Applying genomic meta-analyses to understand disease

Wanted: More Data, the Dirtier the Better

by Esther Landhuis, Quanta Magazine 

“To distill a clear message from growing piles of unruly genomics data, researchers often turn to meta-analysis — a tried-and-true statistical procedure for combining data from multiple studies. But the studies that a meta-analysis might mine for answers can diverge endlessly. Some enroll only men, others only children. Some are done in one country, others across a region like Europe. Some focus on milder forms of a disease, others on more advanced cases. Even if statistical methods can compensate for these kinds of variations, studies rarely use the same protocols and instruments to collect the data, or the same software to analyze it. Researchers performing meta-analyses go to untold lengths trying to clean up the hodgepodge of data to control for these confounding factors.”

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Linking development and disease

Putting developmental diseases on the map

Most people use a map to understand the physical world around them. Now, genetic researchers have a map of their own to understand how developmental diseases work at the genetic level.  

In a recent study, UW graduate student Junyue Cao and Dr. Malte Spielmann from the Max Planck Institute profiled approximately 2 million cells from 61 mice embryos between 9 and 14 days old, resulting in a digital representation of how each cell type develops and its gene expression changes.

When a gene is between 9 and 14 days old, most cells that underlie major developmental diseases can be studied, according to Cao. With further application, Cao believes his study can be used as a reference to help other researchers understand how genetic diseases like autism, breast cancer, and parkinson’s disease develop in humans.

“If we can use this to comprehensively categorize the different cell states and their composition in disease or [the] aging process, then potentially, we can fully understand how they are generated in development and why there are different diseases and aging,” Cao said.

Cao and his team collected the largest single-cell dataset, Mouse Organogenesis Cell Atlas (MOCA), which consists of distinctions between individual cells. This dataset has recently been published and in this publication, the team created a genetic map of organ development.

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Human disease models made in frogs with CRISPR

Modeling human point mutation diseases in Xenopus tropicalis with a modified CRISPR/Cas9 system

Abstract

Precise single-base editing in Xenopus tropicalis would greatly expand the utility of this true diploid frog for modeling human genetic diseases caused by point mutations. Here, we report the efficient conversion of C-to-T or G-to-A in X. tropicalis using the rat apolipoprotein B mRNA editing enzyme catalytic subunit 1–XTEN–clustered regularly interspaced short palindromic repeat–associated protein 9 (Cas9) nickase–uracil DNA glycosylase inhibitor–nuclear localization sequence base editor [base editor 3 (BE3)]. Coinjection of guide RNA and the Cas9 mutant complex mRNA into 1-cell stage X. tropicalis embryos caused precise C-to-T or G-to-A substitution in 14 out of 19 tested sites with efficiencies of 5–75%, which allowed for easy establishment of stable lines. Targeting the conserved T-box 5 R237 and Tyr C28 residues in X. tropicalis with the BE3 system mimicked human Holt-Oram syndrome and oculocutaneous albinism type 1A, respectively. Our data indicate that BE3 is an easy and efficient tool for precise base editing in X. tropicalis.—Shi, Z., Xin, H., Tian, D., Lian, J., Wang, J., Liu, G., Ran, R., Shi, S., Zhang, Z., Shi, Y., Deng, Y., Hou, C., Chen, Y. Modeling human point mutation diseases in Xenopus tropicalis with a modified CRISPR/Cas9 system.

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