Lab meeting 3.11.2016

Inner ear organoid “SFEBq” v Inner ear organoid “SFEBq” Wnt Shh BMP4 TGFβ Day 6 AP2 FGF2 BMP4 100 um

Isolated day 12 vesicles E10.5 OV How well do ES-derived vesicles model a mouse otic vesicle? Can we minimize differences by supplying growth factors or small molecules to regulate transcriptional targets? Can we then make better progenitors and/or set up more efficient maturation?

Retina organoid SFEBq E10.5 eye Chx10 Mitf Day 10 aggregate Chx10 Mitf Rationale: FGF and Wnt signaling are known to be involved in differentiation of NR and RPE. However, transcriptional targets of these signaling pathways during NR and RPE differentiation are not well characterized.

Day 15 endpoint is 5 days away from initial application of CHIR or FGF2. NR-like RPE-like

1) What genes change expression levels after stimulation of competent Day 10 Rx+ tissue with Wnt or Fgf? 2) What genes change expression during maturation? 3) How are transcriptome profiles of RPE- and NR-like tissues different? NR-like RPE-like

Purpose Tool Output Total RNA QC Bioanalyzer RIN cDNA library QC RNASeq data QC FastQC (v0.10.1) Per base and per sequence quality scores, sequence content, GC content, N content, sequence length distribution, duplicates, overrepresented sequences Tophat (v2.0.8b) Alignment to mm10 Quantification of expression Cuffdiff # fragments per individual gene edgeR Normalized expression values per gene per library (CPM) and identification of differentially regulated genes Downstream analysis PCA (prcomp in R) Variability among and within samples Heatmap (pheatmap in R) Expression patterns (based on CPM) 23,246 genes Results expressed in CPM

QC: Total RNA and cDNA library quality 28s Check ratio of 28s to 18s Check RINs 18s Library prep: polyA enrichment  fragmentation  RT and second-strand cDNA synthesis  and end repair and adaptor ligation. Fragment sizes: 324-354 bp

QC: Total RNA and cDNA library quality UM core recommends RINs >7, although specialized kits can handle lower RINs. Also want the RINs to generally be similar between samples. Library amplification – is this standard? PolyA selection?

QC: RNASeq data

QC: Variability in transcriptomes within and among groups # of sequence reads at a nucleotide position in the reference genome at a 100bp resolution. Count of reads at each position then normalized to per million reads of respective library sizes. Data converted to Circos plots.

Results: Differential expression of candidate genes

Main points In-depth methods for RNASeq study on organoid tissue Example of how to analyze and present RNASeq data Important parameters 3 replicates RINs > 7 100bp, paired-end Reference genome: mm10 The study was performed in order to learn more about transcriptional targets of pathways already known to be important in development: Fgf and Wnt. Higher percentage Matrigel (4%) results in a continuous Rx::GFP+ epithelium instead of sprouting optic cups reported previously (2%).

Generating a kidney organoid equivalent to the human fetal kidney in vitro. M Takasato et al. Nature 000, 1-5 (2015) doi:10.1038/nature15695

Cerebral organoids recapitulate gene expression programs underlying cerebral cortex cell biology. Cerebral organoids recapitulate gene expression programs underlying cerebral cortex cell biology. (A) APs express genes involved in ECM production and sensing. The heat map shows expression for each ECM gene for organoid and fetal tissue cells in the order of the monocle lineage. For each gene, the correlation between organoid and fetal tissue lineages is plotted to the right of the organoid heat map. Note that the organoid dataset has proportionally more progenitors than the fetal tissue dataset, whereas the fetal tissue has more neurons. (B–D) A similar comparison as in A is shown for genes involved in aRG delamination (B), Delta/Notch signaling (C), and neurite outgrowth (D). J. Gray Camp et al. PNAS 2015;112:15672-15677 ©2015 by National Academy of Sciences