True single-cell proteomics using advanced ion mobility mass spectrometry

In this thesis, I present the development of a novel mass spectrometry (MS) platform and scan modes in conjunction with a versatile and robust liquid chromatography (LC) platform, which addresses current sensitivity and robustness limitations in MS-based proteomics. I demonstrate how this technology benefits the high-speed and ultra-high sensitivity proteomics studies on a large scale. This culminated in the first of its kind label-free MS-based single-cell proteomics platform and its application to spatial tissue proteomics. I also investigate the vastly underexplored ‘dark matter’ of the proteome, validating novel microproteins that contribute to human cellular function. First, we developed a novel trapped ion mobility spectrometry (TIMS) platform for proteomics applications, which multiplies sequencing speed and sensitivity by ‘parallel accumulation – serial fragmentation’ (PASEF) and applied it to first high-sensitivity and large-scale projects in the biomedical arena. Next, to explore the collisional cross section (CCS) dimension in TIMS, we measured over 1 million peptide CCS values, which enabled us to train a deep learning model for CCS prediction solely based on the linear amino acid sequence. We also translated the principles of TIMS and PASEF to the field of lipidomics, highlighting parallel benefits in terms of throughput and sensitivity. The core of my PhD is the development of a robust ultra-high sensitivity LC-MS platform for the high-throughput analysis of single-cell proteomes. Improvements in ion transfer efficiency, robust, very low flow LC and a PASEF data independent acquisition scan mode together increased measurement sensitivity by up to 100-fold. We quantified single-cell proteomes to a depth of up to 1,400 proteins per cell. A fundamental result from the comparisons to single-cell RNA sequencing data revealed that single cells have a stable core proteome, whereas the transcriptome is dominated by Poisson noise, emphasizing the need for both complementary technologies. Building on our achievements with the single-cell proteomics technology, we elucidated the image-guided spatial and cell-type resolved proteome in whole organs and tissues from minute sample amounts. We combined clearing of rodent and human organs, unbiased 3D-imaging, target tissue identification, isolation and MS-based unbiased proteomics to describe early-stage β-amyloid plaque proteome profiles in a disease model of familial Alzheimer’s. Automated artificial intelligence driven isolation and pooling of single cells of the same phenotype allowed us to analyze the cell-type resolved proteome of cancer tissues, revealing a remarkable spatial difference in the proteome. Last, we systematically elucidated pervasive translation of noncanonical human open reading frames combining state-of-the art ribosome profiling, CRISPR screens, imaging and MS-based proteomics. We performed unbiased analysis of small novel proteins and prove their physical existence by LC-MS as HLA peptides, essential interaction partners of protein complexes and cellular function

Multi-GPU Acceleration of Iterative X-ray CT Image Reconstruction

X-ray computed tomography is a widely used medical imaging modality for screening and diagnosing diseases and for image-guided radiation therapy treatment planning. Statistical iterative reconstruction (SIR) algorithms have the potential to significantly reduce image artifacts by minimizing a cost function that models the physics and statistics of the data acquisition process in X-ray CT. SIR algorithms have superior performance compared to traditional analytical reconstructions for a wide range of applications including nonstandard geometries arising from irregular sampling, limited angular range, missing data, and low-dose CT. The main hurdle for the widespread adoption of SIR algorithms in multislice X-ray CT reconstruction problems is their slow convergence rate and associated computational time. We seek to design and develop fast parallel SIR algorithms for clinical X-ray CT scanners. Each of the following approaches is implemented on real clinical helical CT data acquired from a Siemens Sensation 16 scanner and compared to the straightforward implementation of the Alternating Minimization (AM) algorithm of O’Sullivan and Benac [1]. We parallelize the computationally expensive projection and backprojection operations by exploiting the massively parallel hardware architecture of 3 NVIDIA TITAN X Graphical Processing Unit (GPU) devices with CUDA programming tools and achieve an average speedup of 72X over a straightforward CPU implementation. We implement a multi-GPU based voxel-driven multislice analytical reconstruction algorithm called Feldkamp-Davis-Kress (FDK) [2] and achieve an average overall speedup of 1382X over the baseline CPU implementation by using 3 TITAN X GPUs. Moreover, we propose a novel adaptive surrogate-function based optimization scheme for the AM algorithm, resulting in more aggressive update steps in every iteration. On average, we double the convergence rate of our baseline AM algorithm and also improve image quality by using the adaptive surrogate function. We extend the multi-GPU and adaptive surrogate-function based acceleration techniques to dual-energy reconstruction problems as well. Furthermore, we design and develop a GPU-based deep Convolutional Neural Network (CNN) to denoise simulated low-dose X-ray CT images. Our experiments show significant improvements in the image quality with our proposed deep CNN-based algorithm against some widely used denoising techniques including Block Matching 3-D (BM3D) and Weighted Nuclear Norm Minimization (WNNM). Overall, we have developed novel fast, parallel, computationally efficient methods to perform multislice statistical reconstruction and image-based denoising on clinically-sized datasets

Development of Microscopy Systems for Super-Resolution, Whole-Slide, Hyperspectral, and Confocal Imaging

Optical microscope is an important tool for researchers to study small objects. In this thesis, we will focus on the improvement of traditional microscope systems from several aspects including resolution, field of view, speed, cost, compactness, multimodality. In particular, we will investigate computational imaging methods that bypass the limitations with traditional microscope systems by combining the optical hardware design and image processing algorithm. Examples will include optimizing illumination strategy for the Fourier ptychography (FP), developing field-portable high-resolution microscope using a cellphone lens, investigating pattern-illuminated FP for fluorescence microscopy, demonstrating multimodal microscopic imaging with the use of liquid crystal display, achieving fast and accurate autofocusing for whole slide imaging system

Bio-Inspired Multi-Spectral and Polarization Imaging Sensors for Image-Guided Surgery

Image-guided surgery (IGS) can enhance cancer treatment by decreasing, and ideally eliminating, positive tumor margins and iatrogenic damage to healthy tissue. Current state-of-the-art near-infrared fluorescence imaging systems are bulky, costly, lack sensitivity under surgical illumination, and lack co-registration accuracy between multimodal images. As a result, an overwhelming majority of physicians still rely on their unaided eyes and palpation as the primary sensing modalities to distinguish cancerous from healthy tissue. In my thesis, I have addressed these challenges in IGC by mimicking the visual systems of several animals to construct low power, compact and highly sensitive multi-spectral and color-polarization sensors. I have realized single-chip multi-spectral imagers with 1000-fold higher sensitivity and 7-fold better spatial co-registration accuracy compared to clinical imaging systems in current use by monolithically integrating spectral tapetal and polarization filters with an array of vertically stacked photodetectors. These imaging sensors yield the unique capabilities of imaging simultaneously color, polarization, and multiple fluorophores for near-infrared fluorescence imaging. Preclinical and clinical data demonstrate seamless integration of this technologies in the surgical work flow while providing surgeons with real-time information on the location of cancerous tissue and sentinel lymph nodes, respectively. Due to its low cost, the bio-inspired sensors will provide resource-limited hospitals with much-needed technology to enable more accurate value-based health care

Small business innovation research. Abstracts of completed 1987 phase 1 projects

Non-proprietary summaries of Phase 1 Small Business Innovation Research (SBIR) projects supported by NASA in the 1987 program year are given. Work in the areas of aeronautical propulsion, aerodynamics, acoustics, aircraft systems, materials and structures, teleoperators and robotics, computer sciences, information systems, spacecraft systems, spacecraft power supplies, spacecraft propulsion, bioastronautics, satellite communication, and space processing are covered