This service exclusively searches for literature that cites resources. Please be aware that the total number of searchable documents is limited to those containing RRIDs and does not include all open-access literature.
Single-cell mRNA sequencing (RNA-seq) methods have undergone rapid development in recent years, and transcriptome analysis of relevant cell populations at single-cell resolution has become a key research area of biomedical sciences. We here present single-cell mRNA 3-prime end sequencing (SC3-seq), a practical methodology based on PCR amplification followed by 3-prime-end enrichment for highly quantitative, parallel and cost-effective measurement of gene expression in single cells. The SC3-seq allows excellent quantitative measurement of mRNAs ranging from the 10,000-cell to 1-cell level, and accordingly, allows an accurate estimate of the transcript levels by a regression of the read counts of spike-in RNAs with defined copy numbers. The SC3-seq has clear advantages over other typical single-cell RNA-seq methodologies for the quantitative measurement of transcript levels and at a sequence depth required for the saturation of transcript detection. The SC3-seq distinguishes four distinct cell types in the peri-implantation mouse blastocysts. Furthermore, the SC3-seq reveals the heterogeneity in human-induced pluripotent stem cells (hiPSCs) cultured under on-feeder as well as feeder-free conditions, demonstrating a more homogeneous property of the feeder-free hiPSCs. We propose that SC3-seq might be used as a powerful strategy for single-cell transcriptome analysis in a broad range of investigations in biomedical sciences.
Single transcription factors (TFs) regulate multiple developmental pathways, but the underlying mechanisms remain unclear. Here, we quantitatively characterized the genome-wide occupancy profiles of BLIMP1, a key transcriptional regulator for diverse developmental processes, during the development of three germ-layer derivatives (photoreceptor precursors, embryonic intestinal epithelium and plasmablasts) and the germ cell lineage (primordial germ cells). We identified BLIMP1-binding sites shared among multiple developmental processes, and such sites were highly occupied by BLIMP1 with a stringent recognition motif and were located predominantly in promoter proximities. A subset of bindings common to all the lineages exhibited a new, strong recognition sequence, a GGGAAA repeat. Paradoxically, however, the shared/common bindings had only a slight impact on the associated gene expression. In contrast, BLIMP1 occupied more distal sites in a cell type-specific manner; despite lower occupancy and flexible sequence recognitions, such bindings contributed effectively to the repression of the associated genes. Recognition motifs of other key TFs in BLIMP1-binding sites had little impact on the expression-level changes. These findings suggest that the shared/common sites might serve as potential reservoirs of BLIMP1 that functions at the specific sites, providing the foundation for a unified understanding of the genome regulation by BLIMP1, and, possibly, TFs in general.
Welcome to the FDI Lab - SciCrunch.org Resources search. From here you can search through a compilation of resources used by FDI Lab - SciCrunch.org and see how data is organized within our community.
You are currently on the Community Resources tab looking through categories and sources that FDI Lab - SciCrunch.org has compiled. You can navigate through those categories from here or change to a different tab to execute your search through. Each tab gives a different perspective on data.
If you have an account on FDI Lab - SciCrunch.org then you can log in from here to get additional features in FDI Lab - SciCrunch.org such as Collections, Saved Searches, and managing Resources.
Here is the search term that is being executed, you can type in anything you want to search for. Some tips to help searching:
You can save any searches you perform for quick access to later from here.
We recognized your search term and included synonyms and inferred terms along side your term to help get the data you are looking for.
If you are logged into FDI Lab - SciCrunch.org you can add data records to your collections to create custom spreadsheets across multiple sources of data.
Here are the facets that you can filter your papers by.
From here we'll present any options for the literature, such as exporting your current results.
If you have any further questions please check out our FAQs Page to ask questions and see our tutorials. Click this button to view this tutorial again.
Year:
Count: