Research Roundup: October 8, 2021

New maps of the mouse brain, prime editing enhancements, a multi-molecular measurement method for the nucleus, and more

Susanna M. Hamilton
Credit: Susanna M. Hamilton

Welcome to the October 8, 2021 installment of Research Roundup, a recurring snapshot of recent studies published by scientists at the Broad Institute and their collaborators.

Mapping all cell types in the mouse cerebellum

Cellular diversity in the brain supports functions ranging from movement to sleep, but scientists don’t know all cell types present in the brain. Using single-nucleus RNA-sequencing Velina Kozareva, Caroline Martin, institute member Evan Macosko of the Stanley Center for Psychiatric Research, Wade Regehr (Harvard Medical School), and colleagues found a new cell type in the mouse cerebellum as well as unexpected complexity in known cell types. Their work, published in Nature, is part of a larger NIH-funded effort to characterize all cell types in the brain, and could help scientists better understand brain health and disease. Read more in a Broad news story.

Protecting prime editing guide RNAs

Prime editing has the potential to correct most known disease-causing genetic variations, but editing efficiency varies by cell type and target location in the genome. James Nelson, Peyton Randolph, core institute member and Merkin Institute for Transformative Technologies in Healthcare director David Liu, and colleagues have developed a tool to improve prime editing efficiency. By attaching knot-shaped RNA structures to one end of the prime editing guide RNA (pegRNA, which guides editing machinery to its target and encodes the edit), they protected pegRNAs from degradation, increasing prime editing's efficiency at installing or correcting a range of disease-causing mutations 3-4x in most tested cell types. Published in Nature Biotechnology, this improvement expands the range of potential therapeutic applications of prime editing.

Functional foresight

The vision of functional precision medicine for cancer is to predict a patient’s drug susceptibility by sampling the tumor, exposing the cells to drugs, and using integrative readouts to measure cellular response — yet it’s unclear if this ex vivo testing approach correlates with survival or other patient outcomes. Max Stockslager (Koch Institute), Seth Malinowski (Dana-Farber), associate members Keith Ligon and Scott Manalis of MIT and the Cancer Program, and others used single-cell mass to test a retrospective cohort of 69 neurosphere models from patients with glioblastoma. Mass changes predicted the response of patients to chemotherapy, with a predictive power comparable to the standard-of-care genetic biomarker. Read more in Cell Reports and an MIT News story.

Population panel pinpoints perilous positions

Genome-wide association studies have flagged many human leukocyte antigen (HLA) variations from diverse ancestries as possible HIV risk factors. However, the HLA genes reside in a very complex genomic region, complicating efforts to pin down true disease-causing signals. To help clear the noise, Yang Luo, institute member Soumya Raychaudhuri of the Program in Medical and Population Genetics, and colleagues built a high-resolution, multi-ancestry HLA reference panel using Japan Biological Informatics Consortium, BioBank Japan, Estonian Biobank, the 1000 Genomes, and TOPMed data. Using the panel, they eliminated some previously-reported HLA-HIV associations and revealed one new one. The panel can be broadly applied to explore HLA biology in other immune-mediated traits. Learn more in Nature Genetics and in a tweetorial by Luo.

Insights into insulin resistance

In type 2 diabetes, liver cells become resistant to insulin's glucose-lowering effects. Ben Zhou, associate member Alexander Soukas in the Metabolism Program, and colleagues led a study revealing that serum- and glucocorticoid-induced kinase 1 (SGK1) drives insulin resistance in the liver in response to a high-fat diet, by phosphorylating and inhibiting AMP-activated protein kinase. They demonstrate that SGK1 is the dominant kinase of the SGK family in regulating insulin sensitivity in the liver. Described in Cell Reports, the work suggests that targeting hepatic SGK1 could be helpful in treating type 2 diabetes.

Predicting breaking hearts

Most models for assessing an individual's cardiovascular disease risk incorporate only a small number of factors. Additional factors might improve models' predictive power, if we knew which factors to include. In Patterns, Saaket Agrawal, Marcus Klarqvist, associate member Amit Khera of the Cardiovascular Disease Initiative, and colleagues (including members of the Machine Learning for Health, or ML4H, initiative) unveil ML4HEN-COX, a machine learning model built to nominate additional risk factors and improve coronary artery disease predictions. Using UK Biobank data, ML4HEN-COX selected 51 predictive factors — including polygenic score, waist measurements, socioeconomic factors, and several blood parameters — and outperformed existing tools at predicting impending cardiovascular events. Learn more in Khera's tweetorial.

The role of SMARCA4 in lung cancer

The SWI/SNF complex is commonly mutated in lung adenocarcinoma, and uses the SMARCA4 enzyme to help it remodel chromatin. In Cancer Discovery, Cancer Program associate member Tyler Jacks (MIT) and collaborators showed that mutations in or the absence of Smarca4 plays a role in lung tumor initiation and progression. In certain cell types, loss of Smarca4 increased the malignancy of mouse- and patient-derived tumors. The team profiled chromatin accessibility and found that lung lineage transcription factors were less available with Smarca4 loss, leading to a dedifferentiated metastatic cell state.

inCITE-seq to stir up new molecular discoveries

A wide range of proteins in the nucleus participate in gene regulation. Measuring these nuclear proteins gives an idea of related gene expression patterns, but in tissues (especially frozen patient samples), identifying their targets is challenging. Hattie Chung, core institute member (on leave) Aviv Regev, Emma Magee, core institute member Fei Chen, Christopher Parkhurst and David Artis (Weill Cornell), and colleagues, developed intranuclear cellular indexing of transcriptomes and epitopes (inCITE-seq). This method measures protein and RNA levels in parallel across thousands of nuclei, thus providing an improved understanding of the relationship between regulatory proteins and their target genes across cell types in tissues. The authors explain in Nature Methods how inCITE-seq can help decipher complex phenotypes and regulatory mechanisms.

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