Single-cell genomics improves the discovery of risk variants and genes of atrial fibrillation.

Genome-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.

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.

Systematic Comparison of High-throughput Single-Cell and Single-Nucleus Transcriptomes during Cardiomyocyte Differentiation.

A comprehensive reference map of all cell types in the human body is necessary for improving our understanding of fundamental biological processes and in diagnosing and treating disease. High-throughput single-cell RNA sequencing techniques have emerged as powerful tools to identify and characterize cell types in complex and heterogeneous tissues. However, extracting intact cells from tissues and organs is often technically challenging or impossible, for example in heart or brain tissue. Single-nucleus RNA sequencing provides an alternative way to obtain transcriptome profiles of such tissues. To systematically assess the differences between high-throughput single-cell and single-nuclei RNA-seq approaches, we compared Drop-seq and DroNc-seq, two microfluidic-based 3' RNA capture technologies that profile total cellular and nuclear RNA, respectively, during a time course experiment of human induced pluripotent stem cells (iPSCs) differentiating into cardiomyocytes. Clustering of time-series transcriptomes from Drop-seq and DroNc-seq revealed six distinct cell types, five of which were found in both techniques. Furthermore, single-cell trajectories reconstructed from both techniques reproduced expected differentiation dynamics. We then applied DroNc-seq to postmortem heart tissue to test its performance on heterogeneous human tissue samples. Our data confirm that DroNc-seq yields similar results to Drop-seq on matched samples and can be successfully used to generate reference maps for the human cell atlas.

Computational multifocal microscopy.

Despite recent advances, high performance single-shot 3D microscopy remains an elusive task. By introducing designed diffractive optical elements (DOEs), one is capable of converting a microscope into a 3D "kaleidoscope," in which case the snapshot image consists of an array of tiles and each tile focuses on different depths. However, the acquired multifocal microscopic (MFM) image suffers from multiple sources of degradation, which prevents MFM from further applications. We propose a unifying computational framework which simplifies the imaging system and achieves 3D reconstruction via computation. Our optical configuration omits optical elements for correcting chromatic aberrations and redesigns the multifocal grating to enlarge the tracking area. Our proposed setup features only one single grating in addition to a regular microscope. The aberration correction, along with Poisson and background denoising, are incorporated in our deconvolution-based fully-automated algorithm, which requires no empirical parameter-tuning. In experiments, we achieve spatial resolutions of 0.35um (lateral) and 0.5um (axial), which are comparable to the resolution that can be achieved with confocal deconvolution microscopy. We demonstrate a 3D video of moving bacteria recorded at 25 frames per second using our proposed computational multifocal microscopy technique.

Biphasic growth dynamics control cell division in Caulobacter crescentus.

Cell size is specific to each species and impacts cell function. Various phenomenological models for cell size regulation have been proposed, but recent work in bacteria has suggested an 'adder' model, in which a cell increments its size by a constant amount between each division. However, the coupling between cell size, shape and constriction remains poorly understood. Here, we investigate size control and the cell cycle dependence of bacterial growth using multigenerational cell growth and shape data for single Caulobacter crescentus cells. Our analysis reveals a biphasic mode of growth: a relative timer phase before constriction where cell growth is correlated to its initial size, followed by a pure adder phase during constriction. Cell wall labelling measurements reinforce this biphasic model, in which a crossover from uniform lateral growth to localized septal growth is observed. We present a mathematical model that quantitatively explains this biphasic 'mixer' model for cell size control.

Correlative imaging across microscopy platforms using the fast and accurate relocation of microscopic experimental regions (FARMER) method.

Imaging specific regions of interest (ROIs) of nanomaterials or biological samples with different imaging modalities (e.g., light and electron microscopy) or at subsequent time points (e.g., before and after off-microscope procedures) requires relocating the ROIs. Unfortunately, relocation is typically difficult and very time consuming to achieve. Previously developed techniques involve the fabrication of arrays of features, the procedures for which are complex, and the added features can interfere with imaging the ROIs. We report the Fast and Accurate Relocation of Microscopic Experimental Regions (FARMER) method, which only requires determining the coordinates of 3 (or more) conspicuous reference points (REFs) and employs an algorithm based on geometric operators to relocate ROIs in subsequent imaging sessions. The 3 REFs can be quickly added to various regions of a sample using simple tools (e.g., permanent markers or conductive pens) and do not interfere with the ROIs. The coordinates of the REFs and the ROIs are obtained in the first imaging session (on a particular microscope platform) using an accurate and precise encoded motorized stage. In subsequent imaging sessions, the FARMER algorithm finds the new coordinates of the ROIs (on the same or different platforms), using the coordinates of the manually located REFs and the previously recorded coordinates. FARMER is convenient, fast (3-15 min/session, at least 10-fold faster than manual searches), accurate (4.4 µm average error on a microscope with a 100x objective), and precise (almost all errors are <8 µm), even with deliberate rotating and tilting of the sample well beyond normal repositioning accuracy. We demonstrate this versatility by imaging and re-imaging a diverse set of samples and imaging methods: live mammalian cells at different time points; fixed bacterial cells on two microscopes with different imaging modalities; and nanostructures on optical and electron microscopes. FARMER can be readily adapted to any imaging system with an encoded motorized stage and can facilitate multi-session and multi-platform imaging experiments in biology, materials science, photonics, and nanoscience.