Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease. Knowledge of circulating immune cell types and states associated with SLE remains incomplete. We profiled more than 1.2 million peripheral blood mononuclear cells (162 cases, 99 controls) with multiplexed single-cell RNA sequencing (mux-seq). Cases exhibited elevated expression of type 1 interferon–stimulated genes (ISGs) in monocytes, reduction of naïve CD4 ⁺ T cells that correlated with monocyte ISG expression, and expansion of repertoire-restricted cytotoxic GZMH ⁺ CD8 ⁺ T cells. Cell type–specific expression features predicted case-control status and stratified patients into two molecular subtypes. We integrated dense genotyping data to map cell type–specific cis–expression quantitative trait loci and to link SLE-associated variants to cell type–specific expression. These results demonstrate mux-seq as a systematic approach to characterize cellular composition, identify transcriptional signatures, and annotate genetic variants associated with SLE.

Type I interferon autoantibodies in patients with COVID-19 are associated with poor interferon responses and increased LAIR1 abundance in leukocytes.

Single-cell RNA sequencing (scRNA-seq) has the potential to provide powerful, high-resolution signatures to inform disease prognosis and precision medicine. This paper takes an important first step towards this goal by developing an interpretable machine learning algorithm, CloudPred, to predict individuals’ disease phenotypes from their scRNA-seq data. Predicting phenotype from scRNA-seq is challenging for standard machine learning methods—the number of cells measured can vary by orders of magnitude across individuals and the cell populations are also highly heterogeneous. Typical analysis creates pseudo-bulk samples which are biased toward prior annotations and also lose the single cell resolution. CloudPred addresses these challenges via a novel end-to-end differentiable learning algorithm which is coupled with a biologically informed mixture of cell types model. CloudPred automatically infers the cell subpopulation that are salient for the phenotype without prior annotations. We developed a systematic simulation platform to evaluate the performance of CloudPred and several alternative methods we propose, and find that CloudPred outperforms the alternative methods across several settings. We further validated CloudPred on a real scRNA-seq dataset of 142 lupus patients and controls. CloudPred achieves AUROC of 0.98 while identifying a specific subpopulation of CD4 T cells whose presence is highly indicative of lupus. CloudPred is a powerful new framework to predict clinical phenotypes from scRNA-seq data and to identify relevant cells.

Off-target effects are well established confounders of CRISPR negative selection screens that impair the identification of essential genomic loci. In particular, non-coding regulatory elements and repetitive regions are often difficult to target with specific gRNAs, effectively precluding the unbiased screening of a large portion of the genome. To address this, we developed CRISPR Specificity Correction (CSC), a computational method that corrects for the effect of off-targeting on gRNA depletion. We benchmark CSC with data from the Cancer Dependency Map and show that it significantly improves the overall sensitivity and specificity of viability screens while preserving known essentialities, particularly for genes targeted by highly promiscuous gRNAs. We believe this tool will further enable the functional annotation of the genome as it represents a robust alternative to the traditional filtering strategy of discarding unspecific guides from the analysis. CSC is an open-source software that can be seamlessly integrated into current CRISPR analysis pipelines.

The visual world is richly adorned with texture, which can serve to delineate important elements of natural scenes. In anesthetized macaque monkeys, selectivity for the statistical features of natural texture is weak in V1, but substantial in V2, suggesting that neuronal activity in V2 might directly support texture perception. To test this, we investigated the relation between single cell activity in macaque V1 and V2 and simultaneously measured behavioral judgments of texture. We generated stimuli along a continuum between naturalistic texture and phase-randomized noise and trained two macaque monkeys to judge whether a sample texture more closely resembled one or the other extreme. Analysis of responses revealed that individual V1 and V2 neurons carried much less information about texture naturalness than behavioral reports. However, the sensitivity of V2 neurons, especially those preferring naturalistic textures, was significantly closer to that of behavior compared with V1. The firing of both V1 and V2 neurons predicted perceptual choices in response to repeated presentations of the same ambiguous stimulus in one monkey, despite low individual neural sensitivity. However, neither population predicted choice in the second monkey. We conclude that neural responses supporting texture perception likely continue to develop downstream of V2. Further, combined with neural data recorded while the same two monkeys performed an orientation discrimination task, our results demonstrate that choice-correlated neural activity in early sensory cortex is unstable across observers and tasks, untethered from neuronal sensitivity, and thus unlikely to reflect a critical aspect of the formation of perceptual decisions. Significance statement As visual signals propagate along the cortical hierarchy, they encode increasingly complex aspects of the sensory environment and likely have a more direct relationship with perceptual experience. We replicate and extend previous results from anesthetized monkeys differentiating the selectivity of neurons along the first step in cortical vision from area V1 to V2. However, our results further complicate efforts to establish neural signatures that reveal the relationship between perception and the neuronal activity of sensory populations. We find that choice-correlated activity in V1 and V2 is unstable across different observers and tasks, and also untethered from neuronal sensitivity and other features of nonsensory response modulation.

Chronic pain affects millions worldwide. Its management is complicated by diverse risk factors, symptom variability, and the lack of biomarkers. Research often explores pain through the lens of the human immune system, as immune dysregulation may contribute to pain. However, most studies analyse both sexes together despite known sex differences in immune responses, with women facing higher risks of autoimmunity and chronic pain. Pooling data and adjusting for sex could obscure sex-specific immune signatures linked to chronic pain, limiting our understanding of its origins. Using transcriptomic meta-analysis, we reprocessed several bulk RNA-sequencing studies of human circulating immune cells across multiple chronic pain conditions to examine both common and previously overlooked sex-specific transcriptomic signatures. Our meta-analysis included bulk RNA-sequencing data from circulating immune cells of 142 patients with chronic pain and 154 control subjects across six chronic pain conditions. Combined-sex analysis revealed differential expression of 19 genes in chronic pain. Stratification by sex identified 34 altered genes in women, including TCL1A, which is implicated in autoimmunity. Notably, TCL1A expression correlated with neuropathic symptom severity (P<0.05). Protein-level validation in an independent cohort confirmed these findings in women with neuropathic pain. Through transcriptomic meta-analysis of open-access data, we identified genes conserved across pain conditions and uncovered sex-specific signatures, including increased expression of TCL1A as a potential biomarker for neuropathic pain in a subset of women.