Jay Gopalakrishnan on brain organoids #GRD23: The human brain is one of the most complex tissue systems with many different cell types that have highly orchestrated interactions.
Brain organoids are self-assembling 3D structures that could be used to model neurological disorders
Jay Gopalakrishnan #GRD23: Brain organoids have some of the layers seen in the brain. Have been able to make organoids with two eye-like structures (are light sensitive).
Have worked out a way to make ~1000 nearly homogeneous brain organoids per batch. "Hi-Q" brain organoids.
Jay Gopalakrishnan #GRD23: Discovered a cilium checkpoint, which is key for growth.
Microcephaly neural progenitor cells have premature differentiation, which leads to depletion of these NPCs.
Jay Gopalakrishnan #GRD23: What if NPCs override the cilium checkpoint?
Cilium disassembly and cell cycle progression become uncoupled. Can see that the NPCs vs differentiated cells look 'inside-out'. Recapitulating a rare disease phenotype.
Jay Gopalakrishnan #GRD23: How often are defects in cilia dynamics implicated in rare disorders? Quite frequently (microcephaly, Fragile X, FoxG1, Rett syndrome).
Now want to do high throughput disease modelling of rare neurogenetic conditions.
Jay Gopalakrishnan #GRD23: With these brain organoid models of rare conditions, can screen FDA approved compounds + ASOs. Hoping this can help find treatments.
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Rob Taylor #GRD23 on multi-omic approaches to dissect mitochondrial pathology.
Clinical mitochondrial diseases collectively have a prevalence of ~1 in 4000 and have functional, molecular, clinical, and prognostic heterogeneity.
Rob Taylor #GRD23: Some of this complexity comes from the fact that cells have many copies of the mitochondrial genome. Pathogenic variants can either be homoplasmic (in all copies) or heteroplasmic (in some copies).
Rob Taylor #GRD23: High throughput sequencing has helped identify pathogenic variants + new disease genes.
A few examples shown, including POLRMT, UQCRH, etc.
Kristine Bilgrav Saether #GRD23: Transposable elements jump through the genome via RNA intermediates. They make up ~50% of the genome. Multiple versions, some of which are autonomous (LINE) and some are non-autonomous (SVA).
Kristine Bilgrav Saether #GRD23: Can use methods like MELT (👍 @DrGeneUK), xTEA, etc for short reads. For long reads, look at split reads so they wrote a new one called TELLR.
Studying 1KG and SweGen samples.
Kristine Bilgrav Saether #GRD23: TELLR finds split reads and insertions, then uses DBSCAN to cluster, minmap2 --> TE calls.
Long reads also give methylation information, which is useful since TE activity is controlled via epigenetics.
Kaiyue Ma #GRD23: Mutations in alpha-dystroglycan can lead to dystroglycanopathies.
Developing SMuRF (saturation mutagenesis-reinforced functional assays) to test variants in alpha-DG glycosylation enzymes like FKRP.
Kaiyue Ma #GRD23: SMuRF scores have the expected distributions for variant type (e.g. synonymous look neutral, nonsense functional). Missense mutations in the catalytic domain tend to be more disruptive (vs those in stem). Good correlation of score with ClinVar classifications.
Kaiyue Ma #GRD23: SMuRF, EVE, and REVEL all do well in AUC analysis of computational predictors. REVEL is the best, but high correlation between SMuRF scores and REVEL. Improvements to SMuRF underway.
Gosia Borowiak #GRD23: The field figured out how to make progenitor cells in culture, but trying to get homogenous populations of human beta cells was a challenge.
Time an endocrine progenitor is formed matters in likelihood of developing into a alpha vs beta cell.
Gosia Borowiak #GRD23: Digging into the microenvironment that influence human endocrine development. Found that WNT5A is necessary and sufficient for beta cell in vitro induction.
Gosia Borowiak #GRD23: Re-emphasizes a point from yesterday: need to have a "factory" to be able to make large amounts of high quality cells if you want to do disease modelling / testing.
Figured out a way to allow serial expansion of these cells [MMP2, I believe].
Danny Miller (@danrdanny) #GRD23: Starts with his take home points
- long-read sequencing will fundamentally change clinical genetic testing
- will reduce barriers to accessing comprehensive testing
- this will happen even if the cost of generating other types of data falls to $0
.@danrdanny#GRD23: Traditional genetic workup (mircoarray -> panel -> exome) is diagnostic in <50% of cases.
Pitches that we can use long-read sequencing as a single test that could then be analyzed in different ways (SV -> repeat expansion -> genome, etc).
.@danrdanny#GRD23: Long-reads are 1kb+ in length. Read accuracy varies (90-99%) and the cost is $500-$3k.
Long-read sequencing finds 2x as many structural variants as short reads. See: cell.com/ajhg/pdfExtend…
Now on is David Liu (@davidrliu) walking through programmable nucleases. >200 patients have been treated with therapies enabled by CRISPR nucleases thus far #GRD23
.@davidrliu#GRD23: While nucleases are good for gene disruption, they aren't great for gene correction.
Developed base editing and prime editing to address this gap and have editors now that can address all single nucleotide base changes.