Impact of sequencing depth and read length on single cell RNA sequencing data of T cells

Optimal sequencing budget allocation for trajectory reconstruction of single cells

BACKGROUND

Charting cellular trajectories over gene expression is key to understanding dynamic cellular processes and their underlying mechanisms. While advances in single-cell RNA-sequencing technologies and computational methods have pushed forward the recovery of such trajectories, trajectory inference remains a challenge due to the noisy, sparse, and high-dimensional nature of single-cell data. This challenge can be alleviated by increasing either the number of cells sampled along the trajectory (breadth) or the sequencing depth, i.e. the number of reads captured per cell (depth). Generally, these two factors are coupled due to an inherent breadth-depth tradeoff that arises when the sequencing budget is constrained due to financial or technical limitations.

RESULTS

Here we study the optimal allocation of a fixed sequencing budget to optimize the recovery of trajectory attributes. Empirical results reveal that reconstruction accuracy of internal cell structure in expression space scales with the logarithm of either the breadth or depth of sequencing. We additionally observe a power law relationship between the optimal number of sampled cells and the corresponding sequencing budget. For linear trajectories, non-monotonicity in trajectory reconstruction across the breadth-depth tradeoff can impact downstream inference, such as expression pattern analysis along the trajectory. We demonstrate these results for five single-cell RNA-sequencing datasets encompassing differentiation of embryonic stem cells, pancreatic beta cells, hepatoblast and multipotent hematopoietic cells, as well as induced reprogramming of embryonic fibroblasts into neurons. By addressing the challenges of single-cell data, our study offers insights into maximizing the efficiency of cellular trajectory analysis through strategic allocation of sequencing resources.

Advances in long-read single-cell transcriptomics

Long-read single-cell transcriptomics (scRNA-Seq) is revolutionizing the way we profile heterogeneity in disease. Traditional short-read scRNA-Seq methods are limited in their ability to provide complete transcript coverage, resolve isoforms, and identify novel transcripts. The scRNA-Seq protocols developed for long-read sequencing platforms overcome these limitations by enabling the characterization of full-length transcripts. Long-read scRNA-Seq techniques initially suffered from comparatively poor accuracy compared to short read scRNA-Seq. However, with improvements in accuracy, accessibility, and cost efficiency, long-reads are gaining popularity in the field of scRNA-Seq. This review details the advances in long-read scRNA-Seq, with an emphasis on library preparation protocols and downstream bioinformatics analysis tools.

Comparison of structural variant callers for massive whole-genome sequence data

BACKGROUND

Detecting structural variations (SVs) at the population level using next-generation sequencing (NGS) requires substantial computational resources and processing time. Here, we compared the performances of 11 SV callers: Delly, Manta, GridSS, Wham, Sniffles, Lumpy, SvABA, Canvas, CNVnator, MELT, and INSurVeyor. These SV callers have been recently published and have been widely employed for processing massive whole-genome sequencing datasets. We evaluated the accuracy, sequence depth, running time, and memory usage of the SV callers.

RESULTS

Notably, several callers exhibited better calling performance for deletions than for duplications, inversions, and insertions. Among the SV callers, Manta identified deletion SVs with better performance and efficient computing resources, and both Manta and MELT demonstrated relatively good precision regarding calling insertions. We confirmed that the copy number variation callers, Canvas and CNVnator, exhibited better performance in identifying long duplications as they employ the read-depth approach. Finally, we also verified the genotypes inferred from each SV caller using a phased long-read assembly dataset, and Manta showed the highest concordance in terms of the deletions and insertions.

CONCLUSIONS

Our findings provide a comprehensive understanding of the accuracy and computational efficiency of SV callers, thereby facilitating integrative analysis of SV profiles in diverse large-scale genomic datasets.

Comparative analysis between single-cell RNA-seq and single-molecule RNA FISH indicates that the pyrimidine nucleobase idoxuridine (IdU) globally amplifies transcriptional noise

Stochastic fluctuations (noise) in transcription generate substantial cell-to-cell variability, but the physiological roles of noise have remained difficult to determine in the absence of generalized noise-modulation approaches. Previous single-cell RNA-sequencing (scRNA-seq) suggested that the pyrimidine-base analog (5'-iodo-2'-deoxyuridine, IdU) could generally amplify noise without substantially altering mean-expression levels but scRNA-seq technical drawbacks potentially obscured the penetrance of IdU-induced transcriptional noise amplification. Here we quantify global-vs.-partial penetrance of IdU-induced noise amplification by assessing scRNA-seq data using numerous normalization algorithms and directly quantifying noise using single-molecule RNA FISH (smFISH) for a panel of genes from across the transcriptome. Alternate scRNA-seq analyses indicate IdU-induced noise amplification for ~90% of genes, and smFISH data verified noise amplification for ~90% of tested genes. Collectively, this analysis indicates which scRNA-seq algorithms are appropriate for quantifying noise and argues that IdU is a globally penetrant noise-enhancer molecule that could enable investigations of the physiological impacts of transcriptional noise.

Therapeutic Strategies for Restoring Perturbed Corneal Epithelial Homeostasis in Limbal Stem Cell Deficiency: Current Trends and Future Directions

Limbal stem cells constitute an important cell population required for regeneration of the corneal epithelium. If insults to limbal stem cells or their niche are sufficiently severe, a disease known as limbal stem cell deficiency occurs. In the absence of functioning limbal stem cells, vision-compromising conjunctivalization of the corneal epithelium occurs, leading to opacification, inflammation, neovascularization, and chronic scarring. Limbal stem cell transplantation is the standard treatment for unilateral cases of limbal stem cell deficiency, but bilateral cases require allogeneic transplantation. Herein we review the current therapeutic utilization of limbal stem cells. We also describe several limbal stem cell markers that impact their phenotype and function and discuss the possibility of modulating limbal stem cells and other sources of stem cells to facilitate the development of novel therapeutic interventions. We finally consider several hurdles for widespread adoption of these proposed methodologies and discuss how they can be overcome to realize vision-restoring interventions.

scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic information

Background

Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to capture transcriptomes at single-cell resolution. However, dropout events distort the gene expression levels and underlying biological signals, misleading the downstream analysis of scRNA-seq data.

Results

We develop a statistical model-based multidimensional imputation algorithm, scMTD, that identifies local cell neighbors and specific gene co-expression networks based on the pseudo-time of cells, leveraging information on cell-level, gene-level, and transcriptome dynamic to recover scRNA-seq data. Compared with the state-of-the-art imputation methods through several real-data-based analytical experiments, scMTD effectively recovers biological signals of transcriptomes and consistently outperforms the other algorithms in improving FISH validation, trajectory inference, differential expression analysis, clustering analysis, and identification of cell types.

Conclusions

scMTD maintains the gene expression characteristics, enhances the clustering of cell subpopulations, assists the study of gene expression dynamics, contributes to the discovery of rare cell types, and applies to both UMI-based and non-UMI-based data. Overall, scMTD’s reliability, applicability, and scalability make it a promising imputation approach for scRNA-seq data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13578-022-00886-4.

A human adipose tissue cell-type transcriptome atlas

The importance of defining cell-type-specific genes is well acknowledged. Technological advances facilitate high-resolution sequencing of single cells, but practical challenges remain. Adipose tissue is composed primarily of adipocytes, large buoyant cells requiring extensive, artefact-generating processing for separation and analysis. Thus, adipocyte data are frequently absent from single-cell RNA sequencing (scRNA-seq) datasets, despite being the primary functional cell type. Here, we decipher cell-type-enriched transcriptomes from unfractionated human adipose tissue RNA-seq data. We profile all major constituent cell types, using 527 visceral adipose tissue (VAT) or 646 subcutaneous adipose tissue (SAT) samples, identifying over 2,300 cell-type-enriched transcripts. Sex-subset analysis uncovers a panel of male-only cell-type-enriched genes. By resolving expression profiles of genes differentially expressed between SAT and VAT, we identify mesothelial cells as the primary driver of this variation. This study provides an accessible method to profile cell-type-enriched transcriptomes using bulk RNA-seq, generating a roadmap for adipose tissue biology.

De Novo Transcriptome Sequencing and Analysis of Differential Gene Expression among Various Stages of Tail Regeneration in Hemidactylus flaviviridis

Across the animal kingdom, lizards are the only amniotes capable of regenerating their lost tail through epimorphosis. Of the many reptiles, the northern house gecko, Hemidactylus flaviviridis, is an excellent model system that is used for understanding the mechanism of epimorphic regeneration. A stage-specific transcriptome profile was generated in the current study following an autotomized tail with the HiSeq2500 platform. The reads obtained from de novo sequencing were filtered and high-quality reads were considered for gene ontology (GO) annotation and pathway analysis. Millions of reads were recorded for each stage upon de novo assembly. Up and down-regulated transcripts were categorized for early blastema (EBL), blastema (BL) and differentiation (DF) stages compared to the normal tail (NT) by differential gene expression analysis. The transcripts from developmentally significant pathways such as FGF, Wnt, Shh and TGF-β/BMP were present during tail regeneration. Additionally, differential expression of transcripts was recorded from biological processes, namely inflammation, cell proliferation, apoptosis and cell migration. Overall, the study reveals the stage-wise transcriptome analysis in conjunction with cellular processes as well as molecular signaling pathways during lizard tail regeneration. The knowledge obtained from the data can be extrapolated to configure regenerative responses in other amniotes, including humans, upon loss of a complex organ.

Progress in multicellular human cardiac organoids for clinical applications

Advances in self-organizing cardiac organoids to recapitulate human cardiogenesis have provided a powerful tool for unveiling human cardiac development, studying cardiovascular diseases, testing drugs, and transplantation. Here, we highlight the recent remarkable progress on multicellular cardiac organoids and review the current status for their practical applications. We then introduce key readouts and tools for assessing cardiac organoids for clinical applications, address major challenges, and provide suggestions for each assessment method. Lastly, we discuss the current limitations of cardiac organoids as miniature models of the human heart and suggest a direction for moving forward toward building the mini-heart from cardiac organoids.

A novel method for single-cell data imputation using subspace regression

Recent advances in biochemistry and single-cell RNA sequencing (scRNA-seq) have allowed us to monitor the biological systems at the single-cell resolution. However, the low capture of mRNA material within individual cells often leads to inaccurate quantification of genetic material. Consequently, a significant amount of expression values are reported as missing, which are often referred to as dropouts. To overcome this challenge, we develop a novel imputation method, named single-cell Imputation via Subspace Regression (scISR), that can reliably recover the dropout values of scRNA-seq data. The scISR method first uses a hypothesis-testing technique to identify zero-valued entries that are most likely affected by dropout events and then estimates the dropout values using a subspace regression model. Our comprehensive evaluation using 25 publicly available scRNA-seq datasets and various simulation scenarios against five state-of-the-art methods demonstrates that scISR is better than other imputation methods in recovering scRNA-seq expression profiles via imputation. scISR consistently improves the quality of cluster analysis regardless of dropout rates, normalization techniques, and quantification schemes. The source code of scISR can be found on GitHub at https://github.com/duct317/scISR.

Assessing reproducibility of high-throughput experiments in the case of missing data

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.

Mapping the expression of an ANK3 isoform associated with bipolar disorder in the human brain

The gene ankyrin-3 (ANK3) has been consistently associated with bipolar disorder (BD) in several genome-wide association studies (GWAS). The exact molecular mechanisms underlying this genetic association remain unknown. The discovery of a loss-of-function variant (rs41283526*G) in an alternatively spliced exon (ENSE00001786716) with a protective effect, suggested that elevated expression of this particular isoform could be a risk factor for developing the disorder. We developed a novel approach for measuring the expression level of all splice forms at a challenging genetic locus using a combination of droplet digital PCR and high-throughput sequencing of indexed PCR amplicons. The combined method was performed on a large collection of 568 postmortem brain samples of BD and SCZ cases and controls. We also studied the expression of the splice forms in a child-development cohort of 41 healthy males. We found that our approach can quantify the splice forms in brain samples, although with less precision than ddPCR. We detected highly significant differences in expression of splice forms and transcription start sites between brain regions, notably with higher expression of the BD-associated isoform in the corpus callosum compared to frontal tissue (mean fold change = 1.80, p < 1e-4). Although the patients in our sample expressed the BD-associated splice form at a similar level to controls, adolescents in our child-development cohort had a clearly higher expression level than younger children (mean fold change = 1.97, p = 5e-3). These results suggest that this ANK3 splice form may play a role in the myelin maturation of the human brain.

Prestin and electromotility may serve multiple roles in cochlear outer hair cells

Outer hair cells (OHCs) are innervated by both medial olivocochlear (MOC) efferents and type II afferents, which also innervate supporting cells to form a local neural network. It has also been demonstrated that prestin provides the molecular basis for OHC somatic electromotility, amplifying movements within the organ of Corti. Although not anticipated, early-onset OHC loss was found in two prestin transgenic mouse models that either lack prestin protein or lack electromotility. To uncover the molecular pathways that evoke OHC death, we profiled the coding transcriptome of OHCs from wildtype (WT), prestin-knockout (KO), and 499-knockin (KI) mice using single-cell RNA sequencing (scRNA-seq). scRNA-Seq transcriptomics and pathway analyses did not reveal common pathways associated with OHC loss observed in prestin-KO and 499-KI mice. Clustering enrichment analysis showed that increased gene expression in OHCs from prestin-KO mice was associated with lipid metabolic processes and cell death pathways. These mRNA profiles likely contribute to the OHC loss observed in prestin-KO mice and support the notion that prestin is also a structural protein, important for the normal plasma membrane compartmentalization that is essential to establish MOC efferent synapses. In contrast, the mRNA profile of OHCs from 499-KI mice did not provide a rational explanation of the early-onset OHC loss in this mutant. OHCs from 499-KI mice have normal plasma membrane compartmentalization and normal OHC-MOC contacts. However, 499 prestin lacks electromotility and appears to change the local neural network around OHCs, as more synaptic markers were found near neighboring supporting cells when compared to WT and prestin-KO mice. Thus, OHCs in prestin-KOs (no prestin protein, no electromotility) and 499-KIs (prestin protein present, no electromotility) may influence local neuronal networks in different ways. Collectively, our data suggest that prestin and its motile properties are important for OHC survival and the maintenance of local afferent/efferent circuits, as well as for its role in cochlear amplification.

Single cell transcriptome research in human placenta

Human placenta is a complex and heterogeneous organ interfacing between the mother and the fetus that supports fetal development. Alterations to placental structural components are associated with various pregnancy complications. To reveal the heterogeneity among various placenta cell types in normal and diseased placentas, as well as elucidate molecular interactions within a population of placental cells, a new genomics technology called single cell RNA-seq (or scRNA-seq) has been employed in the last couple of years. Here we review the principles of scRNA-seq technology, and summarize the recent human placenta studies at scRNA-seq level across gestational ages as well as in pregnancy complications, such as preterm birth and preeclampsia. We list the computational analysis platforms and resources available for the public use. Lastly, we discuss the future areas of interest for placenta single cell studies, as well as the data analytics needed to accomplish them.

High-throughput and single-cell T cell receptor sequencing technologies

T cells express T cell receptors (TCRs) composed of somatically recombined TCRα and TCRβ chains, which mediate recognition of major histocompatibility complex (MHC)-antigen complexes and drive the antigen-specific adaptive immune response to pathogens and cancer. The TCR repertoire in each individual is highly diverse, which allows for recognition of a wide array of foreign antigens, but also presents a challenge in analyzing this response using conventional methods. Recent studies have developed high-throughput sequencing technologies to identify TCR sequences, analyze their antigen specificities using experimental and computational tools, and pair TCRs with transcriptional and epigenetic cell state phenotypes in single cells. In this Review, we highlight these technological advances and describe how they have been applied to discover fundamental insights into T cell-mediated immunity.

Creation of a Single Cell RNASeq Meta-Atlas to Define Human Liver Immune Homeostasis

The liver is unique in both its ability to maintain immune homeostasis and in its potential for immune tolerance following solid organ transplantation. Single-cell RNA sequencing (scRNA seq) is a powerful approach to generate highly dimensional transcriptome data to understand cellular phenotypes. However, when scRNA data is produced by different groups, with different data models, different standards, and samples processed in different ways, it can be challenging to draw meaningful conclusions from the aggregated data. The goal of this study was to establish a method to combine ‘human liver’ scRNA seq datasets by 1) characterizing the heterogeneity between studies and 2) using the meta-atlas to define the dominant phenotypes across immune cell subpopulations in healthy human liver. Publicly available scRNA seq data generated from liver samples obtained from a combined total of 17 patients and ~32,000 cells were analyzed. Liver-specific immune cells (CD45+) were extracted from each dataset, and immune cell subpopulations (myeloid cells, NK and T cells, plasma cells, and B cells) were examined using dimensionality reduction (UMAP), differential gene expression, and ingenuity pathway analysis. All datasets co-clustered, but cell proportions differed between studies. Gene expression correlation demonstrated similarity across all studies, and canonical pathways that differed between datasets were related to cell stress and oxidative phosphorylation rather than immune-related function. Next, a meta-atlas was generated via data integration and compared against PBMC data to define gene signatures for each hepatic immune subpopulation. This analysis defined key features of hepatic immune homeostasis, with decreased expression across immunologic pathways and enhancement of pathways involved with cell death. This method for meta-analysis of scRNA seq data provides a novel approach to broadly define the features of human liver immune homeostasis. Specific pathways and cellular phenotypes described in this human liver immune meta-atlas provide a critical reference point for further study of immune mediated disease processes within the liver.

SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data

The single-cell RNA sequencing (scRNA-seq) technologies obtain gene expression at single-cell resolution and provide a tool for exploring cell heterogeneity and cell types. As the low amount of extracted mRNA copies per cell, scRNA-seq data exhibit a large number of dropouts, which hinders the downstream analysis of the scRNA-seq data. We propose a statistical method, SDImpute (Single-cell RNA-seq Dropout Imputation), to implement block imputation for dropout events in scRNA-seq data. SDImpute automatically identifies the dropout events based on the gene expression levels and the variations of gene expression across similar cells and similar genes, and it implements block imputation for dropouts by utilizing gene expression unaffected by dropouts from similar cells. In the experiments, the results of the simulated datasets and real datasets suggest that SDImpute is an effective tool to recover the data and preserve the heterogeneity of gene expression across cells. Compared with the state-of-the-art imputation methods, SDImpute improves the accuracy of the downstream analysis including clustering, visualization, and differential expression analysis.

Single-cell RNA sequencing (scRNA-seq) allows researchers to analyze gene expression of thousands of single cells simultaneously. However, the low amount of extracted mRNA leads to a large number of dropout events, which introduce computational challenges and hinder downstream analysis of data. To address this problem, we developed SDImpute, a novel statistical method to recover the scRNA-seq data based on cell-level and gene-level information in this manuscript. The goal of our algorithm is to denoise the scRNA-seq data while maintaining the biological nature of gene expression. Combining SDImpute with the downstream analysis tools, we considered the matched bulk expression data and known cell labels of the scRNA-seq data as criteria to design experiments to validate the performance of our method in both simulated and real datasets. Moreover, we offer an R package with detailed instructions and an example input dataset. We hope that SDImpute will be beneficial to researchers to identify mechanisms underlying some biological processes by analysis of the scRNA-seq data.

Polar Gini Curve: A Technique to Discover Gene Expression Spatial Patterns from Single-cell RNA-seq Data

In this work, we describe the development of Polar Gini Curve, a method for characterizing cluster markers by analyzing single-cell RNA sequencing (scRNA-seq) data. Polar Gini Curve combines the gene expression and the 2D coordinates ("spatial") information to detect patterns of uniformity in any clustered cells from scRNA-seq data. We demonstrate that Polar Gini Curve can help users characterize the shape and density distribution of cells in a particular cluster, which can be generated during routine scRNA-seq data analysis. To quantify the extent to which a gene is uniformly distributed in a cell cluster space, we combine two polar Gini curves (PGCs)-one drawn upon the cell-points expressing the gene (the "foreground curve") and the other drawn upon all cell-points in the cluster (the "background curve"). We show that genes with highly dissimilar foreground and background curves tend not to uniformly distributed in the cell cluster-thus having spatially divergent gene expression patterns within the cluster. Genes with similar foreground and background curves tend to uniformly distributed in the cell cluster-thus having uniform gene expression patterns within the cluster. Such quantitative attributes of PGCs can be applied to sensitively discover biomarkers across clusters from scRNA-seq data. We demonstrate the performance of the Polar Gini Curve framework in several simulation case studies. Using this framework to analyze a real-world neonatal mouse heart cell dataset, the detected biomarkers may characterize novel subtypes of cardiac muscle cells. The source code and data for Polar Gini Curve could be found at http://discovery.informatics.uab.edu/PGC/ or https://figshare.com/projects/Polar_Gini_Curve/76749.

Recent advances in single-cell multimodal analysis to study immune cells

Recent advances in single-cell technologies have enabled the profiling of the genome, epigenome, transcriptome and proteome, along with temporal and spatial information of individual cells. These technologies have provided unique opportunities to understand mechanisms underpinning the immune system, such as characterizations of the molecular cell state, how the cell state evolves along its lineage and the impact of spatial location on cell state. In this review, we discuss how these mechanisms have been studied through recent advances in single-cell multimodal technologies.

The scRNA-seq Expression Profiling of the Receptor ACE2 and the Cellular Protease TMPRSS2 Reveals Human Organs Susceptible to SARS-CoV-2 Infection

COVID-19 patients always develop multiple organ dysfunction syndromes other than lungs, suggesting the novel virus SARS-CoV-2 also invades other organs. Therefore, studying the viral susceptibility of other organs is important for a deeper understanding of viral pathogenesis. Angiotensin-converting enzyme II (ACE2) is the receptor protein of SARS-CoV-2, and TMPRSS2 promotes virus proliferation and transmission. We investigated the ACE2 and TMPRSS2 expression levels of cell types from 31 organs to evaluate the risk of viral infection using single-cell RNA sequencing (scRNA-seq) data. For the first time, we found that the gall bladder and fallopian tube are vulnerable to SARS-CoV-2 infection. Besides, the nose, heart, small intestine, large intestine, esophagus, brain, testis, and kidney are also identified to be high-risk organs with high expression levels of ACE2 and TMPRSS2. Moreover, the susceptible organs are grouped into three risk levels based on the ACE2 and TMPRSS2 expression. As a result, the respiratory system, digestive system, and urinary system are at the top-risk level for SARS-CoV-2 infection. This study provides evidence for SARS-CoV-2 infection in the human nervous system, digestive system, reproductive system, respiratory system, circulatory system, and urinary system using scRNA-seq data, which helps in the clinical diagnosis and treatment of patients.

The Application of Single-Cell RNA Sequencing in Mammalian Meiosis Studies

Meiosis is a cellular division process that produces gametes for sexual reproduction. Disruption of complex events throughout meiosis, such as synapsis and homologous recombination, can lead to infertility and aneuploidy. To reveal the molecular mechanisms of these events, transcriptome studies of specific substages must be conducted. However, conventional methods, such as bulk RNA-seq and RT-qPCR, are not able to detect the transcriptional variations effectively and precisely, especially for identifying cell types and stages with subtle differences. In recent years, mammalian meiotic transcriptomes have been intensively studied at the single-cell level by using single-cell RNA-seq (scRNA-seq) approaches, especially through two widely used platforms, Smart-seq2 and Drop-seq. The scRNA-seq protocols along with their downstream analysis enable researchers to accurately identify cell heterogeneities and investigate meiotic transcriptomes at a higher resolution. In this review, we compared bulk RNA-seq and scRNA-seq to show the advantages of the scRNA-seq in meiosis studies; meanwhile, we also pointed out the challenges and limitations of the scRNA-seq. We listed recent findings from mammalian meiosis (male and female) studies where scRNA-seq applied. Next, we summarized the scRNA-seq analysis methods and the meiotic marker genes from spermatocytes and oocytes. Specifically, we emphasized the different features of the two scRNA-seq protocols (Smart-seq2 and Drop-seq) in the context of meiosis studies and discussed their strengths and weaknesses in terms of different research purposes. Finally, we discussed the future applications of scRNA-seq in the meiosis field.

Characterisation of CD4+ T-cell subtypes using single cell RNA sequencing and the impact of cell number and sequencing depth

CD4+ T-cells represent a heterogeneous collection of specialised sub-types and are a key cell type in the pathogenesis of many diseases due to their role in the adaptive immune system. By investigating CD4+ T-cells at the single cell level, using RNA sequencing (scRNA-seq), there is the potential to identify specific cell states driving disease or treatment response. However, the impact of sequencing depth and cell numbers, two important factors in scRNA-seq, has not been determined for a complex cell population such as CD4+ T-cells. We therefore generated a high depth, high cell number dataset to determine the effect of reduced sequencing depth and cell number on the ability to accurately identify CD4+ T-cell subtypes. Furthermore, we investigated T-cell signatures under resting and stimulated conditions to assess cluster specific effects of stimulation. We found that firstly, cell number has a much more profound effect than sequencing depth on the ability to classify cells; secondly, this effect is greater when cells are unstimulated and finally, resting and stimulated samples can be combined to leverage additional power whilst still allowing differences between samples to be observed. While based on one individual, these results could inform future scRNA-seq studies to ensure the most efficient experimental design.

Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis

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.

Comparing in vitro human liver models to in vivo human liver using RNA-Seq

The liver plays an important role in xenobiotic metabolism and represents a primary target for toxic substances. Many different in vitro cell models have been developed in the past decades. In this study, we used RNA-sequencing (RNA-Seq) to analyze the following human in vitro liver cell models in comparison to human liver tissue: cancer-derived cell lines (HepG2, HepaRG 3D), induced pluripotent stem cell-derived hepatocyte-like cells (iPSC-HLCs), cancerous human liver-derived assays (hPCLiS, human precision cut liver slices), non-cancerous human liver-derived assays (PHH, primary human hepatocytes) and 3D liver microtissues. First, using CellNet, we analyzed whether these liver in vitro cell models were indeed classified as liver, based on their baseline expression profile and gene regulatory networks (GRN). More comprehensive analyses using non-differentially expressed genes (non-DEGs) and differential transcript usage (DTU) were applied to assess the coverage for important liver pathways. Through different analyses, we noticed that 3D liver microtissues exhibited a high similarity with in vivo liver, in terms of CellNet (C/T score: 0.98), non-DEGs (10,363) and pathway coverage (highest for 19 out of 20 liver specific pathways shown) at the beginning of the incubation period (0 h) followed by a decrease during long-term incubation for 168 and 336 h. PHH also showed a high degree of similarity with human liver tissue and allowed stable conditions for a short-term cultivation period of 24 h. Using the same metrics, HepG2 cells illustrated the lowest similarity (C/T: 0.51, non-DEGs: 5623, and pathways coverage: least for 7 out of 20) with human liver tissue. The HepG2 are widely used in hepatotoxicity studies, however, due to their lower similarity, they should be used with caution. HepaRG models, iPSC-HLCs, and hPCLiS ranged clearly behind microtissues and PHH but showed higher similarity to human liver tissue than HepG2 cells. In conclusion, this study offers a resource of RNA-Seq data of several biological replicates of human liver cell models in vitro compared to human liver tissue.

A Systems-Based Map of Human Brain Cell-Type Enriched Genes and Malignancy-Associated Endothelial Changes

Changes in the endothelium of the cerebral vasculature can contribute to inflammatory, thrombotic, and malignant disorders. The importance of defining cell-type-specific genes and their modification in disease is increasingly recognized. Here, we develop a bioinformatics-based approach to identify normal brain cell-enriched genes, using bulk RNA sequencing (RNA-seq) data from 238 normal human cortex samples from 2 independent cohorts. We compare endothelial cell-enriched gene profiles with astrocyte, oligodendrocyte, neuron, and microglial cell profiles. Endothelial changes in malignant disease are explored using RNA-seq data from 516 lower-grade gliomas and 401 glioblastomas. Lower-grade gliomas appear to be an "endothelial intermediate" between normal brain and glioblastoma. We apply our method for the prediction of glioblastoma-specific endothelial biomarkers, providing potential diagnostic or therapeutic targets. In summary, we provide a roadmap of endothelial cell identity in normal and malignant brain, using a method developed to resolve bulk RNA-seq into constituent cell-type-enriched profiles.

The Application of Single-Cell RNA Sequencing in Vaccinology

Single-cell RNA sequencing allows highly detailed profiling of cellular immune responses from limited-volume samples, advancing prospects of a new era of systems immunology. The power of single-cell RNA sequencing offers various opportunities to decipher the immune response to infectious diseases and vaccines. Here, we describe the potential uses of single-cell RNA sequencing methods in prophylactic vaccine development, concentrating on infectious diseases including COVID-19. Using examples from several diseases, we review how single-cell RNA sequencing has been used to evaluate the immunological response to different vaccine platforms and regimens. By highlighting published and unpublished single-cell RNA sequencing studies relevant to vaccinology, we discuss some general considerations how the field could be enriched with the widespread adoption of this technology.

A Public BCR Present in a Unique Dual-Receptor-Expressing Lymphocyte from Type 1 Diabetes Patients Encodes a Potent T Cell Autoantigen

T and B cells are the two known lineages of adaptive immune cells. Here, we describe a previously unknown lymphocyte that is a dual expresser (DE) of TCR and BCR and key lineage markers of both B and T cells. In type 1 diabetes (T1D), DEs are predominated by one clonotype that encodes a potent CD4 T cell autoantigen in its antigen binding site. Molecular dynamics simulations revealed that this peptide has an optimal binding register for diabetogenic HLA-DQ8. In concordance, a synthetic version of the peptide forms stable DQ8 complexes and potently stimulates autoreactive CD4 T cells from T1D patients, but not healthy controls. Moreover, mAbs bearing this clonotype are autoreactive against CD4 T cells and inhibit insulin tetramer binding to CD4 T cells. Thus, compartmentalization of adaptive immune cells into T and B cells is not absolute, and violators of this paradigm are likely key drivers of autoimmune diseases.

Determining sequencing depth in a single-cell RNA-seq experiment

An underlying question for virtually all single-cell RNA sequencing experiments is how to allocate the limited sequencing budget: deep sequencing of a few cells or shallow sequencing of many cells? Here we present a mathematical framework which reveals that, for estimating many important gene properties, the optimal allocation is to sequence at a depth of around one read per cell per gene. Interestingly, the corresponding optimal estimator is not the widely-used plug-in estimator, but one developed via empirical Bayes.

For single-cell RNA-seq experiments the sequencing budget is limited, and how it should be optimally allocated to maximize information is not clear. Here the authors develop a mathematical framework to show that, for estimating many gene properties, the optimal allocation is to sequence at the depth of one read per cell per gene.

Eleven grand challenges in single-cell data science

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

Molecular profiling of single neurons of known identity in two ganglia from the crab Cancer borealis

Single-cell transcriptional profiling has become a widespread tool in cell identification, particularly in the nervous system, based on the notion that genomic information determines cell identity. However, many cell-type classification studies are unconstrained by other cellular attributes (e.g., morphology, physiology). Here, we systematically test how accurately transcriptional profiling can assign cell identity to well-studied anatomically and functionally identified neurons in 2 small neuronal networks. While these neurons clearly possess distinct patterns of gene expression across cell types, their expression profiles are not sufficient to unambiguously confirm their identity. We suggest that true cell identity can only be determined by combining gene expression data with other cellular attributes such as innervation pattern, morphology, or physiology.

Understanding circuit organization depends on identification of cell types. Recent advances in transcriptional profiling methods have enabled classification of cell types by their gene expression. While exceptionally powerful and high throughput, the ground-truth validation of these methods is difficult: If cell type is unknown, how does one assess whether a given analysis accurately captures neuronal identity? To shed light on the capabilities and limitations of solely using transcriptional profiling for cell-type classification, we performed 2 forms of transcriptional profiling—RNA-seq and quantitative RT-PCR, in single, unambiguously identified neurons from 2 small crustacean neuronal networks: The stomatogastric and cardiac ganglia. We then combined our knowledge of cell type with unbiased clustering analyses and supervised machine learning to determine how accurately functionally defined neuron types can be classified by expression profile alone. The results demonstrate that expression profile is able to capture neuronal identity most accurately when combined with multimodal information that allows for post hoc grouping, so analysis can proceed from a supervised perspective. Solely unsupervised clustering can lead to misidentification and an inability to distinguish between 2 or more cell types. Therefore, this study supports the general utility of cell identification by transcriptional profiling, but adds a caution: It is difficult or impossible to know under what conditions transcriptional profiling alone is capable of assigning cell identity. Only by combining multiple modalities of information such as physiology, morphology, or innervation target can neuronal identity be unambiguously determined.

Optimal sequencing depth design for whole genome re-sequencing in pigs

BACKGROUND

As whole-genome sequencing is becoming a routine technique, it is important to identify a cost-effective depth of sequencing for such studies. However, the relationship between sequencing depth and biological results from the aspects of whole-genome coverage, variant discovery power and the quality of variants is unclear, especially in pigs. We sequenced the genomes of three Yorkshire boars at an approximately 20X depth on the Illumina HiSeq X Ten platform and downloaded whole-genome sequencing data for three Duroc and three Landrace pigs with an approximately 20X depth for each individual. Then, we downsampled the deep genome data by extracting twelve different proportions of 0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 and 0.9 paired reads from the original bam files to mimic the sequence data of the same individuals at sequencing depths of 1.09X, 2.18X, 3.26X, 4.35X, 6.53X, 8.70X, 10.88X, 13.05X, 15.22X, 17.40X, 19.57X and 21.75X to evaluate the influence of genome coverage, the variant discovery rate and genotyping accuracy as a function of sequencing depth. In addition, SNP chip data for Yorkshire pigs were used as a validation for the comparison of single-sample calling and multisample calling algorithms.

RESULTS

Our results indicated that 10X is an ideal practical depth for achieving plateau coverage and discovering accurate variants, which achieved greater than 99% genome coverage. The number of false-positive variants was increased dramatically at a depth of less than 4X, which covered 95% of the whole genome. In addition, the comparison of multi- and single-sample calling showed that multisample calling was more sensitive than single-sample calling, especially at lower depths. The number of variants discovered under multisample calling was 13-fold and 2-fold higher than that under single-sample calling at 1X and 22X, respectively. A large difference was observed when the depth was less than 4.38X. However, more false-positive variants were detected under multisample calling.

CONCLUSIONS

Our research will inform important study design decisions regarding whole-genome sequencing depth. Our results will be helpful for choosing the appropriate depth to achieve the same power for studies performed under limited budgets.

DC3 is a method for deconvolution and coupled clustering from bulk and single-cell genomics data

Characterizing and interpreting heterogeneous mixtures at the cellular level is a critical problem in genomics. Single-cell assays offer an opportunity to resolve cellular level heterogeneity, e.g., scRNA-seq enables single-cell expression profiling, and scATAC-seq identifies active regulatory elements. Furthermore, while scHi-C can measure the chromatin contacts (i.e., loops) between active regulatory elements to target genes in single cells, bulk HiChIP can measure such contacts in a higher resolution. In this work, we introduce DC3 (De-Convolution and Coupled-Clustering) as a method for the joint analysis of various bulk and single-cell data such as HiChIP, RNA-seq and ATAC-seq from the same heterogeneous cell population. DC3 can simultaneously identify distinct subpopulations, assign single cells to the subpopulations (i.e., clustering) and de-convolve the bulk data into subpopulation-specific data. The subpopulation-specific profiles of gene expression, chromatin accessibility and enhancer-promoter contact obtained by DC3 provide a comprehensive characterization of the gene regulatory system in each subpopulation.

Single-cell RNA-Seq: a next generation sequencing tool for a high-resolution view of the individual cell

Communicated by Ramaswamy H. Sarma.

RNA Interactions Are Essential for CTCF-Mediated Genome Organization

The function of the CCCTC-binding factor (CTCF) in the organization of the genome has become an important area of investigation, but the mechanisms by which CTCF dynamically contributes to genome organization are not clear. We previously discovered that CTCF binds to large numbers of endogenous RNAs, promoting its self-association. In this regard, we now report two independent features that disrupt CTCF association with chromatin: inhibition of transcription and disruption of CTCF-RNA interactions through mutations of 2 of its 11 zinc fingers that are not required for CTCF binding to its cognate DNA site: zinc finger 1 (ZF1) or zinc finger 10 (ZF10). These mutations alter gene expression profiles as CTCF mutants lose their ability to form chromatin loops and thus the ability to insulate chromatin domains and to mediate CTCF long-range genomic interactions. Our results point to the importance of CTCF-mediated RNA interactions as a structural component of genome organization.

Characterizing Human Cell Types and Tissue Origin Using the Benford Law

Processing massive transcriptomic datasets in a meaningful manner requires novel, possibly interdisciplinary, approaches. One principle that can address this challenge is the Benford law (BL), which posits that the occurrence probability of a leading digit in a large numerical dataset decreases as its value increases. Here, we analyzed large single-cell and bulk RNA-seq datasets to test whether cell types and tissue origins can be differentiated based on the adherence of specific genes to the BL. Then, we used the Benford adherence scores of these genes as inputs to machine-learning algorithms and tested their separation accuracy. We found that genes selected based on their first-digit distributions can distinguish between cell types and tissue origins. Moreover, despite the simplicity of this novel feature-selection method, its separation accuracy is higher than that of the mean-expression level approach and is similar to that of the differential expression approach. Thus, the BL can be used to obtain biological insights from massive amounts of numerical genomics data—a capability that could be utilized in various biomedical applications, e.g., to resolve samples of unknown primary origin, identify possible sample contaminations, and provide insights into the molecular basis of cancer subtypes.

Reconstructing B-cell receptor sequences from short-read single-cell RNA sequencing with BRAPeS

BRAPeS is a software for B-cell receptor reconstruction in single cells from very short (25–30 bp) read lengths, which achieves similar success rates and accuracy as applying other methods on long reads.

RNA sequencing of single B cells provides simultaneous measurements of the cell state and its antigen specificity as determined by the B-cell receptor (BCR). However, to uncover the latter, further reconstruction of the BCR sequence is needed. We present BRAPeS (“BCR Reconstruction Algorithm for Paired-end Single cells” ), an algorithm for reconstructing BCRs from short-read paired-end single-cell RNA sequencing. BRAPeS is accurate and achieves a high success rate even at very short (25 bp) read length, which can decrease the cost and increase the number of cells that can be analyzed compared with long reads. BRAPeS is publicly available at the following link: https://github.com/YosefLab/BRAPeS.

Single-Cell Transcriptome Analysis of T Cells

Single-cell RNA-seq (scRNA-seq) has provided novel routes to investigate the heterogeneous populations of T cells and is rapidly becoming a common tool for molecular profiling and identification of novel subsets and functions. This chapter offers an experimental and computational workflow for scRNA-seq analysis of T cells. We focus on the analyses of scRNA-seq data derived from plate-based sorted T cells using flow cytometry and full-length transcriptome protocols such as Smart-Seq2. However, the proposed pipeline can be applied to other high-throughput approaches such as UMI-based methods. We describe a detailed bioinformatics pipeline that can be easily reproduced and discuss future directions and current limitations of these methods in the context of T cell biology.

High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes

High-throughput single-cell RNA sequencing is a powerful technique but only generates short reads from one end of a cDNA template, limiting the reconstruction of highly diverse sequences such as antigen receptors. To overcome this limitation, we combined targeted capture and long-read sequencing of T-cell-receptor (TCR) and B-cell-receptor (BCR) mRNA transcripts with short-read transcriptome profiling of barcoded single-cell libraries generated by droplet-based partitioning. We show that Repertoire and Gene Expression by Sequencing (RAGE-Seq) can generate accurate full-length antigen receptor sequences at nucleotide resolution, infer B-cell clonal evolution and identify alternatively spliced BCR transcripts. We apply RAGE-Seq to 7138 cells sampled from the primary tumor and draining lymph node of a breast cancer patient to track transcriptome profiles of expanded lymphocyte clones across tissues. Our results demonstrate that RAGE-Seq is a powerful method for tracking the clonal evolution from large numbers of lymphocytes applicable to the study of immunity, autoimmunity and cancer.

Single cell RNA sequencing generates short reads from one end of a template, providing incomplete transcript coverage and limiting identification of diverse sequences such as antigen receptors. Here the authors combine long read nanopore sequencing with short read profiling of barcoded libraries to generate full-length antigen receptor sequences.

A statistical simulator scDesign for rational scRNA-seq experimental design

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) has revolutionized biological sciences by revealing genome-wide gene expression levels within individual cells. However, a critical challenge faced by researchers is how to optimize the choices of sequencing platforms, sequencing depths and cell numbers in designing scRNA-seq experiments, so as to balance the exploration of the depth and breadth of transcriptome information.

RESULTS

Here we present a flexible and robust simulator, scDesign, the first statistical framework for researchers to quantitatively assess practical scRNA-seq experimental design in the context of differential gene expression analysis. In addition to experimental design, scDesign also assists computational method development by generating high-quality synthetic scRNA-seq datasets under customized experimental settings. In an evaluation based on 17 cell types and 6 different protocols, scDesign outperformed four state-of-the-art scRNA-seq simulation methods and led to rational experimental design. In addition, scDesign demonstrates reproducibility across biological replicates and independent studies. We also discuss the performance of multiple differential expression and dimension reduction methods based on the protocol-dependent scRNA-seq data generated by scDesign. scDesign is expected to be an effective bioinformatic tool that assists rational scRNA-seq experimental design and comparison of scRNA-seq computational methods based on specific research goals.

AVAILABILITY AND IMPLEMENTATION

We have implemented our method in the R package scDesign, which is freely available at https://github.com/Vivianstats/scDesign.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Antigen Receptor Sequence Reconstruction and Clonality Inference from scRNA-Seq Data

In this chapter, we describe TraCeR and BraCeR, our computational tools for reconstruction of paired full-length antigen receptor sequences and clonality inference from single-cell RNA-seq (scRNA-seq) data. In brief, TraCeR reconstructs T-cell receptor (TCR) sequences from scRNA-seq data by extracting sequencing reads derived from TCRs by aligning the reads from each cell against synthetic TCR sequences. TCR-derived reads are then assembled into full-length recombined TCR sequences. BraCeR builds on the TraCeR pipeline and accounts for somatic hypermutations (SHM) and isotype switching. Here we discuss experimental design, use of the tools, and interpretation of the results.

Workflow Development for the Functional Characterization of ncRNAs

During the last decade, ncRNAs have been investigated intensively and revealed their regulatory role in various biological processes. Worldwide research efforts have identified numerous ncRNAs and multiple RNA subtypes, which are attributed to diverse functionalities known to interact with different functional layers, from DNA and RNA to proteins. This makes the prediction of functions for newly identified ncRNAs challenging. Current bioinformatics and systems biology approaches show promising results to facilitate an identification of these diverse ncRNA functionalities. Here, we review (a) current experimental protocols, i.e., for Next Generation Sequencing, for a successful identification of ncRNAs; (b) sequencing data analysis workflows as well as available computational environments; and (c) state-of-the-art approaches to functionally characterize ncRNAs, e.g., by means of transcriptome-wide association studies, molecular network analyses, or artificial intelligence guided prediction. In addition, we present a strategy to cover the identification and functional characterization of unknown transcripts by using connective workflows.

AutoImpute: Autoencoder based imputation of single-cell RNA-seq data

The emergence of single-cell RNA sequencing (scRNA-seq) technologies has enabled us to measure the expression levels of thousands of genes at single-cell resolution. However, insufficient quantities of starting RNA in the individual cells cause significant dropout events, introducing a large number of zero counts in the expression matrix. To circumvent this, we developed an autoencoder-based sparse gene expression matrix imputation method. AutoImpute, which learns the inherent distribution of the input scRNA-seq data and imputes the missing values accordingly with minimal modification to the biologically silent genes. When tested on real scRNA-seq datasets, AutoImpute performed competitively wrt., the existing single-cell imputation methods, on the grounds of expression recovery from subsampled data, cell-clustering accuracy, variance stabilization and cell-type separability.

An Introduction to the Analysis of Single-Cell RNA-Sequencing Data

The recent development of single-cell RNA sequencing has deepened our understanding of the cell as a functional unit, providing new insights based on gene expression profiles of hundreds to hundreds of thousands of individual cells, and revealing new populations of cells with distinct gene expression profiles previously hidden within analyses of gene expression performed on bulk cell populations. However, appropriate analysis and utilization of the massive amounts of data generated from single-cell RNA sequencing experiments are challenging and require an understanding of the experimental and computational pathways taken between preparation of input cells and output of interpretable data. In this review, we will discuss the basic principles of these new technologies, focusing on concepts important in the analysis of single-cell RNA-sequencing data. Specifically, we summarize approaches to quality-control measures for determination of which single cells to include for further examination, methods of data normalization and scaling to overcome the relatively inefficient capture rate of mRNA from each cell, and clustering and visualization algorithms used for dimensional reduction of the data to a two-dimensional plot.

The expanding role of systems immunology in decoding the T cell receptor repertoire

T cells play a crucial role in the immune system's defense against many infectious diseases, including persistent infections for which no effective vaccines currently exist. The T cell component of the adaptive immune system is highly complex involving a constantly evolving landscape of various inter-related T cell populations. These T cell populations are characterized by their phenotypic and functional properties as well as the collection, or repertoire, of T cell receptors (TCR) that mediate T cell recognition of antigenic peptides derived from pathogens. Understanding the various processes and factors that impact the development and evolution of the broader T cell repertoire available to recognize and respond to pathogens and the characteristics of antigen-experienced T cell repertoires associated with effective immune control of pathogens is critical to the rational design of T cell-based vaccines and therapies. In this article we discuss, using examples of recent research, the promise that systems immunology approaches, involving quantitative analysis and mathematical and computational modeling of immunological data, hold for decoding the complex TCR repertoire system in the current era of advancing technologies.

Single Cell RNA Sequencing of Rare Immune Cell Populations

Single-cell RNA sequencing (scRNA-Seq) is transforming our ability to characterize cells, particularly rare cells that are often overlooked in bulk population analytical approaches. This has lead to the discovery of new cell types and cellular states that echo the underlying heterogeneity and plasticity in the immune system. Technologies for the capture, sequencing, and bioinformatic analysis of single cells are rapidly improving, and scRNA-Seq is now becoming much more accessible to non-specialized laboratories. Here, we describe our experiences in adopting scRNA-Seq to the study of rare immune cells in their microanatomical niches.

Clonally diverse CD38+HLA-DR+CD8+ T cells persist during fatal H7N9 disease

Severe influenza A virus (IAV) infection is associated with immune dysfunction. Here, we show circulating CD8+ T-cell profiles from patients hospitalized with avian H7N9, seasonal IAV, and influenza vaccinees. Patient survival reflects an early, transient prevalence of highly activated CD38+HLA-DR+PD-1+ CD8+ T cells, whereas the prolonged persistence of this set is found in ultimately fatal cases. Single-cell T cell receptor (TCR)-αβ analyses of activated CD38+HLA-DR+CD8+ T cells show similar TCRαβ diversity but differential clonal expansion kinetics in surviving and fatal H7N9 patients. Delayed clonal expansion associated with an early dichotomy at a transcriptome level (as detected by single-cell RNAseq) is found in CD38+HLA-DR+CD8+ T cells from patients who succumbed to the disease, suggesting a divergent differentiation pathway of CD38+HLA-DR+CD8+ T cells from the outset during fatal disease. Our study proposes that effective expansion of cross-reactive influenza-specific TCRαβ clonotypes with appropriate transcriptome signatures is needed for early protection against severe influenza disease.