Cell-attribute aware community detection improves differential abundance testing from single-cell RNA-Seq data.

Variations of cell-type proportions within tissues could be informative of biological aging and disease risk. Single-cell RNA-sequencing offers the opportunity to detect such differential abundance patterns, yet this task can be statistically challenging due to the noise in single-cell data, inter-sample variability and because such patterns are often of small effect size. Here we present a differential abundance testing paradigm called ELVAR that uses cell attribute aware clustering when inferring differentially enriched communities within the single-cell manifold. Using simulated and real single-cell and single-nucleus RNA-Seq datasets, we benchmark ELVAR against an analogous algorithm that uses Louvain for clustering, as well as local neighborhood-based methods, demonstrating that ELVAR improves the sensitivity to detect cell-type composition shifts in relation to aging, precancerous states and Covid-19 phenotypes. In effect, leveraging cell attribute information when inferring cell communities can denoise single-cell data, avoid the need for batch correction and help retrieve more robust cell states for subsequent differential abundance testing. ELVAR is available as an open-source R-package.

Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace.

Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose single-cell spatial position associated co-embeddings (scSpace), an integrative method to identify spatially variable cell subpopulations by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We benchmark scSpace with both simulated and biological datasets, and demonstrate that scSpace can accurately and robustly identify spatially variated cell subpopulations. When employed to reconstruct the spatial architectures of complex tissue such as the brain cortex, the small intestinal villus, the liver lobule, the kidney, the embryonic heart, and others, scSpace shows promising performance on revealing the pairwise cellular spatial association within single-cell data. The application of scSpace in melanoma and COVID-19 exhibits a broad prospect in the discovery of spatial therapeutic markers.

Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review.

Single-cell RNA sequencing (scRNA-seq) has become a routinely used technique to quantify the gene expression profile of thousands of single cells simultaneously. Analysis of scRNA-seq data plays an important role in the study of cell states and phenotypes, and has helped elucidate biological processes, such as those occurring during the development of complex organisms, and improved our understanding of disease states, such as cancer, diabetes, and coronavirus disease 2019 (COVID-19). Deep learning, a recent advance of artificial intelligence that has been used to address many problems involving large datasets, has also emerged as a promising tool for scRNA-seq data analysis, as it has a capacity to extract informative and compact features from noisy, heterogeneous, and high-dimensional scRNA-seq data to improve downstream analysis. The present review aims at surveying recently developed deep learning techniques in scRNA-seq data analysis, identifying key steps within the scRNA-seq data analysis pipeline that have been advanced by deep learning, and explaining the benefits of deep learning over more conventional analytic tools. Finally, we summarize the challenges in current deep learning approaches faced within scRNA-seq data and discuss potential directions for improvements in deep learning algorithms for scRNA-seq data analysis.

Robust single-cell matching and multimodal analysis using shared and distinct features.

The ability to align individual cellular information from multiple experimental sources is fundamental for a systems-level understanding of biological processes. However, currently available tools are mainly designed for single-cell transcriptomics matching and integration, and generally rely on a large number of shared features across datasets for cell matching. This approach underperforms when applied to single-cell proteomic datasets due to the limited number of parameters simultaneously accessed and lack of shared markers across these experiments. Here, we introduce a cell-matching algorithm, matching with partial overlap (MARIO) that accounts for both shared and distinct features, while consisting of vital filtering steps to avoid suboptimal matching. MARIO accurately matches and integrates data from different single-cell proteomic and multimodal methods, including spatial techniques and has cross-species capabilities. MARIO robustly matched tissue macrophages identified from COVID-19 lung autopsies via codetection by indexing imaging to macrophages recovered from COVID-19 bronchoalveolar lavage fluid by cellular indexing of transcriptomes and epitopes by sequencing, revealing unique immune responses within the lung microenvironment of patients with COVID.

Transformer for one stop interpretable cell type annotation.

Consistent annotation transfer from reference dataset to query dataset is fundamental to the development and reproducibility of single-cell research. Compared with traditional annotation methods, deep learning based methods are faster and more automated. A series of useful single cell analysis tools based on autoencoder architecture have been developed but these struggle to strike a balance between depth and interpretability. Here, we present TOSICA, a multi-head self-attention deep learning model based on Transformer that enables interpretable cell type annotation using biologically understandable entities, such as pathways or regulons. We show that TOSICA achieves fast and accurate one-stop annotation and batch-insensitive integration while providing biologically interpretable insights for understanding cellular behavior during development and disease progressions. We demonstrate TOSICA's advantages by applying it to scRNA-seq data of tumor-infiltrating immune cells, and CD14+ monocytes in COVID-19 to reveal rare cell types, heterogeneity and dynamic trajectories associated with disease progression and severity.

Single-cell multi-omics integration for unpaired data by a siamese network with graph-based contrastive loss.

BACKGROUND: Single-cell omics technology is rapidly developing to measure the epigenome, genome, and transcriptome across a range of cell types. However, it is still challenging to integrate omics data from different modalities. Here, we propose a variation of the Siamese neural network framework called MinNet, which is trained to integrate multi-omics data on the single-cell resolution by using graph-based contrastive loss. RESULTS: By training the model and testing it on several benchmark datasets, we showed its accuracy and generalizability in integrating scRNA-seq with scATAC-seq, and scRNA-seq with epitope data. Further evaluation demonstrated our model's unique ability to remove the batch effect, a common problem in actual practice. To show how the integration impacts downstream analysis, we established model-based smoothing and cis-regulatory element-inferring method and validated it with external pcHi-C evidence. Finally, we applied the framework to a COVID-19 dataset to bolster the original work with integration-based analysis, showing its necessity in single-cell multi-omics research. CONCLUSIONS: MinNet is a novel deep-learning framework for single-cell multi-omics sequencing data integration. It ranked top among other methods in benchmarking and is especially suitable for integrating datasets with batch and biological variances. With the single-cell resolution integration results, analysis of the interplay between genome and transcriptome can be done to help researchers understand their data and question.

Modeling and analyzing single-cell multimodal data with deep parametric inference.

The proliferation of single-cell multimodal sequencing technologies has enabled us to understand cellular heterogeneity with multiple views, providing novel and actionable biological insights into the disease-driving mechanisms. Here, we propose a comprehensive end-to-end single-cell multimodal analysis framework named Deep Parametric Inference (DPI). DPI transforms single-cell multimodal data into a multimodal parameter space by inferring individual modal parameters. Analysis of cord blood mononuclear cells (CBMC) reveals that the multimodal parameter space can characterize the heterogeneity of cells more comprehensively than individual modalities. Furthermore, comparisons with the state-of-the-art methods on multiple datasets show that DPI has superior performance. Additionally, DPI can reference and query cell types without batch effects. As a result, DPI can successfully analyze the progression of COVID-19 disease in peripheral blood mononuclear cells (PBMC). Notably, we further propose a cell state vector field and analyze the transformation pattern of bone marrow cells (BMC) states. In conclusion, DPI is a powerful single-cell multimodal analysis framework that can provide new biological insights into biomedical researchers. The python packages, datasets and user-friendly manuals of DPI are freely available at https://github.com/studentiz/dpi.

Single-cell RNA-sequencing in asthma research.

Asthma is a complex and heterogeneous disease with multicellular involvement, and knowledge gaps remain in our understanding of the pathogenesis of asthma. Efforts are still being made to investigate the immune pathogenesis of asthma in order to identify possible targets for prevention. Single cell RNA sequencing (scRNA-seq) technology is a useful tool for exploring heterogeneous diseases, identifying rare cell types and distinct cell subsets, enabling elucidation of key processes of cell differentiation, and understanding regulatory gene networks that predict immune function. In this article, we provide an overview of the importance of scRNA-seq for asthma research, followed by an in-depth discussion of the results in recent years, in order to provide new ideas for the pathogenesis, drug development and treatment of asthma.

Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space.

Computational tools for integrative analyses of diverse single-cell experiments are facing formidable new challenges including dramatic increases in data scale, sample heterogeneity, and the need to informatively cross-reference new data with foundational datasets. Here, we present SCALEX, a deep-learning method that integrates single-cell data by projecting cells into a batch-invariant, common cell-embedding space in a truly online manner (i.e., without retraining the model). SCALEX substantially outperforms online iNMF and other state-of-the-art non-online integration methods on benchmark single-cell datasets of diverse modalities, (e.g., single-cell RNA sequencing, scRNA-seq, single-cell assay for transposase-accessible chromatin use sequencing, scATAC-seq), especially for datasets with partial overlaps, accurately aligning similar cell populations while retaining true biological differences. We showcase SCALEX's advantages by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19 patients, each assembled from diverse data sources and growing with every new data. The online data integration capacity and superior performance makes SCALEX particularly appropriate for large-scale single-cell applications to build upon previous scientific insights.

Metacells untangle large and complex single-cell transcriptome networks.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization. RESULTS: We develop a framework called SuperCell to merge highly similar cells into metacells and perform standard scRNA-seq data analyses at the metacell level. Our systematic benchmarking demonstrates that metacells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, metacells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop. CONCLUSIONS: SuperCell is a framework to build and analyze metacells in a way that efficiently preserves the results of scRNA-seq data analyses while significantly accelerating and facilitating them.

Haematopoietic ageing through the lens of single-cell technologies.

Human lifespan is now longer than ever and, as a result, modern society is getting older. Despite that, the detailed mechanisms behind the ageing process and its impact on various tissues and organs remain obscure. In general, changes in DNA, RNA and protein structure throughout life impair their function. Haematopoietic ageing refers to the age-related changes affecting a haematopoietic system. Aged blood cells display different functional aberrations depending on their cell type, which might lead to the development of haematologic disorders, including leukaemias, anaemia or declining immunity. In contrast to traditional bulk assays, which are not suitable to dissect cell-to-cell variation, single-cell-level analysis provides unprecedented insight into the dynamics of age-associated changes in blood. In this Review, we summarise recent studies that dissect haematopoietic ageing at the single-cell level. We discuss what cellular changes occur during haematopoietic ageing at the genomic, transcriptomic, epigenomic and metabolomic level, and provide an overview of the benefits of investigating those changes with single-cell precision. We conclude by considering the potential clinical applications of single-cell techniques in geriatric haematology, focusing on the impact on haematopoietic stem cell transplantation in the elderly and infection studies, including recent COVID-19 research.

Single-Cell Analysis and Tracking of Antigen-Specific T Cells: Integrating Paired Chain AIRR-Seq and Transcriptome Sequencing: A Method by the AIRR Community.

Single-cell adaptive immune receptor repertoire sequencing (scAIRR-seq) offers the possibility to access the nucleotide sequences of paired receptor chains from T-cell receptors (TCR) or B-cell receptors (BCR ). Here we describe two protocols and the downstream bioinformatic approaches that facilitate the integrated analysis of paired T-cell receptor (TR ) alpha/beta (TRA /TRB ) AIRR-seq, RNA sequencing (RNAseq), immunophenotyping, and antigen-binding information. To illustrate the methodologies with a use case, we describe how to identify, characterize, and track SARS-CoV-2-specific T cells over multiple time points following infection with the virus. The first method allows the analysis of pools of memory CD8+ cells, identifying expansions and contractions of clones of interest. The second method allows the study of rare or antigen-specific cells and allows studying their changes over time.

Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq.

BACKGROUND: Multiplexing of samples in single-cell RNA-seq studies allows a significant reduction of the experimental costs, straightforward identification of doublets, increased cell throughput, and reduction of sample-specific batch effects. Recently published multiplexing techniques using oligo-conjugated antibodies or -lipids allow barcoding sample-specific cells, a process called "hashing." RESULTS: Here, we compare the hashing performance of TotalSeq-A and -C antibodies, custom synthesized lipids and MULTI-seq lipid hashes in four cell lines, both for single-cell RNA-seq and single-nucleus RNA-seq. We also compare TotalSeq-B antibodies with CellPlex reagents (10x Genomics) on human PBMCs and TotalSeq-B with different lipids on primary mouse tissues. Hashing efficiency was evaluated using the intrinsic genetic variation of the cell lines and mouse strains. Antibody hashing was further evaluated on clinical samples using PBMCs from healthy and SARS-CoV-2 infected patients, where we demonstrate a more affordable approach for large single-cell sequencing clinical studies, while simultaneously reducing batch effects. CONCLUSIONS: Benchmarking of different hashing strategies and computational pipelines indicates that correct demultiplexing can be achieved with both lipid- and antibody-hashed human cells and nuclei, with MULTISeqDemux as the preferred demultiplexing function and antibody-based hashing as the most efficient protocol on cells. On nuclei datasets, lipid hashing delivers the best results. Lipid hashing also outperforms antibodies on cells isolated from mouse brain. However, antibodies demonstrate better results on tissues like spleen or lung.

RNA-Seq of Single Fish Cells - Seeking Out the Leukocytes Mediating Immunity in Teleost Fishes.

The immune system is a complex and sophisticated biological system, spanning multiple levels of complexity, from the molecular level to that of tissue. Our current understanding of its function and complexity, of the heterogeneity of leukocytes, is a result of decades of concentrated efforts to delineate cellular markers using conventional methods of antibody screening and antigen identification. In mammalian models, this led to in-depth understanding of individual leukocyte subsets, their phenotypes, and their roles in health and disease. The field was further propelled forward by the development of single-cell (sc) RNA-seq technologies, offering an even broader and more integrated view of how cells work together to generate a particular response. Consequently, the adoption of scRNA-seq revealed the unexpected plasticity and heterogeneity of leukocyte populations and shifted several long-standing paradigms of immunology. This review article highlights the unprecedented opportunities offered by scRNA-seq technology to unveil the individual contributions of leukocyte subsets and their crosstalk in generating the overall immune responses in bony fishes. Single-cell transcriptomics allow identifying unseen relationships, and formulating novel hypotheses tailored for teleost species, without the need to rely on the limited number of fish-specific antibodies and pre-selected markers. Several recent studies on single-cell transcriptomes of fish have already identified previously unnoticed expression signatures and provided astonishing insights into the diversity of teleost leukocytes and the evolution of vertebrate immunity. Without a doubt, scRNA-seq in tandem with bioinformatics tools and state-of-the-art methods, will facilitate studying the teleost immune system by not only defining key markers, but also teaching us about lymphoid tissue organization, development/differentiation, cell-cell interactions, antigen receptor repertoires, states of health and disease, all across time and space in fishes. These advances will invite more researchers to develop the tools necessary to explore the immunology of fishes, which remain non-conventional animal models from which we have much to learn.

treekoR: identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data.

High-throughput single-cell technologies hold the promise of discovering novel cellular relationships with disease. However, analytical workflows constructed for these technologies to associate cell proportions with disease often employ unsupervised clustering techniques that overlook the valuable hierarchical structures that have been used to define cell types. We present treekoR, a framework that empirically recapitulates these structures, facilitating multiple quantifications and comparisons of cell type proportions. Our results from twelve case studies reinforce the importance of quantifying proportions relative to parent populations in the analyses of cytometry data - as failing to do so can lead to missing important biological insights.

Multiscale PHATE identifies multimodal signatures of COVID-19.

As the biomedical community produces datasets that are increasingly complex and high dimensional, there is a need for more sophisticated computational tools to extract biological insights. We present Multiscale PHATE, a method that sweeps through all levels of data granularity to learn abstracted biological features directly predictive of disease outcome. Built on a coarse-graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse resolutions for high-level summarizations of data and at fine resolutions for detailed representations of subsets. We apply Multiscale PHATE to a coronavirus disease 2019 (COVID-19) dataset with 54 million cells from 168 hospitalized patients and find that patients who die show CD16hiCD66blo neutrophil and IFN-γ+ granzyme B+ Th17 cell responses. We also show that population groupings from Multiscale PHATE directly fed into a classifier predict disease outcome more accurately than naive featurizations of the data. Multiscale PHATE is broadly generalizable to different data types, including flow cytometry, single-cell RNA sequencing (scRNA-seq), single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), and clinical variables.

PathogenTrack and Yeskit: tools for identifying intracellular pathogens from single-cell RNA-sequencing datasets as illustrated by application to COVID-19.

Pathogenic microbes can induce cellular dysfunction, immune response, and cause infectious disease and other diseases including cancers. However, the cellular distributions of pathogens and their impact on host cells remain rarely explored due to the limited methods. Taking advantage of single-cell RNA-sequencing (scRNA-seq) analysis, we can assess the transcriptomic features at the single-cell level. Still, the tools used to interpret pathogens (such as viruses, bacteria, and fungi) at the single-cell level remain to be explored. Here, we introduced PathogenTrack, a python-based computational pipeline that uses unmapped scRNA-seq data to identify intracellular pathogens at the single-cell level. In addition, we established an R package named Yeskit to import, integrate, analyze, and interpret pathogen abundance and transcriptomic features in host cells. Robustness of these tools has been tested on various real and simulated scRNA-seq datasets. PathogenTrack is competitive to the state-of-the-art tools such as Viral-Track, and the first tools for identifying bacteria at the single-cell level. Using the raw data of bronchoalveolar lavage fluid samples (BALF) from COVID-19 patients in the SRA database, we found the SARS-CoV-2 virus exists in multiple cell types including epithelial cells and macrophages. SARS-CoV-2-positive neutrophils showed increased expression of genes related to type I interferon pathway and antigen presenting module. Additionally, we observed the Haemophilus parahaemolyticus in some macrophage and epithelial cells, indicating a co-infection of the bacterium in some severe cases of COVID-19. The PathogenTrack pipeline and the Yeskit package are publicly available at GitHub.

Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+monocytes and memory CD8+T cells involved in severe COVID-19.

BACKGROUND: Understanding the host genetic architecture and viral immunity contributes to the development of effective vaccines and therapeutics for controlling the COVID-19 pandemic. Alterations of immune responses in peripheral blood mononuclear cells play a crucial role in the detrimental progression of COVID-19. However, the effects of host genetic factors on immune responses for severe COVID-19 remain largely unknown. METHODS: We constructed a computational framework to characterize the host genetics that influence immune cell subpopulations for severe COVID-19 by integrating GWAS summary statistics (N = 969,689 samples) with four independent scRNA-seq datasets containing healthy controls and patients with mild, moderate, and severe symptom (N = 606,534 cells). We collected 10 predefined gene sets including inflammatory and cytokine genes to calculate cell state score for evaluating the immunological features of individual immune cells. RESULTS: We found that 34 risk genes were significantly associated with severe COVID-19, and the number of highly expressed genes increased with the severity of COVID-19. Three cell subtypes that are CD16+monocytes, megakaryocytes, and memory CD8+T cells were significantly enriched by COVID-19-related genetic association signals. Notably, three causal risk genes of CCR1, CXCR6, and ABO were highly expressed in these three cell types, respectively. CCR1+CD16+monocytes and ABO+ megakaryocytes with significantly up-regulated genes, including S100A12, S100A8, S100A9, and IFITM1, confer higher risk to the dysregulated immune response among severe patients. CXCR6+ memory CD8+ T cells exhibit a notable polyfunctionality including elevation of proliferation, migration, and chemotaxis. Moreover, we observed an increase in cell-cell interactions of both CCR1+ CD16+monocytes and CXCR6+ memory CD8+T cells in severe patients compared to normal controls among both PBMCs and lung tissues. The enhanced interactions of CXCR6+ memory CD8+T cells with epithelial cells facilitate the recruitment of this specific population of T cells to airways, promoting CD8+T cell-mediated immunity against COVID-19 infection. CONCLUSIONS: We uncover a major genetics-modulated immunological shift between mild and severe infection, including an elevated expression of genetics-risk genes, increase in inflammatory cytokines, and of functional immune cell subsets aggravating disease severity, which provides novel insights into parsing the host genetic determinants that influence peripheral immune cells in severe COVID-19.

SLAMF7 engagement superactivates macrophages in acute and chronic inflammation.

Macrophages regulate protective immune responses to infectious microbes, but aberrant macrophage activation frequently drives pathological inflammation. To identify regulators of vigorous macrophage activation, we analyzed RNA-seq data from synovial macrophages and identified SLAMF7 as a receptor associated with a superactivated macrophage state in rheumatoid arthritis. We implicated IFN-γ as a key regulator of SLAMF7 expression and engaging SLAMF7 drove a strong wave of inflammatory cytokine expression. Induction of TNF-α after SLAMF7 engagement amplified inflammation through an autocrine signaling loop. We observed SLAMF7-induced gene programs not only in macrophages from rheumatoid arthritis patients but also in gut macrophages from patients with active Crohn's disease and in lung macrophages from patients with severe COVID-19. This suggests a central role for SLAMF7 in macrophage superactivation with broad implications in human disease pathology.

IDEAS: individual level differential expression analysis for single-cell RNA-seq data.

We consider an increasingly popular study design where single-cell RNA-seq data are collected from multiple individuals and the question of interest is to find genes that are differentially expressed between two groups of individuals. Towards this end, we propose a statistical method named IDEAS (individual level differential expression analysis for scRNA-seq). For each gene, IDEAS summarizes its expression in each individual by a distribution and then assesses whether these individual-specific distributions are different between two groups of individuals. We apply IDEAS to assess gene expression differences of autism patients versus controls and COVID-19 patients with mild versus severe symptoms.