Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

SIS-seq, a molecular ‘time machine’, connects single cell fate with gene programs

ABSTRACT: Conventional single cell RNA-seq methods are destructive, such that a given cell cannot also then be tested for fate and function, without a time machine. Here, we develop a clonal method SIS-seq, whereby single cells are allowed to divide, and progeny cells are assayed separately in SISter conditions; some for fate, others by RNA-seq. By cross-correlating fate and gene expression within a clone, and doing this for many clones, we can identify the earliest gene expression signatures of dendritic cell subset development. SIS-seq could be used to study other populations harboring clonal heterogeneity, including stem, reprogrammed and cancer cells to reveal the transcriptional origins of fate decisions.

ORGANISM(S): Mus musculus

PROVIDER: GSE119097 | GEO | 2018/08/28

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

SIS-seq, a molecular ‘time machine’, connects single cell fate with gene programs

Project description:SIS-seq, a molecular ‘time machine’, connects single cell fate with gene programs

| PRJNA488094 | ENA

Clonal multi-omics in DC development

Project description:Despite major developments in single cell multi-omics approaches, a single stem or progenitor cell can only be tested once. Here we develop ‘clonal multi-omics’, where recent daughters of a clone act as surrogates of the parental cell allowing multiple tests of its molecular profile and/or cellular fate under different conditions. We first present SIS-seq – a novel clonal method whereby siblings are examined in parallel for gene expression by RNA-seq, or for fate after culture. We use this method to identify, then validate using CRISPR, the genes expressed in single haematopoietic progenitors that control the development of the different dendritic cell (DC) subtypes. In this way, Bcor is identified as a suppressor of plasmacytoid DC (pDC) and conventional DC subtype 2 (cDC2) numbers during Flt3 ligand-mediated ‘emergency’ DC development. Using a complementary method SIS-skew – where WT and BcorKO siblings of the same clone are examined in parallel for fate – we discover that Bcor restricts clonal expansion, especially for generation of cDC2s, and suppresses clonal fate potential, especially for pDCs. SIS-seq and SIS-skew therefore represent novel and broadly applicable clonal multiomics approaches that can reveal the molecular and cellular mechanisms governing cell function at a single cell level, including after genetic or extrinsic factor perturbation. This SuperSeries is composed of the SubSeries listed below.

2020-07-01 | GSE153676 | GEO

Clonal multi-omics reveals Bcor as a regulator of dendritic cell development and its action at a clonal level

Project description:Despite major developments in single cell multi-omics approaches, a single stem or progenitor cell can only be tested once. Here we develop ‘clonal multi-omics’, where recent daughters of a clone act as surrogates of the parental cell allowing multiple tests of its molecular profile and/or cellular fate under different conditions. We first present SIS-seq – a novel clonal method whereby siblings are examined in parallel for gene expression by RNA-seq, or for fate after culture. We use this method to identify, then validate using CRISPR, the genes expressed in single haematopoietic progenitors that control the development of the different dendritic cell (DC) subtypes. In this way, Bcor is identified as a suppressor of plasmacytoid DC (pDC) and conventional DC subtype 2 (cDC2) numbers during Flt3 ligand-mediated ‘emergency’ DC development. Using a complementary method SIS-skew – where WT and BcorKO siblings of the same clone are examined in parallel for fate – we discover that Bcor restricts clonal expansion, especially for generation of cDC2s, and suppresses clonal fate potential, especially for pDCs. SIS-seq and SIS-skew therefore represent novel and broadly applicable clonal multi-omics approaches that could reveal the molecular and cellular mechanisms governing stem, immune, reprogrammed and cancer cell function at a single cell level, including after genetic or extrinsic factor perturbation.

2020-07-01 | GSE153675 | GEO

Combining data independent acquisition with spike-in SILAC (DIA-SiS) improves proteome coverage and quantification )

Project description:Data Independent Acquisition (DIA) is increasingly preferred over Data Dependent Acquisition (DDA) due to its higher throughput and fewer missing values. Whereas DDA often utilizes stable isotope labeling to improve quantification, DIA mostly relies on label-free approaches. Efforts to integrate DIA with isotope labeling include chemical methods like mTRAQ and dimethyl labeling, which, while effective, complicate sample preparation. Stable isotope labeling by amino acids in cell culture (SILAC) achieves high labeling efficiency through the metabolic incorporation of heavy labels into proteins in vivo. However, the need for metabolic incorporation limits the direct use in clinical scenarios. Spike-in SILAC methods utilize an externally generated heavy sample as an internal reference, enabling SILAC-based quantification even for samples that cannot be directly labeled. Here, we combine DIA with spike-in SILAC (DIA-SiS), leveraging the robust quantification of SILAC without the complexities associated with chemical labeling. We developed and rigorously validated DIA-SiS through a mixed-species benchmark to assess its performance in proteome coverage and quantification. We demonstrate that DIA-SiS significantly improves proteome coverage and quantification compared to label-free approaches and reduces the incidence of incorrectly quantified proteins. Additionally, DIA-SiS proves effective in analyzing proteins in low-input formalin-fixed paraffin-embedded (FFPE) tissue sections. DIA-SiS combines the precision of stable isotope-based quantification with the simplicity of label-free sample preparation, facilitating simple, accurate and comprehensive proteome profiling.

2024-10-14 | PXD052080 | Pride

Global transcriptional profiles of primary and secondary cellular senescence in vivo by oncogene-induced senescence (OIS) and stress-induced senescence (SIS) [Bulk RNA-seq]

Project description:The physiological roles of cellular senescence in vivo are largely unknown. Here, we report primary and secondary senescence-induced transcriptional alterations by oncogene-induced senescence (OIS, ERK activation) and stress-induced senescence (SIS, p38 activation) of liver, intestine, and MEF.

2025-05-19 | GSE242704 | GEO

Global transcriptional profiles of primary and secondary cellular senescence in vivo by oncogene-induced senescence (OIS) and stress-induced senescence (SIS) [scRNA-seq]

Project description:The physiological roles of cellular senescence in vivo are largely unknown. We report primary and secondary senescence-induced transcriptional alteration by oncogene-induced senescence (OIS, ERK activation) and stress-induced senescence (SIS, p38 activation) of mouse hepatocytes. Our analyses revealed that each senescence-induced cell displays different transcriptional profiles explaining various known senescent properties.

2025-05-19 | GSE242705 | GEO

Machine-guided cell-fate engineering

Project description:Machine-guided cell-fate engineering

| PRJNA887788 | ENA

Collier1996 - Delta Notch intercellular signalling and lateral inhibition

Project description:ODE model describing how slight variations in Notch and Delta cellular concentrations, through lateral inhibition, lead to cells with different states of differentiation. Lateral inhibition is a process whereby a given cell adopting a given fate prevents its immediate neighbouring cells from doing likewise. Notch and Delta are interacting transmembrane proteins and according to this model, lateral inhibition is due to a process where the inhibited cells (where notch has been activated by Delta) loose their ability to inhibit other cells (by synthesizing Delta). This process creates a feedback loop where cells with more delta proteins on their surface inhibit their immediate neighbours and adopt a different cell fate than those neighbours.

2024-09-02 | BIOMD0000001047 | BioModels

Study Nocte's functions in transcription and translation in Drosophila eye discs

Project description:Nocte is an RNA binding protein but its functions in Drosophila eye development is unknown. We found that knockdown of nocte by RNAi in Drosophila eyes leads to small and rough eye phenotypes. We detected nocte's functions at mRNA levels by doing RNA-seq with purified polyA mRNA. We detected nocte's functions in mRNA translation by doing Ribo-seq and total RNA-seq to calculate the translation efficiencies.

2023-11-10 | GSE232395 | GEO

ChromatinHD connects single-cell DNA accessibility and conformation to gene expression through scale-adaptive machine learning.

Project description:Gene regulation is inherently multiscale, but scale-adaptive machine learning methods that fully exploit this property in single-nucleus accessibility data are still lacking. Here, we develop ChromatinHD, a pair of scale-adaptive models that uses the raw accessibility data, without peak-calling or windows, to link regions to gene expression and determine differentially accessible chromatin. We show how ChromatinHD consistently outperforms existing peak and window-based approaches and find that this is due to a large number of uniquely captured, functional accessibility changes within and outside of putative cis-regulatory regions. Furthermore, ChromatinHD can delineate collaborating regulatory regions, including their preferential genomic conformations, that drive gene expression. Finally, our models also use changes in ATAC-seq fragment lengths to identify dense binding of transcription factors, a feature not captured by footprinting methods. Altogether, ChromatinHD, available at https://chromatinhd.org , is a suite of computational tools that enables a data-driven understanding of chromatin accessibility at various scales and how it relates to gene expression.

| S-EPMC11697365 | biostudies-literature

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