Project description:Recently, RNA sequencing has achieved single cell resolution, but what is limiting is an effective way to routinely isolate and process large numbers of individual cells for in-depth sequencing, and to do so quantitatively. We have developed a droplet-microfluidic approach for parallel barcoding thousands of individual cells for subsequent RNA profiling by next-generation sequencing. This high-throughput method shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays. Using this technique, we analyzed mouse embryonic stem cells, revealing in detail the population structure and the heterogeneous onset of differentiation after LIF withdrawal. The reproducibility and low noise of this high-throughput single cell data allowed us to deconstruct cell populations and infer gene expression relationships. A total of 8 single cell data sets are submitted: 3 for mouse embryonic stem (ES) cells (1 biological replicate, 2 technical replicates); 3 samples following LIF withdrawal (days 2,4, 7); one pure RNA data set (from human lymphoblast K562 cells); and one sample of single K562 cells.
Project description:Recently, RNA sequencing has achieved single cell resolution, but what is limiting is an effective way to routinely isolate and process large numbers of individual cells for in-depth sequencing, and to do so quantitatively. We have developed a droplet-microfluidic approach for parallel barcoding thousands of individual cells for subsequent RNA profiling by next-generation sequencing. This high-throughput method shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays. Using this technique, we analyzed mouse embryonic stem cells, revealing in detail the population structure and the heterogeneous onset of differentiation after LIF withdrawal. The reproducibility and low noise of this high-throughput single cell data allowed us to deconstruct cell populations and infer gene expression relationships.
Project description:Analysis of histone modifications at single-cell resolution can provide insights into the cellular heterogeneity in activity states of regulatory elements. However, current methods are hindered by lengthy procedures and insufficient sensitivity. Here we present Droplet Paired-Tag, a microfluidic cell barcoding-based method for fast and robust joint analysis of histone modifications and gene expression from single cells. We applied Droplet Paired-Tag to mouse brain and demonstrated its utility in accurately identifying active or repressive regulatory elements, and resolving the dynamic interactions between candidate regulatory elements and putative target genes.
Project description:Single-cell transcriptomics methods have become very popular to study the cellular composition of organs and tissues and characterize the expression profiles of the individual cells that compose them. The main critical step for single-cell transcriptomics methods is sample preparation. Several methods have been developed to preserve cells after sample dissociation to uncouple sample handling from library preparation. Yet, the suitability of these methods depends on the types of cells to be processed. In this project, we perform a systematic comparison of preservation methods for droplet-based single-cell RNA-seq (scRNA-seq) on human neural progenitor cell populations derived from induced pluripotent stem cells (iPSCs) and highlight their strengths and weaknesses. We compared the cellular composition and expression profile of single-cell suspensions from fresh NPCs with that of NPCs preserved with Dimethyl Sulfoxide (DMSO), Methanol, vivoPHIX and Acetil-methanol (ACME). Our results show that while DMSO provides the highest cell quality in terms of RNA molecules and genes detected per cell. Yet, it strongly affects the cellular composition and the expression profile of the resulting datasets. In contrast, methanol fixed samples display a cellular composition like that of fresh samples while providing a good cell quality and smaller expression biases. Taken together, our results show that methanol fixation is the method of choice for performing droplet-based single-cell transcriptomics experiments on neural cell populations.
Project description:Background: Recent developments in droplet-based microfluidics allow the transcriptional profiling of thousands of individual cells, in a quantitative, highly parallel and cost-effective way. A critical, often limiting step is the preparation of cells in an unperturbed state, not compromised by stress or ageing. Another challenge are rare cells that need to be collected over several days, or samples prepared at different times or locations. Results: Here, we used chemical fixation to overcome these problems. Methanol fixation allowed us to stabilize and preserve dissociated cells for weeks. By using mixtures of fixed human and mouse cells, we showed that individual transcriptomes could be confidently assigned to one of the two species. Single-cell gene expression from live and fixed samples correlated well with bulk mRNA-seq data. We then applied methanol fixation to transcriptionally profile primary single cells from dissociated complex tissues. Low RNA content cells from Drosophila embryos, as well as mouse hindbrain and cerebellum cells sorted by FACS, were successfully analysed after fixation, storage and single-cell droplet RNA-seq. We were able to identify diverse cell populations, including neuronal subtypes. As an additional resource, we provide 'dropbead', an R package for exploratory data analysis, visualization and filtering of Drop-seq data. Conclusions: We expect that the availability of a simple cell fixation method will open up many new opportunities in diverse biological contexts to analyse transcriptional dynamics at single cell resolution.
Project description:In this study, we introduce BacDrop, a bacterial droplet-based high throughput scRNA-seq technology that can be applied to large cell numbers. We applied BacDrop to study Klebsiella pneumoniae clinical isolates and elucidated their critical, genome-wide heterogeneity in the absence and presence of antibiotic perturbations.
Project description:Species identification of fragmentary bones remains a challenging task in archeology and forensics. A species identification method for such fragmentary bones that has recently attracted interest is the use of bone collagen proteins. We developed a method similar to DNA barcoding that reads collagen protein sequences in bone and automatically determines the species by performing sequence database searches. We tested our method using bone samples from 30 vertebrate species ranging from mammals to fish.