SpaNCMG: improving spatial domains identification of spatial transcriptomics using neighborhood-complementary mixed-view graph convolutional network.

The advancement of spatial transcriptomics (ST) technology contributes to a more profound comprehension of the spatial properties of gene expression within tissues. However, due to challenges of high dimensionality, pronounced noise and dynamic limitations in ST data, the integration of gene expression and spatial information to accurately identify spatial domains remains challenging. This paper proposes a SpaNCMG algorithm for the purpose of achieving precise spatial domain description and localization based on a neighborhood-complementary mixed-view graph convolutional network. The algorithm enables better adaptation to ST data at different resolutions by integrating the local information from KNN and the global structure from r-radius into a complementary neighborhood graph. It also introduces an attention mechanism to achieve adaptive fusion of different reconstructed expressions, and utilizes KPCA method for dimensionality reduction. The application of SpaNCMG on five datasets from four sequencing platforms demonstrates superior performance to eight existing advanced methods. Specifically, the algorithm achieved highest ARI accuracies of 0.63 and 0.52 on the datasets of the human dorsolateral prefrontal cortex and mouse somatosensory cortex, respectively. It accurately identified the spatial locations of marker genes in the mouse olfactory bulb tissue and inferred the biological functions of different regions. When handling larger datasets such as mouse embryos, the SpaNCMG not only identified the main tissue structures but also explored unlabeled domains. Overall, the good generalization ability and scalability of SpaNCMG make it an outstanding tool for understanding tissue structure and disease mechanisms. Our codes are available at https://github.com/ZhihaoSi/SpaNCMG.

Chromatin region binning of gene expression for improving embryo cell subtype identification.

Mammalian embryonic development is a complex process, characterized by intricate spatiotemporal dynamics and distinct chromatin preferences. However, the quick diversification in early embryogenesis leads to significant cellular diversity and the sparsity of scRNA-seq data, posing challenges in accurately determining cell fate decisions. In this study, we introduce a chromatin region binning method using scChrBin, designed to identify chromatin regions that elucidate the dynamics of embryonic development and lineage differentiation. This method transforms scRNA-seq data into a chromatin-based matrix, leveraging genomic annotations. Our results showed that the scChrBin method achieves high accuracy, with 98.0% and 89.2% on two single-cell embryonic datasets, demonstrating its effectiveness in analyzing complex developmental processes. We also systematically and comprehensively analysis of these key chromatin binning regions and their associated genes, focusing on their roles in lineage and stage development. The perspective of chromatin region binning method enables a comprehensive analysis of transcriptome data at the chromatin level, allowing us to unveil the dynamic expression of chromatin regions across temporal and spatial development. The tool is available as an application at https://github.com/liameihao/scChrBin.

Downregulation of the expression of subgenomic chromosome A7 genes promotes plant height in resynthesized allopolyploid Brassica napus.

KEY MESSAGE: Homoeolog expression bias and the gene dosage effect induce downregulation of genes on chromosome A7, causing a significant increase in the plant height of resynthesized allopolyploid Brassica napus. Gene expression levels in allopolyploid plants are not equivalent to the simple average of the expression levels in the parents and are associated with several non-additive expression phenomena, including homoeolog expression bias. However, hardly any information is available on the effect of homoeolog expression bias on traits. Here, we studied the effects of gene expression-related characteristics on agronomic traits using six isogenic resynthesized Brassica napus lines across the first ten generations. We found a group of genes located on chromosome A7 whose expression levels were significantly negatively correlated with plant height. They were expressed at significantly lower levels than their homoeologous genes, owing to allopolyploidy rather than inheritance from parents. Homoeolog expression bias resulted in resynthesized allopolyploids with a plant height similar to their female Brassica oleracea parent, but significantly higher than that of the male Brassica rapa parent. Notably, aneuploid lines carrying monosomic and trisomic chromosome A7 had the highest and lowest plant heights, respectively, due to changes in the expression bias of homoeologous genes because of alterations in the gene dosage. These findings suggest that the downregulation of the expression of homoeologous genes on a single chromosome can result in the partial improvement of traits to a significant extent in the nascent allopolyploid B. napus.

Deciphering the decisive factors driving fate bifurcations in somatic cell reprogramming.

Single-cell studies have demonstrated that somatic cell reprogramming is a continuous process of cell fates transition. Only partial reprogramming intermediates can overcome the molecular bottlenecks to acquire pluripotency. To decipher the underlying decisive factors driving cell fate, we identified induced pluripotent stem cells or stromal-like cells (iPSCs/SLCs) and iPSCs or trophoblast-like cells (iPSCs/TLCs) fate bifurcations by reconstructing cellular trajectory. The mesenchymal-epithelial transition and the activation of pluripotency networks are the main molecular series in successful reprogramming. Correspondingly, intermediates diverge into SLCs accompanied by the inhibition of cell cycle genes and the activation of extracellular matrix genes, whereas the TLCs fate is characterized by the up-regulation of placenta development genes. Combining putative gene regulatory networks, seven (Taf7, Ezh2, Klf2, etc.) and three key factors (Cdc5l, Klf4, and Nanog) were individually identified as drivers of the successful reprogramming by triggering downstream pluripotent networks during iPSCs/SLCs and iPSCs/TLCs fate bifurcation. Conversely, 11 factors (Cebpb, Sox4, Junb, etc.) and four factors (Gata2, Jund, Ctnnb1, etc.) drive SLCs fate and TLCs fate, respectively. Our study sheds new light on the understanding of decisive factors driving cell fate, which is helpful for improving reprogramming efficiency through manipulating cell fates to avoid alternative fates.

Characterizing Cellular Differentiation Potency and Waddington Landscape via Energy Indicator.

The precise characterization of cellular differentiation potency remains an open question, which is fundamentally important for deciphering the dynamics mechanism related to cell fate transition. We quantitatively evaluated the differentiation potency of different stem cells based on the Hopfield neural network (HNN). The results emphasized that cellular differentiation potency can be approximated by Hopfield energy values. We then profiled the Waddington energy landscape of embryogenesis and cell reprogramming processes. The energy landscape at single-cell resolution further confirmed that cell fate decision is progressively specified in a continuous process. Moreover, the transition of cells from one steady state to another in embryogenesis and cell reprogramming processes was dynamically simulated on the energy ladder. These two processes can be metaphorized as the motion of descending and ascending ladders, respectively. We further deciphered the dynamics of the gene regulatory network (GRN) for driving cell fate transition. Our study proposes a new energy indicator to quantitatively characterize cellular differentiation potency without prior knowledge, facilitating the further exploration of the potential mechanism of cellular plasticity.

EmAtlas: a comprehensive atlas for exploring spatiotemporal activation in mammalian embryogenesis.

The emerging importance of embryonic development research rapidly increases the volume for a professional resource related to multi-omics data. However, the lack of global embryogenesis repository and systematic analysis tools limits the preceding in stem cell research, human congenital diseases and assisted reproduction. Here, we developed the EmAtlas, which collects the most comprehensive multi-omics data and provides multi-scale tools to explore spatiotemporal activation during mammalian embryogenesis. EmAtlas contains data on multiple types of gene expression, chromatin accessibility, DNA methylation, nucleosome occupancy, histone modifications, and transcription factors, which displays the complete spatiotemporal landscape in mouse and human across several time points, involving gametogenesis, preimplantation, even fetus and neonate, and each tissue involves various cell types. To characterize signatures involved in the tissue, cell, genome, gene and protein levels during mammalian embryogenesis, analysis tools on these five scales were developed. Additionally, we proposed EmRanger to deliver extensive development-related biological background annotations. Users can utilize these tools to analyze, browse, visualize, and download data owing to the user-friendly interface. EmAtlas is freely accessible at http://bioinfor.imu.edu.cn/ematlas.

Competitive binding of TET1 and DNMT3A/B cooperates the DNA methylation pattern in human embryonic stem cells.

DNMT3A/B and TET1 play indispensable roles in regulating DNA methylation that undergoes extensive reprogramming during mammalian embryogenesis. Yet the competitive and cooperative relationships between TET1 and DNMT3A/B remain largely unknown in the human embryonic stem cells. Here, we revealed that the main DNA-binding domain of TET1 contains more positive charges by using charge reduction of amino acid alphabet, followed by DNMT3A and DNMT3B. The genome-wide binding profiles showed that TET1 prefers binding to the proximal promoters and CpG islands compared with DNMT3A/B. Moreover, the binding regions of these three transcription factors can be divided into specific and co-binding regions. And a stronger inhibitory effect of DNMT3A on TET1 demethylation was observed in co-binding regions. Furthermore, we integrated TET1 knockout data to further discuss the competitive binding patterns of TET1 and DNMT3A/B. The lack of TET1 increased the occupation of DNMT3A/B at the specific binding regions of TET1 causing focal hypermethylation. The knockout of TET1 was also accompanied by a reduction of DNMT3A/B binding in the co-binding regions, further confirming the cooperative binding function between TET1 and DNMT3A/B. In conclusion, our studies found that the competitive binding of TET1 and DNMT3A/B cooperatively shapes the global DNA methylation pattern in human embryonic stem cells.

Reprogramming barriers in bovine cells nuclear transfer revealed by single-cell RNA-seq analysis.

Many progresses have recently been achieved in animal somatic cell nuclear transfer (SCNT). However, embryos derived from SCNT rarely result in live births. Single-cell RNA sequencing (scRNA-seq) can be used to investigate the development details of SCNT embryos. Here, bovine fibroblasts and three factors bovine iPSCs (3F biPSCs) were used as donors for bovine nuclear transfer, and the single blastomere transcriptome was analysed by scRNA-seq. Compared to in vitro fertilization (IVF) embryos, SCNT embryos exhibited many defects. Abnormally expressed genes were found at each stage of embryos, which enriched in metabolism, and epigenetic modification. The DEGs of the adjacent stage in SCNT embryos did not follow the temporal expression pattern similar to that of IVF embryos. Particularly, SCNT 8-cell stage embryos showed failures in some gene activation, including ZSCAN4, and defects in protein association networks which cored as POLR2K, GRO1, and ANKRD1. Some important signalling pathways also showed incomplete activation at SCNT zygote to morula stage. Interestingly, 3F biPSCNT embryos exhibited more dysregulated genes than SCNT embryos at zygote and 2-cell stage, including genes in KDM family. Pseudotime analysis of 3F biPSCNT embryos showed the different developmental fate from SCNT and IVF embryos. These findings suggested partial reprogrammed 3F biPS cells as donors for bovine nuclear transfer hindered the reprogramming of nuclear transfer embryos. Our studies revealed the abnormal gene expression and pathway activation of SCNT embryos, which could increase our understanding of the development of SCNT embryos and give hints to improve the efficiency of nuclear transfer.

The Cumulative Formation of R-loop Interacts with Histone Modifications to Shape Cell Reprogramming.

R-loop, a three-stranded RNA/DNA structure, plays important roles in modulating genome stability and gene expression, but the molecular mechanism of R-loops in cell reprogramming remains elusive. Here, we comprehensively profiled the genome-wide landscape of R-loops during cell reprogramming. The results showed that the R-loop formation on most different types of repetitive elements is stage-specific in cell reprogramming. We unveiled that the cumulative deposition of an R-loop subset is positively correlated with gene expression during reprogramming. More importantly, the dynamic turnover of this R-loop subset is accompanied by the activation of the pluripotent transcriptional regulatory network (TRN). Moreover, the large accumulation of the active histone marker H3K4me3 and the reduction in H3K27me3 were also observed in these R-loop regions. Finally, we characterized the dynamic network of R-loops that facilitates cell fate transitions in reprogramming. Together, our study provides a new clue for deciphering the interplay mechanism between R-loops and HMs to control cell reprogramming.

WGBS combined with RNA-seq analysis revealed that Dnmt1 affects the methylation modification and gene expression changes during mouse oocyte vitrification.

Understanding the molecular level changes of oocyte cryopreservation and the subsequent warming process is essential for improving the oocyte cryopreservation technologies. Here, we collected the mature metaphase II (MII) oocytes from mice and vitrified. After thawing, single-cell whole-genome bisulphite sequencing (scWGBS) and single-cell RNA sequencing (scRNA-seq) were used to investigate the molecular attributes of this process. Compared to the fresh oocytes, the vitrified oocytes had lower global methylation and gene expression levels, and 1426 genes up-regulated and 3321 genes down-regulated. The 1426 up-regulated differentially expressed genes (DEGs) in the vitrified oocytes were mainly associated with the histone ubiquitination, while the 3321 down-regulated genes were mainly enriched in the mitochondrion organisation and ATP metabolism processes. The differentially methylated regions (DMRs) were mainly located in promoter, intron and exon region of genes, and the length of DMRs in the vitrified oocytes were also significantly lower than that of the fresh oocytes. Notably, there were no significant difference in the expression levels of DNA demethylases (Tet1, Tet2 and Tet3) and methyltransferases (Dnmt3a and Dnmt3b) between two treatments of oocytes. However, Dnmt1 and kcnq1ot1, which are responsible for maintaining DNA methylation, were significantly down regulated in the vitrified oocytes. Gene regulatory network (GRN) analysis showed the Dnmt1 and kcnq1ot1 play a core role in regulating methylation and expression levels of downstream genes. Moreover, some genes associated with oocyte quality were significantly down-regulated in the vitrified oocytes. The present data provides a new perspective for understanding the impact of vitrification on oocytes.

Nuclear Transfer Arrest Embryos Show Massive Dysregulation of Genes Involved in Transcription Pathways.

Somatic cell nuclear transfer (SCNT) technology can reprogram terminally differentiated cell nuclei into a totipotent state. However, the underlying molecular barriers of SCNT embryo development remain incompletely elucidated. Here, we observed that transcription-related pathways were incompletely activated in nuclear transfer arrest (NTA) embryos compared to normal SCNT embryos and in vivo fertilized (WT) embryos, which hinders the development of SCNT embryos. We further revealed the transcription pathway associated gene regulatory networks (GRNs) and found the aberrant transcription pathways can lead to the massive dysregulation of genes in NTA embryos. The predicted target genes of transcription pathways contain a series of crucial factors in WT embryos, which play an important role in catabolic process, pluripotency regulation, epigenetic modification and signal transduction. In NTA embryos, however, these genes were varying degrees of inhibition and show a defect in synergy. Overall, our research found that the incomplete activation of transcription pathways is another potential molecular barrier for SCNT embryos besides the incomplete reprogramming of epigenetic modifications, broadening the understanding of molecular mechanism of SCNT embryonic development.

HelPredictor models single-cell transcriptome to predict human embryo lineage allocation.

The in-depth understanding of cellular fate decision of human preimplantation embryos has prompted investigations on how changes in lineage allocation, which is far from trivial and remains a time-consuming task by experimental methods. It is desirable to develop a novel effective bioinformatics strategy to consider transitions of coordinated embryo lineage allocation and stage-specific patterns. There are rapidly growing applications of machine learning models to interpret complex datasets for identifying candidate development-related factors and lineage-determining molecular events. Here we developed the first machine learning platform, HelPredictor, that integrates three feature selection methods, namely, principal components analysis, F-score algorithm and squared coefficient of variation, and four classical machine learning classifiers that different combinations of methods and classifiers have independent outputs by increment feature selection method. With application to single-cell sequencing data of human embryo, HelPredictor not only achieved 94.9% and 90.9% respectively with cross-validation and independent test, but also fast classified different embryonic lineages and their development trajectories using less HelPredictor-predicted factors. The above-mentioned candidate lineage-specific genes were discussed in detail and were clustered for exploring transitions of embryonic heterogeneity. Our tool can fast and efficiently reveal potential lineage-specific and stage-specific biomarkers and provide insights into how advanced computational tools contribute to development research. The source code is available at https://github.com/liameihao/HelPredictor.

eHSCPr discriminating the cell identity involved in endothelial to hematopoietic transition.

MOTIVATION: Hematopoietic stem cells (HSCs) give rise to all blood cells and play a vital role throughout the whole lifespan through their pluripotency and self-renewal properties. Accurately identifying the stages of early HSCs is extremely important, as it may open up new prospects for extracorporeal blood research. Existing experimental techniques for identifying the early stages of HSCs development are time-consuming and expensive. Machine learning has shown its excellence in massive single-cell data processing and it is desirable to develop related computational models as good complements to experimental techniques. RESULTS: In this study, we presented a novel predictor called eHSCPr specifically for predicting the early stages of HSCs development. To reveal the distinct genes at each developmental stage of HSCs, we compared F-score with three state-of-art differential gene selection methods (limma, DESeq2, edgeR) and evaluated their performance. F-score captured the more critical surface markers of endothelial cells and hematopoietic cells, and the area under receiver operating characteristic curve (ROC) value was 0.987. Based on SVM, the 10-fold cross-validation accuracy of eHSCpr in the independent dataset and the training dataset reached 94.84% and 94.19%, respectively. Importantly, we performed transcription analysis on the F-score gene set, which indeed further enriched the signal markers of HSCs development stages. eHSCPr can be a powerful tool for predicting early stages of HSCs development, facilitating hypothesis-driven experimental design and providing crucial clues for the in vitro blood regeneration studies. AVAILABILITY AND IMPLEMENTATION: http://bioinfor.imu.edu.cn/ehscpr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Dppa2/4 as a trigger of signaling pathways to promote zygote genome activation by binding to CG-rich region.

Developmental pluripotency-associated 2 (Dppa2) and developmental pluripotency-associated 4 (Dppa4) as positive drivers were helpful for transcriptional regulation of zygotic genome activation (ZGA). Here, we systematically assessed the cooperative interplay of Dppa2 and Dppa4 in regulating cell pluripotency and found that simultaneous overexpression of Dppa2/4 can make induced pluripotent stem cells closer to embryonic stem cells (ESCs). Compared with other pluripotency transcription factors, Dppa2/4 can regulate majorities of signaling pathways by binding on CG-rich region of proximal promoter (0-500 bp), of which 85% and 77% signaling pathways were significantly activated by Dppa2 and Dppa4, respectively. Notably, Dppa2/4 also can dramatically trigger the decisive signaling pathways for facilitating ZGA, including Hippo, MAPK and TGF-beta signaling pathways and so on. At last, we found alkaline phosphatase, placental-like 2 (Alppl2) was completely silenced when Dppa2 and 4 single- or double-knockout in ESC, which is consistent with Dux. Moreover, Alppl2 was significantly activated in mouse 2-cell embryos and 4-8 cells stage of human embryos, further predicted that Alppl2 was directly regulated by Dppa2/4 as a ZGA candidate driver to facilitate pre-embryonic development.

Fatty acid metabolism as an indicator for the maternal-to-zygotic transition in porcine IVF embryos revealed by RNA sequencing.

A number of fatty acids have been found in porcine oocytes and early embryos. Recent studies have indicated the importance of fatty acids in the development of pre-implantation porcine embryos, whether derived from in vivo or somatic cell nuclear transfer. However, the effects of fatty acids on porcine embryos produced by in vitro fertilization (IVF) remain poorly defined. This study aimed to investigate the patterns of gene expression and functions of fatty acids in pre-implantation IVF porcine embryos at different stages using transcriptome sequencing. We found that, in IVF porcine embryos, genes related to fatty acid metabolism were positively expressed during early embryonic development. Additionally, the expression of genes related to lipid metabolism changed dramatically during the maternal-to-zygotic transition (MZT), and the genes associated with lipid metabolism were correlated with zygotic genome activation in porcine IVF embryos, suggesting that fatty acid metabolism plays an important role in MZT. In summary, fatty acid metabolism may be an indicator of MZT in porcine IVF embryos, which presents new considerations for exploring the regulatory mechanisms of this process.

Machine Learning of Single-Cell Transcriptome Highly Identifies mRNA Signature by Comparing F-Score Selection with DGE Analysis.

Human preimplantation development is a complex process involving dramatic changes in transcriptional architecture. For a better understanding of their time-spatial development, it is indispensable to identify key genes. Although the single-cell RNA sequencing (RNA-seq) techniques could provide detailed clustering signatures, the identification of decisive factors remains difficult. Additionally, it requires high experimental cost and a long experimental period. Thus, it is highly desired to develop computational methods for identifying effective genes of development signature. In this study, we first developed a predictor called EmPredictor to identify developmental stages of human preimplantation embryogenesis. First, we compared the F-score of feature selection algorithms with differential gene expression (DGE) analysis to find specific signatures of the development stage. In addition, by training the support vector machine (SVM), four types of signature subsets were comprehensively discussed. The prediction results showed that a feature subset with 1,881 genes from the F-score algorithm obtained the best predictive performance, which achieved the highest accuracy of 93.3% on the cross-validation set. Further function enrichment demonstrated that the gene set selected by the feature selection method was involved in more development-related pathways and cell fate determination biomarkers. This indicates that the F-score algorithm should be preferentially proposed for detecting key genes of multi-period data in mammalian early development.

EmExplorer: a database for exploring time activation of gene expression in mammalian embryos.

Understanding early development offers a striking opportunity to investigate genetic disease, stem cell and assisted reproductive technology. Recent advances in high-throughput sequencing technology have led to the rising influx of omics data, which have rapidly boosted our understanding of mammalian developmental mechanisms. Here, we review the database EmExplorer (a database for exploring time activation of gene expression in mammalian embryos), which systematically organizes the genes from development-related pathways, and which we have already established and continue to update it. The current version of EmExplorer incorporates over 26 000 genes obtained from 306 functional pathways in five species. The function annotations of development-related genes were also integrated into EmExplorer. To facilitate data extraction, the database also contains the following information. (i) The dynamic expression values for each development stage are matched to the corresponding genes. (ii) A two-layer search tool which supports multi-option searching, such as by official symbol, pathway name and function annotation. The returned entries can directly link to the analysis results for the corresponding gene or pathway in the analysis module. (iii) The analysis module provides different gene comparisons at the multi-species level and functional pathway level, which shows the species specificity and stage specificity at the gene or pathway level. (iv) The analysis based on the hypergeometric distribution test reveals the enrichment of gene functions at a particular stage of one organism's pathway. (v) The browser is designed for users with ambiguous searching goals and greatly helps new users to get a general idea of the contents of the database. (vi) The experimentally validated pathways are manually curated and shown on the home page. EmExplorer will be helpful for elucidating early developmental mechanisms and exploring time activation genes. EmExplorer is freely available at http://bioinfor.imu.edu.cn/emexplorer .

The spatial binding model of the pioneer factor Oct4 with its target genes during cell reprogramming.

Understanding the target regulation between pioneer factor and its binding genes is crucial for improving the efficiency of TF-mediated reprogramming. Oct4 as the only one factor that cannot be substituted by other POU members, it is urgent need to develop a quantitative model for describing the spatial binding pattern with its target genes. The dynamic profiles of pioneer factor Oct4-binding showed that the major wave occurs at the intermediate stage of cell reprogramming (from day 7 to day 15), and the promoter is the preferred targeting regions. The Oct4-binding distributions perform significant chromosome bias. The overall enrichment on chromosome 1-11 is higher than that on the others. The dramatic event of TF-mediated reprogramming is mainly concentrated on autosomes. We also found that the spatial binding ability of Oct4 binding can be represented quantitatively by using three parameters of peaks (height, width and distance). The dynamic changes of Oct4-binding demonstrated that the width play more important roles in regulating expression of target genes. At last, a multivariate linear regression was introduced to establish the spatial binding model of the Oct4-binding. The evaluation results confirmed that the height and width is positively correlated with the gene expression. And the additive interaction terms of height and width can better optimize the model performance than the multiplicative terms. The best average coefficients of determination of improved model achieved to 81.38%. Our study will provide new insights into the cooperative regulation of spatial binding pattern of pioneer factors in cell reprogramming.