High-throughput sequencing has greatly influenced the amount of data produced and biological questions asked and answered. Sequencing approaches have also enabled rapid development of related technological fields such as single-cell and spatially resolved expression profiling. The introductory parts of this thesis give an overview of the basic molecular and technological apparatus needed to analyse the transcriptome in cells and tissues. This is succeeded by a summary of present investigations that report recent advancements in RNA profiling. RNA integrity needs to be preserved for accurate gene expression analysis. A method providing a low-cost alternative for RNA preservation was reported. Namely, a low concentration of buffered formaldehyde was used for fixation of human cell lines and peripheral blood cells (Paper I). The results from bulk RNA sequencing confirmed gene expression was not negatively impacted with the preservation procedure (r2&gt;0.88) and that long-term storage of such samples was possible (r2=0.95). However, it is important to note that a small population of cells overexpressing a limited amount of genes can skew bulk gene expression analyses making them sufficient only in carefully designed studies. Therefore, gene expression should be investigated at the single cell resolution when possible. A method for high-throughput single cell expression profiling termed microarrayed single-cell sequencing was developed (Paper II). The method incorporated fluorescence-activated cell sorting, sample deposition and profiling of thousands of barcoded single cells in one reaction. After sample attachment to a barcoded array, a high-resolution image was taken which linked the position of each array barcode sequence to each individual deposited cell. The cDNA synthesis efficiency was estimated at 17.3% while detecting 27,427 transcripts per cell on average. Additionally, spatially resolved analysis is important in cell differentiation, organ development and pathological changes. Current methods are limited in terms of throughput, cost and time. For that reason, the spatial transcriptomics method was developed (Paper III). Here, the barcoded microarray was used to obtain spatially resolved expression profiles from tissue sections using the same imaging principle. The mouse olfactory bulb was profiled on a whole-transcriptome scale and the results showed that the expression correlated well (r2=0.94-0.97) as compared to bulk RNA sequencing. The method was 6.9% efficient, reported signal diffusion at ~2 μm and accurately deconvoluted layer-specific transcripts in an unbiased manner. Lastly, the spatial transcriptomics concept was applied to profile human breast tumours in three dimensions (Paper IV). Unbiased clustering revealed previously un-annotated regions and classified them as parts of the immune system, providing a detailed view into complex interactions and crosstalk in the whole tissue volume. Spatial tumour classification divulged that certain parts of the tumour clearly classified as other subtypes as compared to bulk analysis providing useful data for current practice diagnostics. The last part of the thesis discusses a look towards the future, how the presented methods could be used, improved upon or combined in translational research. / <p>QC 20170109</p>

This dissertation proposes a novel approach to achieve circumferential three-dimensional (3D) profiling for dark objects by investigating specular micro-texture photometry. A small patch of a target surface in micro-texture level yields different appearance under different illumination. This photometric property can be used to reconstruct the target surface with pixel-level resolution. However, due to the nature of some material, the surface of whom has stronger specular components than diffuse components, making the usage of general microtexture photometry more difficult. On the other hand, without using micro-texture photometry, the conventional circumferential 3D approaches only utilizes the geometric property of the target surface, compared to which, the proposed is able to reconstruct the target surface with finer detail. The original contributions of this dissertation are threefold. To begin with, the specular component in the micro-texture photometry is investigated to propose the pixel-level 3D profiling. The intensities of the same pixel from different images, which are taken under different lighting conditions are different. The specular components are used to recover the surface normal of the corresponding surface patch of the target surface. Consequently, the proposed specular-photometry-based technique produces pixel-wise measurement on surface normal. Furthermore, the conventional circumferential 3D profiling approach is extended with the proposed specular-photometry-based technique. The result of 3D profiling via the conventional approach is sparse due to its nature. On the other hand, the result of 3D profiling from the integration using the surface normal obtained from the proposed specular-photometry-based technique suffers from accumulative error. A new approach is then proposed to use the result from the conventional approach as global constraint, for the purpose of reducing the accumulative error. The proposed approach is able to achieve pixel-resolution globally bounded profiling because of the dense surface normal measurement from the proposed specular-photometry-based technique and the constraints from the conventional approach. Lastly, a system is developed to apply the proposed circumferential specular-photometry-based 3D profiling approach. The developed system is not only able to acquire data and but also to provide different lighting conditions for both the specular-photometry-based technique and conventional approach using a digital single-lens reflex camera and different lighting devices. With a step motor to rotate the object for three hundred and sixty degrees, the system is able to achieve circumferential scanning / Doctor of Philosophy / This dissertation explains a novel approach to fulfill circumferential 3D profiling with high resolution for dark objects. With the proposed approach, the resolution is able to achieve micro-texture level. The high resolution measurement is achieved by investigating specular micro-texture photometry. As for dark objects, the specular components dominate the reflection. The usage of photometry is explained as follow. A small patch of a target surface yields different appearance under different illumination. For the material of the surface of dark objects, the surface reflects stronger specular components than diffuse components. The proposed approach utilizes this photometric property to recover the small patch's surface normal using its specular components. The recovered surface normal is then used to calculate the surface profile through integration. The conventional circumferential 3D profiling approach, which can only produce low-resolution measurement, is also adopted in the proposed approach to enhance its performance, as the integration method suffers from accumulative error. The result from the conventional approach serves as a global constraint to bound the final profiling result. A system is developed to apply the proposed circumferential specular-photometry-based 3D profiling approach, which is equipped with a step motor to rotate the object for three hundred and sixty degrees for the purpose of circumferential scanning. A series of numerical and experimental studies were conducted to validate the performance of the proposed approach. As it is shown in the result, the proposed approach is able to measure the tire tread with 31µm resolution.

This thesis presents an approach for configuring and calibrating a network of RGB-D sensors used to guide a robotic arm to interact with objects that get rapidly modeled in 3D. The system is based on Microsoft Kinect sensors for 3D data acquisition. The work presented here also details an analysis and experimental study of the Kinect’s depth sensor capabilities and performance. The study comprises examination of the resolution, quantization error, and random distribution of depth data. In addition, the effects of color and reflectance characteristics of an object are also analyzed. The study examines two versions of Kinect sensors, one dedicated to operate with the Xbox 360 video game console and the more recent Microsoft Kinect for Windows version. The study of the Kinect sensor is extended to the design of a rapid acquisition system dedicated to large workspaces by the linkage of multiple Kinect units to collect 3D data over a large object, such as an automotive vehicle. A customized calibration method for this large workspace is proposed which takes advantage of the rapid 3D measurement technology embedded in the Kinect sensor and provides registration accuracy between local sections of point clouds that is within the range of the depth measurements accuracy permitted by the Kinect technology. The method is developed to calibrate all Kinect units with respect to a reference Kinect. The internal calibration of the sensor in between the color and depth measurements is also performed to optimize the alignment between the modalities. The calibration of the 3D vision system is also extended to formally estimate its configuration with respect to the base of a manipulator robot, therefore allowing for seamless integration between the proposed vision platform and the kinematic control of the robot. The resulting vision-robotic system defines the comprehensive calibration of reference Kinect with the robot. The latter can then be used to interact under visual guidance with large objects, such as vehicles, that are positioned within a significantly enlarged field of view created by the network of RGB-D sensors. The proposed design and calibration method is validated in a real world scenario where five Kinect sensors operate collaboratively to rapidly and accurately reconstruct a 180 degrees coverage of the surface shape of various types of vehicles from a set of individual acquisitions performed in a semi-controlled environment, that is an underground parking garage. The vehicle geometrical properties generated from the acquired 3D data are compared with the original dimensions of the vehicle.

This thesis presents an approach for configuring and calibrating a network of RGB-D sensors used to guide a robotic arm to interact with objects that get rapidly modeled in 3D. The system is based on Microsoft Kinect sensors for 3D data acquisition. The work presented here also details an analysis and experimental study of the Kinect’s depth sensor capabilities and performance. The study comprises examination of the resolution, quantization error, and random distribution of depth data. In addition, the effects of color and reflectance characteristics of an object are also analyzed. The study examines two versions of Kinect sensors, one dedicated to operate with the Xbox 360 video game console and the more recent Microsoft Kinect for Windows version. The study of the Kinect sensor is extended to the design of a rapid acquisition system dedicated to large workspaces by the linkage of multiple Kinect units to collect 3D data over a large object, such as an automotive vehicle. A customized calibration method for this large workspace is proposed which takes advantage of the rapid 3D measurement technology embedded in the Kinect sensor and provides registration accuracy between local sections of point clouds that is within the range of the depth measurements accuracy permitted by the Kinect technology. The method is developed to calibrate all Kinect units with respect to a reference Kinect. The internal calibration of the sensor in between the color and depth measurements is also performed to optimize the alignment between the modalities. The calibration of the 3D vision system is also extended to formally estimate its configuration with respect to the base of a manipulator robot, therefore allowing for seamless integration between the proposed vision platform and the kinematic control of the robot. The resulting vision-robotic system defines the comprehensive calibration of reference Kinect with the robot. The latter can then be used to interact under visual guidance with large objects, such as vehicles, that are positioned within a significantly enlarged field of view created by the network of RGB-D sensors. The proposed design and calibration method is validated in a real world scenario where five Kinect sensors operate collaboratively to rapidly and accurately reconstruct a 180 degrees coverage of the surface shape of various types of vehicles from a set of individual acquisitions performed in a semi-controlled environment, that is an underground parking garage. The vehicle geometrical properties generated from the acquired 3D data are compared with the original dimensions of the vehicle.

This thesis presents an approach for configuring and calibrating a network of RGB-D sensors used to guide a robotic arm to interact with objects that get rapidly modeled in 3D. The system is based on Microsoft Kinect sensors for 3D data acquisition. The work presented here also details an analysis and experimental study of the Kinect’s depth sensor capabilities and performance. The study comprises examination of the resolution, quantization error, and random distribution of depth data. In addition, the effects of color and reflectance characteristics of an object are also analyzed. The study examines two versions of Kinect sensors, one dedicated to operate with the Xbox 360 video game console and the more recent Microsoft Kinect for Windows version. The study of the Kinect sensor is extended to the design of a rapid acquisition system dedicated to large workspaces by the linkage of multiple Kinect units to collect 3D data over a large object, such as an automotive vehicle. A customized calibration method for this large workspace is proposed which takes advantage of the rapid 3D measurement technology embedded in the Kinect sensor and provides registration accuracy between local sections of point clouds that is within the range of the depth measurements accuracy permitted by the Kinect technology. The method is developed to calibrate all Kinect units with respect to a reference Kinect. The internal calibration of the sensor in between the color and depth measurements is also performed to optimize the alignment between the modalities. The calibration of the 3D vision system is also extended to formally estimate its configuration with respect to the base of a manipulator robot, therefore allowing for seamless integration between the proposed vision platform and the kinematic control of the robot. The resulting vision-robotic system defines the comprehensive calibration of reference Kinect with the robot. The latter can then be used to interact under visual guidance with large objects, such as vehicles, that are positioned within a significantly enlarged field of view created by the network of RGB-D sensors. The proposed design and calibration method is validated in a real world scenario where five Kinect sensors operate collaboratively to rapidly and accurately reconstruct a 180 degrees coverage of the surface shape of various types of vehicles from a set of individual acquisitions performed in a semi-controlled environment, that is an underground parking garage. The vehicle geometrical properties generated from the acquired 3D data are compared with the original dimensions of the vehicle.