UNC93B1 variants underlie TLR7-dependent autoimmunity.

UNC93B1 is critical for trafficking and function of nucleic acid-sensing Toll-like receptors (TLRs) TLR3, TLR7, TLR8, and TLR9, which are essential for antiviral immunity. Overactive TLR7 signaling induced by recognition of self-nucleic acids has been implicated in systemic lupus erythematosus (SLE). Here, we report UNC93B1 variants (E92G and R336L) in four patients with early-onset SLE. Patient cells or mouse macrophages carrying the UNC93B1 variants produced high amounts of TNF-α and IL-6 and upon stimulation with TLR7/TLR8 agonist, but not with TLR3 or TLR9 agonists. E92G causes UNC93B1 protein instability and reduced interaction with TLR7, leading to selective TLR7 hyperactivation with constitutive type I IFN signaling. Thus, UNC93B1 regulates TLR subtype-specific mechanisms of ligand recognition. Our findings establish a pivotal role for UNC93B1 in TLR7-dependent autoimmunity and highlight the therapeutic potential of targeting TLR7 in SLE.

Emergence of division of labor in tissues through cell interactions and spatial cues.

Most cell types in multicellular organisms can perform multiple functions. However, not all functions can be optimally performed simultaneously by the same cells. Functions incompatible at the level of individual cells can be performed at the cell population level, where cells divide labor and specialize in different functions. Division of labor can arise due to instruction by tissue environment or through self-organization. Here, we develop a computational framework to investigate the contribution of these mechanisms to division of labor within a cell-type population. By optimizing collective cellular task performance under trade-offs, we find that distinguishable expression patterns can emerge from cell-cell interactions versus instructive signals. We propose a method to construct ligand-receptor networks between specialist cells and use it to infer division-of-labor mechanisms from single-cell RNA sequencing (RNA-seq) and spatial transcriptomics data of stromal, epithelial, and immune cells. Our framework can be used to characterize the complexity of cell interactions within tissues.

TACCO unifies annotation transfer and decomposition of cell identities for single-cell and spatial omics.

Transferring annotations of single-cell-, spatial- and multi-omics data is often challenging owing both to technical limitations, such as low spatial resolution or high dropout fraction, and to biological variations, such as continuous spectra of cell states. Based on the concept that these data are often best described as continuous mixtures of cells or molecules, we present a computational framework for the transfer of annotations to cells and their combinations (TACCO), which consists of an optimal transport model extended with different wrappers to annotate a wide variety of data. We apply TACCO to identify cell types and states, decipher spatiomolecular tissue structure at the cell and molecular level and resolve differentiation trajectories using synthetic and biological datasets. While matching or exceeding the accuracy of specialized tools for the individual tasks, TACCO reduces the computational requirements by up to an order of magnitude and scales to larger datasets (for example, considering the runtime of annotation transfer for 1 M simulated dropout observations).