Quantitative single-cell analysis of S. cerevisiae using a microfluidic live-cell imaging platform

Genome-wide manipulations and measurements have made huge progress over the last decades. In Saccharomyces cerevisiae, a well-studied eukaryotic model organism, homologous recombination allows for systematic deletion or alteration of a majority of its genes. Important products of these manipulation techniques are two libraries of modified strains: A deletion library consisting of all viable knockout mutants, and a GFP library in which 4159 proteins are successfully tagged with GFP. In addition, the development of a method that allows for the systematic construction of double mutants led to a virtually infinite number of potential strains of interest. These advancements in combinatorial biology need to be matched by methods of data measurement and analysis. In order to simultaneously observe the spatio-temporal dynamics of thousands of strains from the GFP library, Dénervaud et al. developed a microfluidic platform that allows for parallel imaging of 1152 strains in a single experiment. On this platform, strains can be grown and monitored in a controllable environment for several days, which results in the imaging of several millions of cells during one experiment. To objectively and quantitatively analyze this immense amount of information, we implemented an image analysis pipeline, which can extract experiment-wide information on single-cell protein abundance and subcellular localization. The construction of a supervised classifier to quantify localization information on a single cell level is a new approach and was invaluable to detect dynamic localization changes within the proteome. Using five different stress conditions, we gained insight into temporal changes of abundance and localization of multiple proteins. For example, we found that while localization changes can often be fast and transient, long-term response of a cell is usually enabled by changes in abundance. This shows a well-orchestrated response of a cell to external stimuli. To extend knowledge about cellular mechanisms, we used our microfluidic platform for two separate screens, combining GFP-reporter with additional deletion mutants. The advantage of our platform in comparison to more common approaches lies in its simultaneous measurement of fluorescence and phenotypic information on cell size and growth. For each deletion, we can quantify not only its influence onto the respective GFP-reporter under changing conditions, but also its effect on cell growth and size. We showed that it is advantageous to combine this information, as it allows pointing out possible underlying mechanisms of gene network regulations. In a first screen we investigated the behavior of several gene networks upon UV irradiation damage. We were able to show that four gene deletions influenced the localization of ribonucleotide-diphosphate reductase (Rnr4p). A second screen was designed to find genes that influence the induction of the galactose network. This screen uses more than 500 deletions of genes mostly related to chromatin in combination with two different reporter strains. A main focus of this study was the inheritance of memory during galactose reinduction. We found several previously unknown genes that potentially influence either induction or reinduction and were picked as candidates for further inheritance studies. Our microfluidic platform allows for unprecedented studies of proteomes in flux. [...]

Single-Cell Analysis of Mycobacterial Persistence Using Microfabricated Tools

Meltem Elitas

Bacterial cells behave as individuals despite being genetically identical and subject to the same environment. Although the underlying mechanisms of cellular individuality are not well understood, temporal variation of gene expression and protein levels in individual cells could act as sources for phenotypic heterogeneity of populations. Bacterial persistence in fluctuating environments relies on such diversity, allowing some individuals, by chance, to survive a challenge that would kill an unadapted and phenotypically homogeneous population. Bacterial persistence is a non-genetic, metastable phenomenon that is readily lost upon subculture of surviving cells in the absence of the environmental stress. A medically relevant example of persistence is the fractional killing of bacteria by antibiotics, whereby a subpopulation of cells survives indefinitely despite prolonged exposure to a bactericidal drug. It is hypothesized in the case of tuberculosis (TB) that drug therapy may require administration of multiple drugs for 6-9 months in order to eliminate a subpopulation of "persister" cells. The focus of this thesis is to understand how persisters tolerate antibiotics that kill their genetically identical siblings. To address this question, automated time-lapse fluorescence microcopy was applied in conjunction with microfluidic and microelectromechanical systems (MEMS) to study bacterial responses to antibiotics in real time at the single-cell level. A genetic approach was employed to identify mutants of Mycobacterium smegmatis with altered rates of persistence against the first-line anti-TB drug isoniazid (INH). A microfluidic platform was designed for high-throughput screening of transposon (Tn) mutant libraries. A pilot screen of 1,100 Tn mutants revealed 11 mutants that showed increased (persister-up, PU) or decreased (persister-down, PD) survival when exposed to isoniazid. These mutants were further characterized in comparison to wild-type cells using the single-cell microfluidics platform. This single-cell analysis revealed distinct modes of persistence within each category, which were otherwise masked at the population level. The results indicate that persistence behavior was the cumulative result of the dynamics of cell division and cell lysis. Mutants with decreased persistence exhibited behavior at single-cell level that challenges the accepted mechanism of isoniazid-mediated killing. Therefore, these results yield new insights into antibiotic persistence and isoniazid's mode of action. Persisters cannot be easily studied in non-fractionated populations of cells where they represent a small percentage of the total. As a complementary approach, a method was developed based on dielectrophoretic (DEP) purification of naturally occurred low frequency persister cells from an antibiotic-treated bacterial culture. Purification of persister cells would allow for downstream analysis using conventional bulk/population level transcriptomic and proteomic approaches. The crossover frequencies for live and dead cells were determined to enable selective trapping or release from the device. This approach enabled a proof-of-concept enrichment of live bacteria in the mixture of live and dead cells after INH treatment, based on their dielectric properties. Quantitative confirmation of the enriched/isolated cell populations was attempted using flow cytometry. The high-throughput microfluidic screening and single-cell approaches developed in this thesis could, in principal, be applied to the genetic analysis of a wide variety of dynamic cellular processes that can be imaged by time- lapse fluorescence microscopy, such as gene expression, cell division, chromosome replication and segregation, and protein localization. The biochemical analysis of live cells isolated by dielectrophoresis-based separation would provide useful information about the underlying persistence mechanism. Dielectrophoresis could also be a valuable analytical tool for mutant characterization based on their intrinsic dielectric properties, either at single-cell or population levels. In conclusion, this thesis presents several new microfabricated tools to identify and understand bacterial persistence. The thesis illustrates how integration of microfluidic tools with automated time-lapse microscopy allows hidden characteristics that are present but invisible in the macro- world of batch cultures to be examined in a quantitative and high-throughput manner in the micro-world of single-cell analysis.

EPFL2012

Roles of Epigenetic Dysregulation in Cancer Progression and Metastasis

Mohammad Sadegh Saghafinia

Nearly all the cells of an organism share the same DNA sequence or genome, and yet they show different phenotypes and carry out different functions. This diversity is made possible by a verity of molecular modifications acting on the DNA sequence that collectively define the cell's epigenome. Failure in the proper regulation of epigenome can lead to abnormal activation or inhibition of vital signaling pathways, which contributes to the initiation and progression of multiple diseases, including cancer. In this thesis, I used a combination of in silico, in vitro, and in vivo techniques to explore the roles of epigenetic dysregulation in tumor progression and metastasis. In the first part, I performed a systematic and unbiased investigation of cancer-associated DNA methylation and gene expression changes across human cancers. I designed a novel algorithmic approach (RESET) to identify aberrant DNA methylation and associated gene expression changes across >6,000 human tumors. We identified a DNA Methylation Instability (DMI) phenotype in tumors acquiring numerous hyper and hypomethylated transcription start sites, which is associated with mutations of chromatin remodeling factors and Wnt-signaling. We showed that silenced genes coalesced in specific pathways including apoptosis, transcriptional regulation, and cell metabolism. The majority of the enhanced genes belonged to cancer-germline antigens (CG), whose expression correlated with response to anti-PD-1 in melanoma patients. Finally, we demonstrated the potential of RESET to explore aberrant DNA methylation in pediatric tumors; pediatric Wilms tumors with diffuse anaplasia exhibit DMI, and specific silencing events predict a worse outcome in cases with favorable histology. These results established a new approach to explore pan-cancer epigenetic modifications and provided a resource of candidate oncogenic events for future functional and therapeutic studies. In the second part, I studied the dynamics of cancer progression and metastasis in Pancreatic Neuroendocrine Tumors (PanNET). This rare tumor is the second most common form of pancreatic cancer. Two principal subtypes of PanNET has been identified: insulinomas (IT) and metastasis-like primaries (MLP), corresponding to the low and high grade of human PanNETs, respectively. From the mouse model of PanNET (RT2), we profiled the single-cell, bulk mRNA and miRNA transcriptomes, and the proteomes of primary and metastasis specimens. We demonstrated that the tumor progression from IT to MLP follows the reverse embryonic and postnatal developmental path. Transcriptomic data from human patients showed that aggressive human PanNETs also follow the same reverse developmental trajectory of dedifferentiation. We established one possible mechanism underlying IT-to-MLP transition. Over-expression of the miR-181cd cluster in IT-like cancer cell-lines resulted in acquisition of the MLP phenotypes. miR-181cd mediated its effect through suppressing the expression of Meis2 and, indirectly, inducing the expression of Hmgb3 and Mycn transcription factors. Inhibiting the expression of Hmgb3 in MLP-like cancer cell-lines resulted in a significant growth decrease both in vitro and in vivo, demonstrating the importance of Hmgb3 in the maintenance of MLP-like cell state. These data presented dedifferentiation as a mechanism by which malignant neuroendocrine cancer cells acquire progenitor-like features, enabling them to become more aggressive and metastatic.

EPFL2020

The genetic architecture of complex human traits at the dawn of genomic medicine

Olivier Noël Marie Naret

The focus of the work presented in this thesis is the exploration of the genetic architecture of complex human traits - at the dawn of genomic medicine. The underlying mechanisms explaining the enormously polygenic nature of most human complex traits are still unknown. The first chapter explores a possible explanatory model in which variant effects are due to an indirect mechanism, namely competition among genes for shared intracellular resources such as ribosomes. Our findings show that under most reasonable assumptions, resource competition should not be expected to have much impact on either protein expression levels of individual genes or on complex trait outcomes. The prediction accuracy of polygenic scores (PGS) remains relatively modest compared to what is expected given the estimated heritability of traits. Traditionally, the construction of PGS uses a large number of genetic variations, most of which have weak additive effects. Recent machine learning methods could improve PGS by also aggregating epistatic effects. To evaluate these different methods, we conducted an experiment based on an innovative concept of crowdsourcing, detailed in the second chapter. We collaborated with opensnp.org, an open repository where people share their genotyping data and phenotypic information, and with crowdai.org, a platform that allowed us to create a public competition for the genomic prediction of height. The challenge lasted three months and attracted 138 participants. This was the first crowd-sourcing challenge based on publicly available genome-wide genotyping data. Due to the enormous number of potential combinations of variants, it is difficult to integrate epistatic effects into PGS. In the third chapter, we present a method where we limit the possible combinations to the boundaries of each topologically associated domain (TAD) independently. With the UK Biobank, for the height phenotype, we included 17,560 variants in an artificial neural network (ANN) and compared the variance explained (

R^2

) by the PGS with or without the knowledge of the TADs. We found that it brings a significant improvement with an average

R^2

going from 0.287 to 0.293 (with a p-value

=10E-5

for n=20). We concluded that it should be possible to build better PGS using ANNs and epistasis in TADs. The effect of genetic ancestry on phenotypes is not taken into account in PGS-based risk estimates. Doing so could accelerate the adoption of genomic medicine for underrepresented populations and mixed-race individuals. The fourth chapter presents a method for its integration through a secondary score derived from genome-wide genotyping data, the PC score (PCS). We compared two models, one using only the PGS and the other using both the PGS and the PCS. Using the UK Biobank, we found an improvement in genetic prediction for all phenotypes tested:

EPFL2021