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accession-icon SRP167106
Dynamic transcriptome profiles within spermatogonial and spermatocyte populations during postnatal testis maturation revealed by single-cell sequencing
  • organism-icon Mus musculus
  • sample-icon 8 Downloadable Samples
  • Technology Badge IconNextSeq 500

Description

This analysis represents the first comprehensive sampling of germ cells in the developing testis over time, at high-resolution, single-cell depth. From these analyses, we have not only revealed novel genetic regulatory signatures of murine germ cells over time, but have also demonstrated that cell types positive for a single marker gene have the capacity to change dramatically during testis maturation, and therefore cells of a particular “identity” may differ significantly from postnatal to adult life. Overall design: Single-cell suspensions of mammalian testes ranging from PND6 to adult were processed for single-cell RNAseq (10x Genomics Chromium) and libraries were sequenced on a NextSeq500 (Illumina).

Publication Title

Dynamic transcriptome profiles within spermatogonial and spermatocyte populations during postnatal testis maturation revealed by single-cell sequencing.

Sample Metadata Fields

Age, Disease, Cell line, Subject

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accession-icon SRP096727
Single cell RNAseq characterization of cell types produced over time in an in vitro model of human inhibitory interneuron differentiation.
  • organism-icon Homo sapiens
  • sample-icon 272 Downloadable Samples
  • Technology Badge Icon

Description

Diverse cell types are produced from dorsal and ventral regions of the developing neural tube. In this study we describe a system for generating human inhibitory interneurons by ventralizing human embryonic stem cells in vitro and characterizing the gene expression of the cell types produced over time. We engineered a DCX-Citrine/Y hESC line to sort and characterize progenitor and neuron transcriptomics separately at both the subpopulation and single cell level. The cells generated in vitro were compared to similar populations present in human fetal brain samples by mapping gene expression data from human fetal cells onto the principal component analysis (PCA) space of in vitro-derived populations. Weighted gene co-expression network analysis (WGCNA) was used to determine the discreet cell types present at D24, D54, D100 and D125 of culture, and describe the gene expression changes that occur in progenitor and neuron populations over time. Immature lateral ganglionic eminence and medial ganglionic eminence cells are present at early timepoints, along with MGE-like and dorsal pallium-like neuronal progenitors. At later timepoints we observe the emergence of SST-expressing interneurons, as well as oligodendrocyte and astrocyte progenitors. We also identified genes that were upregulated in somatostatin-expressing interneurons as they mature. Overall design: The transcriptomes of 1732 ventralized single cells were profiled by SmartSeq2 at different timepoints throughout a 125-day differentiation protocol that converted H1 human embryonic stem cells to a variety of ventrally-derived cell types.

Publication Title

Single-Cell Profiling of an In Vitro Model of Human Interneuron Development Reveals Temporal Dynamics of Cell Type Production and Maturation.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP096986
Sub-population RNAseq characterization of cell types produced over time in an in vitro model of human inhibitory interneuron differentiation.
  • organism-icon Homo sapiens
  • sample-icon 37 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Diverse cell types are produced from dorsal and ventral regions of the developing neural tube. In this study we describe a system for generating human inhibitory interneurons by ventralizing human embryonic stem cells in vitro and characterizing the gene expression of the cell types produced over time. We engineered a DCX-Citrine/Y hESC line to sort and characterize progenitor and neuron transcriptomics separately at both the subpopulation and single cell level. The cells generated in vitro were compared to similar populations present in human fetal brain samples by mapping gene expression data from human fetal cells onto the principal component analysis (PCA) space of in vitro-derived populations. Weighted gene co-expression network analysis (WGCNA) was used to determine the discreet cell types present at D24, D54, D100 and D125 of culture, and describe the gene expression changes that occur in progenitor and neuron populations over time. Immature lateral ganglionic eminence and medial ganglionic eminence cells are present at early timepoints, along with MGE-like and dorsal pallium-like neuronal progenitors. At later timepoints we observe the emergence of SST-expressing interneurons, as well as oligodendrocyte and astrocyte progenitors. We also identified genes that were upregulated in somatostatin-expressing interneurons as they mature. Overall design: The transcriptomes of 1732 ventralized single cells were profiled by SmartSeq2 at different timepoints throughout a 125-day differentiation protocol that converted H1 human embryonic stem cells to a variety of ventrally-derived cell types.

Publication Title

Single-Cell Profiling of an In Vitro Model of Human Interneuron Development Reveals Temporal Dynamics of Cell Type Production and Maturation.

Sample Metadata Fields

Specimen part, Subject

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accession-icon SRP055023
Neonatal na誰ve CD8+ T cells have effector-like gene expression that prevents memory cell formation [RNA-seq]
  • organism-icon Mus musculus
  • sample-icon 18 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

Neonates are intrinsically defective at creating memory CD8+ T cells in response to infection with intracellular pathogens. Here we investigated differential of small RNAs, transcription factors, and chemokine receptors regulation in neonates as compared to adults before and during infection. We found that prior to infection, na誰ve cells have a different expression profile for many microRNAs, and gene targets of these microRNAs show widespread expression differences. These targets and other changes in gene expression in na誰ve cells result in neonatal cells that get activated more easily, express chemokine receptors that home to sites of infection, and are less protected from apoptosis during contraction. As a result, changes in neonatal na誰ve cells drive effector cell terminal differentiation at the expense of creating long-lived memory cells. Overall design: total RNAs were sequenced from adult and neonatal CD8+ T cells before and during infection

Publication Title

MicroRNAs and Their Targets Are Differentially Regulated in Adult and Neonatal Mouse CD8+ T Cells.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP055022
Neonatal na誰ve CD8+ T cells have effector-like gene expression that prevents memory cell formation [3''UTR-seq]
  • organism-icon Mus musculus
  • sample-icon 8 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

Neonates are intrinsically defective at creating memory CD8+ T cells in response to infection with intracellular pathogens. Here we investigated differential of small RNAs, transcription factors, and chemokine receptors regulation in neonates as compared to adults before and during infection. We found that prior to infection, na誰ve cells have a different expression profile for many microRNAs, and gene targets of these microRNAs show widespread expression differences. These targets and other changes in gene expression in na誰ve cells result in neonatal cells that get activated more easily, express chemokine receptors that home to sites of infection, and are less protected from apoptosis during contraction. As a result, changes in neonatal na誰ve cells drive effector cell terminal differentiation at the expense of creating long-lived memory cells. Overall design: PolyA RNA was selected and sequenced from adult and neonatal CD8+ T cells before and during infection

Publication Title

MicroRNAs and Their Targets Are Differentially Regulated in Adult and Neonatal Mouse CD8+ T Cells.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP067568
Transcriptome profiling of hnRNP A2/B1 and A1 depleted cells
  • organism-icon Homo sapiens
  • sample-icon 10 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2500

Description

We used NEBNext Ultra Directional RNA Library Prep Kits to prepare RNA-seq libraries of total RNA from hnRNP A2/B1 and A1 depleted A549 cells. Pro-seq libraries were prepared from A549 cells using Illumina adapters Overall design: hnRNP A2/B1 and A1 depleted A549 cells were generated by lentiviral infections of shRNA constructs. RNAs were isolated using Trizol.

Publication Title

A widespread sequence-specific mRNA decay pathway mediated by hnRNPs A1 and A2/B1.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP041011
Rapid proliferation and differentiation impairs the development of memory CD8+ T cells in early life
  • organism-icon Mus musculus
  • sample-icon 4 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

Neonates often generate incomplete immunity against intracellular pathogens, although the mechanism of this defect is poorly understood. An important question is whether the impaired development of memory CD8+ T cells in neonates is due to an immature priming environment or lymphocyte-intrinsic defects. Here we show that neonatal and adult CD8+ T cells adopted different fates when responding to equal amounts of stimulation in the same host. While adult CD8+ T cells differentiated into a heterogeneous pool of effector and memory cells, neonatal CD8+ T cells preferentially gave rise to short-lived effector cells and exhibited a distinct gene expression profile. Surprisingly, impaired neonatal memory formation was not due to a lack of responsiveness, but instead because neonatal CD8+ T cells expanded more rapidly than adult cells and quickly became terminally differentiated. Collectively, these findings demonstrate that neonatal CD8+ T cells exhibit an imbalance in effector and memory CD8+ T cell differentiation, which impairs the formation of memory CD8+ T cells in early life Overall design: mRNA profiles of effector CD8+ T cells from neonatal and adult mice

Publication Title

Rapid proliferation and differentiation impairs the development of memory CD8+ T cells in early life.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE5846
NCI-60 Cancer Cell Line
  • organism-icon Homo sapiens
  • sample-icon 60 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

NCI-60 cancer cell lines were profiled with their genome-wide gene expression patterns using Affymetrix HG-U133A chips.

Publication Title

A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE5845
Bladder Cancer 40 Cell Lines
  • organism-icon Homo sapiens
  • sample-icon 40 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

40 bladder cancer cell lines were profiled with their genome-wide gene expression patterns using Affymetrix HG-U133A chips.

Publication Title

A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP033135
Pseudo-temporal ordering of individual cells reveals regulators of differentiation
  • organism-icon Homo sapiens
  • sample-icon 384 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2500, IlluminaHiSeq2000

Description

Single-cell expression profiling by RNA-Seq promises to exploit cell-to-cell variation in gene expression to reveal regulatory circuitry governing cell differentiation and other biological processes. Here, we describe Monocle, a novel unsupervised algorithm for ordering cells by progress through differentiation that dramatically increases temporal resolution of expression measurements. This reordering unmasks switch-like changes in expression of key regulatory factors, reveals sequentially organized waves of gene regulation, and exposes regulators of cell differentiation. A functional screen confirms that a number of these regulators dramatically alter the efficiency of myoblast differentiation, demonstrating that single-cell expression analysis with Monocle can uncover new regulators even in well-studied systems. Overall design: We selected primary human myoblasts as a model system of cell differentiation to investigate whether ordering cells by progress revealed new regulators of the process. We sequenced RNA-Seq libraries from each of several hundred cells taken over a time-course of serum-induced differentiation. Please note that this dataset is a single-cell RNA-Seq data set, and each cell comes from a capture plate. Thus, each well of the plate was scored and flagged with several QC criteria prior to library construction, which are provided as sample characteristics; CONTROL indicates that this library is a off-chip tube control library constructed from RNA of approximately 250 cells and ''DEBRIS'' indicates that the well contained visible debris (and may or may not include a cell). Libraries marked DEBRIS thus cannot be confirmed to come from a single cell.

Publication Title

The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.

Sample Metadata Fields

No sample metadata fields

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refine.bio is a repository of uniformly processed and normalized, ready-to-use transcriptome data from publicly available sources. refine.bio is a project of the Childhood Cancer Data Lab (CCDL)

fund-icon Fund the CCDL

Developed by the Childhood Cancer Data Lab

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Cite refine.bio

Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler. refine.bio: a resource of uniformly processed publicly available gene expression datasets.
URL: https://www.refine.bio

Note that the contributor list is in alphabetical order as we prepare a manuscript for submission.

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