Home Medicine 3D Local in vivo Environment (LivE) imaging for single cell protein analysis of bone tissue
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3D Local in vivo Environment (LivE) imaging for single cell protein analysis of bone tissue

LivE imaging is a novel methodological platform for linking individual expression profiles of cells within bone tissues to their local remodeling and mechanical environments
  • Carly Taylor EMAIL logo , Ariane Scheuren , Andreas Trüssel and Ralph Müller
Published/Copyright: September 30, 2016
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Current Directions in Biomedical Engineering
From the journal Current Directions in Biomedical Engineering Volume 2 Issue 1

Abstract

The molecular processes behind pathological bone remodelling seen in diseases such as osteoporosis are unclear. However, a recently developed methodological platform known as Local in vivo Environment (LivE) imaging has been used to link cellular expression data to the local remodelling and mechanical environment in 2D sections of bone tissue. The method therefore can be used to give insight into which proteins are important for pathological bone remodelling. However, the cells within bone tissue exist as a 3D network. Therefore extension of LivE to accommodate 3D data may provide additional physiologically relevant information that is not possible to determine using 2D analysis alone. This will have implications for the further understanding of the cellular basis that underlies bone diseases such as osteoporosis. Here the LivE imaging technique is expanded to incorporate data from cells in a three dimensional manner via a serial sectioning technique. The methodological steps involved in the LivE imaging approach are defined and the optimisation steps performed are explained in detail.

Keywords: 3D; ageing; bone; LivE imaging; osteocyte; osteoporosis; protein expression; sclerostin

1 Introduction

Osteoporosis is a bone disease affecting millions of elderly people [1]. Osteocytes are the key mediators of bone remodelling. However, owing to their inaccessible location within the bone matrix, the molecular mechanisms by which osteocytes control pathological bone remodelling during ageing remain poorly understood. Therefore an understanding of the molecular processes governing pathological bone remodelling, would indicate the significance of osteocyte genes and especially proteins in that process. This could lead to the identification of therapeutic targets [2] for osteoporosis and other bone diseases.

Recently, due to the development of a novel methodological platform known as Local in vivo Environment (LivE) imaging it has been possible to link osteocyte expression to the local remodelling and mechanical environment in sections of bone tissue. To accomplish this, LivE imaging uses longitudinal micro-computed tomography (micro-CT) to track bone remodelling in vivo and link it to end point histological data with additional reference to the local remodelling and micro-mechanical strain environment in 2D bone tissue sections.

However, as osteocytes function as a 3D network it is clear that to fully comprehend how osteocyte expression is linked to the local environment, it is necessary to investigate their expression in 3D. In this work, the previously developed LivE imaging platform is expanded to incorporate multiple 2D sections from single bone: Thereby a 3D impression of osteocyte function with regards to bone remodelling can be obtained. Here, the steps constituting the methodological platform are outlined and optimisation steps including development of the sectioning technique are presented.

2 Methods

2.1 Mechanical loading, tissue processing and immunohistochemical staining

The 6th caudal vertebra of two female C57BL/6 mice were loaded cyclically (10 Hz) three times a week for 5 min over a period of 4 weeks at 8 N using a model previously described [3]. One control animal remained unloaded. The vertebrae were imaged at time point zero and following this on a once weekly basis using in vivo micro-CT (vivaCT 40, Scanco Medical, Switzerland) at a voxel resolution of 10.5 μm for 4 weeks. Following the final loading cycle and a period of 24 h the mice were sacrificed and the vertebrae were dissected from the tail. The vertebrae were fixed using paraformaldehyde to preserve protein structures within the tissue and decalcified using 12.5% Ethylene-diamine-tetra-acetic acid in phosphate buffered saline (PBS) over a period of 4 days at 4°C, to allow the bone to be cut with a metal blade. The vertebrae were processed using a tissue processor (TPC 15 Duo; Medite AG, Switzerland) and embedded in paraffin wax. The optimisation of several steps related to production of quality serial sections involving use of a microtome were performed. These included assessment of manual versus automatic sectioning and the determination of optimal section thickness and microtome settings. Following this the whole vertebrae were sectioned manually in a serial fashion at 5 μm onto superfrost plus glass slides using a microtome (HM 355S; Microm AG, Switzerland).

The serial sections were stained using an anti-sclerostin antibody (AF1589; R&D Systems) and secondary conjugated with biotin (BAF017; R&D systems). To improve the quality and repeatability of sclerostin staining, optimal storage and usage conditions were determined. In addition, wash steps with increased stringency were included in the protocol to prevent false positive staining in the tissue sections that had been observed in previous staining rounds using an isotpye control (02-6202; Invitrogen). Briefly the staining protocol included; rehydration of sections through xylene and a series of ethanol washes and blocking steps with hydrogen peroxidase solution followed by rabbit serum. The sclerostin antibody was applied overnight at 4°C. Sections were washed twice in PBS with tween 20 (0.05%) and twice in PBS for 5 min before the secondary antibody was applied for 1 h at room temperature. Sections were washed again and antibody staining was visualised using the Vectastain Elite ABC-Peroxidase Kit and a Metal enhanced DAB Substrate kit (ThermoScientific, Switzerland). Specimens were counterstained using fast green and dehydrated through an ethanol series before mounting. The stained sections were imaged at 20× magnification using a slide scanner and panoramic viewer software package (Slide Scanner Pannoramic 250; 3D Histech, Hungary).

The images recorded using the micro-CT were processed to generate a 3D vertebral reconstruction. A series of scripts written in house were used to determine remodelling activity in the reconstruction including the volume, surface and thickness of formed and resorbed areas within the bone (see Figure 1).

Figure 1 (A) Complete 3D vertebral reconstruction with remodelling data incorporated. Quiescent (grey), resorptive (blue and formative (orange) regions are shown. (B) Central slice of the middle region of the reconstruction. (C) Strain energy density map for the vertebra determined by finite element analysis from low strain (blue) to high strain (red).
Figure 1

(A) Complete 3D vertebral reconstruction with remodelling data incorporated. Quiescent (grey), resorptive (blue and formative (orange) regions are shown. (B) Central slice of the middle region of the reconstruction. (C) Strain energy density map for the vertebra determined by finite element analysis from low strain (blue) to high strain (red).

2.2 Histological image processing and registration

Eight histology images were processed for complete LivE analysis. Initially the histological image was binarised using Adobe photoshop. The bone was segmented using the paint function to exclude muscular tissue (see Figure 2). The binarised histology image was then located within the 3D reconstruction using registration algorithms developed in house (see Figure 3). The feasibility of partial registration was explored for analysis of sections with partially damaged areas.

Figure 2 (A) 2D histological image stained with anti-sclerostin antibody and fast green counter stain. The box highlights the region shown in image 1B. (B) Magnified histological section detailing sclerostin staining (brown). (C) Binarised image created in photoshop.
Figure 2

(A) 2D histological image stained with anti-sclerostin antibody and fast green counter stain. The box highlights the region shown in image 1B. (B) Magnified histological section detailing sclerostin staining (brown). (C) Binarised image created in photoshop.

Figure 3 (A) Overlay of a binarised histological section and matched equivalent section found within the 3D vertebral reconstruction using a registration algorithm. Histology section (red), micro-CT section (green), histology and micro-CT superimposed (yellow). (B) The slice selected from the 3D vertebral reconstruction using the registration algorithm with optimal fit to the binarised input.
Figure 3

(A) Overlay of a binarised histological section and matched equivalent section found within the 3D vertebral reconstruction using a registration algorithm. Histology section (red), micro-CT section (green), histology and micro-CT superimposed (yellow). (B) The slice selected from the 3D vertebral reconstruction using the registration algorithm with optimal fit to the binarised input.

2.3 Finite element analysis

Finite element (FE) analysis was used to determine the mechanical environments (Strain energy density; SED) within the three vertebrae (see Figure 1C). FE models were generated by converting the voxels of the micro-CT images to eight node hexahedral elements, resulting in approximately 1.8 million elements per model. A Young’s modulus of 14.8 GPa and a Poisson’s ratio of 0.3 [3] were assigned. The SED distribution was calculated by applying simulated compressive loads to the vertebrae; 4 N for the non-loaded vertebra [4] and 9 N for the two loaded vertebrae. Each model was solved with ParFE [5] running at the Swiss National Supercomputing Centre (CSCS, Lugano, Switzerland) with 128 CPUs.

2.4 Cellular analysis

The ImageJ programme (U.S National Institutes of Health, U.S.A) was used to manually identify osteocytes and their co-ordinates upon each of the 2D histological sections. Positive and negative sclerostin staining was determined in each cell by eye (see Figure 4). The protocol was adapted to include measures for increasing the stringency of cell classification.

Figure 4 Cells observed during the analysis of sclerostin status using the cell classification protocol. Row (A) Sclerostin positive cells. Row (B) Sclerostin negative cells.
Figure 4

Cells observed during the analysis of sclerostin status using the cell classification protocol. Row (A) Sclerostin positive cells. Row (B) Sclerostin negative cells.

2.5 Evaluation

In the final step of the methodology, the outputs determined in the various steps of the protocol were analysed simultaneously using a programme written in house. The programme determined cellular locations within the 3D vertebral reconstruction and the associated remodelling and strain values around the cells.

3 Results

The results of the study show that it is possible to expand the LivE imaging technique using multiple sections of individual vertebrae. The expansion of the technique required several optimisation steps. The quality in the appearance of sections was improved by optimisation of procedures involving use of the microtome: The section thickness was found to be optimal at 5 μm, the blade was changed frequently to prevent tearing of sections, the flow rate of the water carrying sections from the blade to the waterbath was reduced to a third of the potential maximum (setting 3.5) to prevent stretching and therefore deformation of sections. Additionally, the control provided by manual sectioning was found to be superior to automatic sectioning functions.

Non-specific staining was observed in sections stained with the sclerostin antibody via the use of an istotype control antibody thereby producing false-positive staining (see Figure 5A). The number of wash steps used during the staining protocol was increased from two to four. Additionally 0.05% Tween 20 (detergent) was added to the PBs in the first two washes to increase the stringency of the washes. These steps resulted in the prevention of false positive staining in the Isotype control (see Figure 5B). Filtration of the DAB metal substrate using a 0.2 μm syringe filter was found to reduce artefacts on the section facilitating cellular identification (data not shown). Superfrost plus slides were found to increase the adherence of the tissue to the glass slide and therefore increase the quality of the morphological appearance of sections following staining.

Figure 5 Left. The isotype control prior to increased wash steps. Right. The isotype control following optimised wash steps.
Figure 5

Left. The isotype control prior to increased wash steps. Right. The isotype control following optimised wash steps.

In addition to this the stability of the antibody was evaluated giving an indication of the antibody’s susceptibility to freeze thaw. This was based around the observance of inconsistent staining with the antibody. It was found that the antibody was particularly susceptible to deterioration through freeze thaw. Therefore, the antibody was aliquoted into smaller volumes with a higher dilution factor and once defrosted was stored in the fridge for up to 2 weeks before disposal rather than being refrozen.

Partial registration was explored for sections with focal areas of damage to make the maximum use of each section and increase the number of useful sections per vertebra. It was found that partial registration was a viable option for maximum extraction of useful data from the histological sections and was particularly useful for registering the end regions of the vertebra in cases where the thin central region of the vertebra had become deformed, resulting in poor registration outputs (see Figure 6).

Figure 6 Left. Registration using a full section with morphological deformation caused by processing (partially shown). Right. Increased accuracy via partial registration of the same section.
Figure 6

Left. Registration using a full section with morphological deformation caused by processing (partially shown). Right. Increased accuracy via partial registration of the same section.

Improvements were also made to the cell classification protocol. It was often possible to see partial lacunae of osteocytes and lacunae with indiscriminate staining (see Figure 7). Therefore cells without strong staining or clear lacunae with a pale central region were excluded from the analysis.

Figure 7 Indeterminate sclerostin staining.
Figure 7

Indeterminate sclerostin staining.

4 Conclusion

LivE imaging has been successfully extended to incorporate multiple sections of single vertebrae. Therefore, in the future, it will be possible to obtain 3D data regarding osteocyte protein expression and the local environment. This will allow researchers to gain a greater understanding of the relevance of osteocyte proteins in health and disease and identify therapeutic targets. Further work will include the completion of LivE imaging with the remaining sections of all three vertebrae to determine the connection between sclerostin staining and the local osteocyte environment in 3D.

Acknowledgement

Thanks to Mariya Shtil for assistance with binarising the histology images.

Author’s Statement

Research funding: Partial funding by the MouseAge COST action BM1402 is gratefully acknowledged. Conflict of interest: Authors state no conflict of interest. Material and Methods: Informed consent: Informed consent has been obtained from all individuals included in this study. Ethical approval: The presented animal studies have all complied with the relevant national regulations and institutional policies for the care and use of animals, and has been approved by the veterinary authority of the Canton of Zurich.

References

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Published Online: 2016-9-30
Published in Print: 2016-9-1

©2016 Carly Taylor et al., licensee De Gruyter.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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https://doi.org/10.1515/cdbme-2016-0099
Keywords for this article
3D; ageing; bone; LivE imaging; osteocyte; osteoporosis; protein expression; sclerostin
Creative Commons
BY-NC-ND 4.0

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  73. Medication process in Styrian hospitals
  74. Control tower to surgical theater
  75. Development of a skull phantom for the assessment of implant X-ray visibility
  76. Surgical navigation with QR codes
  77. Investigation of the pressure gradient of embolic protection devices
  78. Computer assistance in femoral derotation osteotomy: a bottom-up approach
  79. Automatic depth scanning system for 3D infrared thermography
  80. A service for monitoring the quality of intraoperative cone beam CT images
  81. Resectoscope with an easy to use twist mechanism for improved handling
  82. In vitro simulation of distribution processes following intramuscular injection
  83. Adjusting inkjet printhead parameters to deposit drugs into micro-sized reservoirs
  84. A flexible standalone system with integrated sensor feedback for multi-pad electrode FES of the hand
  85. Smart control for functional electrical stimulation with optimal pulse intensity
  86. Tactile display on the remaining hand for unilateral hand amputees
  87. Effects of sustained electrical stimulation on spasticity assessed by the pendulum test
  88. An improved tracking framework for ultrasound probe localization in image-guided radiosurgery
  89. Improvement of a subviral particle tracker by the use of a LAP-Kalman-algorithm
  90. Learning discriminative classification models for grading anal intraepithelial neoplasia
  91. Regularization of EIT reconstruction based on multi-scales wavelet transforms
  92. Assessing MRI susceptibility artefact through an indicator of image distortion
  93. EyeGuidance – a computer controlled system to guide eye movements
  94. A framework for feedback-based segmentation of 3D image stacks
  95. Doppler optical coherence tomography as a promising tool for detecting fluid in the human middle ear
  96. 3D Local in vivo Environment (LivE) imaging for single cell protein analysis of bone tissue
  97. Inside-Out access strategy using new trans-vascular catheter approach
  98. US/MRI fusion with new optical tracking and marker approach for interventional procedures inside the MRI suite
  99. Impact of different registration methods in MEG source analysis
  100. 3D segmentation of thyroid ultrasound images using active contours
  101. Designing a compact MRI motion phantom
  102. Cerebral cortex classification by conditional random fields applied to intraoperative thermal imaging
  103. Classification of indirect immunofluorescence images using thresholded local binary count features
  104. Analysis of muscle fatigue conditions using time-frequency images and GLCM features
  105. Numerical evaluation of image parameters of ETR-1
  106. Fabrication of a compliant phantom of the human aortic arch for use in Particle Image Velocimetry (PIV) experimentation
  107. Effect of the number of electrodes on the reconstructed lung shape in electrical impedance tomography
  108. Hardware dependencies of GPU-accelerated beamformer performances for microwave breast cancer detection
  109. Computer assisted assessment of progressing osteoradionecrosis of the jaw for clinical diagnosis and treatment
  110. Evaluation of reconstruction parameters of electrical impedance tomography on aorta detection during saline bolus injection
  111. Evaluation of open-source software for the lung segmentation
  112. Automatic determination of lung features of CF patients in CT scans
  113. Image analysis of self-organized multicellular patterns
  114. Effect of key parameters on synthesis of superparamagnetic nanoparticles (SPIONs)
  115. Radiopacity assessment of neurovascular implants
  116. Development of a desiccant based dielectric for monitoring humidity conditions in miniaturized hermetic implantable packages
  117. Development of an artifact-free aneurysm clip
  118. Enhancing the regeneration of bone defects by alkalizing the peri-implant zone – an in vitro approach
  119. Rapid prototyping of replica knee implants for in vitro testing
  120. Protecting ultra- and hyperhydrophilic implant surfaces in dry state from loss of wettability
  121. Advanced wettability analysis of implant surfaces
  122. Patient-specific hip prostheses designed by surgeons
  123. Plasma treatment on novel carbon fiber reinforced PEEK cages to enhance bioactivity
  124. Wear of a total intervertebral disc prosthesis
  125. Digital health and digital biomarkers – enabling value chains on health data
  126. Usability in the lifecycle of medical software development
  127. Influence of different test gases in a non-destructive 100% quality control system for medical devices
  128. Device development guided by user satisfaction survey on auricular vagus nerve stimulation
  129. Empirical assessment of the time course of innovation in biomedical engineering: first results of a comparative approach
  130. Effect of left atrial hypertrophy on P-wave morphology in a computational model
  131. Simulation of intracardiac electrograms around acute ablation lesions
  132. Parametrization of activation based cardiac electrophysiology models using bidomain model simulations
  133. Assessment of nasal resistance using computational fluid dynamics
  134. Resistance in a non-linear autoregressive model of pulmonary mechanics
  135. Inspiratory and expiratory elastance in a non-linear autoregressive model of pulmonary mechanics
  136. Determination of regional lung function in cystic fibrosis using electrical impedance tomography
  137. Development of parietal bone surrogates for parietal graft lift training
  138. Numerical simulation of mechanically stimulated bone remodelling
  139. Conversion of engineering stresses to Cauchy stresses in tensile and compression tests of thermoplastic polymers
  140. Numerical examinations of simplified spondylodesis models concerning energy absorption in magnetic resonance imaging
  141. Principle study on the signal connection at transabdominal fetal pulse oximetry
  142. Influence of Siluron® insertion on model drug distribution in the simulated vitreous body
  143. Evaluating different approaches to identify a three parameter gas exchange model
  144. Effects of fibrosis on the extracellular potential based on 3D reconstructions from histological sections of heart tissue
  145. From imaging to hemodynamics – how reconstruction kernels influence the blood flow predictions in intracranial aneurysms
  146. Flow optimised design of a novel point-of-care diagnostic device for the detection of disease specific biomarkers
  147. Improved FPGA controlled artificial vascular system for plethysmographic measurements
  148. Minimally spaced electrode positions for multi-functional chest sensors: ECG and respiratory signal estimation
  149. Automated detection of alveolar arches for nasoalveolar molding in cleft lip and palate treatment
  150. Control scheme selection in human-machine- interfaces by analysis of activity signals
  151. Event-based sampling for reducing communication load in realtime human motion analysis by wireless inertial sensor networks
  152. Automatic pairing of inertial sensors to lower limb segments – a plug-and-play approach
  153. Contactless respiratory monitoring system for magnetic resonance imaging applications using a laser range sensor
  154. Interactive monitoring system for visual respiratory biofeedback
  155. Development of a low-cost senor based aid for visually impaired people
  156. Patient assistive system for the shoulder joint
  157. A passive beating heart setup for interventional cardiology training
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