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accession-icon GSE71001
Crosstalk between two bZIP signaling pathways orchestrates salt-induced metabolic reprogramming in Arabidopsis roots
  • organism-icon Arabidopsis thaliana
  • sample-icon 23 Downloadable Samples
  • Technology Badge Icon Affymetrix Arabidopsis ATH1 Genome Array (ath1121501)

Description

Soil salinity increasingly causes crop losses worldwide. Although roots are the primary targets of salt stress, the signaling networks that facilitate metabolic reprogramming to induce stress tolerance are less understood than those in leaves. Here, a combination of transcriptomic and metabolic approaches was performed in salt-treated Arabidopsis thaliana roots, which revealed that the group S1 basic leucine zipper transcription factors bZIP1 and bZIP53 reprogram primary C- and N-metabolism. In particular, gluconeogenesis and amino acid catabolism are affected by these transcription factors. Importantly, bZIP1 expression reflects cellular stress and energy status in roots. In addition to the well-described abiotic stress response pathway initiated by the hormone abscisic acid (ABA) and executed by SnRK2 (Snf1-RELATED-PROTEIN-KINASE2) and AREB-like bZIP factors, we identify a structurally related ABA-independent signaling module consisting of SnRK1s and S1 bZIPs. Crosstalk between these signaling pathways recruits particular bZIP factor combinations to establish at least four distinct gene expression patterns. Understanding this signaling network provides a framework for securing future crop productivity.

Publication Title

Crosstalk between Two bZIP Signaling Pathways Orchestrates Salt-Induced Metabolic Reprogramming in Arabidopsis Roots.

Sample Metadata Fields

Specimen part

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accession-icon GSE54491
Identification of stable markers of the EMT:MET process
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) facilitate breast cancer (BC) metastasis, however stable molecular changes that result as a consequence of these processes remain poorly defined. Therefore, we sought to identify molecular markers that could distinguish tumor cells that had completed the EMT:MET cycle in the hopes of identifying and targeting unique aspects of metastatic tumor outgrowth.Therefore, normal murine mammary gland (NMumG) cells transformed by overexpression of EGFR (NME) cells were cultured in the presence of TGF-beta1 (5 ng/ml) for 4 weeks, at which point TGF-beta1 supplementation was discontinued and the cells were allowed to recover for an additional 4 weeks (Post-TGF-Rec). Total RNA was prepared from unstimulated cells (Pre-TGF) of similar passage and compared by microarray analysis.

Publication Title

Fibroblast growth factor receptor splice variants are stable markers of oncogenic transforming growth factor β1 signaling in metastatic breast cancers.

Sample Metadata Fields

Specimen part

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accession-icon SRP092186
RNAseq of wild type and maternal-zygotic Nanog mutant (MZnanog) zebrafish embryos
  • organism-icon Danio rerio
  • sample-icon 9 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2500

Description

Goal of this study is differential gene expression between wild type and MZnanog mutant during early zebrafish embryogenesis Overall design: Three timepoints - 2 hours post fertilization (hpf), 4 hpf, and 6.5 hpf; two replicates of wild type at each time point, one replicate for MZnanog at each time point

Publication Title

The primary role of zebrafish <i>nanog</i> is in extra-embryonic tissue.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP129440
Smart-seq2 analysis of larval zebrafish habenula from the gng8-GFP transgenic line
  • organism-icon Danio rerio
  • sample-icon 1138 Downloadable Samples
  • Technology Badge IconNextSeq 500

Description

The identification of cell types and marker genes is critical for dissecting neural development and function, but the size and complexity of the brain has hindered the comprehensive discovery of cell types. We combined single-cell RNA-seq with anatomical brain registration to create a comprehensive map of the zebrafish habenula, a conserved forebrain hub involved in pain processing and learning. Single-cell transcriptomes of ~13000 habenular cells (>4x coverage) identified 18 neuronal types and dozens of marker genes. Registration of marker genes onto a common reference atlas created a rich resource for anatomical and functional studies and enabled the mapping of active neurons onto neuronal types following aversive stimuli. Strikingly, despite brain growth and functional maturation, cell types were retained between the larval and adult habenula. This study provides a gene expression atlas to dissect habenular development and function and offers a general framework for the comprehensive characterization of other brain regions. Overall design: gng8-GFP zebrafish heads were dissected, dissociated and FAC sorted into 96 well plates. Single cell libraries were generated in batches of 384 cells using Smart-seq2. A total of 22 gng8-GFP fish were dissected in 3 batches and 384 cells were processed from each using Smart-seq2.

Publication Title

Comprehensive Identification and Spatial Mapping of Habenular Neuronal Types Using Single-Cell RNA-Seq.

Sample Metadata Fields

Specimen part, Subject

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accession-icon SRP055996
Spatial reconstruction of single-cell gene expression
  • organism-icon Danio rerio
  • sample-icon 1138 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Spatial localization is a key determinant of cellular fate and behavior, but spatial RNA assays traditionally rely on staining for a limited number of RNA species. In contrast, single-cell RNA-seq allows for deep profiling of cellular gene expression, but established methods separate cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos, inferring a transcriptome-wide map of spatial patterning. We confirmed Seurat’s accuracy using several experimental approaches, and used it to identify a set of archetypal expression patterns and spatial markers. Additionally, Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems. Overall design: We generated single-cell RNA-seq profiles from dissociated cells from developing zebrafish embryos (late blastula stage - 50% epiboly)

Publication Title

Spatial reconstruction of single-cell gene expression data.

Sample Metadata Fields

Subject

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accession-icon SRP123526
Single-cell RNAseq (SMART-seq2) of wild-type (TLAB) and MZoep (tz57) zebrafish embryos at 50% epiboly stage
  • organism-icon Danio rerio
  • sample-icon 415 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

SMART-seq2 was performed on single cells isolated from visually staged zebrafish embryos. Overall design: Samples were all sequenced in one batch. Some were generated with a 5'' UMI-tagged method, and others are full-length SMART-seq2.

Publication Title

Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis.

Sample Metadata Fields

Subject

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accession-icon SRP124289
Drop-seq analysis of wild-type (TLAB) zebrafish embryos from high to 6-somite stage (12 timepoints)
  • organism-icon Danio rerio
  • sample-icon 28 Downloadable Samples
  • Technology Badge IconNextSeq 500

Description

Wild-type zebrafish embryos were mechanically dissociated and profiled using Drop-seq Overall design: Drop-seq was performed on 28 groups of 20-40 visually staged, mechanically dissociated embryos. Samples were combined and sequenced in batches DS2-DS5.

Publication Title

Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis.

Sample Metadata Fields

Subject

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accession-icon SRP009426
Comprehensive identification of long non-coding RNAs expressed during zebrafish embryogenesis [RNA_seq]
  • organism-icon Danio rerio
  • sample-icon 17 Downloadable Samples
  • Technology Badge IconIlluminaGenomeAnalyzerII, IlluminaHiSeq2000

Description

Long non-coding RNAs (lncRNAs) comprise a diverse class of transcripts that structurally resemble mRNAs but do not encode proteins. Recent genome-wide studies in human and mouse have annotated lncRNAs expressed in cell lines and adult tissues, but a systematic analysis of lncRNAs expressed during vertebrate embryogenesis has been elusive. To identify lncRNAs with potential functions in vertebrate embryogenesis, we performed a time series of RNA-Seq experiments at eight stages during early zebrafish development. We reconstructed 56,535 high-confidence transcripts in 28,912 loci, recovering the vast majority of expressed RefSeq transcripts, while identifying thousands of novel isoforms and expressed loci. We defined a stringent set of 1,133 non-coding multi-exonic transcripts expressed during embryogenesis. These include long intergenic ncRNAs (lincRNAs), intronic overlapping lncRNAs, exonic antisense overlapping lncRNAs, and precursors for small RNAs (sRNAs). Zebrafish lncRNAs share many of the characteristics of their mammalian counterparts: relatively short length, low exon number, low expression, and conservation levels comparable to introns. Subsets of lncRNAs carry chromatin signatures characteristic of genes with developmental functions. The temporal expression profile of lncRNAs revealed two novel properties: lncRNAs are expressed in narrower time windows than protein-coding genes and are specifically enriched in early-stage embryos. In addition, several lncRNAs show tissue-specific expression and distinct subcellular localization patterns. Integrative computational analyses associated individual lncRNAs with specific pathways and functions, ranging from cell cycle regulation to morphogenesis. Our study provides the first comprehensive identification of lncRNAs in a vertebrate embryo and forms the foundation for future genetic, genomic and evolutionary studies. Overall design: RNA-Seq for 8 zebrafish developmental stages, 2 lanes for each stage (3 for shield).

Publication Title

Ribosome profiling reveals resemblance between long non-coding RNAs and 5' leaders of coding RNAs.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP021915
Ribosome Profiling over a Zebrafish Developmental Timecourse
  • organism-icon Danio rerio
  • sample-icon 8 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2000

Description

To experimentally-validate the non-coding status of annotated lncRNAs, we performed ribosome profiling over a developmental timecourse that matched our previously-published (Pauli et al. 2012) developmental transcriptome. We find that many previously-annotated lncRNAs appear to be translated, but in a pattern more akin to 5'' leaders of coding genes. Overall design: Ribosome profiling over 8 stages in early zebrafish development: 2-4 cell, 256 cell, 1K cell, Dome, Shield, Bud, 28hpf and 5dpf

Publication Title

Ribosome profiling reveals resemblance between long non-coding RNAs and 5' leaders of coding RNAs.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP137889
10x analysis of wild-type (TLAB) and MZoep zebrafish embryos at 6-somite stage
  • organism-icon Danio rerio
  • sample-icon 2 Downloadable Samples
  • Technology Badge IconNextSeq 500

Description

Wild-type and MZoep zebrafish embryos were mechanically dissociated and profiled using 10x Genomics pipeline. Overall design: 10x scRNA-seq was performed on visually staged, mechanically dissociated embryos. Samples were combined and sequenced in one batch.

Publication Title

Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis.

Sample Metadata Fields

Subject

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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)

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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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