Differential gene expression analysis for multi-subject single cell RNA sequencing studies with aggregateBioVar

Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets

Type 1 diabetes (T1D) is a chronic condition in which beta cells are destroyed by immune cells. Despite progress in immunotherapies that could delay T1D onset, early detection of autoimmunity remains challenging. Here, we evaluate the utility of machine learning for early prediction of T1D using single-cell analysis of islets. Using gradient-boosting algorithms, we model changes in gene expression of single cells from pancreatic tissues in T1D and non-diabetic organ donors. We assess if mathematical modeling could predict the likelihood of T1D development in non-diabetic autoantibody-positive donors. While most autoantibody-positive donors are predicted to be non-diabetic, select donors with unique gene signatures are classified as T1D. Our strategy also reveals a shared gene signature in distinct T1D-associated models across cell types, suggesting a common effect of the disease on transcriptional outputs of these cells. Our study establishes a precedent for using machine learning in early detection of T1D.

Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies

Differential expression (DE) analysis is a necessary step in the analysis of single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data. Unlike traditional bulk RNA-seq, DE analysis for scRNA-seq or SRT data has unique characteristics that may contribute to the difficulty of detecting DE genes. However, the plethora of DE tools that work with various assumptions makes it difficult to choose an appropriate one. Furthermore, a comprehensive review on detecting DE genes for scRNA-seq data or SRT data from multi-condition, multi-sample experimental designs is lacking. To bridge such a gap, here, we first focus on the challenges of DE detection, then highlight potential opportunities that facilitate further progress in scRNA-seq or SRT analysis, and finally provide insights and guidance in selecting appropriate DE tools or developing new computational DE methods.

Mitochondrial uncoupling proteins protect human airway epithelial ciliated cells from oxidative damage

Apical cilia on epithelial cells defend the lung by propelling pathogens and particulates out of the respiratory airways. Ciliated cells produce ATP that powers cilia beating by densely grouping mitochondria just beneath the apical membrane. However, this efficient localization comes at a cost because electrons leaked during oxidative phosphorylation react with molecular oxygen to form superoxide, and thus, the cluster of mitochondria creates a hotspot for oxidant production. The relatively high oxygen concentration overlying airway epithelia further intensifies the risk of generating superoxide. Thus, airway ciliated cells face a unique challenge of producing harmful levels of oxidants. However, surprisingly, highly ciliated epithelia produce less reactive oxygen species (ROS) than epithelia with few ciliated cells. Compared to other airway cell types, ciliated cells express high levels of mitochondrial uncoupling proteins, UCP2 and UCP5. These proteins decrease mitochondrial protonmotive force and thereby reduce production of ROS. As a result, lipid peroxidation, a marker of oxidant injury, decreases. However, mitochondrial uncoupling proteins exact a price for decreasing oxidant production; they decrease the fraction of mitochondrial respiration that generates ATP. These findings indicate that ciliated cells sacrifice mitochondrial efficiency in exchange for safety from damaging oxidation. Employing uncoupling proteins to prevent oxidant production, instead of relying solely on antioxidants to decrease postproduction oxidant levels, may offer an advantage for targeting a local area of intense ROS generation.

Single-cell transcriptomics of the immune system in ME/CFS at baseline and following symptom provocation

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a serious and poorly understood disease. To understand immune dysregulation in ME/CFS, we use single-cell RNA sequencing (scRNA-seq) to examine immune cells in patient and control cohorts. Postexertional malaise (PEM), an exacerbation of symptoms following strenuous exercise, is a characteristic symptom of ME/CFS. To detect changes coincident with PEM, we applied scRNA-seq on the same cohorts following exercise. At baseline, ME/CFS patients display classical monocyte dysregulation suggestive of inappropriate differentiation and migration to tissue. We identify both diseased and more normal monocytes within patients, and the fraction of diseased cells correlates with disease severity. Comparing the transcriptome at baseline and postexercise challenge, we discover patterns indicative of improper platelet activation in patients, with minimal changes elsewhere in the immune system. Taken together, these data identify immunological defects present at baseline in patients and an additional layer of dysregulation in platelets.

Key Cell Types and Biomarkers in Heart Failure Identified through Analysis of Single-Cell and Bulk RNA Sequencing Data

Heart failure (HF) is a complex clinical syndrome resulting from various cardiac diseases and a significant medical issue worldwide. Although the role of inflammation in HF pathogenesis is well-known, the specific cell types and regulatory molecules involved remain poorly understood. Here, we identified key cell types and novel biomarkers via an analysis of single-cell and bulk RNA sequencing data obtained from patients with two major HF types of ischemic cardiomyopathy and dilated cardiomyopathy. Myeloid cells were identified as the primary cell population involved in HF through cellular fraction and gene set enrichment analysis. Additionally, differential analysis of myeloid cells revealed crosstalk between cellular communication and cytokine-regulated immune responses in HF, with the MIF pathway emerging as a crucial immune regulatory pathway. The CD74/CXCR4 receptor complex in myeloid cell subgroup Mφ2 was significantly upregulated, potentially acting as a crucial regulator in HF. Upon receiving the MIF signal molecule, the CD74/CXCR4 receptor can activate NF-κB signaling to produce chemokines and thereby enhance the inflammatory response. CD74 and CXCR4 may serve as biomarkers and treatment targets for HF.

Best practices for single-cell analysis across modalities

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.

Robustness of single-cell RNA-seq for identifying differentially expressed genes

BACKGROUND

A common feature of single-cell RNA-seq (scRNA-seq) data is that the number of cells in a cell cluster may vary widely, ranging from a few dozen to several thousand. It is not clear whether scRNA-seq data from a small number of cells allow robust identification of differentially expressed genes (DEGs) with various characteristics.

RESULTS

We addressed this question by performing scRNA-seq and poly(A)-dependent bulk RNA-seq in comparable aliquots of human induced pluripotent stem cells-derived, purified vascular endothelial and smooth muscle cells. We found that scRNA-seq data needed to have 2,000 or more cells in a cluster to identify the majority of DEGs that would show modest differences in a bulk RNA-seq analysis. On the other hand, clusters with as few as 50-100 cells may be sufficient for identifying the majority of DEGs that would have extremely small p values or transcript abundance greater than a few hundred transcripts per million in a bulk RNA-seq analysis.

CONCLUSION

Findings of the current study provide a quantitative reference for designing studies that aim for identifying DEGs for specific cell clusters using scRNA-seq data and for interpreting results of such studies.

Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data

The advances of single-cell transcriptomic technologies have led to increasing use of single-cell RNA sequencing (scRNA-seq) data in large-scale patient cohort studies. The resulting high-dimensional data can be summarized and incorporated into patient outcome prediction models in several ways; however, there is a pressing need to understand the impact of analytical decisions on such model quality. In this study, we evaluate the impact of analytical choices on model choices, ensemble learning strategies and integrate approaches on patient outcome prediction using five scRNA-seq COVID-19 datasets. First, we examine the difference in performance between using single-view feature space versus multi-view feature space. Next, we survey multiple learning platforms from classical machine learning to modern deep learning methods. Lastly, we compare different integration approaches when combining datasets is necessary. Through benchmarking such analytical combinations, our study highlights the power of ensemble learning, consistency among different learning methods and robustness to dataset normalization when using multiple datasets as the model input.

Molecular basis of SARS-CoV-2 Omicron variant evasion from shared neutralizing antibody response

A detailed understanding of the molecular features of the neutralizing epitopes developed by viral escape mutants is important for predicting and developing vaccines or therapeutic antibodies against continuously emerging SARS-CoV-2 variants. Here, we report three human monoclonal antibodies (mAbs) generated from COVID-19 recovered individuals during first wave of pandemic in India. These mAbs had publicly shared near germline gene usage and potently neutralized Alpha and Delta, but poorly neutralized Beta and completely failed to neutralize Omicron BA.1 SARS-CoV-2 variants. Structural analysis of these three mAbs in complex with trimeric spike protein showed that all three mAbs are involved in bivalent spike binding with two mAbs targeting class-1 and one targeting class-4 Receptor Binding Domain (RBD) epitope. Comparison of immunogenetic makeup, structure, and function of these three mAbs with our recently reported class-3 RBD binding mAb that potently neutralized all SARS-CoV-2 variants revealed precise antibody footprint, specific molecular interactions associated with the most potent multi-variant binding / neutralization efficacy. This knowledge has timely significance for understanding how a combination of certain mutations affect the binding or neutralization of an antibody and thus have implications for predicting structural features of emerging SARS-CoV-2 escape variants and to develop vaccines or therapeutic antibodies against these.

Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data

Single-cell RNA-sequencing (scRNA-seq) enables researchers to quantify transcriptomes of thousands of cells simultaneously and study transcriptomic changes between cells. scRNA-seq datasets increasingly include multisubject, multicondition experiments to investigate cell-type-specific differential states (DS) between conditions. This can be performed by first identifying the cell types in all the subjects and then by performing a DS analysis between the conditions within each cell type. Naïve single-cell DS analysis methods that treat cells statistically independent are subject to false positives in the presence of variation between biological replicates, an issue known as the pseudoreplicate bias. While several methods have already been introduced to carry out the statistical testing in multisubject scRNA-seq analysis, comparisons that include all these methods are currently lacking. Here, we performed a comprehensive comparison of 18 methods for the identification of DS changes between conditions from multisubject scRNA-seq data. Our results suggest that the pseudobulk methods performed generally best. Both pseudobulks and mixed models that model the subjects as a random effect were superior compared with the naïve single-cell methods that do not model the subjects in any way. While the naïve models achieved higher sensitivity than the pseudobulk methods and the mixed models, they were subject to a high number of false positives. In addition, accounting for subjects through latent variable modeling did not improve the performance of the naïve methods.

Choroidal endothelial and macrophage gene expression in atrophic and neovascular macular degeneration

The human choroid is a heterogeneous, highly vascular connective tissue that dysfunctions in age-related macular degeneration (AMD). In this study, we performed single-cell RNA sequencing on 21 human choroids, 11 of which were derived from donors with early atrophic or neovascular AMD. Using this large donor cohort, we identified new gene expression signatures and immunohistochemically characterized discrete populations of resident macrophages, monocytes/inflammatory macrophages and dendritic cells. These three immune populations demonstrated unique expression patterns for AMD genetic risk factors, with dendritic cells possessing the highest expression of the neovascular AMD-associated MMP9 gene. Additionally, we performed trajectory analysis to model transcriptomic changes across the choroidal vasculature, and we identified expression signatures for endothelial cells from choroidal arterioles and venules. Finally, we performed differential expression analysis between control, early atrophic AMD, and neovascular AMD samples, and we observed that early atrophic AMD samples had high expression of SPARCL1, a gene that has been shown to increase in response to endothelial damage. Likewise, neovascular endothelial cells harbored gene expression changes consistent with endothelial cell damage and demonstrated increased expression of the sialomucins CD34 and ENCM, which were also observed at the protein level within neovascular membranes. Overall, this study characterizes the molecular features of new populations of choroidal endothelial cells and mononuclear phagocytes in a large cohort of AMD and control human donors.

A Single-Cell Atlas of Large and Small Airways at Birth in a Porcine Model of Cystic Fibrosis

Lack of CFTR (cystic fibrosis transmembrane conductance regulator) affects the transcriptome, composition, and function of large and small airway epithelia in people with advanced cystic fibrosis (CF); however, whether lack of CFTR causes cell-intrinsic abnormalities present at birth versus inflammation-dependent abnormalities is unclear. We performed a single-cell RNA-sequencing census of microdissected small airways from newborn CF pigs, which recapitulate CF host defense defects and pathology over time. Lack of CFTR minimally affected the transcriptome of large and small airways at birth, suggesting that infection and inflammation drive transcriptomic abnormalities in advanced CF. Importantly, common small airway epithelial cell types expressed a markedly different transcriptome than corresponding large airway cell types. Quantitative immunohistochemistry and electrophysiology of small airway epithelia demonstrated basal cells that reach the apical surface and a water and ion transport advantage. This single cell atlas highlights the archetypal nature of airway epithelial cells with location-dependent gene expression and function.

BSDE: barycenter single-cell differential expression for case-control studies

MOTIVATION

Single-cell sequencing brings about a revolutionarily high resolution for finding differentially expressed genes (DEGs) by disentangling highly heterogeneous cell tissues. Yet, such analysis is so far mostly focused on comparing between different cell types from the same individual. As single-cell sequencing becomes cheaper and easier to use, an increasing number of datasets from case-control studies are becoming available, which call for new methods for identifying differential expressions between case and control individuals.

RESULTS

To bridge this gap, we propose barycenter single-cell differential expression (BSDE), a nonparametric method for finding DEGs for case-control studies. Through the use of optimal transportation for aggregating distributions and computing their distances, our method overcomes the restrictive parametric assumptions imposed by standard mixed-effect-modeling approaches. Through simulations, we show that BSDE can accurately detect a variety of differential expressions while maintaining the type-I error at a prescribed level. Further, 1345 and 1568 cell type-specific DEGs are identified by BSDE from datasets on pulmonary fibrosis and multiple sclerosis, among which the top findings are supported by previous results from the literature.

AVAILABILITY AND IMPLEMENTATION

R package BSDE is freely available from doi.org/10.5281/zenodo.6332254. For real data analysis with the R package, see doi.org/10.5281/zenodo.6332566. These can also be accessed thorough GitHub at github.com/mqzhanglab/BSDE and github.com/mqzhanglab/BSDE_pipeline. The two single-cell sequencing datasets can be download with UCSC cell browser from cells.ucsc.edu/?ds=ms and cells.ucsc.edu/?ds=lung-pf-control.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.