Three dimensional optofluidic devices for manipulation of particles and cells

Optical forces offer a powerful tool for manipulating single cells noninvasively. Integration of optical functions within microfluidic devices provides a new freedom for manipulating and studying biological samples at the micro scale. In the pursuit to realise such microfluidic devices with integrated optical components, Ultrafast Laser Inscription (ULI) fabrication technology shows great potential. The uniqueness and versatility of the technique in rapid prototyping of 3D complex microfluidic and optical elements as well as the ability to perform one step integration of these elements provides exciting opportunities in fabricating novel devices for biophotonics applications. The work described in this thesis details the development of three dimensional optofluidic devices that can be used for biophotonics applications, in particular for performing cell manipulation and particle separation. Firstly, the potential of optical forces to manipulate cells and particles in ULI microfluidic channels is investigated. The ability to controllably displace particles within a ULI microchannel using a waveguide positioned orthogonal to it is explored in detail. We then prototype a more complex 3D device with multiple functionalities in which a 3D optofluidic device containing a complex microchannel network and waveguides was used for further investigations into optical manipulation and particle separation. The microfluidic channel network and the waveguides within the device possess the capability to manipulate the injected sample fluid through hydrodynamic focusing and optically manipulate the individual particles, respectively. This geometry provided a more efficient way of investigating optical manipulation within the device. Finally, work towards developing a fully optimised 3D cell separator device is presented. Initial functional validation was performed by investigating the capability of the device to route particles through different outlet channels using optical forces. A proof of concept study demonstrates the potential of the device to use for cell separation based on the size of the cells. It was shown that both passive and active cell separation is possible using this device

Acoustic Forces in Cytometry and Biomedical Applications: Multidimensional Acoustophoresis

Over the last decades the ongoing work in the fields of Lab-on-a-Chip and Micro-Total-Analysis-Systems has led to the discovery of new or improved ways to handle and analyse small volumes of biofluids and complex biosuspensions. The benefits of working on the microscale include: miniaturization of the analysis systems with less need for large sample volumes; temporal and spatial control of suspended particle/cell positions; low volume sheath flow lamination or mixing; novel separation techniques by using forces inherent to the microscale domain; precise regulation of sample temperatures and rapid analysis with less volumes needed to be processed. Researchers now seek to implement these techniques in integrated systems to benefit the biomedical research field as well as clinics. Acoustophoresis, a method that utilizes acoustic forces to move particles and cells in microfluidic channels has been gaining increased attention over the last decade. The acoustophoretic method has been shown to handle a number of biosuspensions e.g., blood, cell cultures and raw milk as well as other biofluids, and comes with a variety of available unit operations e.g., free flow separation, binary density separation, particle positioning, contactless trapping, buffer changes, washing, and surface chemistry based sorting that allows integration into a wide range of application. The theoretical and experimental understanding of the acoustic radiation force which is the principal force used to manipulate particles in these systems (often generated with standing waves) has also evolved during this time. Chip-based acoustic systems have been presented in e.g., silicon, glass and PDMS, further illustrating the versatility of the method. This dissertation presents some of the recent developments in the acoustophoretic field to illustrate how acoustic forces can be used in cytometry and biomedical applications, specifically by utilizing multiple acoustic wavelength geometries or two-dimensional particle manipulation. Paper I presents a novel way to pretreat raw milk in order to facilitate rapid quality control. Paper II extends this method by presenting a technique for label free cytometry in raw milk. Paper III showcases the ability to sort particles with fluorescence activated acoustic forces. Paper IV presents a low complexity high precision proof-of-concept sheathless impedance cytometer that can be integrated in other chip based systems. Paper V presents an improved method for concurrent blood component fractionation that requires less manual handling compared to established methods by implementing free flow separation into multiple outlets. The theory section explains the underlying physical laws that govern the microscale fluid systems presented here. Acoustic force theory is explained in detail for better understanding of the acoustic radiation forces that act on the suspended particles and also cause media streaming. The particle manipulation section compares the different methods that are available to researchers in the biomedical microfluidic field. The microfabrication section deals with the design aspects of using various materials. Unit operations and applications specific for acoustophoresis are presented. Biofluids and cell types including blood and raw milk are discussed to underline the challenges that researchers are faced with during system design, handling and analysis. The aim of this dissertation is to provide a foundation for future development of acoustic force applications in cytometry and biomedicine

Lab-on-a-chip platforms for pathogen analysis

Infectious diseases caused by pathogenic microorganisms are a big burden in developed and developing countries. The emergence and rapid global spread of virus and antimicrobial resistant bacteria is a significant threat to patients, healthcare systems and the economy of countries. Early pathogen detection is often hampered by low concentrations present in complex matrices such as food and body fluids.Microfluidic technologies offer new and improved approaches for detection of pathogens on the microscale. Here, two microfluidic platforms for pathogen sorting and molecular identification were investigated: (1) inertial focusing and (2) microscale immiscible filtration. Inertial focusing in two serpentine channel designs etched in glass at different depths was evaluated with different microparticles, bacteria and blood. The shallow design allowed 2.2-fold concentration of Escherichia coli O157 cells, whereas the deep design accomplished recovery of 54% E. coli O157 depleted from 97% red blood cells in 0.81% haematocrit at flowrates of 0.7 mL min-1.A lab-on-a-chip platform based on microscale immiscible filtration was investigated for capture and detection of nucleic acids and bacteria. For nucleic acids, oligo (dT) functionalised magnetic beads or silica paramagnetic particles in GuHCl were used to capture genomic RNA from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and genomic DNA from Neisseria gonorrhoeae, respectively. On-chip amplification and detection were performed via colorimetric loop-mediated isothermal amplification (LAMP). Results showed sensitive and specific detection of targeted nucleic acids (470 RNA copies mL-1 and 5 × 104 DNA copies mL-1) with no cross-reactivity to other RNAs and DNAs tested. The whole workflow was integrated in a single device and time from sample-in to answer-out was within 1h. The platform only required power for a heat source and showed potential for point of care diagnostics in resource-limited settings. For bacteria detection, anti-E. coli O157 functionalised magnetic beads were used to capture cells with > 90% efficiency and on-chip fluorescence in situ hybridisation and a staining assay were explored for bacteria identification.A wide variety of microfluidic approaches for pathogen analysis have been devised in the literature with different advantages and drawbacks. Careful evaluation based on their purpose, integrated steps and end user is critical. Input from stakeholders right from the start of a project and throughout is vital to success. The platforms investigated herein have potential for applications such as sample preparation, pathogen concentration and specific molecular detection of E. coli O157, N. gonorrhoeae DNA, and SARS-CoV-2 RNA. With further development and clinical validation, the widespread use of these systems could facilitate early diagnosis of infectious diseases, allowing timely management of outbreaks and treatment and slowing the incidence of antimicrobial resistance

Microfluidic Liquid Biopsy for Cancer Prognosis

Leukemia is a deadly and common cancer, especially in children and adolescents. The misdiagnosis and unexpected complications during the treatment are some factors that increase the mortality rate of leukemia. The goal of our project was to create a device that would quickly and accurately assess these complications. While there are existing tests that can perform a single test for either metastasis or sepsis, there are none that can test for both simultaneously and rapidly. We propose to modify and combine some of these existing microfluidic designs as well as create a new component to perform a combinatorial assessment. While we were unable to verify the results, we have designed a device that will potentially meet our goals of testing for sepsis, severe sepsis, solid-body metastasis risk, major solid-body metastasis risk, lymphoma metastasis risk, and progression of the primary blood-based cancer (leukemia/lymphoma), from 7.5 mL of blood in just under 2 hours. We believe that this device has the potential to contribute to the medical field due to its speed and efficiency, especially in a pediatric demographic

Cell Separations and Sorting

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Analytical Chemistry, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.analchem.9b05357.NIBIB Grant P41-EB020594COBRE Grant 5P20GM13042

Fast fluorescence lifetime imaging and sensing via deep learning

Error on title page – year of award is 2023.Fluorescence lifetime imaging microscopy (FLIM) has become a valuable tool in diverse disciplines. This thesis presents deep learning (DL) approaches to addressing two major challenges in FLIM: slow and complex data analysis and the high photon budget for precisely quantifying the fluorescence lifetimes. DL's ability to extract high-dimensional features from data has revolutionized optical and biomedical imaging analysis. This thesis contributes several novel DL FLIM algorithms that significantly expand FLIM's scope. Firstly, a hardware-friendly pixel-wise DL algorithm is proposed for fast FLIM data analysis. The algorithm has a simple architecture yet can effectively resolve multi-exponential decay models. The calculation speed and accuracy outperform conventional methods significantly. Secondly, a DL algorithm is proposed to improve FLIM image spatial resolution, obtaining high-resolution (HR) fluorescence lifetime images from low-resolution (LR) images. A computational framework is developed to generate large-scale semi-synthetic FLIM datasets to address the challenge of the lack of sufficient high-quality FLIM datasets. This algorithm offers a practical approach to obtaining HR FLIM images quickly for FLIM systems. Thirdly, a DL algorithm is developed to analyze FLIM images with only a few photons per pixel, named Few-Photon Fluorescence Lifetime Imaging (FPFLI) algorithm. FPFLI uses spatial correlation and intensity information to robustly estimate the fluorescence lifetime images, pushing this photon budget to a record-low level of only a few photons per pixel. Finally, a time-resolved flow cytometry (TRFC) system is developed by integrating an advanced CMOS single-photon avalanche diode (SPAD) array and a DL processor. The SPAD array, using a parallel light detection scheme, shows an excellent photon-counting throughput. A quantized convolutional neural network (QCNN) algorithm is designed and implemented on a field-programmable gate array as an embedded processor. The processor resolves fluorescence lifetimes against disturbing noise, showing unparalleled high accuracy, fast analysis speed, and low power consumption.Fluorescence lifetime imaging microscopy (FLIM) has become a valuable tool in diverse disciplines. This thesis presents deep learning (DL) approaches to addressing two major challenges in FLIM: slow and complex data analysis and the high photon budget for precisely quantifying the fluorescence lifetimes. DL's ability to extract high-dimensional features from data has revolutionized optical and biomedical imaging analysis. This thesis contributes several novel DL FLIM algorithms that significantly expand FLIM's scope. Firstly, a hardware-friendly pixel-wise DL algorithm is proposed for fast FLIM data analysis. The algorithm has a simple architecture yet can effectively resolve multi-exponential decay models. The calculation speed and accuracy outperform conventional methods significantly. Secondly, a DL algorithm is proposed to improve FLIM image spatial resolution, obtaining high-resolution (HR) fluorescence lifetime images from low-resolution (LR) images. A computational framework is developed to generate large-scale semi-synthetic FLIM datasets to address the challenge of the lack of sufficient high-quality FLIM datasets. This algorithm offers a practical approach to obtaining HR FLIM images quickly for FLIM systems. Thirdly, a DL algorithm is developed to analyze FLIM images with only a few photons per pixel, named Few-Photon Fluorescence Lifetime Imaging (FPFLI) algorithm. FPFLI uses spatial correlation and intensity information to robustly estimate the fluorescence lifetime images, pushing this photon budget to a record-low level of only a few photons per pixel. Finally, a time-resolved flow cytometry (TRFC) system is developed by integrating an advanced CMOS single-photon avalanche diode (SPAD) array and a DL processor. The SPAD array, using a parallel light detection scheme, shows an excellent photon-counting throughput. A quantized convolutional neural network (QCNN) algorithm is designed and implemented on a field-programmable gate array as an embedded processor. The processor resolves fluorescence lifetimes against disturbing noise, showing unparalleled high accuracy, fast analysis speed, and low power consumption