Graphical AbstractHighlights d SpliceAI, a 32-layer deep neural network, predicts splicing from a pre-mRNA sequence d 75% of predicted cryptic splice variants validate on RNA-seq d Cryptic splicing may yield 10% of pathogenic variants in neurodevelopmental disorders d Cryptic splice variants frequently give rise to alternative splicing A deep neural network precisely models mRNA splicing from a genomic sequence and accurately predicts noncoding cryptic splice mutations in patients with rare genetic diseases. SUMMARYThe splicing of pre-mRNAs into mature transcripts is remarkable for its precision, but the mechanisms by which the cellular machinery achieves such specificity are incompletely understood. Here, we describe a deep neural network that accurately predicts splice junctions from an arbitrary pre-mRNA transcript sequence, enabling precise prediction of noncoding genetic variants that cause cryptic splicing. Synonymous and intronic mutations with predicted splice-altering consequence validate at a high rate on RNA-seq and are strongly deleterious in the human population. De novo mutations with predicted splice-altering consequence are significantly enriched in patients with autism and intellectual disability compared to healthy controls and validate against RNA-seq in 21 out of 28 of these patients. We estimate that 9%-11% of pathogenic mutations in patients with rare genetic disorders are caused by this previously underappreciated class of disease variation.(legend continued on next page) (F) Relationship between exon-intron length and the strength of the adjoining splice sites, as predicted by SpliceAI-80 nt (local motif score) and SpliceAI-10k. The genome-wide distributions of exon length (yellow) and intron length (pink) are shown in the background. The x axis is in log-scale. (G) A pair of splice acceptor and donor motifs, placed 150 nt apart, are walked along the HMGCR gene. Shown are, at each position, K562 nucleosome signal and the likelihood of the pair forming an exon at that position, as predicted by SpliceAI-10k. The genome-wide Spearman correlation between the two tracks is shown. (H) Average K562 and GM12878 nucleosome signal near private mutations that are predicted by the SpliceAI-10k model to create novel exons in the GTEx cohort.

Sample multiplexing facilitates scRNA-seq by reducing costs and artifacts such as cell doublets. However, universal and scalable sample barcoding strategies have not been described. We therefore developed MULTI-seq: multiplexing using lipid-tagged indices for single-cell and single-nucleus RNA sequencing. MULTI-seq reagents can barcode any cell type or nucleus from any species with an accessible plasma membrane. The method involves minimal sample *

SUMMARY The fungus Cryptococcus neoformans is a leading cause of mortality and morbidity among HIV-infected individuals. We utilized the completed genome sequence and optimized methods for homologous DNA replacement using high-velocity particle bombardment to engineer 1,201 gene knockout mutants. We screened this resource in vivo for proliferation in murine lung tissue and in vitro for three well-recognized virulence attributes — polysaccharide capsule formation, melanization, and growth at body temperature. We identified dozens of previously uncharacterized genes that affect these known attributes as well as 40 infectivity mutants without obvious defects in these traits. The latter mutants affect predicted regulatory factors, secreted proteins, and immune-related factors, and represent powerful tools for elucidating novel virulence mechanisms. In particular, we describe a GATA family transcription factor that inhibits phagocytosis by murine macrophages independently of the capsule, indicating a previously unknown mechanism of innate immune modulation.

Summary Responses to anti-PD-1 immunotherapy occur but are infrequent in bladder cancer. The specific T cells that mediate tumor rejection are unknown. T cells from human bladder tumors and non-malignant tissue were assessed with single-cell RNA and paired T cell receptor (TCR) sequencing of 30,604 T cells from 7 patients. We find that the states and repertoires of CD8 + T cells are not distinct in tumors compared with non-malignant tissues. In contrast, single-cell analysis of CD4 + T cells demonstrates several tumor-specific states, including multiple distinct states of regulatory T cells. Surprisingly, we also find multiple cytotoxic CD4 + T cell states that are clonally expanded. These CD4 + T cells can kill autologous tumors in an MHC class II-dependent fashion and are suppressed by regulatory T cells. Further, a gene signature of cytotoxic CD4 + T cells in tumors predicts a clinical response in 244 metastatic bladder cancer patients treated with anti-PD-L1.

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