Droplet-based microfluidics is a powerful tool permitting massive-scale single-cell analysis in pico-/nano-liter water-in-oil droplets. It has been integrated into various library preparation techniques to accomplish high-throughput scRNA-seq, scDNA-seq, scATAC-seq, scChIP-seq, as well as scMulti-omics-seq. These advanced technologies have been providing unique and novel insights into both normal differentiation and disease development at single-cell level. In this thesis, we develop four new droplet-based tools for single-cell omics profiling. First, the developed Drop-BS is the first droplet-based platform to construct single-cell bisulfite sequencing libraries for DNA methylome profiling and allows production of BS library of 2,000-10,000 single cells within 2 d. We applied the technology to separately profile mixed cell lines, mouse brain tissues, and human brain tissues to reveal cell type heterogeneity. Second, the new Drop-ChIP platform only requires two steps of droplet generation to achieve multiple steps of reactions in droplets such as single-cell lysis, chromatin fragmentation, ChIP, and barcoding. Third, we aim to establish a droplet-based platform to accomplish high-throughput full-length RNA-seq (Drop-full-seq), which both current tube-based and droplet-based methods cannot realize. Last, we constructed an in-house droplet-based tool to assist single-cell ATAC-seq library preparation (Drop-ATAC), which provided a low-cost and facile protocol to conduct scATAC-seq in laboratories without the expensive instrument. / Doctor of Philosophy / Microfluidics is a collection of techniques to manipulate fluids in the micrometer scale. One of microfluidic techniques is called "droplet-based microfluidics". It can manipulate (i.e., generate, merge, sort, split, etc) pico-/nano-liter of water-in-oil droplets. First, since the water phase is separated by the continuous oil phase, these droplets are discrete and individual reactors. Second, droplet-based microfluidics can achieve highly parallel manipulation of thousands to millions of droplets. These two advantages make droplet-based microfluidics an ideal tool to perform single-cell assays. Over the past 10 years, various droplet-based platforms have been developed to study single-cell transcriptome, genome, epigenome, as well as multi-ome. To expand droplet-based tools for single-cell analysis, we aim to develop four novel platforms in this thesis. First, Drop-BS, by integrating droplet generation and droplet fusion techniques, can achieve high-throughput single-cell bisulfite sequencing library preparation. It can generate 10,000 single-cell BS libraries within 2 days which is difficult to achieve for conventional library preparation in tubes/microwells. Second, we developed a novel and facile Drop-ChIP platform to prepare single-cell ChIP-seq library. It is easy to operate since it only requires two steps of droplet generation. It also generates higher quality of data compared to previous work. In addition, we are working on the development and characterization of the other two droplet-based tools to achieve full-length single-cell RNA-seq and single-cell ATAC-seq.

Droplet microfluidics is an emerging technology for analysis of single cells and biomolecules at high throughput. The controlled encapsulation of particles along with the surrounding microenvironment in discrete droplets, which acts as miniaturized reaction vessels, allows millions of particles to be screened in parallel. By utilizing the unit operations developed to generate, manipulate and analyze droplets, this technology platform has been used to miniaturize a wide range of complex biological assays including, but not limited to, directed evolution, rare cell detection, single cell transcriptomics, rare mutation detection and drug screening. The aim of this thesis is to develop droplet microfluidics based methods for analysis of single cells and nucleic acids. In Paper I, a method for time-series analysis of mammalian cells, using automated fluorescence microscopy and image analysis technique is presented. The cell-containing droplets were trapped on-chip and imaged continuously to assess the viability of hundreds of isolated individual cells over time. This method can be used for studying the dynamic behavior of cells. In Paper II, the influence of droplet size on cell division and viability of mammalian cell factories during cultivation in droplets is presented. The ability to achieve continuous cell division in droplets will enable development of mammalian cell factory screening assays in droplets. In Paper III, a workflow for detecting the outcome of droplet PCR assay using fluorescently color-coded beads is presented. This workflow was used to detect the presence of DNA biomarkers associated with poultry pathogens in a sample. The use of color-coded detection beads will help to improve the scalability of the detection panel, to detect multiple targets in a sample. In Paper IV, a novel unit operation for label-free enrichment of particles in droplets using acoustophoresis is presented. This technique will be useful for developing droplet-based assays that require label-free enrichment of cells/particles and removal of droplet content. In general, droplet microfluidics has proven to be a versatile tool for biological analysis. In the years to come, droplet microfluidics could potentially be used to improve clinical diagnostics and bio-based production processes. / <p>QC 20160926</p>

Cette thèse concerne le développement d’outils microfluidique pour l’ingénierie d’enzymes d’intérêt thérapeutique. La microfluidique à base de gouttelettes présente un énorme potentiel dans le domaine de la biologie quantitative. Nous développons des outils microfluidiques pour l’évolution dirigée de l’enzyme L-asparaginase, enzyme utilisée comme traitement de laleucémie lymphoblastique aiguë. Ce traitement est basée sur une enzyme d’origine bactérienne,ce qui conduit à déclencher des réactions immunitaires qui se traduit par l’interruption du traitement, souvent fatale pour le patient. Cependant, une version humaine de l’enzyme L-asparaginase, qui est moins immunogénique, n’est à l’heure actuelle pas suffisamment active pour être utilisée. L’objectif principal de cette thèse est d’alors d’analyser et de cribler des banques de mutants d’enzymes en utilisant des méthodes classiques de mutagenèse et d’analyser chaque mutant individuellement par le biais de la microfluidique. Pour cela, plusieurs systèmes microfluidiques ont été développés et optimisés afin de répondre à différents critères de sélection pour l’analyse et la sélection de l’enzyme L-asparaginase. La version bactérienne a servi de contrôle positif pour l’optimisation des systèmes microfluidiques afin de pouvoir analyser et de cribler des banques de mutants de la version humaine de l’enzyme L-asparaginase. / This thesis deals with the development of microfluidic tools for the engineering ofenzymes of therapeutic interest. Droplet microfluidics has enormous potential in the field ofquantitative biology. We are developing microfluidic tools based on the directed evolutionof the enzyme L-asparaginase, an enzyme used to treat acute lymphoblastic leukemia. Thistreatment is based on an enzyme of bacterial origin, which leads to immune reactions thatresult in the interruption of treatment, often fatal for the patient. However, a human version ofthe enzyme L-asparaginase, which is less immunogenic, is currently not sufficiently active to beused. The main objective of this thesis is to analyze and screen enzyme mutant libraries usingstandard mutagenesis methods and to analyze each mutant individually through microfluidics.For this, several microfluidic systems have been developed and optimized for different selectioncriteria for the analysis and selection of the enzyme L-asparaginase. The bacterial versionserving as a positive control for the optimization of microfluidic workflows to analyze andscreen mutant libraries of the human version of the enzyme L-asparaginase.

Cellular therapy is emerging as a clinically viable strategy in the field of solid organ transplantation, where it is expected to reduce the dependency on conventional immunosuppression. This has produced a demand for highly sensitive methods to monitor the persistence and tissue distribution of administered cells in vivo. However, tracking cells presents significant challenges. In many cases transplanted cells are autologous with the immune system of the transplant recipient, and hence are invisible to typical methods of detection. To enable their differentiation, the cells must be labelled with a suitable, non-toxic and long lifetime label, prior to their administration to patients. In addition, administered cells represent only a small fraction of the recipient's endogenous cells, which necessitates the use of an extremely sensitive detection method. Laser ablation – inductively coupled plasma – mass spectrometry (LA-ICP-MS) is an exquisitely sensitive analytical technique, capable of imaging trace elements in complex samples, at high spatial resolution.

DNA methylation is an important epigenetic mark spanning all of life's kingdoms. In humans, DNA methylation has been associated with a wide range of age-related pathologies, including type II diabetes and cancer. More recently, in humans, changes in DNA methylation at specific positions in the genome have been found to be predictive of chronological age. Interestingly, DNA methylation age is also predictive of health status and time-to-death. A better understanding of what these DNA methylation changes represent and whether they might be causative in the ageing process will be important to ascertain. However, at present there is no animal model system with which this process can be studied at a mechanistic level. Furthermore, it is becoming increasingly apparent that many disease states that increase in prevalence with age are not caused by all cells within the individual, but are often the result of changes to a subset of cells. This underscores the importance of studying these processes at the single cell level. The recent advances in single cell sequencing approaches now mean that we can study multiple layers of biology within the same single cell, such as the epigenome and the transcriptome (scM&T-Seq). Unfortunately, we are still only able to probe these important aspects of single cell biology in a static sense. This is a major limitation in the study of ageing because ageing and age-related disease processes are inherently dynamic. As such, it is incumbent upon us to develop approaches to assay single cell biology in a dynamic manner. 
In this thesis, I describe an epigenetic age predictor in the mouse. This predictor is tissue-independent and can accurately predict age (with an error of 3.33 weeks) and can record deviations in biological age upon interventions including ovariectomy and high fat diet both of which are known to reduce lifespan. Next, I describe the analysis of a homogeneous population of muscle satellite cells (MuSCs) that I have interrogated at the single cell level, using single cell combined transcriptome and methylome sequencing (scM&T-seq). I found that with age there was increased global transcriptional variability and increased feature-specific methylome variability. These findings explain the loss of functionality of these cells with age. Lastly, I describe two imaging approaches to study DNA methylation dynamically in single cells. Using these methods, I demonstrate that it is possible to accurately determine methylation status across a wide spectrum of global methylation levels and that by using such approaches novel information about dynamic methylation processes can be obtained. These methods represent the first to study DNA methylation dynamically.

Miniaturization of biological analysis is a trend in the field of biotechnology aiming to increase resolution and sensitivity in biological assays. Decreasing the reaction volumes to analyze fewer analytes in each reaction vessel enables the detection of rare analytes in a vast background of more common variants. Droplet microfluidics is a high throughput technology for the generation, manipulation and analysis of picoliter scale water droplets an in immiscible oil. The capacity for high throughput processing of discrete reaction vessels makes droplet microfluidics a valuable tool for miniaturization of biological analysis. In the first paper, detection methods compatible with droplet microfluidics was expanded to include SiNR FET sensors. An integrated droplet microfluidics SiNR FET sensor device capable of extracting droplet contents, transferring a train of droplets to the SiNR to measure pH was implemented and tested. In paper II, a workflow was developed for scalable and target flexible multiplex droplet PCR using fluorescently color-coded beads for target detection. The workflow was verified for concurrent detection of two microorganisms infecting poultry. The detection panel was increased to multiple targets in one assay by the use of target specific capture probes on color-coded detection beads.   In paper III, droplet microfluidics has been successfully applied to single cell processing, demonstrated in paper III, where reverse transcription was performed on 65000 individually encapsulated mammalian cells. cDNA yield was approximately equivalent for reactions performed in droplets and in microliter scale. This workflow was further developed in paper IV to perform reverse transcription PCR in microfluidic droplets for detection of exosomes based on 18S RNA content. The identification of single exosomes based on RNA content can be further developed to detect specific RNA biomarkers for disease diagnostics. Droplet microfluidics has great potential for increasing resolution in biological analysis and to become a standard tool in disease diagnostics and clinical research. / <p>QC 20171024</p>

Microorganisms are ubiquitously distributed on the earth and drive the fundamental element cycling in the biosphere. Their metabolic activities serve human societies in countless areas such as biotechnological engineering, food engineering, energy production, waste disposal et cetera. For human beings, and also for animals, microorganisms are imperative for health especially as colonizers of the gut system. Microbial resource management, especially when complex communities are exploited in biotechnology is a key challenge. Therefore, communities are more and more in the focus of basic research in microbiology complementary to pure cultivation technologies. Owing to their complexity, microbial communities are almost exclusively studied on the basis of bulk parameters and empirical expert knowledge. Bulk parameters are representative for an entire community performance but do not allow a segregated analysis of subpopulations or subcommunities, let alone individuals and their disparate functions within a community. This thesis aims to resolve microbial community properties and mechanisms of assembly in managed ecosystems on the individual level (i.e. single cell). For this the flow cytometric toolbox was employed and further expanded, which phenotypically classifies microbial individuals into sub-communities according to their physiological similarities. Workflows for the fast analysis and evaluation of dynamics in community structure, assembly and interaction were developed. Stability properties of communities, i.e. resistance, resilience, displacement speed and elasticity, can now quantitatively be determined based on cytometric data. For resilience behavior an on-line tool was developed. In addition, the relative proportions of neutral and deterministic forces that structure a microbial community can now be unraveled. As consequence, microbial flow cytometry has been proven to be a powerful tool for analysing complex microbial communities, and will allow huge improvements in understanding and control of microbial communities in managed and natural ecosystems.:Contents Summary ............................................................................................................. I Zusammenfassung ........................................................................................... IV 1 Introduction ..................................................................................................... 1 1.1 Microbial community and ecology ........................................................... 1 1.1.1 What is a microbial community? ...................................................... 1 1.1.2 Flow cytometry as a tool to study microbial communities ................ 2 1.2 Community structure and diversity ........................................................ 10 1.2.1 Community structure...................................................................... 10 1.2.2 Diversity metrics ............................................................................ 10 1.2.3 Evaluating structure and diversity with flow cytometry ................... 12 1.3 Community assembly and dynamics ..................................................... 13 1.3.1 Basic assembly processes ............................................................ 13 1.3.2 Evaluating assembly processes with flow cytometry ..................... 16 1.4 Community interactions ......................................................................... 18 1.4.1 Abiotic interactions of microbes and their surroundings................. 18 1.4.2 Biotic interactions of microbial partners ......................................... 18 1.4.3 Evaluating interactions with flow cytometry ................................... 20 1.5 Community functions ............................................................................. 22 1.5.1 Omics approaches to study functions in microbial communities .... 22 1.5.2 Evaluating functions with flow cytometry ....................................... 23 1.6 Aims of this study .................................................................................. 25 2 Publications .................................................................................................. 27 2.1 Overview of publications ....................................................................... 27 2.2 Published articles .................................................................................. 28 2.2.1 Publication 1 .................................................................................. 29 2.2.2 Publication 2 .................................................................................. 42 2.2.3 Publication 3 (under review) .......................................................... 60 3 Discussion .................................................................................................... 81 3.1 The importance of perceiving ecological situations ............................... 81 3.2 Stability properties of a microbial community ........................................ 84 3.3 Assembly processes in insular environments ....................................... 87 3.3.1 Niche differentiation under balanced cultivation conditions ........... 88 3.3.2 Neutral assembly under balanced cultivation conditions ............... 89 3.3.3 From intermediate disturbance to a non‐equilibrium system ......... 90 3.4 On-line analysis of reactor data ............................................................ 93 3.5 Conclusion and outlook ......................................................................... 95 4 References ................................................................................................... 97 5 Acknowledgement ...................................................................................... 105 6 Appendix .................................................................................................... 106 6.1 Declaration of independent work ......................................................... 106 6.2 Author contributions of published articles............................................ 107 6.3 Curriculum vitae .................................................................................. 111 6.4 List of Publications and conference contributions ............................... 112 6.5 Supplementary materials .................................................................... 113 6.5.1 Supplementary material for publication 1 .................................... 113 6.5.2 Supplementary material for publication 2 .................................... 140 6.5.3 Supplementary material for publication 3 .................................... 174

Optical diffraction tomography (ODT) is a tomographic technique that can be used to measure the threedimensional (3D) refractive index distribution within living cells without the requirement of any marker. In principle, ODT can be regarded as a generalization of optical projection tomography which is equivalent to computerized tomography (CT). Both optical tomographic techniques require projection-phase images of cells measured at multiple angles. However, the reconstruction of the 3D refractive index distribution post-measurement differs for the two techniques. It is known that ODT yields better results than projection tomography, because it takes into account diffraction of the imaging light due to the refractive index structure of the sample. Here, we apply ODT to biological cells in a micro uidic chip which combines optical trapping and microfluidic flow to achieve an optofluidic single-cell rotation. In particular, we address the problem that arises when the trapped cell is not rotating about an axis perpendicular to the imaging plane, but instead about an arbitrarily tilted axis. In this paper we show that the 3D reconstruction can be improved by taking into account such a tilted rotational axis in the reconstruction process.

Titanium alloys are frequently used in the medical field as bone implant materials due to their excellent biocompatibility and corrosion resistance. Yet, their elastic modulus is usually significantly higher than the one of bone, which can lead to a reduction of bone tissue at the implant site. The current research is therefore focused on the development of highly porous implants, which promise a low elastic modulus close to that of bone, an enhanced bone ingrowth and an improved vascularization. However, the appropriate pore size for an optimal osseointegration still remains unclear. To that end, a transparent tubular microsystem is developed to mimic such a porous microenvironment in order to study single bone cell behavior and early bone formation processes. The system is fabricated out of an implant material (β-stabilized Ti-45Nb (wt%)). It is demonstrated that the bulk material composition, which is consisting of a high Nb content, can be closely transferred to transparent thin films by using reactive sputtering. These films then self-assemble into tubular microscaffolds (TS) with a diameter range between 10-42 μm. Biological studies are subsequently performed to investigate the response (e.g. cell adhesion, migration, osteogenic differentiation) of human Mesenchymal Stem Cells (MSC) to the TS. It is shown that cells form fewer, more diffuse focal adhesion points inside the TS compared to a planar surface and the spatial confinement causes a switch in between amoeboid and mesenchymal migration modes. In addition, it is demonstrated that cells can survive inside the TS for at least 12 days during osteogenic differentiation and partly mineralize the TS interior. The observed mineralization process is furthermore linked to the formation of hydroxyapatite crystals inside dead cells bodies, which leads to a crystallization over time. All in all, the TS platform offers an easy way to identify key factors of bone cell-implant interactions that can be used to improve the biocompatibility of the bone-implant interface in the future.

Single-cell analysis means to analyze cells on an individual level. This individual analysis enhances the investigation of the heterogeneity among and the classification of individual cells. Single-cell analysis is a broad term and can include various measurements. This thesis utilizes single-cell RNA sequence data that measures RNA sequences representing genes for individual cells. This data is often high-dimensional, with tens of thousands of RNA sequences measured for each cell. Dimension reduction is therefore necessary when analyzing the data. One proposed dimension reduction method is the unsupervised machine learning method variational autoencoders. The scVI framework has previously implemented a variational autoencoder for analyzing single-cell RNA sequence data. The variational autoencoder of the scVI has one latent space with a Gaussian distribution. Several extensions have been made to the scVI framework since its creation. This thesis proposes an additional extension consisting of a variational autoencoder with two latent spaces, called hybridVI. One of these latent spaces has a Gaussian distribution and the other a von Mises-Fisher distribution. The data is separated between these two latent spaces, meaning that some of the genes go through one latent space and the rest go through the other. In this thesis the cell cycle genes go through the von Mises-Fisher latent space and the rest of the genes go through the Gaussian latent space. The motivation behind the von Mises-Fisher latent space is that cell cycle genes are believed to follow a circular distribution. Putting these genes through a von Mises-Fisher latent space instead of a Gaussian latent space could provide additional insights into the data. The main focus of this thesis was to analyze the impact this separation. The analysis consisted of comparing the performance of the hybridVI model, to the original scVI variational autoencoder. The comparison utilized three annotated datasets, one peripheral blood mononuclear cell dataset, one cortex cell dataset, and one B cell dataset collected by the Henriksson lab at Umeå University. The evaluation metrics used were the adjusted rand index, normalized mutual information and a Wilcoxon signed ranks test was used to determine if the results had statistical significance. The results indicate that the size of the dataset was essential for achieving robust and statistically significant results. For the two datasets that yielded statistically significant results, the scVI model performed better than the hybridVI model. However, more research analyzing biological aspects is necessary to declare the hybridVI model’s effect on the biological interpretation of the results. / Individuell cellanalys är en relativt ny metod som möjliggör undersökning av celler på indivudiell nivå. Det här examensarbetet analyserar RNA sekvens data, där RNA sekvenser är specifierade för individuella celler. Den här sortens data är ofta högdimensionell med flera tusen gener noterade för varje cell. För att möjliggöra en analys av den här datan krävs någon form av dimensionreducering. En föreslagen metod är den ovövervakade maskininlärningsmetoden variational autoencoders. Ett ramverk, scVI, har framtagit en variational autoencoder designad för att hantera den här sortens data. Den här modellen har endast en latentrymd med en normalfördelning. Det här examensarbetet föreslår en utökning av det här ramverket med en variational autoencoder med två latentrymder,där den ena är normalfördelad och den andra följer en von Mises-Fisher fördelning. Motiveringen till en sådan fördelning är att cellcykelgener är antagna att tillhöra en cirkulär fördelning. Cellcykelgenerna i datan kan därmed hanteras av den cirkulära latentrymden. Huvudfokuset i den här studien är att undersöka om den här separationen av gener kan förbättra modellens förmåga att hitta korrekta kluster. Experimentet utfördes på tre annoterade dataset, ett som bestod av perifera mononukleära blodceller, ett som bestod av hjärnbarksceller och ett som bestod av B celler insamlat av Henrikssongruppen vid Umeå universitet. Modellen från scVI ramverket jämfördes med den nya metoden med två latentrymder, hybridVI. Måtten som användes för att bedöma de modellerna var adjusted rand index och normaliserad mutual information och ett Wilcoxon Signed-Ranks test användes för att bedöma resultatens statistiska signifikans. Resultaten påvisar att de båda modellerna preseterar bättre och mer konsekvent för större dataset. Två dataset gav statistiskt signifikanta resultat och visade att scVI modellen presterade bättre än hybridmodellen. Det behövs dock en biologisk analys av resultaten för att undersöka vilken modells resultat som har mest biologisk relevans.