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. Highthroughput 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 timeseries 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 DroNcseq yields similar results to Drop-seq on matched samples and can be successfully used to generate reference maps for the human cell atlas.

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.

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.

1 Cell size is specific to each species and impacts their ability to function. While 1 various phenomenological models for cell size regulation have been proposed, recent 2 work in bacteria have demonstrated an adder mechanism, in which a cell increments 3 its size by a constant amount between each division. However, the coupling between 4 cell size, shape and constriction, remain poorly understood. Here, we investigate size 5 control and the cell cycle dependence of bacterial growth, using multigenerational cell 6 growth and shape data for single Caulobacter crescentus cells. Our analysis reveals 7 a biphasic growth mechanism: a relative timer phase before constriction where cell 8 growth is correlated to its initial size, followed by a pure adder phase during constric-9 tion. Cell wall labeling measurements reinforce this biphasic behavior: a crossover 10 from uniform lateral growth to localized septal growth is observed. We develop a 11 mathematical model that quantitatively explains this mixer mechanism for size con-12 trol. 13 14 We recently introduced a technology that enables obtaining unprecedented amounts of precise 15 quantitative information about the shapes of single bacteria as they grow and divide under non-16 crowding and controllable environmental conditions [1, 2]. Others have developed complementary 17 methods [3-6]. These single-cell studies are generating great interest because they reveal unantic-18 ipated relationships between cell size and division control [5]. Recent work in bacteria that utilize 19 these technologies revealed that a constant size increment between successive generations [7] quan-20 titatively describes the strategy for bacterial size maintenance in E. coli [3-5], B. subtilis [5], C. 21 crescentus [4], P. aeruginosa [8] and even in the yeast S. cerevisiae [9]. This phenomenological ob-22 servation has been termed an adder model for cell size control. Competing models for size control 23 include cell division at a critical size (sizer model) [10] or at a constant interdivision time (timer 24 model) [1]. Analysis of single-cell data show that cell size at division is positively correlated with 25the cell size at birth [1, 4, 5, 11, 12], thus precluding a sizer model. In addition, a negative corre-26 lation between initial cell size and interdivision times, as reported here and in refs [1, 4, 5, 12, 13], 27 is inconsistent with the timer model. However, other studies have suggested mixed models of 28 size control, with diverse combinations of sizer, timer and adder mechanisms [14][15][16][17]. The spatial 29 resolution and statistically large size of our data now allow us to revisit these issues with greater 30 precision. 31While cell size serves as an important determinant of growth, the bacterial cell cycle is composed 32 of various coupled processes including DNA replication and cell wall constriction that have to be 33 2 faithfully coordinated for cells to successfully divide [18]. This raises the question of what other 34 cell cycle variables regulate growth and how the ...

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 chromatic correction grating 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 the 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.