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Abstract We present GuideScan2 for memory-efficient, parallelizable construction of high-specificity CRISPR guide RNA (gRNA) databases and user-friendly design and analysis of individual gRNAs and gRNA libraries for targeting coding and non-coding regions in custom genomes. GuideScan2 analysis identifies widespread confounding effects of low-specificity gRNAs in published CRISPR screens and enables construction of a gRNA library that reduces off-target effects in a gene essentiality screen. GuideScan2 also enables the design and experimental validation of allele-specific gRNAs in a hybrid mouse genome. GuideScan2 will facilitate CRISPR experiments across a wide range of applications. 

Molecular virology tools are critical for basic studies of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and for developing new therapeutics. Experimental systems that do not rely on viruses capable of spread are needed for potential use in lower-containment settings. In this work, we use a yeast-based reverse genetics system to develop spike-deleted SARS-CoV-2 self-replicating RNAs. These noninfectious self-replicating RNAs, or replicons, can be trans-complemented with viral glycoproteins to generate replicon delivery particles for single-cycle delivery into a range of cell types. This SARS-CoV-2 replicon system represents a convenient and versatile platform for antiviral drug screening, neutralization assays, host factor validation, and viral variant characterization. 

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood–brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer’s disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM’s ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals’ rates of adverse cognitive change with age.