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.

Neutralizing autoantibodies against type I interferons (IFNs) have been found in some patients with critical coronavirus disease 2019 (COVID-19), the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the prevalence of these antibodies, their longitudinal dynamics across the disease severity scale, and their functional effects on circulating leukocytes remain unknown. Here, in 284 patients with COVID-19, we found type I IFN-specific autoantibodies in peripheral blood samples from 19% of patients with critical disease and 6% of patients with severe disease. We found no type I IFN autoantibodies in individuals with moderate disease. Longitudinal profiling of over 600,000 peripheral blood mononuclear cells using multiplexed single-cell epitope and transcriptome sequencing from 54 patients with COVID-19 and 26 non-COVID-19 controls revealed a lack of type I IFN-stimulated gene (ISG-I) responses in myeloid cells from patients with critical disease. This was especially evident in dendritic cell populations isolated from patients with critical disease producing type I IFN-specific autoantibodies. Moreover, we found elevated expression of the inhibitory receptor leukocyte-associated immunoglobulin-like receptor 1 (LAIR1) on the surface of monocytes isolated from patients with critical disease early in the disease course. LAIR1 expression is inversely correlated with ISG-I expression response in patients with COVID-19 but is not expressed in healthy controls. The deficient ISG-I response observed in patients with critical COVID-19 with and without type I IFN-specific autoantibodies supports a unifying model for disease pathogenesis involving ISG-I suppression through convergent mechanisms.

Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to directly and simultaneously measure cellular composition and state, which can otherwise only be estimated by applying deconvolution methods to bulk RNA-Seq estimates. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical power of cell-type-specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage per-cell sequencing of more samples rather than high-coverage sequencing of fewer samples. We use simulations starting from one of the largest available real single-cell RNA-Seq data from 120 individuals to also show that multiple experimental designs with different numbers of samples, cells per sample and reads per cell could have similar statistical power, and choosing an appropriate design can yield large cost savings especially when multiplexed workflows are considered. Finally, we provide a practical approach on selecting cost-effective designs for maximizing cell-type-specific eQTL power which is available in the form of a web tool.

Type I interferon (IFN-I) neutralizing autoantibodies have been found in some critical COVID-19 patients; however, their prevalence and longitudinal dynamics across the disease severity scale, and functional effects on circulating leukocytes remain unknown. Here, in 284 COVID-19 patients, we found IFN-I autoantibodies in 19% of critical, 6% of severe and none of the moderate cases. Longitudinal profiling of over 600,000 peripheral blood mononuclear cells using multiplexed single-cell epitope and transcriptome sequencing from 54 COVID-19 patients, 15 non-COVID-19 patients and 11 non-hospitalized healthy controls, revealed a lack of IFN-I stimulated gene (ISG-I) response in myeloid cells from critical cases, including those producing anti-IFN-I autoantibodies. Moreover, surface protein analysis showed an inverse correlation of the inhibitory receptor LAIR-1 with ISG-I expression response early in the disease course. This aberrant ISG-I response in critical patients with and without IFN-I autoantibodies, supports a unifying model for disease pathogenesis involving ISG-I suppression via convergent mechanisms.

Most single units recorded from macaque secondary visual cortex (V2) respond with higher firing rates to synthetic texture images containing "naturalistic" higher-order statistics than to spectrally matched "noise" images lacking these statistics. In contrast, few single units in V1 show this property. We explored how the strength and dynamics of response vary across the different layers of visual cortex by recording multiunit (defined as high-frequency power in the local field potential) and gamma-band activity evoked by brief presentations of naturalistic and noise images in V1 and V2 of anesthetized macaque monkeys of both sexes. As previously reported, recordings in V2 showed consistently stronger responses to naturalistic texture than to spectrally matched noise. In contrast to single-unit recordings, V1 multiunit activity showed a preference for images with naturalistic statistics, and in gamma-band activity this preference was comparable across V1 and V2. Sensitivity to naturalistic image structure was strongest in the supragranular and infragranular layers of V1, but weak in granular layers, suggesting that it might reflect feedback from V2. Response timing was consistent with this idea. Visual responses appeared first in V1, followed by V2. Sensitivity to naturalistic texture emerged first in V2, followed by the supragranular and infragranular layers of V1, and finally in the granular layers of V1. Our results demonstrate laminar differences in the encoding of higher-order statistics of natural texture, and suggest that this sensitivity first arises in V2 and is fed back to modulate activity in V1.

Tissue- and organism-level biological processes often involve coordinated action of multiple distinct cell types. Current computational methods for the analysis of single-cell RNA-sequencing (scRNA-seq) data, however, are not designed to capture co-variation of cell states across samples, in part due to the low number of biological samples in most scRNA-seq datasets. Recent advances in sample multiplexing have enabled population-scale scRNA-seq measurements of tens to hundreds of samples. To take advantage of such datasets, here we introduce a computational approach called single-cell Interpretable Tensor Decomposition (scITD). This method extracts multicellular gene expression patterns that vary across different biological samples. These patterns capture how changes in one cell type are connected to changes in other cell types. The multicellular patterns can be further associated with known covariates (e.g., disease, treatment, or technical batch effects) and used to stratify heterogeneous samples. We first validated the performance of scITD using in vitro experimental data and simulations. We then applied scITD to scRNA-seq data on peripheral blood mononuclear cells (PBMCs) from 115 patients with systemic lupus erythematosus and 56 healthy controls. We recapitulated a well-established pan-cell-type signature of interferon-signaling that was associated with the presence of anti-dsDNA autoantibodies and a disease activity index. We further identified a novel multicellular pattern that appears to potentiate renal involvement for patients with anti-dsDNA autoantibodies. This pattern was characterized by an expansion of activated memory B cells along with helper T cell activation and is predicted to be mediated by an increase in ICOSLG-ICOS interaction between monocytes and helper T cells. Finally, we applied scITD to two PBMC datasets from patients with COVID-19 and identified reproducible multicellular patterns that stratify patients by disease severity. Overall, scITD is a flexible method for exploring co-variation of cell states in multi-sample single-cell datasets, which can yield new insights into complex non-cell-autonomous dependencies that define and stratify disease.

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Most single units recorded from macaque V2 respond with higher firing rates to synthetic texture images containing "naturalistic" higher-order statistics than to spectrally matched "noise" images lacking these statistics. In contrast, few single units in V1 show this property. We explored how the strength and dynamics of response vary across the different layers of visual cortex by recording multiunit and gamma band activity evoked by brief presentations of naturalistic and noise images in V1 and V2 of anesthetized macaque monkeys. As previously reported, recordings in V2 showed consistently stronger responses to naturalistic texture than to spectrally matched noise. In contrast to single unit recordings, V1 multiunit activity showed some preference for images with naturalistic statistics, and in gamma band activity this preference was comparable across V1 and V2. Sensitivity to naturalistic image structure was strongest in the supragranular and infragranular layers of V1, but weak in granular layers, suggesting that it might reflect feedback from V2. Response timing was consistent with this idea. Visual responses appeared first in V1, followed by V2. Sensitivity to naturalistic texture emerged first in V2, followed by the supragranular and infragranular layers of V1, and finally in the granular layers of V1. Our results demonstrate laminar differences in the encoding of higher-order statistics of natural texture, and suggest that this sensitivity first arises in V2 and is fed back to modulate activity in V1. Significance StatementThe circuit mechanisms responsible for visual representations of intermediate complexity are largely unknown. We used a well-validated set of synthetic texture stimuli to probe the temporal and laminar profile of sensitivity to the higher-order statistical structure of natural images. We found that this sensitivity emerges first and most strongly in V2 but soon after in V1. However, sensitivity in V1 is higher in the laminae (extragranular) and recording modalities (local field potential) most likely affected by V2 connections, suggesting a feedback origin. Our results show how sensitivity to naturalistic image structure emerges across time and circuitry in the early visual cortex.