18 research outputs found

    T-RHEX-RNAseq – a tagmentation-based, rRNA blocked, random hexamer primed RNAseq method for generating stranded RNAseq libraries directly from very low numbers of lysed cells

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    Abstract Background RNA sequencing has become the mainstay for studies of gene expression. Still, analysis of rare cells with random hexamer priming – to allow analysis of a broader range of transcripts – remains challenging. Results We here describe a tagmentation-based, rRNA blocked, random hexamer primed RNAseq approach (T-RHEX-RNAseq) for generating stranded RNAseq libraries from very low numbers of FACS sorted cells without RNA purification steps. Conclusion T-RHEX-RNAseq provides an easy-to-use, time efficient and automation compatible method for generating stranded RNAseq libraries from rare cells

    Additional file 1 of T-RHEX-RNAseq – a tagmentation-based, rRNA blocked, random hexamer primed RNAseq method for generating stranded RNAseq libraries directly from very low numbers of lysed cells

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    Additional file 1: Figure S1. Schematic overview of the T-RHEX-RNAseq protocol outlining adapters, adapter introduction and primers used to amplify the library. In brief, double stranded cDNA reverse transcription with dUTP incorporated during the second strand synthesis is subjected to tagmentation with Tn5 loaded with i5 adapters. The i7 adapters are introduced by annealing an i7 oligo to the covalently attached part of the i5 adapter. Subsequently, gap fill in combination with ligation is used to covalently attach the i7 adapter. As Phusion is unable to utilize the dUTP containing strand as a template, stranded libraries are then generated by amplification using Pr2 in combination with the i5 completion primer. Figure S2. Strand-specificity of Tn-RNAseq and Directional Tn-RNAseq libraries. (A). Percentage of reads in exons: localized in a matched or mismatched orientation to transcript; or alternatively being localized in regions with overlapping antiparallel transcripts (undetermined). The data from Gertz et al., [1] was downloaded and processed using nf-core and strand-specificity evaluated using RSeq QC. Figure S3. Tracks and duplication rates of T-RHEX-RNAseq libraries from primary hematopoietic stem- and progenitor cells. (A) Tracks showing plus and minus strand reads in the Neat1, Kit, Hspd1 and Hspe1 genomic regions in primary mouse hematopoietic stem cells (HSCs) and lymphoid primed multipotent progenitors (LMPPs). Arrows below the gene names indicate the 5’-3’ direction of the transcript. RNAseq libraries were prepared directly from the indicated numbers of cells lysed in Single cell lysis solution (SCLS). The use of rRNA blocking reagents and dilution of the blocking reagent is indicated in parenthesis. (B) Reoccurrence (duplication rates) of reads in the indicated libraries. Figure S4. T-RHEX-RNAseq provides highly reproducible data. Spearman correlation between rlog of gene expression in samples generated from the indicated population. The use of rRNA blocking reagents and dilution of the blocking reagent is indicated in parenthesis. Hematopoietic stem cell (HSC with or without CD49b expression); Multipotent progenitor (MPP with no or low CD150 expression), lymphoid primed multipotent progenitors (LMPPs); granulocyte/monocyte progenitors (GMP); and antigen specific CD4 T cells (T, from wild-type or Bhlhe40 knockout mice). Data is from proof-of-principle experiments (HSC and LMPP; 250 and 500 cells respectively) or the subsequently generated T-RHEX-RNAseq data from antigen specific CD4 T cells (1000 cells) [1] and hematopoietic stem/progenitor cells (HSPCs; 250-500 cells) [2]. Table S1. Sample metrics and QC. Supplemental working protocol

    CD49b identifies functionally and epigenetically distinct subsets of lineage-biased hematopoietic stem cells

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    Hematopoiesis is maintained by functionally diverse lineage-biased hematopoietic stem cells (HSCs). The functional significance of HSC heterogeneity and the regulatory mechanisms underlying lineage bias are not well understood. However, absolute purification of HSC subtypes with a pre-determined behavior remains challenging, highlighting the importance of continued efforts toward prospective isolation of homogeneous HSC subsets. In this study, we demonstrate that CD49b subdivides the most primitive HSC compartment into functionally distinct subtypes: CD49b(-) HSCs are highly enriched for myeloid-biased and the most durable cells, while CD49b(+) HSCs are enriched for multipotent cells with lymphoid bias and reduced self-renewal ability. We further demonstrate considerable transcriptional similarities between CD49b(-) and CD49b(+) HSCs but distinct differences in chromatin accessibility. Our studies highlight the diversity of HSC functional behaviors and provide insights into the molecular regulation of HSC heterogeneity through transcriptional and epigenetic mechanisms

    FOXO Dictates Initiation of B Cell Development and Myeloid Restriction in Common Lymphoid Progenitors

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    The development of B cells relies on an intricate network of transcription factors critical for developmental progression and lineage commitment. In the B cell developmental trajectory, a temporal switch from predominant Foxo3 to Foxo1 expression occurs at the CLP stage. Utilizing VAV-iCre mediated conditional deletion, we found that the loss of FOXO3 impaired B cell development from LMPP down to B cell precursors, while the loss of FOXO1 impaired B cell commitment and resulted in a complete developmental block at the CD25 negative proB cell stage. Strikingly, the combined loss of FOXO1 and FOXO3 resulted in the failure to restrict the myeloid potential of CLPs and the complete loss of the B cell lineage. This is underpinned by the failure to enforce the early B-lineage gene regulatory circuitry upon a predominantly pre-established open chromatin landscape. Altogether, this demonstrates that FOXO3 and FOXO1 cooperatively govern early lineage restriction and initiation of B-lineage commitment in CLPs

    What journals and societies can do

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    Broadening the model of science - Recognizing different types of contributions

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    Resources for Society for the Improvement of Psychological Science (2016) Meeting - Diversity & Alternative Contribution

    Improving research in individual labs

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    Making replications mainstream

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    Developing norms for data sharing

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