Dissecting mammalian spermatogenesis using spatial transcriptomics

Shikonin mitigates diabetic testicular dysfunction by improving oxidative, apoptotic, and metabolic functions in rats

Type 2 diabetes mellitus (T2DM) is a health concern worldwide, leading to high blood sugar levels and adversely affects various organ systems, including reproductive organs. The present study was conducted by the need to address the fact that T2DM exerts deleterious effects on male fertility via decreasing the circulating testosterone levels and increasing oxidative stress, apoptosis, and metabolic dysregulation in germ cells. The study purposes to explore the protective effect of Shikonin, a bioactive compound, on T2DM-induced reproductive damage. A high-fat diet along with streptozotocin (50 mg/kg) was administered for the induction of T2DM in male albino rats. The five experimental groups included, control, T2DM, T2DM+Shikonin (0.5 mg/kg), Only Shikonin (0.5 mg/kg), and T2DM+Metformin (50 mg/kg) were established for 4 weeks. Notably, Shikonin-treated diabetic rats exhibited significantly (p < 0.0001) increased body weight, Gonadosomatic index (GSI), Testosterone, and antioxidant response reflected in the significant increase (P < 0.05) in superoxide dismutase and catalase levels, while decreasing malondialdehyde. Histological analysis revealed improved testicular health in T2DM rats treated with Shikonin compared to those treated with Metformin. Shikonin treatment led to a further reduction in pro-apoptotic signaling (cytochrome c, caspase 9, and caspase 3) and improved metabolic disturbances by modulating levels of Fibroblast Growth Factor21 (FGF21) and Lactate Dehydrogenase C (LDHC) in T2DM rats. Compared with metformin, Shikonin showed the potential to offer more protective effects on male reproductive health while effectively managing diabetes, hence revealing its dual role. These findings disclose the significant (P < 0.05) potential of Shikonin in protecting reproductive health against T2DM-induced oxidative stress and apoptosis, hence giving a promising avenue for the therapeutic management of diabetes-induced reproductive complications. The study emphasizes the necessity for future human trials to assess the long-term effects and dosing of Shikonin as a therapeutic option for managing T2DM and its reproductive complications.

SATINN v2: automated image analysis for mouse testis histology with multi-laboratory data integration†

Analysis of testis histology is fundamental to the study of male fertility, but it is a slow task with a high skill threshold. Here, we describe new neural network models for the automated classification of cell types and tubule stages from whole-slide brightfield images of mouse testis. The cell type classifier recognizes 14 cell types, including multiple steps of meiosis I prophase, with an external validation accuracy of 96%. The tubule stage classifier distinguishes all 12 canonical tubule stages with external validation accuracy of 63%, which increases to 96% when allowing for ±1 stage tolerance. We addressed generalizability of SATINN, through extensive training diversification and testing on external (non-training population) wildtype and mutant datasets. This allowed us to use SATINN to successfully process data generated in multiple laboratories. We used SATINN to analyze testis images from eight different mutant lines, generated from three different labs with a range of tissue processing protocols. Finally, we show that it is possible to use SATINN output to cluster histology images in latent space, which, when applied to the eight mutant lines, reveals known relationships in their pathology. This work represents significant progress towards a tool for robust, automated testis histopathology that can be used by multiple labs.

Comparison of reproductive performance and functional analysis of spermatogenesis factors between domestic yak and semi-wild blood yak

This study investigates differences in reproductive performance, testicular histology, and transcriptomic profiles between male Subei (SB; semi-wild) yaks and two domestic yaks, Gannan (GN) and Qinghai (QH). Key metrics including mating age, utilization time, breeding capacity, morphometric traits, and testicular indices were analyzed. SB yaks exhibited superior reproductive metrics, including earlier sexual maturity, prolonged utilization periods, and enhanced breeding capacity compared to GN and QH (P < 0.05). Morphologically, SB yaks demonstrated significantly greater body weight, and testicular dimensions. Compared with GN and QH yaks, the seminiferous tubules of SB yaks exhibited significantly larger spermatogenic cells and luminal cavities, along with a notably higher sperm density within the luminal cavity. Transcriptomic analysis identified 2,403 and 4,428 differentially expressed genes (DEGs) in GN vs. SB and QH vs. SB comparisons, respectively. Eight key genes (TPPP3, SMAD3, PAFAH1B3, BMP7, ARSA, CTNNB1, SMAD4, STAT3) and three pathways (Hippo, pluripotency regulation, TGF-β) were implicated in testicular development and spermatogenesis. These findings underscore the genetic and physiological advantages of SB yaks, offering insights for enhancing male yak reproductive performance.

Applications of spatial transcriptomics in studying spermatogenesis

Spermatogenesis is a complex differentiation process that is facilitated by a series of cellular and molecular events. High-throughput genomics approaches, such as single-cell RNA sequencing, have begun to enable the systematic characterization of these events. However, the loss of tissue context because of tissue disassociations in the single-cell isolation protocols limits our ability to understand the regulation of spermatogenesis and how defects in spermatogenesis lead to infertility. The recent advancement of spatial transcriptomics technologies enables the studying of the molecular signatures of various cell types and their interactions in the native tissue context. In this review, we discuss how spatial transcriptomics has been leveraged to identify spatially variable genes, characterize cellular neighborhood, delineate cell‒cell communications, and detect molecular changes under pathological conditions in the mammalian testis. We believe that spatial transcriptomics, along with other emerging spatially resolved omics assays, can be utilized to further our understanding of the underlying causes of male infertility, and to facilitate the development of new treatment approaches.

Cervical function in pregnancy and disease: new insights from single-cell analysis

The uterine cervix plays an essential role in regulating fertility, maintaining pregnancy, remodeling in preparation for parturition, and protecting the reproductive tract from infection. A compromise in cervical function contributes to adverse clinical outcomes. Understanding molecular events that drive the multifunctional and temporally defined roles of the cervix is necessary to effectively treat infertility, reproductive tract infections, preterm birth, labor dystocia, and cervical cancer. The application of single-cell technologies to study cervical pathophysiology, while in its infancy, underscores the potential of these approaches in developing clinically relevant biomarkers of disease and preventative therapies. This review focuses on insights gained from single-cell transcriptomic studies in human and mouse cervical tissue and highlights outstanding questions in the field. One collective advance from single-cell analysis is the dynamic plasticity of cervical epithelial cells during the reproductive cycle in health and disease. Single-cell comparisons between upper and lower regions of the reproductive tract also highlight the distinct and divergent immunological responses elicited in the cervix during the reproductive lifespan. These findings may reconcile prior controversies in the role of proinflammatory mediators during parturition. In addition to providing obstetric insights, single-cell technologies elucidate the molecular pathways that drive cervical cancer progression. Thus far, these technologies have uncovered cellular heterogeneity in the tumor microenvironment and have identified potential cancer stem cells. While single-cell technology alone will not uncover all the molecular underpinnings contributing to preterm birth or cervical cancer, the insights derived from this valuable technology will accelerate our understanding of cervical biology in health and disease, which ultimately will help develop biomarkers for disease prediction and prevention therapies.

Genetic and genomic insights into male reproductive tract development

Genetic and genomic analysis continues to drive important insights into male reproductive tract (MRT) development. Here, we briefly review normal MRT development, highlighting recent discoveries of cell types and cellular processes delivered by single-cell sequencing. We report a systematic review of phenotype terms and genes linked to MRT development, identifying 35 terms from the Human Phenotype Ontology associated with 269 unique genes. A parallel review of mouse data revealed differences in the phenotype terms available and the number and identity of genes linked to MRT defects, indicating opportunities for harmonization of knowledge. We used a published single-cell atlas of the developing testis to characterize the regulation of MRT genes across cell types and stages of fetal testis development. Single-cell RNA sequencing data support the conclusion that Leydig cells and Sertoli cells are the primary testicular cell types expressing MRT genes. Furthermore, we find post-conception weeks 6, 8, and 16 to be the key points of upregulation of testicular MRT genes. New advances, especially in imaging and spatially resolved molecular measurements, provide exciting prospects for MRT research and diagnosis, and we expect rapid progress in the coming years. Continued investigation in this space is essential to understand the genetic basis of MRT development and how MRT defects are related to medical outcomes in adult life.

Essential Regulation of Spermatogonial Stem Cell Fate Decisions and Male Fertility by APBB1 via Interaction with KAT5 and GDF15 in Humans and Mice

Spermatogonial stem cells (SSCs) are essential for initiating and maintaining normal spermatogenesis, and notably, they have important applications in both reproduction and regenerative medicine. Nevertheless, the molecular mechanisms controlling the fate determinations of human SSCs remain elusive. In this study, we identified a selective expression of APBB1 in dormant human SSCs. We demonstrated for the first time that APBB1 interacted with KAT5, which led to the suppression of GDF15 expression and consequent inhibition of human SSC proliferation. Intriguingly, Apbb1-/- mice assumed the disrupted spermatogenesis and markedly reduced fertility. SSC transplantation assays revealed that Apbb1 silencing enhanced SSC colonization and impeded their differentiation, which resulted in the impaired spermatogenesis. Notably, 4 deleterious APBB1 mutation sites were identified in 2,047 patients with non-obstructive azoospermia (NOA), and patients with the c.1940C>G mutation had a similar testicular phenotype with Apbb1-/- mice. Additionally, we observed lower expression levels of APBB1 in NOA patients with spermatogenic arrest than in obstructive azoospermia patients with normal spermatogenesis. Collectively, our findings highlight an essential role of APBB1/KAT5/GDF15 in governing human SSC fate decisions and maintaining normal spermatogenesis and underscore them as therapeutic targets for treating male infertility.

Unveiling Leydig cell heterogeneity and its role in male infertility: A single-cell transcriptomic study of human testicular tissue

Male infertility and impaired spermatogenesis are significant concerns in reproductive health, often linked to disruptions in the cellular and molecular processes within the testis. The cellular composition and transcriptional dynamics of human testicular tissue are crucial for understanding these issues. Previous studies have largely relied on bulk tissue analysis, which obscures the distinct roles and interactions of specific cell types. Here, through a comprehensive single-cell transcriptomic analysis of human testes across various developmental stages and pathological conditions, we reveal the intricate cellular heterogeneity and the molecular mechanisms underlying testicular function. Our study identifies significant disruptions in the differentiation trajectories of Germ cells in conditions such as Klinefelter syndrome (KS), AZFa deletion, and Sertoli-cell-only syndrome (SCOS). We further uncover key transcription factors and regulatory networks governing Leydig cell function, particularly those related to steroidogenesis and hormonal regulation. These findings highlight the organized yet complex cellular and molecular landscape of the testis and uncover critical pathways altered in male infertility. Collectively, our data suggest that targeted therapeutic strategies could be developed to address specific disruptions in testicular cell populations and their associated regulatory networks.

Computational Approaches in Spatial Transcriptomics for the Study of Mammalian Spermatogenesis

Spermatogenesis is a complex and dynamic cellular differentiation process critical to male fertility. Although the full continuum of gene expression patterns from spermatogonial stem cells (SSCs) to spermatozoa in steady state was characterized using single-cell RNA sequencing technologies, the transcriptional dynamics of spermatogenesis within its native tissue context was largely unexplored. The recent development of spatial transcriptomics (ST) technologies has transformed male fertility research from a single-cell level to a two-dimensional spatial coordinate system and facilitated the study of spermatogenesis in the native environment of both the rodent and human testes. The spatial gene expression information generated by these ST technologies requires new computational approaches to extract novel biological insights. These requirements include, but are not limited to, spatial mapping of testicular cell types, identifying spatially variable genes, and understanding the molecular cross-talk between testicular cell types. Here, we review computational approaches that have been used to dissect mammalian spermatogenesis in the context of ST. We also highlight new computational approaches that can be leveraged to reveal novel insights into male fertility.

Gingival spatial analysis reveals geographic immunological variation in a microbiota-dependent and -independent manner

In mucosal barriers, tissue cells and leukocytes collaborate to form specialized niches that support host-microbiome symbiosis. Understanding the spatial organization of these barriers is crucial for elucidating the mechanisms underlying health and disease. The gingiva, a unique mucosal barrier with significant health implications, exhibits intricate tissue architecture and likely contains specialized immunological regions. Through spatial transcriptomic analysis, this study reveals distinct immunological characteristics between the buccal and palate regions of the murine gingiva, impacting natural alveolar bone loss. The microbiota primarily affects gingival immunity in the buccal region. Additionally, a significant influence of the microbiota on the junctional epithelium facing the oral biofilm offers new insights into neutrophil recruitment. The microbiota also regulates the proliferation and barrier-sealing function of the gingival epithelium. This underscores the presence of immunological niches in the gingiva, with the microbiota differentially influencing them, highlighting the high complexity of this oral mucosal barrier.

Human-specific epigenomic states in spermatogenesis

Infertility is becoming increasingly common, affecting one in six people globally. Half of these cases can be attributed to male factors, many driven by abnormalities in the process of sperm development. Emerging evidence from genome-wide association studies, genetic screening of patient cohorts, and animal models highlights an important genetic contribution to spermatogenic defects, but comprehensive identification and characterization of the genes critical for male fertility remain lacking. High divergence of gene regulation in spermatogenic cells across species poses challenges for delineating the genetic pathways required for human spermatogenesis using common model organisms. In this study, we leveraged post-translational histone modification and gene transcription data for 15,491 genes in four mammalian species (human, rhesus macaque, mouse, and opossum), to identify human-specific patterns of gene regulation during spermatogenesis. We combined H3K27me3 ChIP-seq, H3K4me3 ChIP-seq, and RNA-seq data to define epigenetic states for each gene at two stages of spermatogenesis, pachytene spermatocytes and round spermatids, in each species. We identified 239 genes that are uniquely active, poised, or dynamically regulated in human spermatogenic cells distinct from the other three species. While some of these genes have been implicated in reproductive functions, many more have not yet been associated with human infertility and may be candidates for further molecular and epidemiologic studies. Our analysis offers an example of the opportunities provided by evolutionary and epigenomic data for broadly screening candidate genes implicated in reproduction, which might lead to discoveries of novel genetic targets for diagnosis and management of male infertility and male contraception.

Spatial pattern and differential expression analysis with spatial transcriptomic data

The emergence of spatial transcriptomic technologies has opened new avenues for investigating gene activities while preserving the spatial context of tissues. Utilizing data generated by such technologies, the identification of spatially variable (SV) genes is an essential step in exploring tissue landscapes and biological processes. Particularly in typical experimental designs, such as case-control or longitudinal studies, identifying SV genes between groups is crucial for discovering significant biomarkers or developing targeted therapies for diseases. However, current methods available for analyzing spatial transcriptomic data are still in their infancy, and none of the existing methods are capable of identifying SV genes between groups. To overcome this challenge, we developed SPADE for spatial pattern and differential expression analysis to identify SV genes in spatial transcriptomic data. SPADE is based on a machine learning model of Gaussian process regression with a gene-specific Gaussian kernel, enabling the detection of SV genes both within and between groups. Through benchmarking against existing methods in extensive simulations and real data analyses, we demonstrated the preferred performance of SPADE in detecting SV genes within and between groups. The SPADE source code and documentation are publicly available at https://github.com/thecailab/SPADE.

HistoSPACE: Histology-inspired spatial transcriptome prediction and characterization engine

Spatial transcriptomics (ST) enables the visualization of gene expression within the context of tissue morphology. This emerging discipline has the potential to serve as a foundation for developing tools to design precision medicines. However, due to the higher costs and expertise required for such experiments, its translation into a regular clinical practice might be challenging. Despite implementing modern deep learning to enhance information obtained from histological images using AI, efforts have been constrained by limitations in the diversity of information. In this paper, we developed a model, HistoSPACE, that explores the diversity of histological images available with ST data to extract molecular insights from tissue images. Further, our approach allows us to link the predicted expression with disease pathology. Our proposed study built an image encoder derived from a universal image autoencoder. This image encoder was connected to convolution blocks to build the final model. It was further fine-tuned with the help of ST-Data. The number of model parameters is small and requires lesser system memory and relatively lesser training time. Making it lightweight in comparison to traditional histological models. Our developed model demonstrates significant efficiency compared to contemporary algorithms, revealing a correlation of 0.56 in leave-one-out cross-validation. Finally, its robustness was validated through an independent dataset, showing similar prediction with predefined disease pathology. Our code is available at https://github.com/samrat-lab/HistoSPACE.

Spatial Transcriptomics Reveals the Temporal Architecture of the Seminiferous Epithelial Cycle and Precise Sertoli-Germ Synchronization

Spermatogenesis is characterized by the seminiferous epithelial cycle, a periodic pattern of germ cell differentiation with a wave-like progression along the length of seminiferous tubules. While key signaling and metabolic components of the cycle are known, the transcriptional changes across the cycle and the correlations between germ cell and somatic lineages remain undefined. Here, we use spatial transcriptomics via RNA SeqFISH+ to profile 2,638 genes in 216,090 cells in mouse testis and identify a periodic transcriptional pattern across tubules that precisely recapitulates the seminiferous epithelial cycle, enabling us to map cells to specific timepoints along the developmental cycle. Analyzing gene expression in somatic cells reveals that Sertoli cells exhibit a cyclic transcriptional profile closely synchronized with germ cell development while other somatic cells do not demonstrate such synchronization. Remarkably, in mouse testis with drug-induced ablation of germ cells, Sertoli cells independently maintain their cyclic transcriptional dynamics. By analyzing expression data, we identify an innate retinoic acid cycle, a network of transcription factors with cyclic activation, and signaling from germ cells that could interact with this network. Together, this work leverages spatial geometries for mapping the temporal dynamics and reveals a regulatory mechanism in spermatogenesis where Sertoli cells oscillate and coordinate with the cyclical progression of germ cell development.

Perspective on using non-human primates in Exposome research

The physiological and pathological changes in the human body caused by environmental pressures are collectively referred to as the Exposome. Human society is facing escalating environmental pollution, leading to a rising prevalence of associated diseases, including respiratory diseases, cardiovascular diseases, neurological disorders, reproductive development disorders, among others. Vulnerable populations to the pathogenic effects of environmental pollution include those in the prenatal, infancy, and elderly stages of life. Conducting Exposome mechanistic research and proposing effective health interventions are urgent in addressing the current severe environmental pollution. In this review, we address the core issues and bottlenecks faced by current Exposome research, specifically focusing on the most toxic ultrafine nanoparticles. We summarize multiple research models being used in Exposome research. Especially, we discuss the limitations of rodent animal models in mimicking human physiopathological phenotypes, and prospect advantages and necessity of non-human primates in Exposome research based on their evolutionary relatedness, anatomical and physiological similarities to human. Finally, we declare the initiation of NHPE (Non-Human Primate Exposome) project for conducting Exposome research using non-human primates and provide insights into its feasibility and key areas of focus. SYNOPSIS: Non-human primate models hold unique advantages in human Exposome research.

Single-cell spatial transcriptomics in cardiovascular development, disease, and medicine

Cardiovascular diseases (CVDs) impose a significant burden worldwide. Despite the elucidation of the etiology and underlying molecular mechanisms of CVDs by numerous studies and recent discovery of effective drugs, their morbidity, disability, and mortality are still high. Therefore, precise risk stratification and effective targeted therapies for CVDs are warranted. Recent improvements in single-cell RNA sequencing and spatial transcriptomics have improved our understanding of the mechanisms and cells involved in cardiovascular phylogeny and CVDs. Single-cell RNA sequencing can facilitate the study of the human heart at remarkably high resolution and cellular and molecular heterogeneity. However, this technique does not provide spatial information, which is essential for understanding homeostasis and disease. Spatial transcriptomics can elucidate intracellular interactions, transcription factor distribution, cell spatial localization, and molecular profiles of mRNA and identify cell populations causing the disease and their underlying mechanisms, including cell crosstalk. Herein, we introduce the main methods of RNA-seq and spatial transcriptomics analysis and highlight the latest advances in cardiovascular research. We conclude that single-cell RNA sequencing interprets disease progression in multiple dimensions, levels, perspectives, and dynamics by combining spatial and temporal characterization of the clinical phenome with multidisciplinary techniques such as spatial transcriptomics. This aligns with the dynamic evolution of CVDs (e.g., "angina-myocardial infarction-heart failure" in coronary artery disease). The study of pathways for disease onset and mechanisms (e.g., age, sex, comorbidities) in different patient subgroups should improve disease diagnosis and risk stratification. This can facilitate precise individualized treatment of CVDs.

Seasonal modulation of the testis transcriptome reveals insights into hibernation and reproductive adaptation in Onychostoma macrolepis

The Onychostoma macrolepis have a unique survival strategy, overwintering in caves and returning to the river for reproduction in summer. The current knowledge on the developmental status of its testes during winter and summer is still undiscovered. We performed RNA-seq analysis on O. macrolepis testes between January and June, using the published genome (NCBI, ASM1243209v1). Through KEGG and GO enrichment analysis, we were able to identify 2111 differentially expressed genes (DEGs) and demonstrate their functions in signaling networks associated with the development of organism. At the genomic level, we found that during the overwintering phase, genes associated with cell proliferation (ccnb1, spag5, hdac7) were downregulated while genes linked to testicular fat metabolism (slc27a2, scd, pltp) were upregulated. This indicates suppression of both mitosis and meiosis, thereby inhibiting energy expenditure through genetic regulation of testicular degeneration. Furthermore, in January, we observed the regulation of autophagy and apoptosis (becn1, casp13), which may have the function of protecting reproductive organs and ensuring their maturity for the breeding season. The results provide a basis for the development of specialized feed formulations to regulate the expression of specific genes, or editing of genes during the fish egg stage, to ensure that the testes of O. macrolepis can mature more efficiently after overwintering, thereby enhancing reproductive performance.

Graph domain adaptation-based framework for gene expression enhancement and cell type identification in large-scale spatially resolved transcriptomics

Spatially resolved transcriptomics (SRT) technologies facilitate gene expression profiling with spatial resolution in a naïve state. Nevertheless, current SRT technologies exhibit limitations, manifesting as either low transcript detection sensitivity or restricted gene throughput. These constraints result in diminished precision and coverage in gene measurement. In response, we introduce SpaGDA, a sophisticated deep learning-based graph domain adaptation framework for both scenarios of gene expression imputation and cell type identification in spatially resolved transcriptomics data by impartially transferring knowledge from reference scRNA-seq data. Systematic benchmarking analyses across several SRT datasets generated from different technologies have demonstrated SpaGDA's superior effectiveness compared to state-of-the-art methods in both scenarios. Further applied to three SRT datasets of different biological contexts, SpaGDA not only better recovers the well-established knowledge sourced from public atlases and existing scientific literature but also yields a more informative spatial expression pattern of genes. Together, these results demonstrate that SpaGDA can be used to overcome the challenges of current SRT data and provide more accurate insights into biological processes or disease development. The SpaGDA is available in https://github.com/shenrb/SpaGDA.

Spatiotemporal omics for biology and medicine

The completion of the Human Genome Project has provided a foundational blueprint for understanding human life. Nonetheless, understanding the intricate mechanisms through which our genetic blueprint is involved in disease or orchestrates development across temporal and spatial dimensions remains a profound scientific challenge. Recent breakthroughs in cellular omics technologies have paved new pathways for understanding the regulation of genomic elements and the relationship between gene expression, cellular functions, and cell fate determination. The advent of spatial omics technologies, encompassing both imaging and sequencing-based methodologies, has enabled a comprehensive understanding of biological processes from a cellular ecosystem perspective. This review offers an updated overview of how spatial omics has advanced our understanding of the translation of genetic information into cellular heterogeneity and tissue structural organization and their dynamic changes over time. It emphasizes the discovery of various biological phenomena, related to organ functionality, embryogenesis, species evolution, and the pathogenesis of diseases.

STdGCN: spatial transcriptomic cell-type deconvolution using graph convolutional networks

Spatially resolved transcriptomics integrates high-throughput transcriptome measurements with preserved spatial cellular organization information. However, many technologies cannot reach single-cell resolution. We present STdGCN, a graph model leveraging single-cell RNA sequencing (scRNA-seq) as reference for cell-type deconvolution in spatial transcriptomic (ST) data. STdGCN incorporates expression profiles from scRNA-seq and spatial localization from ST data for deconvolution. Extensive benchmarking on multiple datasets demonstrates that STdGCN outperforms 17 state-of-the-art models. In a human breast cancer Visium dataset, STdGCN delineates stroma, lymphocytes, and cancer cells, aiding tumor microenvironment analysis. In human heart ST data, STdGCN identifies changes in endothelial-cardiomyocyte communications during tissue development.

Single-cell RNA sequencing technology in human spermatogenesis: Progresses and perspectives

Spermatogenesis, a key part of the spermiation process, is regulated by a combination of key cells, such as primordial germ cells, spermatogonial stem cells, and somatic cells, such as Sertoli cells. Abnormal spermatogenesis can lead to azoospermia, testicular tumors, and other diseases related to male infertility. The application of single-cell RNA sequencing (scRNA-seq) technology in male reproduction is gradually increasing with its unique insight into deep mining and analysis. The data cover different periods of neonatal, prepubertal, pubertal, and adult stages. Different types of male infertility diseases including obstructive and non-obstructive azoospermia (NOA), Klinefelter Syndrome (KS), Sertoli Cell Only Syndrome (SCOS), and testicular tumors are also covered. We briefly review the principles and application of scRNA-seq and summarize the research results and application directions in spermatogenesis in different periods and pathological states. Moreover, we discuss the challenges of applying this technology in male reproduction and the prospects of combining it with other technologies.

Novel hypoxia-induced HIF-1αactivation in asthma pathogenesis

BACKGROUND

Asthma's complexity, marked by airway inflammation and remodeling, is influenced by hypoxic conditions. This study focuses on the role of Hypoxia-Inducible Factor-1 Alpha (HIF-1α) and P53 ubiquitination in asthma exacerbation.

METHODS

High-throughput sequencing and bioinformatics were used to identify genes associated with asthma progression, with an emphasis on GO and KEGG pathway analyses. An asthma mouse model was developed, and airway smooth muscle cells (ASMCs) were isolated to create an in vitro hypoxia model. Cell viability, proliferation, migration, and apoptosis were assessed, along with ELISA and Hematoxylin and Eosin (H&E) staining.

RESULTS

A notable increase in HIF-1α was observed in both in vivo and in vitro asthma models. HIF-1α upregulation enhanced ASMCs' viability, proliferation, and migration, while reducing apoptosis, primarily via the promotion of P53 ubiquitination through MDM2. In vivo studies showed increased inflammatory cell infiltration and airway structural changes, which were mitigated by the inhibitor IDF-11,774.

CONCLUSION

The study highlights the critical role of the HIF-1α-MDM2-P53 axis in asthma, suggesting its potential as a target for therapeutic interventions. The findings indicate that modulating this pathway could offer new avenues for treating the complex respiratory disorder of asthma.

Understanding testicular single cell transcriptional atlas: from developmental complications to male infertility

Spermatogenesis is a multi-step biological process where mitotically active diploid (2n) spermatogonia differentiate into haploid (n) spermatozoa via regulated meiotic programming. The alarming rise in male infertility has become a global concern during the past decade thereby demanding an extensive profiling of testicular gene expression. Advancements in Next-Generation Sequencing (NGS) technologies have revolutionized our empathy towards complex biological events including spermatogenesis. However, despite multiple attempts made in the past to reveal the testicular transcriptional signature(s) either with bulk tissues or at the single-cell, level, comprehensive reviews on testicular transcriptomics and associated disorders are limited. Notably, technologies explicating the genome-wide gene expression patterns during various stages of spermatogenic progression provide the dynamic molecular landscape of testicular transcription. Our review discusses the advantages of single-cell RNA-sequencing (Sc-RNA-seq) over bulk RNA-seq concerning testicular tissues. Additionally, we highlight the cellular heterogeneity, spatial transcriptomics, dynamic gene expression and cell-to-cell interactions with distinct cell populations within the testes including germ cells (Gc), Sertoli cells (Sc), Peritubular cells (PTc), Leydig cells (Lc), etc. Furthermore, we provide a summary of key finding of single-cell transcriptomic studies that have shed light on developmental mechanisms implicated in testicular disorders and male infertility. These insights emphasize the pivotal roles of Sc-RNA-seq in advancing our knowledge regarding testicular transcriptional landscape and may serve as a potential resource to formulate future clinical interventions for male reproductive health.

Accurate and efficient integrative reference-informed spatial domain detection for spatial transcriptomics

Spatially resolved transcriptomics (SRT) studies are becoming increasingly common and large, offering unprecedented opportunities in mapping complex tissue structures and functions. Here we present integrative and reference-informed tissue segmentation (IRIS), a computational method designed to characterize tissue spatial organization in SRT studies through accurately and efficiently detecting spatial domains. IRIS uniquely leverages single-cell RNA sequencing data for reference-informed detection of biologically interpretable spatial domains, integrating multiple SRT slices while explicitly considering correlations both within and across slices. We demonstrate the advantages of IRIS through in-depth analysis of six SRT datasets encompassing diverse technologies, tissues, species and resolutions. In these applications, IRIS achieves substantial accuracy gains (39-1,083%) and speed improvements (4.6-666.0) in moderate-sized datasets, while representing the only method applicable for large datasets including Stereo-seq and 10x Xenium. As a result, IRIS reveals intricate brain structures, uncovers tumor microenvironment heterogeneity and detects structural changes in diabetes-affected testis, all with exceptional speed and accuracy.

The Role of Retinoic Acid in Spermatogenesis and Its Application in Male Reproduction

Spermatogenesis in mammalian testes is essential for male fertility, ensuring a continuous supply of mature sperm. The testicular microenvironment finely tunes this process, with retinoic acid, an active metabolite of vitamin A, serving a pivotal role. Retinoic acid is critical for various stages, including the differentiation of spermatogonia, meiosis in spermatogenic cells, and the production of mature spermatozoa. Vitamin A deficiency halts spermatogenesis, leading to the degeneration of numerous germ cells, a condition reversible with retinoic acid supplementation. Although retinoic acid can restore fertility in some males with reproductive disorders, it does not work universally. Furthermore, high doses may adversely affect reproduction. The inconsistent outcomes of retinoid treatments in addressing infertility are linked to the incomplete understanding of the molecular mechanisms through which retinoid signaling governs spermatogenesis. In addition to the treatment of male reproductive disorders, the role of retinoic acid in spermatogenesis also provides new ideas for the development of male non-hormone contraceptives. This paper will explore three facets: the synthesis and breakdown of retinoic acid in the testes, its role in spermatogenesis, and its application in male reproduction. Our discussion aims to provide a comprehensive reference for studying the regulatory effects of retinoic acid signaling on spermatogenesis and offer insights into its use in treating male reproductive issues.

The limitations of testicular organoids: are they truly as promising as we believe?

Organoid systems have revolutionised various facets of biological research by offering a three-dimensional (3D), physiologically relevant in vitro model to study complex organ systems. Over recent years, testicular organoids have been publicised as promising platforms for reproductive studies, disease modelling, drug screening, and fertility preservation. However, the full potential of these systems has yet to be realised due to inherent limitations. This paper offers a comprehensive analysis of the current challenges associated with testicular organoid models. Firstly, we address the inability of current organoid systems to fully replicate the intricate spatial organisation and cellular diversity of the in vivo testis. Secondly, we scrutinise the fidelity of germ cell maturation within the organoids, highlighting incomplete spermatogenesis and epigenetic inconsistencies. Thirdly, we consider the technical challenges faced during organoid culture, including nutrient diffusion limits, lack of vasculature, and the need for specialised growth factors. Finally, we discuss the ethical considerations surrounding the use of organoids for human reproduction research. Addressing these limitations in combination with integrating complementary approaches, will be essential if we are to advance our understanding of testicular biology and develop novel strategies for addressing reproductive health issues in males.

Applications of single-cell RNA sequencing in spermatogenesis and molecular evolution

Spermatogenic cell heterogeneity is determined by the complex process of spermatogenesis differentiation. However, effectively revealing the regulatory mechanisms underlying mammalian spermatogenic cell development and differentiation via traditional methods is difficult. Advances in technology have led to the emergence of many single-cell transcriptome sequencing protocols, which have partially addressed these challenges. In this review, we detail the principles of 10x Genomics technology and summarize the methods for downstream analysis of single-cell transcriptome sequencing data. Furthermore, we explore the role of single-cell transcriptome sequencing in revealing the heterogeneity of testicular ecological niche cells, delineating the establishment and disruption of testicular immune homeostasis during human spermatogenesis, investigating abnormal spermatogenesis in humans, and, ultimately, elucidating the molecular evolution of mammalian spermatogenesis.

Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models

Spatial transcriptomics (ST) assays represent a revolution in how the architecture of tissues is studied by allowing for the exploration of cells in their spatial context. A common element in the analysis is delineating tissue domains or "niches" followed by detecting differentially expressed genes to infer the biological identity of the tissue domains or cell types. However, many studies approach differential expression analysis by using statistical approaches often applied in the analysis of non-spatial scRNA data (e.g., two-sample t-tests, Wilcoxon's rank sum test), hence neglecting the spatial dependency observed in ST data. In this study, we show that applying linear mixed models with spatial correlation structures using spatial random effects effectively accounts for the spatial autocorrelation and reduces inflation of type-I error rate observed in non-spatial based differential expression testing. We also show that spatial linear models with an exponential correlation structure provide a better fit to the ST data as compared to non-spatial models, particularly for spatially resolved technologies that quantify expression at finer scales (i.e., single-cell resolution).

spVC for the detection and interpretation of spatial gene expression variation

Spatially resolved transcriptomics technologies have opened new avenues for understanding gene expression heterogeneity in spatial contexts. However, existing methods for identifying spatially variable genes often focus solely on statistical significance, limiting their ability to capture continuous expression patterns and integrate spot-level covariates. To address these challenges, we introduce spVC, a statistical method based on a generalized Poisson model. spVC seamlessly integrates constant and spatially varying effects of covariates, facilitating comprehensive exploration of gene expression variability and enhancing interpretability. Simulation and real data applications confirm spVC's accuracy in these tasks, highlighting its versatility in spatial transcriptomics analysis.

The human infertility single-cell testis atlas (HISTA): an interactive molecular scRNA-Seq reference of the human testis

BACKGROUND

Single-cell RNA-seq (scRNA-Seq) has been widely adopted to study gene expression of the human testis. Several datasets of scRNA-Seq from human testis have been generated from different groups processed with different informatics pipelines. An integrated atlas of scRNA-Seq expression constructed from multiple donors, developmental ages, and fertility states would be widely useful for the testis research community.

OBJECTIVE

To describe the generation and use of the human infertility single-cell testis atlas (HISTA), an interactive web tool for understanding human spermatogenesis through scRNA-Seq analysis.

METHODS

We obtained scRNA-Seq datasets derived from 12 donors, including healthy adult controls, juveniles, and several infertility cases, and reprocessed these data using methods to remove batch effects. Using Shiny, an open-source environment for data visualization, we created numerous interactive tools for exploring the data, some of which support simple statistical hypothesis testing. We used the resulting HISTA browser and its underlying data to demonstrate HISTA's value for testis researchers.

RESULTS

A primary application of HISTA is to search by a single gene or a set of genes; thus, we present various analyses that quantify and visualize gene expression across the testis cells and pathology. HISTA also contains machine-learning-derived gene modules ("components") that capture the entire transcriptional landscape of the testis tissue. We show how the use of these components can simplify the highly complex data in HISTA and assist with the interpretation of genes with unknown functions. Finally, we demonstrate the diverse ways HISTA can be used for new data analysis, including hypothesis testing.

DISCUSSION AND CONCLUSIONS

HISTA is a research environment that can help scientists organize and understand the high-dimensional transcriptional landscape of the human testis. HISTA has already contributed to published testis research and can be updated as needed with input from the research community or downloaded and modified for individual needs.

Revealing cell-cell communication pathways with their spatially coupled gene programs

Inference of cell-cell communication (CCC) provides valuable information in understanding the mechanisms of many important life processes. With the rise of spatial transcriptomics in recent years, many methods have emerged to predict CCCs using spatial information of cells. However, most existing methods only describe CCCs based on ligand-receptor interactions, but lack the exploration of their upstream/downstream pathways. In this paper, we proposed a new method to infer CCCs, called Intercellular Gene Association Network (IGAN). Specifically, it is for the first time that we can estimate the gene associations/network between two specific single spatially adjacent cells. By using the IGAN method, we can not only infer CCCs in an accurate manner, but also explore the upstream/downstream pathways of ligands/receptors from the network perspective, which are actually exhibited as a new panoramic cell-interaction-pathway graph, and thus provide extensive information for the regulatory mechanisms behind CCCs. In addition, IGAN can measure the CCC activity at single cell/spot resolution, and help to discover the CCC spatial heterogeneity. Interestingly, we found that CCC patterns from IGAN are highly consistent with the spatial microenvironment patterns for each cell type, which further indicated the accuracy of our method. Analyses on several public datasets validated the advantages of IGAN.

Diabetes-induced male infertility: potential mechanisms and treatment options

Male infertility is a physiological phenomenon in which a man is unable to impregnate a fertile woman during a 12-month period of continuous, unprotected sexual intercourse. A growing body of clinical and epidemiological evidence indicates that the increasing incidence of male reproductive problems, especially infertility, shows a very similar trend to the incidence of diabetes within the same age range. In addition, a large number of previous in vivo and in vitro experiments have also suggested that the complex pathophysiological changes caused by diabetes may induce male infertility in multiple aspects, including hypothalamic-pituitary-gonadal axis dysfunction, spermatogenesis and maturation disorders, testicular interstitial cell damage erectile dysfunction. Based on the above related mechanisms, a large number of studies have focused on the potential therapeutic association between diabetes progression and infertility in patients with diabetes and infertility, providing important clues for the treatment of this population. In this paper, we summarized the research results of the effects of diabetes on male reproductive function in recent 5 years, elaborated the potential pathophysiological mechanisms of male infertility induced by diabetes, and reviewed and prospected the therapeutic measures.

MENDER: fast and scalable tissue structure identification in spatial omics data

Tissue structure identification is a crucial task in spatial omics data analysis, for which increasingly complex models, such as Graph Neural Networks and Bayesian networks, are employed. However, whether increased model complexity can effectively lead to improved performance is a notable question in the field. Inspired by the consistent observation of cellular neighborhood structures across various spatial technologies, we propose Multi-range cEll coNtext DEciphereR (MENDER), for tissue structure identification. Applied on datasets of 3 brain regions and a whole-brain atlas, MENDER, with biology-driven design, offers substantial improvements over modern complex models while automatically aligning labels across slices, despite using much less running time than the second-fastest. MENDER's identification power allows the uncovering of previously overlooked spatial domains that exhibit strong associations with brain aging. MENDER's scalability makes it freely appliable on a million-level brain spatial atlas. MENDER's discriminative power enables the differentiation of breast cancer patient subtypes obscured by single-cell analysis.

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.

Spatial transcriptomics deconvolution at single-cell resolution using Redeconve

Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution. Here we present Redeconve, an algorithm to deconvolute spatial transcriptomics data at single-cell resolution, enabling interpretation of spatial transcriptomics data with thousands of nuanced cell states. We benchmark Redeconve with the state-of-the-art algorithms on diverse spatial transcriptomics platforms and datasets and demonstrate the superiority of Redeconve in terms of accuracy, resolution, robustness, and speed. Application to a human pancreatic cancer dataset reveals cancer-clone-specific T cell infiltration, and application to lymph node samples identifies differential cytotoxic T cells between IgA+ and IgG+ spots, providing novel insights into tumor immunology and the regulatory mechanisms underlying antibody class switch.

Cascading effects of hypobaric hypoxia on the testis: insights from a single-cell RNA sequencing analysis

Most mammals tolerate exposure to hypobaric hypoxia poorly as it may affect multiple regulatory mechanisms and inhibit cell proliferation, promote apoptosis, limit tissue vascularization, and disrupt the acid-base equilibrium. Here, we quantified the functional state of germ cell development and demonstrated the interaction between the germ and somatic cells via single-cell RNA sequencing (scRNA-seq). The present study elucidated the regulatory effects of hypobaric hypoxia exposure on germ cell formation and sperm differentiation by applying enrichment analysis to genomic regions. Hypobaric hypoxia downregulates the genes controlling granule secretion and organic matter biosynthesis, upregulates tektin 1 (TEKT1) and kinesin family member 2C (KIF2C), and downregulates 60S ribosomal protein 11 (RPL11) and cilia- and flagella-associated protein 206 (CFAP206). Our research indicated that prosaposin-G protein-coupled receptor 37 (PSAP-GPR37) ligands mediate the damage to supporting cells caused by hypobaric hypoxic exposure. The present work revealed that hypoxia injures peritubular myoid (PTM) cells and spermatocytes in the S phase. It also showed that elongating spermatids promote maturation toward the G2 phase and increase their functional reserve for sperm-egg binding. The results of this study provide a theoretical basis for future investigations on prophylactic and therapeutic approaches toward protecting the reproductive system against the harmful effects of hypobaric hypoxic exposure.

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.

Technical Advances and Applications of Spatial Transcriptomics

Transcriptomics is one of the largest areas of research in biological sciences. Aside from RNA expression levels, the significance of RNA spatial context has also been unveiled in the recent decade, playing a critical role in diverse biological processes, from subcellular kinetic regulation to cell communication, from tissue architecture to tumor microenvironment, and more. To systematically unravel the positional patterns of RNA molecules across subcellular, cellular, and tissue levels, spatial transcriptomics techniques have emerged and rapidly became an irreplaceable tool set. Herein, we review and compare current spatial transcriptomics techniques on their methods, advantages, and limitations, as well as applications across a wide range of biological investigations. This review serves as a comprehensive guide to spatial transcriptomics for researchers interested in adopting this powerful suite of technologies.

Harnessing computational spatial omics to explore the spatial biology intricacies

Spatially resolved transcriptomics (SRT) has unlocked new dimensions in our understanding of intricate tissue architectures. However, this rapidly expanding field produces a wealth of diverse and voluminous data, necessitating the evolution of sophisticated computational strategies to unravel inherent patterns. Two distinct methodologies, gene spatial pattern recognition (GSPR) and tissue spatial pattern recognition (TSPR), have emerged as vital tools in this process. GSPR methodologies are designed to identify and classify genes exhibiting noteworthy spatial patterns, while TSPR strategies aim to understand intercellular interactions and recognize tissue domains with molecular and spatial coherence. In this review, we provide a comprehensive exploration of SRT, highlighting crucial data modalities and resources that are instrumental for the development of methods and biological insights. We address the complexities and challenges posed by the use of heterogeneous data in developing GSPR and TSPR methodologies and propose an optimal workflow for both. We delve into the latest advancements in GSPR and TSPR, examining their interrelationships. Lastly, we peer into the future, envisaging the potential directions and perspectives in this dynamic field.

Male infertility risk and gut microbiota: a Mendelian randomization study

Background

In recent decades, the decline of male sperm quality has become a worldwide phenomenon, with sperm quality of critical importance for the ability to conceive naturally. Recent studies suggest that male fertility function is closely linked to the gut microbiota, however, the cause-and-effect association between the gut microbiota and male infertility risk is currently unclear.

Methods

We performed one two-sample Mendelian randomization (MR) study, which uses summary data on human gut microbiota from the MiBioGen consortium as factors of exposure. FinnGen Consortium R8 data was used to obtain GWAS data for male infertility. To evaluate cause-and-effect associations linking gut microbiota and male infertility risk with multiple Mendelian randomization methods, we included inverse variance weighted (IVW), MR-Egger, and Maximum Likelihood (ML) Ratio. The heterogeneity of instrumental variables was evaluated through Cochran's Q, Rucker's Q, and leave-one-out analysis methods.

Results

We found a positive association between Allisonella, Anaerotruncus, Barnesiella, Intestinibacter, and Lactococcus with male infertility risk according to the MR analysis results. Bacteroides Romboutsia, Ruminococcaceae (NK4A2140group), and Ruminococcaceae (UCG011) play a protective function in male infertility pathogenesis.

Conclusion

It was found that gut microbiota and infertility are causally related in this study. In subsequent studies, there is a need to build a larger and more comprehensive GWAS database on male infertility, which will reveal the underlying mechanisms for gut microbiota and male infertility. There is a need for randomized controlled trials for validating the protective effect of the associated gut microbiota against male infertility risk, and for exploring the associated mechanisms.

Spatial Transcriptomic Technologies

Spatial transcriptomic technologies enable measurement of expression levels of genes systematically throughout tissue space, deepening our understanding of cellular organizations and interactions within tissues as well as illuminating biological insights in neuroscience, developmental biology and a range of diseases, including cancer. A variety of spatial technologies have been developed and/or commercialized, differing in spatial resolution, sensitivity, multiplexing capability, throughput and coverage. In this paper, we review key enabling spatial transcriptomic technologies and their applications as well as the perspective of the techniques and new emerging technologies that are developed to address current limitations of spatial methodologies. In addition, we describe how spatial transcriptomics data can be integrated with other omics modalities, complementing other methods in deciphering cellar interactions and phenotypes within tissues as well as providing novel insight into tissue organization.

An update on the role and potential mechanisms of clock genes regulating spermatogenesis: A systematic review of human and animal experimental studies

Circadian clocks can be traced in nearly all life kingdoms, with the male reproductive system no exception. However, our understanding of the circadian clock in spermatogenesis seems to fall behind other scenarios. The present review aims to summarize the current knowledge about the role and especially the potential mechanisms of clock genes in spermatogenesis regulation. Accumulating studies have revealed rhythmic oscillation in semen parameters and some physiological events of spermatogenesis. Disturbing the clock gene expression by genetic mutations or environmental changes will also notably damage spermatogenesis. On the other hand, the mechanisms of spermatogenetic regulation by clock genes remain largely unclear. Some recent studies, although not revealing the entire mechanisms, indeed attempted to shed light on this issue. Emerging clues hinted that gonadal hormones, retinoic acid signaling, homologous recombination, and the chromatoid body might be involved in the regulation of spermatogenesis by clock genes. Then we highlight the challenges and the promising directions for future studies so as to stimulate attention to this critical field which has not gained adequate concern.

Applications of multi-omics analysis in human diseases

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.

Dissecting the spermatogonial stem cell niche using spatial transcriptomics

Spermatogonial stem cells (SSCs) in the testis support the lifelong production of sperm. SSCs reside within specialized microenvironments called "niches," which are essential for SSC self-renewal and differentiation. However, our understanding of the molecular and cellular interactions between SSCs and niches remains incomplete. Here, we combine spatial transcriptomics, computational analyses, and functional assays to systematically dissect the molecular, cellular, and spatial composition of SSC niches. This allows us to spatially map the ligand-receptor (LR) interaction landscape in both mouse and human testes. Our data demonstrate that pleiotrophin regulates mouse SSC functions through syndecan receptors. We also identify ephrin-A1 as a potential niche factor that influences human SSC functions. Furthermore, we show that the spatial re-distribution of inflammation-related LR interactions underlies diabetes-induced testicular injury. Together, our study demonstrates a systems approach to dissect the complex organization of the stem cell microenvironment in health and disease.

Transcriptional metabolic reprogramming implements meiotic fate decision in mouse testicular germ cells

Nutrient starvation drives yeast meiosis, whereas retinoic acid (RA) is required for mammalian meiosis through its germline target Stra8. Here, by using single-cell transcriptomic analysis of wild-type and Stra8-deficient juvenile mouse germ cells, our data show that the expression of nutrient transporter genes, including Slc7a5, Slc38a2, and Slc2a1, is downregulated in germ cells during meiotic initiation, and this process requires Stra8, which binds to these genes and induces their H3K27 deacetylation. Consequently, Stra8-deficient germ cells sustain glutamine and glucose uptake in response to RA and exhibit hyperactive mTORC1/protein kinase A (PKA) activities. Importantly, expression of Slc38a2, a glutamine importer, is negatively correlated with meiotic genes in the GTEx dataset, and Slc38a2 knockdown downregulates mTORC1/PKA activities and induces meiotic gene expression. Thus, our study indicates that RA via Stra8, a chordate morphogen pathway, induces meiosis partially by generating a conserved nutrient restriction signal in mammalian germ cells by downregulating their nutrient transporter expression.

SMDB: a Spatial Multimodal Data Browser

Understanding the relationship between fine-scale spatial organization and biological function necessitates a tool that effectively combines spatial positions, morphological information, and spatial transcriptomics (ST) data. We introduce the Spatial Multimodal Data Browser (SMDB, https://www.biosino.org/smdb), a robust visualization web service for interactively exploring ST data. By integrating multimodal data, such as hematoxylin and eosin (H&E) images, gene expression-based molecular clusters, and more, SMDB facilitates the analysis of tissue composition through the dissociation of two-dimensional (2D) sections and the identification of gene expression-profiled boundaries. In a digital three-dimensional (3D) space, SMDB allows researchers to reconstruct morphology visualizations based on manually filtered spots or expand anatomical structures using high-resolution molecular subtypes. To enhance user experience, it offers customizable workspaces for interactive exploration of ST spots in tissues, providing features like smooth zooming, panning, 360-degree rotation in 3D and adjustable spot scaling. SMDB is particularly valuable in neuroscience and spatial histology studies, as it incorporates Allen's mouse brain anatomy atlas for reference in morphological research. This powerful tool provides a comprehensive and efficient solution for examining the intricate relationships between spatial morphology, and biological function in various tissues.

Spatial Transcriptomic Cell-type Deconvolution Using Graph Neural Networks

Spatially resolved transcriptomics performs high-throughput measurement of transcriptomes while preserving spatial information about the cellular organizations. However, many spatially resolved transcriptomic technologies can only distinguish spots consisting of a mixture of cells instead of working at single-cell resolution. Here, we present STdGCN, a graph neural network model designed for cell type deconvolution of spatial transcriptomic (ST) data that can leverage abundant single-cell RNA sequencing (scRNA-seq) data as reference. STdGCN is the first model incorporating the expression profiles from single cell data as well as the spatial localization information from the ST data for cell type deconvolution. Extensive benchmarking experiments on multiple ST datasets showed that STdGCN outperformed 14 published state-of-the-art models. Applied to a human breast cancer Visium dataset, STdGCN discerned spatial distributions between stroma, lymphocytes and cancer cells for tumor microenvironment dissection. In a human heart ST dataset, STdGCN detected the changes of potential endothelial-cardiomyocyte communications during tissue development.

The expanding vistas of spatial transcriptomics

The formation and maintenance of tissue integrity requires complex, coordinated activities by thousands of genes and their encoded products. Until recently, transcript levels could only be quantified for a few genes in tissues, but advances in DNA sequencing, oligonucleotide synthesis and fluorescence microscopy have enabled the invention of a suite of spatial transcriptomics technologies capable of measuring the expression of many, or all, genes in situ. These technologies have evolved rapidly in sensitivity, multiplexing and throughput. As such, they have enabled the determination of the cell-type architecture of tissues, the querying of cell-cell interactions and the monitoring of molecular interactions between tissue components. The rapidly evolving spatial genomics landscape will enable generalized high-throughput genomic measurements and perturbations to be performed in the context of tissues. These advances will empower hypothesis generation and biological discovery and bridge the worlds of tissue biology and genomics.

Advances in spatial transcriptomics and related data analysis strategies

Spatial transcriptomics technologies developed in recent years can provide various information including tissue heterogeneity, which is fundamental in biological and medical research, and have been making significant breakthroughs. Single-cell RNA sequencing (scRNA-seq) cannot provide spatial information, while spatial transcriptomics technologies allow gene expression information to be obtained from intact tissue sections in the original physiological context at a spatial resolution. Various biological insights can be generated into tissue architecture and further the elucidation of the interaction between cells and the microenvironment. Thus, we can gain a general understanding of histogenesis processes and disease pathogenesis, etc. Furthermore, in silico methods involving the widely distributed R and Python packages for data analysis play essential roles in deriving indispensable bioinformation and eliminating technological limitations. In this review, we summarize available technologies of spatial transcriptomics, probe into several applications, discuss the computational strategies and raise future perspectives, highlighting the developmental potential.

Application of single-cell RNA sequencing on human testicular samples: a comprehensive review

So far there has been no comprehensive review using systematic literature search strategies to show the application of single-cell RNA sequencing (scRNA-seq) in the human testis of the whole life cycle (from embryos to aging males). Here, we summarized the application of scRNA-seq analyses on various human testicular biological samples. A systematic search was conducted in PubMed and Gene Expression Omnibus (GEO), focusing on English researches published after 2009. Articles related to GEO data-series were also retrieved in PubMed or BioRxiv. 81 full-length studies were finally included in the review. ScRNA-seq has been widely used on different human testicular samples with various library strategies, and new cell subtypes such as State 0 spermatogonial stem cells (SSC) and stage_a/b/c Sertoli cells (SC) were identified. For the development of normal testes, scRNA-seq-based evidence showed dynamic transcriptional changes of both germ cells and somatic cells from embryos to adults. And dysregulated metabolic signaling or hedgehog signaling were revealed by scRNA-seq in aged SC or Leydig cells (LC), respectively. For infertile males, scRNA-seq studies revealed profound changes of testes, such as the increased proportion of immature SC/LC of Klinefelter syndrome, the somatic immaturity and altered germline autophagy of patients with non-obstructive azoospermia, and the repressed differentiation of SSC in trans-females receiving testosterone inhibition therapy. Besides, the re-analyzing of public scRNA-seq data made further discoveries such as the potential vulnerability of testicular SARS-CoV-2 infection, and both evolutionary conservatism and divergence among species. ScRNA-seq analyses would unveil mechanisms of testes' development and changes so as to help developing novel treatments for male infertility.