Biology subjects

Dobrzycki, T.

Publications and source records attributed to Dobrzycki, T..

4 recordsLinked to original sources

bioRxiv2019

Single cell transcriptome analysis reveals an essential role for Gata2b in hematopoietic lineage decisions in zebrafish

Hematopoietic stem cells (HSCs) are tightly controlled to keep a balance between myeloid and lymphoid cell differentiation. Gata2 is a pivotal hematopoietic transcription factor required for HSC generation and maintenance. We generated a zebrafish mutant for the mammalian Gata2 orthologue, gata2b. We found that in adult zebrafish, gata2b is required for both neutrophilic- and monocytic lineage differentiation. Single cell transcriptome analysis revealed that the myeloid defect present in Gata2b deficient zebrafish arise in the most immature hematopoietic stem and progenitor cell (HSPC) compartment and that this population is instead committed towards the lymphoid and erythroid lineage. Taken together, we find that Gata2b is vital for the fate choice between the myeloid and lymphoid lineages.

immunology↗

bioRxiv2019

A zebrafish model for MonoMAC syndrome identifies an earlier role for gata2 in haemogenic endothelium programming and generation of haematopoietic stem cells

Haematopoietic stem and progenitor cells (HSPCs) maintain the vertebrate blood system throughout life and their emergence from haemogenic endothelium (HE) is regulated by transcription factors such as Gata2. Here we deleted a conserved enhancer (i4 enhancer) driving pan-endothelial expression of gata2a and showed that Gata2a is required for HE programming by regulating expression of runx1 and of the second zebrafish Gata2 orthologue, gata2b. By 5 days, homozygous gata2a{Delta}i4/{Delta}i4 larvae showed normal numbers of HSPCs, a recovery mediated by Notch signalling driving gata2b and runx1 expression in HE. However, gata2a{Delta}i4/{Delta}i4 adults showed oedema, susceptibility to infections and marrow hypo-cellularity, consistent with bone marrow failure found in GATA2 deficiency syndromes. Thus, gata2a expression driven by the i4 enhancer is required for HE programming in embryos and maintenance of steady-state haematopoietic stem cell output in the adult. These enhancer mutants are a new paradigm to explore the pathophysiology of GATA2-related deficiencies in vivo.

developmental biology↗

bioRxiv2017

An optimized pipeline for parallel image-based quantification of gene expression and genotyping after in situ hybridization

Advances in genome engineering have resulted in the generation of numerous zebrafish mutant lines. A commonly used method to assess gene expression in the mutants is in situ hybridization (ISH). Because the embryos can be distinguished by genotype after ISH, comparing gene expression between wild type and mutant siblings can be done blinded and in parallel. Such experimental design reduces the technical variation between samples and minimises the risk of bias. This approach, however, requires an efficient method of genomic DNA extraction from post-ISH fixed zebrafish samples to ascribe phenotype to genotype. Here we describe a method to obtain PCR-quality DNA from 95-100% of zebrafish embryos, suitable for genotyping after ISH. In addition, we provide an image analysis protocol for quantifying gene expression of ISH-probed embryos, adaptable for the analysis of different expression patterns. Finally, we show that intensity-based image analysis enables accurate representation of the variability of gene expression detected by ISH and that it can complement quantitative methods like qRT-PCR. By combining genotyping after ISH and computer-based image analysis, we have established a high-confidence, unbiased methodology to assign gene expression levels to specific genotypes, and applied it to the analysis of molecular phenotypes of newly generated lmo4a mutants.\n\nSUMMARY STATEMENTOur optimized protocol to genotype zebrafish mutant embryos after in situ hybridization and digitally quantify the in situ signal will help to standardize existing experimental designs and methods of analysis.

developmental biology↗

bioRxiv2017

Brain Development: Machine Learning Analysis Of Individual Stem Cells In Live 3D Tissue

A major challenge in cell and developmental biology is the automated identification and quantitation of cells in complex multilayered tissues. We developed CytoCensus: an easily deployed implementation of supervised machine learning that extends convenient 2D \"point- and-click\" user training to 3D detection of cells in challenging datasets with ill-defined cell boundaries. In tests on these datasets, CytoCensus outperforms other freely available image analysis software in accuracy and speed of cell detection. We used CytoCensus to count stem cells and their progeny, and to quantify individual cell divisions from time-lapse movies of explanted Drosophila larval brains, comparing wild-type and mutant phenotypes. We further illustrate the general utility and future potential of CytoCensus by analysing the 3D organisation of multiple cell classes in Zebrafish retinal organoids and cell distributions in mouse embryos. CytoCensus opens the possibility of straightforward and robust automated analysis of developmental phenotypes in complex tissues.\n\nSummaryHailstone et al. develop CytoCensus, a \"point-and-click\" supervised machine-learning image analysis software to quantitatively identify defined cell classes and divisions from large multidimensional data sets of complex tissues. They demonstrate its utility in analysing challenging developmental phenotypes in living explanted Drosophila larval brains, mammalian embryos and zebrafish organoids. They further show, in comparative tests, a significant improvement in performance over existing easy-to-use image analysis software.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=198 HEIGHT=200 SRC=\"FIGDIR/small/137406v4_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (93K):\norg.highwire.dtl.DTLVardef@68912corg.highwire.dtl.DTLVardef@11300bdorg.highwire.dtl.DTLVardef@95810corg.highwire.dtl.DTLVardef@14b77a6_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LICytoCensus: machine learning quantitation of cell types in complex 3D tissues\nC_LIO_LISingle cell analysis of division rates from movies of living Drosophila brains in 3D\nC_LIO_LIDiverse applications in the analysis of developing vertebrate tissues and organoids\nC_LIO_LIOutperforms other image analysis software on challenging, low SNR datasets tested\nC_LI

developmental biology↗