BACKGROUND: Generalized linear mixed models (GLMMs), such as the negative-binomial or Poisson linear mixed model, are widely applied to single-cell RNA sequencing data to compare transcript expression between different conditions determined at the subject level. However, the model is computationally intensive, and its relative statistical performance to pseudobulk approaches is poorly understood.
RESULTS: We propose offset-pseudobulk as a lightweight alternative to GLMMs. We prove that a count-based pseudobulk equipped with a proper offset variable has the same statistical properties as GLMMs in terms of both point estimates and standard errors. We confirm our findings using simulations based on real data. Offset-pseudobulk is substantially faster (>×10) and numerically more stable than GLMMs.
METHODS: Offset pseudobulk can be easily implemented in any generalized linear model software by tweaking a few options. The codes can be found at https://github.com/hanbin973/pseudobulk_is_mm.

Neurologic Rosai-Dorfman disease (RDD) is a rare type of non-Langerhans cell histiocytosis that affects the central nervous system. Most neurologic RDDs grow like meningiomas, have clear boundaries, and can be completely resected. However, a few RDDs are invasive and aggressive, and no effective treatment options are available because the molecular mechanisms involved remain unknown. Here, we report a case of deadly and glucocorticoid-resistant neurologic RDD and explore its possible pathogenic mechanisms via single-cell RNA sequencing. First, we identified two distinct but evolutionarily related histiocyte subpopulations (the C1Q+ and SPP1+ histiocytes) that accumulated in the biopsy sample. The expression of genes in the KRAS signaling pathway was upregulated, indicating gain-of-function of KRAS mutations. The C1Q+ and SPP1+ histiocytes were highly differentiated and arrested in the G1 phase, excluding the idea that RDD is a lympho-histio-proliferative disorder. Second, although C1Q+ histiocytes were the primary RDD cell type, SPP1+ histiocytes highly expressed several severe inflammation-related and invasive factors, such as WNT5A, IL-6, and MMP12, suggesting that SPP1+ histiocytes plays a central role in driving the progression of this disease. Third, oligodendrocytes were found to be the prominent cell type that initiates RDD via MIF and may resist glucocorticoid treatment via the MDK and PTN signaling pathways. In summary, in this case, we report a rare presentation of neurologic RDD and provided new insight into the pathogenic mechanisms of progressive neurologic RDD. This study will also offer evidence for developing precision therapies targeting this complex disease.

Due to the high dimensionality and sparsity of the gene expression matrix in single-cell RNA-sequencing (scRNA-seq) data, coupled with significant noise generated by shallow sequencing, it poses a great challenge for cell clustering methods. While numerous computational methods have been proposed, the majority of existing approaches center on processing the target dataset itself. This approach disregards the wealth of knowledge present within other species and batches of scRNA-seq data. In light of this, our paper proposes a novel method named graph-based deep embedding clustering (GDEC) that leverages transfer learning across species and batches. GDEC integrates graph convolutional networks, effectively overcoming the challenges posed by sparse gene expression matrices. Additionally, the incorporation of DEC in GDEC enables the partitioning of cell clusters within a lower-dimensional space, thereby mitigating the adverse effects of noise on clustering outcomes. GDEC constructs a model based on existing scRNA-seq datasets and then applying transfer learning techniques to fine-tune the model using a limited amount of prior knowledge gleaned from the target dataset. This empowers GDEC to adeptly cluster scRNA-seq data cross different species and batches. Through cross-species and cross-batch clustering experiments, we conducted a comparative analysis between GDEC and conventional packages. Furthermore, we implemented GDEC on the scRNA-seq data of uterine fibroids. Compared results obtained from the Seurat package, GDEC unveiled a novel cell type (epithelial cells) and identified a notable number of new pathways among various cell types, thus underscoring the enhanced analytical capabilities of GDEC. Availability and implementation: https://github.com/YuzhiSun/GDEC/tree/main.

This study investigated immune cell characteristics in chronic hypersensitivity pneumonitis (HP), focusing on CD39-expressing cells\' impact on inflammation and tissue remodelling. Lung tissue from an HP patient was analysed using single-cell transcriptomics, flow cytometry, and gene expression profiling. The tissue revealed diverse cell types like macrophages, T cells, fibroblasts, and regulatory T cells (Tregs). CD39-expressing Tregs exhibited heightened ATP hydrolysis capacity and regulatory gene expression. CD39hi cells displayed markers of both Tregs and proinflammatory Th17 cells, suggesting transitional properties. Communication networks involving molecules like SPP1, collagen, CSF1, and IL-1β were identified, hinting at interactions between cell types in HP pathogenesis. This research provides insights into the immune response and cell interactions in chronic HP. CD39-expressing cells dual nature as Tregs and Th17 cells suggests a role in modulating lung inflammation, potentially affecting disease progression. These findings lay the groundwork for further research, underscoring CD39-expressing cells as potential therapeutic targets in HP.

High-throughput experiments are an essential part of modern biological and biomedical research. The outcomes of high-throughput biological experiments often have a lot of missing observations due to signals below detection levels. For example, most single-cell RNA-seq (scRNA-seq) protocols experience high levels of dropout due to the small amount of starting material, leading to a majority of reported expression levels being zero. Though missing data contain information about reproducibility, they are often excluded in the reproducibility assessment, potentially generating misleading assessments. In this article, we develop a regression model to assess how the reproducibility of high-throughput experiments is affected by the choices of operational factors (eg, platform or sequencing depth) when a large number of measurements are missing. Using a latent variable approach, we extend correspondence curve regression, a recently proposed method for assessing the effects of operational factors to reproducibility, to incorporate missing values. Using simulations, we show that our method is more accurate in detecting differences in reproducibility than existing measures of reproducibility. We illustrate the usefulness of our method using a single-cell RNA-seq dataset collected on HCT116 cells. We compare the reproducibility of different library preparation platforms and study the effect of sequencing depth on reproducibility, thereby determining the cost-effective sequencing depth that is required to achieve sufficient reproducibility.

Single-cell analysis has become one of the main cornerstones of biotechnology, inspiring the advent of various microfluidic compartments for cell cultivation such as microwells, microtrappers, microcapillaries, and droplets. A fundamental assumption for using such microfluidic compartments is that unintended stress or harm to cells derived from the microenvironments is insignificant, which is a crucial condition for carrying out unbiased single-cell studies. Despite the significance of this assumption, simple viability or growth tests have overwhelmingly been the assay of choice for evaluating culture conditions while empirical studies on the sub-lethal effect on cellular functions have been insufficient in many cases. In this work, we assessed the effect of culturing cells in droplets on the cellular function using yeast morphology as an indicator. Quantitative morphological analysis using CalMorph, an image-analysis program, demonstrated that cells cultured in flasks, large droplets, and small droplets significantly differed morphologically. From these differences, we identified that the cell cycle was delayed in droplets during the G1 phase and during the process of bud growth likely due to the checkpoint mechanism and impaired mitochondrial function, respectively. Furthermore, comparing small and large droplets, cells cultured in large droplets were morphologically more similar to those cultured in a flask, highlighting the advantage of increasing the droplet size. These results highlight a potential source of bias in cell analysis using droplets and reinforce the significance of assessing culture conditions of microfluidic cultivation methods for specific study cases.

Single-cell analysis is a powerful technology that has found widespread use in recent years. For diseases with involvement of adaptive immunity, single-cell analysis of antigen-specific T cells and B cells is particularly informative. In autoimmune diseases, the adaptive immune system is obviously at play, yet the ability to identify the culprit T and B cells recognizing disease-relevant antigen can be difficult. Celiac disease, a widespread disorder with autoimmune components, is unique in that disease-relevant antigens for both T cells and B cells are well defined. Furthermore, the celiac disease gut lesion is readily accessible allowing for sampling of tissue-resident cells. Thus, disease-relevant T cells and B cells from the gut and blood can be studied at the level of single cells. Here we review single-cell studies providing information on such adaptive immune cells and outline some future perspectives in the area of single-cell analysis in autoimmune diseases.

An increasing number of \'-omics\' datasets, generated by labs all across the world, are becoming available. They contain a wealth of data that are largely unexplored. Not every scientist, however, will have access to the required resources and expertise to analyze such data from scratch. Fortunately, a growing number of investigators is dedicating their time and effort to the development of user friendly, online applications that allow researchers to use and investigate these datasets. Here, we will illustrate the usefulness of such an approach. Using regulation of Wnt7b expression as an example, we will highlight a selection of accessible tools and resources that are available to researchers in the area of mammary gland biology. We show how they can be used for in silico analyses of gene regulatory mechanisms, resulting in new hypotheses and providing leads for experimental follow up. We also call out to the mammary gland community to join forces in a coordinated effort to generate and share additional tissue-specific \'-omics\' datasets and thereby expand the in silico toolbox.

Bacteria respond to changes in their environment with specific transcriptional programmes, but even within genetically identical populations these programmes are not homogenously expressed1. Such transcriptional heterogeneity between individual bacteria allows genetically clonal communities to develop a complex array of phenotypes1, examples of which include persisters that resist antibiotic treatment and metabolically specialized cells that emerge under nutrient-limiting conditions2. Fluorescent reporter constructs have played a pivotal role in deciphering heterogeneous gene expression within bacterial populations3 but have been limited to recording the activity of single genes in a few genetically tractable model species, whereas the vast majority of bacteria remain difficult to engineer and/or even to cultivate. Single-cell transcriptomics is revolutionizing the analysis of phenotypic cell-to-cell variation in eukaryotes, but technical hurdles have prevented its robust application to prokaryotes. Here, using an improved poly(A)-independent single-cell RNA-sequencing protocol, we report the faithful capture of growth-dependent gene expression patterns in individual Salmonella and Pseudomonas bacteria across all RNA classes and genomic regions. These transcriptomes provide important reference points for single-cell RNA-sequencing of other bacterial species, mixed microbial communities and host-pathogen interactions.

Drug-induced hypersensitivity syndrome/drug reaction with eosinophilia and systemic symptoms (DiHS/DRESS) is a potentially fatal multiorgan inflammatory disease associated with herpesvirus reactivation and subsequent onset of autoimmune diseases1-4. Pathophysiology remains elusive and therapeutic options are limited. Cases refractory to corticosteroid therapy pose a clinical challenge1,5 and approximately 30% of patients with DiHS/DRESS develop complications, including infections and inflammatory and autoimmune diseases1,2,5. Progress in single-cell RNA sequencing (scRNA-seq) provides an opportunity to dissect human disease pathophysiology at unprecedented resolutions6, particularly in diseases lacking animal models, such as DiHS/DRESS. We performed scRNA-seq on skin and blood from a patient with refractory DiHS/DRESS, identifying the JAK-STAT signaling pathway as a potential target. We further showed that central memory CD4+ T cells were enriched with DNA from human herpesvirus 6b. Intervention via tofacitinib enabled disease control and tapering of other immunosuppressive agents. Tofacitinib, as well as antiviral agents, suppressed culprit-induced T cell proliferation in vitro, further supporting the roles of the JAK-STAT pathway and herpesviruses in mediating the adverse drug reaction. Thus, scRNA-seq analyses guided successful therapeutic intervention in the patient with refractory DiHS/DRESS. scRNA-seq may improve our understanding of complicated human disease pathophysiology and provide an alternative approach in personalized medicine.