Snapshot 3D tracking of insulin granules in live cells

Rapid and accurate volumetric imaging remains a challenge, yet has the potential to enhance understanding of cell function. We developed and used a multifocal microscope (MFM) for 3D snapshot imaging to allow 3D tracking of insulin granules labeled with mCherry in MIN6 cells. MFM employs a special diffractive optical element (DOE) to simultaneously image multiple focal planes. This simultaneous acquisition of information determines the 3D location of single objects at a speed only limited by the frame rate of array detector . We validated the accuracy of MFM imaging and tracking with fluorescence beads; the 3D positions and trajectories of single fluorescence beads can be determined accurately over a wide range of spatial and temporal scales. The 3D positions and trajectories of single insulin granules in a 3.2 micro meter deep volume were determined with imaging processing that combines 3D decovolution, shift correction, and finally tracking using the Imaris software package. We find that the motion of the granules is super-diffusive, but less so in 3D than 2D for cells grown on coverslip surfaces, suggesting an anisotropy in the cytoskeleton (e.g. microtubules and action)

Single-cell Genomic Approaches for Interpreting the Genetic Architecture of Complex Traits

Variation in DNA sequence influences change in one or many molecular intermediates in a functional pathway, ultimately leading to a change in an organismal-level trait. This creates a causal chain of events, as governed by the Central Dogma of molecular biology, where deleterious DNA variants cause dysregulation of gene expression and/or protein levels, leading to a disease state at the organismal-level. Determining which and how DNA variants are causal for the disease phenotype is a major challenge in the field of genetics and is of major interest due to its potential for unraveling new knowledge about regulatory biology and discovering new genetic therapies for diseases. Single nucleotide variants (SNVs), or just variants, can be classified into two classes: namely single-nucleotide polymorphism (SNPs) which occur at some frequency in the human population, and somatic point mutations which occur throughout the lifespan of the organism. The vast majority of disease-associated variants tend to be in the non-coding part of the genome, leading to complex and variable interactions with genes. Perhaps the best understood of these non-coding variants are regulatory variants which reside in DNA regulatory elements such as promoters, enhancers and repressors. The activity of regulatory elements has been shown to be cell-type and state specific, which motivates the need for single-cell technologies for further dissecting disease-related variants and the putative genes they target. In this dissertation, I develop a framework for utilizing single-cell ‘omics data to interpret the germline SNPs and somatic point mutations associated with disease states. In Chapter 1, I explore methods for detecting somatic mutations in individual cancer cells and nominate genes whose expression is altered in cells with somatic mutations using single-cell RNA-sequencing data. However, obtaining single-cell RNA-sequencing data from bulk tissues such as solid tumors, presents its own challenges. Due to the complexity of the intracellular matrix of adult bulk tissues, such as solid tumors, obtaining single cell suspensions is not always possible. In Chapter 2, I performed a systematic analysis between single-cell and nucleus RNA-sequencing data on a model system of induced-pluripotent stem cells differentiating into cardiomyocytes. Finally, I developed a framework in Chapter 3 for utilizing single-nucleus ATAC-seq and single-nucleus RNA-seq to interpret the germline SNPs found in atrial fibrillation (AF) GWAS, the most common cardiac arrhythmia. Risk variants of Atrial Fibrillation (AF) are >10-fold enriched in cardiomyocytes (CMs) but not other cell types. Taking advantage of this enrichment pattern, we used a Bayesian statistical framework to fine-map causal variants of AF, favoring variants in CM open chromatin regions. I developed a novel computational procedure that aggregates all putative causal variants and combines multiple sources of information linking SNPs to genes. Through this procedure, I nominate genes that are not found by GWAS alone

mDrop-seq: Massively parallel single-cell RNA-seq of <i>Saccharomyces cerevisiae</i> and <i>Candida albicans</i>

AbstractAdvances in high-throughput single-cell mRNA sequencing (scRNA-seq) have been limited till date by technical challenges like tough cell walls and low RNA quantity that prevented transcriptomic profiling of microbial species at throughput. We present microbial Drop-seq or mDrop-seq, a high-throughput scRNA-seq technique that is used on two yeast species, Saccharomyces cerevisiae, a popular model organism and Candida albicans, a common opportunistic pathogen. We benchmarked mDrop-seq for sensitivity and specificity and used it to profile 35,109 S. cerevisiae cells to detect variation in mRNA levels between them. As a proof of concept, we quantified expression differences in heat-shocked S. cerevisiae using mDrop-seq. We detected differential activation of stress response genes within a seemingly homogenous population of S. cerevisiae under heat-shock. We also applied mDrop-seq to C. albicans cells, a polymorphic and clinically relevant yeast species with thicker cell wall compared to S. cerevisiae. Single cell transcriptomes in 39,705 C. albicans cells was characterized using mDrop-seq under different conditions, including exposure to fluconazole, a common anti-fungal drug. We noted differential regulation in stress response and drug target pathways between C. albicans cells, changes in cell cycle patterns and marked increases in histone activity. These experiments are among the first high throughput single cell RNA-seq on different yeast species and demonstrate mDrop-seq as an affordable, easily implementable, and scalable technique that can quantify the variability in gene expression in different yeast species. We hope that mDrop-seq will lead to better understanding of genetic variation in yeasts in response to stimuli and find immediate applications in investigating drug resistance and infection outcome.</jats:p

mDrop-Seq: Massively Parallel Single-Cell RNA-Seq of <i>Saccharomyces cerevisiae</i> and <i>Candida albicans</i>

Advances in high-throughput single-cell RNA sequencing (scRNA-seq) have been limited by technical challenges such as tough cell walls and low RNA quantity that prevent transcriptomic profiling of microbial species at throughput. We present microbial Drop-seq or mDrop-seq, a high-throughput scRNA-seq technique that is demonstrated on two yeast species, Saccharomyces cerevisiae, a popular model organism, and Candida albicans, a common opportunistic pathogen. We benchmarked mDrop-seq for sensitivity and specificity and used it to profile 35,109 S. cerevisiae cells to detect variation in mRNA levels between them. As a proof of concept, we quantified expression differences in heat shock S. cerevisiae using mDrop-seq. We detected differential activation of stress response genes within a seemingly homogenous population of S. cerevisiae under heat shock. We also applied mDrop-seq to C. albicans cells, a polymorphic and clinically relevant species of yeast with a thicker cell wall compared to S. cerevisiae. Single-cell transcriptomes in 39,705 C. albicans cells were characterized using mDrop-seq under different conditions, including exposure to fluconazole, a common anti-fungal drug. We noted differential regulation in stress response and drug target pathways between C. albicans cells, changes in cell cycle patterns and marked increases in histone activity when treated with fluconazole. We demonstrate mDrop-seq to be an affordable and scalable technique that can quantify the variability in gene expression in different yeast species. We hope that mDrop-seq will lead to a better understanding of genetic variation in pathogens in response to stimuli and find immediate applications in investigating drug resistance, infection outcome and developing new drugs and treatment strategies

mDrop-Seq: Massively Parallel Single-Cell RNA-Seq of Saccharomyces cerevisiae and Candida albicans

Advances in high-throughput single-cell RNA sequencing (scRNA-seq) have been limited by technical challenges such as tough cell walls and low RNA quantity that prevent transcriptomic profiling of microbial species at throughput. We present microbial Drop-seq or mDrop-seq, a high-throughput scRNA-seq technique that is demonstrated on two yeast species, Saccharomyces cerevisiae, a popular model organism, and Candida albicans, a common opportunistic pathogen. We benchmarked mDrop-seq for sensitivity and specificity and used it to profile 35,109 S. cerevisiae cells to detect variation in mRNA levels between them. As a proof of concept, we quantified expression differences in heat shock S. cerevisiae using mDrop-seq. We detected differential activation of stress response genes within a seemingly homogenous population of S. cerevisiae under heat shock. We also applied mDrop-seq to C. albicans cells, a polymorphic and clinically relevant species of yeast with a thicker cell wall compared to S. cerevisiae. Single-cell transcriptomes in 39,705 C. albicans cells were characterized using mDrop-seq under different conditions, including exposure to fluconazole, a common anti-fungal drug. We noted differential regulation in stress response and drug target pathways between C. albicans cells, changes in cell cycle patterns and marked increases in histone activity when treated with fluconazole. We demonstrate mDrop-seq to be an affordable and scalable technique that can quantify the variability in gene expression in different yeast species. We hope that mDrop-seq will lead to a better understanding of genetic variation in pathogens in response to stimuli and find immediate applications in investigating drug resistance, infection outcome and developing new drugs and treatment strategies.</jats:p

Biphasic growth dynamics during<i>Caulobacter crescentus</i>division

Cell size is specific to each species and impacts their ability to function. While various phenomenological models for cell size regulation have been proposed, recent work in bacteria have demonstrated anaddermodel, in which a cell increments its size by a constant amount between each division. However, the coupling between cell size, shape and constriction, remain poorly understood. Here, we investigate size control and the cell cycle dependence of bacterial growth, using multigenerational cell growth and shape data for singleCaulobacter crescentuscells. Our analysis reveals a biphasic mode of growth:a relative timerphase before constriction where cell growth is correlated to its initial size, followed by apure adderphase during constriction. Cell wall labeling measurements reinforce this biphasic model: a crossover from uniform lateral growth to localized septal growth is observed. We present a mathematical model that quantitatively explains this biphasicmixermodel for cell size control.</jats:p

Single-cell genomics improves the discovery of risk variants and genes of Atrial Fibrillation

AbstractGenome-wide association studies (GWAS) have linked hundreds of loci to cardiac diseases. However, in most loci the causal variants and their target genes remain unknown. We developed a combined experimental and analytical approach that integrates single cell epigenomics with GWAS to prioritize risk variants and genes. We profiled accessible chromatin in single cells obtained from human hearts and leveraged the data to study genetics of Atrial Fibrillation (AF), the most common cardiac arrhythmia. Enrichment analysis of AF risk variants using cell-type-resolved open chromatin regions (OCRs) implicated cardiomyocytes as the main mediator of AF risk. We then performed statistical fine-mapping, leveraging the information in OCRs, and identified putative causal variants in 122 AF-associated loci. Taking advantage of the fine-mapping results, our novel statistical procedure for gene discovery prioritized 46 high-confidence risk genes, highlighting transcription factors and signal transduction pathways important for heart development. In summary, our analysis provides a comprehensive map of AF risk variants and genes, and a general framework to integrate single-cell genomics with genetic studies of complex traits.</jats:p