Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics

Allometry of cell types in planarians by single-cell transcriptomics

Allometry explores the relationship between an organism's body size and its various components, offering insights into ecology, physiology, metabolism, and disease. The cell is the basic unit of biological systems, and yet the study of cell-type allometry remains relatively unexplored. Single-cell RNA sequencing (scRNA-seq) provides a promising tool for investigating cell-type allometry. Planarians, capable of growing and degrowing following allometric scaling rules, serve as an excellent model for these studies. We used scRNA-seq to examine cell-type allometry in asexual planarians of different sizes, revealing that they consist of the same basic cell types but in varying proportions. Notably, the gut basal cells are the most responsive to changes in size, suggesting a role in energy storage. We capture the regulated gene modules of distinct cell types in response to body size. This research sheds light on the molecular and cellular aspects of cell-type allometry in planarians and underscores the utility of scRNA-seq in these investigations.

Unveiling cell-type-specific mode of evolution in comparative single-cell expression data

While methodology for determining the mode of evolution in coding sequences has been well established, evaluation of adaptation events in emerging types of phenotype data needs further development. Here we propose an analysis framework (expression variance decomposition, EVaDe) for comparative single-cell expression data based on phenotypic evolution theory. After decomposing the gene expression variance into separate components, we use two strategies to identify genes exhibiting large between-taxon expression divergence and small within-cell-type expression noise in certain cell types, attributing this pattern to putative adaptive evolution. In a dataset of primate prefrontal cortex, we find that such human-specific key genes enrich with neurodevelopment-related functions, while most other genes exhibit neutral evolution patterns. Specific neuron types are found to harbor more of these key genes than other cell types, thus likely to have experienced more extensive adaptation. Reassuringly, at molecular sequence level, the key genes are significantly associated with the rapidly evolving conserved non-coding elements. An additional case analysis comparing the naked mole-rat (NMR) with the mouse suggests that innate-immunity-related genes and cell types have undergone putative expression adaptation in NMR. Overall, the EVaDe framework may effectively probe adaptive evolution mode in single-cell expression data.

Regeneration leads to global tissue rejuvenation in aging sexual planarians

The possibility of reversing the adverse impacts of aging could significantly reduce age-related diseases and improve quality of life in older populations. Here we report that the sexual lineage of the planarian Schmidtea mediterranea exhibits physiological decline within 18 months of birth, including altered tissue architecture, impaired fertility and motility, and increased oxidative stress. Single-cell profiling of young and older planarian heads uncovered loss of neurons and muscle, increase of glia, and revealed minimal changes in somatic pluripotent stem cells, along with molecular signatures of aging across tissues. Remarkably, amputation followed by regeneration of lost tissues in older planarians led to reversal of these age-associated changes in tissues both proximal and distal to the injury at physiological, cellular and molecular levels. Our work suggests mechanisms of rejuvenation in both new and old tissues concurring with planarian regeneration, which may provide valuable insights for antiaging interventions.

Application of bioinformatic tools in cell type classification for single-cell RNA-seq data

The advancements in single-cell RNA sequencing (scRNAseq) technology have significantly transformed genomics research, enabling the handling of thousands of cells in each experiment. As of now, 32,068 research studies have been cataloged in the Pubmed database. The primary aim of scRNAseq investigations is to identify cell types, understand the antitumor immune response, and identify new and uncommon cell types. Traditional techniques for identifying cell types include microscopy, histology, and pathological characteristics. However, the complexity of instruments and the need for precise experimental design make it difficult to fully capture the overall heterogeneity. Unsupervised clustering and supervised classification methods have been used to solve this task. Supervised cell type classification methods have gained popularity as large-scale, high-quality, well-annotated and more robust results compared to clustering methods. A recent study showed that support vector machine (SVM) gives a high-quality classification performance in different scenarios. In this article, we compare and evaluate the performance of four different kernels (sigmoid, linear, radial, polynomial) of SVM. The results of the experiments on three standard scRNA-seq datasets indicate that SVM with linear and SVM with sigmoid kernel classify the cells more accurately (approx. 99 %) where SVM linear kernel method has remarkably fast computation time and we also evaluate the results using some single cell specific evaluation matrices F-1 score, MCC, AUC value. Additionally, it sheds light on the potential use of kernels of SVM to give underlying information of single-cell RNA-Seq data more effectively.

Chromatin remodeling protein BPTF mediates chromatin accessibility at gene promoters in planarian stem cells

BACKGROUND

The regulation of chromatin accessibility is essential in eukaryotic cells as one of several mechanisms that ensure gene activation occurs at appropriate times and in appropriate cell types. Accordingly, mutations in chromatin remodeling proteins are linked to many different developmental disorders and cancers. One example of a chromatin protein that has been linked to both developmental abnormalities and cancer is BPTF/NURF301, the largest subunit of the Nucleosome Remodeling Factor (NuRF) complex. The BPTF subunit is not only important for the formation of NuRF but also helps direct its activity to particular regions of chromatin by preferentially binding histone H3 lysine four trimethylation (H3K4me3). Notably, defects caused by knockdown of bptf in Xenopus embryos mimic those caused by knockdown of wdr5, a core subunit of all H3K4me3 methyltransferase complexes. However, the mechanistic details of how and where BPTF/NuRF is recruited to regulate gene expression vary between studies and have been largely tested in vitro and/or in cultured cells. Improving our understanding of how this chromatin remodeling complex targets specific gene loci and regulates their expression in an organismal context will provide important insight into how pathogenic mutations disrupt its normal, in vivo, cellular functions.

RESULTS

Here, we report our findings on the role of BPTF in maintaining chromatin accessibility and essential function in planarian (Schmidtea mediterranea) stem cells. We find that depletion of planarian BPTF primarily affects accessibility at gene promoters near transcription start sites (TSSs). BPTF-dependent loss of accessibility did not correlate with decreased gene expression when we considered all affected loci. However, we found that genes marked by Set1-dependent H3K4me3, but not MLL1/2-dependent H3K4me3, showed increased sensitivity to the loss of BPTF-dependent accessibility. In addition, knockdown of bptf (Smed-bptf) produces loss-of-function phenotypes similar to those caused by knockdown of Smed-set1.

CONCLUSIONS

The S.mediterranea homolog of NuRF protein BPTF (SMED-BPTF) is essential for normal homeostasis in planarian tissues, potentially through its role in maintaining chromatin accessibility at a specific subset of gene promoters in planarian stem cells. By identifying loci that lose both chromatin accessibility and gene expression after depletion of BPTF, we have identified a cohort of genes that may have important functions in stem cell biology.

scMUG: deep clustering analysis of single-cell RNA-seq data on multiple gene functional modules

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity by providing gene expression data at the single-cell level. Unlike bulk RNA-seq, scRNA-seq allows identification of different cell types within a given tissue, leading to a more nuanced comprehension of cell functions. However, the analysis of scRNA-seq data presents challenges due to its sparsity and high dimensionality. Since bioinformatics plays an important role in the analysis of big data and its utility for the welfare of living beings, it has been widely applied in analyzing scRNA-seq data. To address these challenges, we introduce the scMUG computational pipeline, which incorporates gene functional module information to enhance scRNA-seq clustering analysis. The pipeline includes data preprocessing, cell representation generation, cell-cell similarity matrix construction, and clustering analysis. The scMUG pipeline also introduces a novel similarity measure that combines local density and global distribution in the latent cell representation space. As far as we can tell, this is the first attempt to integrate gene functional associations into scRNA-seq clustering analysis. We curated nine human scRNA-seq datasets to evaluate our scMUG pipeline. With the help of gene functional information and the novel similarity measure, the clustering results from scMUG pipeline present deep insights into functional relationships between gene expression patterns and cellular heterogeneity. In addition, our scMUG pipeline also presents comparable or better clustering performances than other state-of-the-art methods. All source codes of scMUG have been deposited in a GitHub repository with instructions for reproducing all results (https://github.com/degiminnal/scMUG).

Gene module reconstruction identifies cellular differentiation processes and the regulatory logic of specialized secretion in zebrafish

During differentiation, cells become structurally and functionally specialized, but comprehensive views of the underlying remodeling processes are elusive. Here, we leverage single-cell RNA sequencing (scRNA-seq) developmental trajectories to reconstruct differentiation using two secretory tissues as models-the zebrafish notochord and hatching gland. First, we integrated expression and functional similarities to identify gene modules, revealing dozens of modules representing known and newly associated differentiation processes and their dynamics. Second, we focused on the unfolded protein response (UPR) transducer module to study how general versus cell-type-specific secretory functions are regulated. Profiling loss- and gain-of-function embryos identified that the UPR transcription factors creb3l1, creb3l2, and xbp1 are master regulators of a general secretion program. creb3l1/creb3l2 additionally activate an extracellular matrix secretion program, while xbp1 partners with bhlha15 to activate a gland-like secretion program. Our study presents module identification via multi-source integration for reconstructing differentiation (MIMIR) and illustrates how transcription factors confer general and specialized cellular functions.

Stem cells (neoblasts) and positional information jointly dominate regeneration in planarians

Regeneration is the ability to accurately regrow missing body parts. The unparalleled regenerative capacity and incredible tissue plasticity of planarians, both resulting from the presence of abundant adult stem cells referred to as neoblasts, offer a unique opportunity to investigate the cellular and molecular principles underlying regeneration. Neoblasts are capable of self-renewal and differentiation into the desired cell types for correct replacement of lost parts after tissue damage. Positional information in muscle cells governs the polarity and patterning of the body plan during homeostasis and regeneration. For planarians, removal of neoblasts disables the regenerative feats and disruption of positional information results in the regeneration of inappropriate missing body regions, only the combination of neoblasts and positional information enables regeneration. Here, I summarize the current state of the field in neoblast lineage potential, subclasses and specification, and in the roles of positional information for proper tissue turnover and regeneration in planarians.

Transcriptional profile of the rat cardiovascular system at single-cell resolution

We sought to characterize cellular composition across the cardiovascular system of the healthy Wistar rat, an important model in preclinical cardiovascular research. We performed single-nucleus RNA sequencing (snRNA-seq) in 78 samples in 10 distinct regions, including the four chambers of the heart, ventricular septum, sinoatrial node, atrioventricular node, aorta, pulmonary artery, and pulmonary veins, which produced 505,835 nuclei. We identified 26 distinct cell types and additional subtypes, with different cellular composition across cardiac regions and tissue-specific transcription for each cell type. Several cell subtypes were region specific, including a subtype of vascular smooth muscle cells enriched in the large vasculature. We observed tissue-enriched cellular communication networks, including heightened Nppa-Npr1/2/3 signaling in the sinoatrial node. The existence of tissue-restricted cell types suggests regional regulation of cardiovascular physiology. Our detailed transcriptional characterization of each cell type offers the potential to identify novel therapeutic targets and improve preclinical models of cardiovascular disease.

Benchmarking cross-species single-cell RNA-seq data integration methods: towards a cell type tree of life

Cross-species single-cell RNA-seq data hold immense potential for unraveling cell type evolution and transferring knowledge between well-explored and less-studied species. However, challenges arise from interspecific genetic variation, batch effects stemming from experimental discrepancies and inherent individual biological differences. Here, we benchmarked nine data-integration methods across 20 species, encompassing 4.7 million cells, spanning eight phyla and the entire animal taxonomic hierarchy. Our evaluation reveals notable differences between the methods in removing batch effects and preserving biological variance across taxonomic distances. Methods that effectively leverage gene sequence information capture underlying biological variances, while generative model-based approaches excel in batch effect removal. SATURN demonstrates robust performance across diverse taxonomic levels, from cross-genus to cross-phylum, emphasizing its versatility. SAMap excels in integrating species beyond the cross-family level, especially for atlas-level cross-species integration, while scGen shines within or below the cross-class hierarchy. As a result, our analysis offers recommendations and guidelines for selecting suitable integration methods, enhancing cross-species single-cell RNA-seq analyses and advancing algorithm development.

Challenges and Opportunities in the Clinical Translation of High-Resolution Spatial Transcriptomics

Pathology has always been fueled by technological advances. Histology powered the study of tissue architecture at single-cell resolution and remains a cornerstone of clinical pathology today. In the last decade, next-generation sequencing has become informative for the targeted treatment of many diseases, demonstrating the importance of genome-scale molecular information for personalized medicine. Today, revolutionary developments in spatial transcriptomics technologies digitalize gene expression at subcellular resolution in intact tissue sections, enabling the computational analysis of cell types, cellular phenotypes, and cell-cell communication in routinely collected and archival clinical samples. Here we review how such molecular microscopes work, highlight their potential to identify disease mechanisms and guide personalized therapies, and provide guidance for clinical study design. Finally, we discuss remaining challenges to the swift translation of high-resolution spatial transcriptomics technologies and how integration of multimodal readouts and deep learning approaches is bringing us closer to a holistic understanding of tissue biology and pathology.

Mitochondrial dynamics govern whole-body regeneration through stem cell pluripotency and mitonuclear balance

Tissue regeneration is a complex process involving large changes in cell proliferation, fate determination, and differentiation. Mitochondrial dynamics and metabolism play a crucial role in development and wound repair, but their function in large-scale regeneration remains poorly understood. Planarians offer an excellent model to investigate this process due to their remarkable regenerative abilities. In this study, we examine mitochondrial dynamics during planarian regeneration. We find that knockdown of the mitochondrial fusion gene, opa1, impairs both tissue regeneration and stem cell pluripotency. Interestingly, the regeneration defects caused by opa1 knockdown are rescued by simultaneous knockdown of the mitochondrial fission gene, drp1, which partially restores mitochondrial dynamics. Furthermore, we discover that Mitolow stem cells exhibit an enrichment of pluripotency due to their fate choices at earlier stages. Transcriptomic analysis reveals the delicate mitonuclear balance in metabolism and mitochondrial proteins in regeneration, controlled by mitochondrial dynamics. These findings highlight the importance of maintaining mitochondrial dynamics in large-scale tissue regeneration and suggest the potential for manipulating these dynamics to enhance stem cell functionality and regenerative processes.

An AI-Cyborg System for Adaptive Intelligent Modulation of Organoid Maturation

Recent advancements in flexible bioelectronics have enabled continuous, long-term stable interrogation and intervention of biological systems. However, effectively utilizing the interrogated data to modulate biological systems to achieve specific biomedical and biological goals remains a challenge. In this study, we introduce an AI-driven bioelectronics system that integrates tissue-like, flexible bioelectronics with cyber learning algorithms to create a long-term, real-time bidirectional b ioelectronic interface with o ptimized a daptive intelligent m odulation (BIO-AIM). When integrated with biological systems as an AI-cyborg system, BIO-AIM continuously adapts and optimizes stimulation parameters based on stable cell state mapping, allowing for real-time, closed-loop feedback through tissue-embedded flexible electrode arrays. Applied to human pluripotent stem cell-derived cardiac organoids, BIO-AIM identifies optimized stimulation conditions that accelerate functional maturation. The effectiveness of this approach is validated through enhanced extracellular spike waveforms, increased conduction velocity, and improved sarcomere organization, outperforming both fixed and no stimulation conditions.

Maximizing single cell dissociation protocol for individual zebrafish embryo

Single-cell sequencing has revolutionized our understanding of cellular heterogeneity and cell state, enabling investigations across diverse fields such as developmental biology, immunology, and cancer biology. However, obtaining a high-quality single-cell suspension is still challenging, particularly when starting with limited materials like Zebrafish embryos, a powerful animal model for studying developmental processes and human diseases. Here, we present an optimized protocol for isolating single cells from individual zebrafish embryos, offering a valuable resource for researchers interested in working with limited starting material. The protocol facilitates unique investigations utilizing individual embryos, such as inter-individual genetic differences and embryo-specific lineage tracing analysis. Using a refined single-cell isolation protocol alongside zebrafish as a model organism, researchers can access a resource for exploring the emergence of all types and states of cells, advancing our understanding of cellular processes and disease mechanisms.

Enhancing Single-Cell RNA-seq Data Completeness with a Graph Learning Framework

Single cell RNA sequencing (scRNA-seq) is a powerful tool to capture gene expression snapshots in individual cells. However, a low amount of RNA in the individual cells results in dropout events, which introduce huge zero counts in the single cell expression matrix. We have developed VAImpute, a variational graph autoencoder based imputation technique that learns the inherent distribution of a large network/graph constructed from the scRNA-seq data leveraging copula correlation () among cells/genes. The trained model is utilized to predict the dropouts events by computing the probability of all non-edges (cell-gene) in the network. We devise an algorithm to impute the missing expression values of the detected dropouts. The performance of the proposed model is assessed on both simulated and real scRNA-seq datasets, comparing it to established single-cell imputation methods. VAImpute yields significant improvements to detect dropouts, thereby achieving superior performance in cell clustering, detecting rare cells, and differential expression. All codes and datasets are given in the github link: https://github.com/sumantaray/VAImputeAvailability.

Planarian LDB and SSDP proteins scaffold transcriptional complexes for regeneration and patterning

Sequence-specific transcription factors often function as components of large regulatory complexes. LIM-domain binding protein (LDB) and single-stranded DNA-binding protein (SSDP) function as core scaffolds of transcriptional complexes in animals and plants. Little is known about potential partners and functions for LDB/SSDP complexes in the context of tissue regeneration. In this work, we find that planarian LDB1 and SSDP2 promote tissue regeneration, with a particular function in anterior regeneration and mediolateral polarity reestablishment. We find that LDB1 and SSDP2 interact with one another and with characterized planarian LIM-HD proteins Arrowhead, Islet1, and Lhx1/5-1. We also show that SSDP2 and LDB1 function with islet1 in polarity reestablishment and with lhx1/5-1 in serotonergic neuron maturation. Finally, we find new roles for LDB1 and SSDP2 in regulating gene expression in the planarian intestine and parenchyma; these functions are likely LIM-HD-independent. Together, our work provides insight into LDB/SSDP complexes in a highly regenerative organism. Further, our work provides a strong starting point for identifying and characterizing potential binding partners of LDB1 and SSDP2 and for exploring roles for these proteins in diverse aspects of planarian physiology.

Mining of potentially stem cell-related miRNAs in planarians

Stem cells and regenerative medicine have recently become important research topics. However, the complex stem cell regulatory networks involved in various microRNA (miRNA)-mediated mechanisms have not yet been fully elucidated. Planarians are ideal animal models for studying stem cells owing to their rich stem cell populations (neoblasts) and extremely strong regeneration capacity. The roles of planarian miRNAs in stem cells and regeneration have long attracted attention. However, previous studies have generally provided simple datasets lacking integrative analysis. Here, we have summarized the miRNA family reported in planarians and highlighted conservation in both sequence and function. Furthermore, we summarized miRNA data related to planarian stem cells and regeneration and screened potential involved candidates. Nevertheless, the roles of these miRNAs in planarian regeneration and stem cells remain unclear. The identification of potential stem cell-related miRNAs offers more precise suggestions and references for future investigations of miRNAs in planarians. Furthermore, it provides potential research avenues for understanding the mechanisms of stem cell regulatory networks. Finally, we compiled a summary of the experimental methods employed for studying planarian miRNAs, with the aim of highlighting special considerations in certain procedures and providing more convenient technical support for future research endeavors.

A comparative analysis of planarian genomes reveals regulatory conservation in the face of rapid structural divergence

The planarian Schmidtea mediterranea is being studied as a model species for regeneration, but the assembly of planarian genomes remains challenging. Here, we report a high-quality haplotype-phased, chromosome-scale genome assembly of the sexual S2 strain of S. mediterranea and high-quality chromosome-scale assemblies of its three close relatives, S. polychroa, S. nova, and S. lugubris. Using hybrid gene annotations and optimized ATAC-seq and ChIP-seq protocols for regulatory element annotation, we provide valuable genome resources for the planarian research community and a first comparative perspective on planarian genome evolution. Our analyses reveal substantial divergence in protein-coding sequences and regulatory regions but considerable conservation within promoter and enhancer annotations. We also find frequent retrotransposon-associated chromosomal inversions and interchromosomal translocations within the genus Schmidtea and, remarkably, independent and nearly complete losses of ancestral metazoan synteny in Schmidtea and two other flatworm groups. Overall, our results suggest that platyhelminth genomes can evolve without syntenic constraints.

The Role of Polycystic Kidney Disease-Like Homologs in Planarian Nervous System Regeneration and Function

Planarians are an excellent model for investigating molecular mechanisms necessary for regenerating a functional nervous system. Numerous studies have led to the generation of extensive genomic resources, especially whole-animal single-cell RNA-seq resources. These have facilitated in silico predictions of neuronal subtypes, many of which have been anatomically mapped by in situ hybridization. However, our knowledge of the function of dozens of neuronal subtypes remains poorly understood. Previous investigations identified that polycystic kidney disease (pkd)-like genes in planarians are strongly expressed in sensory neurons and have roles in mechanosensation. Here, we examine the expression and function of all the pkd genes found in the Schmidtea mediterranea genome and map their expression in the asexual and hermaphroditic strains. Using custom behavioral assays, we test the function of pkd genes in response to mechanical stimulation and in food detection. Our work provides insight into the physiological function of sensory neuron populations and protocols for creating inexpensive automated setups for acquiring and analyzing mechanosensory stimulation in planarians.

Melastatin subfamily Transient Receptor Potential channels support spermatogenesis in planarian flatworms

The Transient Receptor Potential superfamily of proteins (TRPs) form cation channels that are abundant in animal sensory systems. Amongst TRPs, the Melastatin-related subfamily (TRPMs) is composed of members that respond to temperature, pH, sex hormones, and various other stimuli. Some TRPMs exhibit enriched expression in gonads of vertebrate and invertebrate species, but their contributions to germline development remain to be determined. We identified twenty-one potential TRPMs in the planarian flatworm Schmidtea mediterranea and analyzed their anatomical distribution of expression by whole-mount in situ hybridization. Enriched expression of two TRPMs (Smed-TRPM-c and Smed-TRPM-l) was detected in testis, whereas eight TRPM genes had detectable expression in patterns representative of neuronal and/or sensory cell types. Functional analysis of TRPM homologs by RNA-interference (RNAi) revealed that disruption of Smed-TRPM-c expression results in reduced sperm development, indicating a role for this receptor in supporting spermatogenesis. Smed-TRPM-l RNAi did not result in a detectable phenotype, but it increased sperm development deficiencies when combined with Smed-TRPM-c RNAi. Fluorescence in situ hybridization revealed expression of Smed-TRPM-c in early spermatogenic cells within testes, suggesting cell-autonomous regulatory functions in germ cells for this gene. In addition, Smed-TRPM-c RNAi resulted in reduced numbers of presumptive germline stem cell clusters in asexual planarians, suggesting that Smed-TRPM-c supports establishment, maintenance, and/or expansion of spermatogonial germline stem cells. While further research is needed to identify the factors that trigger Smed-TRPM-c activity, these findings reveal one of few known examples for TRPM function in direct regulation of sperm development.

Decoding changes in tumor-infiltrating leukocytes through dynamic experimental models and single-cell technologies

The ability to characterize immune cells and explore the molecular interactions that govern their functions has never been greater, fueled in recent years by the revolutionary advance of single-cell analysis platforms. However, precisely how immune cells respond to different stimuli and where differentiation processes and effector functions operate remain incompletely understood. Inferring cellular fate within single-cell transcriptomic analyses is now omnipresent, despite the assumptions typically required in such analyses. Recently developed experimental models support dynamic analyses of the immune response, providing insights into the temporal changes that occur within cells and the tissues in which such transitions occur. Here we will review these approaches and discuss how these can be combined with single-cell technologies to develop a deeper understanding of the immune responses that should support the development of better therapeutic options for patients.

The role of polycystic kidney disease-like homologs in planarian nervous system regeneration and function

Planarians are an excellent model for investigating molecular mechanisms necessary for regenerating a functional nervous system. Numerous studies have led to the generation of extensive genomic resources, especially whole-animal single-cell RNA-seq resources. These have facilitated in silico predictions of neuronal subtypes, many of which have been anatomically mapped by in situ hybridization. However, our knowledge of the function of dozens of neuronal subtypes remains poorly understood. Previous investigations identified that polycystic kidney disease (pkd)-like genes in planarians are strongly expressed in sensory neurons and have roles in mechanosensation. Here, we examine the expression and function of all the pkd genes found in the Schmidtea mediterranea genome and map their expression in the asexual and hermaphroditic strains. Using custom behavioral assays, we test the function of pkd genes in response to mechanical stimulation and in food detection. Our work provides insight into the physiological function of sensory neuron populations and protocols for creating inexpensive automated setups for acquiring and analyzing mechanosensory stimulation in planarians.

Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning

Cell type identification is an important task for single-cell RNA-sequencing (scRNA-seq) data analysis. Many prediction methods have recently been proposed, but the predictive accuracy of difficult cell type identification tasks is still low. In this work, we proposed a novel Gaussian noise augmentation-based scRNA-seq contrastive learning method (GsRCL) to learn a type of discriminative feature representations for cell type identification tasks. A large-scale computational evaluation suggests that GsRCL successfully outperformed other state-of-the-art predictive methods on difficult cell type identification tasks, while the conventional random genes masking augmentation-based contrastive learning method also improved the accuracy of easy cell type identification tasks in general.

Identifying cell states in single-cell RNA-seq data at statistically maximal resolution

Single-cell RNA sequencing (scRNA-seq) has become a popular experimental method to study variation of gene expression within a population of cells. However, obtaining an accurate picture of the diversity of distinct gene expression states that are present in a given dataset is highly challenging because of the sparsity of the scRNA-seq data and its inhomogeneous measurement noise properties. Although a vast number of different methods is applied in the literature for clustering cells into subsets with 'similar' expression profiles, these methods generally lack rigorously specified objectives, involve multiple complex layers of normalization, filtering, feature selection, dimensionality-reduction, employ ad hoc measures of distance or similarity between cells, often ignore the known measurement noise properties of scRNA-seq measurements, and include a large number of tunable parameters. Consequently, it is virtually impossible to assign concrete biophysical meaning to the clusterings that result from these methods. Here we address the following problem: Given raw unique molecule identifier (UMI) counts of an scRNA-seq dataset, partition the cells into subsets such that the gene expression states of the cells in each subset are statistically indistinguishable, and each subset corresponds to a distinct gene expression state. That is, we aim to partition cells so as to maximally reduce the complexity of the dataset without removing any of its meaningful structure. We show that, given the known measurement noise structure of scRNA-seq data, this problem is mathematically well-defined and derive its unique solution from first principles. We have implemented this solution in a tool called Cellstates which operates directly on the raw data and automatically determines the optimal partition and cluster number, with zero tunable parameters. We show that, on synthetic datasets, Cellstates almost perfectly recovers optimal partitions. On real data, Cellstates robustly identifies subtle substructure within groups of cells that are traditionally annotated as a common cell type. Moreover, we show that the diversity of gene expression states that Cellstates identifies systematically depends on the tissue of origin and not on technical features of the experiments such as the total number of cells and total UMI count per cell. In addition to the Cellstates tool we also provide a small toolbox of software to place the identified cellstates into a hierarchical tree of higher-order clusters, to identify the most important differentially expressed genes at each branch of this hierarchy, and to visualize these results.

A niche-derived nonribosomal peptide triggers planarian sexual development

Germ cells are regulated by local microenvironments (niches), which secrete instructive cues. Conserved developmental signaling molecules act as niche-derived regulatory factors, yet other types of niche signals remain to be identified. Single-cell RNA-sequencing of sexual planarians revealed niche cells expressing a nonribosomal peptide synthetase (nrps). Inhibiting nrps led to loss of female reproductive organs and testis hyperplasia. Mass spectrometry detected the dipeptide β-alanyl-tryptamine (BATT), which is associated with reproductive system development and requires nrps and a monoamine-transmitter-synthetic enzyme Aromatic L-amino acid decarboxylase (AADC) for its production. Exogenous BATT rescued the reproductive defects after nrps or aadc inhibition, restoring fertility. Thus, a nonribosomal, monoamine-derived peptide provided by niche cells acts as a critical signal to trigger planarian reproductive development. These findings reveal an unexpected function for monoamines in niche-germ cell signaling. Furthermore, given the recently reported role for BATT as a male-derived factor required for reproductive maturation of female schistosomes, these results have important implications for the evolution of parasitic flatworms and suggest a potential role for nonribosomal peptides as signaling molecules in other organisms.

Chromatin remodeling protein BPTF regulates transcriptional stability in planarian stem cells

Trimethylation of histone H3 lysine 4 (H3K4me3) correlates strongly with gene expression in many different organisms, yet the question of whether it plays a causal role in transcriptional activity remains unresolved. Although H3K4me3 does not directly affect chromatin accessibility, it can indirectly affect genome accessibility by recruiting the ATP-dependent chromatin remodeling complex NuRF (Nucleosome Remodeling Factor). The largest subunit of NuRF, BPTF/NURF301, binds H3K4me3 specifically and recruits the NuRF complex to loci marked by this modification. Studies have shown that the strength and duration of BPTF binding likely also depends on additional chromatin features at these loci, such as lysine acetylation and variant histone proteins. However, the exact details of this recruitment mechanism vary between studies and have largely been tested in vitro. Here, we use stem cells isolated directly from live planarian animals to investigate the role of BPTF in regulating chromatin accessibility in vivo. We find that BPTF operates at gene promoters and is most effective at facilitating transcription at genes marked by Set1-dependent H3K4me3 peaks, which are significantly broader than those added by the lysine methyltransferase MLL1/2. Moreover, BPTF is essential for planarian stem cell biology and its loss of function phenotype mimics that of Set1 knockdown. Together, these data suggest that BPTF and H3K4me3 are important mediators of both transcription and in vivo stem cell function.

An atlas of spider development at single-cell resolution provides new insights into arthropod embryogenesis

Spiders are a diverse order of chelicerates that diverged from other arthropods over 500 million years ago. Research on spider embryogenesis, particularly studies using the common house spider Parasteatoda tepidariorum, has made important contributions to understanding the evolution of animal development, including axis formation, segmentation, and patterning. However, we lack knowledge about the cells that build spider embryos, their gene expression profiles and fate. Single-cell transcriptomic analyses have been revolutionary in describing these complex landscapes of cellular genetics in a range of animals. Therefore, we carried out single-cell RNA sequencing of P. tepidariorum embryos at stages 7, 8 and 9, which encompass the establishment and patterning of the body plan, and initial differentiation of many tissues and organs. We identified 20 cell clusters, from 18.5 k cells, which were marked by many developmental toolkit genes, as well as a plethora of genes not previously investigated. We found differences in the cell cycle transcriptional signatures, suggestive of different proliferation dynamics, which related to distinctions between endodermal and some mesodermal clusters, compared with ectodermal clusters. We identified many Hox genes as markers of cell clusters, and Hox gene ohnologs were often present in different clusters. This provided additional evidence of sub- and/or neo-functionalisation of these important developmental genes after the whole genome duplication in an arachnopulmonate ancestor (spiders, scorpions, and related orders). We also examined the spatial expression of marker genes for each cluster to generate a comprehensive cell atlas of these embryonic stages. This revealed new insights into the cellular basis and genetic regulation of head patterning, hematopoiesis, limb development, gut development, and posterior segmentation. This atlas will serve as a platform for future analysis of spider cell specification and fate, and studying the evolution of these processes among animals at cellular resolution.

scLENS: data-driven signal detection for unbiased scRNA-seq data analysis

High dimensionality and noise have limited the new biological insights that can be discovered in scRNA-seq data. While dimensionality reduction tools have been developed to extract biological signals from the data, they often require manual determination of signal dimension, introducing user bias. Furthermore, a common data preprocessing method, log normalization, can unintentionally distort signals in the data. Here, we develop scLENS, a dimensionality reduction tool that circumvents the long-standing issues of signal distortion and manual input. Specifically, we identify the primary cause of signal distortion during log normalization and effectively address it by uniformizing cell vector lengths with L2 normalization. Furthermore, we utilize random matrix theory-based noise filtering and a signal robustness test to enable data-driven determination of the threshold for signal dimensions. Our method outperforms 11 widely used dimensionality reduction tools and performs particularly well for challenging scRNA-seq datasets with high sparsity and variability. To facilitate the use of scLENS, we provide a user-friendly package that automates accurate signal detection of scRNA-seq data without manual time-consuming tuning.

Annelid adult cell type diversity and their pluripotent cellular origins

Many annelids can regenerate missing body parts or reproduce asexually, generating all cell types in adult stages. However, the putative adult stem cell populations involved in these processes, and the diversity of cell types generated by them, are still unknown. To address this, we recover 75,218 single cell transcriptomes of the highly regenerative and asexually-reproducing annelid Pristina leidyi. Our results uncover a rich cell type diversity including annelid specific types as well as novel types. Moreover, we characterise transcription factors and gene networks that are expressed specifically in these populations. Finally, we uncover a broadly abundant cluster of putative stem cells with a pluripotent signature. This population expresses well-known stem cell markers such as vasa, piwi and nanos homologues, but also shows heterogeneous expression of differentiated cell markers and their transcription factors. We find conserved expression of pluripotency regulators, including multiple chromatin remodelling and epigenetic factors, in piwi+ cells. Finally, lineage reconstruction analyses reveal computational differentiation trajectories from piwi+ cells to diverse adult types. Our data reveal the cell type diversity of adult annelids by single cell transcriptomics and suggest that a piwi+ cell population with a pluripotent stem cell signature is associated with adult cell type differentiation.

Intestinal cell diversity and treatment responses in a parasitic nematode at single cell resolution

BACKGROUND

Parasitic nematodes, significant pathogens for humans, animals, and plants, depend on diverse organ systems for intra-host survival. Understanding the cellular diversity and molecular variations underlying these functions holds promise for developing novel therapeutics, with specific emphasis on the neuromuscular system's functional diversity. The nematode intestine, crucial for anthelmintic therapies, exhibits diverse cellular phenotypes, and unraveling this diversity at the single-cell level is essential for advancing knowledge in anthelmintic research across various organ systems.

RESULTS

Here, using novel single-cell transcriptomics datasets, we delineate cellular diversity within the intestine of adult female Ascaris suum, a parasitic nematode species that infects animals and people. Gene transcripts expressed in individual nuclei of untreated intestinal cells resolved three phenotypic clusters, while lower stringency resolved additional subclusters and more potential diversity. Clusters 1 and 3 phenotypes displayed variable congruence with scRNA phenotypes of C. elegans intestinal cells, whereas the A. suum cluster 2 phenotype was markedly unique. Distinct functional pathway enrichment characterized each A. suum intestinal cell cluster. Cluster 2 was distinctly enriched for Clade III-associated genes, suggesting it evolved within clade III nematodes. Clusters also demonstrated differential transcriptional responsiveness to nematode intestinal toxic treatments, with Cluster 2 displaying the least responses to short-term intra-pseudocoelomic nematode intestinal toxin treatments.

CONCLUSIONS

This investigation presents advances in knowledge related to biological differences among major cell populations of adult A. suum intestinal cells. For the first time, diverse nematode intestinal cell populations were characterized, and associated biological markers of these cells were identified to support tracking of constituent cells under experimental conditions. These advances will promote better understanding of this and other parasitic nematodes of global importance, and will help to guide future anthelmintic treatments.

Partial label learning for automated classification of single-cell transcriptomic profiles

Single-cell RNA sequencing (scRNASeq) data plays a major role in advancing our understanding of developmental biology. An important current question is how to classify transcriptomic profiles obtained from scRNASeq experiments into the various cell types and identify the lineage relationship for individual cells. Because of the fast accumulation of datasets and the high dimensionality of the data, it has become challenging to explore and annotate single-cell transcriptomic profiles by hand. To overcome this challenge, automated classification methods are needed. Classical approaches rely on supervised training datasets. However, due to the difficulty of obtaining data annotated at single-cell resolution, we propose instead to take advantage of partial annotations. The partial label learning framework assumes that we can obtain a set of candidate labels containing the correct one for each data point, a simpler setting than requiring a fully supervised training dataset. We study and extend when needed state-of-the-art multi-class classification methods, such as SVM, kNN, prototype-based, logistic regression and ensemble methods, to the partial label learning framework. Moreover, we study the effect of incorporating the structure of the label set into the methods. We focus particularly on the hierarchical structure of the labels, as commonly observed in developmental processes. We show, on simulated and real datasets, that these extensions enable to learn from partially labeled data, and perform predictions with high accuracy, particularly with a nonlinear prototype-based method. We demonstrate that the performances of our methods trained with partially annotated data reach the same performance as fully supervised data. Finally, we study the level of uncertainty present in the partially annotated data, and derive some prescriptive results on the effect of this uncertainty on the accuracy of the partial label learning methods. Overall our findings show how hierarchical and non-hierarchical partial label learning strategies can help solve the problem of automated classification of single-cell transcriptomic profiles, interestingly these methods rely on a much less stringent type of annotated datasets compared to fully supervised learning methods.

Topological and geometric analysis of cell states in single-cell transcriptomic data

Single-cell RNA sequencing (scRNA-seq) enables dissecting cellular heterogeneity in tissues, resulting in numerous biological discoveries. Various computational methods have been devised to delineate cell types by clustering scRNA-seq data, where clusters are often annotated using prior knowledge of marker genes. In addition to identifying pure cell types, several methods have been developed to identify cells undergoing state transitions, which often rely on prior clustering results. The present computational approaches predominantly investigate the local and first-order structures of scRNA-seq data using graph representations, while scRNA-seq data frequently display complex high-dimensional structures. Here, we introduce scGeom, a tool that exploits the multiscale and multidimensional structures in scRNA-seq data by analyzing the geometry and topology through curvature and persistent homology of both cell and gene networks. We demonstrate the utility of these structural features to reflect biological properties and functions in several applications, where we show that curvatures and topological signatures of cell and gene networks can help indicate transition cells and the differentiation potential of cells. We also illustrate that structural characteristics can improve the classification of cell types.

A transcription factor atlas of stem cell fate in planarians

Whole-body regeneration requires the ability to produce the full repertoire of adult cell types. The planarian Schmidtea mediterranea contains over 125 cell types, which can be regenerated from a stem cell population called neoblasts. Neoblast fate choice can be regulated by the expression of fate-specific transcription factors (FSTFs). How fate choices are made and distributed across neoblasts versus their post-mitotic progeny remains unclear. We used single-cell RNA sequencing to systematically map fate choices made in S/G2/M neoblasts and, separately, in their post-mitotic progeny that serve as progenitors for all adult cell types. We defined transcription factor expression signatures associated with all detected fates, identifying numerous new progenitor classes and FSTFs that regulate them. Our work generates an atlas of stem cell fates with associated transcription factor signatures for most cell types in a complete adult organism.

Purification and transcriptomic characterization of proliferative cells of Mesocestoides corti selectively affected by irradiation

Flatworms depend on stem cells for continued tissue growth and renewal during their life cycles, making these cells valuable drug targets. While neoblasts are extensively characterized in the free-living planarian Schmidtea mediterranea, and similar stem cells have been characterized in the trematode Schistosoma mansoni, their identification and characterization in cestodes is just emerging. Since stem cells are generally affected by irradiation, in this work we used this experimental approach to study the stem cells of the model cestode Mesocestoides corti. We found that gamma irradiation produces a dose-dependent decrease in proliferative cells, requiring higher doses than in other flatworms to completely abolish proliferation. The treatment results in the downregulation of candidate marker genes. Transcriptomic studies reveal that several genes downregulated after irradiation are conserved with other flatworms, and are related to cell cycle, DNA replication and repair functions. Furthermore, proliferative cells were isolated by cell sorting and also characterized transcriptomically. We found that the set of genes characteristic of proliferative cells agrees well with those downregulated during irradiation, and have a significant overlap with those expressed in planarian neoblasts or S. mansoni stem cells. Our study highlights that conserved mechanisms of stem cell biology may be functional in flatworms, suggesting that these could be relevant targets to evaluate in the control of parasitic species.

Quantifying the clusterness and trajectoriness of single-cell RNA-seq data

Among existing computational algorithms for single-cell RNA-seq analysis, clustering and trajectory inference are two major types of analysis that are routinely applied. For a given dataset, clustering and trajectory inference can generate vastly different visualizations that lead to very different interpretations of the data. To address this issue, we propose multiple scores to quantify the "clusterness" and "trajectoriness" of single-cell RNA-seq data, in other words, whether the data looks like a collection of distinct clusters or a continuum of progression trajectory. The scores we introduce are based on pairwise distance distribution, persistent homology, vector magnitude, Ripley's K, and degrees of connectivity. Using simulated datasets, we demonstrate that the proposed scores are able to effectively differentiate between cluster-like data and trajectory-like data. Using real single-cell RNA-seq datasets, we demonstrate the scores can serve as indicators of whether clustering analysis or trajectory inference is a more appropriate choice for biological interpretation of the data.

Mapping Cell Atlases at the Single-Cell Level

Recent advancements in single-cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential to provide detailed information about every cell type in different organisms, enabling the characterization of cellular diversity at the single-cell level. Global efforts in developing comprehensive cell atlases have profound implications for both basic research and clinical applications. This review provides a broad overview of the cellular diversity and dynamics across various biological systems. In addition, the incorporation of machine learning techniques into cell atlas analyses opens up exciting prospects for the field of integrative biology.

Ribosome inactivation regulates translation elongation in neurons

Cellular plasticity is crucial for adapting to ever-changing stimuli. As a result, cells consistently reshape their translatome, and, consequently, their proteome. The control of translational activity has been thoroughly examined at the stage of translation initiation. However, the regulation of ribosome speed in cells is widely unknown. In this study, we utilized a timed ribosome runoff approach, along with proteomics and transmission electron microscopy, to investigate global translation kinetics in cells. We found that ribosome speeds vary among various cell types, such as astrocytes, induced pluripotent human stem cells, human neural stem cells, and human and rat neurons. Of all cell types studied, mature cortical neurons exhibit the highest rate of translation. This finding is particularly remarkable because mature cortical neurons express the eukaryotic elongation factor 2 (eEF2) at lower levels than other cell types. Neurons solve this conundrum by inactivating a fraction of their ribosomes. As a result, the increase in eEF2 levels leads to a reduction of inactive ribosomes and an enhancement of active ones. Processes that alter the demand for active ribosomes, like neuronal excitation, cause increased inactivation of redundant ribosomes in an eEF2-dependent manner. Our data suggest a novel regulatory mechanism in which neurons dynamically inactivate ribosomes to facilitate translational remodeling. These findings have important implications for developmental brain disorders characterized by, among other things, aberrant translation.

Cell cycle specific, differentially tagged ribosomal proteins to measure phase specific transcriptomes from asynchronously cycling cells

Asynchronously cycling cells pose a challenge to the accurate characterization of phase-specific gene expression. Current strategies, including RNAseq, survey the steady state gene expression across the cell cycle and are inherently limited by their inability to resolve dynamic gene regulatory networks. Single cell RNAseq (scRNAseq) can identify different cell cycle transcriptomes if enough cycling cells are present, however some cells are not amenable to scRNAseq. Therefore, we merged two powerful strategies, the CDT1 and GMNN degrons used in Fluorescent Ubiquitination-based Cell Cycle Indicator (FUCCI) cell cycle sensors and the ribosomal protein epitope tagging used in RiboTrap/Tag technologies to isolate cell cycle phase-specific mRNA for sequencing. The resulting cell cycle dependent, tagged ribosomal proteins (ccTaggedRP) were differentially expressed during the cell cycle, had similar subcellular locations as endogenous ribosomal proteins, incorporated into ribosomes and polysomes, and facilitated the recovery of cell cycle phase-specific RNA for sequencing. ccTaggedRP has broad applications to investigate phase-specific gene expression in complex cell populations.

A resource of single-cell gene expression profiles in a planarian Dugesia japonica

The freshwater planarian Dugesia japonica maintains an abundant heterogeneous cell population called neoblasts, which include adult pluripotent stem cells. Thus, it is an excellent model organism for stem cell and regeneration research. Recently, many single-cell RNA sequencing (scRNA-seq) databases of several model organisms, including other planarian species, have become publicly available; these are powerful and useful resources to search for gene expression in various tissues and cells. However, the only scRNA-seq dataset for D. japonica has been limited by the number of genes detected. Herein, we collected D. japonica cells, and conducted an scRNA-seq analysis. A novel, automatic, iterative cell clustering strategy produced a dataset of 3,404 cells, which could be classified into 63 cell types based on gene expression profiles. We introduced two examples for utilizing the scRNA-seq dataset in this study using D. japonica. First, the dataset provided results consistent with previous studies as well as novel functionally relevant insights, that is, the expression of DjMTA and DjP2X-A genes in neoblasts that give rise to differentiated cells. Second, we conducted an integrative analysis of the scRNA-seq dataset and time-course bulk RNA-seq of irradiated animals, demonstrating that the dataset can help interpret differentially expressed genes captured via bulk RNA-seq. Using the R package "Seurat" and GSE223927, researchers can easily access and utilize this dataset.

MRLC controls apoptotic cell death and functions to regulate epidermal development during planarian regeneration and homeostasis

Adult stem cells (ASCs) are pluripotent cells with the capacity to self-renew and constantly replace lost cells due to physiological turnover or injury. Understanding the molecular mechanisms of the precise coordination of stem cell proliferation and proper cell fate decision is important to regeneration and organismal homeostasis. The planarian epidermis provides a highly tractable model to study ASC complex dynamic due to the distinct spatiotemporal differentiation stages during lineage development. Here, we identified the myosin regulatory light chain (MRLC) homologue in the Dugesia japonica transcriptome. We found high expression levels of MRLC in wound region during regeneration and also expressed in late epidermal progenitors as an essential regulator of the lineage from neoblasts to mature epidermal cells. We investigated the function of MRLC using in situ hybridization, real-time polymerase chain reaction and double fluorescent and uncovered the potential mechanism. Knockdown of MRLC leads to a remarkable increase in cell death, causes severe abnormalities during regeneration and homeostasis and eventually leads to animal death. The global decrease in epidermal cell in MRLC RNAi animals induces accelerated epidermal proliferation and differentiation. Additionally, we find that MRLC is co-expressed with cdc42 and acts cooperatively to control the epidermal lineage development by affecting cell death. Our results uncover an important role of MRLC, as an inhibitor of apoptosis, involves in epidermal development.

Purification of Planarian Stem Cells Using a Draq5-Based FACS Approach

Planarians are flatworms that have the remarkable ability to regenerate entirely new animals. This regenerative ability requires abundant adult stem cells called neoblasts, which are relatively small in size, sensitive to irradiation and the only proliferative cells in the animal. Despite the lack of cell surface markers, fluorescence-activated cell sorting (FACS) protocols have been developed to discriminate and isolate neoblasts, based on DNA content. Here, we describe a protocol that combines staining of far-red DNA dye Draq5, Calcein-AM and DAPI, along with a shortened processing time. This profiling strategy can be used to functionally characterize the neoblast population in pharmacologically-treated or gene knockdown animals. Highly purified neoblasts can be analyzed with downstream assays, such as in situ hybridization and RNA sequencing.

Single-cell analysis of shared signatures and transcriptional diversity during zebrafish development

During development, animals generate distinct cell populations with specific identities, functions, and morphologies. We mapped transcriptionally distinct populations across 489,686 cells from 62 stages during wild-type zebrafish embryogenesis and early larval development (3-120 h post-fertilization). Using these data, we identified the limited catalog of gene expression programs reused across multiple tissues and their cell-type-specific adaptations. We also determined the duration each transcriptional state is present during development and identify unexpected long-term cycling populations. Focused clustering and transcriptional trajectory analyses of non-skeletal muscle and endoderm identified transcriptional profiles and candidate transcriptional regulators of understudied cell types and subpopulations, including the pneumatic duct, individual intestinal smooth muscle layers, spatially distinct pericyte subpopulations, and recently discovered best4+ cells. To enable additional discoveries, we make this comprehensive transcriptional atlas of early zebrafish development available through our website, Daniocell.

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.

A niche-derived non-ribosomal peptide triggers planarian sexual development

Germ cells are regulated by local microenvironments (niches), which secrete instructive cues. Conserved developmental signaling molecules act as niche-derived regulatory factors, yet other types of niche signals remain to be identified. Single-cell RNA-sequencing of sexual planarians revealed niche cells expressing a non-ribosomal peptide synthetase (nrps). Inhibiting nrps led to loss of female reproductive organs and testis hyperplasia. Mass spectrometry detected the dipeptide β-alanyl-tryptamine (BATT), which is associated with reproductive system development and requires nrps and a monoamine-transmitter-synthetic enzyme (AADC) for its production. Exogenous BATT rescued the reproductive defects after nrps or aadc inhibition, restoring fertility. Thus, a non-ribosomal, monoamine-derived peptide provided by niche cells acts as a critical signal to trigger planarian reproductive development. These findings reveal an unexpected function for monoamines in niche-germ cell signaling. Furthermore, given the recently reported role for BATT as a male-derived factor required for reproductive maturation of female schistosomes, these results have important implications for the evolution of parasitic flatworms and suggest a potential role for non-ribosomal peptides as signaling molecules in other organisms.

LIM-HD transcription factors control axial patterning and specify distinct neuronal and intestinal cell identities in planarians

Adult planarians can regenerate the gut, eyes and even a functional brain. Proper identity and patterning of the newly formed structures require signals that guide and commit their adult stem cells. During embryogenesis, LIM-homeodomain (LIM-HD) transcription factors act in a combinatorial 'LIM code' to control cell fate determination and differentiation. However, our understanding about the role these genes play during regeneration and homeostasis is limited. Here, we report the full repertoire of LIM-HD genes in Schmidtea mediterranea. We found that lim homeobox (lhx) genes appear expressed in complementary patterns along the cephalic ganglia and digestive system of the planarian, with some of them being co-expressed in the same cell types. We have identified that Smed-islet1, -lhx1/5-1, -lhx2/9-3, -lhx6/8, -lmx1a/b-2 and -lmx1a/b-3 are essential to pattern and size the planarian brain as well as for correct regeneration of specific subpopulations of dopaminergic, serotonergic, GABAergic and cholinergic neurons, while Smed-lhx1/5.2 and -lhx2/9.2 are required for the proper expression of intestinal cell type markers, specifically the goblet subtype. LIM-HD are also involved in controlling axonal pathfinding (lhx6/8), axial patterning (islet1, lhx1/5-1, lmx1a/b-3), head/body proportions (islet2) and stem cell proliferation (lhx3/4, lhx2/9-3, lmx1a/b-2, lmx1a/b-3). Altogether, our results suggest that planarians might present a combinatorial LIM code that controls axial patterning and axonal growing and specifies distinct neuronal and intestinal cell identities.

Human γδ T cell identification from single-cell RNA sequencing datasets by modular TCR expression

Accurately identifying γδ T cells in large single-cell RNA sequencing (scRNA-seq) datasets without additional single-cell γδ T cell receptor sequencing (sc-γδTCR-seq) or CITE-seq (cellular indexing of transcriptomes and epitopes sequencing) data remains challenging. In this study, we developed a TCR module scoring strategy for human γδ T cell identification (i.e. based on modular gene expression of constant and variable TRA/TRB and TRD genes). We evaluated our method using 5' scRNA-seq datasets comprising both sc-αβTCR-seq and sc-γδTCR-seq as references and demonstrated that it can identify γδ T cells in scRNA-seq datasets with high sensitivity and accuracy. We observed a stable performance of this strategy across datasets from different tissues and different subtypes of γδ T cells. Thus, we propose this analysis method, based on TCR gene module scores, as a standardized tool for identifying and reanalyzing γδ T cells from 5'-end scRNA-seq datasets.

Cell type and gene regulatory network approaches in the evolution of spiralian biomineralisation

Biomineralisation is the process by which living organisms produce hard structures such as shells and bone. There are multiple independent origins of biomineralised skeletons across the tree of life. This review gives a glimpse into the diversity of spiralian biominerals and what they can teach us about the evolution of novelty. It discusses different levels of biological organisation that may be informative to understand the evolution of biomineralisation and considers the relationship between skeletal and non-skeletal biominerals. More specifically, this review explores if cell type and gene regulatory network approaches could enhance our understanding of the evolutionary origins of biomineralisation.

Single-cell transcriptomics refuels the exploration of spiralian biology

Spiralians represent the least studied superclade of bilaterian animals, despite exhibiting the widest diversity of organisms. Although spiralians include iconic organisms, such as octopus, earthworms and clams, a lot remains to be discovered regarding their phylogeny and biology. Here, we review recent attempts to apply single-cell transcriptomics, a new pioneering technology enabling the classification of cell types and the characterisation of their gene expression profiles, to several spiralian taxa. We discuss the methodological challenges and requirements for applying this approach to marine organisms and explore the insights that can be brought by such studies, both from a biomedical and evolutionary perspective. For instance, we show that single-cell sequencing might help solve the riddle of the homology of larval forms across spiralians, but also to better characterise and compare the processes of regeneration across taxa. We highlight the capacity of single-cell to investigate the origin of evolutionary novelties, as the mollusc shell or the cephalopod visual system, but also to interrogate the conservation of the molecular fingerprint of cell types at long evolutionary distances. We hope that single-cell sequencing will open a new window in understanding the biology of spiralians, and help renew the interest for these overlooked but captivating organisms.

Fate specification is spatially intermingled across planarian stem cells

Regeneration requires mechanisms for producing a wide array of cell types. Neoblasts are stem cells in the planarian Schmidtea mediterranea that undergo fate specification to produce over 125 adult cell types. Fate specification in neoblasts can be regulated through expression of fate-specific transcription factors. We utilize multiplexed error-robust fluorescence in situ hybridization (MERFISH) and whole-mount FISH to characterize fate choice distribution of stem cells within planarians. Fate choices are often made distant from target tissues and in a highly intermingled manner, with neighboring neoblasts frequently making divergent fate choices for tissues of different location and function. We propose that pattern formation is driven primarily by the migratory assortment of progenitors from mixed and spatially distributed fate-specified stem cells and that fate choice involves stem-cell intrinsic processes.

Transcriptional profile of the rat cardiovascular system at single cell resolution

Background

Despite the critical role of the cardiovascular system, our understanding of its cellular and transcriptional diversity remains limited. We therefore sought to characterize the cellular composition, phenotypes, molecular pathways, and communication networks between cell types at the tissue and sub-tissue level across the cardiovascular system of the healthy Wistar rat, an important model in preclinical cardiovascular research. We obtained high quality tissue samples under controlled conditions that reveal a level of cellular detail so far inaccessible in human studies.

Methods and Results

We performed single nucleus RNA-sequencing in 78 samples in 10 distinct regions including the four chambers of the heart, ventricular septum, sinoatrial node, atrioventricular node, aorta, pulmonary artery, and pulmonary veins (PV), which produced an aggregate map of 505,835 nuclei. We identified 26 distinct cell types and additional subtypes, including a number of rare cell types such as PV cardiomyocytes and non-myelinating Schwann cells (NMSCs), and unique groups of vascular smooth muscle cells (VSMCs), endothelial cells (ECs) and fibroblasts (FBs), which gave rise to a detailed cell type distribution across tissues. We demonstrated differences in the cellular composition across different cardiac regions and tissue-specific differences in transcription for each cell type, highlighting the molecular diversity and complex tissue architecture of the cardiovascular system. Specifically, we observed great transcriptional heterogeneities among ECs and FBs. Importantly, several cell subtypes had a unique regional localization such as a subtype of VSMCs enriched in the large vasculature. We found the cellular makeup of PV tissue is closer to heart tissue than to the large arteries. We further explored the ligand-receptor repertoire across cell clusters and tissues, and observed tissue-enriched cellular communication networks, including heightened Nppa - Npr1/2/3 signaling in the sinoatrial node.

Conclusions

Through a large single nucleus sequencing effort encompassing over 500,000 nuclei, we broadened our understanding of cellular transcription in the healthy cardiovascular system. The existence of tissue-restricted cellular phenotypes suggests regional regulation of cardiovascular physiology. The overall conservation in gene expression and molecular pathways across rat and human cell types, together with our detailed transcriptional characterization of each cell type, offers the potential to identify novel therapeutic targets and improve preclinical models of cardiovascular disease.