Single-cell multiomics sequencing reveals the functional regulatory landscape of early embryos

Exploring the untapped potential of single-cell and spatial omics in plant biology

Advances in single-cell and spatial omics technologies have revolutionised biology by revealing the diverse molecular states of individual cells and their spatial organization within tissues. The field of plant biology has widely adopted single-cell transcriptome and chromatin accessibility profiling and spatial transcriptomics, which extend traditional cell biology and genomics analyses and provide unique opportunities to reveal molecular and cellular dynamics of tissues. Using these technologies, comprehensive cell atlases have been generated in several model plant species, providing valuable platforms for discovery and tool development. Other emerging technologies related to single-cell and spatial omics, such as multiomics, lineage tracing, molecular recording, and high-content genetic and chemical perturbation phenotyping, offer immense potential for deepening our understanding of plant biology yet remain underutilised due to unique technical challenges and resource availability. Overcoming plant-specific barriers, such as cell wall complexity and limited antibody resources, alongside community-driven efforts in developing more complete reference atlases and computational tools, will accelerate progress. The synergy between technological innovation and targeted biological questions is poised to drive significant discoveries, advancing plant science. This review highlights the current applications of single-cell and spatial omics technologies in plant research and introduces emerging approaches with the potential to transform the field.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

Moving toward totipotency: the molecular and cellular features of totipotent and naive pluripotent stem cells

BACKGROUND

Dissecting the key molecular mechanism of embryonic development provides novel insights into embryogenesis and potential intervention strategies for clinical practices. However, the ability to study the molecular mechanisms of early embryo development in humans, such as zygotic genome activation and lineage segregation, is meaningfully constrained by methodological limitations and ethical concerns. Totipotent stem cells have an extended developmental potential to differentiate into embryonic and extraembryonic tissues, providing a suitable model for studying early embryo development. Recently, a series of ground-breaking results on stem cells have identified totipotent-like cells or induced pluripotent stem cells into totipotent-like cells.

OBJECTIVE AND RATIONALE

This review followed the PRISMA guidelines, surveys the current works of literature on totipotent, naive, and formative pluripotent stem cells, introduces the molecular and biological characteristics of those stem cells, and gives advice for future research.

SEARCH METHODS

The search method employed the terms 'totipotent' OR 'naive pluripotent stem cell' OR 'formative pluripotent stem cell' for unfiltered search on PubMed, Web of Science, and Cochrane Library. Papers included were those with information on totipotent stem cells, naive pluripotent stem cells, or formative pluripotent stem cells until June 2024 and were published in the English language. Articles that have no relevance to stem cells, or totipotent, naive pluripotent, or formative pluripotent cells were excluded.

OUTCOMES

There were 152 records included in this review. These publications were divided into four groups according to the species of the cells included in the studies: 67 human stem cell studies, 70 mouse stem cell studies, 9 porcine stem cell studies, and 6 cynomolgus stem cell studies. Naive pluripotent stem cell models have been established in other species such as porcine and cynomolgus. Human and mouse totipotent stem cells, e.g. human 8-cell-like cells, human totipotent blastomere-like cells, and mouse 2-cell-like cells, have been successfully established and exhibit high developmental potency for both embryonic and extraembryonic contributions. However, the observed discrepancies between these cells and real embryos in terms of epigenetics and transcription suggest that further research is warranted. Our results systematically reviewed the established methods, molecular characteristics, and developmental potency of different naive, formative pluripotent, and totipotent stem cells. Furthermore, we provide a parallel comparison between animal and human models, and offer recommendations for future applications to advance early embryo research and assisted reproduction technologies.

WIDER IMPLICATIONS

Totipotent cell models provide a valuable resource to understand the underlying mechanisms of embryo development and forge new paths toward future treatment of infertility and regenerative medicine. However, current in vitro cell models exhibit epigenetic and transcriptional differences from in vivo embryos, and many cell models are unstable across passages, thus imperfectly recapitulating embryonic development. In this regard, standardizing and expanding current research on totipotent stem cell models are essential to enhance our capability to resemble and decipher embryogenesis.

Single-cell metabolomics reveals that bisphosphoglycerate mutase influences oocyte maturation through glucose metabolism

The spatiotemporal turnover of metabolites is essential for oocyte maturation, embryonic development, and cell lineage differentiation. Here, we analyzed the metabolic profiles of individual living mouse oocytes and studied how bisphosphoglycerate mutase (BPGM), an important maternal factor, influences metabolite regulation during oocyte maturation. We found that BPGM is expressed in mouse follicles, oocytes, and embryos, as well as in human embryos. Notably, deletion of Bpgm significantly reduced the rate of oocyte maturation and reduced mouse fertility, which was observed as reduced pups per litter. Also, the expression levels for meiosis-related genes and genes related to glucose metabolic pathways (glycolysis, tricarboxylic acid cycle, and pentose phosphate pathway) were altered in BPGM-deficient mouse oocytes. We used a highly sensitive, live-cell sampling approach to carry out metabolite assays using induced nanoelectrospray-ionization mass spectrometry technology on 1 picolitre of aspirated cytoplasm from oocytes. BPGM gene disruption impaired glucose metabolism pathways, tyrosine metabolism, and amino acid biosynthesis. Together, our findings indicate that Bpgm participates in oocyte and embryo development, and we demonstrate the feasibility of studying metabolite composition and other phenotypic features of single oocytes.

Genome-coverage single-cell histone modifications for embryo lineage tracing

Substantial epigenetic resetting during early embryo development from fertilization to blastocyst formation ensures zygotic genome activation and leads to progressive cellular heterogeneities1-3. Mapping single-cell epigenomic profiles of core histone modifications that cover each individual cell is a fundamental goal in developmental biology. Here we develop target chromatin indexing and tagmentation (TACIT), a method that enabled genome-coverage single-cell profiling of seven histone modifications across mouse early embryos. We integrated these single-cell histone modifications with single-cell RNA sequencing data to chart a single-cell resolution epigenetic landscape. Multimodal chromatin-state annotations showed that the onset of zygotic genome activation at the early two-cell stage already primes heterogeneities in totipotency. We used machine learning to identify totipotency gene regulatory networks, including stage-specific transposable elements and putative transcription factors. CRISPR activation of a combination of these identified transcription factors induced totipotency activation in mouse embryonic stem cells. Together with single-cell co-profiles of multiple histone modifications, we developed a model that predicts the earliest cell branching towards the inner cell mass and the trophectoderm in latent multimodal space and identifies regulatory elements and previously unknown lineage-specifying transcription factors. Our work provides insights into single-cell epigenetic reprogramming, multimodal regulation of cellular lineages and cell-fate priming during mouse pre-implantation development.

Chromatin accessibility landscape of mouse early embryos revealed by single-cell NanoATAC-seq2

In mammals, fertilized eggs undergo genome-wide epigenetic reprogramming to generate the organism. However, our understanding of epigenetic dynamics during preimplantation development at single-cell resolution remains incomplete. Here, we developed scNanoATAC-seq2, a single-cell assay for transposase-accessible chromatin using long-read sequencing for scarce samples. We present a detailed chromatin accessibility landscape of mouse preimplantation development, revealing distinct chromatin signatures in the epiblast, primitive endoderm, and trophectoderm during lineage segregation. Differences between zygotes and two-cell embryos highlight reprogramming in chromatin accessibility during the maternal-to-zygotic transition. Single-cell long-read sequencing enables in-depth analysis of chromatin accessibility in noncanonical imprinting, imprinted X chromosome inactivation, and low-mappability genomic regions, such as repetitive elements and paralogs. Our data provide insights into chromatin dynamics during mammalian preimplantation development and lineage differentiation.

Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective

The development of single-cell and spatial transcriptomics has revolutionized our capacity to investigate cellular properties, functions, and interactions in both cellular and spatial contexts. Despite this progress, the analysis of single-cell and spatial omics data remains challenging. First, single-cell sequencing data are high-dimensional and sparse, and are often contaminated by noise and uncertainty, obscuring the underlying biological signal. Second, these data often encompass multiple modalities, including gene expression, epigenetic modifications, metabolite levels, and spatial locations. Integrating these diverse data modalities is crucial for enhancing prediction accuracy and biological interpretability. Third, while the scale of single-cell sequencing has expanded to millions of cells, high-quality annotated datasets are still limited. Fourth, the complex correlations of biological tissues make it difficult to accurately reconstruct cellular states and spatial contexts. Traditional feature engineering approaches struggle with the complexity of biological networks, while deep learning, with its ability to handle high-dimensional data and automatically identify meaningful patterns, has shown great promise in overcoming these challenges. Besides systematically reviewing the strengths and weaknesses of advanced deep learning methods, we have curated 21 datasets from nine benchmarks to evaluate the performance of 58 computational methods. Our analysis reveals that model performance can vary significantly across different benchmark datasets and evaluation metrics, providing a useful perspective for selecting the most appropriate approach based on a specific application scenario. We highlight three key areas for future development, offering valuable insights into how deep learning can be effectively applied to transcriptomic data analysis in biological, medical, and clinical settings.

Understanding developing kidneys and Wilms tumors one cell at a time

Single-cell sequencing-based techniques are revolutionizing all fields of biomedical sciences, including normal kidney development and how this is disturbed in the development of Wilms tumor. The many different techniques and the differences between them can obscure which technique is best used to answer which question. In this review we summarize the techniques currently available, discuss which have been used in kidney development or Wilms tumor context, and which techniques can or should be combined to maximize the increase in biological understanding we can get from them.

Capture of Totipotency in Mouse Embryonic Stem Cells in the Absence of Pdzk1

Totipotent cells can differentiate into three lineages: the epiblast, primitive endoderm, and trophectoderm. Naturally, only early fertilized embryos possess totipotency, and they lose this ability as they develop. The expansion of stem cell differentiation potential has been a hot topic in developmental biology for years, particularly with respect to the generation totipotent-like stem cells. Here, the study describes the establishment of totipotency in embryonic stem cells (ESCs) via the deletion of a single gene, Pdzk1. Pdzk1-knockout (KO) ESCs substantially contribute to the fetus, placenta, and yolk sac in chimera assays but can also self-organize to form standard blastocyst-like structures containing the three lineages efficiently; thus, they exhibit full developmental potential as early blastomeres. Single-cell transcriptome and bulk RNA-seq comprehensive analyses revealed that Pdzk1-KO activates several lineage inducers (C1qa, C1qb, Fgf5, and Cdx2) to break down barriers between embryonic and extraembryonic tissues, making these lineages switch smoothly and resulting in a totipotent-like state. This versatile and scalable system provides a robust experimental model for differentiation potency and cell fate studies.

Reconstructing Waddington Landscape from Cell Migration and Proliferation

The Waddington landscape was initially proposed to depict cell differentiation, and has been extended to explain phenomena such as reprogramming. The landscape serves as a concrete representation of cellular differentiation potential, yet the precise representation of this potential remains an unsolved problem, posing significant challenges to reconstructing the Waddington landscape. The characterization of cellular differentiation potential relies on transcriptomic signatures of known markers typically. Numerous computational models based on various energy indicators, such as Shannon entropy, have been proposed. While these models can effectively characterize cellular differentiation potential, most of them lack corresponding dynamical interpretations, which are crucial for enhancing our understanding of cell fate transitions. Therefore, from the perspective of cell migration and proliferation, a feasible framework was developed for calculating the dynamically interpretable energy indicator to reconstruct Waddington landscape based on sparse autoencoders and the reaction diffusion advection equation. Within this framework, typical cellular developmental processes, such as hematopoiesis and reprogramming processes, were dynamically simulated and their corresponding Waddington landscapes were reconstructed. Furthermore, dynamic simulation and reconstruction were also conducted for special developmental processes, such as embryogenesis and Epithelial-Mesenchymal Transition process. Ultimately, these diverse cell fate transitions were amalgamated into a unified Waddington landscape.

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

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

Integration of unpaired single cell omics data by deep transfer graph convolutional network

The rapid advance of large-scale atlas-level single cell RNA sequences and single-cell chromatin accessibility data provide extraordinary avenues to broad and deep insight into complex biological mechanism. Leveraging the datasets and transfering labels from scRNA-seq to scATAC-seq will empower the exploration of single-cell omics data. However, the current label transfer methods have limited performance, largely due to the lower capable of preserving fine-grained cell populations and intrinsic or extrinsic heterogeneity between datasets. Here, we present a robust deep transfer model based graph convolutional network, scTGCN, which achieves versatile performance in preserving biological variation, while achieving integration hundreds of thousands cells in minutes with low memory consumption. We show that scTGCN is powerful to the integration of mouse atlas data and multimodal data generated from APSA-seq and CITE-seq. Thus, scTGCN shows high label transfer accuracy and effectively knowledge transfer across different modalities.

LncRNA XLOC-040580 targeted by TPRA1 coordinate zygotic genome activation during porcine embryonic development

Long noncoding RNAs (lncRNAs) are crucial in porcine preimplantation embryonic development, yet their regulatory role during zygote genome activation (ZGA) is poorly understood. We analyzed transcriptome data from porcine fetal fibroblasts (PEF), induced pluripotent stem cells (iPS), and preimplantation embryos, identifying ZGA-specific lncRNAs like XLOC-040580, and further predicted its potentially interacting genes TPRA1 and BCL2L1 via co-expression network. XLOC-040580 was knocked down by siRNA microinjection and the expression of ZGA-related genes was detected by qRT-PCR. After microinjecting siRNA targeting TPRA1 and BCL2L1 at the one-cell stage, we counted the blastocyst development rate. The blastocyst development rate was consistent with the results from si-XLOC-040580 after si-TPRA1. Through dual-luciferase reporter assays, we found that XLOC-040580 was a downstream target of TPRA1. To further elucidate the mechanism of XLOC-040580, Single-cell mRNA sequencing after XLOC-040580 knockdown revealed its regulatory network involved in embryonic developmental defects. Transcriptome analysis revealed that XLOC-040580 was specifically expressed during zygote activation. Knockdown of XLOC-040580 decreased the blastocyst development rate and reduced both the total blastocyst cell number and TE cell number. TPRA1 and BCL2L1 were specifically co-expressed with XLOC-040580 during ZGA stage, and TPRA1 could interact with the promoter region of XLOC-040580 and regulate its expression. Knockdown of TPRA1 or XLOC-040580 blocked porcine embryonic development by affecting the expression of ZGA-related genes. We found and validated that lncRNA XLOC-040580 played a key role in the ZGA process, which was regulated by TPRA1. These results implied that the functional axis of TPRA1-XLOC-040580-downstream genes involved in ZGA-related functions also coordinated early embryonic development in porcine.

Unraveling the mysteries of early embryonic arrest: genetic factors and molecular mechanisms

Early embryonic arrest (EEA) is a critical impediment in assisted reproductive technology (ART), affecting 40% of infertile patients by halting the development of early embryos from the zygote to blastocyst stage, resulting in a lack of viable embryos for successful pregnancy. Despite its prevalence, the molecular mechanism underlying EEA remains elusive. This review synthesizes the latest research on the genetic and molecular factors contributing to EEA, with a focus on maternal, paternal, and embryonic factors. Maternal factors such as irregularities in follicular development and endometrial environment, along with mutations in genes like NLRP5, PADI6, KPNA7, IGF2, and TUBB8, have been implicated in EEA. Specifically, PATL2 mutations are hypothesized to disrupt the maternal-zygotic transition, impairing embryo development. Paternal contributions to EEA are linked to chromosomal variations, epigenetic modifications, and mutations in genes such as CFAP69, ACTL7A, and M1AP, which interfere with sperm development and lead to infertility. Aneuploidy may disrupt spindle assembly checkpoints and pathways including Wnt, MAPK, and Hippo signaling, thereby contributing to EEA. Additionally, key genes involved in embryonic genome activation-such as ZSCAN4, DUXB, DUXA, NANOGNB, DPPA4, GATA6, ARGFX, RBP7, and KLF5-alongside functional disruptions in epigenetic modifications, mitochondrial DNA, and small non-coding RNAs, play critical roles in the onset of EEA. This review provides a comprehensive understanding of the genetic and molecular underpinnings of EEA, offering a theoretical foundation for the diagnosis and potential therapeutic strategies aimed at improving pregnancy outcomes.

Multi-omics PGT: re-evaluation of euploid blastocysts for implantation potential based on RNA sequencing

STUDY QUESTION

In addition to chromosomal euploidy, can the transcriptome of blastocysts be used as a novel predictor of embryo implantation potential?

SUMMARY ANSWER

This retrospective analysis showed that based on differentially expressed genes (DEGs) between euploid blastocysts which resulted and did not result in a clinical pregnancy, machine learning models could help improve implantation rates by blastocyst optimization.

WHAT IS KNOWN ALREADY

Embryo implantation is a multifaceted process, with implantation loss and pregnancy failure related not only to blastocyst euploidy but also to the intricate dialog between blastocyst and endometrium. Although in vitro studies have revealed the characteristics of trophectoderm (TE) differentiation in implanted blastocysts and the function of TE placentation at the implantation site, the precise molecular mechanisms of embryo implantation and their clinical application remain to be fully elucidated.

STUDY DESIGN, SIZE, DURATION

This study involved 102 patients who underwent 111 cycles for preimplantation genetic testing for aneuploidies (PGT-A) between March 2022 and July 2023.

PARTICIPANTS/MATERIALS, SETTING, METHODS

The study included 412 blastocysts biopsied at Day 5 [D5] or Day 6 [D6] for patients who underwent PGT-A. The biopsy lysates were split and subjected to DNA and RNA sequencing (DNA- and RNA-seq). One part was used for PGT-A to detect DNA copy number variations, whereas the other part was assessed simultaneously by RNA-seq to determine the transcriptome characteristics. To validate the reliability and accuracy of RNA-seq obtained from this strategy, we initially analyzed the transcriptome of blastocysts with chromosomal aneuploidies. Subsequently, we compared the transcriptomic features of blastocysts with different rates of formation (D5 vs D6) and investigated the network of interactions between key blastulation genes and the receptive endometrium. Then to evaluate the implantation potential of euploid blastocysts, we identified DEGs between euploid blastocysts that resulted in clinical pregnancy (defined as the presence of a gestational sac detected by ultrasound after 5 weeks) and those that did not. These DEGs were then employed to construct a predictive model for optimizing blastocyst selection.

MAIN RESULTS AND THE ROLE OF CHANCE

The successful detection rate of PGT-A was remarkably high at 99.8%. The RNA data may infer aneuploidy for both trisomy and monosomy. Between the euploid blastocysts that formed on D5 and D6, 187 DEGs were predominantly involved in cell differentiation for embryonic placenta development, the PPAR signaling pathway, and the Notch signaling pathway. These D5/D6 DEGs also exhibited a functional dialog with the receptive phase endometrium-specific genes through protein-protein interaction networks, indicating that the embryo undergoes further differentiation for post-implantation development. Furthermore, a modeling strategy using 280 DEGs between blastocysts leading to successful clinical pregnancies or failing to produce clinical pregnancies was implemented to refine the euploid embryo optimization, achieving areas under the curves of 0.88, 0.71, and 0.84 for the random forest (RF), support vector machine, and linear discriminant analysis models, respectively. Finally, a retrospective analysis of 83 transferred euploid blastocysts using the RF model identified three types of euploid embryos with a decreasing trend in implantation potential. Notably, the implantation rate of the good group was significantly higher than that of the moderate group (88.6% vs 50.0% P = 0.001) and that of the moderate group was higher than that of the poor group (50.0% vs 20.8%, P = 0.035).

LIMITATIONS, REASONS FOR CAUTION

The sample size was insufficient; thus, a prospective study is needed to verify the clinical effectiveness of the above model. Because we did not analyze blastocysts that led only to biochemical pregnancies but failed clinical pregnancies separately, our classification system still must be modified to screen these embryos.

WIDER IMPLICATIONS OF THE FINDINGS

Transcriptomic analysis of blastocysts offers a novel approach for predicting embryo implantation potential, which can be utilized to optimize clinical embryo selection. The ranking system may be effective in reducing the times and costs involved in achieving a clinical pregnancy.

STUDY FUNDING/COMPETING INTEREST(S)

This study was funded by the "Pioneer" and "Leading Goose" R&D Program of Zhejiang (No. 2023C03034), the National Natural Science Foundation of China (82101709), and the National Key Research and Development Program for Young Scientists of China (No. 2022YFC2702300). The authors state no competing interests.

TRIAL REGISTRATION NUMBER

N/A.

Nuclear receptor-SINE B1 network modulates expanded pluripotency in blastoids and blastocysts

Embryonic stem cells possess the remarkable ability to self-organize into blastocyst-like structures upon induction. These stem cell-based embryo models serve as invaluable platforms for studying embryogenesis and therapeutic developments. Nevertheless, the specific intrinsic regulators that govern this potential for blastoid formation remain unknown. Here we demonstrate an intrinsic program that plays a crucial role in both blastoids and blastocysts across multiple species. We first establish metrics for grading the resemblance of blastoids to mouse blastocysts, and identify the differential activation of gene regulons involved in lineage specification among various blastoid grades. Notably, abrogation of nuclear receptor subfamily 1, group H, member 2 (Nr1h2) drastically reduces blastoid formation. Nr1h2 activation alone is sufficient to rewire conventional ESC into a distinct pluripotency state, enabling them to form blastoids with enhanced implantation capacity in the uterus and contribute to both embryonic and extraembryonic lineages in vivo. Through integrative multi-omics analyses, we uncover the broad regulatory role of Nr1h2 in the transcriptome, chromatin accessibility and epigenome, targeting genes associated with embryonic lineage and the transposable element SINE-B1. The Nr1h2-centred intrinsic program governs and drives the development of both blastoids and early embryos.

An atlas of small non-coding RNAs in human preimplantation development

Understanding the molecular circuitries that govern early embryogenesis is important, yet our knowledge of these in human preimplantation development remains limited. Small non-coding RNAs (sncRNAs) can regulate gene expression and thus impact blastocyst formation, however, the expression of specific biotypes and their dynamics during preimplantation development remains unknown. Here we identify the abundance of and kinetics of piRNA, rRNA, snoRNA, tRNA, and miRNA from embryonic day (E)3-7 and isolate specific miRNAs and snoRNAs of particular importance in blastocyst formation and pluripotency. These sncRNAs correspond to specific genomic hotspots: an enrichment of the chromosome 19 miRNA cluster (C19MC) in the trophectoderm (TE), and the chromosome 14 miRNA cluster (C14MC) and MEG8-related snoRNAs in the inner cell mass (ICM), which may serve as 'master regulators' of potency and lineage. Additionally, we observe a developmental transition with 21 isomiRs and in tRNA fragment (tRF) codon usage and identify two novel miRNAs. Our analysis provides a comprehensive measure of sncRNA biotypes and their corresponding dynamics throughout human preimplantation development, providing an extensive resource. Better understanding the sncRNA regulatory programmes in human embryogenesis will inform strategies to improve embryo development and outcomes of assisted reproductive technologies. We anticipate broad usage of our data as a resource for studies aimed at understanding embryogenesis, optimising stem cell-based models, assisted reproductive technology, and stem cell biology.

SMARTdb: An Integrated Database for Exploring Single-cell Multi-omics Data of Reproductive Medicine

Single-cell multi-omics sequencing has greatly accelerated reproductive research in recent years, and the data are continually growing. However, utilizing these data resources is challenging for wet-lab researchers. A comprehensive platform for exploring single-cell multi-omics data related to reproduction is urgently needed. Here, we introduce the single-cell multi-omics atlas of reproduction (SMARTdb), an integrative and user-friendly platform for exploring molecular dynamics of reproductive development, aging, and disease, which covers multi-omics, multi-species, and multi-stage data. We curated and analyzed single-cell transcriptomic and epigenomic data of over 2.0 million cells from 6 species across the entire lifespan. A series of powerful functionalities are provided, such as "Query gene expression", "DIY expression plot", "DNA methylation plot", and "Epigenome browser". With SMARTdb, we found that the male germ cell-specific expression pattern of RPL39L and RPL10L is conserved between human and other model animals. Moreover, DNA hypomethylation and open chromatin may collectively regulate the specific expression pattern of RPL39L in both male and female germ cells. In summary, SMARTdb is a powerful platform for convenient data mining and gaining novel insights into reproductive development, aging, and disease. SMARTdb is publicly available at https://smart-db.cn.

AMAR-seq: Automated Multimodal Sequencing of DNA Methylation, Chromatin Accessibility, and RNA Expression with Single-Cell Resolution

Parallel single-cell multimodal sequencing is the most intuitive and precise tool for cellular status research. In this study, we propose AMAR-seq to automate methylation, chromatin accessibility, and RNA expression coanalysis with single-cell precision. We validated the accuracy and robustness of AMAR-seq in comparison with standard single-omics methods. The high gene detection rate and genome coverage of AMAR-seq enabled us to establish a genome-wide gene expression regulatory atlas and triple-omics landscape with single base resolution and implement single-cell copy number variation analysis. Applying AMAR-seq to investigate the process of mouse embryonic stem cell differentiation, we revealed the dynamic coupling of the epigenome and transcriptome, which may contribute to unraveling the molecular mechanisms of early embryonic development. Collectively, we propose AMAR-seq for the in-depth and accurate establishment of single-cell multiomics regulatory patterns in a cost-effective, efficient, and automated manner, paving the way for insightful dissection of complex life processes.

Assessing the necessity of screening ≤5 Mb segmental aneuploidy in routine PGT for aneuploidies

RESEARCH QUESTION

Does routine clinical practice require an increase in the resolution of preimplantation genetic testing for aneuploidies (PGT-A) to detect segmental aneuploidies ≤5 Mb?

DESIGN

This retrospective study analysed 963 trophectoderm biopsies from 346 couples undergoing PGT between 2019 and 2023. Segmental aneuploidies ≥1 Mb were reported. The characteristics, clinical interpretation and concordance of segmental aneuploidies ≤5 Mb were analysed.

RESULTS

The incidence of segmental aneuploidies was 15.1% (145/963) in blastocysts, with segmental aneuploidies of ≤5 Mb accounting for 2.3% (22/963). The size of the segmental aneuploidies showed a skewed distribution. Segmental aneuploidies ≤5 Mb were found to occur more frequently on the q arm of the chromosome, compared with the p arm. Losses of ≤5 Mb segmental aneuploidies were more prevalent than gains, with 17 deletions compared with 5 duplications. Of the segmental aneuploidies, 63.6% (14/22) ≤5 Mb were de novo, and 50.0% (7/14) of de-novo segmental aneuploidies were pathogenic/likely pathogenic (P/LP) copy number variations, accounting for 0.7% of 963 blastocysts. For blastocysts carrying ≤5 Mb segmental aneuploidies, a re-analysis of back-up biopsy samples showed that 35.7% of de-novo segmental aneuploidies (5/14) were not detected in the back-up samples. Cases were reported in which prenatal diagnosis (amniocentesis) revealed the absence of embryonic ≤5 Mb segmental aneuploidies detected at the blastocyst stage.

CONCLUSIONS

The incidence of P/LP de-novo ≤5 Mb segmental aneuploidies in human blastocysts is extremely low. There is no compelling need to increase the resolution of PGT-A to 5 Mb in routine clinical practice.

Progress in multifactorial single-cell chromatin profiling methods

Chromatin states play a key role in shaping overall cellular states and fates. Building a complete picture of the functional state of chromatin in cells requires the co-detection of several distinct biochemical aspects. These span DNA methylation, chromatin accessibility, chromosomal conformation, histone posttranslational modifications, and more. While this certainly presents a challenging task, over the past few years many new and creative methods have been developed that now enable co-assay of these different aspects of chromatin at single cell resolution. This field is entering an exciting phase, where a confluence of technological improvements, decreased sequencing costs, and computational innovation are presenting new opportunities to dissect the diversity of chromatin states present in tissues, and how these states may influence gene regulation. In this review, I discuss the spectrum of current experimental approaches for multifactorial chromatin profiling, highlight some of the experimental and analytical challenges, as well as some areas for further innovation.

Advances in single-cell sequencing technology in microbiome research

With the rapid development of histological techniques and the widespread application of single-cell sequencing in eukaryotes, researchers desire to explore individual microbial genotypes and functional expression, which deepens our understanding of microorganisms. In this review, the history of the development of microbial detection technologies was revealed and the difficulties in the application of single-cell sequencing in microorganisms were dissected as well. Moreover, the characteristics of the currently emerging microbial single-cell sequencing (Microbe-seq) technology were summarized, and the prospects of the application of Microbe-seq in microorganisms were distilled based on the current development status. Despite its mature development, the Microbe-seq technology was still in the optimization stage. A retrospective study was conducted, aiming to promote the widespread application of single-cell sequencing in microorganisms and facilitate further improvement in the technology.

REUNION: transcription factor binding prediction and regulatory association inference from single-cell multi-omics data

MOTIVATION

Profiling of gene expression and chromatin accessibility by single-cell multi-omics approaches can help to systematically decipher how transcription factors (TFs) regulate target gene expression via cis-region interactions. However, integrating information from different modalities to discover regulatory associations is challenging, in part because motif scanning approaches miss many likely TF binding sites.

RESULTS

We develop REUNION, a framework for predicting genome-wide TF binding and cis-region-TF-gene "triplet" regulatory associations using single-cell multi-omics data. The first component of REUNION, Unify, utilizes information theory-inspired complementary score functions that incorporate TF expression, chromatin accessibility, and target gene expression to identify regulatory associations. The second component, Rediscover, takes Unify estimates as input for pseudo semi-supervised learning to predict TF binding in accessible genomic regions that may or may not include detected TF motifs. Rediscover leverages latent chromatin accessibility and sequence feature spaces of the genomic regions, without requiring chromatin immunoprecipitation data for model training. Applied to peripheral blood mononuclear cell data, REUNION outperforms alternative methods in TF binding prediction on average performance. In particular, it recovers missing region-TF associations from regions lacking detected motifs, which circumvents the reliance on motif scanning and facilitates discovery of novel associations involving potential co-binding transcriptional regulators. Newly identified region-TF associations, even in regions lacking a detected motif, improve the prediction of target gene expression in regulatory triplets, and are thus likely to genuinely participate in the regulation.

AVAILABILITY AND IMPLEMENTATION

All source code is available at https://github.com/yangymargaret/REUNION.

Integration of single-cell transcriptome and chromatin accessibility and its application on tumor investigation

The advent of single-cell sequencing techniques has not only revolutionized the investigation of biological processes but also significantly contributed to unraveling cellular heterogeneity at unprecedented levels. Among the various methods, single-cell transcriptome sequencing stands out as the best established, and has been employed in exploring many physiological and pathological activities. The recently developed single-cell epigenetic sequencing techniques, especially chromatin accessibility sequencing, have further deepened our understanding of gene regulatory networks. In this review, we summarize the recent breakthroughs in single-cell transcriptome and chromatin accessibility sequencing methodologies. Additionally, we describe current bioinformatic strategies to integrate data obtained through these single-cell sequencing methods and highlight the application of this analysis strategy on a deeper understanding of tumorigenesis and tumor progression. Finally, we also discuss the challenges and anticipated developments in this field.

Population epigenetics: DNA methylation in the plant omics era

DNA methylation plays an important role in many biological processes. The mechanisms underlying the establishment and maintenance of DNA methylation are well understood thanks to decades of research using DNA methylation mutants, primarily in Arabidopsis (Arabidopsis thaliana) accession Col-0. Recent genome-wide association studies (GWASs) using the methylomes of natural accessions have uncovered a complex and distinct genetic basis of variation in DNA methylation at the population level. Sequencing following bisulfite treatment has served as an excellent method for quantifying DNA methylation. Unlike studies focusing on specific accessions with reference genomes, population-scale methylome research often requires an additional round of sequencing beyond obtaining genome assemblies or genetic variations from whole-genome sequencing data, which can be cost prohibitive. Here, we provide an overview of recently developed bisulfite-free methods for quantifying methylation and cost-effective approaches for the simultaneous detection of genetic and epigenetic information. We also discuss the plasticity of DNA methylation in a specific Arabidopsis accession, the contribution of DNA methylation to plant adaptation, and the genetic determinants of variation in DNA methylation in natural populations. The recently developed technology and knowledge will greatly benefit future studies in population epigenomes.

Nr5a2 ensures inner cell mass formation in mouse blastocyst

Recent studies have elucidated Nr5a2's role in activating zygotic genes during early mouse embryonic development. Subsequent research, however, reveals that Nr5a2 is not critical for zygotic genome activation but is vital for the gene program between the 4- and 8-cell stages. A significant gap exists in experimental evidence regarding its function during the first lineage differentiation's pivotal period. In this study, we observed that approximately 20% of embryos developed to the blastocyst stage following Nr5a2 ablation. However, these blastocysts lacked inner cell mass (ICM), highlighting Nr5a2's importance in first lineage differentiation. Mechanistically, using RNA sequencing and CUT&Tag, we found that Nr5a2 transcriptionally regulates ICM-specific genes, such as Oct4, to establish the pluripotent network. Interference with or overexpression of Nr5a2 in single blastomeres of 2-cell embryos can alter the fate of daughter cells. Our results indicate that Nr5a2 works as a doorkeeper to ensure ICM formation in mouse blastocyst.

Single-cell multiplex chromatin and RNA interactions in aging human brain

The dynamically organized chromatin complexes often involve multiplex chromatin interactions and sometimes chromatin-associated RNA (caRNA) 1-3. Chromatin complex compositions change during cellular differentiation and aging, and are expected to be highly heterogeneous among terminally differentiated single cells 4-7. Here we introduce the Multi-Nucleic Acid Interaction Mapping in Single Cell (MUSIC) technique for concurrent profiling of multiplex chromatin interactions, gene expression, and RNA-chromatin associations within individual nuclei. Applied to 14 human frontal cortex samples from elderly donors, MUSIC delineates diverse cortical cell types and states. We observed the nuclei exhibiting fewer short-range chromatin interactions are correlated with an "older" transcriptomic signature and with Alzheimer's pathology. Furthermore, the cell type exhibiting chromatin contacts between cis expression quantitative trait loci (cis eQTLs) and a promoter tends to be the cell type where these cis eQTLs specifically affect their target gene's expression. Additionally, the female cortical cells exhibit highly heterogeneous interactions between the XIST non-coding RNA and Chromosome X, along with diverse spatial organizations of the X chromosomes. MUSIC presents a potent tool for exploring chromatin architecture and transcription at cellular resolution in complex tissues.

Advances in single-cell omics and multiomics for high-resolution molecular profiling

Single-cell omics technologies have revolutionized molecular profiling by providing high-resolution insights into cellular heterogeneity and complexity. Traditional bulk omics approaches average signals from heterogeneous cell populations, thereby obscuring important cellular nuances. Single-cell omics studies enable the analysis of individual cells and reveal diverse cell types, dynamic cellular states, and rare cell populations. These techniques offer unprecedented resolution and sensitivity, enabling researchers to unravel the molecular landscape of individual cells. Furthermore, the integration of multimodal omics data within a single cell provides a comprehensive and holistic view of cellular processes. By combining multiple omics dimensions, multimodal omics approaches can facilitate the elucidation of complex cellular interactions, regulatory networks, and molecular mechanisms. This integrative approach enhances our understanding of cellular systems, from development to disease. This review provides an overview of the recent advances in single-cell and multimodal omics for high-resolution molecular profiling. We discuss the principles and methodologies for representatives of each omics method, highlighting the strengths and limitations of the different techniques. In addition, we present case studies demonstrating the applications of single-cell and multimodal omics in various fields, including developmental biology, neurobiology, cancer research, immunology, and precision medicine.

Spotlight on iron overload and ferroptosis: Research progress in female infertility

Iron is an essential trace element for organisms. However, iron overload, which is common in haematological disorders (e.g. haemochromatosis, myelodysplastic syndromes, aplastic anaemia, and thalassaemia, blood transfusion-dependent or not), can promote reactive oxygen species generation and induce ferroptosis, a novel form of programmed cell death characterised by excess iron and lipid peroxidation, thus causing cell and tissue damage. Infertility is a global health concern. Recent evidence has indicated the emerging role of iron overload and ferroptosis in female infertility by inducing hypogonadism, causing ovary dysfunction, impairing preimplantation embryos, attenuating endometrial receptivity, and crosstalk between subfertility-related disorders, such as polycystic ovary syndrome and endometriosis. In addition, gut microbiota and their metabolites are involved in iron metabolism, ferroptosis, and female infertility. In this review, we systematically elaborate on the current research progress in female infertility with a novel focus on iron overload and ferroptosis and summarise promising therapies targeting iron overload and ferroptosis to recover fertility in women. In summary, our study provides new insights into female infertility and offers literature references for the clinical management of female infertility associated with iron overload and ferroptosis, which may be beneficial for females with haematopoietic disorders suffering from both iron overload and infertility.

Novel MEI1 mutations cause chromosomal and DNA methylation abnormalities leading to embryonic arrest and implantation failure

This study presents a case of a female infertile patient suffering from embryonic arrest and recurrent implantation failure. The primary objective was to assess the copy number variations (CNVs) and DNA methylation of her embryos. Genetic diagnosis was conducted by whole-exome sequencing and validated through Sanger sequencing. CNV evaluation of two cleavage stage embryos was performed using whole-genome sequencing, while DNA methylation and CNV assessment of two blastocysts were carried out using whole-genome bisulfite sequencing. We identified two novel pathogenic frameshift variants in the MEI1 gene (NM_152513.3, c.3002delC, c.2264_2268 + 11delGTGAGGTATGGACCAC) in the proband. These two variants were inherited from her heterozygous parents, consistent with autosomal recessive genetic transmission. Notably, two Day 3 embryos and two Day 6 blastocysts were all aneuploid, with numerous monosomy and trisomy events. Moreover, global methylation levels greatly deviated from the optimized window of 0.25-0.27, measuring 0.344 and 0.168 for the respective blastocysts. This study expands the mutational spectrum of MEI1 and is the first to document both aneuploidy and abnormal methylation levels in embryos from a MEI1-affected female patient presenting with embryonic arrest. Given that females affected by MEI1 mutations might experience either embryonic arrest or monospermic androgenetic hydatidiform moles due to the extrusion of all maternal chromosomes, the genetic makeup of the arrested embryos of MEI1 patients provides important clues for understanding the different disease mechanisms of the two phenotypes.

moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud

Single-cell omics sequencing has rapidly advanced, enabling the quantification of diverse omics profiles at a single-cell resolution. To facilitate comprehensive biological insights, such as cellular differentiation trajectories, precise annotation of cell subtypes is essential. Conventional methods involve clustering cells and manually assigning subtypes based on canonical markers, a labor-intensive and expert-dependent process. Hence, an automated computational prediction framework is crucial. While several classification frameworks for predicting cell subtypes from single-cell RNA sequencing datasets exist, these methods solely rely on single-omics data, offering insights at a single molecular level. They often miss inter-omic correlations and a holistic understanding of cellular processes. To address this, the integration of multi-omics datasets from individual cells is essential for accurate subtype annotation. This article introduces moSCminer, a novel framework for classifying cell subtypes that harnesses the power of single-cell multi-omics sequencing datasets through an attention-based neural network operating at the omics level. By integrating three distinct omics datasets-gene expression, DNA methylation, and DNA accessibility-while accounting for their biological relationships, moSCminer excels at learning the relative significance of each omics feature. It then transforms this knowledge into a novel representation for cell subtype classification. Comparative evaluations against standard machine learning-based classifiers demonstrate moSCminer's superior performance, consistently achieving the highest average performance on real datasets. The efficacy of multi-omics integration is further corroborated through an in-depth analysis of the omics-level attention module, which identifies potential markers for cell subtype annotation. To enhance accessibility and scalability, moSCminer is accessible as a user-friendly web-based platform seamlessly connected to a cloud system, publicly accessible at http://203.252.206.118:5568. Notably, this study marks the pioneering integration of three single-cell multi-omics datasets for cell subtype identification.

Data integration and inference of gene regulation using single-cell temporal multimodal data with scTIE

Single-cell technologies offer unprecedented opportunities to dissect gene regulatory mechanisms in context-specific ways. Although there are computational methods for extracting gene regulatory relationships from scRNA-seq and scATAC-seq data, the data integration problem, essential for accurate cell type identification, has been mostly treated as a standalone challenge. Here we present scTIE, a unified method that integrates temporal multimodal data and infers regulatory relationships predictive of cellular state changes. scTIE uses an autoencoder to embed cells from all time points into a common space by using iterative optimal transport, followed by extracting interpretable information to predict cell trajectories. Using a variety of synthetic and real temporal multimodal data sets, we show scTIE achieves effective data integration while preserving more biological signals than existing methods, particularly in the presence of batch effects and noise. Furthermore, on the exemplar multiome data set we generated from differentiating mouse embryonic stem cells over time, we show scTIE captures regulatory elements highly predictive of cell transition probabilities, providing new potentials to understand the regulatory landscape driving developmental processes.

The impact of selective HDAC inhibitors on the transcriptome of early mouse embryos

BACKGROUND

Histone acetylation, which is regulated by histone acetyltransferases (HATs) and histone deacetylases (HDACs), plays a crucial role in the control of gene expression. HDAC inhibitors (HDACi) have shown potential in cancer therapy; however, the specific roles of HDACs in early embryos remain unclear. Moreover, although some pan-HDACi have been used to maintain cellular undifferentiated states in early embryos, the specific mechanisms underlying their effects remain unknown. Thus, there remains a significant knowledge gap regarding the application of selective HDACi in early embryos.

RESULTS

To address this gap, we treated early embryos with two selective HDACi (MGCD0103 and T247). Subsequently, we collected and analyzed their transcriptome data at different developmental stages. Our findings unveiled a significant effect of HDACi treatment during the crucial 2-cell stage of zygotes, leading to a delay in embryonic development after T247 and an arrest at 2-cell stage after MGCD0103 administration. Furthermore, we elucidated the regulatory targets underlying this arrested embryonic development, which pinpointed the G2/M phase as the potential period of embryonic development arrest caused by MGCD0103. Moreover, our investigation provided a comprehensive profile of the biological processes that are affected by HDACi, with their main effects being predominantly localized in four aspects of zygotic gene activation (ZGA): RNA splicing, cell cycle regulation, autophagy, and transcription factor regulation. By exploring the transcriptional regulation and epigenetic features of the genes affected by HDACi, we made inferences regarding the potential main pathways via which HDACs affect gene expression in early embryos. Notably, Hdac7 exhibited a distinct response, highlighting its potential as a key player in early embryonic development.

CONCLUSIONS

Our study conducted a comprehensive analysis of the effects of HDACi on early embryonic development at the transcriptional level. The results demonstrated that HDACi significantly affected ZGA in embryos, elucidated the distinct actions of various selective HDACi, and identified specific biological pathways and mechanisms via which these inhibitors modulated early embryonic development.

Progress in single-cell multimodal sequencing and multi-omics data integration

With the rapid advance of single-cell sequencing technology, cell heterogeneity in various biological processes was dissected at different omics levels. However, single-cell mono-omics results in fragmentation of information and could not provide complete cell states. In the past several years, a variety of single-cell multimodal omics technologies have been developed to jointly profile multiple molecular modalities, including genome, transcriptome, epigenome, and proteome, from the same single cell. With the availability of single-cell multimodal omics data, we can simultaneously investigate the effects of genomic mutation or epigenetic modification on transcription and translation, and reveal the potential mechanisms underlying disease pathogenesis. Driven by the massive single-cell omics data, the integration method of single-cell multi-omics data has rapidly developed. Integration of the massive multi-omics single-cell data in public databases in the future will make it possible to construct a cell atlas of multi-omics, enabling us to comprehensively understand cell state and gene regulation at single-cell resolution. In this review, we summarized the experimental methods for single-cell multimodal omics data and computational methods for multi-omics data integration. We also discussed the future development of this field.

Rescuing lung development through embryonic inhibition of histone acetylation

A major barrier to the impact of genomic diagnosis in patients with congenital malformations is the lack of understanding regarding how sequence variants contribute to disease pathogenesis and whether this information could be used to generate patient-specific therapies. Congenital diaphragmatic hernia (CDH) is among the most common and severe of all structural malformations; however, its underlying mechanisms are unclear. We identified loss-of-function sequence variants in the epigenomic regulator gene SIN3A in two patients with complex CDH. Tissue-specific deletion of Sin3a in mice resulted in defects in diaphragm development, lung hypoplasia, and pulmonary hypertension, the cardinal features of CDH and major causes of CDH-associated mortality. Loss of SIN3A in the lung mesenchyme resulted in reduced cellular differentiation, impaired cell proliferation, and increased DNA damage. Treatment of embryonic Sin3a mutant mice with anacardic acid, an inhibitor of histone acetyltransferase, reduced DNA damage, increased cell proliferation and differentiation, improved lung and pulmonary vascular development, and reduced pulmonary hypertension. These findings demonstrate that restoring the balance of histone acetylation can improve lung development in the Sin3a mouse model of CDH.

Leveraging metabolic modeling and machine learning to uncover modulators of quiescence depth

Quiescence, a temporary withdrawal from the cell cycle, plays a key role in tissue homeostasis and regeneration. Quiescence is increasingly viewed as a continuum between shallow and deep quiescence, reflecting different potentials to proliferate. The depth of quiescence is altered in a range of diseases and during aging. Here, we leveraged genome-scale metabolic modeling (GEM) to define the metabolic and epigenetic changes that take place with quiescence deepening. We discovered contrasting changes in lipid catabolism and anabolism and diverging trends in histone methylation and acetylation. We then built a multi-cell type machine learning model that accurately predicts quiescence depth in diverse biological contexts. Using both machine learning and genome-scale flux simulations, we performed high-throughput screening of chemical and genetic modulators of quiescence and identified novel small molecule and genetic modulators with relevance to cancer and aging.

DIRECT: Digital Microfluidics for Isolation-Free Shared Library Construction of Single-Cell DNA Methylome and Transcriptome

Simultaneous profiling of DNA methylation and gene expression within single cells is a powerful technology to dissect complex gene regulatory network of cells. However, existing methods are based on picking a single-cell in a tube and split single-cell lysate into two parts for transcriptome and methylome library construction, respectively, which is costly and cumbersome. Here, DIRECT is proposed, a digital microfluidics-based method for high-efficiency single-cell isolation and simultaneous analysis of the methylome and transcriptome in a single library construction. The accuracy of DIRECT is demonstrated in comparison with bulk and single-omics data, and the high CpG site coverage of DIRECT allows for precise analysis of copy number variation information, enabling expansion of single cell analysis from two- to three-omics. By applying DIRECT to monitor the dynamics of mouse embryonic stem cell differentiation, the relationship between DNA methylation and changes in gene expression during differentiation is revealed. DIRECT enables accurate, robust, and reproducible single-cell DNA methylation and gene expression co-analysis in a more cost-effective, simpler library preparation and automated manner, broadening the application scenarios of single-cell multi-omics analysis and revealing a more comprehensive and fine-grained map of cellular regulatory landscapes.

Multi-omics analysis in developmental bone biology

Single-cell omics and multi-omics have revolutionized our understanding of molecular and cellular biological processes at a single-cell level. In bone biology, the combination of single-cell RNA-sequencing analyses and in vivo lineage-tracing approaches has successfully identified multi-cellular diversity and dynamics of skeletal cells. This established a new concept that bone growth and regeneration are regulated by concerted actions of multiple types of skeletal stem cells, which reside in spatiotemporally distinct niches. One important subtype is endosteal stem cells that are particularly abundant in young bone marrow. The discovery of this new skeletal stem cell type has been facilitated by single-cell multi-omics, which simultaneously measures gene expression and chromatin accessibility. Using single-cell omics, it is now possible to computationally predict the immediate future state of individual cells and their differentiation potential. In vivo validation using histological approaches is the key to interpret the computational prediction. The emerging spatial omics, such as spatial transcriptomics and epigenomics, have major advantage in retaining the location of individual cells within highly complex tissue architecture. Spatial omics can be integrated with other omics to further obtain in-depth insights. Single-cell multi-omics are now becoming an essential tool to unravel intricate multicellular dynamics and intercellular interactions of skeletal cells.

Dissecting mammalian reproduction with spatial transcriptomics

BACKGROUND

Mammalian reproduction requires the fusion of two specialized cells: an oocyte and a sperm. In addition to producing gametes, the reproductive system also provides the environment for the appropriate development of the embryo. Deciphering the reproductive system requires understanding the functions of each cell type and cell-cell interactions. Recent single-cell omics technologies have provided insights into the gene regulatory network in discrete cellular populations of both the male and female reproductive systems. However, these approaches cannot examine how the cellular states of the gametes or embryos are regulated through their interactions with neighboring somatic cells in the native tissue environment owing to tissue disassociations. Emerging spatial omics technologies address this challenge by preserving the spatial context of the cells to be profiled. These technologies hold the potential to revolutionize our understanding of mammalian reproduction.

OBJECTIVE AND RATIONALE

We aim to review the state-of-the-art spatial transcriptomics (ST) technologies with a focus on highlighting the novel biological insights that they have helped to reveal about the mammalian reproductive systems in the context of gametogenesis, embryogenesis, and reproductive pathologies. We also aim to discuss the current challenges of applying ST technologies in reproductive research and provide a sneak peek at what the field of spatial omics can offer for the reproduction community in the years to come.

SEARCH METHODS

The PubMed database was used in the search for peer-reviewed research articles and reviews using combinations of the following terms: 'spatial omics', 'fertility', 'reproduction', 'gametogenesis', 'embryogenesis', 'reproductive cancer', 'spatial transcriptomics', 'spermatogenesis', 'ovary', 'uterus', 'cervix', 'testis', and other keywords related to the subject area. All relevant publications until April 2023 were critically evaluated and discussed.

OUTCOMES

First, an overview of the ST technologies that have been applied to studying the reproductive systems was provided. The basic design principles and the advantages and limitations of these technologies were discussed and tabulated to serve as a guide for researchers to choose the best-suited technologies for their own research. Second, novel biological insights into mammalian reproduction, especially human reproduction revealed by ST analyses, were comprehensively reviewed. Three major themes were discussed. The first theme focuses on genes with non-random spatial expression patterns with specialized functions in multiple reproductive systems; The second theme centers around functionally interacting cell types which are often found to be spatially clustered in the reproductive tissues; and the thrid theme discusses pathological states in reproductive systems which are often associated with unique cellular microenvironments. Finally, current experimental and computational challenges of applying ST technologies to studying mammalian reproduction were highlighted, and potential solutions to tackle these challenges were provided. Future directions in the development of spatial omics technologies and how they will benefit the field of human reproduction were discussed, including the capture of cellular and tissue dynamics, multi-modal molecular profiling, and spatial characterization of gene perturbations.

WIDER IMPLICATIONS

Like single-cell technologies, spatial omics technologies hold tremendous potential for providing significant and novel insights into mammalian reproduction. Our review summarizes these novel biological insights that ST technologies have provided while shedding light on what is yet to come. Our review provides reproductive biologists and clinicians with a much-needed update on the state of art of ST technologies. It may also facilitate the adoption of cutting-edge spatial technologies in both basic and clinical reproductive research.

scapGNN: A graph neural network-based framework for active pathway and gene module inference from single-cell multi-omics data

Although advances in single-cell technologies have enabled the characterization of multiple omics profiles in individual cells, extracting functional and mechanistic insights from such information remains a major challenge. Here, we present scapGNN, a graph neural network (GNN)-based framework that creatively transforms sparse single-cell profile data into the stable gene-cell association network for inferring single-cell pathway activity scores and identifying cell phenotype-associated gene modules from single-cell multi-omics data. Systematic benchmarking demonstrated that scapGNN was more accurate, robust, and scalable than state-of-the-art methods in various downstream single-cell analyses such as cell denoising, batch effect removal, cell clustering, cell trajectory inference, and pathway or gene module identification. scapGNN was developed as a systematic R package that can be flexibly extended and enhanced for existing analysis processes. It provides a new analytical platform for studying single cells at the pathway and network levels.

Developmental, cytogenetic and epigenetic consequences of removing complex proteins and adding melatonin during in vitro maturation of bovine oocytes

Background

In vitro maturation (IVM) of germinal vesicle intact oocytes prior to in vitro fertilization (IVF) is practiced widely in animals. In human assisted reproduction it is generally reserved for fertility preservation or where ovarian stimulation is contraindicated. Standard practice incorporates complex proteins (CP), in the form of serum and/or albumin, into IVM media to mimic the ovarian follicle environment. However, the undefined nature of CP, together with batch variation and ethical concerns regarding their origin, necessitate the development of more defined formulations. A known component of follicular fluid, melatonin, has multifaceted roles including that of a metabolic regulator and antioxidant. In certain circumstances it can enhance oocyte maturation. At this stage in development, the germinal-vesicle intact oocyte is prone to aneuploidy and epigenetic dysregulation.

Objectives

To determine the developmental, cytogenetic and epigenetic consequences of removing CP and including melatonin during bovine IVM.

Materials and methods

The study comprised a 2 x 2 factorial arrangement comparing (i) the inclusion or exclusion of CP, and (ii) the addition (100 nM) or omission of melatonin, during IVM. Cumulus-oocyte complexes (COCs) were retrieved from stimulated cycles. Following IVM and IVF, putative zygotes were cultured to Day 8 in standard media. RNAseq was performed on isolated cumulus cells, cytogenetic analyses (SNP-based algorithms) on isolated trophectoderm cells, and DNA methylation analysis (reduced representation bisulfite sequencing) on isolated cells of the inner-cell mass.

Results

Removal of CP during IVM led to modest reductions in blastocyst development, whilst added melatonin was beneficial in the presence but detrimental in the absence of CP. The composition of IVM media did not affect the nature or incidence of chromosomal abnormalities but cumulus-cell transcript expression indicated altered metabolism (primarily lipid) in COCs. These effects preceded the establishment of distinct metabolic and epigenetic signatures several days later in expanded and hatching blastocysts.

Conclusions

These findings highlight the importance of lipid, particularly sterol, metabolism by the COC during IVM. They lay the foundation for future studies that seek to develop chemically defined systems of IVM for the generation of transferrable embryos that are both cytogenetically and epigenetically normal.

Single-cell sequencing technology applied to epigenetics for the study of tumor heterogeneity

BACKGROUND

Previous studies have traditionally attributed the initiation of cancer cells to genetic mutations, considering them as the fundamental drivers of carcinogenesis. However, recent research has shed light on the crucial role of epigenomic alterations in various cell types present within the tumor microenvironment, suggesting their potential contribution to tumor formation and progression. Despite these significant findings, the progress in understanding the epigenetic mechanisms regulating tumor heterogeneity has been impeded over the past few years due to the lack of appropriate technical tools and methodologies.

RESULTS

The emergence of single-cell sequencing has enhanced our understanding of the epigenetic mechanisms governing tumor heterogeneity by revealing the distinct epigenetic layers of individual cells (chromatin accessibility, DNA/RNA methylation, histone modifications, nucleosome localization) and the diverse omics (transcriptomics, genomics, multi-omics) at the single-cell level. These technologies provide us with new insights into the molecular basis of intratumoral heterogeneity and help uncover key molecular events and driving mechanisms in tumor development.

CONCLUSION

This paper provides a comprehensive review of the emerging analytical and experimental approaches of single-cell sequencing in various omics, focusing specifically on epigenomics. These approaches have the potential to capture and integrate multiple dimensions of individual cancer cells, thereby revealing tumor heterogeneity and epigenetic features. Additionally, this paper outlines the future trends of these technologies and their current technical limitations.

Single-cell multi-omics sequencing of human spermatogenesis reveals a DNA demethylation event associated with male meiotic recombination

Human spermatogenesis is a highly ordered process; however, the roles of DNA methylation and chromatin accessibility in this process remain largely unknown. Here by simultaneously investigating the chromatin accessibility, DNA methylome and transcriptome landscapes using the modified single-cell chromatin overall omic-scale landscape sequencing approach, we revealed that the transcriptional changes throughout human spermatogenesis were correlated with chromatin accessibility changes. In particular, we identified a set of transcription factors and cis elements with potential functions. A round of DNA demethylation was uncovered upon meiosis initiation in human spermatogenesis, which was associated with male meiotic recombination and conserved between human and mouse. Aberrant DNA hypermethylation could be detected in leptotene spermatocytes of certain nonobstructive azoospermia patients. Functionally, the intervention of DNA demethylation affected male meiotic recombination and fertility. Our work provides multi-omics landscapes of human spermatogenesis at single-cell resolution and offers insights into the association between DNA demethylation and male meiotic recombination.

Omics Views of Mechanisms for Cell Fate Determination in Early Mammalian Development

During mammalian preimplantation development, a totipotent zygote undergoes several cell cleavages and two rounds of cell fate determination, ultimately forming a mature blastocyst. Along with compaction, the establishment of apicobasal cell polarity breaks the symmetry of an embryo and guides subsequent cell fate choice. Although the lineage segregation of the inner cell mass (ICM) and trophectoderm (TE) is the first symbol of cell differentiation, several molecules have been shown to bias the early cell fate through their inter-cellular variations at much earlier stages, including the 2- and 4-cell stages. The underlying mechanisms of early cell fate determination have long been an important research topic. In this review, we summarize the molecular events that occur during early embryogenesis, as well as the current understanding of their regulatory roles in cell fate decisions. Moreover, as powerful tools for early embryogenesis research, single-cell omics techniques have been applied to both mouse and human preimplantation embryos and have contributed to the discovery of cell fate regulators. Here, we summarize their applications in the research of preimplantation embryos, and provide new insights and perspectives on cell fate regulation.

Integrative analysis of single-cell embryo data reveals transcriptome signatures for the human pre-implantation inner cell mass

As the source of embryonic stem cells (ESCs), inner cell mass (ICM) can form all tissues of the embryo proper, however, its role in early human lineage specification remains controversial. Although a stepwise differentiation model has been proposed suggesting the existence of ICM as a distinct developmental stage, the underlying molecular mechanism remains unclear. In the present study, we perform an integrated analysis on the public human preimplantation embryonic single-cell transcriptomic data and apply a trajectory inference algorithm to measure the cell plasticity. In our results, ICM population can be clearly discriminated on the dimension-reduced graph and confirmed by compelling evidences, thus validating the two-step hypothesis of lineage commitment. According to the branch probabilities and differentiation potential, we determine the precise time points for two lineage segregations. Further analysis on gene expression dynamics and regulatory network indicates that transcription factors including GSC, PRDM1, and SPIC may underlie the decisions of ICM fate. In addition, new human ICM marker genes, such as EPHA4 and CCR8 are discovered and validated by immunofluorescence. Given the potential clinical applications of ESCs, our analysis provides a further understanding of human ICM cells and facilitates the exploration of more unique characteristics in early human development.

Decoding Human Biology and Disease Using Single-cell Omics Technologies

Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.

The technological landscape and applications of single-cell multi-omics

Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.

N4-acetylcytidine of Nop2 mRNA is required for the transition of morula-to-blastocyst

N-acetyltransferase 10 (NAT10)-mediated N4-acetylcytidine (ac4C) modification is crucial for mRNA stability and translation efficiency, yet the underlying function in mammalian preimplantation embryos remains unclear. Here, we characterized the ac4C modification landscape in mouse early embryos and found that the majority of embryos deficient in ac4C writer-NAT10 failed to develop into normal blastocysts. Through single-cell sequencing, RNA-seq, acetylated RNA immunoprecipitation combined with PCR (acRIP-PCR), and embryonic phenotype monitoring, Nop2 was screened as a target gene of Nat10. Mechanistically, Nat10 knockdown decreases the ac4C modification on Nop2 mRNA and reduces RNA and protein abundance by affecting the mRNA stability of Nop2. Then, depletion of NOP2 may inhibit the translation of transcription factor TEAD4, resulting in defective expression of the downstream lineage-specific gene Cdx2, and ultimately preventing blastomeres from undergoing the trophectoderm (TE) fate. However, exogenous Nop2 mRNA partially reverses this abnormal development. In conclusion, our findings demonstrate that defective ac4C modification of Nop2 mRNA hinders the morula-to-blastocyst transition by influencing the first cell fate decision in mice.

Dynamic DNA 5-hydroxylmethylcytosine and RNA 5-methycytosine Reprogramming During Early Human Development

After implantation, complex and highly specialized molecular events render functionally distinct organ formation, whereas how the epigenome shapes organ-specific development remains to be fully elucidated. Here, nano-hmC-Seal, RNA bisulfite sequencing (RNA-BisSeq), and RNA sequencing (RNA-Seq) were performed, and the first multilayer landscapes of DNA 5-hydroxymethylcytosine (5hmC) and RNA 5-methylcytosine (m5C) epigenomes were obtained in the heart, kidney, liver, and lung of the human foetuses at 13-28 weeks with 123 samples in total. We identified 70,091 and 503 organ- and stage-specific differentially hydroxymethylated regions (DhMRs) and m5C-modified mRNAs, respectively. The key transcription factors (TFs), T-box transcription factor 20 (TBX20), paired box 8 (PAX8), krueppel-like factor 1 (KLF1), transcription factor 21 (TCF21), and CCAAT enhancer binding protein beta (CEBPB), specifically contribute to the formation of distinct organs at different stages. Additionally, 5hmC-enriched Alu elements may participate in the regulation of expression of TF-targeted genes. Our integrated studies reveal a putative essential link between DNA modification and RNA methylation, and illustrate the epigenetic maps during human foetal organogenesis, which provide a foundation for for an in-depth understanding of the epigenetic mechanisms underlying early development and birth defects.