Liam tackles complex multimodal single-cell data integration challenges.

Multi-omics characterization of single cells holds outstanding potential for profiling the dynamics and relations of gene regulatory states of thousands of cells. How to integrate multimodal data is an open problem, especially when aiming to combine data from multiple sources or conditions containing both biological and technical variation. We introduce liam, a flexible model for the simultaneous horizontal and vertical integration of paired single-cell multimodal data and mosaic integration of paired with unimodal data. Liam learns a joint low-dimensional representation of the measured modalities, which proves beneficial when the information content or quality of the modalities differ. Its integration accounts for complex batch effects using a tunable combination of conditional and adversarial training, which can be optimized using replicate information while retaining selected biological variation. We demonstrate liam's superior performance on multiple paired multimodal data types, including Multiome and CITE-seq data, and in mosaic integration scenarios. Our detailed benchmarking experiments illustrate the complexities and challenges remaining for integration and the meaningful assessment of its success.

Intricacies of single-cell multi-omics data integration.

A wealth of single-cell protocols makes it possible to characterize different molecular layers at unprecedented resolution. Integrating the resulting multimodal single-cell data to find cell-to-cell correspondences remains a challenge. We argue that data integration needs to happen at a meaningful biological level of abstraction and that it is necessary to consider the inherent discrepancies between modalities to strike a balance between biological discovery and noise removal. A survey of current methods reveals that a distinction between technical and biological origins of presumed unwanted variation between datasets is not yet commonly considered. The increasing availability of paired multimodal data will aid the development of improved methods by providing a ground truth on cell-to-cell matches.

Body Temperature Cycles Control Rhythmic Alternative Splicing in Mammals.

The core body temperature of all mammals oscillates with the time of the day. However, direct molecular consequences of small, physiological changes in body temperature remain largely elusive. Here we show that body temperature cycles drive rhythmic SR protein phosphorylation to control an alternative splicing (AS) program. A temperature change of 1°C is sufficient to induce a concerted splicing switch in a large group of functionally related genes, rendering this splicing-based thermometer much more sensitive than previously described temperature-sensing mechanisms. AS of two exons in the 5' UTR of the TATA-box binding protein (Tbp) highlights the general impact of this mechanism, as it results in rhythmic TBP protein levels with implications for global gene expression in vivo. Together our data establish body temperature-driven AS as a core clock-independent oscillator in mammalian peripheral clocks.

Epilepsy-causing STX1B mutations translate altered protein functions into distinct phenotypes in mouse neurons.

Syntaxin 1B (STX1B) is a core component of the N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complex that is critical for the exocytosis of synaptic vesicles in the presynapse. SNARE-mediated vesicle fusion is assisted by Munc18-1, which recruits STX1B in the auto-inhibited conformation, while Munc13 catalyses the fast and efficient pairing of helices during SNARE complex formation. Mutations within the STX1B gene are associated with epilepsy. Here we analysed three STX1B mutations by biochemical and electrophysiological means. These three paradigmatic mutations cause epilepsy syndromes of different severity, from benign fever-associated seizures in childhood to severe epileptic encephalopathies. An insertion/deletion (K45/RMCIE, L46M) mutation (STX1BInDel), causing mild epilepsy and located in the early helical Habc domain, leads to an unfolded protein unable to sustain neurotransmission. STX1BG226R, causing epileptic encephalopathies, strongly compromises the interaction with Munc18-1 and reduces expression of both proteins, the size of the readily releasable pool of vesicles, and Ca2+-triggered neurotransmitter release when expressed in STX1-null neurons. The mutation STX1BV216E, also causing epileptic encephalopathies, only slightly diminishes Munc18-1 and Munc13 interactions, but leads to enhanced fusogenicity and increased vesicular release probability, also in STX1-null neurons. Even though the synaptic output remained unchanged in excitatory hippocampal STX1B+/- neurons exogenously expressing STX1B mutants, the manifestation of clear and distinct molecular disease mechanisms by these mutants suggest that certain forms of epilepsies can be conceptualized by assigning mutations to structurally sensitive regions of the STX1B-Munc18-1 interface, translating into distinct neurophysiological phenotypes.

Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000 exomes.

Here we present an exome-wide rare genetic variant association study for 30 blood biomarkers in 191,971 individuals in the UK Biobank. We compare gene-based association tests for separate functional variant categories to increase interpretability and identify 193 significant gene-biomarker associations. Genes associated with biomarkers were ~ 4.5-fold enriched for conferring Mendelian disorders. In addition to performing weighted gene-based variant collapsing tests, we design and apply variant-category-specific kernel-based tests that integrate quantitative functional variant effect predictions for missense variants, splicing and the binding of RNA-binding proteins. For these tests, we present a computationally efficient combination of the likelihood-ratio and score tests that found 36% more associations than the score test alone while also controlling the type-1 error. Kernel-based tests identified 13% more associations than their gene-based collapsing counterparts and had advantages in the presence of gain of function missense variants. We introduce local collapsing by amino acid position for missense variants and use it to interpret associations and identify potential novel gain of function variants in PIEZO1. Our results show the benefits of investigating different functional mechanisms when performing rare-variant association tests, and demonstrate pervasive rare-variant contribution to biomarker variability.