Spatial transcriptomics herald a new era of transcriptome research

Tea tree oil inhibits hydrogen sulfide-induced oxidative damage in chicken lungs through CYP450s/ROS pathway

A large amount of hydrogen sulfide (H2S) is produced in the process of chicken breeding, which can cause serious inflammation and oxidative damage to the respiratory system of chickens. Tea tree oil (TTO) has antioxidant and anti-inflammatory properties. No studies have been reported on the use of TTO in H2S-induced lung injury in chickens. Therefore, in this study, 240 one-day-old Roman pink laying hens were randomly and equally divided into 3 groups: control group (CON), H2S exposure group (AVG, containing H2S), and TTO treatment group (TTG, containing H2S and 0.02 mL/L TTO) to establish an experimental model of TTO treatment with H2S exposure for a period of 42 d. Hematoxylin and eosin (H&E) staining was used to detect lung histopathology. Gene expression profiles were analyzed using transcriptomics. The underlying mechanism of the amelioration of lung injury by TTO was further revealed by antioxidant enzyme assays and qRT-PCR. The results showed that H2S exposure induced significant gene expression of CYP450s (CYP1B1 and CYP1C1) (P < 0.05), and caused intense oxidative stress, apoptosis and inflammation compared with CON. TTO could reduce ROS production and enhance antioxidant capacity (SOD, CAT, T-AOC, and GSH-PX) by regulating the CYP450s/ROS pathway (P < 0.05). Compared with the control group, the treatment group showed significantly decreased expression of apoptotic (Caspase-8, Caspase-3, Bid and Fas) (P < 0.05) and inflammatory (IL-4, IL-16, NF-κB, TNF-α and IFN-γ) (P < 0.05) factors in the lung. This study revealed that TTO regulated CYP450s/ROS pathway to alleviate H2S-induced lung injury in chickens. These results enrich the theory of the action mechanism of TTO on H2S-exposed chicken lungs and are of great value for the treatment of H2S-exposed animals.

Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine

Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.

Integrating spatial transcriptomics and bulk RNA-seq: predicting gene expression with enhanced resolution through graph attention networks

Spatial transcriptomics data play a crucial role in cancer research, providing a nuanced understanding of the spatial organization of gene expression within tumor tissues. Unraveling the spatial dynamics of gene expression can unveil key insights into tumor heterogeneity and aid in identifying potential therapeutic targets. However, in many large-scale cancer studies, spatial transcriptomics data are limited, with bulk RNA-seq and corresponding Whole Slide Image (WSI) data being more common (e.g. TCGA project). To address this gap, there is a critical need to develop methodologies that can estimate gene expression at near-cell (spot) level resolution from existing WSI and bulk RNA-seq data. This approach is essential for reanalyzing expansive cohort studies and uncovering novel biomarkers that have been overlooked in the initial assessments. In this study, we present STGAT (Spatial Transcriptomics Graph Attention Network), a novel approach leveraging Graph Attention Networks (GAT) to discern spatial dependencies among spots. Trained on spatial transcriptomics data, STGAT is designed to estimate gene expression profiles at spot-level resolution and predict whether each spot represents tumor or non-tumor tissue, especially in patient samples where only WSI and bulk RNA-seq data are available. Comprehensive tests on two breast cancer spatial transcriptomics datasets demonstrated that STGAT outperformed existing methods in accurately predicting gene expression. Further analyses using the TCGA breast cancer dataset revealed that gene expression estimated from tumor-only spots (predicted by STGAT) provides more accurate molecular signatures for breast cancer sub-type and tumor stage prediction, and also leading to improved patient survival and disease-free analysis. Availability: Code is available at https://github.com/compbiolabucf/STGAT.

Molecular features of luminal breast cancer defined through spatial and single-cell transcriptomics

BACKGROUND

Intratumour heterogeneity is a hallmark of most solid tumours, including breast cancers. We applied spatial transcriptomics and single-cell RNA-sequencing on patient-derived xenografts (PDXs) to profile spatially resolved cell populations within oestrogen receptor-positive (ER+ ) breast cancer and to elucidate their importance in oestrogen-dependent tumour growth.

METHODS

Two PDXs of 'ER-high' breast cancers with opposite oestrogen-mediated growth responses were investigated: oestrogen-suppressed GS3 (80-100% ER) and oestrogen-dependent SC31 (40-90% ER) models. The observation was validated via single-cell analyses on an 'ER-low' PDX, GS1 (5% ER). The results from our spatial and single-cell analyses were further supported by a public ER+ breast cancer single-cell dataset and protein-based dual immunohistochemistry (IHC) of SC31 examining important luminal cancer markers (i.e., ER, progesterone receptor and Ki67). The translational implication of our findings was assessed by clinical outcome analyses on publicly available cohorts.

RESULTS

Our space-gene-function study revealed four spatially distinct compartments within ER+ breast cancers. These compartments showed functional diversity (oestrogen-responsive, proliferative, hypoxia-induced and inflammation-related). The 'proliferative' population, rather than the 'oestrogen-responsive' compartment, was crucial for oestrogen-dependent tumour growth, leading to the acquisition of luminal B-like features. The cells expressing typical oestrogen-responsive genes like PGR were not directly linked to oestrogen-dependent proliferation. Dual IHC analyses demonstrated the distinct contribution of the Ki67+ proliferative cells toward oestrogen-mediated growth and their response to a CDK4/6 inhibitor. The gene signatures derived from the proliferative, hypoxia-induced and inflammation-related compartments were significantly correlated with worse clinical outcomes, while patients with the oestrogen-responsive signature showed better prognoses, suggesting that this compartment would not be directly associated with oestrogen-dependent tumour progression.

CONCLUSIONS

Our study identified the gene signature in our 'proliferative' compartment as an important determinant of luminal cancer subtypes. This 'proliferative' cell population is a causative feature of luminal B breast cancer, contributing toward its aggressive behaviours.

Exploring myometrial microenvironment changes at the single-cell level from nonpregnant to term pregnant states

The microenvironment and cell populations within the myometrium play crucial roles in maintaining uterine structural integrity and protecting the fetus during pregnancy. However, the specific changes occurring at the single-cell level in the human myometrium between nonpregnant (NP) and term pregnant (TP) states remain unexplored. In this study, we used single-cell RNA sequencing (scRNA-Seq) and spatial transcriptomics (ST) to construct a transcriptomic atlas of individual cells in the myometrium of NP and TP women. Integrated analysis of scRNA-Seq and ST data revealed spatially distinct transcriptional characteristics and examined cell-to-cell communication patterns based on ligand-receptor interactions. We identified and categorized 87,845 high-quality individual cells into 12 populations from scRNA-Seq data of 12 human myometrium tissues. Our findings demonstrated alterations in the proportions of five subpopulations of smooth muscle cells in TP. Moreover, an increase in monocytic cells, particularly M2 macrophages, was observed in TP myometrium samples, suggesting their involvement in the anti-inflammatory response. This study provides unprecedented single-cell resolution of the NP and TP myometrium, offering new insights into myometrial remodeling during pregnancy.NEW & NOTEWORTHY Using single-cell RNA sequencing and spatial transcriptomics, the myometrium was examined at the single-cell level during pregnancy. We identified spatially distinct cell populations and observed alterations in smooth muscle cells and increased M2 macrophages in term pregnant women. These findings offer unprecedented insights into myometrial remodeling and the anti-inflammatory response during pregnancy. The study advances our understanding of pregnancy-related myometrial changes.

Crosstalk between alternative splicing and inflammatory bowel disease: Basic mechanisms, biotechnological progresses and future perspectives

BACKGROUND

Alternative splicing (AS) is an omnipresent regulatory mechanism of gene expression that enables the generation of diverse splice isoforms from a single gene. Recently, AS events have gained considerable momentum in the pathogenesis of inflammatory bowel disease (IBD).

METHODS

Our review has summarized the complex process of RNA splicing, and firstly highlighted the potential involved molecules that target aberrant splicing events in IBD. The quantitative transcriptome analyses such as microarrays, next-generation sequencing (NGS) for AS events in IBD have been also discussed.

RESULTS

Available evidence suggests that some abnormal splicing RNAs can lead to multiple intestinal disorders during the onset of IBD as well as the progression to colitis-associated cancer (CAC), including gut microbiota perturbations, intestinal barrier dysfunctions, innate/adaptive immune dysregulations, pro-fibrosis activation and some other risk factors. Moreover, current data show that the advanced technologies, including microarrays and NGS, have been pioneeringly employed to screen the AS candidates and elucidate the potential regulatory mechanisms of IBD. Besides, other biotechnological progresses such as the applications of third-generation sequencing (TGS), single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST), will be desired with great expectations.

CONCLUSIONS

To our knowledge, the current review is the first one to evaluate the potential regulatory mechanisms of AS events in IBD. The expanding list of aberrantly spliced genes in IBD along with the developed technologies provide us new clues to how IBD develops, and how these important AS events can be explored for future treatment.