Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Characterizing the temporal dynamics of gene expression in single cells with sci-fate

ABSTRACT: Gene expression is a dynamic process on multiple scales, e.g. the cell cycle, response to stimuli, normal differentiation and development, etc. However, nearly all techniques for profiling gene expression in single cells fail to directly capture these temporal dynamics, which limits the scope of biology that can be effectively investigated. Towards addressing this, we developed sci-fate, a new technique that combines S4U labeling of newly synthesized mRNA with single cell combinatorial indexing (sci-), in order to concurrently profile the whole and newly synthesized transcriptome in each of many single cells. As a proof-of-concept, we applied sci-fate to a model system of cortisol response, and characterized expression dynamics in over 6,000 single cells. From these data, we quantify the dynamics of the cell cycle and of glucocorticoid receptor activation, while also exploring their intersection. We furthermore use these data to develop a framework for estimating cell state transition probabilities, and to identify factors whose dynamic expression potentially regulates these transitions. The experimental and computational methods described here may be broadly applicable to quantitatively characterize cell state dynamics in in vitro systems.

ORGANISM(S): Mus musculus Homo sapiens

PROVIDER: GSE131351 | GEO | 2020/04/12

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Characterizing the temporal dynamics of gene expression in single cells with sci-fate

Project description:Characterizing the temporal dynamics of gene expression in single cells with sci-fate

| PRJNA543318 | ENA

NASC-seq monitors RNA synthesis in single cells

Project description:Sequencing of newly synthesized RNA can monitor transcriptional dynamics with great sensitivity and high temporal resolution, but is currently restricted to populations of cells. Here, we develop new transcriptome alkylation-dependent single-cell RNA sequencing (NASC-seq), to monitor newly synthesized and pre-existing RNA in single cells. We validate the method on pre-alkylated RNA, and by demonstrating that more newly synthesized RNA was detected for genes with known high mRNA turnover. NASC-seq reveals rapidly up- and down-regulated genes during T-cell activation, and RNA arising from induced genes is essentially only newly synthesized. The newly synthesized and pre-existing transcriptomes after T-cell activation are distinct, confirming that we simultaneously measure gene expression at two time points in single cells. Altogether, NASC-seq is an accessible and powerful tool to investigate transcriptional dynamics that enables the precise monitoring of RNA synthesis at flexible time periods during homeostasis, perturbation responses and cellular differentiation.

2019-07-18 | GSE128273 | GEO

Flexible and scalable control of T cell memory by a reversible epigenetic switch (sci-fate-seq)

Project description:In this study, we use a minimal ex vivo culture system to track CD8 T cell differentiation at the single cell and clonal lineage level during activation. To characterize early heterogeneity in memory and effector differentiation in this system, we subjected cells to temporally-resolved single-cell transcriptomic profiling using sci-fate-seq (Cao et al., Nature Biotech, 202).

2023-07-25 | GSE237829 | GEO

An accessible and convenient assay to profile single-cell newly synthesized transcriptome reveals transcription factors and regulon dynamics during early-stage T-cell activation

Project description:Sequencing newly synthesized transcriptome in addition to the regular transcriptome in single cells enable the study of gene expression temporal dynamics during rapid chromatin and gene regulation processes. However, current single-cell newly synthesized transcriptome assays require in-house technology expertise to achieve a high cellular throughput, preventing their widespread application. Here, we develop NOTE-seq to simultaneously profile regular and newly synthesized transcriptome in single cells. NOTE-seq combines 4-thiouridine labeling of newly synthesized RNA, thiol-alkylation-based chemical conversion, and the streamlined 10X Genomics platform, offering a high cellular throughput accessible to and convenient for regular biology labs without single-cell technology expertise. Using NOTE-seq, we investigate the temporal dynamics of gene expression during early-stage T-cell activation in human Jurkat T cells and mouse naïve T cells, characterize transcription factors and regulons, and discover Fli-1 as a master transcription factor for gene regulation upon T-cell activation. Interestingly, Fli-1 level in T cells is sensitive to the treatment of camptothecin, a topoisomerase inhibitor used in cancer chemotherapy, indicating its potential complication on the immune system.

2024-08-23 | GSE270292 | GEO

Sci-Space E14 Mouse Embryo Data

Project description:Spatial patterns of gene expression span many scales, and are shaped by both local (e.g. cell-cell interactions) and global (e.g. tissue, organ) context. However, most in situ methods for profiling gene expression either average local contexts or are restricted to limited fields of view. Here we introduce sci-Space, a scale-flexible method that retains single cell resolution while resolving spatial heterogeneity in gene expression at larger scales. As a proof-of-concept, we apply sci-Space to the developing mouse embryo (E14), capturing the approximate spatial coordinates of profiled cells from whole embryo serial sections.

2021-07-01 | GSE166692 | GEO

sci-RNA-seq data

Project description:sci-RNA-seq data

| PRJNA785988 | ENA

sci-ATAC-seq data

Project description:sci-ATAC-seq data

| PRJNA785974 | ENA

Profiling immunological phenotypes in individuals during the first year after traumatic spinal cord injury (SCI): a longitudinal analysis

Project description:Individuals with SCI are severely affected by immune system changes, resulting in a SCI-induced immune deficiency syndrome (SCI-IDS). While recent data support that acute SCI-IDS symptoms differ from those present in the chronic phase, only limited data in humans is available. To characterize dynamic molecular and cellular immune phenotypes over the first year, we assess RNA (bulk-RNA sequencing), protein, and flow cytometry (FACS) profiles of blood samples from 12 individuals with SCI at 0-3 days and at 3, 6, and 12 months post injury (MPI) compared to 23 uninjured individuals (controls). We identified 967 differentially expressed (DE) genes in individuals with SCI (FDR<0.001) compared to controls. Within the first 6 MPI we detected a reduced expression of NK cell genes, consistent with reduced frequencies of CD56bright, CD56dim NK cells present at 12 MPI. Over 6MPI, we observed increased and prolonged expression of genes associated with inflammation (e.g. HMGB1, Toll-like receptor signaling) and expanded frequencies of monocytes acutely. Canonical T-cell related DE genes (e.g. FOXP3, TCF7, CD4) were upregulated during the first 6 MPI and increased frequencies of activated T cells at 3-12 MPI were observed. Neurological injury severity was reflected in distinct whole blood gene expression profiles at any time after SCI, indicating a persistent ‘neurogenic’ imprint. Overall, 2876 DE genes emerge when comparing profiles of motor complete to motor incomplete SCI (ANOVA, FDR<0.05), including genes related to neutrophils, inflammation, and infection. In summary, we identify dynamic SCI-IDS determinants in humans, including molecular and cellular profiles, which may provide potential targets to reduce inflammation, improve immunity, or serve as candidate biomarkers of injury severity.

2023-05-26 | GSE226238 | GEO

Large-scale multiplexed profiling of mouse kidney fibrogenesis with sci-RNA-seq3

Project description:We employed single-cell combinatorial indexing RNA-seq (sci-RNA-seq), a scRNA-seq technology with high throughput, high sample multiplexing capacity and low costs, to decipher the molecular events involved in mouse kidney fibrogenesis. With the hypothesis that different types of kidney insults may lead to distinct cellular injury responses, we leveraged sci-RNA-seq to profile mouse kidneys collected from two mouse kidney fibrogenesis models, unilateral ischemia-reperfusion injury (uni-IRI) and unilateral ureteral obstruction (UUO), at multiple stages. We described an atlas of kidney fibrogenesis (available at http://humphreyslab.com/SingleCell/) with a total of 309,666 cells profiled from 11 biological conditions and 24 samples in one experiment. We discovered that uni-IRI and UUO produced two types of early-stage injured PT cells with different transcriptomic signature. Further investigation on the two cell states highlighted their distinct mechanisms of metabolic regulation. Analysis of other structures of TECs revealed a common cellular response to injury and repair. In addition, we described the heterogeneity within kidney stroma and the dynamics of cell-cell communications in kidney fibrogenesis.

2022-10-01 | GSE190887 | GEO

Improving the study of RNA dynamics through advances in RNA-seq with metabolic labeling and nucleotide-recoding chemistry

Project description:RNA metabolic labeling using 4-thiouridine (s4U) can be used to capture the dynamics of RNA synthesis and decay. The power of this approach is dependent on appropriate quantification of labeled and unlabeled sequencing reads, which can be compromised by the apparent loss of s4U-labeled reads in a process we refer to as dropout. Here we show that s4U-containing transcripts can be selectively lost when RNA samples are handled under sub-optimal conditions, but that this loss can be minimized using an optimized protocol. We demonstrate a second cause of dropout in nucleotiderecoding and RNA sequencing (NR-seq) experiments that is computational and downstream of library preparation. NR-seq experiments involve chemically converting s4U from a uridine analog to a cytidine analog and the apparent T-to-C mutations are then used to identify the populations of newly synthesized RNA. We show that high levels of T-to-C mutations can prevent read alignment with some computational pipelines, but that this bias can be overcome using improved alignment pipelines. Importantly, kinetic parameter estimates are affected by dropout independent of the NR chemistry employed, and all chemistries are practically indistinguishable in bulk, short-read RNA-seq experiments. Dropout is an avoidable problem that can be identified by including unlabeled controls, and mitigated through improved sample handing and read alignment that together improve the robustness and reproducibiltiy of NR-seq experiments.

2023-05-25 | GSE233139 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data