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Mus musculus
924 Downloadable Samples
NextSeq 500
Description
We report RNA-seq of single nuclei isolated from the adult C57BL/6 male mouse Hippocampus region. Majority of the nuclei were isolated from 12 weeks old mice (4 different animal), with an additional set of nuclei from 3 months and 2 years old animals. In addition a set of GFP labeled nuclei driven by a VGAT promoter . Overall design: Microdissections of dentate gyrus, CA1 and CA2/3 regions of the Hippocampus were placed into ice-cold RNA-later for fixation and stored at 4°c overnight, then stored in -80°c. Nuclei were isolated by sucrose gradient centrifugation and kept on ice until sorting using Fluorescence Activated Cell Sorting (FACS) into 96 well plates containing RNA lysis buffer. Single nucleus RNA was first purified then derived cDNA libraries were generated following a modified Smart-seq2 protocol. For VGAT nuclei: high titer AAV1/2 of pAAV-EF1a-DIO-EYFP-KASH-WPRE-hGH-polyA was injected into dorsal and/or ventral Hippocampus, animals were sacrificed two weeks after injections, and GFP labeled nuclei were sorted into plates and processed as described above.
Publication Title
Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons.
Sample Metadata Fields
Age, Cell line, Subject
View Samples- Mus musculus
- 154 Downloadable Samples
- Illumina HiSeq 2500
Description
We present a detailed single cell time course of the macrophage response to Salmonella infection. By combining phenotypic fluorescent labels with single cell expression analysis we are able to identify gene modules associated with bacterial exposure and bacterial infection. We also identify other genetic clusters that are expressed heterogenously, ananlyzing both their regulation and their impact on infection Overall design: Analysis of 192 single cells across 4 time points after Salmonella exposure (MOI 1:1) with one of three different fluorescent labels indicating whether a given cell contained no intracellular bacteria (non-fluorescent), contained dead intracellular bacteria (only pHrodo positive), or contained live intracellular bacteria (pHrodo and GFP positive)
Publication Title
Pathogen Cell-to-Cell Variability Drives Heterogeneity in Host Immune Responses.
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus
- 96 Downloadable Samples
- Illumina HiSeq 2500
Description
We present a detailed single cell analysis of the macrophage response to LPS from Salmonella enterica. By combining single cell transcriptional analysis, fluorescently labeled, LPS-coated beads, and cytometry we are able to distinguish the responses of macrophages that have internalized LPS-coated beads and those that have not. Overall design: Analysis of 96 single macrophages that were either: left untreated, were exposed to but did not internalize uncoated beads, were exposed to and internalized uncoated beads, were exposed to but did not internalize LPS-coated beads, or were exposed to and did internalize LPS-coated beads.
Publication Title
Pathogen Cell-to-Cell Variability Drives Heterogeneity in Host Immune Responses.
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus
- 20 Downloadable Samples
- Illumina HiSeq 2000
Description
A time course of the macrophage response to Salmonella exposure analyzing the effects of input cell number as a control for single cell studies Overall design: Mouse macrophages were exposed to Salmonella enterica for different lengths of time. Libraries were constructed using either approximately 500,00 macrophages lysed directly on a tissue culture dish (bulk) or using only 150 cells isolated using FACS (sorted). All libraries were constructed in duplicate (bulk) or triplicate (sorted). All replicates are biological replicates
Publication Title
Pathogen Cell-to-Cell Variability Drives Heterogeneity in Host Immune Responses.
Sample Metadata Fields
No sample metadata fields
View Samples
GSE12275- Mus musculus
- 4 Downloadable Samples
Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
FAN (Factor associated with neutral sphingomyelinase activation) is an adaptor protein that constitutively binds to TNF-R1. Microarray analysis was performed in fibroblasts derived from wild-type or FAN knockout mouse embryos to evaluate the role of FAN in TNF-induced gene expression.
Publication Title
FAN stimulates TNF(alpha)-induced gene expression, leukocyte recruitment, and humoral response.
Sample Metadata Fields
Treatment
View Samples- Homo sapiens
- 192 Downloadable Samples
- NextSeq500
Description
A cell supsension containing an equal mix of HEK and 3T3 cells was used in the Fluidigm C1 Overall design: Suspensions of 3T3 and HEK cells were diluted down to a concentration of 250,000 per mL and mixed 1:1, then loaded onto two medium C1 cell capture chips.
Publication Title
Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus
- 86 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Dynamic regulatory network controlling TH17 cell differentiation.
Sample Metadata Fields
Specimen part, Treatment
View Samples- Mus musculus
- 61 Downloadable Samples
- Illumina Genome Analyzer
Description
Despite their enormous importance, the molecular circuits that control the differentiation of Th17 cells remain largely unknown. Recent studies have reconstructed regulatory networks in mammalian cells, but have focused on short-term responses and relied on perturbation approaches that cannot be applied to primary T cells. Here, we develop a systematic strategy – combining transcriptional profiling at high temporal resolution, novel computational algorithms, and innovative nanowire-based tools for performing gene perturbations in primary T cells – to derive and experimentally validate a temporal model of the dynamic regulatory network that controls Th17 differentiation. The network is arranged into two self-reinforcing and mutually antagonistic modules that either suppress or promote Th17 differentiation. The two modules contain 12 novel regulators with no previous implication in Th17 differentiation, which may be essential to maintain the appropriate balance of Th17 and other CD4+ T cell subsets. Overall, our study identifies and validates 39 regulatory factors that are embedded within a comprehensive temporal network and identifies novel drug targets and organizational principles for the differentiation of Th17 cells. Overall design: RNA-seq of knockdown of 12 genes in Th17 cell differentiation
Publication Title
Dynamic regulatory network controlling TH17 cell differentiation.
Sample Metadata Fields
Specimen part, Cell line, Treatment, Subject
View Samples- Mus musculus
- 58 Downloadable Samples
Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
Despite their enormous importance, the molecular circuits that control the differentiation of Th17 cells remain largely unknown. Recent studies have reconstructed regulatory networks in mammalian cells, but have focused on short-term responses and relied on perturbation approaches that cannot be applied to primary T cells. Here, we develop a systematic strategy combining transcriptional profiling at high temporal resolution, novel computational algorithms, and innovative nanowire-based tools for performing gene perturbations in primary T cells to derive and experimentally validate a temporal model of the dynamic regulatory network that controls Th17 differentiation. The network is arranged into two self-reinforcing and mutually antagonistic modules that either suppress or promote Th17 differentiation. The two modules contain 12 novel regulators with no previous implication in Th17 differentiation, which may be essential to maintain the appropriate balance of Th17 and other CD4+ T cell subsets. Overall, our study identifies and validates 39 regulatory factors that are embedded within a comprehensive temporal network and identifies novel drug targets and organizational principles for the differentiation of Th17 cells.
Publication Title
Dynamic regulatory network controlling TH17 cell differentiation.
Sample Metadata Fields
Specimen part, Treatment
View Samples- Mus musculus
- 20 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
Despite their enormous importance, the molecular circuits that control the differentiation of Th17 cells remain largely unknown. Recent studies have reconstructed regulatory networks in mammalian cells, but have focused on short-term responses and relied on perturbation approaches that cannot be applied to primary T cells. Here, we develop a systematic strategy combining transcriptional profiling at high temporal resolution, novel computational algorithms, and innovative nanowire-based tools for performing gene perturbations in primary T cells to derive and experimentally validate a temporal model of the dynamic regulatory network that controls Th17 differentiation. The network is arranged into two self-reinforcing and mutually antagonistic modules that either suppress or promote Th17 differentiation. The two modules contain 12 novel regulators with no previous implication in Th17 differentiation, which may be essential to maintain the appropriate balance of Th17 and other CD4+ T cell subsets. Overall, our study identifies and validates 39 regulatory factors that are embedded within a comprehensive temporal network and identifies novel drug targets and organizational principles for the differentiation of Th17 cells.
Publication Title
Dynamic regulatory network controlling TH17 cell differentiation.
Sample Metadata Fields
Specimen part, Treatment
View Samples