Recent advances in micro technologies have equipped researches with novel tools for interacting with biological molecules and cells. This thesis focuses on the design, fabrication and application of microfluidic platforms for stimuli-responsive drug delivery and the electromechanical characterization of single cells. Stimuli-responsive hydrogels are promising materials for controlled drug delivery due to their ability to respond to changes in local environmental conditions. In particular, nanohydrogel particles have been a topic of considerable interest due to their rapid response times compared to micro and macro-scale counterparts. Owing to their small size and thus low drug-loading capacity, these materials are unsuitable for prolonged drug delivery. To address this issue, stimuli-responsive implantable drug delivery micro devices by integrating microfabricated drug reservoirs with smart nano-hydrogel particles embedded composite membranes have been proposed. In one proposed glucose-responsive micro device, crosslinked glucose oxidase enables the oxidation of glucose into gluconic acid, producing a microenvironment with lower pH values to modulate the pH-responsive nanoparticles. In vitro glucose-responsive drug release profiles were characterized and normoglycemic glucose levels in diabetic rats with device implantation were also recorded. The biophysical properties of single cells have recently been demonstrated as an important indicator of disease diagnosis. Existing technologies are capable of characterizing single parameter either electrical or mechanical rapidly, but not both, which could only collect limited information for cell status evaluation. To address this issue, two microfluidic platforms capable of simultaneously characterizing both the electrical and mechanical properties of single cells based on electrodeformation and integrated impedance spectroscopy with micropipette aspiration have been proposed. In one proposed microfluidic device, a negative pressure was used to suck cells continuously through the aspiration channel with impedance profiles measured. By interpreting impedance profiles, transit time and impedance amplitude ratio can be quantified as cellular mechanical and electrical property indicators. Neural network based cell classification was conducted, demonstrating that two biophysical parameters could provide a higher cell classification success rate than using electrical or mechanical parameter alone.

We have employed a differential detector combined with dielectrophoretic (DEP) translation in a microfluidic channel to monitor dielectric response of single cells and particularly to track phenomenon related to apoptosis. Two different cell lines were studied; Chinese hamster ovary cells (CHO) and MRC5 cells. Dielectric response was quantified by a factor called Force Index. Force Index was studied statistically to identify apoptotic subpopulations. Another direction of this work was to monitor changes in the cytoplasm conductivity following inhibition of mitochondrial ATP production by Oligomycin. To make the DEP response mostly sensitive to the cytoplasm conductivity, medium conductivity and DEP frequency were adjusted such that Clausius Mossotti factor and hence DEP response become less sensitive to cell radius. Chinese hamster ovary cells were used in this work and the impact of different concentrations of Oligomycin has been studied. We show that following exposure to Oligomycin at 8 μg/ml, cytoplasm conductivity drops. The majority of the changes takes place within one hour of exposure to the drug. Furthermore, double shell models has been used to estimate cytoplasm conductivity in a medium with conductivity of 0.42 S/m and the drop in the cytoplasm conductivity following treatment with Oligomycin was estimated to be ≈ 0.16 S/m. The magnitude of the decrease in the cytoplasm conductivity is evidence that Glycolysis is active as an energy production pathway within the cell. This approach can be used to quantify Glycolysis versus mitochondria ATP production which has an application in Warburg effect in cancer cells and monitoring bioprocesses.

Single cell analysis devices have become important tools to obtain unique information on cells to improve current medical techniques, such as tissue engineering, or diagnosis of cancer at an early stage. This thesis documents the development of a "cell cross flow system" (CFS), which aims to capture magnetically tagged (MT) cells from a heterogeneous population of cells, and array these cells in pre-determined locations using magnetic force. The CFS integrates a “magnetic single cell micro array” (MSCMA), and a gasket assembly to achieve this. Current single cell technology, relevant fluid and magnetic theory, CFS design process, finite element method (FEM) simulation, and cross flow experiments are detailed in this thesis. The CFS was successful in capturing MT Jurkat cells, and the experimental results were consistent with the FEM simulation analysis. It was found that the CFS was capable of capturing MT Jurkat cells up to a ratio of 1 to 103 (MT to non-magnetically tagged cells) using a cell concentration of 105 cells/mL. Although these results are promising, non-magnetically tagged Jurkat cells were found to adhere to the chip and could not be easily removed. Several recommendations were suggested for future iterations, including changing the gasket assembly design, optimizing the flow rate and cell concentration, and using smaller trap sizes for the MSCMA design.

Recent proceedings in biotechnology have enabled completely new avenues in life science research to be explored. By allowing increased parallelization an ever-increasing complexity of cell samples or experiments can be investigated in shorter time and at a lower cost. This facilitates for example large-scale efforts to study cell heterogeneity at the single cell level, by analyzing cells in parallel that also can include global genomic analyses. The work presented in this thesis focuses on massively parallel analysis of cells or nucleic acid samples, demonstrating technology developments in the field as well as use of the technology in life sciences. In stem cell research issues such as cell morphology, cell differentiation and effects of reprogramming factors are frequently studied, and to obtain information on cell heterogeneity these experiments are preferably carried out on single cells. In paper I we used a high-density microwell device in silicon and glass for culturing and screening of stem cells. Maintained pluripotency in stem cells from human and mouse was demonstrated in a screening assay by antibody staining and the chip was furthermore used for studying neural differentiation. The chip format allows for low sample volumes and rapid high-throughput analysis of single cells, and is compatible with Fluorescence Activated Cell Sorting (FACS) for precise cell selection. Massively parallel DNA sequencing is revolutionizing genomics research throughout the life sciences by constantly producing increasing amounts of data from one sequencing run. However, the reagent costs and labor requirements in current massively parallel sequencing protocols are still substantial. In paper II-IV we have focused on flow-sorting techniques for improved sample preparation in bead-based massive sequencing platforms, with the aim of increasing the amount of quality data output, as demonstrated on the Roche/454 platform. In paper II we demonstrate a rapid alternative to the existing shotgun sample titration protocol and also use flow-sorting to enrich for beads that carry amplified template DNA after emulsion PCR, thus obtaining pure samples and with no downstream sacrifice of DNA sequencing quality. This should be seen in comparison to the standard 454-enrichment protocol, which gives rise to varying degrees of sample purity, thus affecting the sequence data output of the sequencing run. Massively parallel sequencing is also useful for deep sequencing of specific PCR-amplified targets in parallel. However, unspecific product formation is a common problem in amplicon sequencing and since these shorter products may be difficult to fully remove by standard procedures such as gel purification, and their presence inevitably reduces the number of target sequence reads that can be obtained in each sequencing run. In paper III a gene-specific fluorescent probe was used for target-specific FACS enrichment to specifically enrich for beads with an amplified target gene on the surface. Through this procedure a nearly three-fold increase in fraction of informative sequences was obtained and with no sequence bias introduced. Barcode labeling of different DNA libraries prior to pooling and emulsion PCR is standard procedure to maximize the number of experiments that can be run in one sequencing lane, while also decreasing the impact of technical noise. However, variation between libraries in quality and GC content affects amplification efficiency, which may result in biased fractions of the different libraries in the sequencing data. In paper IV barcode specific labeling and flow-sorting for normalization of beads with different barcodes on the surface was used in order to weigh the proportion of data obtained from different samples, while also removing mixed beads, and beads with no or poorly amplified product on the surface, hence also resulting in an increased sequence quality. In paper V, cell heterogeneity within a human being is being investigated by low-coverage whole genome sequencing of single cell material. By focusing on the most variable portion of the human genome, polyguanine nucleotide repeat regions, variability between different cells is investigated and highly variable polyguanine repeat loci are identified. By selectively amplifying and sequencing polyguanine nucleotide repeats from single cells for which the phylogenetic relationship is known, we demonstrate that massively parallel sequencing can be used to study cell-cell variation in length of these repeats, based on which a phylogenetic tree can be drawn. / QC 20111031

L'inférence des réseaux de régulation de gènes (RRG) à partir de données d'expression est un défi majeur en biologie. L’arrivée des technologies de mesure de transcriptomique à l’échelle de la cellule a suscité de nombreux espoirs, mais paradoxalement elles montrent une nouvelle complexité du problème d’inférence des RRG qui limite encore les approches existantes. Nous avons commencé par montrer, à partir de données d'expression en cellules uniques acquises sur un modèle aviaire de différenciation érythrocytaire, que les RRG sont des systèmes stochastiques à l'échelle de la cellule et qu'il y a une évolution dynamique de cette stochasticité au cours du processus de différenciation (Richard et al, PLOS Comp.Biol., 2016). C'est pourquoi nous avons développé par la suite un modèle de RRG mécaniste qui inclus cette stochasticité afin d'exploiter au maximum l'information des données expérimentales à l'échelle de la cellule (Herbach et al, BMC Sys.Biol., 2017). Ce modèle décrit les interactions entre gènes comme un couplage de processus de Markov déterministes par morceaux. En régime stationnaire une formule explicite de la distribution jointe est dérivée du modèle et peut servir à inférer des réseaux simples. Afin d'exploiter l'information dynamique et d'intégrer d'autres données expérimentales (protéomique, demi-vie des ARN), j’ai développé à partir du modèle précédent une approche itérative, intégrative et parallèle, baptisée WASABI qui est basé sur le concept de vague d'expression (Bonnaffoux et al, en révision, 2018). Cette approche originale a été validée sur des modèles in-silico de RRG, puis sur nos données in-vitro. Les RRG inférés affichent une structure de réseau originale au regard de la littérature, avec un rôle central du stimulus et une topologie très distribuée et limitée. Les résultats montrent que WASABI surmonte certaines limitations des approches existantes et sera certainement utile pour aider les biologistes dans l’analyse et l’intégration de leurs données. / Inference of gene regulatory networks from gene expression data has been a long-standing and notoriously difficult task in systems biology. Recently, single-cell transcriptomic data have been massively used for gene regulatory network inference, with both successes and limitations.In the present work we propose an iterative algorithm called WASABI, dedicated to inferring a causal dynamical network from timestamped single-cell data, which tackles some of the limitations associated with current approaches. We first introduce the concept of waves, which posits that the information provided by an external stimulus will affect genes one-byone through a cascade, like waves spreading through a network. This concept allows us to infer the network one gene at a time, after genes have been ordered regarding their time of regulation. We then demonstrate the ability of WASABI to correctly infer small networks, which have been simulated in-silico using a mechanistic model consisting of coupled piecewise-deterministic Markov processes for the proper description of gene expression at the single-cell level. We finally apply WASABI on in-vitro generated data on an avian model of erythroid differentiation. The structure of the resulting gene regulatory network sheds a fascinating new light on the molecular mechanisms controlling this process. In particular, we find no evidence for hub genes and a much more distributed network structure than expected. Interestingly, we find that a majority of genes are under the direct control of the differentiation-inducing stimulus. Together, these results demonstrate WASABI versatility and ability to tackle some general gene regulatory networks inference issues. It is our hope that WASABI will prove useful in helping biologists to fully exploit the power of time-stamped single-cell data.

INTRODUCTION: One important characteristic of solid tumors is heterogeneity at multiple levels of genetic and non-genetic organization. This can include gene mutations, epigenetic alterations, copy number changes, and chromosomal aberrations. Collectively, these alterations contribute as parts of a genome-defined system. Thus, when genetic information is passed from mother to daughter cell in the context of cancer evolution, in contrast to normal cellular processes, an altered system inheritance is often transmitted. When the genome of a somatic cell is highly unstable, such as during certain phases of cancer initiation and progression, many novel alterations to the genome can be introduced in a short timeframe, effectively resulting in the macro-evolution of the somatic cell population (i.e., through the transition stages of cancer, including transformation, metastasis, and drug resistance). Unfortunately, these continually introduced, non-clonal alterations to the cell’s genetic information have often been described as background “noise” that does not function significantly in cancer. Rather, the driving force of cancer has largely been attributed to the accumulation of gene mutations in several key, driver genes. Despite the presumed significance of these driver genes by the gene mutation and clonal evolutionary theories of cancer, recent sequencing efforts have failed to identify common driver genes in the majority of cancer types. Based on this fact, and on the overwhelming presence of non-clonal alterations at multiple levels of organization in the cells comprising tumors, the paradigm of cancer research requires re-examination. A better understanding of genome-level heterogeneity is necessary, as the genome, rather than individual genes, defines system boundaries and unifies the diverse individual molecular mechanisms of cancer through their contribution to major evolutionary transitions. Because inheritance is traditionally defined as a precise process of relaying bio-information with extreme low frequencies of errors, it is challenging to explain how genetics work in cancer evolution. It is thus timely to consider that potentially novel processes of inheritance occur in many types of cancer. The maintenance of a massive extent of multi-level heterogeneity in the cells of solid tumors over generations suggests that a less precise process is taking place. We have described this with a new term, “fuzzy inheritance,” wherein a range of variants, rather than specific variants (such as specific gene mutations or chromosomal aberrations), is recapitulated in the cell division process. This study aimed to elucidate the mechanism of fuzzy inheritance by examining the relationship between genome instability-linked karyotypic heterogeneity and growth heterogeneity, based on single-cell analysis of an in vitro cell culture model. By demonstrating that increased genome-level heterogeneity is reflected by increased and more variable levels of growth heterogeneity, it was hoped to establish that fuzzy inheritance correctly explains the maintenance of high levels of heterogeneity in these somatic cell populations. An example of this phenomenon was also studied in giant cancer cells, as they undergo division processes which appear to contribute to and facilitate genome instability. METHODS: To examine these concepts, various cellular profiling methods were used, including in-situ cell growth, cellular morphological comparison, and karyotype analysis. We first quantified the extent of variation in the growth rates of single cells; by selecting the fastest- and slowest-growing colonies from the parent population, and examining the extent to which growth heterogeneity was passed in subsequent generations of cells, the correlation between genome-level heterogeneity (as reflected by the karyotype) and growth heterogeneity was determined. We then examined an extreme example of fuzzy inheritance, wherein giant cancer cells containing massive amounts of DNA undergo extremely abnormal cell division events, yielding many normal-sized daughter cells with genomes significantly different from those of both the parent cell and other daughter cells. By studying the frequency and other aspects of these cells in two unequally stable cell lines, we sought to gain insight on one specific mechanism of fuzzy inheritance. RESULTS: The data suggested that fuzzy inheritance can be demonstrated in multiple cell culture models. The extent and variability of karyotypic heterogeneity was reflected by those of growth heterogeneity, indicating the karyotype’s importance in facilitating cancer evolutionary processes. Moreover, the cells with giant nuclei can generate diverse genome-level heterogeneity. DISCUSSION: Because fuzzy inheritance allows for the less precise passage of bio-information over generations in cancer cell populations, and for the effective introduction of numerous alterations to the genome in often brief spans of time, the cell population can constantly increase its evolutionary potential, which is essential for the major transition steps of cancer evolution. The mechanism of fuzzy inheritance should be explored further, due to its clear importance in the processes underlying cancer initiation, progression, and drug resistance.

Sir Alec Jeffreys was the first to describe human identification with deoxyribonucleic acid (DNA) in his seminal work in 1985 (1); the result was the birth of forensic DNA analysis. Since then, DNA has become the primary substance used to conduct human identification testing. Forensic DNA analysis has evolved since the work of Jeffreys and now incorporates the analysis of 15 to 24 STR (short tandem repeat) locations, or loci (2-4). The simultaneous amplification and subsequent electrophoresis of tens of STR polymorphisms results in analysis that are highly discriminating. DNA target masses of 0.5 to 2 nanograms (ng) are sufficient to obtain a full STR profile (4); however, pertinent information can still be obtained if low copy numbers of DNA are collected from the crime scene or evidentiary material (4-9). Despite the sensitivity of polymerase chain reaction (PCR) - capillary electrophoresis (CE) based technology, low copy DNA signal can be difficult to interpret due to the preponderance of low signal-to-noise ratios. Due to the complicated nature of low template signal, optimization of the DNA laboratory process such that high-fidelity signal is regularly produced is necessary; studies designed to effectively hone in on optimized laboratory conditions are presented herein. The STR regions of a set of samples containing 0.0078 ng of DNA were amplified for 29 cycles; the amplified fragments were separated using two types of CE platforms: an ABI 3130 Genetic Analyzer and an ABI 3500 Genetic Analyzer. The result is a genetic trace, or electropherogram (EPG), comprised of three signal components that include noise, artifact, and allele. The EPGs were analyzed using two peak detection software programs. In addition, a tool, termed Simulating Evidentiary Electropherograms (SEEIt) (10, 11), was utilized to simulate EPG signal obtained when one copy of DNA is processed through the forensic pipeline. SEEIt was parameterized to simulate data corresponding to two laboratory scenarios: the amplification of a single copy of DNA injected on an ABI 3130 Genetic Analyzer and on an ABI 3500 Genetic Analyzer. In total, 20,000 allele peaks and 20,000 noise peaks were generated for each CE platform. Comparison of simulated and experimental data was used to elucidate features that are difficult to ascertain by experimental work alone. The data demonstrate that experimental signal obtained with the ABI 3500 platform results in signal that is, on average, a factor of four larger than signal obtained from the ABI 3130 platform. When a histogram of the signal is plotted, a multi modal distribution is observed. The first mode is hypothesized to be the result of noise, while the second, third, etc. modes are the signal obtained when one, two, etc. target DNA molecules are amplified. By evaluating the data in this way, full signal resolution between noise and allelic signal is visualized. Therefore, this methodology may be used to: 1) optimize post-PCR laboratory conditions to obtain excellent resolution between noise and allelic signal; and 2) determine an analytical threshold (AT) that results in few false detections and few cases of allelic dropout. A χ2 test for independence of the experimental signal in noise positions and the experimental signal within allele positions < 12 relative fluorescence units (RFU), i.e. signal in the noise regime, indicate the populations are not independent when sufficient signal-to-noise resolution is obtained. Once sufficient resolution is achieved, optimized ATs may be acquired by evaluating and minimizing the false negative and false positive detection rates. Here, a false negative is defined as the non-detection of an allele and a false positive is defined as the detection of noise. An AT of 15 RFU was found to be the optimal AT for samples injected on the ABI 3130 for at least 10 seconds (sec) as 99.42% of noise peaks did not exceed this critical value while allelic dropout was kept to a minimum, 36.97%, at this AT. Similarily, in examining signal obtained from the ABI 3500, 99.41% and 99.0% of noise fell under an AT of 50 RFU for data analyzed with GeneMapper ID-X (GM) and OSIRIS (OS), respectively. Allelic dropout was 36.34% and 36.55% for GM and OS, respectively, at this AT.

BACKGROUND: Previous work has shown that follicular dendritic cells (FDCs) play an important role in selecting B cells such that antigens are responded to in a specific manner. FcγRΙΙB (CD32) is an antibody constant-region receptor found on FDCs and mutation of this receptor in humans is associated with Systemic Lupus Erythematosus (SLE). In addition, previous work has demonstrated that autoreactive germinal centers are the product of expression of interferon alpha (ΙFNα) by FDCs, so FcγRIIB signaling may involve modulation of IFNα signaling. OBJECTIVE: Because FcγRIIB mutation is associated with SLE and FDCs have been shown to be important in orchestrating B cell responses, understanding FcγRIIB on FDCs helps characterize B cell repertoire development in response to antigen—whether the antigen is foreign or self, as is the case in autoimmunity. Better characterization of the role of FcγRIIB could have consequences for autoimmune and cancer therapy. This study seeks to determine the role of FcγRΙΙB on FDCs in germinal center B cell selection dynamics within single, autoreactive germinal centers. METHODS: This study compares transplanted wild-type (B6) B cells—that are driven to be autoimmune by simultaneously transplanted autoimmune B cells—in two stromal cell settings: first, in germinal centers containing wild-type FDCs and second, in germinal centers containing FcγRIIB-knockout FDCs. Transplanted B6 B cell populations express photoactivatable protein, which allows for sorting of B cells from individual germinal centers. B cell sequences from single germinal centers were analyzed to determine how focused each germinal center response was and how the B cells differ in maturity and affinity for antigen. Finally, mice expressing a lineage-tracing system were treated with IFNα in order to observe the cytokine’s effect on B cell selection. RESULTS: Cells sorted from germinal centers containing FcγRIIB-knockout FDCs contain a distinguished population of less-developed B cells, as quantified by population-based analysis of their variable heavy chain genes. Overall, the IgM sequences from B cells sorted from germinal centers (GCs) containing FcγRIIB-knockout follicular dendritic cells displayed lower levels of somatic hypermutation (SHM) (p<.05) and shorter hypervariable regions (CDR3) (p<.05) compared to other B cell populations. Values computed to summarize how many different B cell lineages were present in a GC—its “clonality”—did not vary between the two mouse populations, although FcγRIIB-knockout FDC germinal centers displayed a correlation between clonality and immunoglobulin (Ig) isotype expression (R2= .85). Finally, lineage tracing mice receiving injections of interferon alpha (IFNα) displayed no difference in GC clonality compared to those receiving vehicle and assays of IFNα downstream signaling genes also displayed no change. CONCLUSIONS: FcγRIIB encourages more stringent selection of immature B cells in germinal centers as evidenced by survival of less developed B cells as defined by degree of somatic hypermutation and CDR3 length in GCs comprising FcγRIIB-knockout FDCs. In spite of this, sequence-based measures of germinal center clonality as completed here may fail to capture the functional results of B cell selection. In addition, the link between FcγRIIB and IFNα requires further investigation. / 2019-07-03T00:00:00Z

The current work entails the development of a novel high content platform for the measurement of kinetic ligand responses across cell signalling networks at the single-cell level in distinct PBMC subtypes ex vivo. Using automated sample preparation, fluorescent cellular barcoding and flow cytometry the platform is capable of detecting 21, 840 parallel cell signalling responses in each PBMC sample. We apply this platform to characterize the effects of neuropsychiatric treatments and CNS ligands on the T cell signalling repertoire. We apply it to define cell signalling network abnormalities in PBMCs from drug-naïve first-onset schizophrenia patients (n=12) relative to healthy controls (n=12) which are subsequently normalized in PBMCs from the same patients (n=10) after a six week course of clinical treatment with the atypical antipsychotic olanzapine. We then validate the abnormal cell signalling responses in PBMCs from an independent cohort of drug-naïve first-onset schizophrenia patients (n=25) relative to controls (n=25) and investigate the specificity of the abnormal PBMC responses in schizophrenia as compared to major depression (n=25), bipolar disorder (n=25) and autism spectrum disorder (n=25). Subsequently we conduct a phenotypic drug screen using the US Food and Drug Administration (FDA) approved compound library, in addition to experimental neuropsychiatric drug candidates and nutraceuticals, to identify compounds which selectively normalize the schizophrenia-associated cell signalling response. Finally these candidate compounds are characterized using structure-activity relationships to reveal specific chemical moieties implicated in the putative therapeutic effect.

The stochasticity of cellular processes and the small number of molecules in a cell make deterministic models inappropriate for modelling chemical reactions at the single cell level. The Chemical Master Equation (CME) is widely used to describe the evolution of biochemical reactions inside cells stochastically but is computationally expensive. The Linear Noise Approximation (LNA) is a popular method for approximating the CME in order to carry out inference and parameter estimation in stochastic models. Data from stochastic systems is often aggregated over time. One such example is in luminescence bioimaging, where a luciferase reporter gene allows us to quantify the activity of proteins inside a cell. The luminescence intensity emitted from the luciferase experiments is collected from single cells and is integrated over a time period (usually 15 to 30 minutes), which is then collected as a single data point. In this work we consider stochastic systems that we approximate using the Linear Noise Approximation (LNA). We demonstrate our method by learning the parameters of three different models from which aggregated data was simulated, an Ornstein-Uhlenbeck model, a Lotka-Voltera model and a gene transcription model. We have additionally compared our approach to the existing approach and find that our method is outperforming the existing one. Finally, we apply our method in microscopy data from a translation inhibition experiment.