Uncovering an Organ's Molecular Architecture at Single-Cell Resolution by Spatially Resolved Transcriptomics

Single-cell sequencing, spatial transcriptome ad periodontitis: Rethink pathogenesis and classification

OBJECTIVE

This narrative review illuminates on the application of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) in periodontitis and highlights the probability of relating cell population and gene signatures to the pathogenesis of the disease for a better diagnosis.

METHODS

An electronic search of the literature in the PubMed database for the keywords, "single cell sequencing" OR "spatial transcriptomics" and "periodontitis" OR "gingiva" OR "oral mucosa" yielded 486 research articles and reviews. After filtering duplicates and careful curation, 22 papers conducted in humans were retained.

RESULTS

The molecular mechanisms underlying periodontitis are complex and involve the interaction of multiple cells and various gene expressions. Most residing cells in periodontal tissues participate in maintaining homeostasis and health, while in addition to infiltrating immune cells contribute to the fight against the bacterial insult.

CONCLUSION

scRNA-seq and ST have provided new insights into the cellular and molecular changes associated with periodontitis for a better diagnosis and clinical outcome. New functions of cells and genes are revealed with these techniques; however, no cells or gene signatures are attributed to periodontitis so far.

Simulating multiple variability in spatially resolved transcriptomics with scCube

A pressing challenge in spatially resolved transcriptomics (SRT) is to benchmark the computational methods. A widely-used approach involves utilizing simulated data. However, biases exist in terms of the currently available simulated SRT data, which seriously affects the accuracy of method evaluation and validation. Herein, we present scCube ( https://github.com/ZJUFanLab/scCube ), a Python package for independent, reproducible, and technology-diverse simulation of SRT data. scCube not only enables the preservation of spatial expression patterns of genes in reference-based simulations, but also generates simulated data with different spatial variability (covering the spatial pattern type, the resolution, the spot arrangement, the targeted gene type, and the tissue slice dimension, etc.) in reference-free simulations. We comprehensively benchmark scCube with existing single-cell or SRT simulators, and demonstrate the utility of scCube in benchmarking spot deconvolution, gene imputation, and resolution enhancement methods in detail through three applications.

Spatial transcriptomics in cancer research and potential clinical impact: a narrative review

Spatial transcriptomics (ST) provides novel insights into the tumor microenvironment (TME). ST allows the quantification and illustration of gene expression profiles in the spatial context of tissues, including both the cancer cells and the microenvironment in which they are found. In cancer research, ST has already provided novel insights into cancer metastasis, prognosis, and immunotherapy responsiveness. The clinical precision oncology application of next-generation sequencing (NGS) and RNA profiling of tumors relies on bulk methods that lack spatial context. The ability to preserve spatial information is now possible, as it allows us to capture tumor heterogeneity and multifocality. In this narrative review, we summarize precision oncology, discuss tumor sequencing in the clinic, and review the available ST research methods, including seqFISH, MERFISH (Vizgen), CosMx SMI (NanoString), Xenium (10x), Visium (10x), Stereo-seq (STOmics), and GeoMx DSP (NanoString). We then review the current ST literature with a focus on solid tumors organized by tumor type. Finally, we conclude by addressing an important question: how will spatial transcriptomics ultimately help patients with cancer?

Spatiotemporal transcriptomic landscape of rice embryonic cells during seed germination

Characterizing cellular features during seed germination is crucial for understanding the complex biological functions of different embryonic cells in regulating seed vigor and seedling establishment. We performed spatially enhanced resolution omics sequencing (Stereo-seq) and single-cell RNA sequencing (scRNA-seq) to capture spatially resolved single-cell transcriptomes of germinating rice embryos. An automated cell-segmentation model, employing deep learning, was developed to accommodate the analysis requirements. The spatial transcriptomes of 6, 24, 36, and 48 h after imbibition unveiled both known and previously unreported embryo cell types, including two unreported scutellum cell types, corroborated by in situ hybridization and functional exploration of marker genes. Temporal transcriptomic profiling delineated gene expression dynamics in distinct embryonic cell types during seed germination, highlighting key genes involved in nutrient metabolism, biosynthesis, and signaling of phytohormones, reprogrammed in a cell-type-specific manner. Our study provides a detailed spatiotemporal transcriptome of rice embryo and presents a previously undescribed methodology for exploring the roles of different embryonic cells in seed germination.

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.

Astrocyte-Microglia Crosstalk: A Novel Target for the Treatment of Migraine

Migraine is a pervasive neurologic disease closely related to neurogenic inflammation. The astrocytes and microglia in the central nervous system are vital in inducing neurogenic inflammation in migraine. Recently, it has been found that there may be a crosstalk phenomenon between microglia and astrocytes, which plays a crucial part in the pathology and treatment of Alzheimer's disease and other central nervous system diseases closely related to inflammation, thus becoming a novel hotspot in neuroimmune research. However, the role of the crosstalk between microglia and astrocytes in the pathogenesis and treatment of migraine is yet to be discussed. Based on the preliminary literature reports, we have reviewed relevant evidence of the crosstalk between microglia and astrocytes in the pathogenesis of migraine and summarized the crosstalk pathways, thereby hoping to provide novel ideas for future research and treatment.

Integrating Single-Cell and Spatial Transcriptomics Reveals Heterogeneity of Early Pig Skin Development and a Subpopulation with Hair Placode Formation

The dermis and epidermis, crucial structural layers of the skin, encompass appendages, hair follicles (HFs), and intricate cellular heterogeneity. However, an integrated spatiotemporal transcriptomic atlas of embryonic skin has not yet been described and would be invaluable for studying skin-related diseases in humans. Here, single-cell and spatial transcriptomic analyses are performed on skin samples of normal and hairless fetal pigs across four developmental periods. The cross-species comparison of skin cells illustrated that the pig epidermis is more representative of the human epidermis than mice epidermis. Moreover, Phenome-wide association study analysis revealed that the conserved genes between pigs and humans are strongly associated with human skin-related diseases. In the epidermis, two lineage differentiation trajectories describe hair follicle (HF) morphogenesis and epidermal development. By comparing normal and hairless fetal pigs, it is found that the hair placode (Pc), the most characteristic initial structure in HFs, arises from progenitor-like OGN+/UCHL1+ cells. These progenitors appear earlier in development than the previously described early Pc cells and exhibit abnormal proliferation and migration during differentiation in hairless pigs. The study provides a valuable resource for in-depth insights into HF development, which may serve as a key reference atlas for studying human skin disease etiology using porcine models.

Bayesian hidden mark interaction model for detecting spatially variable genes in imaging-based spatially resolved transcriptomics data

Recent technology breakthroughs in spatially resolved transcriptomics (SRT) have enabled the comprehensive molecular characterization of cells whilst preserving their spatial and gene expression contexts. One of the fundamental questions in analyzing SRT data is the identification of spatially variable genes whose expressions display spatially correlated patterns. Existing approaches are built upon either the Gaussian process-based model, which relies on ad hoc kernels, or the energy-based Ising model, which requires gene expression to be measured on a lattice grid. To overcome these potential limitations, we developed a generalized energy-based framework to model gene expression measured from imaging-based SRT platforms, accommodating the irregular spatial distribution of measured cells. Our Bayesian model applies a zero-inflated negative binomial mixture model to dichotomize the raw count data, reducing noise. Additionally, we incorporate a geostatistical mark interaction model with a generalized energy function, where the interaction parameter is used to identify the spatial pattern. Auxiliary variable MCMC algorithms were employed to sample from the posterior distribution with an intractable normalizing constant. We demonstrated the strength of our method on both simulated and real data. Our simulation study showed that our method captured various spatial patterns with high accuracy; moreover, analysis of a seqFISH dataset and a STARmap dataset established that our proposed method is able to identify genes with novel and strong spatial patterns.

Spatial transcriptomics reveals segregation of tumor cell states in glioblastoma and marked immunosuppression within the perinecrotic niche

Glioblastoma (GBM) remains an untreatable malignant tumor with poor patient outcomes, characterized by palisading necrosis and microvascular proliferation. While single-cell technology made it possible to characterize different lineage of glioma cells into neural progenitor-like (NPC-like), oligodendrocyte-progenitor-like (OPC-like), astrocyte-like (AC-like) and mesenchymal like (MES-like) states, it does not capture the spatial localization of these tumor cell states. Spatial transcriptomics empowers the study of the spatial organization of different cell types and tumor cell states and allows for the selection of regions of interest to investigate region-specific and cell-type-specific pathways. Here, we obtained paired 10x Chromium single-nuclei RNA-sequencing (snRNA-seq) and 10x Visium spatial transcriptomics data from three GBM patients to interrogate the GBM microenvironment. Integration of the snRNA-seq and spatial transcriptomics data reveals patterns of segregation of tumor cell states. For instance, OPC-like tumor and NPC-like tumor significantly segregate in two of the three samples. Our differentially expressed gene and pathway analyses uncovered significant pathways in functionally relevant niches. Specifically, perinecrotic regions were more immunosuppressive than the endogenous GBM microenvironment, and perivascular regions were more pro-inflammatory. Our gradient analysis suggests that OPC-like tumor cells tend to reside in areas closer to the tumor vasculature compared to tumor necrosis, which may reflect increased oxygen requirements for OPC-like cells. In summary, we characterized the localization of cell types and tumor cell states, the gene expression patterns, and pathways in different niches within the GBM microenvironment. Our results provide further evidence of the segregation of tumor cell states and highlight the immunosuppressive nature of the necrotic and perinecrotic niches in GBM.

The crucial prognostic signaling pathways of pancreatic ductal adenocarcinoma were identified by single-cell and bulk RNA sequencing data

Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with poor prognosis and high mortality. Although a large number of studies have explored its potential prognostic markers using traditional RNA sequencing (RNA-Seq) data, they have not achieved good prediction effect. In order to explore the possible prognostic signaling pathways leading to the difference in prognosis, we identified differentially expressed genes from one scRNA-seq cohort and four GEO cohorts, respectively. Then Cox and Lasso regression analysis showed that 12 genes were independent prognostic factors for PDAC. AUC and calibration curve analysis showed that the prognostic model had good discrimination and calibration. Compared with the low-risk group, the high-risk group had a higher proportion of gene mutations than the low-risk group. Immune infiltration analysis revealed differences in macrophages and monocytes between the two groups. Prognosis related genes were mainly distributed in fibroblasts, macrophages and type 2 ducts. The results of cell communication analysis showed that there was a strong communication between cancer-associated fibroblasts (CAF) and type 2 ductal cells, and collagen formation was the main interaction pathway.

Implementing Whole Genome Sequencing (WGS) in Clinical Practice: Advantages, Challenges, and Future Perspectives

The integration of whole genome sequencing (WGS) into all aspects of modern medicine represents the next step in the evolution of healthcare. Using this technology, scientists and physicians can observe the entire human genome comprehensively, generating a plethora of new sequencing data. Modern computational analysis entails advanced algorithms for variant detection, as well as complex models for classification. Data science and machine learning play a crucial role in the processing and interpretation of results, using enormous databases and statistics to discover new and support current genotype-phenotype correlations. In clinical practice, this technology has greatly enabled the development of personalized medicine, approaching each patient individually and in accordance with their genetic and biochemical profile. The most propulsive areas include rare disease genomics, oncogenomics, pharmacogenomics, neonatal screening, and infectious disease genomics. Another crucial application of WGS lies in the field of multi-omics, working towards the complete integration of human biomolecular data. Further technological development of sequencing technologies has led to the birth of third and fourth-generation sequencing, which include long-read sequencing, single-cell genomics, and nanopore sequencing. These technologies, alongside their continued implementation into medical research and practice, show great promise for the future of the field of medicine.

Spatial transcriptomics and the anatomical pathologist: Molecular meets morphology

In recent years anatomical pathology has been revolutionised by the incorporation of molecular findings into routine diagnostic practice, and in some diseases the presence of specific molecular alterations are now essential for diagnosis. Spatial transcriptomics describes a group of technologies that provide up to transcriptome-wide expression profiling while preserving the spatial origin of the data, with many of these technologies able to provide these data using a single tissue section. Spatial transcriptomics allows expression profiling of highly specific areas within a tissue section potentially to subcellular resolution, and allows correlation of expression data with morphology, tissue type and location relative to other structures. While largely still research laboratory-based, several spatial transcriptomics methods have now achieved compatibility with formalin-fixed paraffin-embedded tissue (FFPE), allowing their use in diagnostic tissue samples, and with further development potentially leading to their incorporation in routine anatomical pathology practice. This mini review provides an overview of spatial transcriptomics methods, with an emphasis on platforms compatible with FFPE tissue, approaches to assess the data and potential applications in anatomical pathology practice.

Spatial transcriptomics reveals prognosis-associated cellular heterogeneity in the papillary thyroid carcinoma microenvironment

BACKGROUND

Papillary thyroid carcinoma (PTC) is the most common malignant endocrine tumour, and its incidence and prevalence are increasing considerably. Cellular heterogeneity in the tumour microenvironment is important for PTC prognosis. Spatial transcriptomics is a powerful technique for cellular heterogeneity study.

METHODS

In conjunction with a clinical pathologist identification method, spatial transcriptomics was employed to characterise the spatial location and RNA profiles of PTC-associated cells within the tissue sections. The spatial RNA-clinical signature genes for each cell type were extracted and applied to outlining the distribution regions of specific cells on the entire section. The cellular heterogeneity of each cell type was further revealed by ContourPlot analysis, monocle analysis, trajectory analysis, ligand-receptor analysis and Gene Ontology enrichment analysis.

RESULTS

The spatial distribution region of tumour cells, typical and atypical follicular cells (FCs and AFCs) and immune cells were accurately and comprehensively identified in all five PTC tissue sections. AFCs were identified as a transitional state between FCs and tumour cells, exhibiting a higher resemblance to the latter. Three tumour foci were shared among all patients out of the 13 observed. Notably, tumour foci No. 2 displayed elevated expression levels of genes associated with lower relapse-free survival in PTC patients. We discovered key ligand-receptor interactions, including LAMB3-ITGA2, FN1-ITGA3 and FN1-SDC4, involved in the transition of PTC cells from FCs to AFCs and eventually to tumour cells. High expression of these patterns correlated with reduced relapse-free survival. In the tumour immune microenvironment, reduced interaction between myeloid-derived TGFB1 and TGFBR1 in tumour focus No. 2 contributed to tumourigenesis and increased heterogeneity. The spatial RNA-clinical analysis method developed here revealed prognosis-associated cellular heterogeneity in the PTC microenvironment.

CONCLUSIONS

The occurrence of tumour foci No. 2 and three enhanced ligand-receptor interactions in the AFC area/tumour foci reduced the relapse-free survival of PTC patients, potentially leading to improved prognostic strategies and targeted therapies for PTC patients.

Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics

Most sequencing-based spatial transcriptomics (ST) technologies do not achieve single-cell resolution where each captured location (spot) may contain a mixture of cells from heterogeneous cell types, and several cell-type decomposition methods have been proposed to estimate cell type proportions of each spot by integrating with single-cell RNA sequencing (scRNA-seq) data. However, these existing methods did not fully consider the effect of distribution difference between scRNA-seq and ST data for decomposition, leading to biased cell-type-specific genes derived from scRNA-seq for ST data. To address this issue, we develop an instance-based transfer learning framework to adjust scRNA-seq data by ST data to correctly match cell-type-specific gene expression. We evaluate the effect of raw and adjusted scRNA-seq data on cell-type decomposition by eight leading decomposition methods using both simulated and real datasets. Experimental results show that data adjustment can effectively reduce distribution difference and improve decomposition, thus enabling for a more precise depiction on spatial organization of cell types. We highlight the importance of data adjustment in integrative analysis of scRNA-seq with ST data and provide guidance for improved cell-type decomposition.

Spatial omics: An innovative frontier in aging research

Disentangling the impact of aging on health and disease has become critical as population aging progresses rapidly. Studying aging at the molecular level is complicated by the diverse aging profiles and dynamics. However, the examination of cellular states within aging tissues in situ is hampered by the lack of high-resolution spatial data. Emerging spatial omics technologies facilitate molecular and spatial analysis of tissues, providing direct access to precise information on various functional regions and serving as a favorable tool for unraveling the heterogeneity of aging. In this review, we summarize the recent advances in spatial omics application in multi-organ aging research, which has enhanced the understanding of aging mechanisms from multiple standpoints. We also discuss the main challenges in spatial omics research to date, the opportunities for further developing the technology, and the potential applications of spatial omics in aging and aging-related diseases.

Mechanisms of multi-omics and network pharmacology to explain traditional chinese medicine for vascular cognitive impairment: A narrative review

BACKGROUND

The term "vascular cognitive impairment" (VCI) describes various cognitive conditions that include vascular elements. It increases the risk of morbidity and mortality in the elderly population and is the most common cognitive impairment associated with cerebrovascular disease. Understanding the etiology of VCI may aid in identifying approaches to target its possible therapy for the condition. Treatment of VCI has focused on vascular risk factors. There are no authorized conventional therapies available right now. The medications used to treat VCI are solely approved for symptomatic relief and are not intended to prevent or slow the development of VCI.

PURPOSE

The function of Chinese medicine in treating VCI has not yet been thoroughly examined. This review evaluates the preclinical and limited clinical evidence to comprehend the "multi-component, multi-target, multi-pathway" mechanism of Traditional Chinese medicine (TCM). It investigates the various multi-omics approaches in the search for the pathological mechanisms of VCI, as well as the new research strategies, in the hopes of supplying supportive evidence for the clinical treatment of VCI.

METHODS

This review used the Preferred Reporting Items for Preferred reporting items for systematic reviews and meta-analyses (PRISMA) statements. Using integrated bioinformatics and network pharmacology approaches, a thorough evaluation and analysis of 25 preclinical studies published up to July 1, 2023, were conducted to shed light on the mechanisms of TCM for vascular cognitive impairment. The studies for the systematic review were located using the following databases: PubMed, Web of Science, Scopus, Cochrane, and ScienceDirect.

RESULTS

We discovered that the multi-omics analysis approach would hasten the discovery of the role of TCM in the treatment of VCI. It will explore components, compounds, targets, and pathways, slowing the progression of VCI from the perspective of inhibiting oxidative stress, stifling neuroinflammation, increasing cerebral blood flow, and inhibiting iron deposition by a variety of molecular mechanisms, which have significant implications for the treatment of VCI.

CONCLUSION

TCM is a valuable tool for developing dementia therapies, and further research is needed to determine how TCM components may affect the operation of the neurovascular unit. There are still some limitations, although several research have offered invaluable resources for searching for possible anti-dementia medicines and treatments. To gain new insights into the molecular mechanisms that precisely modulate the key molecules at different levels during pharmacological interventions-a prerequisite for comprehending the mechanism of action and determining the potential therapeutic value of the drugs-further research should employ more standardized experimental methods as well as more sophisticated science and technology. Given the results of this review, we advocate integrating chemical and biological component analysis approaches in future research on VCI to provide a more full and objective assessment of the standard of TCM. With the help of bioinformatics, a multi-omics analysis approach will hasten the discovery of the role of TCM in the treatment of VCI, which has significant implications for the treatment of VCI.

Bayesian Hidden Mark Interaction Model for Detecting Spatially Variable Genes in Imaging-Based Spatially Resolved Transcriptomics Data

Recent technology breakthroughs in spatially resolved transcriptomics (SRT) have enabled the comprehensive molecular characterization of cells whilst preserving their spatial and gene expression contexts. One of the fundamental questions in analyzing SRT data is the identification of spatially variable genes whose expressions display spatially correlated patterns. Existing approaches are built upon either the Gaussian process-based model, which relies on ad hoc kernels, or the energy-based Ising model, which requires gene expression to be measured on a lattice grid. To overcome these potential limitations, we developed a generalized energy-based framework to model gene expression measured from imaging-based SRT platforms, accommodating the irregular spatial distribution of measured cells. Our Bayesian model applies a zero-inflated negative binomial mixture model to dichotomize the raw count data, reducing noise. Additionally, we incorporate a geostatistical mark interaction model with a generalized energy function, where the interaction parameter is used to identify the spatial pattern. Auxiliary variable MCMC algorithms were employed to sample from the posterior distribution with an intractable normalizing constant. We demonstrated the strength of our method on both simulated and real data. Our simulation study showed that our method captured various spatial patterns with high accuracy; moreover, analysis of a seqFISH dataset and a STARmap dataset established that our proposed method is able to identify genes with novel and strong spatial patterns.

Revealing spatial multimodal heterogeneity in tissues with SpaTrio

Capturing and depicting the multimodal tissue information of tissues at the spatial scale remains a significant challenge owing to technical limitations in single-cell multi-omics and spatial transcriptomics sequencing. Here, we developed a computational method called SpaTrio that can build spatial multi-omics data by integrating these two datasets through probabilistic alignment and enabling further analysis of gene regulation and cellular interactions. We benchmarked SpaTrio using simulation datasets and demonstrated its accuracy and robustness. Next, we evaluated SpaTrio on biological datasets and showed that it could detect topological patterns of cells and modalities. SpaTrio has also been applied to multiple sets of actual data to uncover spatially multimodal heterogeneity, understand the spatiotemporal regulation of gene expression, and resolve multimodal communication among cells. Our data demonstrated that SpaTrio could accurately map single cells and reconstruct the spatial distribution of various biomolecules, providing valuable multimodal insights into spatial biology.

Reconstructing Spatial Transcriptomics at the Single-cell Resolution with BayesDeep

Spatially resolved transcriptomics (SRT) techniques have revolutionized the characterization of molecular profiles while preserving spatial and morphological context. However, most next-generation sequencing-based SRT techniques are limited to measuring gene expression in a confined array of spots, capturing only a fraction of the spatial domain. Typically, these spots encompass gene expression from a few to hundreds of cells, underscoring a critical need for more detailed, single-cell resolution SRT data to enhance our understanding of biological functions within the tissue context. Addressing this challenge, we introduce BayesDeep, a novel Bayesian hierarchical model that leverages cellular morphological data from histology images, commonly paired with SRT data, to reconstruct SRT data at the single-cell resolution. BayesDeep effectively model count data from SRT studies via a negative binomial regression model. This model incorporates explanatory variables such as cell types and nuclei-shape information for each cell extracted from the paired histology image. A feature selection scheme is integrated to examine the association between the morphological and molecular profiles, thereby improving the model robustness. We applied BayesDeep to two real SRT datasets, successfully demonstrating its capability to reconstruct SRT data at the single-cell resolution. This advancement not only yields new biological insights but also significantly enhances various downstream analyses, such as pseudotime and cell-cell communication.

Multiplexed RNA-FISH-guided Laser Capture Microdissection RNA Sequencing Improves Breast Cancer Molecular Subtyping, Prognostic Classification, and Predicts Response to Antibody Drug Conjugates

On a retrospective cohort of 1,082 FFPE breast tumors, we demonstrated the analytical validity of a test using multiplexed RNA-FISH-guided laser capture microdissection (LCM) coupled with RNA-sequencing (mFISHseq), which showed 93% accuracy compared to immunohistochemistry. The combination of these technologies makes strides in i) precisely assessing tumor heterogeneity, ii) obtaining pure tumor samples using LCM to ensure accurate biomarker expression and multigene testing, and iii) providing thorough and granular data from whole transcriptome profiling. We also constructed a 293-gene intrinsic subtype classifier that performed equivalent to the research based PAM50 and AIMS classifiers. By combining three molecular classifiers for consensus subtyping, mFISHseq alleviated single sample discordance, provided near perfect concordance with other classifiers (κ > 0.85), and reclassified 30% of samples into different subtypes with prognostic implications. We also use a consensus approach to combine information from 4 multigene prognostic classifiers and clinical risk to characterize high, low, and ultra-low risk patients that relapse early (< 5 years), late (> 10 years), and rarely, respectively. Lastly, to identify potential patient subpopulations that may be responsive to treatments like antibody drug-conjugates (ADC), we curated a list of 92 genes and 110 gene signatures to interrogate their association with molecular subtype and overall survival. Many genes and gene signatures related to ADC processing (e.g., antigen/payload targets, endocytosis, and lysosome activity) were independent predictors of overall survival in multivariate Cox regression models, thus highlighting potential ADC treatment-responsive subgroups. To test this hypothesis, we constructed a unique 19-feature classifier using multivariate logistic regression with elastic net that predicted response to trastuzumab emtansine (T-DM1; AUC = 0.96) better than either ERBB2 mRNA or Her2 IHC alone in the T-DM1 arm of the I-SPY2 trial. This test was deployed in a research-use only format on 26 patients and revealed clinical insights into patient selection for novel therapies like ADCs and immunotherapies and de-escalation of adjuvant chemotherapy.

Post-Stroke Neuropsychiatric Complications: Types, Pathogenesis, and Therapeutic Intervention

Almost all stroke survivors suffer physical disabilities and neuropsychiatric disturbances, which can be briefly divided into post-stroke neurological diseases and post-stroke psychiatric disorders. The former type mainly includes post-stroke pain, post-stroke epilepsy, and post-stroke dementia while the latter one includes post-stroke depression, post-stroke anxiety, post-stroke apathy and post-stroke fatigue. Multiple risk factors are related to these post-stroke neuropsychiatric complications, such as age, gender, lifestyle, stroke type, medication, lesion location, and comorbidities. Recent studies have revealed several critical mechanisms underlying these complications, namely inflammatory response, dysregulation of the hypothalamic pituitary adrenal axis, cholinergic dysfunction, reduced level of 5-hydroxytryptamine, glutamate-mediated excitotoxicity and mitochondrial dysfunction. Moreover, clinical efforts have successfully given birth to many practical pharmaceutic strategies, such as anti-inflammatory medications, acetylcholinesterase inhibitors, and selective serotonin reuptake inhibitors, as well as diverse rehabilitative modalities to help patients physically and mentally. However, the efficacy of these interventions is still under debate. Further investigations into these post-stroke neuropsychiatric complications, from both basic and clinical perspectives, are urgent for the development of effective treatment strategies.

STGNNks: Identifying cell types in spatial transcriptomics data based on graph neural network, denoising auto-encoder, and k-sums clustering

BACKGROUND

Spatial transcriptomics technologies fully utilize spatial location information, tissue morphological features, and transcriptional profiles. Integrating these data can greatly advance our understanding about cell biology in the morphological background.

METHODS

We developed an innovative spatial clustering method called STGNNks by combining graph neural network, denoising auto-encoder, and k-sums clustering. First, spatial resolved transcriptomics data are preprocessed and a hybrid adjacency matrix is constructed. Next, gene expressions and spatial context are integrated to learn spots' embedding features by a deep graph infomax-based graph convolutional network. Third, the learned features are mapped to a low-dimensional space through a zero-inflated negative binomial (ZINB)-based denoising auto-encoder. Fourth, a k-sums clustering algorithm is developed to identify spatial domains by combining k-means clustering and the ratio-cut clustering algorithms. Finally, it implements spatial trajectory inference, spatially variable gene identification, and differentially expressed gene detection based on the pseudo-space-time method on six 10x Genomics Visium datasets.

RESULTS

We compared our proposed STGNNks method with five other spatial clustering methods, CCST, Seurat, stLearn, Scanpy and SEDR. For the first time, four internal indicators in the area of machine learning, that is, silhouette coefficient, the Davies-Bouldin index, the Caliniski-Harabasz index, and the S_Dbw index, were used to measure the clustering performance of STGNNks with CCST, Seurat, stLearn, Scanpy and SEDR on five spatial transcriptomics datasets without labels (i.e., Adult Mouse Brain (FFPE), Adult Mouse Kidney (FFPE), Human Breast Cancer (Block A Section 2), Human Breast Cancer (FFPE), and Human Lymph Node). And two external indicators including adjusted Rand index (ARI) and normalized mutual information (NMI) were applied to evaluate the performance of the above six methods on Human Breast Cancer (Block A Section 1) with real labels. The comparison experiments elucidated that STGNNks obtained the smallest Davies-Bouldin and S_Dbw values and the largest Silhouette Coefficient, Caliniski-Harabasz, ARI and NMI, significantly outperforming the above five spatial transcriptomics analysis algorithms. Furthermore, we detected the top six spatially variable genes and the top five differentially expressed genes in each cluster on the above five unlabeled datasets. And the pseudo-space-time tree plot with hierarchical layout demonstrated a flow of Human Breast Cancer (Block A Section 1) progress in three clades branching from three invasive ductal carcinoma regions to multiple ductal carcinoma in situ sub-clusters.

CONCLUSION

We anticipate that STGNNks can efficiently improve spatial transcriptomics data analysis and further boost the diagnosis and therapy of related diseases. The codes are publicly available at https://github.com/plhhnu/STGNNks.

High-quality single-cell transcriptomics from ovarian histological sections during folliculogenesis

High-quality, straightforward single-cell RNA sequencing (RNA-seq) with spatial resolution remains challenging. Here, we developed DRaqL (direct RNA recovery and quenching for laser capture microdissection), an experimental approach for efficient cell lysis of tissue sections, directly applicable to cDNA amplification. Single-cell RNA-seq combined with DRaqL allowed transcriptomic profiling from alcohol-fixed sections with efficiency comparable with that of profiling from freshly dissociated cells, together with effective exon-exon junction profiling. The combination of DRaqL with protease treatment enabled robust and efficient single-cell transcriptome analysis from formalin-fixed tissue sections. Applying this method to mouse ovarian sections, we were able to predict the transcriptome of oocytes by their size and identified an anomaly in the size-transcriptome relationship relevant to growth retardation of oocytes, in addition to detecting oocyte-specific splice isoforms. Furthermore, we identified differentially expressed genes in granulosa cells in association with their proximity to the oocytes, suggesting distinct epigenetic regulations and cell-cycle activities governing the germ-soma relationship. Thus, DRaqL is a versatile, efficient approach for high-quality single-cell RNA-seq from tissue sections, thereby revealing histological heterogeneity in folliculogenic transcriptome.

Spatially Resolved Transcriptomics Technology Facilitates Cancer Research

Single cell RNA sequencing (scRNA-seq) provides a great convenience for studying tumor occurrence and development for its ability to study gene expression at the individual cell level. However, patient-derived tumor tissues are composed of multiple types of cells including tumor cells and adjacent non-malignant cells such as stromal cells and immune cells. The spatial locations of various cells in situ tissues plays a pivotal role in the occurrence and development of tumors, which cannot be elucidated by scRNA-seq alone. Spatially resolved transcriptomics (SRT) technology emerges timely to explore the unrecognized relationship between the spatial background of a particular cell and its functions, and is increasingly used in cancer research. This review provides a systematic overview of the SRT technologies that are developed, in particular the more widely used cutting-edge SRT technologies based on next-generation sequencing (NGS). In addition, the main achievements by SRT technologies in precisely unveiling the underappreciated spatial locations on gene expression and cell function with unprecedented high-resolution in cancer research are emphasized, with the aim of developing more effective clinical therapeutics oriented to a deeper understanding of the interaction between tumor cells and surrounding non-malignant cells.

Decoding Human Biology and Disease Using Single-cell Omics Technologies

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

New insight into the role of fibroblasts in the epithelial immune microenvironment in the single-cell era

The skin is exposed to environmental challenges and contains heterogeneous cell populations such as epithelial cells, stromal cells, and skin-resident immune cells. As the most abundant type of stromal cells, fibroblasts have been historically considered silent observers in the immune responses of the cutaneous epithelial immune microenvironment (EIME), with little research conducted on their heterogeneity and immune-related functions. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have overcome the limitations of bulk RNA sequencing and help recognize the functional and spatial heterogeneity of fibroblasts, as well as their crosstalk with other types of cells in the cutaneous EIME. Recently, emerging single-cell sequencing data have demonstrated that fibroblasts notably participate in the immune responses of the EIME and impact the initiation and progression of inflammatory skin diseases. Here, we summarize the latest advances in the role of fibroblasts in the cutaneous EIME of inflammatory skin diseases and discuss the distinct functions and molecular mechanisms of activated fibroblasts in fibrotic skin diseases and non-fibrotic inflammatory skin diseases. This review help unveil the multiple roles of fibroblasts in the cutaneous EIME and offer new promising therapeutic strategies for the management of inflammatory skin diseases by targeting fibroblasts or the fibroblast-centered EIME.

Identifying spatial domains of spatially resolved transcriptomics via multi-view graph convolutional networks

MOTIVATION

Recent advances in spatially resolved transcriptomics (ST) technologies enable the measurement of gene expression profiles while preserving cellular spatial context. Linking gene expression of cells with their spatial distribution is essential for better understanding of tissue microenvironment and biological progress. However, effectively combining gene expression data with spatial information to identify spatial domains remains challenging.

RESULTS

To deal with the above issue, in this paper, we propose a novel unsupervised learning framework named STMGCN for identifying spatial domains using multi-view graph convolution networks (MGCNs). Specifically, to fully exploit spatial information, we first construct multiple neighbor graphs (views) with different similarity measures based on the spatial coordinates. Then, STMGCN learns multiple view-specific embeddings by combining gene expressions with each neighbor graph through graph convolution networks. Finally, to capture the importance of different graphs, we further introduce an attention mechanism to adaptively fuse view-specific embeddings and thus derive the final spot embedding. STMGCN allows for the effective utilization of spatial context to enhance the expressive power of the latent embeddings with multiple graph convolutions. We apply STMGCN on two simulation datasets and five real spatial transcriptomics datasets with different resolutions across distinct platforms. The experimental results demonstrate that STMGCN obtains competitive results in spatial domain identification compared with five state-of-the-art methods, including spatial and non-spatial alternatives. Besides, STMGCN can detect spatially variable genes with enriched expression patterns in the identified domains. Overall, STMGCN is a powerful and efficient computational framework for identifying spatial domains in spatial transcriptomics data.

A Systems Nephrology Approach to Diabetic Kidney Disease Research and Practice

BACKGROUND

Diagnosis and staging of diabetic kidney disease (DKD) via the serial assessment of routine laboratory indices lacks the granularity required to resolve the heterogeneous disease mechanisms driving progression in the individual patient. A systems nephrology approach may help resolve mechanisms underlying this clinically apparent heterogeneity, paving a way for targeted treatment of DKD.

SUMMARY

Given the limited access to kidney tissue in routine clinical care of patients with DKD, data derived from renal tissue in preclinical model systems, including animal and in vitro models, can play a central role in the development of a targeted systems-based approach to DKD. Multi-centre prospective cohort studies, including the Kidney Precision Medicine Project (KPMP) and the European Nephrectomy Biobank (ENBiBA) project, will improve access to human diabetic kidney tissue for research purposes. Integration of diverse data domains from such initiatives including clinical phenotypic data, renal and retinal imaging biomarkers, histopathological and ultrastructural data, and an array of molecular omics (transcriptomics, proteomics, etc.) alongside multi-dimensional data from preclinical modelling offers exciting opportunities to unravel individual-level mechanisms underlying progressive DKD. The application of machine and deep learning approaches may particularly enhance insights derived from imaging and histopathological/ultrastructural data domains.

KEY MESSAGES

Integration of data from multiple model systems (in vitro, animal models, and patients) and from diverse domains (clinical phenotypic, imaging, histopathological/ultrastructural, and molecular omics) offers potential to create a precision medicine approach to DKD care wherein the right treatments are offered to the right patients at the right time.

Laser capture microdissection for biomedical research: towards high-throughput, multi-omics, and single-cell resolution

Spatial omics technologies have become powerful methods to provide valuable insights into cells and tissues within a complex context, significantly enhancing our understanding of the intricate and multifaceted biological system. With an increasing focus on spatial heterogeneity, there is a growing need for unbiased, spatially resolved omics technologies. Laser capture microdissection (LCM) is a cutting-edge method for acquiring spatial information that can quickly collect regions of interest (ROIs) from heterogeneous tissues, with resolutions ranging from single cells to cell populations. Thus, LCM has been widely used for studying the cellular and molecular mechanisms of diseases. This review focuses on the differences among four types of commonly used LCM technologies and their applications in omics and disease research. Key attributes of application cases are also highlighted, such as throughput and spatial resolution. In addition, we comprehensively discuss the existing challenges and the great potential of LCM in biomedical research, disease diagnosis, and targeted therapy from the perspective of high-throughput, multi-omics, and single-cell resolution.

A deep generative framework with embedded vector arithmetic and classifier for sample generation, label transfer, and clustering of single-cell data

Multiple-source single-cell datasets have accumulated quickly and need computational methods to integrate and decompose into meaningful components. Here, we present inClust (integrated clustering), a flexible deep generative framework that enables embedding auxiliary information, latent space vector arithmetic, and clustering. All functional parts are relatively modular, independent in implementation but interrelated at runtime, resulting in an all-in general framework that could work in supervised, semi-supervised, or unsupervised mode. We show that inClust is superior to most data integration methods in benchmark datasets. Then, we demonstrate the capability of inClust in the tasks of conditional out-of-distribution generation in supervised mode, label transfer in semi-supervised mode, and spatial domain identification in unsupervised mode. In these examples, inClust could accurately express the effect of each covariate, distinguish the query-specific cell types, or segment spatial domains. The results support that inClust is an excellent general framework for multiple-task harmonization and data decomposition.

Spatial transcriptomics: recent developments and insights in respiratory research

The respiratory system's complex cellular heterogeneity presents unique challenges to researchers in this field. Although bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) have provided insights into cell types and heterogeneity in the respiratory system, the relevant specific spatial localization and cellular interactions have not been clearly elucidated. Spatial transcriptomics (ST) has filled this gap and has been widely used in respiratory studies. This review focuses on the latest iterative technology of ST in recent years, summarizing how ST can be applied to the physiological and pathological processes of the respiratory system, with emphasis on the lungs. Finally, the current challenges and potential development directions are proposed, including high-throughput full-length transcriptome, integration of multi-omics, temporal and spatial omics, bioinformatics analysis, etc. These viewpoints are expected to advance the study of systematic mechanisms, including respiratory studies.

Chromogenic Visualization of Single RNA Molecules In Situ with Duplex Capability by Rolling Circle Amplification with Alkaline Phosphatase

Visualizing spatial patterns of gene expression by optical microscopy at single-molecule resolution represents a long-standing challenge for imaging and molecular engineering technologies. In this study, we developed a method for visualizing mRNA with duplex capability by optical microscopy using rolling circle amplification with streptavidin-modified alkaline phosphatase (SA-ALP) to provide highly selective and sensitive RNA detection. ALP-based RNA detection provides comparable sensitivity and specificity to fluorescence-based in situ assays and similar performance to the current RNAscope technique for single-molecule RNA detection, but with improved ease of operation. This versatile and relatively user-friendly method of single-molecule RNA visualization can also overcome common problems of background interference. Our findings show that the red spots generated by the Fast Red staining in situ are readily quantified by image analysis, even in samples with heavy melanin deposition, supporting the clinical translation of this assay to improve diagnostic assays for recalcitrant tissues. This system was adaptable for duplex assays with multiple probes and multiple stains, which is ALP with horseradish peroxidase to produce red and brown signals to simultaneously visualize two different RNA targets. The duplex assay can be successfully applied to quantify mRNA expression from two genes in situ within single cells and multiple cell types. With the advantages of high sensitivity and low hardware requirements, this versatile and user-friendly method of RNA visualization may enable low-resource institutions to conduct previously inaccessible diagnostic or research questions about the in situ expression and distribution of RNAs at single-molecule resolution.

SONAR enables cell type deconvolution with spatially weighted Poisson-Gamma model for spatial transcriptomics

Recent advancements in spatial transcriptomic technologies have enabled the measurement of whole transcriptome profiles with preserved spatial context. However, limited by spatial resolution, the measured expressions at each spot are often from a mixture of multiple cells. Computational deconvolution methods designed for spatial transcriptomic data rarely make use of the valuable spatial information as well as the neighboring similarity information. Here, we propose SONAR, a Spatially weighted pOissoN-gAmma Regression model for cell-type deconvolution with spatial transcriptomic data. SONAR directly models the raw counts of spatial transcriptomic data and applies a geographically weighted regression framework that incorporates neighboring information to enhance local estimation of regional cell type composition. In addition, SONAR applies an additional elastic weighting step to adaptively filter dissimilar neighbors, which effectively prevents the introduction of local estimation bias in transition regions with sharp boundaries. We demonstrate the performance of SONAR over other state-of-the-art methods on synthetic data with various spatial patterns. We find that SONAR can accurately map region-specific cell types in real spatial transcriptomic data including mouse brain, human heart and human pancreatic ductal adenocarcinoma. We further show that SONAR can reveal the detailed distributions and fine-grained co-localization of immune cells within the microenvironment at the tumor-normal tissue margin in human liver cancer.

Methods and applications for single-cell and spatial multi-omics

The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome from single cells is transforming our understanding of cell biology in health and disease. In less than a decade, the field has seen tremendous technological revolutions that enable crucial new insights into the interplay between intracellular and intercellular molecular mechanisms that govern development, physiology and pathogenesis. In this Review, we highlight advances in the fast-developing field of single-cell and spatial multi-omics technologies (also known as multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers. We demonstrate their impact on fundamental cell biology and translational research, discuss current challenges and provide an outlook to the future.

Promise of spatially resolved omics for tumor research

Tumors are spatially heterogeneous tissues that comprise numerous cell types with intricate structures. By interacting with the microenvironment, tumor cells undergo dynamic changes in gene expression and metabolism, resulting in spatiotemporal variations in their capacity for proliferation and metastasis. In recent years, the rapid development of histological techniques has enabled efficient and high-throughput biomolecule analysis. By preserving location information while obtaining a large number of gene and molecular data, spatially resolved metabolomics (SRM) and spatially resolved transcriptomics (SRT) approaches can offer new ideas and reliable tools for the in-depth study of tumors. This review provides a comprehensive introduction and summary of the fundamental principles and research methods used for SRM and SRT techniques, as well as a review of their applications in cancer-related fields.

Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

Best practices for single-cell analysis across modalities

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.

The heterocellular heart: identities, interactions, and implications for cardiology

The heterocellular nature of the heart has been receiving increasing attention in recent years. In addition to cardiomyocytes as the prototypical cell type of the heart, non-myocytes such as endothelial cells, fibroblasts, or immune cells are coming more into focus. The rise of single-cell sequencing technologies enables  identification of ever more subtle differences and has reignited the question of what defines a cell's identity. Here we provide an overview of the major cardiac cell types, describe their roles in homeostasis, and outline recent findings on non-canonical functions that may be of relevance for cardiology. We highlight modes of biochemical and biophysical interactions between different cardiac cell types and discuss the potential implications of the heterocellular nature of the heart for basic research and therapeutic interventions.

Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity in health and disease. However, the lack of physical relationships among dissociated cells has limited its applications. To address this issue, we present CeLEry (Cell Location recovEry), a supervised deep learning algorithm that leverages gene expression and spatial location relationships learned from spatial transcriptomics to recover the spatial origins of cells in scRNA-seq. CeLEry has an optional data augmentation procedure via a variational autoencoder, which improves the method's robustness and allows it to overcome noise in scRNA-seq data. We show that CeLEry can infer the spatial origins of cells in scRNA-seq at multiple levels, including 2D location and spatial domain of a cell, while also providing uncertainty estimates for the recovered locations. Our comprehensive benchmarking evaluations on multiple datasets generated from brain and cancer tissues using Visium, MERSCOPE, MERFISH, and Xenium demonstrate that CeLEry can reliably recover the spatial location information for cells using scRNA-seq data.

Dissecting the brain with spatially resolved multi-omics

Recent studies have highlighted spatially resolved multi-omics technologies, including spatial genomics, transcriptomics, proteomics, and metabolomics, as powerful tools to decipher the spatial heterogeneity of the brain. Here, we focus on two major approaches in spatial transcriptomics (next-generation sequencing-based technologies and image-based technologies), and mass spectrometry imaging technologies used in spatial proteomics and spatial metabolomics. Furthermore, we discuss their applications in neuroscience, including building the brain atlas, uncovering gene expression patterns of neurons for special behaviors, deciphering the molecular basis of neuronal communication, and providing a more comprehensive explanation of the molecular mechanisms underlying central nervous system disorders. However, further efforts are still needed toward the integrative application of multi-omics technologies, including the real-time spatial multi-omics analysis in living cells, the detailed gene profile in a whole-brain view, and the combination of functional verification.

Inflammation and aging: signaling pathways and intervention therapies

Aging is characterized by systemic chronic inflammation, which is accompanied by cellular senescence, immunosenescence, organ dysfunction, and age-related diseases. Given the multidimensional complexity of aging, there is an urgent need for a systematic organization of inflammaging through dimensionality reduction. Factors secreted by senescent cells, known as the senescence-associated secretory phenotype (SASP), promote chronic inflammation and can induce senescence in normal cells. At the same time, chronic inflammation accelerates the senescence of immune cells, resulting in weakened immune function and an inability to clear senescent cells and inflammatory factors, which creates a vicious cycle of inflammation and senescence. Persistently elevated inflammation levels in organs such as the bone marrow, liver, and lungs cannot be eliminated in time, leading to organ damage and aging-related diseases. Therefore, inflammation has been recognized as an endogenous factor in aging, and the elimination of inflammation could be a potential strategy for anti-aging. Here we discuss inflammaging at the molecular, cellular, organ, and disease levels, and review current aging models, the implications of cutting-edge single cell technologies, as well as anti-aging strategies. Since preventing and alleviating aging-related diseases and improving the overall quality of life are the ultimate goals of aging research, our review highlights the critical features and potential mechanisms of inflammation and aging, along with the latest developments and future directions in aging research, providing a theoretical foundation for novel and practical anti-aging strategies.

Meningeal immunity and neurological diseases: new approaches, new insights

The meninges, membranes surrounding the central nervous system (CNS) boundary, harbor a diverse array of immunocompetent immune cells, and therefore, serve as an immunologically active site. Meningeal immunity has emerged as a key factor in modulating proper brain function and social behavior, performing constant immune surveillance of the CNS, and participating in several neurological diseases. However, it remains to be determined how meningeal immunity contributes to CNS physiology and pathophysiology. With the advances in single-cell omics, new approaches, such as single-cell technologies, unveiled the details of cellular and molecular mechanisms underlying meningeal immunity in CNS homeostasis and dysfunction. These new findings contradict some previous dogmas and shed new light on new possible therapeutic targets. In this review, we focus on the complicated multi-components, powerful meningeal immunosurveillance capability, and its crucial involvement in physiological and neuropathological conditions, as recently revealed by single-cell technologies.

Network pharmacology approaches for research of Traditional Chinese Medicines

Pharmacodynamics material basis and effective mechanisms are the two main issues to decipher the mechnisms of action of Traditional Chinese medicines (TCMs) for the treatment of diseases. TCMs, in "multi-component, multi-target, multi-pathway" paradigm, show satisfactory clinical results in complex diseases. New ideas and methods are urgently needed to explain the complex interactions between TCMs and diseases. Network pharmacology (NP) provides a novel paradigm to uncover and visualize the underlying interaction networks of TCMs against multifactorial diseases. The development and application of NP has promoted the safety, efficacy, and mechanism investigations of TCMs, which then reinforces the credibility and popularity of TCMs. The current organ-centricity of medicine and the "one disease-one target-one drug" dogma obstruct the understanding of complex diseases and the development of effective drugs. Therefore, more attentions should be paid to shift from "phenotype and symptom" to "endotype and cause" in understanding and redefining current diseases. In the past two decades, with the advent of advanced and intelligent technologies (such as metabolomics, proteomics, transcriptomics, single-cell omics, and artificial intelligence), NP has been improved and deeply implemented, and presented its great value and potential as the next drug-discovery paradigm. NP is developed to cure causal mechanisms instead of treating symptoms. This review briefly summarizes the recent research progress on NP application in TCMs for efficacy research, mechanism elucidation, target prediction, safety evaluation, drug repurposing, and drug design.

Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace

Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose single-cell spatial position associated co-embeddings (scSpace), an integrative method to identify spatially variable cell subpopulations by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We benchmark scSpace with both simulated and biological datasets, and demonstrate that scSpace can accurately and robustly identify spatially variated cell subpopulations. When employed to reconstruct the spatial architectures of complex tissue such as the brain cortex, the small intestinal villus, the liver lobule, the kidney, the embryonic heart, and others, scSpace shows promising performance on revealing the pairwise cellular spatial association within single-cell data. The application of scSpace in melanoma and COVID-19 exhibits a broad prospect in the discovery of spatial therapeutic markers.

Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST

Spatial transcriptomics technologies generate gene expression profiles with spatial context, requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample integration, and cell-type deconvolution. We present GraphST, a graph self-supervised contrastive learning method that fully exploits spatial transcriptomics data to outperform existing methods. It combines graph neural networks with self-supervised contrastive learning to learn informative and discriminative spot representations by minimizing the embedding distance between spatially adjacent spots and vice versa. We demonstrated GraphST on multiple tissue types and technology platforms. GraphST achieved 10% higher clustering accuracy and better delineated fine-grained tissue structures in brain and embryo tissues. GraphST is also the only method that can jointly analyze multiple tissue slices in vertical or horizontal integration while correcting batch effects. Lastly, GraphST demonstrated superior cell-type deconvolution to capture spatial niches like lymph node germinal centers and exhausted tumor infiltrating T cells in breast tumor tissue.

Reconstruction of the tumor spatial microenvironment along the malignant-boundary-nonmalignant axis

Although advances in spatial transcriptomics (ST) enlarge to unveil spatial landscape of tissues, it remains challenging to delineate pathology-relevant and cellular localizations, and interactions exclusive to a spatial niche (e.g., tumor boundary). Here, we develop Cottrazm, integrating ST with hematoxylin and eosin histological image, and single-cell transcriptomics to delineate the tumor boundary connecting malignant and non-malignant cell spots in tumor tissues, deconvolute cell-type composition at spatial location, and reconstruct cell type-specific gene expression profiles at sub-spot level. We validate the performance of Cottrazm along the malignant-boundary-nonmalignant spatial axis. We identify specific macrophage and fibroblast subtypes localized around tumor boundary that interacted with tumor cells to generate a structural boundary, which limits T cell infiltration and promotes immune exclusion in tumor microenvironment. In this work, Cottrazm provides an integrated tool framework to dissect the tumor spatial microenvironment and facilitates the discovery of functional biological insights, thereby identifying therapeutic targets in oncologic ST datasets.

Shoot-root signal circuit: Phytoremediation of heavy metal contaminated soil

High concentrations of heavy metals in the environment will cause serious harm to ecosystems and human health. It is urgent to develop effective methods to control soil heavy metal pollution. Phytoremediation has advantages and potential for soil heavy metal pollution control. However, the current hyperaccumulators have the disadvantages of poor environmental adaptability, single enrichment species and small biomass. Based on the concept of modularity, synthetic biology makes it possible to design a wide range of organisms. In this paper, a comprehensive strategy of "microbial biosensor detection - phytoremediation - heavy metal recovery" for soil heavy metal pollution control was proposed, and the required steps were modified by using synthetic biology methods. This paper summarizes the new experimental methods that promote the discovery of synthetic biological elements and the construction of circuits, and combs the methods of producing transgenic plants to facilitate the transformation of constructed synthetic biological vectors. Finally, the problems that should be paid more attention to in the remediation of soil heavy metal pollution based on synthetic biology were discussed.

The Evolution of Single-Cell RNA Sequencing Technology and Application: Progress and Perspectives

As an emerging sequencing technology, single-cell RNA sequencing (scRNA-Seq) has become a powerful tool for describing cell subpopulation classification and cell heterogeneity by achieving high-throughput and multidimensional analysis of individual cells and circumventing the shortcomings of traditional sequencing for detecting the average transcript level of cell populations. It has been applied to life science and medicine research fields such as tracking dynamic cell differentiation, revealing sensitive effector cells, and key molecular events of diseases. This review focuses on the recent technological innovations in scRNA-Seq, highlighting the latest research results with scRNA-Seq as the core technology in frontier research areas such as embryology, histology, oncology, and immunology. In addition, this review outlines the prospects for its innovative application in traditional Chinese medicine (TCM) research and discusses the key issues currently being addressed by scRNA-Seq and its great potential for exploring disease diagnostic targets and uncovering drug therapeutic targets in combination with multiomics technologies.

Single-cell and spatial transcriptomics: Advances in heart development and disease applications

Current transcriptomics technologies, including bulk RNA-seq, single-cell RNA sequencing (scRNA-seq), single-nucleus RNA-sequencing (snRNA-seq), and spatial transcriptomics (ST), provide novel insights into the spatial and temporal dynamics of gene expression during cardiac development and disease processes. Cardiac development is a highly sophisticated process involving the regulation of numerous key genes and signaling pathways at specific anatomical sites and developmental stages. Exploring the cell biological mechanisms involved in cardiogenesis also contributes to congenital heart disease research. Meanwhile, the severity of distinct heart diseases, such as coronary heart disease, valvular disease, cardiomyopathy, and heart failure, is associated with cellular transcriptional heterogeneity and phenotypic alteration. Integrating transcriptomic technologies in the clinical diagnosis and treatment of heart diseases will aid in advancing precision medicine. In this review, we summarize applications of scRNA-seq and ST in the cardiac field, including organogenesis and clinical diseases, and provide insights into the promise of single-cell and spatial transcriptomics in translational research and precision medicine.