Dataset Information

SLAM-ITseq: sequencing cell type-specific transcriptomes without cell sorting.

ABSTRACT: Cell type-specific transcriptome analysis is an essential tool for understanding biological processes in which diverse types of cells are involved. Although cell isolation methods such as fluorescence-activated cell sorting (FACS) in combination with transcriptome analysis have widely been used so far, their time-consuming and harsh procedures limit their applications. Here, we report a novel in vivo metabolic RNA sequencing method, SLAM-ITseq, which metabolically labels RNA with 4-thiouracil in a specific cell type in vivo followed by detection through an RNA-seq-based method that specifically distinguishes the thiolated uridine by base conversion. This method has successfully identified the cell type-specific transcriptome in three different tissues: endothelial cells in brain, epithelial cells in intestine and adipocytes in white adipose tissue. As this method does not require isolation of cells or RNA prior to the transcriptomic analysis, SLAM-ITseq provides an easy yet accurate snapshot of the transcriptional state in vivo.

SUBMITTER: Matsushima W

PROVIDER: S-EPMC6053661 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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SLAM-ITseq: sequencing cell type-specific transcriptomes without cell sorting.

Matsushima Wayo W Herzog Veronika A VA Neumann Tobias T Gapp Katharina K Zuber Johannes J Ameres Stefan L SL Miska Eric A EA

Development (Cambridge, England) 20180711 13

Cell type-specific transcriptome analysis is an essential tool for understanding biological processes in which diverse types of cells are involved. Although cell isolation methods such as fluorescence-activated cell sorting (FACS) in combination with transcriptome analysis have widely been used so far, their time-consuming and harsh procedures limit their applications. Here, we report a novel <i>in vivo</i> metabolic RNA sequencing method, SLAM-ITseq, which metabolically labels RNA with 4-thiour ...[more]

PMID: 29945865

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Project description:BackgroundOrgan-specific gene expression contains rich information about in vivo biological processes. This kind of information, previously gathered through microarray profiling, has been proven fruitful to the understanding of specific mutants, regulatory events, signaling, and development. With the advent of the next-generation sequencing (NGS) of RNAs, more quantitative and detailed information of gene expressions than previously available can now be collected for each organ or organ developmental stages. The combination of an object-oriented experimental design and an efficient treatment of the high volume information generated through a NGS platform may offer a powerful tool for inferring previously intractable developmental processes.ResultsWe collected transcriptomic data over a Solexa/Illumina platform on samples of Ipomoea leaf, sepal, and petals (at three developmental stages), and presented a method for analyzing transcriptomic variations within and between organs. We demonstrated that in vivo signals of transcriptomes can be retrieved de novo through the NGS techniques, proper data handling, bioinformatic tools, and the current understanding of molecular networks. We found that numbers of transcribed genes from both nuclear and chloroplast genomes decreased by the same order of leaf → sepal →petal. Petal resembled leaf in cell division patterns and abundance level of commonly expressed organelle genes. Its chloroplast transcripts constituted a subset of those in leaf. Moreover, reconstructions of multiple metabolic networks for each organ enabled inferences of substance flow, providing transcript evidence for the path of sucrose in leaf to anthocyanin synthesis in petal.ConclusionOur results attest that developmental transcriptomes are highly informative for exploring connections between morphological traits and the associated molecular networks. Significant hypotheses have been developed, including that the petal is a derived organ of leaf and that its color can be modified by fluctuations of substance flow within the associated metabolic networks among organs.

Project description:Microglial cells form a context-dependent network of brain immunoeffector cells. Despite their indispensable roles, unresolved questions exist around biomarker discovery relevant to their cellular localization, self-renewing potential, and brain developmental dynamics. To resolve the existent gap in the annotation of candidate biomarkers, we conducted a meta-analysis of brain cells using available high-throughput data sets for deciphering microglia-specific expression profiles. We have identified 3,290 significant genes specific to microglia and further selected the top 20 dysregulated genes on the basis of p-value and log2FC. To this list, we added 7 known microglia-specific markers making the candidate list comprising 27 genes for further downstream analyses. Next, we established a connectome of these potential markers with their putative protein partners, which demonstrated strong associations of upregulated genes like Dedicator of cytokinesis 2 (DOCK2) with early/mature microglial markers such as Sphingosine kinase 1 (SPHK1), CD68, and CD45. To elucidate their respective brain anatomical location, we deconvoluted the BrainSpan Atlas expression data. This analysis showed high expression of the majority of candidate genes in microglia-dense regions (Amygdala, Hippocampus, Striatum) in the postnatal brain. Furthermore, to decipher their localized expression across brain ages, we constructed a developmental dynamics map (DDM) comprising extensive gene expression profiles throughout prenatal to postnatal stages, which resulted in the discovery of novel microglia-specific gene signatures. One of the interesting readout from DDM is that all the microglia-dense regions exhibit dynamic regulation of few genes at 37 post conception week (pcw), the transition period between pre- and postnatal stages. To validate these findings and correlate them as potential biomarkers, we analyzed the expression of corresponding proteins in hESC-derived human microglia precursors. The cultured microglial precursors showed expression of Pentraxin 3 (PTX3) and SPHK1 as well as several known markers like CD68, Allograft inflammatory factor 1 (AIF1/IBA1). In summary, this study has furnished critical insights into microglia dynamics across human brain ages and cataloged potential transcriptomic fingerprints that can be further exploited for designing novel neurotherapeutics.

Dataset Information

SLAM-ITseq: sequencing cell type-specific transcriptomes without cell sorting.

Publications

SLAM-ITseq: sequencing cell type-specific transcriptomes without cell sorting.

Similar Datasets

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