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Dobrzycki, T.

Publications and source records attributed to Dobrzycki, T..

2 recordsLinked to original sources

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

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