ABSTRACT: Transcriptomes of monkey primary visual cortex at the single-cell resolution. The dataset includes all cell types, including both glia and neurons.
Project description:The mammalian neocortex is a layered sheet of neural tissue that mediates complex cognitive processes including perception and cognition. Despite its importance, we still lack detailed knowledge of the cellular components of the neocortex. Integrating morphological, electrophysiological and molecular classification schemes into a common framework for defining cell types is challenging due to the limited scope of most single-cell analyses. Here, we developed a protocol for high-throughput electrophysiological and transcriptomic analysis of single neurons that combines whole-cell recordings and single-cell RNA-sequencing, which we call Patch-seq. Using this approach, we fully characterized the electrophysiological and molecular profiles of ~50 neocortical neurons, and show that gene expression patterns can be used to infer the morphological and physiological properties of individual neurons and their corresponding cell type. Our results shed light on the molecular underpinnings of neuronal diversity and demonstrate a path forward for comprehensive cell type characterization in the nervous system.
Project description:Endovascular biopsy and fluorescence activated cell sorting was used to enrich for viable endothelial cells (ECs) from a vertebrobasilar aneurysm and the femoral artery. scRNAseq was then performed on 24 aneurysmal endothelial cells and 23 patient-matched non-aneurysmal femoral artery endothelial cells. cDNA libraries were prepared using the Smart-seq2 protocol on a Fluidigm C1 system (Fluidigm, South San Francisco, California) and sequenced on a HiSeq2500 machine (Illumina, San Diego, California).
Project description:We sequenced the mRNAs of embryonic stem cells (ESCs) cultured in different conditions. The two lines M (male) and F (female) used in this study were derived from E4 blastocysts of the same cross between a C57BL/6J (Mus musculus domesticus) and CAST/EiJ (Mus castaneus) male. mESCs were cultured in 2i and LIF as the ground state condition or in serum and LIF as the conventional condition. Epistem cell lines were also generated from the two lines by culturing them with Activin A and FGF2. In order to study more advanced development, we differentiated the two mESC lines through embryonic body formation to postmitotic motor neurons using retinoic acid and the smoothened agonist SAG. This differentiation process also results in the derivation of several types of interneurons. We picked single cells from all different conditions and generated sequencing libraries using the Smart-seq2 and Tn5 protocol. For simplicity, we designate the different condition as ES2i, ES, Epi and Neuron from hereon. We also obtained preimplantation inner cell mass and epiblast cells from E3.5 ICM (inner cell mass) and E4.5 blastocysts of the crossbred mice (male CAST/EiJ Ã female C57BL/6J) as well as postimplantation epiblast cells from E5.5 embryos of C57BL/6J mice Examination of gene expression profile in individual male and female embryonic stem cell lines along developmental progression
Project description:Generally, when LCM is used in diverse transcriptomic analyses, several hundred, if not thousands, of cells are needed to obtain high quality of RNA-seq data. As some cellular populations are very small and tissue often in scarcity, we aimed to carefully document the lowest number of cells needed to retrieve sequencable libraries. We started with capturing 120 cells and subsequently scaled down to 50 cells, 30 cells, 10 cells, 5 cells, 2 cells and finally 1 cell. By optimizing multiple steps in the procedure, including direct lysis of cells without performing RNA isolation, we developed LCM-seq that couples LCM with Smart-seq2 for robust and efficient polyA-based RNA sequencing. We applied LCM-seq to mouse and human neuron samples, and demonstrated that LCM-seq can allow us to acquire high quality RNA-seq data from mouse and human tissues to conduct various transcriptomic studies. Developing new sequencing technology LCM-seq to efficiently sequence mouse and human tissues
Project description:Experiment was done as a part of multi-methodological description of Sst-neurons subtypes in mouse VTA, aiming to reveal their transcriptional profiles and neurotransmitter nature.
Project description:With improved whole-cell isolation protocols, we performed single-cell RNA sequencing (scRNA-seq) and profiled the transcriptomes from adult non-human primate brain. We identified discriminative cell populations with canonical and novel markers. Cross-species projection demonstrated the evolutionary conservation among mouse, monkey, and human. This dataset serves as a detailed transcriptomic atlas for understanding the adult primate central nervous system.
Project description:We performed single-cell RNA sequencing of dorsal forebrain organoids at day 53 of differentiation upon treatment with Hyper-IL-6. The study aimed at investigation of the effects of Hyper-IL-6 on transcriptional profiles of dorsal forebrain organoids at single-cell level.
Project description:To isolation of high-yield and viable brain cells including neurons and neural progenitors from adult primate brains, we have developed a reproducible whole-cell dissociation procedure for isolation of primary brain cells from adult and aged primates, with high-yield progenitor, immature and mature neural cells. We verified the viability of isolated cells and identified the main cell type with canonical markers. Furthermore, isolated primate neural progenitors by this our protocol is viable enough for culturing in vitro. This dataset is a detailed single cell RNA-sequencing results for two primate brain regions: primary visual cortex and prefrontal cortex.
Project description:To comprehensively profile cell types in the human retina, we performed single cell RNA-sequencing on 20,009 cells obtained post-mortem from three donors and compiled a reference transcriptome atlas. Using unsupervised clustering analysis, we identified 18 transcriptionally distinct clusters representing all known retinal cells: rod photoreceptors, cone photoreceptors, Müller glia cells, bipolar cells, amacrine cells, retinal ganglion cells, horizontal cells, retinal astrocytes and microglia.