Paracrine brassinosteroid signaling at the stem cell niche controls cellular regeneration

Stem cell regeneration is crucial for both cell turnover and tissue healing in multicellular organisms. In Arabidopsis roots, a reduced group of cells known as the quiescent center (QC) acts as a cell reservoir for surrounding stem cells during both normal growth and in response to external damage. Although cells of the QC have a very low mitotic activity, plant hormones such as brassinosteroids (BR) can promote QC divisions. Here, we used a tissue-specific strategy to investigate the spatial signaling requirements of BR-mediated QC divisions. We generated stem cell niche-specific receptor knockout lines by placing an artificial microRNA against BRI1 (BRASSINOSTEROID RESPONSE INSENSITIVE 1) under the control of the QC-specific promoter WOX5. Additionally, QC-specific knock-in lines for BRI1 and its downstream transcription factor BES1 (BRI1-EMS-SUPPRESOR1) were also created using the WOX5 promoter. By analyzing the roots of these lines, we uncover that BES1-mediated signaling cell-autonomously promotes QC divisions, that BRI1 is essential for sensing nearby inputs and triggering QC divisions, and that DNA damage promotes BR-dependent paracrine signaling in the stem cell niche as a prerequisite to stem cell replenishment

Robust temporal map of human in vitro myelopoiesis using single-cell genomics

We thank the Cellular Genetics wet lab support team, Cellular Genetics IT team, Sanger Sequencing operations and Sanger Cytometry Core facility for their essential help. We thank the Gene Editing team for providing iPSC knock-out lines. We would also like to thank Ruxandra Tesloianu and Luz Garcia-Alonso for their help setting up the scATAC-seq computational analysis. We thank Jana Eliasova for her help with figure design and Christina Usher and Aidan Maartens for their edits in the text. This work was mainly funded by the Open Targets consortium (OTAR026 and OTAR032 project) and the Wellcome Sanger core funding (WT206194) with additional support from Open Targets projects OTAR037, OTAR2065, OTAR2071. The authors are grateful to the funders for their support and additional care given to their members during the COVID-19 pandemic. D.A.-E. thanks CERCA Programme/Generalitat de Catalunya and the Josep Carreras Foundation for institutional support. This study makes use of cell lines and data generated by the HiPSci Consortium, funded by The Wellcome Trust and the MRC (Medical Research Council). For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. This publication is part of the Human Cell Atlas- www.humancellatlas.org/publications.We thank the Cellular Genetics wet lab support team, Cellular Genetics IT team, Sanger Sequencing operations and Sanger Cytometry Core facility for their essential help. We thank the Gene Editing team for providing iPSC knock-out lines. We would also like to thank Ruxandra Tesloianu and Luz Garcia-Alonso for their help setting up the scATAC-seq computational analysis. We thank Jana Eliasova for her help with figure design and Christina Usher and Aidan Maartens for their edits in the text. This work was mainly funded by the Open Targets consortium (OTAR026 and OTAR032 project) and the Wellcome Sanger core funding (WT206194) with additional support from Open Targets projects OTAR037, OTAR2065, OTAR2071. The authors are grateful to the funders for their support and additional care given to their members during the COVID-19 pandemic. D.A.-E. thanks CERCA Programme/Generalitat de Catalunya and the Josep Carreras Foundation for institutional support. This study makes use of cell lines and data generated by the HiPSci Consortium, funded by The Wellcome Trust and the MRC (Medical Research Council). For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. This publication is part of the Human Cell Atlas-www.humancellatlas.org/publications.Myeloid cells are central to homeostasis and immunity. Characterising in vitro myelopoiesis protocols is imperative for their use in research, immunotherapies, and understanding human myelopoiesis. Here, we generate a >470K cells molecular map of human induced pluripotent stem cells (iPSC) differentiation into macrophages. Integration with in vivo single-cell atlases shows in vitro differentiation recapitulates features of yolk sac hematopoiesis, before definitive hematopoietic stem cells (HSC) emerge. The diversity of myeloid cells generated, including mast cells and monocytes, suggests that HSC-independent hematopoiesis can produce multiple myeloid lineages. We uncover poorly described myeloid progenitors and conservation between in vivo and in vitro regulatory programs. Additionally, we develop a protocol to produce iPSC-derived dendritic cells (DC) resembling cDC2. Using CRISPR/Cas9 knock-outs, we validate the effects of key transcription factors in macrophage and DC ontogeny. This roadmap of myeloid differentiation is an important resource for investigating human fetal hematopoiesis and new therapeutic opportunities

Methods for modeling brassinosteroid-mediated signaling in plant development

Mathematical modeling of biological processes is a useful tool to draw conclusions that are contained in the data, but not directly reachable, as well as to make predictions and select the most efficient follow-up experiments. Here we outline a method to model systems of a few proteins that interact transcriptionally and/or posttranscriptionally, by representing the system as Ordinary Differential Equations and to study the model dynamics and stationary states. We exemplify this method by focusing on the regulation by the brassinosteroid (BR) signaling component BRASSINOSTEROID INSENSITIVE1 ETHYL METHYL SULFONATE SUPPRESSOR1 (BES1) of BRAVO, a quiescence-regulating transcription factor expressed in the quiescent cells of Arabidopsis thaliana roots. The method to extract the stationary states and the dynamics is provided as a Mathematica code and requires basic knowledge of the Mathematica software to be executed.D.F. and M.I. acknowledge support from the Ministerio de Economía y Competitividad (Spain) and FEDER (EU) through grant FIS2015-66503-C3-3-P and from the Generalitat de Catalunya through Grup de Recerca Consolidat 2014 SGR 878. AIC-D acknowledges financial support from the Spanish Ministry of Economy and Competitiveness, through the ‘Severo Ochoa Programme for Centres of Excellence in R&D’ 2016–2019 (SEV-2015-0533). AIC-D is a recipient of a BIO2013-43873 grant from the Spanish Ministry of Economy and Competitiveness and European Research Council, ERC Consolidator Grant (ERC-2015-CoG – 683163).Peer reviewe

Whole-genome fingerprint of the DNA methylome during human B cell differentiation

We analyzed the DNA methylome of ten subpopulations spanning the entire B cell differentiation program by whole-genome bisulfite sequencing and high-density microarrays. We observed that non-CpG methylation disappeared upon B cell commitment, whereas CpG methylation changed extensively during B cell maturation, showing an accumulative pattern and affecting around 30% of all measured CpG sites. Early differentiation stages mainly displayed enhancer demethylation, which was associated with upregulation of key B cell transcription factors and affected multiple genes involved in B cell biology. Late differentiation stages, in contrast, showed extensive demethylation of heterochromatin and methylation gain at Polycomb-repressed areas, and genes with apparent functional impact in B cells were not affected. This signature, which has previously been linked to aging and cancer, was particularly widespread in mature cells with an extended lifespan. Comparing B cell neoplasms with their normal counterparts, we determined that they frequently acquire methylation changes in regions already undergoing dynamic methylation during normal B cell differentiation