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A Comprehensive Roadmap of Murine Spermatogenesis Defined by Single-Cell RNA-Seq.
Spermatogenesis requires intricate interactions between the germline and somatic cells. Within a given cross section of a seminiferous tubule, multiple germ and somatic cell types co-occur. This cellular heterogeneity has made it difficult to profile distinct cell types at different stages of development. To address this challenge, we collected single-cell RNA sequencing data from ∼35,000 cells from the adult mouse testis and identified all known germ and somatic cells, as well as two unexpected somatic cell types. Our analysis revealed a continuous developmental trajectory of germ cells from spermatogonia to spermatids and identified candidate transcriptional regulators at several transition points during differentiation. Focused analyses delineated four subtypes of spermatogonia and nine subtypes of Sertoli cells; the latter linked to histologically defined developmental stages over the seminiferous epithelial cycle. Overall, this high-resolution cellular atlas represents a community resource and foundation of knowledge to study germ cell development and in vivo gametogenesis.
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Evaluation of out-of-distribution detection methods for data shifts in single-cell transcriptomics.
Automatic cell-type annotation methods assign cell-type labels to new, unlabeled datasets by leveraging relationships from a reference RNA-seq atlas. However, new datasets may include labels absent from the reference dataset or exhibit feature distributions that diverge from it. These scenarios can significantly affect the reliability of cell type predictions, a factor often overlooked in current automatic annotation methods. The field of out-of-distribution detection (OOD), primarily focused on computer vision, addresses the identification of instances that differ from the training distribution. Therefore, the implementation of OOD methods in the context of novel cell type annotation and data shift detection for single-cell transcriptomics may enhance annotation accuracy and trustworthiness. We evaluate six OOD detection methods: LogitNorm, MC dropout, Deep Ensembles, Energy-based OOD, Deep NN, and Posterior networks, for their annotation and OOD detection performance in both synthetical and real-life application settings. We show that OOD detection methods can accurately identify novel cell types and demonstrate potential to detect significant data shifts in non-integrated datasets. Moreover, we find that integration of the OOD datasets does not interfere with OOD detection of novel cell types.
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Advancing our understanding of root development: Technologies and insights from diverse studies.
Understanding root development is critical for enhancing plant growth and health, and advanced technologies are essential for unraveling the complexities of these processes. In this review, we highlight select technological innovations in the study of root development, with a focus on the transformative impact of single-cell gene expression analysis. We provide a high-level overview of recent advancements, illustrating how single-cell RNA sequencing (scRNA-seq) has become a pivotal tool in plant biology. scRNA-seq has revolutionized root biology by enabling detailed, cell-specific analysis of gene expression. This has allowed researchers to create comprehensive root atlases, predict cell development, and map gene regulatory networks (GRNs) with unprecedented precision. Complementary technologies, such as multimodal profiling and bioinformatics, further enrich our understanding of cellular dynamics and gene interactions. Innovations in imaging and modeling, combined with genetic tools like CRISPR, continue to deepen our knowledge of root formation and function. Moreover, the integration of these technologies with advanced biosensors and microfluidic devices has advanced our ability to study plant-microbe interactions and phytohormone signaling at high resolution. These tools collectively provide a more comprehensive understanding of root system architecture and its regulation by environmental factors. As these technologies evolve, they promise to drive further breakthroughs in plant science, with substantial implications for agriculture and sustainability.
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Cell-Cell Communication at the Embryo Implantation Site of Mouse Uterus Revealed by Single-Cell Analysis.
As a crucial step for human reproduction, embryo implantation is a low-efficiency process. Despite rapid advances in recent years, the molecular mechanism underlying embryo implantation remains poorly understood. Here, we used the mouse as an animal model and generated a single-cell transcriptomic atlas of embryo implantation sites. By analyzing inter-implantation sites of the uterus as control, we were able to identify global gene expression changes associated with embryo implantation in each cell type. Additionally, we predicted signaling interactions between uterine luminal epithelial cells and mural trophectoderm of blastocysts, which represent the key mechanism of embryo implantation. We also predicted signaling interactions between uterine epithelial-stromal crosstalk at implantation sites, which are crucial for post-implantation development. Our data provide a valuable resource for deciphering the molecular mechanism underlying embryo implantation.
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Cell atlas of the foetal human heart and implications for autoimmune-mediated congenital heart block.
AIMS: Investigating human heart development and applying this to deviations resulting in disease is incomplete without molecular characterization of the cell types required for normal functioning. We investigated foetal human heart single-cell transcriptomes from mid-gestational healthy and anti-SSA/Ro associated congenital heart block (CHB) samples. METHODS AND RESULTS: Three healthy foetal human hearts (19th to 22nd week of gestation) and one foetal heart affected by autoimmune-associated CHB (21st week of gestation) were subjected to enzymatic dissociation using the Langendorff preparation to obtain single-cell suspensions followed by 10× Genomics- and Illumina-based single-cell RNA-sequencing (scRNA-seq). In addition to the myocytes, fibroblasts, immune cells, and other minor cell types, previously uncharacterized diverse sub-populations of endothelial cells were identified in the human heart. Differential gene expression analysis revealed increased and heterogeneous interferon responses in varied cell types of the CHB heart compared with the healthy controls. In addition, we also identified matrisome transcripts enriched in CHB stromal cells that potentially contribute to extracellular matrix deposition and subsequent fibrosis. CONCLUSION: These data provide an information-rich resource to further our understanding of human heart development, which, as illustrated by comparison to a heart exposed to a maternal autoimmune environment, can be leveraged to provide insight into the pathogenesis of disease.
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High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin.
Understanding cell type identity in a multicellular organism requires the integration of gene expression profiles from individual cells with their spatial location in a particular tissue. Current technologies allow whole-transcriptome sequencing of spatially identified cells but lack the throughput needed to characterize complex tissues. Here we present a high-throughput method to identify the spatial origin of cells assayed by single-cell RNA-sequencing within a tissue of interest. Our approach is based on comparing complete, specificity-weighted mRNA profiles of a cell with positional gene expression profiles derived from a gene expression atlas. We show that this method allocates cells to precise locations in the brain of the marine annelid Platynereis dumerilii with a success rate of 81%. Our method is applicable to any system that has a reference gene expression database of sufficiently high resolution.
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Cellular Heterogeneity and Lineage Restriction during Mouse Digit Tip Regeneration at Single-Cell Resolution.
Innate regeneration following digit tip amputation is one of the few examples of epimorphic regeneration in mammals. Digit tip regeneration is mediated by the blastema, the same structure invoked during limb regeneration in some lower vertebrates. By genetic lineage analyses, the digit tip blastema has been defined as a population of heterogeneous, lineage-restricted progenitor cells. These previous studies, however, do not comprehensively evaluate blastema heterogeneity or address lineage restriction of closely related cell types. In this report, we present single-cell RNA sequencing of over 38,000 cells from mouse digit tip blastemas and unamputated control digit tips and generate an atlas of the cell types participating in digit tip regeneration. We computationally define differentiation trajectories of vascular, monocytic, and fibroblastic lineages over regeneration, and while our data confirm broad lineage restriction of progenitors, our analysis reveals 67 genes enriched in blastema fibroblasts including a novel regeneration-specific gene, Mest.
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Identification of Intercellular Crosstalk between Decidual Cells and Niche Cells in Mice.
Decidualization is a crucial step for human reproduction, which is a prerequisite for embryo implantation, placentation and pregnancy maintenance. Despite rapid advances over recent years, the molecular mechanism underlying decidualization remains poorly understood. Here, we used the mouse as an animal model and generated a single-cell transcriptomic atlas of a mouse uterus during decidualization. By analyzing the undecidualized inter-implantation site of the uterus as a control, we were able to identify global gene expression changes associated with decidualization in each cell type. Additionally, we identified intercellular crosstalk between decidual cells and niche cells, including immune cells, endothelial cells and trophoblast cells. Our data provide a valuable resource for deciphering the molecular mechanism underlying decidualization.
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Spatial mapping of single cells in the Drosophila embryo from transcriptomic data based on topological consistency.
The advancement in single-cell RNA sequencing technologies allow us to obtain transcriptome at single cell resolution. However, the original spatial context of cells, a crucial knowledge for understanding cellular and tissue-level functions, is often lost during sequencing. To address this issue, the DREAM Single Cell Transcriptomics Challenge launched a community-wide effort to seek computational solutions for spatial mapping of single cells in tissues using single-cell RNAseq (scRNA-seq) data and a reference atlas obtained from in situ hybridization data. As a top-performing team in this competition, we approach this problem in three steps. The first step involves identifying a set of most informative genes based on the consistency between gene expression similarity and cell proximity. For this step, we propose two different approaches, i.e., an unsupervised approach that does not utilize the gold standard location of the cells provided by the challenge organizers, and a supervised approach that relies on the gold standard locations. In the second step, a Particle Swarm Optimization algorithm is used to optimize the weights of different genes in order to maximize matches between the predicted locations and the gold standard locations. Finally, the information embedded in the cell topology is used to improve the predicted cell-location scores by weighted averaging of scores from neighboring locations. Evaluation results based on DREAM scores show that our method accurately predicts the location of single cells, and the predictions lead to successful recovery of the spatial expression patterns for most of landmark genes. In addition, investigating the selected genes demonstrates that most predictive genes are cluster specific, and stable across our supervised and unsupervised gene selection frameworks. Overall, the promising results obtained by our methods in DREAM challenge demonstrated that topological consistency is a useful concept in identifying marker genes and constructing predictive models for spatial mapping of single cells.
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Polled Digital Cell Sorter (p-DCS): Automatic identification of hematological cell types from single cell RNA-sequencing clusters.
BACKGROUND: Single cell RNA sequencing (scRNA-seq) brings unprecedented opportunities for mapping the heterogeneity of complex cellular environments such as bone marrow, and provides insight into many cellular processes. Single cell RNA-seq has a far larger fraction of missing data reported as zeros (dropouts) than traditional bulk RNA-seq, and unsupervised clustering combined with Principal Component Analysis (PCA) can be used to overcome this limitation. After clustering, however, one has to interpret the average expression of markers on each cluster to identify the corresponding cell types, and this is normally done by hand by an expert curator. RESULTS: We present a computational tool for processing single cell RNA-seq data that uses a voting algorithm to automatically identify cells based on approval votes received by known molecular markers. Using a stochastic procedure that accounts for imbalances in the number of known molecular signatures for different cell types, the method computes the statistical significance of the final approval score and automatically assigns a cell type to clusters without an expert curator. We demonstrate the utility of the tool in the analysis of eight samples of bone marrow from the Human Cell Atlas. The tool provides a systematic identification of cell types in bone marrow based on a list of markers of immune cell types, and incorporates a suite of visualization tools that can be overlaid on a t-SNE representation. The software is freely available as a Python package at https://github.com/sdomanskyi/DigitalCellSorter . CONCLUSIONS: This methodology assures that extensive marker to cell type matching information is taken into account in a systematic way when assigning cell clusters to cell types. Moreover, the method allows for a high throughput processing of multiple scRNA-seq datasets, since it does not involve an expert curator, and it can be applied recursively to obtain cell sub-types. The software is designed to allow the user to substitute the marker to cell type matching information and apply the methodology to different cellular environments.