1. Lab meeting

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

3. 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?

4. 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.

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

7. 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

12. 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

13. 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: bp

14. 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?

15. QC: RNASeq data

16. 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.

17. Results: Differential expression of candidate genes

18. 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%).

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

20. 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:
©2015 by National Academy of Sciences