Regulation of RNA levels is critical for the response to external stimuli and determined through the interplay between RNA production, processing and degradation. Despite the centrality of these processes, most global studies of RNA regulation do not distinguish their separate contributions and relatively little is known about how they are temporally integrated. Here, we combine metabolic labeling of RNA with advanced RNA quantification assays and computational modeling to estimate RNA transcription and degradation during the response of immune dendritic cells (DCs) to pathogens, a critical and tightly regulated step in innate immunity. We find that transcription regulation plays a major role in shaping most temporal changes in RNA levels, but that changes in degradation rate are important for shaping sharp ‘peaked’ responses. We find that transcription changes precede corresponding RNA changes by a small lag (15-30 min), which is shorter for induced than for repressed genes. Massively parallel sequencing of the entire RNA population – including non-polyadenylated transcripts – allows us to estimate RNA processing, and identify specific groups of transcripts, mostly cytokines and transcription factors, undergoing enhanced mRNA maturation. This suggests an additional role for splicing in regulating mRNA maturation. Our method provides a new quantitative approach to study key steps in the integrative process of RNA regulation. Overall design: Sequencing of 4sU-labeled RNA taken from a 7 samples time-series (one sample every 1 hour) during the response of DCs to LPS stimulation. 4-thiouridine was added 45 minutes prior to sample collection. Data presented here for six timepoints: 0, 1, 3-6 hrs. 2hr timepoint not included.
Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells.
No sample metadata fields
View SamplesFour Kcng4-cre;stop-YFP mouse retinas from two mice were dissected, dissociated and FACS sorted, and single cell RNA-seq libraries were generated for 384 single cells using Smart-seq2. Aligned bam files are generated for 383 samples as one failed to align. Overall design: Four mouse retinas (labeled 1la, 1Ra, and 2la, 2Ra respective from the two mice) were used, and 96 single cells from each were processed using Smart-seq2. Total 384 cells Smart-seq2 analysis of P17 FACS sorted retinal cells from the Kcng4-cre;stop-YFP mice (Kcng4tm1.1(cre)Jrs mice [Duan et al., Cell 158, 793-807, 2015] crossed to the cre-dependent reporter Thy1-stop-YFP Line#1 [Buffelli et al., Nature 424, 430-434, 2003])
Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics.
Specimen part, Subject
View SamplesRNA-Seq is an effective method to study the transcriptome, but can be difficult to apply to scarce or degraded RNA from fixed clinical samples, rare cell populations, or cadavers. Recent studies have proposed several methods for RNA-Seq of low quality and/or low quantity samples, but their relative merits have not been systematically analyzed. Here, we compare five such methods using a comprehensive set of metrics, relevant to applications such as transcriptome annotation, transcript discovery, and gene expression. Using a single human RNA sample, we constructed and deeply sequenced 10 libraries with these methods and two control libraries. We find that the RNase H method performed best for low quality RNA, and can even effectively replace oligo (dT) based methods for standard RNA-Seq. SMART and NuGEN had distinct strengths for low quantity RNA. Our analysis allows biologists to select the most suitable methods and provides a benchmark for future method development. Overall design: Examination of 9 different RNA-Seq libraries starting from total RNA from 5 distinct methods; also 3 control RNA-Seq libraries
Comparative analysis of RNA sequencing methods for degraded or low-input samples.
Specimen part, Cell line, Subject
View Samples15,000 GFP+ cells were collected from two replicates of the Htr3a GFP line into RNAlater (ThermoFisher, AM7024). RNA was purified and bulk RNA-seq was performed using the Ovation RNA-seq system V2 (Nugen, 7102-32) Overall design: Bulk RNA-seq analysis of Type 5 retinal bipolar cells (2 biological replicates)
Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics.
Specimen part, Subject
View SamplesAging is a major risk factor for both genetic and sporadic neurodegenerative disorders. However, it is unclear how aging interacts with genetic predispositions to promote neurodegeneration. Here we investigate how partial loss-of-function of TBK1, a major genetic cause for amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) comorbidity, leads to age-dependent neurodegeneration. We show that TBK1 is an endogenous inhibitor of RIPK1 and the embryonic lethality of Tbk1-/- mice is dependent on RIPK1 kinase activity. In aging human brains, another endogenous RIPK1 inhibitor, TAK1, exhibits a marked decrease in expression. We show that in Tbk1+/- mice, the reduced myeloid TAK1 expression promotes all the key hallmarks of ALS/FTD, including neuroinflammation, TDP-43 aggregation, axonal degeneration, neuronal loss and behavior deficits, which are blocked upon inhibition of RIPK1. Thus, aging facilitates RIPK1 activation by reducing TAK1 expression, which cooperates with genetic risk factors to promote the onset of ALS/FTD. Overall design: mRNA profiles of primary microglia derived from 2-day old wild type (WT), Tbk1+/-, Tbk1+/-;Ripk1D138N/D138N, Tak1?M/+, Tbk1+/-;Tak1?M/+ and Tbk1+/-;Tak1?M/+;RIpk1D138N/+ mice were generated by bulk RNA sequencing, in triplicate.
TBK1 Suppresses RIPK1-Driven Apoptosis and Inflammation during Development and in Aging.
No sample metadata fields
View SamplesThe mammalian brain is complex, with multiple cell types performing a variety of diverse functions, but exactly how each cell type is affected in aging remains largely unknown. Here we performed a single-cell transcriptomic analysis of young and old mouse brains. We provide comprehensive datasets of aging-related genes, pathways and ligand–receptor interactions in nearly all brain cell types. Our analysis identified gene signatures that vary in a coordinated manner across cell types and gene sets that are regulated in a cell-type specific manner, even at times in opposite directions. These data reveal that aging, rather than inducing a universal program, drives a distinct transcriptional course in each cell population, and they highlight key molecular processes, including ribosome biogenesis, underlying brain aging. Overall, these large-scale datasets provide a resource for the neuroscience community that will facilitate additional discoveries directed towards understanding and modifying the aging process. Overall design: Total of 16 mice brains with raw data for 50,212 single cells and processed data for 37,089 single cells
Single-cell transcriptomic profiling of the aging mouse brain.
Specimen part, Subject
View SamplesThis SuperSeries is composed of the SubSeries listed below.
Integrative analysis of the melanoma transcriptome.
Disease, Disease stage
View SamplesPaired end sequencing of cDNA isolated from individual melanoma samples via the Illumina sequencing platform to identify genetic aberrations that may play a role in melanoma genesis.
Integrative analysis of the melanoma transcriptome.
No sample metadata fields
View SamplesWe profiled the gene expression levels from 8 melanoma short-term cultures and 1 melanoma cell line in order to compare to expression level estimates obtained by RNA-seq.
Integrative analysis of the melanoma transcriptome.
Disease, Disease stage
View SamplesSickle cell disease is characterized by hemolysis, vaso-occlusion and ischemia reperfusion injury. These events cause endothelial dysfunction and vasculopathies in multiple systems
Global gene expression profiling of endothelium exposed to heme reveals an organ-specific induction of cytoprotective enzymes in sickle cell disease.
Specimen part, Treatment
View Samples