Single cell genomics: advances and future perspectives. PLoS Genet. 2014;10:e1004126. [PMID: 24497842 PMCID: PMC3907301 DOI: 10.1371/journal.pgen.1004126]

Transcriptomics, single-cell sequencing and spatial sequencing-based studies of cerebral ischemia

With high disability and mortality rate as well as highly complex pathogenesis, cerebral ischemia is highly morbid, prone to recurrence. To comprehensively understand the pathophysiological process of cerebral ischemia and to find new therapeutic strategies, a new approach to cerebral ischemia transcriptomics has emerged in recent years. By integrating data from multiple levels of transcriptomics, such as transcriptomics, single-cell transcriptomics, and spatial transcriptomics, this new approach can provide powerful help in revealing the molecular mechanisms of cerebral ischemia occurrence and development. Key findings highlight the critical roles of inflammation, blood-brain barrier dysfunction, and mitochondrial dysregulation in cerebral ischemia, offering potential biomarkers and therapeutic targets for early diagnosis and personalized treatment. A review of the research progress of cerebral ischemic injury mechanism under the analysis of the comprehensive transcriptomics research method was presented in this article, aiming to study the potential mechanism to provide new, innovative therapeutic strategies for this disease.

Large-scale acoustic single cell trapping and selective releasing

Recent advancements in single-cell analysis have underscored the need for precise isolation and manipulation of individual cells. Traditional techniques for single-cell manipulation are often limited by the number of cells that can be parallel trapped and processed and usually require complex devices or instruments to operate. Here, we introduce an acoustic microfluidic platform that efficiently traps and selectively releases individual cells using spherical air cavities embedded in a polydimethylsiloxane (PDMS) substrate for large scale manipulation. Our device utilizes the principle of acoustic impedance mismatches to generate near-field acoustic potential gradients that create trapping sites for single cells. These single cell traps can be selectively disabled by illuminating a near-infrared laser pulse, allowing targeted release of trapped cells. This method ensures minimal impact on cell viability and proliferation, making it ideal for downstream single-cell analysis. Experimental results demonstrate our platform's capability to trap and release synthetic microparticles and biological cells with high efficiency and biocompatibility. Our device can handle a wide range of cell sizes (8-30 μm) across a large active manipulation area of 1 cm2 with 20 000 single-cell traps, providing a versatile and robust platform for single-cell applications. This acoustic microfluidic platform offers a cost-effective and practical method for large scale single-cell trapping and selective releasing with potential applications in genomics, proteomics, and other fields requiring precise single-cell manipulation.

SVCFit: Inferring structural variant cellular fraction in tumors

Many tumors evolve through cellular mutation and selection, where subpopulations of cells (subclones) with shared ancestry compete for dominance. Introduction of next generation sequencing enables subclone identification using small somatic. However, there are several advantages to marking subclones with structural variants: they have greater functional impact, play a crucial role in late-stage tumors, and provide a more complete view of genomic instability driving tumor evolution. Here, we present SVCFit, a scalable method to estimate the cellular prevalence of somatic deletions, duplications and inversions. We demonstrate that cellular prevalence estimation can be improved by incorporating distinct read patterns for each structural variant type. Additionally, this improvement is achieved without prior knowledge of tumor purity, which is often inaccurate. Using a combination of simulated data and patient-derived metastatic samples with known mixture proportions, we show that our algorithm achieves significantly greater accuracy than state-of-the-art in estimating the structural variants cellular prevalence (p<0.05). The speed of SVCFit estimation from cost-effective bulk whole-genome sequencing (WGS) makes it well-suited for analyzing large cohorts of sequenced tumor samples, enhancing the accessibility of SV-based clonal reconstruction.

Understanding Genetic Screening: Harnessing Health Information to Prevent Disease Risks

Genetic screening analyzes an individual's genetic information to assess disease risk and provide personalized health recommendations. This article introduces the public to genetic screening, explaining its definition, principles, history, and common types, including prenatal, newborn, adult disease risk, cancer, and pharmacogenetic screening. It elaborates on the benefits of genetic screening, such as early risk detection, personalized prevention, family risk assessment, and reproductive decision-making. The article also notes limitations, including result interpretation uncertainty, psychological and ethical issues, and privacy and discrimination risks. It provides advice on selecting suitable screening, consulting professionals, choosing reliable institutions, and understanding screening purposes and limitations. Finally, it discusses applying screening results through lifestyle adjustments, regular check-ups, and preventive treatments. By comprehensively introducing genetic screening, the article aims to raise public awareness and encourage utilizing this technology to prevent disease and maintain health.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Replication stress increases de novo CNVs across the malaria parasite genome

Changes in the copy number of large genomic regions, termed copy number variations (CNVs), contribute to important phenotypes in many organisms. CNVs are readily identified using conventional approaches when present in a large fraction of the cell population. However, CNVs that are present in only a few genomes across a population are often overlooked but important; if beneficial under specific conditions, a de novo CNV that arises in a single genome can expand during selection to create a larger population of cells with novel characteristics. While the reach of single cell methods to study de novo CNVs is increasing, we continue to lack information about CNV dynamics in rapidly evolving microbial populations. Here, we investigated de novo CNVs in the genome of the Plasmodium parasite that causes human malaria. The highly AT-rich P. falciparum genome readily accumulates CNVs that facilitate rapid adaptation to new drugs and host environments. We employed a low-input genomics approach optimized for this unique genome as well as specialized computational tools to evaluate the de novo CNV rate both before and after the application of stress. We observed a significant increase in genomewide de novo CNVs following treatment with a replication inhibitor. These stress-induced de novo CNVs encompassed genes that contribute to various cellular pathways and tended to be altered in clinical parasite genomes. This snapshot of CNV dynamics emphasizes the connection between replication stress, DNA repair, and CNV generation in this important microbial pathogen.

Single-Cell Lipidomics: An Automated and Accessible Microfluidic Workflow Validated by Capillary Sampling

We report the first demonstration of a microfluidics-based approach to measure lipids in single living cells using widely available liquid chromatography mass spectrometry (LC-MS) instrumentation. The method enables the rapid sorting of live cells into liquid chambers formed on standard Petri dishes and their subsequent dispensing into vials for analysis using LC-MS. This approach facilitates automated sampling, data acquisition, and analysis and carries the additional advantage of chromatographic separation, aimed at reducing matrix effects present in shotgun lipidomics approaches. We demonstrate that our method detects comparable numbers of features at around 200 lipids in populations of single cells versus established live single-cell capillary sampling methods and with greater throughput, albeit with the loss of spatial resolution. We also show the importance of optimization steps in addressing challenges from lipid contamination, especially in blanks, and demonstrate a 75% increase in the number of lipids identified. This work opens up a novel, accessible, and high-throughput way to obtain single-cell lipid profiles and also serves as an important validation of single-cell lipidomics through the use of different sampling methods.

Exome Sequencing Starting from Single Cells

Single-cell genomic analysis enables researchers to gain novel insights across diverse research areas, including developmental biology, tumor heterogeneity, and disease pathogenesis. Conducting single-cell genomic analysis using next-generation sequencing (NGS) methods has traditionally been challenging as the amount of genomic DNA present in a single cell is limited. Advancements in multiple displacement amplification (MDA) technologies allow the unbiased amplification of limited quantities of DNA under conditions that maintain its integrity. This method of amplification results in high yield and facilitates the generation of high-complexity NGS libraries that ensure the highest coverage to effectively allow variant calling. With the introduction of new sequencing platforms and chemistry, whole genome sequencing became a more cost-effective application, but enrichment of specific regions of interest further reduces the amount of required sequencing output and associated costs. There are two enrichment methods, polymerase chain reaction (PCR)-based and hybrid-capture-based methods. PCR-based methods are very flexible and highly effective but focus on specific loci, typically known to be associated with disease. Inherited diseases of unknown genetic origin require a more comprehensive approach to capture the genetic variation that is not yet associated with a specific disease. Hybrid capture enrichment methods require considerable amounts of DNA such that exome enrichment from single cells is only possible after preamplification of this limited material. This article describes the complete workflow from single cells and small quantities of DNA to exome-NGS libraries for Illumina sequencing instruments and includes the following protocols: © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Whole genome amplification from single cells or small amounts of gDNA Basic Protocol 2: NGS library generation of MDA-amplified material Basic Protocol 3: Exome enrichment.

Molecular and functional profiling of cell diversity and identity in the lateral superior olive, an auditory brainstem center with ascending and descending projections

The lateral superior olive (LSO), a prominent integration center in the auditory brainstem, contains a remarkably heterogeneous population of neurons. Ascending neurons, predominantly principal neurons (pLSOs), process interaural level differences for sound localization. Descending neurons (lateral olivocochlear neurons, LOCs) provide feedback into the cochlea and are thought to protect against acoustic overload. The molecular determinants of the neuronal diversity in the LSO are largely unknown. Here, we used patch-seq analysis in mice at postnatal days P10-12 to classify developing LSO neurons according to their functional and molecular profiles. Across the entire sample (n = 86 neurons), genes involved in ATP synthesis were particularly highly expressed, confirming the energy expenditure of auditory neurons. Two clusters were identified, pLSOs and LOCs. They were distinguished by 353 differentially expressed genes (DEGs), most of which were novel for the LSO. Electrophysiological analysis confirmed the transcriptomic clustering. We focused on genes affecting neuronal input-output properties and validated some of them by immunohistochemistry, electrophysiology, and pharmacology. These genes encode proteins such as osteopontin, Kv11.3, and Kvβ3 (pLSO-specific), calcitonin-gene-related peptide (LOC-specific), or Kv7.2 and Kv7.3 (no DEGs). We identified 12 "Super DEGs" and 12 genes showing "Cluster similarity." Collectively, we provide fundamental and comprehensive insights into the molecular composition of individual ascending and descending neurons in the juvenile auditory brainstem and how this may relate to their specific functions, including developmental aspects.

Single-cell transcriptomic analysis reveals transcriptional and cell subpopulation differences between human and pig immune cells

BACKGROUND

The pig is a promising donor candidate for xenotransplantation. Understanding the differences between human and swine immune systems is critical for addressing xenotransplant rejection and hematopoietic reconstitution. The gene transcriptional profile differences between human and pig immune cell subpopulations have not been studied. To assess the similarities and differences between pigs and humans at the levels of gene transcriptional profiles or cell subpopulations are important for better understanding the cross-species similarity of humans and pigs, and it would help establish the fundamental principles necessary to genetically engineer donor pigs and improve xenotransplantation.

OBJECTIVE

To assess the gene transcriptional similarities and differences between pigs and humans.

METHODS

Two pigs and two healthy humans' PBMCs were sorted for 10 × genomics single-cell sequence. We generated integrated human-pig scRNA-seq data from human and pig PBMCs and defined the overall gene expression landscape of pig peripheral blood immune cell subpopulations by updating the set of human-porcine homologous genes. The subsets of immune cells were detected by flow cytometry.

RESULTS

There were significantly less T cells, NK cells and monocytes but more B cells in pig peripheral blood than those in human peripheral blood. High oxidative phosphorylation, HIF-1, glycolysis, and lysosome-related gene expressions in pig CD14+ monocytes were observed, whereas pig CD14+ monocytes exhibited lower levels of cytokine receptors and JAK-STAT-related genes. Pig activated CD4+T cells decreased cell adhesion and inflammation, while enriched for migration and activation processes. Porcine GNLY+CD8+T cells reduced cytotoxicity and increased proliferation compared with human GNLY+CD8+T cells. Pig CD2+CD8+γδT cells were functionally homologous to human CD2+CD4+ γδT cells. Pig CD2-CD8-γδT cells expressed genes with quiescent and precursor characteristics, while CD2-CD8+γδT cells expressed migration and memory-related molecules. Pig CD24+ and CD5+B cells are associated with inflammatory responses.

CONCLUSION

Our research with integrated scRNA-seq assays identified the different distribution of pig immune cell subpopulations and the different transcriptional profiles of human and pig immune cells. This study enables a deeper understanding of the development and function of porcine immune cells.

Closing the genome of unculturable cable bacteria using a combined metagenomic assembly of long and short sequencing reads

Many environmentally relevant micro-organisms cannot be cultured, and even with the latest metagenomic approaches, achieving complete genomes for specific target organisms of interest remains a challenge. Cable bacteria provide a prominent example of a microbial ecosystem engineer that is currently unculturable. They occur in low abundance in natural sediments, but due to their capability for long-distance electron transport, they exert a disproportionately large impact on the biogeochemistry of their environment. Current available genomes of marine cable bacteria are highly fragmented and incomplete, hampering the elucidation of their unique electrogenic physiology. Here, we present a metagenomic pipeline that combines Nanopore long-read and Illumina short-read shotgun sequencing. Starting from a clonal enrichment of a cable bacterium, we recovered a circular metagenome-assembled genome (5.09 Mbp in size), which represents a novel cable bacterium species with the proposed name Candidatus Electrothrix scaldis. The closed genome contains 1109 novel identified genes, including key metabolic enzymes not previously described in incomplete genomes of cable bacteria. We examined in detail the factors leading to genome closure. Foremost, native, non-amplified long reads are crucial to resolve the many repetitive regions within the genome of cable bacteria, and by analysing the whole metagenomic assembly, we found that low strain diversity is key for achieving genome closure. The insights and approaches presented here could help achieve genome closure for other keystone micro-organisms present in complex environmental samples at low abundance.

Manual Dissociation of Mammalian Preimplantation Embryos for Single-Cell Genomics

Single-cell genomics allow the characterization and quantification of molecular heterogeneity from a wide variety of tissues. Here, we describe the manual dissociation and collection of single cells, a method adapted for the characterization of precious small tissues like preimplantation embryos. We also describe the acquisition of mouse embryos by flushing of the oviducts. The cells can then be used in multiple sequencing protocols, for example, Smart-seq2, Smart-seq3, smallseq, and scBSseq.

Single-cell bisulfite-free 5mC and 5hmC sequencing with high sensitivity and scalability

Existing single-cell bisulfite-based DNA methylation analysis is limited by low DNA recovery, and the measurement of 5hmC at single-base resolution remains challenging. Here, we present a bisulfite-free single-cell whole-genome 5mC and 5hmC profiling technique, named Cabernet, which can characterize 5mC and 5hmC at single-base resolution with high genomic coverage. Cabernet utilizes Tn5 transposome for DNA fragmentation, which enables the discrimination between different alleles for measuring hemi-methylation status. Using Cabernet, we revealed the 5mC, hemi-5mC and 5hmC dynamics during early mouse embryo development, uncovering genomic regions exclusively governed by active or passive demethylation. We show that hemi-methylation status can be used to distinguish between pre- and post-replication cells, enabling more efficient cell grouping when integrated with 5mC profiles. The property of Tn5 naturally enables Cabernet to achieve high-throughput single-cell methylome profiling, where we probed mouse cortical neurons and embryonic day 7.5 (E7.5) embryos, and constructed the library for thousands of single cells at high efficiency, demonstrating its potential for analyzing complex tissues at substantially low cost. Together, we present a way of high-throughput methylome and hydroxymethylome detection at single-cell resolution, enabling efficient analysis of the epigenetic status of biological systems with complicated nature such as neurons and cancer cells.

Mr. Toad's Wild Fungi: Fungal Isolate Diversity on Colorado Boreal Toads and their Capacity for Pathogen Inhibition

The amphibian skin pathogen Batrachochytrium dendrobatidis (Bd) has caused an ongoing biodiversity crisis, including in the locally endangered Colorado boreal toad (Anaxyrus boreas boreas). Although researchers have investigated the bacteria living on amphibian skin and how they interact with Bd, there is less information about fungal community members. This study describes (1) the diversity of culturable fungi from boreal toad skin, (2) which subset of these isolates is Bd-inhibitory, and (3) how Bd affects these isolates' growth and morphology. Most isolates were from the orders Capnodiales, Helotiales, and Pleosporales. Of 16 isolates tested for Bd-inhibition, two from the genus Neobulgaria and three from Pseudeurotium inhibited Bd. Fungal growth in co-culture with Bd varied with weak statistical support for Neobulgaria sp. (isolate BTF_36) and cf Psychrophila (isolate BTF_60) (p-values = 0.076 and 0.092, respectively). Fungal morphology remained unchanged in co-culture with Bd, however, these results could be attributed to low replication per isolate. Nonetheless, two fungal isolates' growth may have been affected by Bd, implying that fungal growth changes in Bd co-culture could be a variable worth measuring in the future (with higher replication). These findings add to the sparse but growing literature on amphibian-associated fungi and suggest further study may uncover the relevance of fungi to amphibian health and Bd infection.

Single cell genomics applications in forensic science: Current state and future directions

Standard methods of mixture analysis involve subjecting a dried crime scene sample to a "bulk" DNA extraction method such that the resulting isolate compromises a homogenized DNA mixture from the individual donors. If, however, instead of bulk DNA extraction, a sufficient number of individual cells from the mixed stain are subsampled prior to genetic analysis then it should be possible to recover highly probative single source, non-mixed scDNA profiles from each of the donors. This approach can detect low DNA level minor donors to a mixture that otherwise would not be identified using standard methods and can also resolve rare mixtures comprising first degree relatives and thereby also prevent the false inclusion of non-donor relatives. This literature landscape review and associated commentary reports on the history and increasing interest in current and potential future applications of scDNA in forensic genomics, and critically evaluates opportunities and impediments to further progress.

Single-cell RNA sequencing reveals blastomere heterogeneity of 2-cell embryos in pigs

In mammals, single blastomeres from as early as 2-cell embryos demonstrate heterogeneous developmental capacity and fate decision into different cell lineages. However, mechanisms underlying blastomere heterogeneity of 2-cell embryos remain largely unresolved. Here, we analysed the molecular heterogeneity of full-length mRNAs and their 3'UTR regions, based on the single-cell RNA-seq data of pig 2-cell embryos generated from in vivo fertilization (in vivo), in vitro fertilization (in vitro) and parthenogenetic activation (PA), respectively. First, unsupervised clustering helped discover two different groups of blastomeres for 2-cell pig embryos. Between these two groups of blastomeres in pig 2-cell embryos, 35, 301 and 428 full-length mRNAs respectively in in vivo, in vitro and PA embryo types were identified to be differentially expressed (padj  ≤ .05 and |log2 [fold change]| ≥1) (DE mRNAs), while 92, 89 and 42 mRNAs were shown to be with significantly different 3'UTR lengths (3'UTR DE) (padj  ≤ .05). Gene enrichment for both DE mRNAs and 3'UTR DE mRNAs found multiple signalling pathways, including cell cycle, RNA processing. Few numbers of common DE mRNAs and 3'UTR DE mRNAs existed between in vitro and in vivo blastomeres derived from 2-cell embryos, indicating the larger differences between in vitro and in vivo fertilized embryos. Integrative genomics viewer analysis further identified that 3'UTRs of HSDL2 and SGTA (in vivo), FAM204A and phosphoserine phosphatase (in vitro), PRPF40A and RPIA (PA) had >100 nt average length changes. Moreover, numbers and locations of regulatory elements (polyadenylation site, cytoplasmic polyadenylation element and microRNA binding sites) within 3'UTRs of these DE mRNAs were predicted. These results indicate that molecular heterogeneity existed among blastomeres from different types of pig 2-cell embryos, providing useful information and novel insights into future functional investigation on its relationship with the subsequent embryo development and differentiation.

Microbial single-cell mass spectrometry: status, challenges, and prospects

Single-cell analysis uncovers phenotypic differences between cells in a population and dissects their individual physiological states and differences on all omics levels from genome to phenome. Spectrometric observation allows label-free analysis of the metabolome and proteome of individual cells, but is still mainly limited to the analysis of mammalian single cells. Recent progress in mass spectrometry approaches now enables the analysis of microbial single cells - mainly by miniaturizing cell handling, incubation, and improving chip-coupling concepts for analyte ionization by interfacing microfluidic chips and mass spectrometers. This review aims at distilling the enabling principles behind microbial single-cell mass spectrometry and puts them into perspective for the future of the field.

Current Status of Next-Generation Sequencing in Bone Genetic Diseases

The development of next-generation sequencing (NGS) has dramatically increased the speed and volume of genetic analysis. Furthermore, the range of applications of NGS is rapidly expanding to include genome, epigenome (such as DNA methylation), metagenome, and transcriptome analyses (such as RNA sequencing and single-cell RNA sequencing). NGS enables genetic research by offering various sequencing methods as well as combinations of methods. Bone tissue is the most important unit supporting the body and is a reservoir of calcium and phosphate ions, which are important for physical activity. Many genetic diseases affect bone tissues, possibly because metabolic mechanisms in bone tissue are complex. For instance, the presence of specialized immune cells called osteoclasts in the bone tissue, which absorb bone tissue and interact with osteoblasts in complex ways to support normal vital functions. Moreover, the many cell types in bones exhibit cell-specific proteins for their respective activities. Mutations in the genes encoding these proteins cause a variety of genetic disorders. The relationship between age-related bone tissue fragility (also called frailty) and genetic factors has recently attracted attention. Herein, we discuss the use of genomic, epigenomic, transcriptomic, and metagenomic analyses in bone genetic disorders.

Methods and applications for single-cell and spatial multi-omics

The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome from single cells is transforming our understanding of cell biology in health and disease. In less than a decade, the field has seen tremendous technological revolutions that enable crucial new insights into the interplay between intracellular and intercellular molecular mechanisms that govern development, physiology and pathogenesis. In this Review, we highlight advances in the fast-developing field of single-cell and spatial multi-omics technologies (also known as multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers. We demonstrate their impact on fundamental cell biology and translational research, discuss current challenges and provide an outlook to the future.

Liver in infections: a single-cell and spatial transcriptomics perspective

The liver is an immune organ that plays a vital role in the detection, capture, and clearance of pathogens and foreign antigens that invade the human body. During acute and chronic infections, the liver transforms from a tolerant to an active immune state. The defence mechanism of the liver mainly depends on a complicated network of intrahepatic and translocated immune cells and non-immune cells. Therefore, a comprehensive liver cell atlas in both healthy and diseased states is needed for new therapeutic target development and disease intervention improvement. With the development of high-throughput single-cell technology, we can now decipher heterogeneity, differentiation, and intercellular communication at the single-cell level in sophisticated organs and complicated diseases. In this concise review, we aimed to summarise the advancement of emerging high-throughput single-cell technologies and re-define our understanding of liver function towards infections, including hepatitis B virus, hepatitis C virus, Plasmodium, schistosomiasis, endotoxemia, and corona virus disease 2019 (COVID-19). We also unravel previously unknown pathogenic pathways and disease mechanisms for the development of new therapeutic targets. As high-throughput single-cell technologies mature, their integration into spatial transcriptomics, multiomics, and clinical data analysis will aid in patient stratification and in developing effective treatment plans for patients with or without liver injury due to infectious diseases.

Elevated intracellular pH of zygotes during mouse aging causes mitochondrial dysfunction associated with poor embryo development

The mortality of preimplantation embryos is positively correlated with maternal age. However, the underlying mechanism for the poor quality of embryos remains unclear. Here, we found that aging caused elevated intracellular pH (pHi) in zygotes, which could trigger aberrant mitochondrial membrane potential, increased reactive oxygen species (ROS) levels, and poor embryo development. Moreover, single-cell transcriptome sequencing of mouse zygotes identified 120 genes that were significantly differentially expressed (DE) between young and older zygotes. These include genes such as Slc14a1, Fxyd5, CD74, and Bst, which are related to cell division, ion transporter, and cell differentiation. Further analysis indicated that these DE genes were enriched in apoptosis, the NF-kappa B signaling pathway, and the chemokine signaling pathway, which might be the key regulatory pathway affecting the quality of zygotes and subsequent embryo development. Taken together, our study helps elucidate the poor quality and development of older preimplantation embryos.

Identification and Functional Analysis of Transcriptome Profiles, Long Non-Coding RNAs, Single-Nucleotide Polymorphisms, and Alternative Splicing from the Oocyte to the Preimplantation Stage of Sheep by Single-Cell RNA Sequencing

Numerous dynamic and complicated processes characterize development from the oocyte to the embryo. However, given the importance of functional transcriptome profiles, long non-coding RNAs, single-nucleotide polymorphisms, and alternative splicing during embryonic development, the effect that these features have on the blastomeres of 2-, 4-, 8-, 16-cell, and morula stages of development has not been studied. Here, we carried out experiments to identify and functionally analyze the transcriptome profiles, long non-coding RNAs, single-nucleotide polymorphisms (SNPs), and alternative splicing (AS) of cells from sheep from the oocyte to the blastocyst developmental stages. We found between the oocyte and zygote groups significantly down-regulated genes and the second-largest change in gene expression occurred between the 8- and 16-cell stages. We used various methods to construct a profile to characterize cellular and molecular features and systematically analyze the related GO and KEGG profile of cells of all stages from the oocyte to the blastocyst. This large-scale, single-cell atlas provides key cellular information and will likely assist clinical studies in improving preimplantation genetic diagnosis.

Spatial single cell metabolomics: Current challenges and future developments

Single cell metabolomics is a rapidly advancing field of bio-analytical chemistry which aims to observe cellular biology with the greatest detail possible. Mass spectrometry imaging and selective cell sampling (e.g. using nanocapillaries) are two common approaches within the field. Recent achievements such as observation of cell-cell interactions, lipids determining cell states and rapid phenotypic identification demonstrate the efficacy of these approaches and the momentum of the field. However, single cell metabolomics can only continue with the same impetus if the universal challenges to the field are met, such as the lack of strategies for standardisation and quantification, and lack of specificity/sensitivity. Mass spectrometry imaging and selective cell sampling come with unique advantages and challenges which, in many cases are complementary to each other. We propose here that the challenges specific to each approach could be ameliorated with collaboration between the two communities driving these approaches.

Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis

In the analysis of single-cell RNA-sequencing (scRNA-seq) data, how to effectively and accurately identify cell clusters from a large number of cell mixtures is still a challenge. Low-rank representation (LRR) method has achieved excellent results in subspace clustering. But in previous studies, most LRR-based methods usually choose the original data matrix as the dictionary. In addition, the methods based on LRR usually use spectral clustering algorithm to complete cell clustering. Therefore, there is a matching problem between the spectral clustering method and the affinity matrix, which is difficult to ensure the optimal effect of clustering. Considering the above two points, we propose the DLNLRR method to better identify the cell type. First, DLNLRR can update the dictionary during the optimization process instead of using the predefined fixed dictionary, so it can realize dictionary learning and LRR learning at the same time. Second, DLNLRR can realize subspace clustering without relying on spectral clustering algorithm, that is, we can perform clustering directly based on the low-rank matrix. Finally, we carry out a large number of experiments on real single-cell datasets and experimental results show that DLNLRR is superior to other scRNA-seq data analysis algorithms in cell type identification.

Multi-omics research strategies in ischemic stroke: A multidimensional perspective

Ischemic stroke (IS) is a multifactorial and heterogeneous neurological disorder with high rate of death and long-term impairment. Despite years of studies, there are still no stroke biomarkers for clinical practice, and the molecular mechanisms of stroke remain largely unclear. The high-throughput omics approach provides new avenues for discovering biomarkers of IS and explaining its pathological mechanisms. However, single-omics approaches only provide a limited understanding of the biological pathways of diseases. The integration of multiple omics data means the simultaneous analysis of thousands of genes, RNAs, proteins and metabolites, revealing networks of interactions between multiple molecular levels. Integrated analysis of multi-omics approaches will provide helpful insights into stroke pathogenesis, therapeutic target identification and biomarker discovery. Here, we consider advances in genomics, transcriptomics, proteomics and metabolomics and outline their use in discovering the biomarkers and pathological mechanisms of IS. We then delineate strategies for achieving integration at the multi-omics level and discuss how integrative omics and systems biology can contribute to our understanding and management of IS.

Approaches to benchmark and characterize in vitro human model systems

In vitro human models, such as gastruloids and organoids, are complex three-dimensional (3D) structures often consist of cells from multiple germ layers that possess some attributes of a developing embryo or organ. To use these models to interrogate human development and organogenesis, these 3D models must accurately recapitulate aspects of their in vivo counterparts. Recent advances in single-cell technologies, including sequencing and spatial approaches, have enabled efforts to better understand and directly compare organoids with native tissues. For example, single-cell genomic efforts have created cell and organ atlases that enable benchmarking of in vitro models and can also be leveraged to gain novel biological insights that can be used to further improve in vitro models. This Spotlight discusses the state of current in vitro model systems, the efforts to create large publicly available atlases of the developing human and how these data are being used to improve organoids. Limitations and perspectives on future efforts are also discussed.

Single-cell sequencing: A cutting edge tool in molecular medical research

The rapid development of advanced high throughput technologies and introduction of high resolution "omics" data through analysis of biological molecules has revamped medical research. Single-cell sequencing in recent years, is in fact revolutionising the field by providing a deeper, spatio-temporal analyses of individual cells within tissues and their relevance to disease. Like conventional sequencing, the single-cell approach deciphers the sequence of nucleotides in a given Deoxyribose Nucleic Acid (DNA), Ribose Nucleic Acid (RNA), Micro Ribose Nucleic Acid (miRNA), epigenetically modified DNA or chromatin DNA; however, the unit of analyses is changed to single cells rather than the entire tissue. Further, a large number of single cells analysed from a single tissue generate a unique holistic perception capturing all kinds of perturbations across different cells in the tissue that increases the precision of data. Inherently, execution of the technique generates a large amount of data, which is required to be processed in a specific manner followed by customised bioinformatic analysis to produce meaningful results. The most crucial role of single-cell sequencing technique is in elucidating the inter-cell genetic, epigenetic, transcriptomic and proteomic heterogeneity in health and disease. The current review presents a brief overview of this cutting-edge technology and its applications in medical research.

Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges

With the advent of single-cell RNA-sequencing (scRNA-seq), it is possible to measure the expression dynamics of genes at the single-cell level. Through scRNA-seq, a huge amount of expression data for several thousand(s) of genes over million(s) of cells are generated in a single experiment. Differential expression analysis is the primary downstream analysis of such data to identify gene markers for cell type detection and also provide inputs to other secondary analyses. Many statistical approaches for differential expression analysis have been reported in the literature. Therefore, we critically discuss the underlying statistical principles of the approaches and distinctly divide them into six major classes, i.e., generalized linear, generalized additive, Hurdle, mixture models, two-class parametric, and non-parametric approaches. We also succinctly discuss the limitations that are specific to each class of approaches, and how they are addressed by other subsequent classes of approach. A number of challenges are identified in this study that must be addressed to develop the next class of innovative approaches. Furthermore, we also emphasize the methodological challenges involved in differential expression analysis of scRNA-seq data that researchers must address to draw maximum benefit from this recent single-cell technology. This study will serve as a guide to genome researchers and experimental biologists to objectively select options for their analysis.

HBV genome-enriched single cell sequencing revealed heterogeneity in HBV-driven hepatocellular carcinoma (HCC)

BACKGROUND

Hepatitis B virus (HBV) related hepatocellular carcinoma (HCC) is heterogeneous and frequently contains multifocal tumors, but how the multifocal tumors relate to each other in terms of HBV integration and other genomic patterns is not clear.

METHODS

To interrogate heterogeneity of HBV-HCC, we developed a HBV genome enriched single cell sequencing (HGE-scSeq) procedure and a computational method to identify HBV integration sites and infer DNA copy number variations (CNVs).

RESULTS

We performed HGE-scSeq on 269 cells from four tumor sites and two tumor thrombi of a HBV-HCC patient. HBV integrations were identified in 142 out of 269 (53%) cells sequenced, and were enriched in two HBV integration hotspots chr1:34,397,059 (CSMD2) and chr8:118,557,327 (MED30/EXT1). There were also 162 rare integration sites. HBV integration sites were enriched in DNA fragile sites and sequences around HBV integration sites were enriched for microhomologous sequences between human and HBV genomes. CNVs were inferred for each individual cell and cells were grouped into four clonal groups based on their CNVs. Cells in different clonal groups had different degrees of HBV integration heterogeneity. All of 269 cells carried chromosome 1q amplification, a recurrent feature of HCC tumors, suggesting that 1q amplification occurred before HBV integration events in this case study. Further, we performed simulation studies to demonstrate that the sequential events (HBV infecting transformed cells) could result in the observed phenotype with biologically reasonable parameters.

CONCLUSION

Our HGE-scSeq data reveals high heterogeneity of HCC tumor cells in terms of both HBV integrations and CNVs. There were two HBV integration hotspots across cells, and cells from multiple tumor sites shared some HBV integration and CNV patterns.

Somatic variant calling from single-cell DNA sequencing data

Single-cell sequencing has gained popularity in recent years. Despite its numerous applications, single-cell DNA sequencing data is highly error-prone due to technical biases arising from uneven sequencing coverage, allelic dropout, and amplification error. With these artifacts, the identification of somatic genomic variants becomes a challenging task, and over the years, several methods have been developed explicitly for this type of data. Single-cell variant callers implement distinct strategies, make different use of the data, and typically result in many discordant calls when applied to real data. Here, we review current approaches for single-cell variant calling, emphasizing single nucleotide variants. We highlight their potential benefits and shortcomings to help users choose a suitable tool for their data at hand.

Single-cell RNA sequencing technologies and applications: A brief overview

Single-cell RNA sequencing (scRNA-seq) technology has become the state-of-the-art approach for unravelling the heterogeneity and complexity of RNA transcripts within individual cells, as well as revealing the composition of different cell types and functions within highly organized tissues/organs/organisms. Since its first discovery in 2009, studies based on scRNA-seq provide massive information across different fields making exciting new discoveries in better understanding the composition and interaction of cells within humans, model animals and plants. In this review, we provide a concise overview about the scRNA-seq technology, experimental and computational procedures for transforming the biological and molecular processes into computational and statistical data. We also provide an explanation of the key technological steps in implementing the technology. We highlight a few examples on how scRNA-seq can provide unique information for better understanding health and diseases. One important application of the scRNA-seq technology is to build a better and high-resolution catalogue of cells in all living organism, commonly known as atlas, which is key resource to better understand and provide a solution in treating diseases. While great promises have been demonstrated with the technology in all areas, we further highlight a few remaining challenges to be overcome and its great potentials in transforming current protocols in disease diagnosis and treatment.

Single-cell genome-wide concurrent haplotyping and copy-number profiling through genotyping-by-sequencing

Single-cell whole-genome haplotyping allows simultaneous detection of haplotypes associated with monogenic diseases, chromosome copy-numbering and subsequently, has revealed mosaicism in embryos and embryonic stem cells. Methods, such as karyomapping and haplarithmisis, were deployed as a generic and genome-wide approach for preimplantation genetic testing (PGT) and are replacing traditional PGT methods. While current methods primarily rely on single-nucleotide polymorphism (SNP) array, we envision sequencing-based methods to become more accessible and cost-efficient. Here, we developed a novel sequencing-based methodology to haplotype and copy-number profile single cells. Following DNA amplification, genomic size and complexity is reduced through restriction enzyme digestion and DNA is genotyped through sequencing. This single-cell genotyping-by-sequencing (scGBS) is the input for haplarithmisis, an algorithm we previously developed for SNP array-based single-cell haplotyping. We established technical parameters and developed an analysis pipeline enabling accurate concurrent haplotyping and copy-number profiling of single cells. We demonstrate its value in human blastomere and trophectoderm samples as application for PGT for monogenic disorders. Furthermore, we demonstrate the method to work in other species through analyzing blastomeres of bovine embryos. Our scGBS method opens up the path for single-cell haplotyping of any species with diploid genomes and could make its way into the clinic as a PGT application.

A high-throughput technique to map cell images to cell positions using a 3D imaging flow cytometer

This article demonstrates a high-throughput technique to map cell images to cell positions. The technology uses a three-dimensional (3D) imaging flow cytometer to record multiparameter 3D cell images at a throughput of 1,000 cells/s and a cell placement robot to place the exiting cells from the imaging system on a filter plate in a first-in–first-out manner so the cells on the plate have the same order as the cells that are imaged. Innovative algorithms were developed to match the cell sequences from the imaging and placement modules to detect and eliminate errors to ensure high accuracy. The technology forms an unprecedented bridge between single-cell molecular analysis and single-cell image analysis to connect phenotype and genotype analysis with single-cell resolution.

We develop a high-throughput technique to relate positions of individual cells to their three-dimensional (3D) imaging features with single-cell resolution. The technique is particularly suitable for nonadherent cells where existing spatial biology methodologies relating cell properties to their positions in a solid tissue do not apply. Our design consists of two parts, as follows: recording 3D cell images at high throughput (500 to 1,000 cells/s) using a custom 3D imaging flow cytometer (3D-IFC) and dispensing cells in a first-in–first-out (FIFO) manner using a robotic cell placement platform (CPP). To prevent errors due to violations of the FIFO principle, we invented a method that uses marker beads and DNA sequencing software to detect errors. Experiments with human cancer cell lines demonstrate the feasibility of mapping 3D side scattering and fluorescent images, as well as two-dimensional (2D) transmission images of cells to their locations on the membrane filter for around 100,000 cells in less than 10 min. While the current work uses our specially designed 3D imaging flow cytometer to produce 3D cell images, our methodology can support other imaging modalities. The technology and method form a bridge between single-cell image analysis and single-cell molecular analysis.

CellPhy: accurate and fast probabilistic inference of single-cell phylogenies from scDNA-seq data

We introduce CellPhy, a maximum likelihood framework for inferring phylogenetic trees from somatic single-cell single-nucleotide variants. CellPhy leverages a finite-site Markov genotype model with 16 diploid states and considers amplification error and allelic dropout. We implement CellPhy into RAxML-NG, a widely used phylogenetic inference package that provides statistical confidence measurements and scales well on large datasets with hundreds or thousands of cells. Comprehensive simulations suggest that CellPhy is more robust to single-cell genomics errors and outperforms state-of-the-art methods under realistic scenarios, both in accuracy and speed. CellPhy is freely available at https://github.com/amkozlov/cellphy .

The emerging roles of NGS in clinical oncology and personalized medicine

Next-generation sequencing (NGS) has been increasingly popular in genomics studies over the last decade, as new sequencing technology has been created and improved. Recently, NGS started to be used in clinical oncology to improve cancer therapy through diverse modalities ranging from finding novel and rare cancer mutations, discovering cancer mutation carriers to reaching specific therapeutic approaches known as personalized medicine (PM). PM has the potential to minimize medical expenses by shifting the current traditional medical approach of treating cancer and other diseases to an individualized preventive and predictive approach. Currently, NGS can speed up in the early diagnosis of diseases and discover pharmacogenetic markers that help in personalizing therapies. Despite the tremendous growth in our understanding of genetics, NGS holds the added advantage of providing more comprehensive picture of cancer landscape and uncovering cancer development pathways. In this review, we provided a complete overview of potential NGS applications in scientific and clinical oncology, with a particular emphasis on pharmacogenomics in the direction of precision medicine treatment options.

Advancement of Single-Cell Sequencing in Medulloblastoma

Single-cell sequencing is a promising attempt to investigate the genomic, transcriptomic, and multiomic level of individual cell in the larger population of cells. The outward evolution of the technique from a manual method to the automation of single-cell sequencing is cogent. Lately, single-cell sequencing is widely used in various fields of science and has applications in neurobiology, immunity, cancer, microbiology, reproduction, and digestion. This chapter introduces the reader to the details of single-cell sequencing, currently used in several small-scale and commercial platforms. The advancement of single-cell sequencing in brain cancer sheds light on questions unanswered so far in the field of oncology.

Spatial mapping of cancer tissues by OMICS technologies

Spatial mapping of heterogeneity in gene expression in cancer tissues can improve our understanding of cancers and help in the rapid detection of cancers with high accuracy and reliability. Significant advancements have been made in recent years in OMICS technologies, which possess the strong potential to be applied in the spatial mapping of biopsy tissue samples and their molecular profiling to a single-cell level. The clinical application of OMICS technologies in spatial profiling of cancer tissues is also advancing. The current review presents recent advancements and prospects of applying OMICS technologies to the spatial mapping of various analytes in cancer tissues. We benchmark the current state of the art in the field to advance existing OMICS technologies for high throughput spatial profiling. The factors taken into consideration include spatial resolution, types of biomolecules, number of different biomolecules that can be detected from the same assay, labeled versus label-free approaches, and approximate time required for each assay. Further advancements are still needed for the widespread application of OMICs technologies in performing fast and high throughput spatial mapping of cancer tissues as well as their effective use in research and clinical applications.

Droplet-based microfluidics in biomedical applications

Droplet-based microfluidic systems have been employed to manipulate discrete fluid volumes with immiscible phases. Creating the fluid droplets at microscale has led to a paradigm shift in mixing, sorting, encapsulation, sensing, and designing high throughput devices for biomedical applications. Droplet microfluidics has opened many opportunities in microparticle synthesis, molecular detection, diagnostics, drug delivery, and cell biology. In the present review, we first introduce standard methods for droplet generation (i.e., passive and active methods) and discuss the latest examples of emulsification and particle synthesis approaches enabled by microfluidic platforms. Then, the applications of droplet-based microfluidics in different biomedical applications are detailed. Finally, a general overview of the latest trends along with the perspectives and future potentials in the field are provided.

Single-Cell RNA-Seq Revealed the Gene Expression Pattern during the In Vitro Maturation of Donkey Oocytes

Donkeys are an important domesticated animal, providing labor, meat, milk, and medicinal materials for humans. However, the donkey population is continuously declining and even at risk of extinction. The application of modern animal production technology, such as oocyte in vitro maturation, is a promising method to improve the donkey population. In this study, we explore the gene expression patterns of donkey germinal vesicle (GV) and in vitro matured metaphase II (MII) oocytes using single cell RNA-seq of the candidate genes along with the regulatory mechanisms that affect donkey oocyte maturation. We identified a total of 24,164 oocyte genes of which 9073 were significant differentially expressed in the GV and MII oocytes. Further Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that these genes were associated with the meiotic cell cycle, mitochondrion activity, and N-glycan biosynthesis, which might be the key genes and regulatory mechanisms affecting the maturation of donkey oocytes. Our study provides considerable understanding regarding the maturation of donkey oocytes and serves as a theoretical basis for improving the development of donkey oocytes, which could ultimately benefit the expansion of the donkey population and conservation of biodiversity and genetic resources.

Complex small-world regulatory networks emerge from the 3D organisation of the human genome

The discovery that overexpressing one or a few critical transcription factors can switch cell state suggests that gene regulatory networks are relatively simple. In contrast, genome-wide association studies (GWAS) point to complex phenotypes being determined by hundreds of loci that rarely encode transcription factors and which individually have small effects. Here, we use computer simulations and a simple fitting-free polymer model of chromosomes to show that spatial correlations arising from 3D genome organisation naturally lead to stochastic and bursty transcription as well as complex small-world regulatory networks (where the transcriptional activity of each genomic region subtly affects almost all others). These effects require factors to be present at sub-saturating levels; increasing levels dramatically simplifies networks as more transcription units are pressed into use. Consequently, results from GWAS can be reconciled with those involving overexpression. We apply this pan-genomic model to predict patterns of transcriptional activity in whole human chromosomes, and, as an example, the effects of the deletion causing the diGeorge syndrome.

Pushing the Boundaries: Forensic DNA Phenotyping Challenged by Single-Cell Sequencing

Single-cell sequencing is a fast developing and very promising field; however, it is not commonly used in forensics. The main motivation behind introducing this technology into forensics is to improve mixture deconvolution, especially when a trace consists of the same cell type. Successful studies demonstrate the ability to analyze a mixture by separating single cells and obtaining CE-based STR profiles. This indicates a potential use of the method in other forensic investigations, like forensic DNA phenotyping, in which using mixed traces is not fully recommended. For this study, we collected single-source autopsy blood from which the white cells were first stained and later separated with the DEPArray™ N×T System. Groups of 20, 10, and 5 cells, as well as 20 single cells, were collected and submitted for DNA extraction. Libraries were prepared using the Ion AmpliSeq™ PhenoTrivium Panel, which includes both phenotype (HIrisPlex-S: eye, hair, and skin color) and ancestry-associated SNP-markers. Prior to sequencing, half of the single-cell-based libraries were additionally amplified and purified in order to improve the library concentrations. Ancestry and phenotype analysis resulted in nearly full consensus profiles resulting in correct predictions not only for the cells groups but also for the ten re-amplified single-cell libraries. Our results suggest that sequencing of single cells can be a promising tool used to deconvolute mixed traces submitted for forensic DNA phenotyping.

Application of NGS Technology in Understanding the Pathology of Autoimmune Diseases

NGS technologies have transformed clinical diagnostics and broadly used from neonatal emergencies to adult conditions where the diagnosis cannot be made based on clinical symptoms. Autoimmune diseases reveal complicate molecular background and traditional methods could not fully capture them. Certainly, NGS technologies meet the needs of modern exploratory research, diagnostic and pharmacotherapy. Therefore, the main purpose of this review was to briefly present the application of NGS technology used in recent years in the understanding of autoimmune diseases paying particular attention to autoimmune connective tissue diseases. The main issues are presented in four parts: (a) panels, whole-genome and -exome sequencing (WGS and WES) in diagnostic, (b) Human leukocyte antigens (HLA) as a diagnostic tool, (c) RNAseq, (d) microRNA and (f) microbiome. Although all these areas of research are extensive, it seems that epigenetic impact on the development of systemic autoimmune diseases will set trends for future studies on this area.

scDPN for High-throughput Single-cell CNV Detection to Uncover Clonal Evolution During HCC Recurrence

Single-cell genomics provides substantial resources for dissecting cellular heterogeneity and cancer evolution. Unfortunately, classical DNA amplification-based methods have low throughput and introduce coverage bias during sample preamplification. We developed a single-cell DNA library preparation method without preamplification in nanolitre scale (scDPN) to address these issues. The method achieved a throughput of up to 1800 cells per run for copy number variation (CNV) detection. Also, our approach demonstrated a lower level of amplification bias and noise than the multiple displacement amplification (MDA) method and showed high sensitivity and accuracy for cell line and tumor tissue evaluation. We used this approach to profile the tumor clones in paired primary and relapsed tumor samples of hepatocellular carcinoma (HCC). We identified three clonal subpopulations with a multitude of aneuploid alterations across the genome. Furthermore, we observed that a minor clone of the primary tumor containing additional alterations in chromosomes 1q, 10q, and 14q developed into the dominant clone in the recurrent tumor, indicating clonal selection during recurrence in HCC. Overall, this approach provides a comprehensive and scalable solution to understand genome heterogeneity and evolution

Single cell RNA-seq reveals genes vital to in vitro fertilized embryos and parthenotes in pigs

Successful early embryo development requires the correct reprogramming and configuration of gene networks by the timely and faithful execution of zygotic genome activation (ZGA). However, the regulatory principle of molecular elements and circuits fundamental to embryo development remains largely obscure. Here, we profiled the transcriptomes of single zygotes and blastomeres, obtained from in vitro fertilized (IVF) or parthenogenetically activated (PA) porcine early embryos (1- to 8-cell), focusing on the gene expression dynamics and regulatory networks associated with maternal-to-zygote transition (MZT) (mainly maternal RNA clearance and ZGA). We found that minor and major ZGAs occur at 1-cell and 4-cell stages for both IVF and PA embryos, respectively. Maternal RNAs gradually decay from 1- to 8-cell embryos. Top abundantly expressed genes (CDV3, PCNA, CDR1, YWHAE, DNMT1, IGF2BP3, ARMC1, BTG4, UHRF2 and gametocyte-specific factor 1-like) in both IVF and PA early embryos identified are of vital roles for embryo development. Differentially expressed genes within IVF groups are different from that within PA groups, indicating bi-parental and maternal-only embryos have specific sets of mRNAs distinctly decayed and activated. Pathways enriched from DEGs showed that RNA associated pathways (RNA binding, processing, transport and degradation) could be important. Moreover, mitochondrial RNAs are found to be actively transcribed, showing dynamic expression patterns, and for DNA/H3K4 methylation and transcription factors as well. Taken together, our findings provide an important resource to investigate further the epigenetic and genome regulation of MZT events in early embryos of pigs.

Single-cell sequencing of the small and AT-skewed genome of malaria parasites

Single-cell genomics is a rapidly advancing field; however, most techniques are designed for mammalian cells. We present a single-cell sequencing pipeline for an intracellular parasite, Plasmodium falciparum, with a small genome of extreme base content. Through optimization of a quasi-linear amplification method, we target the parasite genome over contaminants and generate coverage levels allowing detection of minor genetic variants. This work, as well as efforts that build on these findings, will enable detection of parasite heterogeneity contributing to P. falciparum adaptation. Furthermore, this study provides a framework for optimizing single-cell amplification and variant analysis in challenging genomes.

A single-cell genomics pipeline for environmental microbial eukaryotes

Single-cell sequencing of environmental microorganisms is an essential component of the microbial ecology toolkit. However, large-scale targeted single-cell sequencing for the whole-genome recovery of uncultivated eukaryotes is lagging. The key challenges are low abundance in environmental communities, large complex genomes, and cell walls that are difficult to break. We describe a pipeline composed of state-of-the art single-cell genomics tools and protocols optimized for poorly studied and uncultivated eukaryotic microorganisms that are found at low abundance. This pipeline consists of seven distinct steps, beginning with sample collection and ending with genome annotation, each equipped with quality review steps to ensure high genome quality at low cost. We tested and evaluated each step on environmental samples and cultures of early-diverging lineages of fungi and Chromista/SAR. We show that genomes produced using this pipeline are almost as good as complete reference genomes for functional and comparative genomics for environmental microbial eukaryotes.

Genomics of the Parasitic Nematode Ascaris and Its Relatives

Nematodes of the genus Ascaris are important parasites of humans and swine, and the phylogenetically related genera (Parascaris, Toxocara, and Baylisascaris) infect mammals of veterinary interest. Over the last decade, considerable genomic resources have been established for Ascaris, including complete germline and somatic genomes, comprehensive mRNA and small RNA transcriptomes, as well as genome-wide histone and chromatin data. These datasets provide a major resource for studies on the basic biology of these parasites and the host-parasite relationship. Ascaris and its relatives undergo programmed DNA elimination, a highly regulated process where chromosomes are fragmented and portions of the genome are lost in embryonic cells destined to adopt a somatic fate, whereas the genome remains intact in germ cells. Unlike many model organisms, Ascaris transcription drives early development beginning prior to pronuclear fusion. Studies on Ascaris demonstrated a complex small RNA network even in the absence of a piRNA pathway. Comparative genomics of these ascarids has provided perspectives on nematode sex chromosome evolution, programmed DNA elimination, and host-parasite coevolution. The genomic resources enable comparison of proteins across diverse species, revealing many new potential drug targets that could be used to control these parasitic nematodes.

Multi-omics approaches to improve malaria therapy

Malaria contributes to the most widespread infectious diseases worldwide. Even though current drugs are commercially available, the ever-increasing drug resistance problem by malaria parasites poses new challenges in malaria therapy. Hence, searching for efficient therapeutic strategies is of high priority in malaria control. In recent years, multi-omics technologies have been extensively applied to provide a more holistic view of functional principles and dynamics of biological mechanisms. We briefly review multi-omics technologies and focus on recent malaria progress conducted with the help of various omics methods. Then, we present up-to-date advances for multi-omics approaches in malaria. Next, we describe resistance phenomena to established antimalarial drugs and underlying mechanisms. Finally, we provide insight into novel multi-omics approaches, new drugs and vaccine developments and analyze current gaps in multi-omics research. Although multi-omics approaches have been successfully used in malaria studies, they are still limited. Many gaps need to be filled to bridge the gap between basic research and treatment of malaria patients. Multi-omics approaches will foster a better understanding of the molecular mechanisms of Plasmodium that are essential for the development of novel drugs and vaccines to fight this disastrous disease.

Origin of Sex-Biased Mental Disorders: An Evolutionary Perspective

Sexual dimorphism or sex bias in diseases and mental disorders have two biological causes: sexual selection and sex hormones. We review the role of sexual selection theory and bring together decades of molecular studies on the variation and evolution of sex-biased genes and provide a theoretical basis for the causes of sex bias in disease and health. We present a Sexual Selection-Sex Hormone theory and show that male-driven evolution, including sexual selection, leads to: (1) increased male vulnerability due to negative pleiotropic effects associated with male-driven sexual selection and evolution; (2) increased rates of male-driven mutations and epimutations in response to early fitness gains and at the cost of late fitness; and (3) enhanced female immunity due to antagonistic responses to mutations that are beneficial to males but harmful to females, reducing female vulnerability to diseases and increasing the thresholds for disorders such as autism. Female-driven evolution, such as reproduction-related fluctuation in female sex hormones in association with stress and social condition, has been shown to be associated with increased risk of certain mental disorders such as major depression disorder in women. Bodies have history, cells have memories. An evolutionary framework, such as the Sexual Selection–Sex Hormone theory, provides a historical perspective for understanding how the differences in the sex-biased diseases and mental disorders have evolved over time. It has the potential to direct the development of novel preventive and treatment strategies.

Single-cell RNA-seq reveals mRNAs and lncRNAs important for oocytes in vitro matured in pigs

The faithful execution of molecular programme underlying oocyte maturation and meiosis is vital to generate competent haploid gametes for efficient mammalian reproduction. However, the organization and principle of molecular circuits and modules for oocyte meiosis remain obscure. Here, we employed the recently developed single-cell RNA-seq technique to profile the transcriptomes of germinal vesicle (GV) and metaphase II (MII) oocytes, aiming to discover the dynamic changes of mRNAs and long non-coding RNAs (lncRNAs) during oocyte in vitro meiotic maturation. During the transition from GV to MII, total number of detected RNAs (mRNAs and lncRNAs) in oocytes decreased. Moreover, 1,807 (602 up- and 1,205 down-regulated) mRNAs and 313 (177 up- and 136 down-regulated) lncRNAs were significantly differentially expressed (DE), i.e., more mRNAs down-regulated, but more lncRNAs up-regulated. During maturation of pig oocytes, mitochondrial mRNAs were actively transcribed, eight of which (ND6, ND5, CYTB, ND1, ND2, COX1, COX2 and COX3) were significantly up-regulated. Both DE mRNAs and targets of DE lncRNAs were enriched in multiple biological and signal pathways potentially associated with oocyte meiosis. Highly abundantly expressed mRNAs (including DNMT1, UHRF2, PCNA, ARMC1, BTG4, ASNS and SEP11) and lncRNAs were also discovered. Weighted gene co-expression network analysis (WGCNA) revealed 20 hub mRNAs in three modules to be important for oocyte meiosis and maturation. Taken together, our findings provide insights and resources for further functional investigation of mRNAs/lncRNAs in in vitro meiotic maturation of pig oocytes.