Computational modeling for deciphering tissue microenvironment heterogeneity from spatially resolved transcriptomics.

Spatial transcriptomics techniques, while measuring gene expression, retain spatial location information, aiding in situ studies of organismal tissue architecture and the progression of pathological processes. These techniques generate vast amounts of omics data, necessitating the development of computational methods to reveal the underlying tissue microenvironment heterogeneity. The main directions in spatial transcriptomics data analysis are spatial domain detection and spatial deconvolution, which can identify spatial functional regions and parse the distribution of cell types in spatial transcriptomics data by integrating single-cell transcriptomics data. In these two research directions, many computational methods have been successively proposed. This article will categorize them into three types: machine learning-based methods, probabilistic models-based methods, and deep learning-based methods. It will list and discuss the representative algorithms of each type along with their advantages and disadvantages and describe the datasets and evaluation metrics used to assess these computational methods, facilitating researchers in selecting suitable computational methods according to their research needs. Finally, combining the latest technological developments and the advantages and disadvantages of current algorithms, this article will look forward to the future directions of computational method development.

Multi-modal domain adaptation for revealing spatial functional landscape from spatially resolved transcriptomics.

Spatially resolved transcriptomics (SRT) has emerged as a powerful tool for investigating gene expression in spatial contexts, providing insights into the molecular mechanisms underlying organ development and disease pathology. However, the expression sparsity poses a computational challenge to integrate other modalities (e.g. histological images and spatial locations) that are simultaneously captured in SRT datasets for spatial clustering and variation analyses. In this study, to meet such a challenge, we propose multi-modal domain adaption for spatial transcriptomics (stMDA), a novel multi-modal unsupervised domain adaptation method, which integrates gene expression and other modalities to reveal the spatial functional landscape. Specifically, stMDA first learns the modality-specific representations from spatial multi-modal data using multiple neural network architectures and then aligns the spatial distributions across modal representations to integrate these multi-modal representations, thus facilitating the integration of global and spatially local information and improving the consistency of clustering assignments. Our results demonstrate that stMDA outperforms existing methods in identifying spatial domains across diverse platforms and species. Furthermore, stMDA excels in identifying spatially variable genes with high prognostic potential in cancer tissues. In conclusion, stMDA as a new tool of multi-modal data integration provides a powerful and flexible framework for analyzing SRT datasets, thereby advancing our understanding of intricate biological systems.

Spatially aware self-representation learning for tissue structure characterization and spatial functional genes identification.

Spatially resolved transcriptomics (SRT) enable the comprehensive characterization of transcriptomic profiles in the context of tissue microenvironments. Unveiling spatial transcriptional heterogeneity needs to effectively incorporate spatial information accounting for the substantial spatial correlation of expression measurements. Here, we develop a computational method, SpaSRL (spatially aware self-representation learning), which flexibly enhances and decodes spatial transcriptional signals to simultaneously achieve spatial domain detection and spatial functional genes identification. This novel tunable spatially aware strategy of SpaSRL not only balances spatial and transcriptional coherence for the two tasks, but also can transfer spatial correlation constraint between them based on a unified model. In addition, this joint analysis by SpaSRL deciphers accurate and fine-grained tissue structures and ensures the effective extraction of biologically informative genes underlying spatial architecture. We verified the superiority of SpaSRL on spatial domain detection, spatial functional genes identification and data denoising using multiple SRT datasets obtained by different platforms and tissue sections. Our results illustrate SpaSRL's utility in flexible integration of spatial information and novel discovery of biological insights from spatial transcriptomic datasets.

Correlation analysis of serum reproductive hormones and metabolites during multiple ovulation in sheep.

BACKGROUND: The establishment of non-invasive diagnostic method for multiple ovulation prediction is helpful to improve the efficiency of multiple ovulation. The blood hormones and metabolites would be suitable indexes for this subject. METHODS: In this study, 86 estrus ewes (65 of induced estrus (IE) and 21 of spontaneous estrus (SE)) were selected and the blood samples were collected at the day before follicle-stimulating hormone (FSH) injection (1st) and before artificial insemination (2nd). The serum reproductive hormones ofFSH, luteinizing hormone (LH), 17ß-Estradiol (E2), progesterone (P4) and anti-Mullerian hormone (AMH) were measured through enzyme linked immunosorbent assay (ELISA) and the untargeted metabolomics analysis was processed through LC-MS/MS. The embryos were collected after 6.5 days of artificial insemination. RESULTS: In total, 975 and 406 embryos were collected in IE and SE group, respectively. The analysis of reproductive hormones showed that concentrations of FSH, E2 and AMH were positive correlated with the embryo yield while concentrations of LH and P4 were negative correlated in both group at 1st detection. At 2nd detection, the trends of reproductive hormones were similar with 1st except P4, which was positive correlated with embryo yield. The metabolomics analysis showed that 1158 metabolites (721 in positive iron mode and 437 in negative iron mode) were detected and 617 were annotated. In 1st comparation of high and low embryonic yield populations, 56 and 53 differential metabolites were identified in IE and SE group, respectively. The phosphatidyl choline (PC) (19:0/20:5) and PC (18:2/18:3) were shared in two groups. In 2nd comparation, 48 and 49 differential metabolites were identified in IE and SE group, respectively. The PC (18:1/18:2) and pentadecanoic acid were shared. Most differential metabolites were significantly enriched in amino acid, fatty acid metabolism, digestive system secretion and ovarian steroidogenesis pathways. CONCLUSIONS: This study showed that FSH, P4, AMH, the PC relevant metabolites and some anomic acids could be potential biomarkers for embryonic yield prediction in ovine multiple ovulation. The results would help to explain the relation between blood material and ovarian function and provide a theoretical basis for the multiple ovulation prediction.

RDAClone: Deciphering Tumor Heterozygosity through Single-Cell Genomics Data Analysis with Robust Deep Autoencoder.

Rapid advances in single-cell genomics sequencing (SCGS) have allowed researchers to characterize tumor heterozygosity with unprecedented resolution and reveal the phylogenetic relationships between tumor cells or clones. However, high sequencing error rates of current SCGS data, i.e., false positives, false negatives, and missing bases, severely limit its application. Here, we present a deep learning framework, RDAClone, to recover genotype matrices from noisy data with an extended robust deep autoencoder, cluster cells into subclones by the Louvain-Jaccard method, and further infer evolutionary relationships between subclones by the minimum spanning tree. Studies on both simulated and real datasets demonstrate its robustness and superiority in data denoising, cell clustering, and evolutionary tree reconstruction, particularly for large datasets.

Deciphering extrachromosomal circular DNA in Arabidopsis.

Extrachromosomal circular DNA (eccDNA) is independent of the chromosome and exists in many eukaryotes. However, the nature and origin of eccDNA in plants remains unclear. In this study, we sequenced 12 samples from four tissues (leaf, flower, stem and root) with three biological replicates. In total, we found 743 eccDNAs found in at least two samples. Most of eccDNA have inverted repeats ranging from 4 to 12 bp in the boundaries. Interestingly, eccDNA is not only related to transposon activity, but also hosts tRNA genes, suggesting that the eccDNAs may be associated with tRNA abundance which controls protein synthesis under conditions of stress. Our results provide an unprecedented view of eccDNA, which is still naïve in scope.

CropCircDB: a comprehensive circular RNA resource for crops in response to abiotic stress.

Circular RNA (circRNAs) may mediate mRNA expression as miRNA sponge. Since the community has paid more attention on circRNAs, a lot of circRNA databases have been developed for plant. However, a comprehensive collection of circRNAs in crop response to abiotic stress is still lacking. In this work, we applied a big-data approach to take full advantage of large-scale sequencing data, and developed a rich circRNA resource: CropCircDB for maize and rice, later extending to incorporate more crop species. We also designed a metric: stress detections score, which is specifically for detecting circRNAs under stress condition. In summary, we systematically investigated 244 and 288 RNA-Seq samples for maize and rice, respectively, and found 38 785 circRNAs in maize, and 63 048 circRNAs in rice. This resource not only supports user-friendly JBrowser to visualize genome easily, but also provides elegant view of circRNA structures and dynamic profiles of circRNA expression in all samples. Together, this database will host all predicted and validated crop circRNAs response to abiotic stress.

Survey of knowledge, attitude, and practice regarding reproductive health among urban men in China: a descriptive study.

There has been little focus on men's reproductive health (RH) in China. This descriptive study conducted in Yiling District, Yichang, China, surveyed male knowledge of sexual physiology and RH to assess levels of knowledge, attitudes and practices (KAPs) regarding prevention of sexually transmitted diseases (STDs). A total of 3933 men, aged 18-59 years (mean, 40.3 years), were recruited by cluster random sampling. They completed a questionnaire in the presence of an interviewer, with items related to subject characteristics, RH knowledge, and subjective symptoms of the reproductive system. Physical examination and reproductive system disease diagnosis were performed. Participants' occupations were predominantly skilled labor (80.5%). Nearly four-fifths (78.5%) respondents had at least one reproductive disease. Over half of respondents were aware of and declared a positive attitude about sexual physiology and safe sex, and 70% of them selected to visit a doctor when they had a reproductive disorder. However, only 41.9% believed human immunodeficiency virus/acquired immunodeficiency syndrome could be transmitted through breastfeeding, and 64.6% incorrectly thought they could avoid contracting STDs by cleaning their genitals after intercourse. In addition, 45% discriminated against and were unwilling to be friends with infected persons. Nearly 45% of those with a reproductive system disorder refused to discuss it with friends or family members. These results indicate that this cohort of Chinese men had a certain degree of KAP about RH, whereas some aspects require further public health education in the general population. It is necessary to disseminate accurate knowledge of STD risk in China based on sociodemographic characteristics.