Comparative Analysis of Droplet-Based Ultra-High-Throughput Single-Cell RNA-Seq Systems.

Since its establishment in 2009, single-cell RNA sequencing (RNA-seq) has been a major driver behind progress in biomedical research. In developmental biology and stem cell studies, the ability to profile single cells confers particular benefits. Although most studies still focus on individual tissues or organs, the recent development of ultra-high-throughput single-cell RNA-seq has demonstrated potential power in characterizing more complex systems or even the entire body. However, although multiple ultra-high-throughput single-cell RNA-seq systems have attracted attention, no systematic comparison of these systems has been performed. Here, with the same cell line and bioinformatics pipeline, we developed directly comparable datasets for each of three widely used droplet-based ultra-high-throughput single-cell RNA-seq systems, inDrop, Drop-seq, and 10X Genomics Chromium. Although each system is capable of profiling single-cell transcriptomes, their detailed comparison revealed the distinguishing features and suitable applications for each system.

SARS-CoV2 infection in whole lung primarily targets macrophages that display subset-specific responses.

Deciphering the initial steps of SARS-CoV-2 infection, that influence COVID-19 outcomes, is challenging because animal models do not always reproduce human biological processes and in vitro systems do not recapitulate the histoarchitecture and cellular composition of respiratory tissues. To address this, we developed an innovative ex vivo model of whole human lung infection with SARS-CoV-2, leveraging a lung transplantation technique. Through single-cell RNA-seq, we identified that alveolar and monocyte-derived macrophages (AMs and MoMacs) were initial targets of the virus. Exposure of isolated lung AMs, MoMacs, classical monocytes and non-classical monocytes (ncMos) to SARS-CoV-2 variants revealed that while all subsets responded, MoMacs produced higher levels of inflammatory cytokines than AMs, and ncMos contributed the least. A Wuhan lineage appeared to be more potent than a D614G virus, in a dose-dependent manner. Amidst the ambiguity in the literature regarding the initial SARS-CoV-2 cell target, our study reveals that AMs and MoMacs are dominant primary entry points for the virus, and suggests that their responses may conduct subsequent injury, depending on their abundance, the viral strain and dose. Interfering on virus interaction with lung macrophages should be considered in prophylactic strategies.

A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections.

A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell-cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.

Identification of four mitochondria-related genes in sepsis based on RNA sequencing technology.

OBJECTIVES: The purpose of this study was to identify and analyze the mitochondrial genes associated with sepsis patients in order to elucidate the underlying mechanism of sepsis immunity and provide new ideas for the clinical treatment of sepsis. METHODS: The hospitalized cases of sepsis (n = 20) and systemic inflammatory response syndrome (SIRS) (n = 12) admitted to the Emergency Intensive Care Unit (EICU) of the Affiliated Hospital of Southwest Medical University from January 2019 to December 2019 were collected consecutively. RNA-seq was used to sequence the RNA (mRNA) of peripheral blood cells. Bioinformatics techniques were used to screen and identify differentially expressed RNAs, with an absolute value of fold change (FC) greater than or equal to 1.2 and a false discovery rate (FDR) less than 0.05. At the same time, mitochondrial genes were obtained from the MitoCarta 3.0 database. Differential genes were then intersected with mitochondrial genes. The resulting crossover genes were subjected to GO, KEGG, and PPI analysis. Subsequently, the GSE65682 dataset was downloaded from the GEO database for survival analysis to assess the prognostic value of core genes, and GSE67652 was downloaded for ROC curve analysis to validate the diagnostic value of core genes. Finally, the localization of core genes was clarified through 10X single-cell sequencing. RESULTS: The crossing of 314 sepsis differential genes and 1136 mitochondrial genes yielded 28 genes. GO and KEGG analysis showed that the crossover genes were mainly involved in the mitochondrion, mitochondrial matrix, and mitochondrial inner membrane. Survival analysis screened four genes that were significantly negatively associated with the prognosis of sepsis, namely FIS1, FKBP8, GLRX5, and GUK1. A comparison of peripheral blood RNA-seq results between the sepsis group and the SIRS group showed that the expression levels of these four genes were significantly decreased in the sepsis group compared to the SIRS group. ROC curve analysis based on GSE67652 indicates these four genes' high sensitivity and specificity for sepsis detection. Additionally, single-cell RNA sequencing found that the core genes were mainly expressed in macrophages, T cells, and B cells. CONCLUSIONS: Mitochondria-related genes (FIS1, FKBP8, GLRX5, GUK1) were underexpressed in the sepsis group, negatively correlated with survival, and mainly distributed in immune cells. This finding may guide studying the immune-related mechanisms of sepsis. This study protocol was reviewed by the Ethics Committee of the Affiliated Hospital of Southwest Medical University (ethics number: KY2018029), the clinical trial registration number is ChiCTR1900021261, and the registration date is February 4, 2019.

Cryopreservation of cerebrospinal fluid cells preserves the transcriptional landscape for single-cell analysis.

Cerebrospinal fluid (CSF) matrix biomarkers have become increasingly valuable surrogate markers of neuropsychiatric diseases in research and clinical practice. In contrast, CSF cells have been rarely investigated due to their relative scarcity and fragility, and lack of common collection and cryopreservation protocols, with limited exceptions for neurooncology and primary immune-based diseases like multiple sclerosis. the advent of a microfluidics-based multi-omics approach to studying individual cells has allowed for the study of cellular phenotyping, intracellular dynamics, and intercellular relationships that provide multidimensionality unable to be obtained through acellular fluid-phase analyses. challenges to cell-based research include site-to-site differences in handling, storage, and thawing methods, which can lead to inaccuracy and inter-assay variability. In the present study, we performed single-cell RNA sequencing (10x Genomics) on fresh or previously cryopreserved human CSF samples from three alternative cryopreservation methods: Fetal Bovine Serum with Dimethyl sulfoxide (FBS/DMSO), FBS/DMSO after a DNase step (a step often included in epigenetic studies), and cryopreservation using commercially available Recovery© media. In comparing relative differences between fresh and cryopreserved samples, we found little effect of the cryopreservation method on being able to resolve donor-linked cell type proportions, markers of cellular stress, and overall gene expression at the single-cell level, whereas donor-specific differences were readily discernable. We further demonstrate the compatibility of fresh and cryopreserved CSF immune cell sequencing using biologically relevant sexually dimorphic gene expression differences by donor. Our findings support the utility and interchangeability of FBS/DMSO and Recovery cryopreservation with fresh sample analysis, providing a methodological grounding that will enable researchers to further expand our understanding of the CSF immune cell contributions to neurological and psychiatric disease.

Comparative Analysis of Single-Cell RNA Sequencing Methods with and without Sample Multiplexing.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating biological heterogeneity at the single-cell level in human systems and model organisms. Recent advances in scRNA-seq have enabled the pooling of cells from multiple samples into single libraries, thereby increasing sample throughput while reducing technical batch effects, library preparation time, and the overall cost. However, a comparative analysis of scRNA-seq methods with and without sample multiplexing is lacking. In this study, we benchmarked methods from two representative platforms: Parse Biosciences (Parse; with sample multiplexing) and 10x Genomics (10x; without sample multiplexing). By using peripheral blood mononuclear cells (PBMCs) obtained from two healthy individuals, we demonstrate that demultiplexed scRNA-seq data obtained from Parse showed similar cell type frequencies compared to 10x data where samples were not multiplexed. Despite relatively lower cell capture affecting library preparation, Parse can detect rare cell types (e.g., plasmablasts and dendritic cells) which is likely due to its relatively higher sensitivity in gene detection. Moreover, a comparative analysis of transcript quantification between the two platforms revealed platform-specific distributions of gene length and GC content. These results offer guidance for researchers in designing high-throughput scRNA-seq studies.

Comparison of the Illumina NextSeq 2000 and GeneMind Genolab M sequencing platforms for spatial transcriptomics.

BACKGROUND: The Illumina sequencing systems demonstrate high efficiency and power and remain the most popular platforms. Platforms with similar throughput and quality profiles but lower costs are under intensive development. In this study, we compared two platforms Illumina NextSeq 2000 and GeneMind Genolab M for 10x Genomics Visium spatial transcriptomics. RESULTS: The performed comparison demonstrates that GeneMind Genolab M sequencing platform produces highly consistent with Illumina NextSeq 2000 sequencing results. Both platforms have similar performance in terms of sequencing quality and detection of UMI, spatial barcode, and probe sequence. Raw read mapping and following read counting produced highly comparable results that is confirmed by quality control metrics and strong correlation between expression profiles in the same tissue spots. Downstream analysis including dimension reduction and clustering demonstrated similar results, and differential gene expression analysis predominantly detected the same genes for both platforms. CONCLUSIONS: GeneMind Genolab M instrument is similar to Illumina sequencing efficacy and is suitable for 10x Genomics Visium spatial transcriptomics.

Single cell transcriptome sequencing on the Nanopore platform with ScNapBar.

The current ecosystem of single cell RNA-seq platforms is rapidly expanding, but robust solutions for single cell and single molecule full- length RNA sequencing are virtually absent. A high-throughput solution that covers all aspects is necessary to study the complex life of mRNA on the single cell level. The Nanopore platform offers long read sequencing and can be integrated with the popular single cell sequencing method on the 10x Chromium platform. However, the high error-rate of Nanopore reads poses a challenge in downstream processing (e.g. for cell barcode assignment). We propose a solution to this particular problem by using a hybrid sequencing approach on Nanopore and Illumina platforms. Our software ScNapBar enables cell barcode assignment with high accuracy, especially if sequencing satura- tion is low. ScNapBar uses unique molecular identifier (UMI) or Naive Bayes probabilistic approaches in the barcode assignment, depending on the available Illumina sequencing depth. We have benchmarked the two approaches on simulated and real Nanopore datasets. We further applied ScNapBar to pools of cells with an active or a silenced non-sense mediated RNA decay pathway. Our Nanopore read assignment distinguishes the respective cell populations and reveals characteristic nonsense-mediated mRNA decay events depending on cell status.

spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data.

BACKGROUND: Spatially-resolved transcriptomics has now enabled the quantification of high-throughput and transcriptome-wide gene expression in intact tissue while also retaining the spatial coordinates. Incorporating the precise spatial mapping of gene activity advances our understanding of intact tissue-specific biological processes. In order to interpret these novel spatial data types, interactive visualization tools are necessary. RESULTS: We describe spatialLIBD, an R/Bioconductor package to interactively explore spatially-resolved transcriptomics data generated with the 10x Genomics Visium platform. The package contains functions to interactively access, visualize, and inspect the observed spatial gene expression data and data-driven clusters identified with supervised or unsupervised analyses, either on the user's computer or through a web application. CONCLUSIONS: spatialLIBD is available at https://bioconductor.org/packages/spatialLIBD . It is fully compatible with SpatialExperiment and the Bioconductor ecosystem. Its functionality facilitates analyzing and interactively exploring spatially-resolved data from the Visium platform.

Single-cell RNA-sequencing of Nicotiana attenuata corolla cells reveals the biosynthetic pathway of a floral scent.

High-throughput single-cell RNA sequencing (scRNA-Seq) identifies distinct cell populations based on cell-to-cell heterogeneity in gene expression. By examining the distribution of the density of gene expression profiles, we can observe the metabolic features of each cell population. Here, we employ the scRNA-Seq technique to reveal the entire biosynthetic pathway of a flower volatile. The corolla of the wild tobacco Nicotiana attenuata emits a bouquet of scents that are composed mainly of benzylacetone (BA). Protoplasts from the N. attenuata corolla limbs and throat cups were isolated at three different time points, and the transcript levels of > 16 000 genes were analyzed in 3756 single cells. We performed unsupervised clustering analysis to determine which cell clusters were involved in BA biosynthesis. The biosynthetic pathway of BA was uncovered by analyzing gene co-expression in scRNA-Seq datasets and by silencing candidate genes in the corolla. In conclusion, the high-resolution spatiotemporal atlas of gene expression provided by scRNA-Seq reveals the molecular features underlying cell-type-specific metabolism in a plant.

A Chromosome-Scale Genome Assembly of the Okapi (Okapia johnstoni).

The okapi (Okapia johnstoni), or forest giraffe, is the only species in its genus and the only extant sister group of the giraffe within the family Giraffidae. The species is one of the remaining large vertebrates surrounded by mystery because of its elusive behavior as well as the armed conflicts in the region where it occurs, making it difficult to study. Deforestation puts the okapi under constant anthropogenic pressure, and it is currently listed as "Endangered" on the IUCN Red List. Here, we present the first annotated de novo okapi genome assembly based on PacBio continuous long reads, polished with short reads, and anchored into chromosome-scale scaffolds using Hi-C proximity ligation sequencing. The final assembly (TBG_Okapi_asm_v1) has a length of 2.39 Gbp, of which 98% are represented by 28 scaffolds > 3.9 Mbp. The contig N50 of 61 Mbp and scaffold N50 of 102 Mbp, together with a BUSCO score of 94.7%, and 23 412 annotated genes, underline the high quality of the assembly. This chromosome-scale genome assembly is a valuable resource for future conservation of the species and comparative genomic studies among the giraffids and other ruminants.

WASP: a versatile, web-accessible single cell RNA-Seq processing platform.

BACKGROUND: The technology of single cell RNA sequencing (scRNA-seq) has gained massively in popularity as it allows unprecedented insights into cellular heterogeneity as well as identification and characterization of (sub-)cellular populations. Furthermore, scRNA-seq is almost ubiquitously applicable in medical and biological research. However, these new opportunities are accompanied by additional challenges for researchers regarding data analysis, as advanced technical expertise is required in using bioinformatic software. RESULTS: Here we present WASP, a software for the processing of Drop-Seq-based scRNA-Seq data. Our software facilitates the initial processing of raw reads generated with the ddSEQ or 10x protocol and generates demultiplexed gene expression matrices including quality metrics. The processing pipeline is realized as a Snakemake workflow, while an R Shiny application is provided for interactive result visualization. WASP supports comprehensive analysis of gene expression matrices, including detection of differentially expressed genes, clustering of cellular populations and interactive graphical visualization of the results. The R Shiny application can be used with gene expression matrices generated by the WASP pipeline, as well as with externally provided data from other sources. CONCLUSIONS: With WASP we provide an intuitive and easy-to-use tool to process and explore scRNA-seq data. To the best of our knowledge, it is currently the only freely available software package that combines pre- and post-processing of ddSEQ- and 10x-based data. Due to its modular design, it is possible to use any gene expression matrix with WASP's post-processing R Shiny application. To simplify usage, WASP is provided as a Docker container. Alternatively, pre-processing can be accomplished via Conda, and a standalone version for Windows is available for post-processing, requiring only a web browser.

Choice of pre-processing pipeline influences clustering quality of scRNA-seq datasets.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) has quickly become one of the most dominant techniques in modern transcriptome assessment. In particular, 10X Genomics' Chromium system, with its high throughput approach, turn key and thorough user guide made this cutting-edge technique accessible to many laboratories using diverse animal models. However, standard pre-processing, including the alignment and cell filtering pipelines might not be ideal for every organism or tissue. Here we applied an alternative strategy, based on the pseudoaligner kallisto, on twenty-two publicly available single cell sequencing datasets from a wide range of tissues of eight organisms and compared the results with the standard 10X Genomics' Cell Ranger pipeline. RESULTS: In most of the tested samples, kallisto produced higher sequencing read alignment rates and total gene detection rates in comparison to Cell Ranger. Although datasets processed with Cell Ranger had higher cell counts, outside of human and mouse datasets, these additional cells were routinely of low quality, containing low gene detection rates. Thorough downstream analysis of one kallisto processed dataset, obtained from the zebrafish pineal gland, revealed clearer clustering, allowing the identification of an additional photoreceptor cell type that previously went undetected. The finding of the new cluster suggests that the photoreceptive pineal gland is essentially a bi-chromatic tissue containing both green and red cone-like photoreceptors and implies that the alignment and pre-processing pipeline can affect the discovery of biologically-relevant cell types. CONCLUSION: While Cell Ranger favors higher cell numbers, using kallisto results in datasets with higher median gene detection per cell. We could demonstrate that cell type identification was not hampered by the lower cell count, but in fact improved as a result of the high gene detection rate and the more stringent filtering. Depending on the acquired dataset, it can be beneficial to favor high quality cells and accept a lower cell count, leading to an improved classification of cell types.

Single nucleus transcriptomics data integration recapitulates the major cell types in human liver.

AIM: The aim of this study was to explore the benefits of data integration from different platforms for single nucleus transcriptomics profiling to characterize cell populations in human liver. METHODS: We generated single-nucleus RNA sequencing data from Chromium 10X Genomics and Drop-seq for a human liver sample. We utilized state of the art bioinformatics tools to undertake a rigorous quality control and to integrate the data into a common space summarizing the gene expression variation from the respective platforms, while accounting for known and unknown confounding factors. RESULTS: Analysis of single nuclei transcriptomes from both 10X and Drop-seq allowed identification of the major liver cell types, while the integrated set obtained enough statistical power to separate a small population of inactive hepatic stellate cells that was not characterized in either of the platforms. CONCLUSIONS: Integration of droplet-based single nucleus transcriptomics data enabled identification of a small cluster of inactive hepatic stellate cells that highlights the potential of our approach. We suggest single-nucleus RNA sequencing integrative approaches could be utilized to design larger and cost-effective studies.

Identification of immune and non-immune cells in regenerating axolotl limbs by single-cell sequencing.

Immune cells are known to be critical for successful limb regeneration in the axolotl (Ambystoma mexicanum), but many details regarding their identity, behavior, and function are yet to be resolved. We isolated peripheral leukocytes from the blood of adult axolotls and then created two samples for single-cell sequencing: 1) peripheral leukocytes (N = 7889) and 2) peripheral leukocytes with presumptive macrophages from the intraperitoneal cavity (N = 4998). Using k-means clustering, we identified 6 cell populations from each sample that presented gene expression patterns indicative of erythrocyte, thrombocyte, neutrophil, B-cell, T-cell, and myeloid cell populations. A seventh, presumptive macrophage cell population was identified uniquely from sample 2. We then isolated cells from amputated axolotl limbs at 1 and 6 days post-amputation (DPA) and performed single cell sequencing (N = 8272 and 9906 cells respectively) to identify immune and non-immune cell populations. Using k-means clustering, we identified 8 cell populations overall, with the majority of cells expressing erythrocyte-specific genes. Even though erythrocytes predominated, we used an unbiased approach to identify infiltrating neutrophil, macrophage, and lymphocyte populations at both time points. Additionally, populations expressing genes for epidermal cells, fibroblast-like cells, and endothelial cells were also identified. Consistent with results from previous experimental studies, neutrophils were more abundant at 1 DPA than 6 DPA, while macrophages and non-immune cells exhibited inverse abundance patterns. Of note, we identified a small population of fibroblast-like cells at 1 DPA that was represented by considerably more cells at 6 DPA. We hypothesize that these are early progenitor cells that give rise to the blastema. The enriched gene sets from our work will aid future single-cell investigations of immune cell diversity and function during axolotl limb regeneration.

Draft de novo Genome Assembly of the Elusive Jaguarundi, Puma yagouaroundi.

The Puma lineage within the family Felidae consists of 3 species that last shared a common ancestor around 4.9 million years ago. Whole-genome sequences of 2 species from the lineage were previously reported: the cheetah (Acinonyx jubatus) and the mountain lion (Puma concolor). The present report describes a whole-genome assembly of the remaining species, the jaguarundi (Puma yagouaroundi). We sequenced the genome of a male jaguarundi with 10X Genomics linked reads and assembled the whole-genome sequence. The assembled genome contains a series of scaffolds that reach the length of chromosome arms and is similar in scaffold contiguity to the genome assemblies of cheetah and puma, with a contig N50 = 100.2 kbp and a scaffold N50 = 49.27 Mbp. We assessed the assembled sequence of the jaguarundi genome using BUSCO, aligned reads of the sequenced individual and another published female jaguarundi to the assembled genome, annotated protein-coding genes, repeats, genomic variants and their effects with respect to the protein-coding genes, and analyzed differences of the 2 jaguarundis from the reference mitochondrial genome. The jaguarundi genome assembly and its annotation were compared in quality, variants, and features to the previously reported genome assemblies of puma and cheetah. Computational analyzes used in the study were implemented in transparent and reproducible way to allow their further reuse and modification.

Long live the king: chromosome-level assembly of the lion (Panthera leo) using linked-read, Hi-C, and long-read data.

BACKGROUND: The lion (Panthera leo) is one of the most popular and iconic feline species on the planet, yet in spite of its popularity, the last century has seen massive declines for lion populations worldwide. Genomic resources for endangered species represent an important way forward for the field of conservation, enabling high-resolution studies of demography, disease, and population dynamics. Here, we present a chromosome-level assembly from a captive African lion from the Exotic Feline Rescue Center (Center Point, IN) as a resource for current and subsequent genetic work of the sole social species of the Panthera clade. RESULTS: Our assembly is composed of 10x Genomics Chromium data, Dovetail Hi-C, and Oxford Nanopore long-read data. Synteny is highly conserved between the lion, other Panthera genomes, and the domestic cat. We find variability in the length of runs of homozygosity across lion genomes, indicating contrasting histories of recent and possibly intense inbreeding and bottleneck events. Demographic analyses reveal similar ancient histories across all individuals during the Pleistocene except the Asiatic lion, which shows a more rapid decline in population size. We show a substantial influence on the reference genome choice in the inference of demographic history and heterozygosity. CONCLUSIONS: We demonstrate that the choice of reference genome is important when comparing heterozygosity estimates across species and those inferred from different references should not be compared to each other. In addition, estimates of heterozygosity or the amount or length of runs of homozygosity should not be taken as reflective of a species, as these can differ substantially among individuals. This high-quality genome will greatly aid in the continuing research and conservation efforts for the lion, which is rapidly moving towards becoming a species in danger of extinction.

The first complete mitochondrial genome of the sand dollar Sinaechinocyamus mai (Echinoidea: Clypeasteroida).

Morphologic and molecular data often lead to different hypotheses of phylogenetic relationships. Such incongruence has been found in the echinoderm class Echinoidea. In particular, the phylogenetic status of the order Clypeasteroida is not well resolved. Complete mitochondrial genomes are currently available for 29 echinoid species, but no clypeasteroid had been sequenced to date. DNA extracted from a single live individual of Sinaechinocyamus mai was sequenced with 10× Genomics technology. This first complete mitochondrial genome (mitogenome) for the order Clypeasteroida is 15,756 base pairs in length. Phylogenomic analysis based on 34 ingroup taxa belonging to nine orders of the class Echinoidea show congruence between our new genetic inference and published trees based on morphologic characters, but also includes some intriguing differences that imply the need for additional investigation.

Hap10: reconstructing accurate and long polyploid haplotypes using linked reads.

BACKGROUND: Haplotype information is essential for many genetic and genomic analyses, including genotype-phenotype associations in human, animals and plants. Haplotype assembly is a method for reconstructing haplotypes from DNA sequencing reads. By the advent of new sequencing technologies, new algorithms are needed to ensure long and accurate haplotypes. While a few linked-read haplotype assembly algorithms are available for diploid genomes, to the best of our knowledge, no algorithms have yet been proposed for polyploids specifically exploiting linked reads. RESULTS: The first haplotyping algorithm designed for linked reads generated from a polyploid genome is presented, built on a typical short-read haplotyping method, SDhaP. Using the input aligned reads and called variants, the haplotype-relevant information is extracted. Next, reads with the same barcodes are combined to produce molecule-specific fragments. Then, these fragments are clustered into strongly connected components which are then used as input of a haplotype assembly core in order to estimate accurate and long haplotypes. CONCLUSIONS: Hap10 is a novel algorithm for haplotype assembly of polyploid genomes using linked reads. The performance of the algorithms is evaluated in a number of simulation scenarios and its applicability is demonstrated on a real dataset of sweet potato.

Single-Cell Virus Sequencing of Influenza Infections That Trigger Innate Immunity.

Influenza virus-infected cells vary widely in their expression of viral genes and only occasionally activate innate immunity. Here, we develop a new method to assess how the genetic variation in viral populations contributes to this heterogeneity. We do this by determining the transcriptome and full-length sequences of all viral genes in single cells infected with a nominally "pure" stock of influenza virus. Most cells are infected by virions with defects, some of which increase the frequency of innate-immune activation. These immunostimulatory defects are diverse and include mutations that perturb the function of the viral polymerase protein PB1, large internal deletions in viral genes, and failure to express the virus's interferon antagonist NS1. However, immune activation remains stochastic in cells infected by virions with these defects and occasionally is triggered even by virions that express unmutated copies of all genes. Our work shows that the diverse spectrum of defects in influenza virus populations contributes to-but does not completely explain-the heterogeneity in viral gene expression and immune activation in single infected cells.IMPORTANCE Because influenza virus has a high mutation rate, many cells are infected by mutated virions. But so far, it has been impossible to fully characterize the sequence of the virion infecting any given cell, since conventional techniques such as flow cytometry and single-cell transcriptome sequencing (scRNA-seq) only detect if a protein or transcript is present, not its sequence. Here we develop a new approach that uses long-read PacBio sequencing to determine the sequences of virions infecting single cells. We show that viral genetic variation explains some but not all of the cell-to-cell variability in viral gene expression and innate immune induction. Overall, our study provides the first complete picture of how viral mutations affect the course of infection in single cells.