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Myelination is a key biological process wherein glial cells such as oligodendrocytes wrap myelin around neuronal axons, forming an insulative sheath that accelerates signal propagation down the axon. A major obstacle to understanding myelination is the challenge of visualizing and reproducibly quantifying this inherently three-dimensional process in vitro . To this end, we previously developed artificial axons (AAs), a biocompatible platform consisting of 3D-printed hydrogel-based axon mimics designed to more closely recapitulate the micrometer-scale diameter and sub-kilopascal mechanical stiffness of biological axons. First, we present our platform for fabricating AAs with tunable axon diameter, stiffness, and inter-axonal spacing. Second, we demonstrate that increasing the Young’s modulus E or stiffness of polymer comprising the AAs increases the extent of myelin ensheathment by rat oligodendrocytes. Third, we demonstrate that the responses of oligodendrocytes to pro-myelinating compounds are also dependent on axon stiffness, which can affect compounds efficacy and the relative ranking. These results reinforce the importance of studying myelination in mechanically representative environments, and highlight the importance of considering biophysical cues when conducting drug screening studies.

The field of single-cell biology is growing rapidly, generating large amounts of data from a variety of species, disease conditions, tissues, and organs. Coordinated efforts such as CZI CELLxGENE, HuBMAP, Broad Institute Single Cell Portal, and DISCO allow researchers to access large volumes of curated datasets, including more than just scRNA-seq data. These resources have created an opportunity to build and expand the computational biology ecosystem to develop tools necessary for data reuse and for extracting novel biological insights. We highlight achievements made so far, areas where further development is needed, and specific challenges that need to be overcome.

Gilean McVean and colleagues report the results of a large-scale clinical genome sequencing project spanning a broad spectrum of disorders. They identify factors influencing successful genetic diagnosis and highlight the challenges of interpreting findings for genetically heterogeneous disorders. To assess factors influencing the success of whole-genome sequencing for mainstream clinical diagnosis, we sequenced 217 individuals from 156 independent cases or families across a broad spectrum of disorders in whom previous screening had identified no pathogenic variants. We quantified the number of candidate variants identified using different strategies for variant calling, filtering, annotation and prioritization. We found that jointly calling variants across samples, filtering against both local and external databases, deploying multiple annotation tools and using familial transmission above biological plausibility contributed to accuracy. Overall, we identified disease-causing variants in 21% of cases, with the proportion increasing to 34% (23/68) for mendelian disorders and 57% (8/14) in family trios. We also discovered 32 potentially clinically actionable variants in 18 genes unrelated to the referral disorder, although only 4 were ultimately considered reportable. Our results demonstrate the value of genome sequencing for routine clinical diagnosis but also highlight many outstanding challenges.

Genome-wide association studies (GWAS) have identified over 150,000 links between common genetic variants and human traits or complex diseases. Over 80% of these associations map to polymorphisms in non-coding DNA. Therefore, the challenge is to identify disease-causing variants, the genes they affect, and the cells in which these effects occur. We have developed a platform using ATAC-seq, DNaseI footprints, NG Capture-C and machine learning to address this challenge. Applying this approach to red blood cell traits identifies a significant proportion of known causative variants and their effector genes, which we show can be validated by direct in vivo modelling. Footnotes * Extended version including all supplemental figures.