ABSTRACT: Purpose: We applied cDNA molecule counting using unique molecular identifiers combined with high-throughput sequencing to study the transcriptome of individual mouse embryonic stem cells, with spike-in controls to monitor technical performance. We further examined transcriptional noise in the embryonic stem cells. One 96-well plate of single-stranded cDNA libraries generated from 96 single R1 mouse embryonic stem cells sequenced on two lanes, and one 96-well plate of the same libraries further amplified by 9 PCR cycles sequenced on one lane.
Single-cell RNA sequencing (RNA-seq) is a powerful tool to reveal cellular heterogeneity, discover new cell types and characterize tumor microevolution. However, losses in cDNA synthesis and bias in cDNA amplification lead to severe quantitative errors. We show that molecular labels--random sequences that label individual molecules--can nearly eliminate amplification noise, and that microfluidic sample preparation and optimized reagents produce a fivefold improvement in mRNA capture efficiency. ...[more]
Project description:We have applied a recently developed, highly accurate and sensitive single-cell RNA-seq method (STRT/C1) to perform a molecular census of two regions of the mouse cerebral cortex: the somatosensory cortex and hippocampus CA1. We isolated cells fresh from somatosensory cortex (S1) and hippocampus CA1 area of juvenile (P22 - P32) CD1 mice, 33 males and 34 females. Cells were collected without selection, except that 116 cells were obtained by FACS from 5HT3a-BACEGFP transgenic mice. A total of 76 Fluidigm C1 runs were performed, each attempting 96 cell captures and resulting in 3005 high-quality single-cell cDNAs, containing Unique Molecular Identifiers allowing counting of individual mRNA molecules, even after PCR amplification.
Project description:In order to establish a consensus catalog of dorsal rott ganglion cell types, we used comprehensive transcriptome analysis of single cells for unsupervised identification and molecular classification of sensory neurons independent of any a priori knowledge of sensory subtypes. RNA-Seq was performed on 799 dissociated single cells dissected from the mouse lumbar dorsal root ganglion distributed over a total of nine 96-well plates
Project description:5069 transcriptomes of single oligodendrocyte cells from spinal cord, substantia nigra-ventral tegmental area, striatum, amygdala, hypothalamic nuclei, zona incerta, hippocampus, and somatosensory cortex of male and female mice between post-natal day 21 and 90. The study aimed at identifying diverse populations of oligodendrocytes, and revealing dynamics of oligodendrocyte maturation. 5069 individual cells were sampled from CNS regions of mice of various strains as detailed in the protocols section
Project description:The molecular mechanism regulating phasic corticotropin-releasing hormone (CRH) release from parvocellular neurons (PVN) remains poorly understood. Here, we find a cohort of parvocellular cells interspersed with magnocellular PVN neurons expressing secretagogin. Single-cell transcriptome analysis combined with protein interactome profiling identifies secretagogin neurons as a distinct CRH-releasing neuron population reliant on secretagogin’s Ca2+ sensor properties and protein interactions with the vesicular traffic and exocytosis release machineries to liberate this key hypothalamic releasing hormone. single cells from the PVN region juvenile (21-28 days) mice were dissected and subject to whole transcriptome analysis
Project description:We successfully sequenced and annotated more than 400 cells from child, adult control, type 1 diabetes and type 2 diabetes donors. We detect donor-type specific transcript variation. We also report that cells from child donors have less defined gene signature. Cells from type 2 diabetes donors resemble juvenile cells in gene expression. Cells from three adult controls (56, 74, 92), one donor with type 1 diabetes (91), two donors with type 2 diabetes (75, 143), and two child donors (40, 72) were sequenced. Numbers in parathesis indicates number of cells sequenced.
Project description:Three libraries from 100 HEK293 cells each were prepared using a Smartseq based custom library preparation approach with unique molecular identifiers. Libraries were sequenced on a Illumina NextSeq 500 HEK293 cell (100 cells) 5' selective RNAseq profiling, N4H4 unique molecular identifiers, 3 replicates
Project description:Five libraries from 100 HEK293 cells each were prepared using a Smartseq based custom library preparation approach with unique molecular identifiers. One batch of 2 replicates (A) and one batch of 3 replicates (B) were prepared from different cell cultures. Libraries were sequenced on an Ion Proton HEK293 cell (100 cells) 5' selective RNAseq profiling, N4H4 unique molecular identifiers, 2 replicates (A) and 3 replicates (B)
Project description:Studies on the epigenetic regulation of gene expression during germ cell development are still at the beginning stage. In the present study, we used our highly specific 5hmC-labeling and enrichment technique coupled with DNA deep-sequencing to profile the global 5hmC distribution in 8 serial stages of male germ cells during spermatogenesis, as well as in the the Sertoli cells (SE) which are the only somatic cell type inside seminiferous tubules. We analyzed the genomic distribution and dynamic changes of 5hmC during spermatogenesis. Moreover, to dissect the functional significance of 5hmC modifications for transcriptional regulation of gene expression, we also performed RNA-Seq transcriptome analysis in all of the 8 corresponding stages of male germ cells and found that 5hmC is positively correlated with gene expression. RNA-seq: Examination of the transcriptomes during mouse spermatogenesis. 5hmC-seq: Identification of 5hmC enriched genmoic regions in mouse germ cell.
Project description:Many library preparation methods are available for gene expression quantification. Here, we sequenced and analysed Universal Human Reference RNA (UHRR) prepared using Smart-Seq2, TruSeq (public data) and a protocol using unique molecular identifiers (UMIs) that all include the ERCC spike-in mRNAs to investigate the effects of amplification bias on expression quantification. UHRR 10 and 12 replicates for Smart-seq2 and UMI-seq library preparation methods, respectively.