Cross-Species Analysis of Single-Cell Transcriptomic Data

Integrating phylogenies into single-cell RNA sequencing analysis allows comparisons across species, genes, and cells

Comparisons of single-cell RNA sequencing (scRNA-seq) data across species can reveal links between cellular gene expression and the evolution of cell functions, features, and phenotypes. These comparisons evoke evolutionary histories, as depicted by phylogenetic trees, that define relationships between species, genes, and cells. This Essay considers each of these in turn, laying out challenges and solutions derived from a phylogenetic comparative approach and relating these solutions to previously proposed methods for the pairwise alignment of cellular dimensional maps. This Essay contends that species trees, gene trees, cell phylogenies, and cell lineages can all be reconciled as descriptions of the same concept-the tree of cellular life. By integrating phylogenetic approaches into scRNA-seq analyses, challenges for building informed comparisons across species can be overcome, and hypotheses about gene and cell evolution can be robustly tested.

Application of Single-Cell Assay for Transposase-Accessible Chromatin with High Throughput Sequencing in Plant Science: Advances, Technical Challenges, and Prospects

The Single-cell Assay for Transposase-Accessible Chromatin with high throughput sequencing (scATAC-seq) has gained increasing popularity in recent years, allowing for chromatin accessibility to be deciphered and gene regulatory networks (GRNs) to be inferred at single-cell resolution. This cutting-edge technology now enables the genome-wide profiling of chromatin accessibility at the cellular level and the capturing of cell-type-specific cis-regulatory elements (CREs) that are masked by cellular heterogeneity in bulk assays. Additionally, it can also facilitate the identification of rare and new cell types based on differences in chromatin accessibility and the charting of cellular developmental trajectories within lineage-related cell clusters. Due to technical challenges and limitations, the data generated from scATAC-seq exhibit unique features, often characterized by high sparsity and noise, even within the same cell type. To address these challenges, various bioinformatic tools have been developed. Furthermore, the application of scATAC-seq in plant science is still in its infancy, with most research focusing on root tissues and model plant species. In this review, we provide an overview of recent progress in scATAC-seq and its application across various fields. We first conduct scATAC-seq in plant science. Next, we highlight the current challenges of scATAC-seq in plant science and major strategies for cell type annotation. Finally, we outline several future directions to exploit scATAC-seq technologies to address critical challenges in plant science, ranging from plant ENCODE(The Encyclopedia of DNA Elements) project construction to GRN inference, to deepen our understanding of the roles of CREs in plant biology.

Species-agnostic transfer learning for cross-species transcriptomics data integration without gene orthology

Novel hypotheses in biomedical research are often developed or validated in model organisms such as mice and zebrafish and thus play a crucial role. However, due to biological differences between species, translating these findings into human applications remains challenging. Moreover, commonly used orthologous gene information is often incomplete and entails a significant information loss during gene-id conversion. To address these issues, we present a novel methodology for species-agnostic transfer learning with heterogeneous domain adaptation. We extended the cross-domain structure-preserving projection toward out-of-sample prediction. Our approach not only allows knowledge integration and translation across various species without relying on gene orthology but also identifies similar GO among the most influential genes composing the latent space for integration. Subsequently, during the alignment of latent spaces, each composed of species-specific genes, it is possible to identify functional annotations of genes missing from public orthology databases. We evaluated our approach with four different single-cell sequencing datasets focusing on cell-type prediction and compared it against related machine-learning approaches. In summary, the developed model outperforms related methods working without prior knowledge when predicting unseen cell types based on other species' data. The results demonstrate that our novel approach allows knowledge transfer beyond species barriers without the dependency on known gene orthology but utilizing the entire gene sets.

scPML: pathway-based multi-view learning for cell type annotation from single-cell RNA-seq data

Recent developments in single-cell technology have enabled the exploration of cellular heterogeneity at an unprecedented level, providing invaluable insights into various fields, including medicine and disease research. Cell type annotation is an essential step in its omics research. The mainstream approach is to utilize well-annotated single-cell data to supervised learning for cell type annotation of new singlecell data. However, existing methods lack good generalization and robustness in cell annotation tasks, partially due to difficulties in dealing with technical differences between datasets, as well as not considering the heterogeneous associations of genes in regulatory mechanism levels. Here, we propose the scPML model, which utilizes various gene signaling pathway data to partition the genetic features of cells, thus characterizing different interaction maps between cells. Extensive experiments demonstrate that scPML performs better in cell type annotation and detection of unknown cell types from different species, platforms, and tissues.

Marine Invertebrates One Cell at A Time: Insights from Single-Cell Analysis

Over the past decade, single-cell RNA-sequencing (scRNA-seq) has made it possible to study the cellular diversity of a broad range of organisms. Technological advances in single-cell isolation and sequencing have expanded rapidly, allowing the transcriptomic profile of individual cells to be captured. As a result, there has been an explosion of cell type atlases created for many different marine invertebrate species from across the tree of life. Our focus in this review is to synthesize current literature on marine invertebrate scRNA-seq. Specifically, we provide perspectives on key insights from scRNA-seq studies, including descriptive studies of cell type composition, how cells respond in dynamic processes such as development and regeneration, and the evolution of new cell types. Despite these tremendous advances, there also lie several challenges ahead. We discuss the important considerations that are essential when making comparisons between experiments, or between datasets from different species. Finally, we address the future of single-cell analyses in marine invertebrates, including combining scRNA-seq data with other 'omics methods to get a fuller understanding of cellular complexities. The full diversity of cell types across marine invertebrates remains unknown and understanding this diversity and evolution will provide rich areas for future study.

Evolutionary reversion in tumorigenesis

Cells forming malignant tumors are distinguished from those forming normal tissues based on several features: accelerated/dysregulated cell division, disruption of physiologic apoptosis, maturation/differentiation arrest, loss of polarity, and invasive potential. Among them, accelerated cell division and differentiation arrest make tumor cells similar to stem/progenitor cells, and this is why tumorigenesis is often regarded as developmental reversion. Here, in addition to developmental reversion, we propose another insight into tumorigenesis from a phylogeny viewpoint. Based on the finding that tumor cells also share some features with unicellular organisms, we propose that tumorigenesis can be regarded as "evolutionary reversion". Recent advances in sequencing technologies and the ability to identify gene homologous have made it possible to perform comprehensive cross-species transcriptome comparisons and, in our recent study, we found that leukemic cells resulting from a polycomb dysfunction transcriptionally resemble unicellular organisms. Analyzing tumorigenesis from the viewpoint of phylogeny should reveal new aspects of tumorigenesis in the near future, and contribute to overcoming malignant tumors by developing new therapies.

Benchmarking strategies for cross-species integration of single-cell RNA sequencing data

The growing number of available single-cell gene expression datasets from different species creates opportunities to explore evolutionary relationships between cell types across species. Cross-species integration of single-cell RNA-sequencing data has been particularly informative in this context. However, in order to do so robustly it is essential to have rigorous benchmarking and appropriate guidelines to ensure that integration results truly reflect biology. Here, we benchmark 28 combinations of gene homology mapping methods and data integration algorithms in a variety of biological settings. We examine the capability of each strategy to perform species-mixing of known homologous cell types and to preserve biological heterogeneity using 9 established metrics. We also develop a new biology conservation metric to address the maintenance of cell type distinguishability. Overall, scANVI, scVI and SeuratV4 methods achieve a balance between species-mixing and biology conservation. For evolutionarily distant species, including in-paralogs is beneficial. SAMap outperforms when integrating whole-body atlases between species with challenging gene homology annotation. We provide our freely available cross-species integration and assessment pipeline to help analyse new data and develop new algorithms.

Cross-species comparative analysis of single presynapses

Comparing brain structure across species and regions enables key functional insights. Leveraging publicly available data from a novel mass cytometry-based method, synaptometry by time of flight (SynTOF), we applied an unsupervised machine learning approach to conduct a comparative study of presynapse molecular abundance across three species and three brain regions. We used neural networks and their attractive properties to model complex relationships among high dimensional data to develop a unified, unsupervised framework for comparing the profile of more than 4.5 million single presynapses among normal human, macaque, and mouse samples. An extensive validation showed the feasibility of performing cross-species comparison using SynTOF profiling. Integrative analysis of the abundance of 20 presynaptic proteins revealed near-complete separation between primates and mice involving synaptic pruning, cellular energy, lipid metabolism, and neurotransmission. In addition, our analysis revealed a strong overlap between the presynaptic composition of human and macaque in the cerebral cortex and neostriatum. Our unique approach illuminates species- and region-specific variation in presynapse molecular composition.

Integrated single-cell analysis-based classification of vascular mononuclear phagocytes in mouse and human atherosclerosis

AIMS

Accumulation of mononuclear phagocytes [monocytes, macrophages, and dendritic cells (DCs)] in the vessel wall is a hallmark of atherosclerosis. Using integrated single-cell analysis of mouse and human atherosclerosis, we here aimed to refine the nomenclature of mononuclear phagocytes in atherosclerotic vessels and to compare their transcriptomic profiles in mouse and human disease.

METHODS AND RESULTS

We integrated 12 single-cell RNA-sequencing (scRNA-seq) datasets of immune cells isolated from healthy or atherosclerotic mouse aortas, and data from 11 patients (n = 4 coronary vessels, n = 7 carotid endarterectomy specimens) from two studies. Integration of mouse data identified subpopulations with discrete transcriptomic signatures within previously described populations of aortic resident (Lyve1), inflammatory (Il1b), as well as foamy (Trem2hi) macrophages. We identified unique transcriptomic features distinguishing aortic intimal resident macrophages from atherosclerosis-associated Trem2hi macrophages. Also, populations of Xcr1+ Type 1 classical DCs (cDC1), Cd209a+ cDC2, and mature DCs (Ccr7, Fscn1) with a 'mreg-DC' signature were detected. In humans, we uncovered macrophage and DC populations with gene expression patterns similar to those observed in mice. In particular, core transcripts of the foamy/Trem2hi signature (TREM2, SPP1, GPNMB, CD9) mapped to a specific population of macrophages in human lesions. Comparison of mouse and human data and direct cross-species data integration suggested transcriptionally similar macrophage and DC populations in mice and humans.

CONCLUSIONS

We refined the nomenclature of mononuclear phagocytes in mouse atherosclerotic vessels, and show conserved transcriptomic features of macrophages and DCs in atherosclerosis in mice and humans, emphasizing the relevance of mouse models to study mononuclear phagocytes in atherosclerosis.

An atlas of rabbit development as a model for single-cell comparative genomics

Traditionally, the mouse has been the favoured vertebrate model for biomedical research, due to its experimental and genetic tractability. However, non-rodent embryological studies highlight that many aspects of early mouse development, such as its egg-cylinder gastrulation and method of implantation, diverge from other mammals, thus complicating inferences about human development. Like the human embryo, rabbits develop as a flat-bilaminar disc. Here we constructed a morphological and molecular atlas of rabbit development. We report transcriptional and chromatin accessibility profiles for over 180,000 single cells and high-resolution histology sections from embryos spanning gastrulation, implantation, amniogenesis and early organogenesis. Using a neighbourhood comparison pipeline, we compare the transcriptional landscape of rabbit and mouse at the scale of the entire organism. We characterize the gene regulatory programmes underlying trophoblast differentiation and identify signalling interactions involving the yolk sac mesothelium during haematopoiesis. We demonstrate how the combination of both rabbit and mouse atlases can be leveraged to extract new biological insights from sparse macaque and human data. The datasets and computational pipelines reported here set a framework for a broader cross-species approach to decipher early mammalian development, and are readily adaptable to deploy single-cell comparative genomics more broadly across biomedical research.

A pan-grass transcriptome reveals patterns of cellular divergence in crops

Different plant species within the grasses were parallel targets of domestication, giving rise to crops with distinct evolutionary histories and traits1. Key traits that distinguish these species are mediated by specialized cell types2. Here we compare the transcriptomes of root cells in three grass species-Zea mays, Sorghum bicolor and Setaria viridis. We show that single-cell and single-nucleus RNA sequencing provide complementary readouts of cell identity in dicots and monocots, warranting a combined analysis. Cell types were mapped across species to identify robust, orthologous marker genes. The comparative cellular analysis shows that the transcriptomes of some cell types diverged more rapidly than those of others-driven, in part, by recruitment of gene modules from other cell types. The data also show that a recent whole-genome duplication provides a rich source of new, highly localized gene expression domains that favour fast-evolving cell types. Together, the cell-by-cell comparative analysis shows how fine-scale cellular profiling can extract conserved modules from a pan transcriptome and provide insight on the evolution of cells that mediate key functions in crops.

Spinal cords: Symphonies of interneurons across species

Vertebrate movement is orchestrated by spinal inter- and motor neurons that, together with sensory and cognitive input, produce dynamic motor behaviors. These behaviors vary from the simple undulatory swimming of fish and larval aquatic species to the highly coordinated running, reaching and grasping of mice, humans and other mammals. This variation raises the fundamental question of how spinal circuits have changed in register with motor behavior. In simple, undulatory fish, exemplified by the lamprey, two broad classes of interneurons shape motor neuron output: ipsilateral-projecting excitatory neurons, and commissural-projecting inhibitory neurons. An additional class of ipsilateral inhibitory neurons is required to generate escape swim behavior in larval zebrafish and tadpoles. In limbed vertebrates, a more complex spinal neuron composition is observed. In this review, we provide evidence that movement elaboration correlates with an increase and specialization of these three basic interneuron types into molecularly, anatomically, and functionally distinct subpopulations. We summarize recent work linking neuron types to movement-pattern generation across fish, amphibians, reptiles, birds and mammals.

Dissection of Adult Mouse Stria Vascularis for Single-Nucleus Sequencing or Immunostaining

Endocochlear potential, which is generated by the stria vascularis, is essential to maintain an environment conducive to appropriate hair cell mechanotransduction and ultimately hearing. Pathologies of the stria vascularis can result in a decreased hearing. Dissection of the adult stria vascularis allows for focused single-nucleus capture and subsequent single-nucleus sequencing and immunostaining. These techniques are used to study stria vascularis pathophysiology at the single-cell level. Single-nucleus sequencing can be used in the setting of transcriptional analysis of the stria vascularis. Meanwhile, immunostaining continues to be useful in identifying specific populations of cells. Both methods require proper stria vascularis dissection as a prerequisite, which can prove to be technically challenging.

Unraveling Psychiatric Disorders through Neural Single-Cell Transcriptomics Approaches

The development of single-cell and single-nucleus transcriptome technologies is enabling the unraveling of the molecular and cellular heterogeneity of psychiatric disorders. The complexity of the brain and the relationships between different brain regions can be better understood through the classification of individual cell populations based on their molecular markers and transcriptomic features. Analysis of these unique cell types can explain their involvement in the pathology of psychiatric disorders. Recent studies in both human and animal models have emphasized the importance of transcriptome analysis of neuronal cells in psychiatric disorders but also revealed critical roles for non-neuronal cells, such as oligodendrocytes and microglia. In this review, we update current findings on the brain transcriptome and explore molecular studies addressing transcriptomic alterations identified in human and animal models in depression and stress, neurodegenerative disorders (Parkinson's and Alzheimer's disease), schizophrenia, opioid use disorder, and alcohol and psychostimulant abuse. We also comment on potential future directions in single-cell and single-nucleus studies.

scAEGAN: Unification of single-cell genomics data by adversarial learning of latent space correspondences

Recent progress in Single-Cell Genomics has produced different library protocols and techniques for molecular profiling. We formulate a unifying, data-driven, integrative, and predictive methodology for different libraries, samples, and paired-unpaired data modalities. Our design of scAEGAN includes an autoencoder (AE) network integrated with adversarial learning by a cycleGAN (cGAN) network. The AE learns a low-dimensional embedding of each condition, whereas the cGAN learns a non-linear mapping between the AE representations. We evaluate scAEGAN using simulated data and real scRNA-seq datasets, different library preparations (Fluidigm C1, CelSeq, CelSeq2, SmartSeq), and several data modalities as paired scRNA-seq and scATAC-seq. The scAEGAN outperforms Seurat3 in library integration, is more robust against data sparsity, and beats Seurat 4 in integrating paired data from the same cell. Furthermore, in predicting one data modality from another, scAEGAN outperforms Babel. We conclude that scAEGAN surpasses current state-of-the-art methods and unifies integration and prediction challenges.

Current advances in primate genomics: novel approaches for understanding evolution and disease

Primate genomics holds the key to understanding fundamental aspects of human evolution and disease. However, genetic diversity and functional genomics data sets are currently available for only a few of the more than 500 extant primate species. Concerted efforts are under way to characterize primate genomes, genetic polymorphism and divergence, and functional landscapes across the primate phylogeny. The resulting data sets will enable the connection of genotypes to phenotypes and provide new insight into aspects of the genetics of primate traits, including human diseases. In this Review, we describe the existing genome assemblies as well as genetic variation and functional genomic data sets. We highlight some of the challenges with sample acquisition. Finally, we explore how technological advances in single-cell functional genomics and induced pluripotent stem cell-derived organoids will facilitate our understanding of the molecular foundations of primate biology.

Cross-species cell-type assignment from single-cell RNA-seq data by a heterogeneous graph neural network

Cross-species comparative analyses of single-cell RNA sequencing (scRNA-seq) data allow us to explore, at single-cell resolution, the origins of the cellular diversity and evolutionary mechanisms that shape cellular form and function. Cell-type assignment is a crucial step to achieve that. However, the poorly annotated genome and limited known biomarkers hinder us from assigning cell identities for nonmodel species. Here, we design a heterogeneous graph neural network model, CAME, to learn aligned and interpretable cell and gene embeddings for cross-species cell-type assignment and gene module extraction from scRNA-seq data. CAME achieves significant improvements in cell-type characterization across distant species owing to the utilization of non-one-to-one homologous gene mapping ignored by early methods. Our large-scale benchmarking study shows that CAME significantly outperforms five classical methods in terms of cell-type assignment and model robustness to insufficiency and inconsistency of sequencing depths. CAME can transfer the major cell types and interneuron subtypes of human brains to mouse and discover shared cell-type-specific functions in homologous gene modules. CAME can align the trajectories of human and macaque spermatogenesis and reveal their conservative expression dynamics. In short, CAME can make accurate cross-species cell-type assignments even for nonmodel species and uncover shared and divergent characteristics between two species from scRNA-seq data.

The E3 ubiquitin ligase WWP2 regulates pro-fibrogenic monocyte infiltration and activity in heart fibrosis

Non-ischemic cardiomyopathy (NICM) can cause left ventricular dysfunction through interstitial fibrosis, which corresponds to the failure of cardiac tissue remodeling. Recent evidence implicates monocytes/macrophages in the etiopathology of cardiac fibrosis, but giving their heterogeneity and the antagonizing roles of macrophage subtypes in fibrosis, targeting these cells has been challenging. Here we focus on WWP2, an E3 ubiquitin ligase that acts as a positive genetic regulator of human and murine cardiac fibrosis, and show that myeloid specific deletion of WWP2 reduces cardiac fibrosis in hypertension-induced NICM. By using single cell RNA sequencing analysis of immune cells in the same model, we establish the functional heterogeneity of macrophages and define an early pro-fibrogenic phase of NICM that is driven by Ccl5-expressing Ly6c^high monocytes. Among cardiac macrophage subtypes, WWP2 dysfunction primarily affects Ly6c^high monocytes via modulating Ccl5, and consequentially macrophage infiltration and activation, which contributes to reduced myofibroblast trans-differentiation. WWP2 interacts with transcription factor IRF7, promoting its non-degradative mono-ubiquitination, nuclear translocation and transcriptional activity, leading to upregulation of Ccl5 at transcriptional level. We identify a pro-fibrogenic macrophage subtype in non-ischemic cardiomyopathy, and demonstrate that WWP2 is a key regulator of IRF7-mediated Ccl5/Ly6c^high monocyte axis in heart fibrosis.

Deep learning of cross-species single-cell landscapes identifies conserved regulatory programs underlying cell types

Despite extensive efforts to generate and analyze reference genomes, genetic models to predict gene regulation and cell fate decisions are lacking for most species. Here, we generated whole-body single-cell transcriptomic landscapes of zebrafish, Drosophila and earthworm. We then integrated cell landscapes from eight representative metazoan species to study gene regulation across evolution. Using these uniformly constructed cross-species landscapes, we developed a deep-learning-based strategy, Nvwa, to predict gene expression and identify regulatory sequences at the single-cell level. We systematically compared cell-type-specific transcription factors to reveal conserved genetic regulation in vertebrates and invertebrates. Our work provides a valuable resource and offers a new strategy for studying regulatory grammar in diverse biological systems.

Cell types as species: Exploring a metaphor

The concept of "cell type," though fundamental to cell biology, is controversial. Cells have historically been classified into types based on morphology, physiology, or location. More recently, single cell transcriptomic studies have revealed fine-scale differences among cells with similar gross phenotypes. Transcriptomic snapshots of cells at various stages of differentiation, and of cells under different physiological conditions, have shown that in many cases variation is more continuous than discrete, raising questions about the relationship between cell type and cell state. Some researchers have rejected the notion of fixed types altogether. Throughout the history of discussions on cell type, cell biologists have compared the problem of defining cell type with the interminable and often contentious debate over the definition of arguably the most important concept in systematics and evolutionary biology, "species." In the last decades, systematics, like cell biology, has been transformed by the increasing availability of molecular data, and the fine-grained resolution of genetic relationships have generated new ideas about how that variation should be classified. There are numerous parallels between the two fields that make exploration of the "cell types as species" metaphor timely. These parallels begin with philosophy, with discussion of both cell types and species as being either individuals, groups, or something in between (e.g., homeostatic property clusters). In each field there are various different types of lineages that form trees or networks that can (and in some cases do) provide criteria for grouping. Developing and refining models for evolutionary divergence of species and for cell type differentiation are parallel goals of the two fields. The goal of this essay is to highlight such parallels with the hope of inspiring biologists in both fields to look for new solutions to similar problems outside of their own field.

Detecting signatures of selection on gene expression

A substantial amount of phenotypic diversity results from changes in gene expression levels and patterns. Understanding how the transcriptome evolves is therefore a key priority in identifying mechanisms of adaptive change. However, in contrast to powerful models of sequence evolution, we lack a consensus model of gene expression evolution. Furthermore, recent work has shown that many of the comparative approaches used to study gene expression are subject to biases that can lead to false signatures of selection. Here we first outline the main approaches for describing expression evolution and their inherent biases. Next, we bridge the gap between the fields of phylogenetic comparative methods and transcriptomics to reinforce the main pitfalls of inferring selection on expression patterns and use simulation studies to show that shifts in tissue composition can heavily bias inferences of selection. We close by highlighting the multi-dimensional nature of transcriptional variation and identifying major unanswered questions in disentangling how selection acts on the transcriptome.

Marsupials and Multi-Omics: Establishing New Comparative Models of Neural Crest Patterning and Craniofacial Development

Studies across vertebrates have revealed significant insights into the processes that drive craniofacial morphogenesis, yet we still know little about how distinct facial morphologies are patterned during development. Studies largely point to evolution in GRNs of cranial progenitor cell types such as neural crest cells, as the major driver underlying adaptive cranial shapes. However, this hypothesis requires further validation, particularly within suitable models amenable to manipulation. By utilizing comparative models between related species, we can begin to disentangle complex developmental systems and identify the origin of species-specific patterning. Mammals present excellent evolutionary examples to scrutinize how these differences arise, as sister clades of eutherians and marsupials possess suitable divergence times, conserved cranial anatomies, modular evolutionary patterns, and distinct developmental heterochrony in their NCC behaviours and craniofacial patterning. In this review, I lend perspectives into the current state of mammalian craniofacial biology and discuss the importance of establishing a new marsupial model, the fat-tailed dunnart, for comparative research. Through detailed comparisons with the mouse, we can begin to decipher mammalian conserved, and species-specific processes and their contribution to craniofacial patterning and shape disparity. Recent advances in single-cell multi-omics allow high-resolution investigations into the cellular and molecular basis of key developmental processes. As such, I discuss how comparative evolutionary application of these tools can provide detailed insights into complex cellular behaviours and expression dynamics underlying adaptive craniofacial evolution. Though in its infancy, the field of "comparative evo-devo-omics" presents unparalleled opportunities to precisely uncover how phenotypic differences arise during development.

Triiodothyronine (T3) promotes brown fat hyperplasia via thyroid hormone receptor α mediated adipocyte progenitor cell proliferation

The thyroid hormone (TH)-controlled recruitment process of brown adipose tissue (BAT) is not fully understood. Here, we show that long-term treatment of T3, the active form of TH, increases the recruitment of thermogenic capacity in interscapular BAT of male mice through hyperplasia by promoting the TH receptor α-mediated adipocyte progenitor cell proliferation. Our single-cell analysis reveals the heterogeneous nature and hierarchical trajectory within adipocyte progenitor cells of interscapular BAT. Further analyses suggest that T3 facilitates cell state transition from a more stem-like state towards a more committed adipogenic state and promotes cell cycle progression towards a mitotic state in adipocyte progenitor cells, through mechanisms involving the action of Myc on glycolysis. Our findings elucidate the mechanisms underlying the TH action in adipocyte progenitors residing in BAT and provide a framework for better understanding of the TH effects on hyperplastic growth and adaptive thermogenesis in BAT depot at a single-cell level.

Thyroid hormone (TH) action regulates brown adipose tissue thermogenic capacity through incompletely understood mechanisms. Here the authors report that T3, the active form of TH, increases thermogenic capacity via thyroid hormone receptor α-mediated hyperplasia of brown adipose tissue adipocyte progenitor cells.

Behavioral Neuroscience in the Era of Genomics: Tools and Lessons for Analyzing High-Dimensional Datasets

Behavioral neuroscience underwent a technology-driven revolution with the emergence of machine-vision and machine-learning technologies. These technological advances facilitated the generation of high-resolution, high-throughput capture and analysis of complex behaviors. Therefore, behavioral neuroscience is becoming a data-rich field. While behavioral researchers use advanced computational tools to analyze the resulting datasets, the search for robust and standardized analysis tools is still ongoing. At the same time, the field of genomics exploded with a plethora of technologies which enabled the generation of massive datasets. This growth of genomics data drove the emergence of powerful computational approaches to analyze these data. Here, we discuss the composition of a large behavioral dataset, and the differences and similarities between behavioral and genomics data. We then give examples of genomics-related tools that might be of use for behavioral analysis and discuss concepts that might emerge when considering the two fields together.

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

Towards Tabula Gallus

The Tabula Gallus is a proposed project that aims to create a map of every cell type in the chicken body and chick embryos. Chickens (Gallus gallus) are one of the most recognized model animals that recapitulate the development and physiology of mammals. The Tabula Gallus will generate a compendium of single-cell transcriptome data from Gallus gallus, characterize each cell type, and provide tools for the study of the biology of this species, similar to other ongoing cell atlas projects (Tabula Muris and Tabula Sapiens/Human Cell Atlas for mice and humans, respectively). The Tabula Gallus will potentially become an international collaboration between many researchers. This project will be useful for the basic scientific study of Gallus gallus and other birds (e.g., cell biology, molecular biology, developmental biology, neuroscience, physiology, oncology, virology, behavior, ecology, and evolution). It will eventually be beneficial for a better understanding of human health and diseases.

Challenges and Opportunities for the Translation of Single-Cell RNA Sequencing Technologies to Dermatology

Skin is a complex and heterogeneous organ at the cellular level. This complexity is beginning to be understood through the application of single-cell genomics and computational tools. A large number of datasets that shed light on how the different human skin cell types interact in homeostasis-and what ceases to work in diverse dermatological diseases-have been generated and are publicly available. However, translation of these novel aspects to the clinic is lacking. This review aims to summarize the state-of-the-art of skin biology using single-cell technologies, with a special focus on skin pathologies and the translation of mechanistic findings to the clinic. The main implications of this review are to summarize the benefits and limitations of single-cell analysis and thus help translate the emerging insights from these novel techniques to the bedside.

Gene family evolution underlies cell-type diversification in the hypothalamus of teleosts

Hundreds of cell types form the vertebrate brain but it is largely unknown how similar cellular repertoires are between or within species or how cell-type diversity evolves. To examine cell-type diversity across and within species, we performed single-cell RNA sequencing of ~130,000 hypothalamic cells from zebrafish (Danio rerio) and surface and cave morphs of Mexican tetra (Astyanax mexicanus). We found that over 75% of cell types were shared between zebrafish and Mexican tetra, which diverged from a common ancestor over 150 million years ago. Shared cell types displayed shifts in paralogue expression that were generated by subfunctionalization after genome duplication. Expression of terminal effector genes, such as neuropeptides, was more conserved than the expression of their associated transcriptional regulators. Species-specific cell types were enriched for the expression of species-specific genes and characterized by the neofunctionalization of expression patterns of members of recently expanded or contracted gene families. Comparisons between surface and cave morphs revealed differences in immune repertoires and transcriptional changes in neuropeptidergic cell types associated with genomic differences. The single-cell atlases presented here are a powerful resource to explore hypothalamic cell types and reveal how gene family evolution and shifts in paralogue expression contribute to cellular diversity.

Single-cell RNA sequencing deconvolutes the in vivo heterogeneity of human bone marrow-derived mesenchymal stem cells

Bone marrow-derived mesenchymal stem cells (BM-MSCs) are multipotent stromal cells that have a critical role in the maintenance of skeletal tissues such as bone, cartilage, and the fat in bone marrow. In addition to providing microenvironmental support for hematopoietic processes, BM-MSCs can differentiate into various mesodermal lineages including osteoblast/osteocyte, chondrocyte, and adipocyte that are crucial for bone metabolism. While BM-MSCs have high cell-to-cell heterogeneity in gene expression, the cell subtypes that contribute to this heterogeneity in vivo in humans have not been characterized. To investigate the transcriptional diversity of BM-MSCs, we applied single-cell RNA sequencing (scRNA-seq) on freshly isolated CD271+ BM-derived mononuclear cells (BM-MNCs) from two human subjects. We successfully identified LEPRhiCD45low BM-MSCs within the CD271+ BM-MNC population, and further codified the BM-MSCs into distinct subpopulations corresponding to the osteogenic, chondrogenic, and adipogenic differentiation trajectories, as well as terminal-stage quiescent cells. Biological functional annotations of the transcriptomes suggest that osteoblast precursors induce angiogenesis coupled with osteogenesis, and chondrocyte precursors have the potential to differentiate into myocytes. We also discovered transcripts for several clusters of differentiation (CD) markers that were either highly expressed (e.g., CD167b, CD91, CD130 and CD118) or absent (e.g., CD74, CD217, CD148 and CD68) in BM-MSCs, representing potential novel markers for human BM-MSC purification. This study is the first systematic in vivo dissection of human BM-MSCs cell subtypes at the single-cell resolution, revealing an insight into the extent of their cellular heterogeneity and roles in maintaining bone homeostasis.

From Cell Types to an Integrated Understanding of Brain Evolution: The Case of the Cerebral Cortex

With the discovery of the incredible diversity of neurons, Cajal and coworkers laid the foundation of modern neuroscience. Neuron types are not only structural units of nervous systems but also evolutionary units, because their identities are encoded in the genome. With the advent of high-throughput cellular transcriptomics, neuronal identities can be characterized and compared systematically across species. The comparison of neurons in mammals, reptiles, and birds indicates that the mammalian cerebral cortex is a mosaic of deeply conserved and recently evolved neuron types. Using the cerebral cortex as a case study, this review illustrates how comparing neuron types across species is key to reconciling observations on neural development, neuroanatomy, circuit wiring, and physiology for an integrated understanding of brain evolution.

Macrophage phenotypes in tissue repair and the foreign body response: Implications for biomaterial-based regenerative medicine strategies

Macrophages are a highly heterogeneous and plastic population of cells that are crucial for tissue repair and regeneration. This has made macrophages a particularly attractive target for biomaterial-directed regenerative medicine strategies. However, macrophages also contribute to adverse inflammatory and fibrotic responses to implanted biomaterials, typically related to the foreign body response (FBR). The traditional model in the field asserts that the M2 macrophage phenotype is pro-regenerative and associated with positive wound healing outcomes, whereas the M1 phenotype is pro-inflammatory and associated with pathogenesis. However, recent studies indicate that both M1 and M2 macrophages play different, but equally vital, roles in promoting tissue repair. Furthermore, recent technological developments such as single-cell RNA sequencing have allowed for unprecedented insights into the heterogeneity within the myeloid compartment, related to activation state, niche, and ontogenetic origin. A better understanding of the phenotypic and functional characteristics of macrophages critical to tissue repair and FBR processes will allow for rational design of biomaterials to promote biomaterial-tissue integration and regeneration. In this review, we discuss the role of temporal and ontogenetic macrophage heterogeneity on tissue repair processes and the FBR and the potential implications for biomaterial-directed regenerative medicine applications. STATEMENT OF SIGNIFICANCE: This review outlines the contributions of different macrophage phenotypes to different phases of wound healing and angiogenesis. Pathological outcomes, such as chronic inflammation, fibrosis, and the foreign body response, related to disruption of the macrophage inflammation-resolution process are also discussed. We summarize recent insights into the vast heterogeneity of myeloid cells related to their niche, especially the biomaterial microenvironment, and ontogenetic origin. Additionally, we present a discussion on novel tools that allow for resolution of cellular heterogeneity at the single-cell level and how these can be used to build a better understanding of macrophage heterogeneity in the biomaterial immune microenvironment to better inform immunomodulatory biomaterial design.

Plant Cell Identity in the Era of Single-Cell Transcriptomics

High-throughput single-cell transcriptomic approaches have revolutionized our view of gene expression at the level of individual cells, providing new insights into their heterogeneity, identities, and functions. Recently, technical challenges to the application of single-cell transcriptomics to plants have been overcome, and many plant organs and tissues have now been subjected to analyses at single-cell resolution. In this review, we describe these studies and their impact on our understanding of the diversity, differentiation, and activities of plant cells. We particularly highlight their impact on plant cell identity, including unprecedented views of cell transitions and definitions of rare and novel cell types. We also point out current challenges and future opportunities for the application and analyses of single-cell transcriptomics in plants. Expected final online publication date for the Annual Review of Genetics, Volume 55 is November 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Single-Cell Analysis for Whole-Organism Datasets

Cell atlases are essential companions to the genome as they elucidate how genes are used in a cell type-specific manner or how the usage of genes changes over the lifetime of an organism. This review explores recent advances in whole-organism single-cell atlases, which enable understanding of cell heterogeneity and tissue and cell fate, both in health and disease. Here we provide an overview of recent efforts to build cell atlases across species and discuss the challenges that the field is currently facing. Moreover, we propose the concept of having a knowledgebase that can scale with the number of experiments and computational approaches and a new feedback loop for development and benchmarking of computational methods that includes contributions from the users. These two aspects are key for community efforts in single-cell biology that will help produce a comprehensive annotated map of cell types and states with unparalleled resolution.

Determination of the dynamic cellular transcriptional profiles during kidney development from birth to maturity in rats by single-cell RNA sequencing

Recent single-cell RNA sequencing (scRNA-seq) analyses have offered much insight into the gene expression profiles in early-stage kidney development. However, comprehensive gene expression profiles from mid- and late-stage kidney development are lacking. In the present study, by using the scRNA-seq technique, we analyzed 54,704 rat kidney cells from just after birth to adulthood (six time points: postnatal days 0, 2, 5, 10, 20, and 56) including the mid and late stages of kidney development. Twenty-five original clusters and 13 different cell types were identified during these stages. Gene expression in these 13 cell types was mapped, and single cell atlas of the rat kidney from birth to maturity ( http://youngbearlab.com ) was built to enable users to search for a gene of interest and to evaluate its expression in different cells. The variation trend of six major types of kidney cells-intercalated cells of the collecting duct (CD-ICs), principal cells of the collecting duct (CD-PCs), cells of the distal convoluted tubules (DCTs), cells of the loop of Henle (LOH), podocytes (PDs), and cells of the proximal tubules (PTs)-during six postnatal time points was demonstrated. The trajectory of rat kidney development and the order of induction of the six major types of kidney cells from just after birth to maturity were determined. In addition, features of the dynamically changing genes as well as transcription factors during postnatal rat kidney development were identified. The present study provides a resource for achieving a deep understanding of the molecular basis of and regulatory events in the mid and late stages of kidney development.

Assessing evolutionary and developmental transcriptome dynamics in homologous cell types

Background

During development, complex organ patterns emerge through the precise temporal and spatial specification of different cell types. On an evolutionary timescale, these patterns can change, resulting in morphological diversification. It is generally believed that homologous anatomical structures are built—largely—by homologous cell types. However, whether a common evolutionary origin of such cell types is always reflected in the conservation of their intrinsic transcriptional specification programs is less clear.

Results

Here, we developed a user‐friendly bioinformatics workflow to detect gene co‐expression modules and test for their conservation across developmental stages and species boundaries. Using a paradigm of morphological diversification, the tetrapod limb, and single‐cell RNA‐sequencing data from two distantly related species, chicken and mouse, we assessed the transcriptional dynamics of homologous cell types during embryonic patterning. With mouse limb data as reference, we identified 19 gene co‐expression modules with varying tissue or cell type‐restricted activities. Testing for co‐expression conservation revealed modules with high evolutionary turnover, while others seemed maintained—to different degrees, in module make‐up, density or connectivity—over developmental and evolutionary timescales.

Conclusions

We present an approach to identify evolutionary and developmental dynamics in gene co‐expression modules during patterning‐relevant stages of homologous cell type specification using single‐cell RNA‐sequencing data.

We present an approach to identify evolutionary and developmental dynamics in gene co‐expression modules during patterning‐relevant stages of homologous cell type specification using single‐cell RNA‐sequencing data.

Single-cell RNA sequencing in human lung cancer: Applications, challenges, and pathway towards personalized therapy

Lung cancer is one of the most prevalent human cancers, and single-cell RNA sequencing (scRNA-seq) has been widely used to study human lung cancer at the cellular, genetic, and molecular level. Even though there are published reviews, which summarized the applications of scRNA-seq in human cancers like breast cancer, there is lack of a comprehensive review, which could effectively highlight the broad use of scRNA-seq in studying lung cancer. This review, therefore, was aimed to summarize the various applications of scRNA-seq in human lung cancer research based on the findings from different published in vitro, in vivo, and clinical studies. The review would first briefly outline the concept and principle of scRNA-seq, followed by the discussion on the applications of scRNA-seq in studying human lung cancer. Finally, the challenges faced when using scRNA-seq to study human lung cancer would be discussed, and the potential applications and challenges of scRNA-seq to facilitate the development of personalized cancer therapy in the future would be explored.

Evolutionary Cell Type Mapping with Single-Cell Genomics

A fundamental characteristic of animal multicellularity is the spatial coexistence of functionally specialized cell types that are all encoded by a single genome sequence. Cell type transcriptional programs are deployed and maintained by regulatory mechanisms that control the asymmetric, differential access to genomic information in each cell. This genome regulation ultimately results in specific cellular phenotypes. However, the emergence, diversity, and evolutionary dynamics of animal cell types remain almost completely unexplored beyond a few species. Single-cell genomics is emerging as a powerful tool to build comprehensive catalogs of cell types and their associated gene regulatory programs in non-traditional model species. We review the current state of sampling efforts across the animal tree of life and challenges ahead for the comparative study of cell type programs. We also discuss how the phylogenetic integration of cell atlases can lead to the development of models of cell type evolution and a phylogenetic taxonomy of cells.

Mapping single-cell atlases throughout Metazoa unravels cell type evolution

Comparing single-cell transcriptomic atlases from diverse organisms can elucidate the origins of cellular diversity and assist the annotation of new cell atlases. Yet, comparison between distant relatives is hindered by complex gene histories and diversifications in expression programs. Previously, we introduced the self-assembling manifold (SAM) algorithm to robustly reconstruct manifolds from single-cell data (Tarashansky et al., 2019). Here, we build on SAM to map cell atlas manifolds across species. This new method, SAMap, identifies homologous cell types with shared expression programs across distant species within phyla, even in complex examples where homologous tissues emerge from distinct germ layers. SAMap also finds many genes with more similar expression to their paralogs than their orthologs, suggesting paralog substitution may be more common in evolution than previously appreciated. Lastly, comparing species across animal phyla, spanning sponge to mouse, reveals ancient contractile and stem cell families, which may have arisen early in animal evolution.

Computational principles and challenges in single-cell data integration

The development of single-cell multimodal assays provides a powerful tool for investigating multiple dimensions of cellular heterogeneity, enabling new insights into development, tissue homeostasis and disease. A key challenge in the analysis of single-cell multimodal data is to devise appropriate strategies for tying together data across different modalities. The term 'data integration' has been used to describe this task, encompassing a broad collection of approaches ranging from batch correction of individual omics datasets to association of chromatin accessibility and genetic variation with transcription. Although existing integration strategies exploit similar mathematical ideas, they typically have distinct goals and rely on different principles and assumptions. Consequently, new definitions and concepts are needed to contextualize existing methods and to enable development of new methods.

Endothelial cell plasticity at the single-cell level

The vascular endothelium is characterized by a remarkable level of plasticity, which is the driving force not only of physiological repair/remodeling of adult tissues but also of pathological angiogenesis. The resulting heterogeneity of endothelial cells (ECs) makes targeting the endothelium challenging, no less because many EC phenotypes are yet to be identified and functionally inventorized. Efforts to map the vasculature at the single-cell level have been instrumental to capture the diversity of EC types and states at a remarkable depth in both normal and pathological states. Here, we discuss new EC subtypes and functions emerging from recent single-cell studies in health and disease. Interestingly, such studies revealed distinct metabolic gene signatures in different EC phenotypes, which deserve further consideration for therapy. We highlight how this metabolic targeting strategy could potentially be used to promote (for tissue repair) or block (in tumor) angiogenesis in a tissue or even vascular bed-specific manner.

ACME dissociation: a versatile cell fixation-dissociation method for single-cell transcriptomics

Single-cell sequencing technologies are revolutionizing biology, but they are limited by the need to dissociate live samples. Here, we present ACME (ACetic-MEthanol), a dissociation approach for single-cell transcriptomics that simultaneously fixes cells. ACME-dissociated cells have high RNA integrity, can be cryopreserved multiple times, and are sortable and permeable. As a proof of principle, we provide single-cell transcriptomic data of different species, using both droplet-based and combinatorial barcoding single-cell methods. ACME uses affordable reagents, can be done in most laboratories and even in the field, and thus will accelerate our knowledge of cell types across the tree of life.

Single-Cell RNA-Seq Mapping of Human Thymopoiesis Reveals Lineage Specification Trajectories and a Commitment Spectrum in T Cell Development

The challenges in recapitulating in vivo human T cell development in laboratory models have posed a barrier to understanding human thymopoiesis. Here, we used single-cell RNA sequencing (sRNA-seq) to interrogate the rare CD34⁺ progenitor and the more differentiated CD34^- fractions in the human postnatal thymus. CD34⁺ thymic progenitors were comprised of a spectrum of specification and commitment states characterized by multilineage priming followed by gradual T cell commitment. The earliest progenitors in the differentiation trajectory were CD7^- and expressed a stem-cell-like transcriptional profile, but had also initiated T cell priming. Clustering analysis identified a CD34⁺ subpopulation primed for the plasmacytoid dendritic lineage, suggesting an intrathymic dendritic specification pathway. CD2 expression defined T cell commitment stages where loss of B cell potential preceded that of myeloid potential. These datasets delineate gene expression profiles spanning key differentiation events in human thymopoiesis and provide a resource for the further study of human T cell development.

miRNA Regulatory Functions in Farm Animal Diseases, and Biomarker Potentials for Effective Therapies

MicroRNAs (miRNAs) are small endogenous RNAs that regulate gene expression post-transcriptionally by targeting either the 3′ untranslated or coding regions of genes. They have been reported to play key roles in a wide range of biological processes. The recent remarkable developments of transcriptomics technologies, especially next-generation sequencing technologies and advanced bioinformatics tools, allow more in-depth exploration of messenger RNAs (mRNAs) and non-coding RNAs (ncRNAs), including miRNAs. These technologies have offered great opportunities for a deeper exploration of miRNA involvement in farm animal diseases, as well as livestock productivity and welfare. In this review, we provide an overview of the current knowledge of miRNA roles in major farm animal diseases with a particular focus on diseases of economic importance. In addition, we discuss the steps and future perspectives of using miRNAs as biomarkers and molecular therapy for livestock disease management as well as the challenges and opportunities for understanding the regulatory mechanisms of miRNAs related to disease pathogenesis.

Multi-level convergence of complex traits and the evolution of bioluminescence

Evolutionary convergence provides natural opportunities to investigate how, when, and why novel traits evolve. Many convergent traits are complex, highlighting the importance of explicitly considering convergence at different levels of biological organization, or 'multi-level convergent evolution'. To investigate multi-level convergent evolution, we propose a holistic and hierarchical framework that emphasizes breaking down traits into several functional modules. We begin by identifying long-standing questions on the origins of complexity and the diverse evolutionary processes underlying phenotypic convergence to discuss how they can be addressed by examining convergent systems. We argue that bioluminescence, a complex trait that evolved dozens of times through either novel mechanisms or conserved toolkits, is particularly well suited for these studies. We present an updated estimate of at least 94 independent origins of bioluminescence across the tree of life, which we calculated by reviewing and summarizing all estimates of independent origins. Then, we use our framework to review the biology, chemistry, and evolution of bioluminescence, and for each biological level identify questions that arise from our systematic review. We focus on luminous organisms that use the shared luciferin substrates coelenterazine or vargulin to produce light because these organisms convergently evolved bioluminescent proteins that use the same luciferins to produce bioluminescence. Evolutionary convergence does not necessarily extend across biological levels, as exemplified by cases of conservation and disparity in biological functions, organs, cells, and molecules associated with bioluminescence systems. Investigating differences across bioluminescent organisms will address fundamental questions on predictability and contingency in convergent evolution. Lastly, we highlight unexplored areas of bioluminescence research and advances in sequencing and chemical techniques useful for developing bioluminescence as a model system for studying multi-level convergent evolution.

Insect lifestyle and evolution of brain morphology

Insect lifestyles are extremely diversified and have important consequences for brain function. Lifestyle determines the resources and information that brains might access and also those that are required to produce adaptive behaviors. Most of the observed adaptations in brain morphology to variation in lifestyle are related to the first stages of sensory information processing (e.g. adaptations to diel habits). However, morphological signatures of lifestyles related to higher order processing of information are more difficult to demonstrate. Co-option of existing neural structures for new behaviors might hinder the detection of morphological changes at a large scale. Current methodological advances will make it possible to investigate finer structural changes (e.g. variation in the connectivity between neurons) and might shed light on whether or not some lifestyles (e.g. eusociality) require morphological adaptations.

Emerging Roles of Single-Cell Multi-Omics in Studying Developmental Temporal Patterning

The complexity of brain structure and function is rooted in the precise spatial and temporal regulation of selective developmental events. During neurogenesis, both vertebrates and invertebrates generate a wide variety of specialized cell types through the expansion and specification of a restricted set of neuronal progenitors. Temporal patterning of neural progenitors rests on fine regulation between cell-intrinsic and cell-extrinsic mechanisms. The rapid emergence of high-throughput single-cell technologies combined with elaborate computational analysis has started to provide us with unprecedented biological insights related to temporal patterning in the developing central nervous system (CNS). Here, we present an overview of recent advances in Drosophila and vertebrates, focusing both on cell-intrinsic mechanisms and environmental influences. We then describe the various multi-omics approaches that have strongly contributed to our current understanding and discuss perspectives on the various -omics approaches that hold great potential for the future of temporal patterning research.

Vertebrate brain regeneration - a community effort of fate-restricted precursor cell types

The process of regeneration describes the full restoration of tissue after destruction from injury or disease. Most mammals show very limited ability for regeneration of adult organs, while vertebrate models of regeneration such as fish and salamanders, allow to study regeneration mechanism of the brain, heart, limbs, retina, and other organs in adults. The regenerative abilities of teleost fish are well documented, but the cellular sources for regeneration, the specificity of source cells for restored cell types, as well as the extent and fidelity of cell replacement are only beginning to be revealed for many regeneration paradigms. Here, we highlight recent analyses of adult neurogenesis and regeneration after injury in teleost fish that address these issues, and we discuss how such analyses can help to evaluate the role of different cells in tissues in the regeneration process.

Computational methods for the integrative analysis of single-cell data

Recent advances in single-cell technologies are providing exciting opportunities for dissecting tissue heterogeneity and investigating cell identity, fate and function. This is a pristine, exploding field that is flooding biologists with a new wave of data, each with its own specificities in terms of complexity and information content. The integrative analysis of genomic data, collected at different molecular layers from diverse cell populations, holds promise to address the full-scale complexity of biological systems. However, the combination of different single-cell genomic signals is computationally challenging, as these data are intrinsically heterogeneous for experimental, technical and biological reasons. Here, we describe the computational methods for the integrative analysis of single-cell genomic data, with a focus on the integration of single-cell RNA sequencing datasets and on the joint analysis of multimodal signals from individual cells.

Cancer systems immunology

Tumor immunology is undergoing a renaissance due to the recent profound clinical successes of tumor immunotherapy. These advances have coincided with an exponential growth in the development of -omics technologies. Armed with these technologies and their associated computational and modeling toolsets, systems biologists have turned their attention to tumor immunology in an effort to understand the precise nature and consequences of interactions between tumors and the immune system. Such interactions are inherently multivariate, spanning multiple time and size scales, cell types, and organ systems, rendering systems biology approaches particularly amenable to their interrogation. While in its infancy, the field of 'Cancer Systems Immunology' has already influenced our understanding of tumor immunology and immunotherapy. As the field matures, studies will move beyond descriptive characterizations toward functional investigations of the emergent behavior that govern tumor-immune responses. Thus, Cancer Systems Immunology holds incredible promise to advance our ability to fight this disease.

Novel classifier orthologs of bovine and human oocytes matured in different melatonin environments

It has been demonstrated that melatonin influences the developmental competence of both in vivo and in vitro matured oocytes. It modulates oocyte-specific gene expression patterns among mammalian species. Due to differences among study systems, the identification of the classifier orthologs-the homologous genes related among mammals that could universally categorize oocytes matured in environments with varied melatonin levels is still limitedly studied. To gain insight into such orthologs, cross-species transcription profiling meta-analysis of in vitro matured bovine oocytes and in vivo matured human oocytes in low and high melatonin environments was demonstrated in the current study. RNA-Seq data of bovine and human oocytes were retrieved from the Sequence Read Archive database and pre-processed. The used datasets of bovine oocytes obtained from culturing in the absence of melatonin and human oocytes from old patients were regarded as oocytes in the low melatonin environment (Low). Datasets from bovine oocytes cultured in 10-9 M melatonin and human oocytes from young patients were considered as oocytes in the high melatonin environment (High). Candidate orthologs differentially expressed between Low and High melatonin environments were selected by a linear model, and were further verified by Zero-inflated regression analysis. Support Vector Machine (SVM) was applied to determine the potentials of the verified orthologs as classifiers of melatonin environments. According to the acquired results, linear model analysis identified 284 candidate orthologs differentially expressed between Low and High melatonin environments. Among them, only 15 candidate orthologs were verified by Zero-inflated regression analysis (FDR ≤ 0.05). Utilization of the verified orthologs as classifiers in SVM resulted in the precise classification of oocyte learning datasets according to their melatonin environments (Misclassification rates < 0.18, area under curves > 0.9). In conclusion, the cross-species RNA-Seq meta-analysis to identify novel classifier orthologs of matured oocytes under different melatonin environments was successfully demonstrated in this study-delivering candidate orthologs for future studies at biological levels. Such verified orthologs might provide valuable evidence about melatonin sufficiency in target oocytes-by which, the decision on melatonin supplementation could be implied.