more sophisticated future implementations, involving the use of marks during the experimental phase and drift corrections at the data analysis level.

iv Sammanfattning

NK-celler är en typ av lymfocyter som hör till det ospecifika immunförsvaret. NK- celler utför cellmedierad cytotoxicitet samt utsöndrar proinflommatoriska cytokiner mot cancerceller. Dock har In vivo-tester visat på minskad aktivitet av NK-celler mot solida tumörer - troligtvis på grund av negativ påverkan från tumörens mikromiljö och dess tillhörande hämmande kroniska inflammation. Multicellulära tumörsfäroider kan innebära en fördelaktig modell inom cancerbiologi för att studera mekanismerna bakom immunoediting vid cancer eftersom de bättre speglar komplexiteten hos den mänskliga kroppen jämfört med motsvarande 2D-modell. Denna studie undersöker interaktionen mellan NK-celler isolerade från blod och tumörsfäroider erhållna från A498 njurkarcinomceller, med hjälp av light-sheet-mikrosopi som möjliggör tillfredsställande cellspårning i de inre skikten av sfäroiderna. NK-celler interagerar inte bara med tumörsfäroider. Många av dem kunde även tränga in i sfäroiderna, vilket inducerade förändringar i sfäroidernas struktur. NK-celler studerades även över tid, vilket visade migreringsvägen samt tillät beräkning av rörelsehastigheten. Intensiteten hos NK-cellernas flourescens visade sig reducerad så snart de penetrerat sfäroiden. Omvänt verkade hastigheten öka inuti sfäroiden - ett möjligt tecken på spårningsalgoritmens felbarhet i det specifika fallet. Vi föreslår lösningar för mer sofistikerade framtida implementeringar, med användning av markörer under experimentfasen och korrigering av drift under dataanalysen.

v Acknowledgements

I would like to thank the professor Björn Önfelt for the opportunity to participate in this project, including me in his research group. I would like to thank my supervisor Quentin Verron for guiding me during the whole thesis project with useful suggestions and constructive critiques. I would like to thank Valentina Carannante and Steven Edwards involved in the experiments setting and data collection. Lastly, but not least, I want to thank my family and my friends for their emotional support. Also worth special mention my sister Noemi with whom I share an unique and indissoluble bond and my best friend Alice who is my role model and my confidant.

vi Contents

Abstract iv

Sammanfattning v

Acknowledgements vi

List of Figures ix

Glossary x

1 Introduction 1

2 Methods 3 2.1 Experimental setup and data acquisition ...... 3 2.2 First approach to the data ...... 4 2.3 Pre processing ...... 5 2.4 Segmentation and the Batch Pipeline ...... 5 2.5 How to evaluate infiltration ...... 7 2.6 Natural Killer cells tracking ...... 8

3 Results 10 3.1 Infiltration ...... 10 3.2 Tracking ...... 12 3.3 Killing ...... 14

4 Discussion 16

5 Conclusions 18

References 19

vii CONTENTS

A State of the Art 24 A.1 Human immune system ...... 24 A.2 Optical Microscopy ...... 29 A.3 Image processing and recognition for biological images ...... 33

B List of Parameters 36 B.1 Surfaces creation parameters ...... 36 B.2 Spots and Tracks creation parameters ...... 37

viii List of Figures

2.1.1 Sample setup ...... 3 2.2.1 Data displayed and segmented in ImageJ ...... 4 2.3.1 Background Subtraction ...... 5 2.4.1 Segmentation ...... 6 2.5.1 Infiltration ...... 7 2.5.2Shells approach ...... 8 2.6.1 Tracking groups ...... 9 2.6.2Tracking ...... 9

3.1.1 Labeled spheroids ...... 10 3.1.2 Number of cells inside the spheroid ...... 11 3.1.3 The drift of the spheroids ...... 11 3.1.4 Distance distribution over time ...... 12 3.1.5 NK cell infiltration related to the spheroid volume ...... 13 3.2.1 Number of tracks of a given duration ...... 14 3.2.2NK cells speed distribution ...... 14 3.2.3NK cells intensity mean ...... 15 3.3.1 Killing ...... 15

A.1.1 Inhibition and activation of NK cells through receptors activity . . . . . 25 A.1.2Internal structure of a tumor spheroid ...... 28 A.1.3The creation of MCTS using agarose as coating for the wells ...... 29 A.1.4The creation of multicellular tumor spheroids using ultrasonic standing waves ...... 30 A.2.1Confocal microscope ...... 31 A.2.2The principle of light sheet microscopy ...... 32

ix Glossary

CCD Charged-coupled device, a transistorized light sensor on an integrated circuit

DNAM-1 Activating receptor expressed on subsets of natural killer and T cells

ECM Extracellular matrix

FOV Field of view

LSFM Light sheet fluorescence microscopy

MCTS Multicellular tumor spheroid

MHC Major histocompatibility complex, group of genes that code for proteins found on the surfaces of cells that help the immune system recognize foreign substances

NK Natural killer cell

PVR Poliovirus receptor, a cell adhesion protein involved in the transendothelial migration of leukocytes

TIGIT T cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine- based inhibition motif domains present on some T cells and Natural Killer Cells

USW Ultrasonic standing wave

x Chapter 1

Introduction

Understanding the mechanisms behind treatment resistance is a challenging task for oncological research. It has been shown that natural killer (NK) cells have dynamic cytotoxic activity against tumor cells also thanks to the ability to discriminate between ‘‘normal and altered self’’ through MHC class I-specific receptors considering that MHC-I expression levels can be altered upon cell stress and tumor transformation. The tumor microenvironment may interfere with NK-cell activation pathways or the complex receptor array that regulate NK-cell activation and antitumor activity. Now the research is focused on how functional NK cells can be efficiently delivered and maintained at the tumor site, in spite of the numerous suppressive actions promoted by the tumor [1, 2].

Traditionally, cancer cell proliferation is first measured, culturing and treating the cells in standard microplates and later transferred to a 3D in vivo model (typically animal) to study the drug effect in an environment more similar to the human body. Multicellular tumor spheroids are models of increasing interest since they allow considering cellular interactions in exploring cell cycle and cell division mechanisms, relieving laboratories of the financial burden and the ethical aspect of animal testing. However, 3D imaging of cell division in living tumor spheroids is often time-consuming, arduous, and lack reproducibility, in addition to the issue of poorly visualized core region [3–5].

As compared to other techniques, fluorescence microscopy offers the possibility to image from multiple views large, living and fluorescently labeled samples preserving a sufficient spatio-temporal resolution. Long-term live fluorescence imaging allows to observe time-dependent processes in single cells, tissues, as well as in entire

1 CHAPTER 1. INTRODUCTION multicellular structures, as tumor spheroids. With the light-sheet microscope, the specimen is irradiated with low energy and the occurrence of photo-bleaching and phototoxicity is reduced [6, 7].

With the adoption of automated microscopy technologies, the volume and complexity of image data has increased to the point that it is no longer feasible to extract information without using computerized analysis. Images generated during high- throughput microscopy of large numbers of samples under a variety of conditions require image analysis tools to search for biologically relevant features, to follow relevant objects across space and time and to control the living condition of the cells over the time [8].

The aim of this project is to evaluate the interaction between resting NK cells and tumor spheroids from a dataset of 3D images previously acquired with light-sheet microscopy technique. To verify the scientific potential of the dataset, a method to quantify the number of NK cells able to penetrate the tumor was found together with a description in terms of total track duration, cell speed and displacement over time. The information collected may be used to make prediction about tumor killing but most important to confirm light sheet microscopy as adequate technique for experiments involving tumor spheroids.

2 Chapter 2

Methods

2.1 Experimental setup and data acquisition

The data used in this project derives from an experiment performed on May 2019 in SciLifelab using light-sheet microscopy on a sample of human NK cells and tumor spheroids. The NK cells were isolated from blood and stained using a CellTrace Violet cell proliferation kit. Tumor spheroids were produced from A498 renal carcinoma cells expressing a cytosolic RFP (red fluorescent protein) in agarose multi-well 3D petri dishes.

NK cells and tumour spheroids were seeded into a neutralized type 1 collagen solution (PureCol EZ Gel) which was injected with a syringe above the PDMS plug and allowed to polymerise at 37 °C for 1hour.

Leibovitz L-15 medium was added above the collagen to fill the FEP tube. The opening of the FEP tube was covered with parafilm to prevent evaporation of the media. Figure 2.1.1: Sample Setup. The sample contained in the probe. Courtesy of The FEP tube containing spheroids organized Steven Edwards. as described above was introduced to the chamber of a Zeiss Light Sheet Z.1 microscope containing PBS at 37 °C and imaged using a 10x 0.5NA detection objective.

3 CHAPTER 2. METHODS

2.2 First approach to the data

The acquisition resulted in 108 images corresponding to different time frames (10 minutes distant form each other) scanned with laser wavelengths of 561 nm for tumor spheroids (magenta) and 405 nm for NK cells (cyan). The 2-channel images have a resolution of 1920x1920 Pixels with an image size of 878.61 μm x 878.61 μm (scaled). Along the z axis, one image includes 67 slides which lead to a thickness of 165 μm.

ImageJ was used to conduct a first estimation of the sample parameters in a single z plane (2D). Many automatic tools used later on need the largest expected diameter of tumor spheroids and NK cells together with the minimum distance between their centers of mass in order to perform segmentation, tracking and other image analysis processes. The largest spheroid has a diameter of approximately 150 µm, while for NK cells 9.4 µm was found. At this early stage of the project a board approximation was sufficient.

Figure 2.2.1: Data displayed and segmented in ImageJ. On the left, tumor spheroids are colored with magenta and NK cells with cyan. On the right, thresholding was performed separately for the 2 channels and the area of the objects was measured

4 CHAPTER 2. METHODS

2.3 Pre processing

Having two channels brings the advantage to adjust brightness, enhance features and correct artifacts of tumor spheroids and NK cells separately. Furthermore, image processing can be accomplished on single channels. Turning off the tumor spheroids channel (Cam2-T1), the halo of the spheroids was still visible on the NK cells channel (Cam2-T2). This artifact is known with various names, such as spectral overlap, bleed- through, or crossover. When imaging a sample stained with multiple fluorescent labels simultaneously, the emission profiles often share the same spectral region, especially in the longer wavelengths. To overcome this issue a restricted-wavelength bandpass emission filter could have been implemented in the experimental phase, otherwise as post-processing solution, Background Subtraction applies a Gaussian filter to define the background at each voxel and then performs a Baseline Subtraction of this variable background.

Figure 2.3.1: Background Subtraction. One image of the dataset before and after the Background Subtraction correction.

2.4 Segmentation and the Batch Pipeline

It was possible in Imaris to segment the spheroids and the cells using respectively, Surfaces and Spots. Surfaces encapsulate the volume object, creating an artificial solid

5 CHAPTER 2. METHODS structure that mimic the real shape, whereas Spots automatically detects point-like structures objects, providing an editor to manually correct detection errors, a viewer to visualize the structures as spheres, and statistics output. The reason why the tool Spots was used to model NK cells is based on the morphology of the sample. Using Surfaces on NK cells will merge them in an unique block in most of the cases since the NK cells are so close to each other and splitting them manually is difficult and time-consuming. Furthermore, Imaris has useful built-in functions which analyse the correlation between different classes (surfaces and spots) rather than the same class, such as Find Spots Close To Surfaces and Compute Distance between Spots and Surfaces.

Figure 2.4.1: Segmentation. An example of the output after performing Surfaces and Spots on one image of the dataset. The original dataset was temporarily hidden for exhibition purposes.

Imaris provides the Batch Pipeline function to create surfaces and spots automatically for all the images of the dataset. A specific protocol was designed with an initial image processing phase, background subtraction followed by the simultaneous creation of surfaces and spots. It is worthwhile to note that for Spots the option Region Growing was enabled whereas the opposite was done for Surfaces. The region growing algorithm for segmentation starts defining seed points, then the regions start to grow around each points; the growing stops only when the regions meet. This supports the

6 CHAPTER 2. METHODS separation of two or more objects that are identified as one. About surfaces, it is more important that the region respect accurately the real volume even if the result will be two merged spheroids. Conversely, spots need to be identified as singular objects even inside clusters to give useful information for the infiltration analysis. The complete list of used parameters is contained in Appendix B.

2.5 How to evaluate infiltration

All the spots have coordinates in the 3D space that can be used to develop the infiltration approach. Imaris also offers the function Find Spots Close To Surface which is made exactly for this purpose. Unfortunately, it was not possible to run this function for all the files simultaneously so the use of external function was necessary.

In Imaris it is possible to build new functions using MATLAB, Python or other programming languages or download them on the Bitplane website (Xtension). The downloaded function used for infiltration was called XT Spots Split Into Surface Objects (Copyright Bitplane AG 2011, modified by Matthew J. Gastinger 1/14/2013), written in MATLAB and modified to create an Excel files for every image processed. The processing time was around 3 hours.

Figure 2.5.1: Infiltration. An example of the output after performing the function, Find Spots Close To Surface on one image of the dataset. The function detects only the spots inside surfaces, providing the corresponding coordinates in the 3D space.

7 CHAPTER 2. METHODS

The spots inside a surface were collected with an ID and 3D coordinates whereas the rest of them was not considered. Also the surfaces received an ID together with the coordinates of their center of mass and the volumes. These information made possible to count the NK cells inside a particular spheroid and to calculate the distance of the spots from the center of mass of the spheroid.

Figure 2.5.2: Shells approach. One Another approach to evaluate the infiltration is spheroid is illustrated with concentric to consider the spheroids divided into virtual virtual shells to simulate different shells with variable radius and centered in levels of infiltration, from inner layers to the surrounding area. the spheroid itself. This model may be more intuitive and highlights the different stages of infiltration, from the neighbourhood to the core of the spheroid but it was abandoned due to the complexity of creating inner shells and the inability to run it automatically for all the images.

2.6 Natural Killer cells tracking

Tracking cells consists in the identification of the same object over time, in a time- lapse sequence. In Imaris, tracking is a feature of Spots and it can be performed automatically but not before having created a sequence of images. The option Add Time Points permit to merge two or more images as different time points, obtaining a 4D image file.

In this case, the excessive size of the final file (162 GB) made the automatic tracking impossible for all the images. The solution was to divide the dataset in smaller groups maintaining one image present in adjacent groups. Specifically, the last image of a group is the same of the first image of the next group. Choosing 10 images as length of the groups 12 groups resulted. The concatenation of the resulting group statistics was possible in Excel but but the ID of the tracks was different for every group. For this reason it was necessary to find the original track ID in one group (starting from group 1) and match it with the new IDs of the next groups.

8 CHAPTER 2. METHODS

Figure 2.6.1: Tracking groups. A graphic representation of the groups division required to perform the tracking for all the images. Noteworthy the concatenation of the images, keeping the last image of a group identical to the first one of the following group.

Then, the tracking was computed for all the groups choosing Autoregressive Motion as algorithm, a MaxDistance of 30 μm and a MaxGapSize of 1 frame. This algorithm is used for objects with approximately continuous motion, looking one timepoint back it predicts the object will move in the same direction and distance. The Maximum Distance is the distance that a spot is allowed to deviate from the predicted position to tolerate some directional changes and maximum gap size different from zero allows the “disappearing” objects to reconnect with their original track if they reappear in a future time point.

Figure 2.6.2: Tracking. An example of the output after performing the tracking on one group of the dataset. The color of the track indicates track duration in frames, e.g. red tracks last for more frames than blue tracks.

9 Chapter 3

Results

3.1 Infiltration

In this section, some experimental results about the infiltration of the NK cells inside the tumor spheroids will be illustrated. The spheroids are labeled in figure 3.1.1 and the labels are maintained for the successive figures to avoid misinterpretation. Meanwhile the number of cells detected inside the spheroid over time (108 frames) is shown in Figure 3.1.2. It is possible to observe a common decreasing trend for all the surfaces. Spheroids characterized by a larger volume (B and C) presents an higher number of NK cells at the beginning of the calculation and this number decreases more slowly over time. From the frontal prospective (xy plane) surface A is not entirely present in the Field of view (FOV) since the first acquisition and the reduced volume leads to a small amount of cell infiltrated.

Figure 3.1.1: Labelled spheroids. The spheroids are labeled as following: surface A with cyan, surface B with blue, surface C with yellow, surface D with red.

10 CHAPTER 3. RESULTS

Figure 3.1.2: Number of cells inside the spheroid. The graph shows the trend of NK cells infiltration inside the tumor spheroids. The number of NK cells decreases over the time.

It is interesting to notice that for three surfaces (A-B-D) the trend moves to zero, going forward on the frame axis. This aspect suggest that the spheroids are not static but instead, they are shifting outside the field of interest. Also the apoptosis of one or more tumor spheroids, induced by infiltrated NK cells, can occur and the consequent disintegration reduce the volume of the spheroids and then the number of NK cells inside them. Figure 3.1.3 shows the drift of the objects which is more evident for tumor spheroids. The upwards trajectory is predominant but the drift is not spared in other directions.

Figure 3.1.3: The drift of the spheroids. The position of the tumor spheroids in different time frames. overlapping the images and using different colors is it possible to estimate the drift in all 3 spatial directions. Frontal view on the left (XY plane) and side view on the right (XZ plane).

11 CHAPTER 3. RESULTS

Considering the spheroid which stays, even if partially, in the FOV for all the dataset (Surface C), in figure 3.1.4 the distance between its center and the infiltrated NK cells was plotted in 3 different frames. A constant decreasing of the value of the distance occurs from the early stage until the last frames. It may look promising to observe that the NK cells are deeply penetrating the spheroid but the result need to be related with the volume trend.

Figure 3.1.4: Distance distribution over time. The graph shows the distribution of the distances of the NK cells from the center of mass of the tumor spheroids (Surface C) for 3 different images. Specifically, the data come from 3 different time frames.

On the top, figure 3.1.5 illustrates the number of infiltrating NK cells penetrated normalized by the volume of the spheroids. For surface B and C the trend is relatively stable for the entire acquisition time, although surface D exhibits peaks and valleys at irregular rate. At the bottom of the figure, the evolution of the spheroids volume is displayed with a reduction of the values for all the surfaces over time. Notably, the volume of surfaces A and D drops to zero (even if at different times). The green circle represents a critical moment, after which the collected data cannot seen as trustworthy due to the disappearance of big portion of the spheroids from the FOV.

3.2 Tracking

The figure 3.2.1 illustrate the number of tracks of a given duration (in time frames). As long as the number of frames increases there are fewer and fewer detected tracks, probably due to miscalculation of the tracking algorithm. The graph starts from 11

12 CHAPTER 3. RESULTS

Figure 3.1.5: NK cell infiltration related to the spheroid volume. The graph on the top shows the density of NK cells inside the tumor spheroids and it is associated with the graph of the spheroids volume over the time. One area is outlined to show when the results lose credibility due to the vanishing of the tumor spheroids from the FOV. frames to exclude tracks belonging to just one group (see Method 2.6).

The speed of the NK cells is a significant statistic obtained with the tracking. Figure 3.2.2 is a box plot of the speed distribution outside and inside the spheroids. According to another study performed in SciLifeLab [9] the migration average speed of individual activated NK cells in 3D resulted 0.053 ± 0.283 μm/s whereas for un-activated human NK cells in 2D 0.026 ± 0.01 μm/s. The case in exam is an intermediate situation considering that the NK cells were un-activated but in 3D; in fact the values in the plot have the same order of magnitude. The speed has higher values inside the spheroids instead of the other way around; however any explanation of this behavior risks to result speculative in the absent of other specific experiments.

The intensity mean distribution inside and outside the spheroids is shown in figure 3.2.3. The intensity is higher outside the spheroid probably because the spheroids

13 CHAPTER 3. RESULTS

Figure 3.2.1: Number of tracks of a given duration. For Estimation of how many tracks survived until the last frame. Starting with 11 frames to include at least 2 groups.

Figure 3.2.2: NK cells speed distribution. Values of the speed outside and inside the tumor spheroids calculated for all the time frames. cause scattering, resulting in lower intensities for the cells inside. This may also explain the unexpected difference in speed inside/outside since lower intensities mean higher risk for mistakes in segmentation and thus in tracking.

3.3 Killing

Looking at the time-lapse video of the dataset, figure 3.3.1, it is possible to notice on the surface of the spheroids episodes of killing. Far from being easy to prove, killing still posses some characteristics that make possible the identification: irregularities

14 CHAPTER 3. RESULTS

Figure 3.2.3: NK cells intensity mean. Values of the intensity outside and inside the tumor spheroids calculated for all the time frames. in the texture, disaggregation, blebbing and obviously the penetration of NK cells. Nevertheless a trained eye is still required.

Figure 3.3.1: Killing. Focusing of one tumor spheroid surface which exhibits killing occurrence. The NK cell approaches the tumor cell (1), engages in a contact (2), induces tumor cell death as marked by membrane blebbing (3) and rupture (4).

15 Chapter 4

Discussion

The results of segmentation are satisfying. A visual inspection allows to observe that tumor spheroids were accurately encapsulated by the surfaces and NK cells were represented as different entities even in the most dense clusters. The creation of the batch pipeline accelerated the segmentation with a processing time of just few minutes for all the dataset.

About the NK cells infiltration, the MATLAB function used to detect the cells inside the spheroids did not show any irregularities. The drift did not compromise the counting of the cells inside the spheroid since it is a static analysis, computed in a particular time frame. Oppositely, the distance from the center of mass of the tumor becomes unsuitable to evaluate NK cell penetration. When a spheroid partially leaves the FOV, the center of mass is calculated based only on the part which is still visible. This produces a shifted location for the center, making the NK cells falsely appear to be approaching it very fast.

Many factors can cause the drift and mostly of them belong to the experimental phase. During the acquisition time, gravity and variation of temperature and humidity affect the stability of the sample. Collagen shrinkage is another variable that affect the data acquisition [10].Talking about cells tracking, it is not possible to make genuine statements about the trajectory of the NK cells towards the spheroids, despite the presence of many tracks close the surfaces of spheroids which proves a certain grade of activeness in that area. When it comes to track infiltrated cells, the situation is even more problematic. Having a big group of cells in a restricted volume makes the tracking more unreliable.

16 CHAPTER 4. DISCUSSION

There are several ways to correct drift. One that is worthy of note is Nearest neighbors algorithm. Static objects in the sample, such as dead cells, can be used to measure and correct drift of the sample. For heterogeneous drift across the field of view, drifting can be corrected locally by finding neighboring immobile objects around the cell of interest.

Microscope-stage drift can be calculated using fiducial markers as points of reference. These markers should be: photostable as much as possible, biochemically inert with the sample and easy to implement. The list of the most used markers includes: fluorescent beads or dyes, microfabricated patterns, quantum dots and gold nanoparticles. Despite the suitable features of a marker, tracking errors can still occur so manual checking is usually performed in any case. Photobleaching, change in greyscale noise, localization and tracking algorithms are just some of the possible reasons [11].

About killing, the chromium-release assay developed in in the 1960s is still the most commonly used method to measure cytotoxicity by T cells and by natural killer cells. Tumor cells are loaded with radioactive chromium that is released in the supernatant when the cells die and then measured. High levels of chromium are translated with the lysis of many tumor cells but unfortunately it is not possible to image the killing itself. As an alternative approach to chromium release, flow cytometric assays that use fluorescent dyes such as calcein, Annexin V or SYTOX to distinguish live and dead cells, have been created. The principle is simple, SYTOX, for example, does not penetrate the membrane of the target cell until the latter dies; at this point the stain enters and binds with the DNA so that the intensity spikes up [12].

17 Chapter 5

Conclusions

In this project the size of the files was one the biggest limiting factor together with the drift of the structures. However, segmentation and tracking were possible to implement and they can be improved with customized functions and correcting algorithms. New experiments using functionalized NK cells and fiducial markers as reference can be performed with light-sheet microscopy since this technique provide high quality images. As future recommendation, a shorter acquisition time would reduce tracking inaccuracies and binning can increase in signal-to-noise ratio. On the other hand, shorter acquisition time means more frames so an increment in terms of memory and highly binned images may present low resolution. Based on the purpose of the experiments, an advantageous compromise need to be found.

18 References

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[19] Anft, Moritz, Netter, Petra, Urlaub, Doris, Prager, Isabel, Schaffner, Samantha, and Watzl, Carsten. “NK cell detachment from target cells is regulated by successful cytotoxicity and influences cytokine production”. In: Cellular & molecular immunology (2019), pp. 1–9.

[20] Davis, Zachary B, Felices, Martin, Verneris, Michael R, and Miller, Jeffrey S. “Natural killer cell adoptive transfer therapy: exploiting the first line of defense against cancer”. In: Cancer journal (Sudbury, Mass.) 21.6 (2015), p. 486.

[21] Loyon, Romain, Picard, Emilie, Mauvais, Olivier, Queiroz, Lise, Mougey, Virginie, Pallandre, Jean-René, Galaine, Jeanne, Mercier-Letondal, Patricia, Kellerman, Guillaume, Chaput, Nathalie, et al. “IL-21–induced MHC class II+ NK cells promote the expansion of human uncommitted CD4+ central memory T cells in a macrophage migration inhibitory factor–dependent manner”. In: The Journal of Immunology 197.1 (2016), pp. 85–96.

[22] De Andrade, Lucas Ferrari, Smyth, Mark J, and Martinet, Ludovic. “DNAM-1 control of natural killer cells functions through nectin and nectin-like proteins”. In: Immunology and cell biology 92.3 (2014), pp. 237–244.

[23] Bowers, Jonathan R, Readler, James M, Sharma, Priyanka, and Excoffon, Katherine JDA. “Poliovirus receptor: more than a simple viral receptor”. In: Virus research 242 (2017), pp. 1–6.

[24] Nishiwada, Satoshi, Sho, Masayuki, Yasuda, Satoshi, Shimada, Keiji, Yamato, Ichiro, Akahori, Takahiro, Kinoshita, Shoichi, Nagai, Minako, Konishi, Noboru, and Nakajima, Yoshiyuki. “Clinical significance of CD155 expression in human pancreatic cancer”. In: Anticancer research 35.4 (2015), pp. 2287–2297.

[25] Huntington, Nicholas D, Martinet, Ludovic, and Smyth, Mark J. “DNAM-1: would the real natural killer cell please stand up!” In: Oncotarget 6.30 (2015), p. 28537.

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[27] Fennema, Eelco, Rivron, Nicolas, Rouwkema, Jeroen, Blitterswijk, Clemens van, and Boer, Jan de. “Spheroid culture as a tool for creating 3D complex tissues”. In: Trends in biotechnology 31.2 (2013), pp. 108–115. ￿ [28] Costa, Elisabete C, Moreira, André F, Melo-Diogo, Duarte de, Gaspar, Vıtor M, ￿ Carvalho, Marco P, and Correia, Ilıdio J. “3D tumor spheroids: an overview on the tools and techniques used for their analysis”. In: Biotechnology advances 34.8 (2016), pp. 1427–1441.

[29] Nath, Sritama and Devi, Gayathri R. “Three-dimensional culture systems in cancer research: Focus on tumor spheroid model”. In: Pharmacology & therapeutics 163 (2016), pp. 94–108.

[30] Gong, Xue, Lin, Chao, Cheng, Jian, Su, Jiansheng, Zhao, Hang, Liu, Tianlin, Wen, Xuejun, and Zhao, Peng. “Generation of multicellular tumor spheroids with microwell-based agarose scaffolds for drug testing”. In: PloS one 10.6 (2015).

[31] Christakou, Athanasia E, Ohlin, Mathias, Önfelt, Björn, and Wiklund, Martin. “Ultrasonic three-dimensional on-chip cell culture for dynamic studies of tumor immune surveillance by natural killer cells”. In: Lab on a Chip 15.15 (2015), pp. 3222–3231.

[32] Olofsson, Karl, Carannante, V, Ohlin, Mathias, Frisk, Thomas, Kushiro, K, Takai, M, Lundqvist, A, Önfelt, Björn, and Wiklund, Martin. “Acoustic formation of multicellular tumor spheroids enabling on-chip functional and structural imaging”. In: Lab on a Chip 18.16 (2018), pp. 2466–2476.

[33] Reyes-Aldasoro, Constantino Carlos. Biomedical image analysis recipes in MATLAB: for life scientists and engineers. John Wiley & Sons, 2015.

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[35] Lindon, John C, Tranter, George E, and Koppenaal, David. Encyclopedia of spectroscopy and spectrometry. Academic Press, 2016, pp. 627–631.

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[37] Ford, Brian J. and Shannon, Robert R. “Microscope”. In: (2020). URL: https: //www.britannica.com/technology/microscope.

[38] Weber, Michael, Mickoleit, Michaela, and Huisken, Jan. “Light sheet microscopy”. In: Methods in cell biology. Vol. 123. Elsevier, 2014, pp. 193–215.

[39] Chen, Weiyang, Li, Weiwei, Dong, Xiangjun, and Pei, Jialun. “A review of biological image analysis”. In: Current Bioinformatics 13.4 (2018), pp. 337– 343.

[40] Uchida, Seiichi. “Image processing and recognition for biological images”. In: Development, growth & differentiation 55.4 (2013), pp. 523–549.

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[42] Litjens, Geert, Kooi, Thijs, Bejnordi, Babak Ehteshami, Setio, Arnaud Arindra Adiyoso, Ciompi, Francesco, Ghafoorian, Mohsen, Van Der Laak, Jeroen Awm, Van Ginneken, Bram, and Sánchez, Clara I. “A survey on deep learning in medical image analysis”. In: Medical image analysis 42 (2017), pp. 60–88.

23 Appendix A

State of the Art

A.1 Human immune system

The immune system identifies a multitude of threats, like viruses, bacteria and parasites, discerning them from the body’s own healthy tissue. It is responsible for warding off potentially damaging foreign threats or limiting their ability to spread and move throughout the body [13, 14].

Adaptive, or specific, immunity is able to recognize antigens and to create antibodies specifically designed to them. T lymphocytes (T cells) and B lymphocytes (B cells) are the major components of the adaptive immune system [13, 14]. Innate, or nonspecific, immunity refers to defenses existing from birth that can be activated immediately once a pathogen attacks. The innate immune system includes: physical barriers (skin, the respiratory tract, the gastrointestinal tract, the nasopharynx, cilia, etc.), defense mechanism (secretions, gastric acid, mucous, saliva, bile, tears, and sweat) and the general immune responses (inflammation, complement, and non-specific cellular responses). In particular, inflammation brings immune cells where the infection occurs by increasing blood flow to the area. Complement is an immune response that marks pathogens for destruction and makes cavity in the cell membrane of the pathogen [13–15].

A.1.1 NK cells

Due to their early production of cytokines and chemokines, and the ability to lyse target cells without prior sensitization, Natural killer cell (NK) cells are lymphocytes of the

24 APPENDIX A. STATE OF THE ART innate immune system [16]. Analogously to cytotoxic T lymphocytes, NK cells kill tumor cells by inducing them to undergo apoptosis before the latter has had a chance to replicate. However, NK cells do not express antigen-specific receptors like T-cells and they do not rearrange their germline DNA to gain specificity [15, 17].

Naïve NK cells can be activated by varied proinflammatory cytokines which stimulate the proliferation of the NK cells and enhance their cytotoxicity, producing granzymes and perforin which kill transformed cells [2, 17]. All healthy nucleated cells express MHC class I proteins on their surface which act as ligands for inhibitory receptors and contribute to the self-tolerance of NK cells. However, tumor cells lose surface MHC class I expression, producing lower inhibitory signal in NK cells and consequentially shifting the balance toward NK cell activation and elimination of target cells [15, 18].

Additionally, NK cells modulate the immune response, secreting cytotoxic granules and pro-inflammatory cytokines which can restrict tumor angiogenesis and increase MHC-II expression on tumor cells and antigen-presenting cells. The upregulation of MHC-II molecules promote T cell proliferation and this mechanism creates a bridge between the innate and adaptive immune responses [19–21].

Figure A.1.1: Inhibition and activation of NK cells through receptors activity. The predominance of bonded inhibitory receptors produces NK cells inactivity. Conversely, when activating signal dominates cytoxic granules are released inducing degranulation and apoptosis in the tumor cell. Figure created with Biorender

25 APPENDIX A. STATE OF THE ART

A.1.2 Receptors: PVR, DNAM-1 and TIGIT

NK cells represent key innate immune components and express a wide set of inhibitory and activating receptors that alert them against cellular stress without damaging healthy cells [22]. It has been reported that Poliovirus receptor (PVR), a type I transmembrane glycoprotein, is differently expressed in cancerous cells and some studies have shown a strong correlation between increased PVR expression and cancer. In light of these recent discoveries one potential application of PVR would be as a biomarker for clinical use in cancer pathology [23].

The interaction of the human PVR with its ligand T-cell immunoreceptor with IG and ITIM domains (TIGIT) on NK cell inhibits cell proliferation and cytokine production [24]. On the other hand, PVR is also bound to DNAX accessory molecule-1 (DNAM- 1) on NK cells, a cell surface glycoprotein that conversely increases cytotoxic function stimulating the release of cytokines that ultimately lead to the apoptosis of target cells [24, 25].

So PVR can inhibit or activate the cell mediated response, by interacting with TIGIT and DNAM-1 respectively. However, given the higher affinity of TIGIT than DNAM-1 for the same ligands, TIGIT can physically interfere with DNAM-1 binding. Blocking this ubiquitous receptor may represent a successful strategy to enhance DNAM-1 functions [22, 24, 26].

A.1.3 Tumor Spheroids

In cell biology most employed pre-clinical studies use the cells of interest cultured on cell culture-compatible polystyrene in a 2D monolayer, such as culture flasks or Petri dishes. This approach guarantees easy handling, cost-effectiveness, good reproducibility and the ability to grow multiple different cell types in a well-controlled and homogeneous environment. However, monolayers are unable to reproduce the real complexity and 3D structure found in the human body, cell–cell and cell–matrix interactions, in particular [27, 28].

On the contrary, 3D cell culture methods confer a high degree of clinical and biological relevance to in vitro models for screening new therapeutics, both at a small and large scale. In spheroid cultures, cells secrete the Extracellular matrix (ECM) in which they reside, and they can interact with cells from their original microenvironment.

26 APPENDIX A. STATE OF THE ART

3D Spheroids have also been used as a tool to investigate the role of adhesion molecules in tumor biology since they have the capacity to more accurately mimic some features of solid tumors, such as their spatial architecture, physiological responses, secretion of soluble mediators, gene expression patterns and drug resistance mechanisms [27, 28].

Multicellular tumor spheroid (MCTS) can be composed purely of cancer cells (homotypic spheroids) or of cancer cells cultured with other cell types (heterotypic spheroids) such as fibroblasts, endothelial cells or immune cells. A cross-section of the MCTSs shows an internal structure characterized by different cell layers. The external layer is composed of highly proliferative cells and migratory cells, followed by a middle layer of quiescent viable cells and then by the core, which contains necrotic cells.

The easier access to oxygen and nutrients in the peripheral area explains the high proliferation rate of cells while the absence of oxygen (hypoxia) and nutrients induces a senescent or necrotic state in the inner layers. Additionally, in hypoxic environments, cancer cells convert pyruvate to lactate to obtain energy (Warburg effect) with a consequent accumulation of lactate which is responsible for the acidification of the spheroid’s interior (pH of 6.5–7.2). This aspect is identified as being one of the main reason for the impaired therapeutic efficacy of anticancer drugs. In the hypoxic region, drugs that promote cellular death through the formation of reactive oxygen species are less effective. [27–29].

A.1.4 Techniques for generating MCTSs

From this standpoint, 3D tumor spheroids cells have emerged as the most promising culture model for screening anticancer therapeutics and different techniques to realize spheroids have been developed over the years but not all of them will be explained comprehensively. Two main categories of spheroids can be distinguished based on the fabrication method: scaffold based 3D cell cultures (hydrogels and inserts) and non- scaffold based 3D cell cultures. Each technique has advantages and limitations, related with the scope and the characteristic that the spheroid should possess.

Typically, cultures of 48 hours generate small spheroids (200μm in diameter) of uniform size and homogeneity whereas long-term cultures of more than 4 days generate large (>500μm in diameter) heterogeneous spheroids with hypoxic core [28, 29].

27 APPENDIX A. STATE OF THE ART

Figure A.1.2: Internal structure of a tumor spheroid. Moving from the external proliferation layer to the necrotic core, conditions and properties drastically change. Low values of pH, oxygen and nutrients characterized the inner part of the tumor spheroids. The image is taken from ”3D tumor spheroids: an overview on the tools and techniques used for their analysis” [15]

Scaffold-based MCTS

In scaffold-based MCTS, the biologically active scaffolds, beside the support function, have the role of promoting cell-cell and cell–matrix interactions. The scaffolds, commonly used in 3D culture systems, are: ECM-based natural hydrogels, synthetic hydrogels, and engineered hydrogels that mimic native ECM. Hydrogels are water- insoluble and characterized by interconnected microscopic pores, which facilitates easy transport of oxygen, nutrients, metabolic wastes and other soluble factors through the porous channels. Matrigel is one of the most popular ECM-based natural hydrogels available on the market [29].

Scaffold-free MCTS

On the contrary, the ECM present in scaffold-free 3D cell cultures is composed of proteins produced by cells during the formation of the culture and the tumor cells undergo self-aggregation into highly organized three-dimensional tissue-like structures. In ultra-low attachment plates the wells can be made or coated with an inert substrate (e.g.agarose or polystyrene), which inhibits cell adhesion, forcing the cells in

28 APPENDIX A. STATE OF THE ART suspension to aggregate and form a visible spheroid. Using hanging drop technique, cells spontaneously aggregate in the bottom of a drop after inverting a plate with drops of cell suspension whereas magnetic levitation exploits magnetic forces to guide self- assembly of cells into spheroids [29, 30].

Figure A.1.3: The creation of multicellular tumor spheroids using agarose as coating for the wells. The wells where the cells are deposited are coated with agarose which inhibits cell adhesion and promotes cell aggregation. The image come from the “Half- time Review” of the Phd student, Valentina Carannante, with her permission

A recent method to create 3D cell culture uses the acoustic radiation force of Ultrasonic standing wave (USW) in a multi-well microplate. USWs have been largely employed for the purpose of trapping, concentrating or separating particles and cells, in microfluidic systems. The USW is induced in a resonator cavity with a width equal to a multiple of half the wavelength and the cell in suspension arranges into a single aggregate in each well of the microplate. Exploiting the stable pressure nodes defined by the USW, cells are trapped and 3D cell structures will be formed by two and seven day [31, 32].

A.2 Optical Microscopy

A.2.1 Bright-field

Bright field microscopy is one of the simplest techniques used in light microscopy to observe cells “in vitro”, slices of tissue or live organisms. Placing the samples

29 APPENDIX A. STATE OF THE ART

Figure A.1.4: The creation of MCTS using ultrasonic standing waves. The use USWs is another method to produce MCTS promoting the cell aggregation without interfering with the biological structure of the spheroids. The image come from the “Half-time Review” of the Phd student, Valentina Carannante, with her permission of interest on the stage of a microscope, they are illuminated with white light that passes through the samples themselves, getting attenuated in the dense areas, and then through a series of lenses until it reaches the eye of the observer or a camera. The applications of image analysis with images acquired through bright field are many; correction of shading, segmentation and measurement of cells are just some examples. However, limitations of bright-field microscopy include low resolution due to the blurry appearance of out-of-focus material and low contrast for weakly absorbing samples. The use of fluorescent substances or illuminating the samples sideways to exploit the contrast of the image is typical of other microscopic techniques [33, 34].

A.2.2 Fluorescence

The emission of light by a substance consequentially the illumination and therefore the absorption of other electromagnetic radiation is known as fluorescence. In fluorescence microscopy, the sample is excited with light of a relatively short wavelength, usually blue or ultraviolet and then inspected through a barrier filter that removes the short-wavelength light used for illumination and transmits the fluorescence. High sensitivity and specificity are the most considerable advantages

30 APPENDIX A. STATE OF THE ART that fluorescence microscopy can provide over other forms of microscopy [35].

A.2.3 Confocal microscopy

Confocal microscopy is based on the principle that excitation and detection are focused onto the same diffraction-limited region of the sample, from this the term ‘‘con’’-focal. A pinhole is placed in the image plane of the sample to guarantee that only light from a small region of the sample reaches the detector, another pinhole can be placed in the excitation path to ensure that the excitation light is focused at the position of the detection of the sample. Nowadays, using laser light which can be highly collimated, the excitation pinhole is not always necessary.

The confocal detection pinhole defines what light reaches the detector, out-of-focus light can be reduced and the signal-to-noise ratio significantly improved compared to bright-field methods. Three-dimensional images can be reconstructed by scanning several optical planes at different focal depths in the specimen (known as a Z-series) [36, 37].

Figure A.2.1: Confocal microscope. Laser light is directed to a defined portion at a specific depth of the sample. The resulting emission of fluorescent light from the illuminated area reaches the detector passing thought a confocal pinholes, cutting off the signal that is out of focus. The image is taken from ©2012 Encyclopædia Britannica, Inc.

31 APPENDIX A. STATE OF THE ART

A.2.4 Light sheet fluorescence

Light sheet fluorescence microscopy (LSFM) combines two distinct optical planes, one for fast wide-field detection and one for illumination with a thin sheet of light, orthogonally to the detection planes [38]. Images are recorded one plane at a time with a CCD camera providing high temporal resolution, facilitating imaging of live samples and limiting phototoxicity. Scanning different planes of light, 3D images of a large specimen can be recorded at a much higher speed but slightly lower resolution compared to confocal imaging[3].

Traditional microscopic imaging strategies in developmental and cell biology, tend to stress and overexpose the sample during four-dimensional (4D; x, y, z and t) analysis. When the whole sample volume is illuminated for imaging a single section, the risk of fluorophore bleaching and phototoxicity dramatically increases [7].

Figure A.2.2: The principle of light sheet microscopy. The sample is illuminated only in a single plane at a time and the emitted light is detected from perpendicular direction. In this way, the measurement of out-of-focus fluorescence is avoided, preventing phototoxic effects in regions that are not being scanned. The image is taken from the book ”Methods in cell biology” pag. 196 [38].

In LSFM, only the focal plane of the detection objective is selectively illuminated, resulting in a consistent decrease in energy input. The use of fast and sensitive cameras allows the acquisition of large image datasets in less time still offering an acceptable signal-to-noise ratio and minimal phototoxicity. This makes light sheet

32 APPENDIX A. STATE OF THE ART microscopy the ideal technique to follow fast and dynamic developmental processes without any impact on the living specimen. Wanting to achieve high spatial and temporal resolution leads to enormous amounts of image data which require strong computation power and massive storage space to process and archive the information. Thus, the speed at which the samples can be imaged is limited by these factors and a suitable compromise between image quality and data size need to be found by the user [38].

A.3 Image processing and recognition for biological images

In the recent times, it has been an increasing trend of applying bio-imaging techniques to generate a multitude of biological images. Biologists often perform visual inspection and manual measurement for bio-image analysis which may be biased by subjective observation and require considerable effort as soon as image data sets become larger and larger. For this reason, there is a growing need to replace manual labor by quantitative computerized image processing and analysis, also referred to as bioimage- informatics, increasing the sensitivity, accuracy, objectivity, and reproducibility of data analysis [39–41].

A.3.1 Image filtering

For image filtering is intended the series of modifications through which an input image is converted to another image with different properties and it is usually the first step of image processing. Among the most acknowledged examples of filters, smoothing (minimizing gray-level difference between neighboring pixels) and edge detection (enhancing edge pixel by detecting discontinuities in brightness) are reckoned [40].

A.3.2 Image segmentation

Image segmentation is one of the most beneficial steps in image processing for biological images and it allows quantitative analysis of meaningful parameters related to volume and shape. The goal of segmentation is to distinguish the foreground (the interested objects) from background and the typical adopted method is based on the

33 APPENDIX A. STATE OF THE ART gray threshold for the evenly illuminated images. Computers often suffer from its complexity since biological images have ambiguous boundaries. Many tasks can be accomplished as soon as segmentation is feasible: recognizing and counting objects, measuring the distribution of the objects (in space or time), measuring the shape of individual objects, localizing objects for tracking, removing unnecessary regions, etc [39, 40, 42].

A.3.3 Visual object tracking

One of the major challenges of biological research is the estimation of single molecules (or complexes) movement and cell migrations. This is translated with the acquisition of time-lapse image series and the tracking of objects over time. For studies in this field, typical 3D time-lapse data sets consist of thousands of images which are impossible to analyze manually; therefore computerized, quantitative cell tracking and motion analysis have been developed since the ‘90s. Simply, tracking methods consist of two consecutive steps, the detection of individual structures per time frame and the linking in successive frames.Due to obscure boundaries, noise and photobleaching effects, simple methods based on intensity thresholding may be defective and still require manual checking and correction of the results. Recently, model-based segmentation methods have been considered in research, which allow the incorporation of prior knowledge about object shape; active contours (also called snakes) and active surfaces are examples of such methods [41].

A.3.4 Image registration

Registration, or spatial alignment, is a common image analysis task in which a coordinate transform is derived from one image to another. In other words, image registration is to fit a reference image to its deformed version or vice versa with the purpose of evaluating the relative deformation, represented by the optimized fitting function between the two images. Usually this is performed in an iterative framework where a specific type of (non-)parametric transformation is used as a reference and a pre-determined metric is optimized [40, 42].

34 APPENDIX A. STATE OF THE ART

A.3.5 Image pattern recognition

Image pattern recognition consists in assigning a predefined class label to an image (or a part of an image) and generally includes two modules: feature extraction (converting an input image as a set of values, a vector) and classification (classifying the input feature vector into a class according to some rule, a classifier). Nearest neighbor and discriminant function are two main methods of classification [40].

35 Appendix B

List of Parameters

B.1 Surfaces creation parameters

[Algorithm] Enable Region Of Interest = false Enable Region Growing = false Enable Tracking = false Enable Shortest Distance = true [Source Channel] Source Channel Index = 1 Enable Smooth = true Surface Grain Size = 10.0 um Enable Eliminate Background = true Diameter Of Largest Sphere = 60.0 um [Threshold] Enable Automatic Threshold = false Manual Threshold Value = 150.319 Active Threshold = true Enable Automatic Threshold B = true Manual Threshold Value B = 5067.91 Active Threshold B = false [Classify Surfaces] ”Number of Voxels Img=1” above 10.0

36 APPENDIX B. LIST OF PARAMETERS

B.2 Spots and Tracks creation parameters

Algorithm ] Enable Region Of Interest = true Process Entire Image = true Enable Region Growing = true Enable Tracking = true Enable Region Growing = true Enable Shortest Distance = true [Region of Interest] Region1: XYZT from [443 243 1 1] to [1215 1033 67 1] [Source Channel] Source Channel Index = 2 Estimated XY Diameter = 4.00 um Estimated Z Diameter = 5.00 um Background Subtraction = true [Classify Spots] ”Intensity Mean Ch=2 Img=1” above 277 [Spot Region Type] Region Growing Type = Local Contrast [Spot Regions] Region Growing Automatic Treshold = false Region Growing Manual Threshold = 233.548 Region Growing Diameter = Diameter From Volume Create Region Channel = false Tracking ] Algorithm Name = Autoregressive Motion MaxDistance = 30.0 um MaxGapSize = 1 F i l l Gap Enable = f a l s e

37

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