Unraveling temporal and spatial biomarkers of epithelial-mesenchymal transition in colorectal cancer: insights into the crucial role of immunosuppressive cells

The occurrence and progression of tumors can be established through a complex interplay among tumor cells undergoing epithelial-mesenchymal transition (EMT), invasive factors and immune cells. In this study, we employed single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (ST) to evaluate the pseudotime trajectory and spatial interactive relationship between EMT-invasive malignant tumors and immune cells in primary colorectal cancer (CRC) tissues at different stages (stage I/II and stage III with tumor deposit). Our research characterized the spatiotemporal relationship among different invasive tumor programs by constructing pseudotime endpoint-EMT-invasion tumor programs (EMTPs) located at the edge of ST, utilizing evolution trajectory analysis integrated with EMT-invasion genes. Strikingly, the invasive and expansive process of tumors undergoes remarkable spatial reprogramming of regulatory and immunosuppressive cells, such as myeloid-derived suppressor cells (MDSCs), tumor-associated macrophages (TAMs), regulatory T cells (Treg), and exhausted T cells (Tex). These EMTP-adjacent cell are linked to EMT-related invasion genes, especially the C-X-C motif ligand 1 (CXCL1) and CXCL8 genes that are important for CRC prognosis. Interestingly, the EMTPs in stage I mainly produce an inflammatory margin invasive niche, while the EMTPs in stage III tissues likely produce a hypoxic pre-invasive niche. Our data demonstrate the crucial role of regulatory and immunosuppressive cells in tumor formation and progression of CRC. This study provides a framework to delineate the spatiotemporal invasive niche in CRC samples.

Architecture of skin inflammation in psoriasis revealed by spatial transcriptomics

Psoriasis is a chronic inflammatory skin disease, thought to be predominantly mediated by TH17 cells. Significance of other inflammatory pathways and the innate immune system is not well understood and the spatial heterogeneity of inflammation in the skin has largely been overlooked. Our aim was to create a comprehensive map of skin inflammation in psoriasis, exploring the tissue patterning of inflammation. In situ whole transcriptome sequencing (spatial sequencing) was performed on lesional psoriatic skin in four patients with moderate-to-severe disease to quantify all expressed genes within a tissue section. Transcriptional analysis revealed three major inflammatory niches in psoriasis skin, each with distinct cytokine circuits and chemokines: the hyperplastic epidermis, upper (papillary) dermis, and reticular dermis. Interestingly, key cytokines such as IL-23, IL-17 s, and TNFα were not notably present in the skin's transcriptomic signature. Unexpectedly, IL-32 showed strong expression in the dermis. Our findings underscore the complexity of psoriatic inflammation, highlighting its architectural heterogeneity and the roles of innate cytokines. Both IL-32 and IL-1 family cytokines appear to play critical roles in the dermal and epidermal inflammation, respectively, and may provide pharmacological targets to improve the control of the inflammatory process.

Microglial contribution to the pathology of neurodevelopmental disorders in humans

Microglia are the brain's resident macrophages, which guide various developmental processes crucial for brain maturation, activity, and plasticity. Microglial progenitors enter the telencephalic wall by the 4th postconceptional week and colonise the fetal brain in a manner that spatiotemporally tracks key neurodevelopmental processes in humans. However, much of what we know about how microglia shape neurodevelopment comes from rodent studies. Multiple differences exist between human and rodent microglia warranting further focus on the human condition, particularly as microglia are emerging as critically involved in the pathological signature of various cognitive and neurodevelopmental disorders. In this article, we review the evidence supporting microglial involvement in basic neurodevelopmental processes by focusing on the human species. We next concur on the neuropathological evidence demonstrating whether and how microglia contribute to the aetiology of two neurodevelopmental disorders: autism spectrum conditions and schizophrenia. Next, we highlight how recent technologies have revolutionised our understanding of microglial biology with a focus on how these tools can help us elucidate at unprecedented resolution the links between microglia and neurodevelopmental disorders. We conclude by reviewing which current treatment approaches have shown most promise towards targeting microglia in neurodevelopmental disorders and suggest novel avenues for future consideration.

Combined near infrared photoacoustic imaging and ultrasound detects vulnerable atherosclerotic plaque

Atherosclerosis is an inflammatory process resulting in the deposition of cholesterol and cellular debris, narrowing of the vessel lumen and clot formation. Characterization of the morphology and vulnerability of the lesion is essential for effective clinical management. Here, near-infrared auto-photoacoustic (NIRAPA) imaging is shown to detect plaque components and, when combined with ultrasound imaging, to differentiate stable and vulnerable plaque. In an ex vivo study of photoacoustic imaging of excised plaque from 25 patients, 88.2% sensitivity and 71.4% specificity were achieved using a clinically-relevant protocol. In order to determine the origin of the NIRAPA signal, immunohistochemistry, spatial transcriptomics and spatial proteomics were co-registered with imaging and applied to adjacent plaque sections. The highest NIRAPA signal was spatially correlated with bilirubin and associated blood-based residue and with the cytoplasmic contents of inflammatory macrophages bearing CD74, HLA-DR, CD14 and CD163 markers. In summary, we establish the potential to apply the NIRAPA-ultrasound imaging combination to detect vulnerable carotid plaque and a methodology for fusing molecular imaging with spatial transcriptomic and proteomic methods.

Spatial enrichment of the type 1 interferon signature in the brain of a neuropsychiatric lupus murine model

Among systemic lupus erythematosus (SLE) patients, neuropsychiatric symptoms are highly prevalent, being observed in up to 80% of adult and 95% of pediatric patients. Type 1 interferons, particularly interferon alpha (IFNα), have been implicated in the pathogenesis of SLE and its associated neuropsychiatric symptoms (NPSLE). However, it remains unclear how type 1 interferon signaling in the central nervous system (CNS) might result in neuropsychiatric sequelae. In this study, we validate an NPSLE mouse model and find an elevated peripheral type 1 interferon signature alongside clinically relevant NPSLE symptoms such as anxiety and fatigue. Unbiased single-nucleus sequencing of the hindbrain and hippocampus revealed that interferon-stimulated genes (ISGs) were among the most highly upregulated genes in both regions and that gene pathways involved in cellular interaction and neuronal development were generally repressed among astrocytes, oligodendrocytes, and neurons. Using image-based spatial transcriptomics, we found that the type 1 interferon signature is enriched as spatially distinct patches within the brain parenchyma of these mice. Our results suggest that type 1 interferon in the CNS may play an important mechanistic role in mediating NPSLE behavioral phenotypes by repressing general cellular communication pathways, and that type 1 interferon signaling modulators are a potential therapeutic option for NPSLE.

GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging

When analyzing data from in situ RNA detection technologies, cell segmentation is an essential step in identifying cell boundaries, assigning RNA reads to cells, and studying the gene expression and morphological features of cells. We developed a deep-learning-based method, GeneSegNet, that integrates both gene expression and imaging information to perform cell segmentation. GeneSegNet also employs a recursive training strategy to deal with noisy training labels. We show that GeneSegNet significantly improves cell segmentation performances over existing methods that either ignore gene expression information or underutilize imaging information.

Spatial transcriptomics reveal basal sex differences in supraoptic nucleus gene expression of adult rats related to cell signaling and ribosomal pathways

BACKGROUND

The supraoptic nucleus (SON) of the hypothalamus contains magnocellular neurosecretory cells that secrete the hormones vasopressin and oxytocin. Sex differences in SON gene expression have been relatively unexplored. Our study used spatially resolved transcriptomics to visualize gene expression profiles in the SON of adult male (n = 4) and female (n = 4) Sprague-Dawley rats using Visium Spatial Gene Expression (10x Genomics).

METHODS

Briefly, 10-μm coronal sections (~ 4 × 4 mm) containing the SON were collected from each rat and processed using Visium slides and recommended protocols. Data were analyzed using 10x Genomics' Space Ranger and Loupe Browser applications and other bioinformatic tools. Two unique differential expression (DE) analysis methods, Loupe Browser and DESeq2, were used.

RESULTS

Loupe Browser DE analysis of the SON identified 116 significant differentially expressed genes (DEGs) common to both sexes (e.g., Avp and Oxt), 31 significant DEGs unique to the males, and 73 significant DEGs unique to the females. DESeq2 analysis revealed 183 significant DEGs between the two groups. Gene Ontology (GO) enrichment and pathway analyses using significant genes identified via Loupe Browser revealed GO terms and pathways related to (1) neurohypophyseal hormone activity, regulation of peptide hormone secretion, and regulation of ion transport for the significant genes common to both males and females, (2) Gi signaling/G-protein mediated events for the significant genes unique to males, and (3) potassium ion transport/voltage-gated potassium channels for the significant genes unique to females, as some examples. GO/pathway analyses using significant genes identified via DESeq2 comparing female vs. male groups revealed GO terms/pathways related to ribosomal structure/function. Ingenuity Pathway Analysis (IPA) identified additional sex differences in canonical pathways (e.g., 'Mitochondrial Dysfunction', 'Oxidative Phosphorylation') and upstream regulators (e.g., CSF3, NFKB complex, TNF, GRIN3A).

CONCLUSION

There was little overlap in the IPA results for the two different DE methods. These results suggest sex differences in SON gene expression that are associated with cell signaling and ribosomal pathways.

Dual spatially resolved transcriptomics for human host-pathogen colocalization studies in FFPE tissue sections

Technologies to study localized host-pathogen interactions are urgently needed. Here, we present a spatial transcriptomics approach to simultaneously capture host and pathogen transcriptome-wide spatial gene expression information from human formalin-fixed paraffin-embedded (FFPE) tissue sections at a near single-cell resolution. We demonstrate this methodology in lung samples from COVID-19 patients and validate our spatial detection of SARS-CoV-2 against RNAScope and in situ sequencing. Host-pathogen colocalization analysis identified putative modulators of SARS-CoV-2 infection in human lung cells. Our approach provides new insights into host response to pathogen infection through the simultaneous, unbiased detection of two transcriptomes in FFPE samples.

Pan-cancer spatially resolved single-cell analysis reveals the crosstalk between cancer-associated fibroblasts and tumor microenvironment

Cancer-associated fibroblasts (CAFs) are a heterogeneous cell population that plays a crucial role in remodeling the tumor microenvironment (TME). Here, through the integrated analysis of spatial and single-cell transcriptomics data across six common cancer types, we identified four distinct functional subgroups of CAFs and described their spatial distribution characteristics. Additionally, the analysis of single-cell RNA sequencing (scRNA-seq) data from three additional common cancer types and two newly generated scRNA-seq datasets of rare cancer types, namely epithelial-myoepithelial carcinoma (EMC) and mucoepidermoid carcinoma (MEC), expanded our understanding of CAF heterogeneity. Cell-cell interaction analysis conducted within the spatial context highlighted the pivotal roles of matrix CAFs (mCAFs) in tumor angiogenesis and inflammatory CAFs (iCAFs) in shaping the immunosuppressive microenvironment. In patients with breast cancer (BRCA) undergoing anti-PD-1 immunotherapy, iCAFs demonstrated heightened capacity in facilitating cancer cell proliferation, promoting epithelial-mesenchymal transition (EMT), and contributing to the establishment of an immunosuppressive microenvironment. Furthermore, a scoring system based on iCAFs showed a significant correlation with immune therapy response in melanoma patients. Lastly, we provided a web interface ( https://chenxisd.shinyapps.io/pancaf/ ) for the research community to investigate CAFs in the context of pan-cancer.

Spatial transcriptomic analysis of virtual prostate biopsy reveals confounding effect of tissue heterogeneity on genomic signatures

Genetic signatures have added a molecular dimension to prognostics and therapeutic decision-making. However, tumour heterogeneity in prostate cancer and current sampling methods could confound accurate assessment. Based on previously published spatial transcriptomic data from multifocal prostate cancer, we created virtual biopsy models that mimic conventional biopsy placement and core size. We then analysed the gene expression of different prognostic signatures (OncotypeDx®, Decipher®, Prostadiag®) using a step-wise approach with increasing resolution from pseudo-bulk analysis of the whole biopsy, to differentiation by tissue subtype (benign, stroma, tumour), followed by distinct tumour grade and finally clonal resolution. The gene expression profile of virtual tumour biopsies revealed clear differences between grade groups and tumour clones, compared to a benign control, which were not reflected in bulk analyses. This suggests that bulk analyses of whole biopsies or tumour-only areas, as used in clinical practice, may provide an inaccurate assessment of gene profiles. The type of tissue, the grade of the tumour and the clonal composition all influence the gene expression in a biopsy. Clinical decision making based on biopsy genomics should be made with caution while we await more precise targeting and cost-effective spatial analyses.

The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics

Cell-cell interactions instruct cell fate and function. These interactions are hijacked to promote cancer development. Single-cell transcriptomics and spatial transcriptomics have become powerful new tools for researchers to profile the transcriptional landscape of cancer at unparalleled genetic depth. In this review, we discuss the rapidly growing array of computational tools to infer cell-cell interactions from non-spatial single-cell RNA-sequencing and the limited but growing number of methods for spatial transcriptomics data. Downstream analyses of these computational tools and applications to cancer studies are highlighted. We finish by suggesting several directions for further extensions that anticipate the increasing availability of multi-omics cancer data.

Emerging Concepts in Precision Medicine in Axial Spondyloarthritis

PURPOSE OF REVIEW

Axial spondyloarthritis (AxSpA) is among the rheumatology's most heritable complex diseases, yet precision medicine at clinics still needs to be explored. We reviewed the emerging concepts and recent developments in polygenic risk scores, Mendelian randomization, pharmacogenomics, single-cell sequencing, and spatial transcriptomics.

RECENT FINDINGS

Polygenic risk score has resulted in encouraging results with potential diagnostic utility as it appears to outperform current diagnostic tools. Its performance and generalizability vary with ethnicity. Mendelian randomization has elucidated multiple causal associations, particularly between inflammatory bowel disease and AxSpA. Single-cell transcriptomics (particularly scRNA-seq and scATAC-seq) has identified numerous cell types, including synovial and blood immunological cells, to understand the contribution of both innate and adaptative immunity in AxSpA. Current molecular tools provide an exciting opportunity to advance precision medicine for AxSpA patients. However, extensive research and implementation strategies are still required before they can have an impact in the clinic.

Spatial transcriptomics analysis of neoadjuvant cabozantinib and nivolumab in advanced hepatocellular carcinoma identifies independent mechanisms of resistance and recurrence

BACKGROUND

Novel immunotherapy combination therapies have improved outcomes for patients with hepatocellular carcinoma (HCC), but responses are limited to a subset of patients. Little is known about the inter- and intra-tumor heterogeneity in cellular signaling networks within the HCC tumor microenvironment (TME) that underlie responses to modern systemic therapy.

METHODS

We applied spatial transcriptomics (ST) profiling to characterize the tumor microenvironment in HCC resection specimens from a prospective clinical trial of neoadjuvant cabozantinib, a multi-tyrosine kinase inhibitor that primarily blocks VEGF, and nivolumab, a PD-1 inhibitor in which 5 out of 15 patients were found to have a pathologic response at the time of resection.

RESULTS

ST profiling demonstrated that the TME of responding tumors was enriched for immune cells and cancer-associated fibroblasts (CAF) with pro-inflammatory signaling relative to the non-responders. The enriched cancer-immune interactions in responding tumors are characterized by activation of the PAX5 module, a known regulator of B cell maturation, which colocalized with spots with increased B cell marker expression suggesting strong activity of these cells. HCC-CAF interactions were also enriched in the responding tumors and were associated with extracellular matrix (ECM) remodeling as there was high activation of FOS and JUN in CAFs adjacent to the tumor. The ECM remodeling is consistent with proliferative fibrosis in association with immune-mediated tumor regression. Among the patients with major pathologic responses, a single patient experienced early HCC recurrence. ST analysis of this clinical outlier demonstrated marked tumor heterogeneity, with a distinctive immune-poor tumor region that resembles the non-responding TME across patients and was characterized by HCC-CAF interactions and expression of cancer stem cell markers, potentially mediating early tumor immune escape and recurrence in this patient.

CONCLUSIONS

These data show that responses to modern systemic therapy in HCC are associated with distinctive molecular and cellular landscapes and provide new targets to enhance and prolong responses to systemic therapy in HCC.

Spatial Transcriptomics of Aging Rat Ovaries Reveals Unexplored Cell Subpopulations with Reduced Antioxidative Defense

INTRODUCTION

Ovarian aging is characterized by a gradual decline in quantity and quality of oocytes and lower chance of fertility. Better understanding the genetic modulation during ovarian aging can further address available treatment options for aging-related ovarian diseases and fertility preservation.

METHODS

A novel technique spatial transcriptomics (ST) was used to investigate the spatial transcriptome features of rat ovaries. Transcriptomes from ST spots in the young and aged ovaries were clustered using differentially expressed genes. These data were analyzed to determine the spatial organization of age-induced heterogeneity and potential mechanisms underlying ovarian aging.

RESULTS

In this study, ST technology was applied to profile the comprehensive spatial imaging in young and aged rat ovary. Fifteen ovarian cell clusters with distinct gene-expression signatures were identified. The gene expression dynamics of granulosa cell clusters revealed three sub-types with sequential developmental stages. Aged ovary showed a significant decrease in the number of granulosa cells from the antral follicle. Besides, a remarkable rearrangement of interstitial gland cells was detected in aging ovary. Further analysis of aging-associated transcriptional changes revealed that the disturbance of oxidative pathway was a crucial factor in ovarian aging.

CONCLUSIONS

This study firstly described an aging-related spatial transcriptome changes in ovary and identified the potential targets for prevention of ovarian aging. These data may provide the basis for further investigations of the diagnosis and treatment of aging-related ovarian disorders.

Progress in research on tumor microenvironment-based spatial omics technologies

Spatial omics technology integrates the concept of space into omics research and retains the spatial information of tissues or organs while obtaining molecular information. It is characterized by the ability to visualize changes in molecular information and yields intuitive and vivid visual results. Spatial omics technologies include spatial transcriptomics, spatial proteomics, spatial metabolomics, and other technologies, the most widely used of which are spatial transcriptomics and spatial proteomics. The tumor microenvironment refers to the surrounding microenvironment in which tumor cells exist, including the surrounding blood vessels, immune cells, fibroblasts, bone marrow-derived inflammatory cells, various signaling molecules, and extracellular matrix. A key issue in modern tumor biology is the application of spatial omics to the study of the tumor microenvironment, which can reveal problems that conventional research techniques cannot, potentially leading to the development of novel therapeutic agents for cancer. This paper summarizes the progress of research on spatial transcriptomics and spatial proteomics technologies for characterizing the tumor immune microenvironment.

Technical optimization of spatially resolved single-cell transcriptomic datasets to study clinical liver disease

Single cell and spatially resolved 'omic' techniques have enabled deep characterization of clinical pathologies that remain poorly understood, providing unprecedented insights into molecular mechanisms of disease. However, transcriptomic platforms are costly, limiting sample size, which increases the possibility of pre-analytical variables such as tissue processing and storage procedures impacting RNA quality and downstream analyses. Furthermore, spatial transcriptomics have not yet reached single cell resolution, leading to the development of multiple deconvolution methods to predict individual cell types within each transcriptome 'spot' on tissue sections. In this study, we performed spatial transcriptomics and single nucleus RNA sequencing (snRNASeq) on matched specimens from patients with either histologically normal or advanced fibrosis to establish important aspects of tissue handling, data processing, and downstream analyses of biobanked liver samples. We observed that tissue preservation technique impacts transcriptomic data, especially in fibrotic liver. Deconvolution of the spatial transcriptome using paired snRNASeq data generated a spatially resolved, single cell dataset with 24 unique liver cell phenotypes. We determined that cell-cell interactions predicted using ligand-receptor analysis of snRNASeq data poorly correlated with celullar relationships identified using spatial transcriptomics. Our study provides a framework for generating spatially resolved, single cell datasets to study gene expression and cell-cell interactions in biobanked clinical samples with advanced liver disease.

VT3D: a visualization toolbox for 3D transcriptomic data

Data visualization empowers researchers to communicate their results that support scientific reasoning in an intuitive way. Three-dimension (3D) spatially resolved transcriptomic atlases constructed from multi-view and high-dimensional data have rapidly emerged as a powerful tool to unravel spatial gene expression patterns and cell type distribution in biological samples, revolutionizing the understanding of gene regulatory interactions and cell niches. However, limited accessible tools for data visualization impede the potential impact and application of this technology. Here we introduce VT3D, a visualization toolbox that allows users to explore 3D transcriptomic data, enabling gene expression projection to any 2D plane of interest, 2D virtual slice creation and visualization, and interactive 3D data browsing with surface model plots. In addition, it can either work on personal devices in standalone mode or be hosted as a web-based server. We apply VT3D to multiple datasets produced by the most popular techniques, including both sequencing-based approaches (Stereo-seq, spatial transcriptomics, and Slide-seq) and imaging-based approaches (MERFISH and STARMap), and successfully build a 3D atlas database that allows interactive data browsing. We demonstrate that VT3D bridges the gap between researchers and spatially resolved transcriptomics, thus accelerating related studies such as embryogenesis and organogenesis processes. The source code of VT3D is available at https://github.com/BGI-Qingdao/VT3D, and the modeled atlas database is available at http://www.bgiocean.com/vt3d_example.

Improved in situ sequencing for high-resolution targeted spatial transcriptomic analysis in tissue sections

Spatial transcriptomics enables the study of localization-indexed gene expression activity in tissues, providing the transcriptional landscape that in turn indicates the potential regulatory networks of gene expression. In situ sequencing (ISS) is a targeted spatial transcriptomic technique, based on padlock probe and rolling circle amplification combined with next-generation sequencing chemistry, for highly multiplexed in situ gene expression profiling. Here, we present improved in situ sequencing (IISS) that exploits a new probing and barcoding approach, combined with advanced image analysis pipelines for high-resolution targeted spatial gene expression profiling. We develop an improved combinatorial probe anchor ligation chemistry using a 2-base encoding strategy for barcode interrogation. The new encoding strategy results in higher signal intensity as well as improved specificity for in situ sequencing, while maintaining a streamlined analysis pipeline for targeted spatial transcriptomics. We show that IISS can be applied to both fresh frozen tissue and formalin-fixed paraffin-embedded tissue sections for single-cell level spatial gene expression analysis, based on which the developmental trajectory and cell-cell communication networks can also be constructed.

Spatial transcriptomics: Technologies, applications and experimental considerations

The diverse cell types of an organ have a highly structured organization to enable their efficient and correct function. To fully appreciate gene functions in a given cell type, one needs to understand how much, when and where the gene is expressed. Classic bulk RNA sequencing and popular single cell sequencing destroy cell structural organization and fail to provide spatial information. However, the spatial location of gene expression or of the cell in a complex tissue provides key clues to comprehend how the neighboring genes or cells cross talk, transduce signals and work together as a team to complete the job. The functional requirement for the spatial content has been a driving force for rapid development of the spatial transcriptomics technologies in the past few years. Here, we present an overview of current spatial technologies with a special focus on the commercially available or currently being commercialized technologies, highlight their applications by category and discuss experimental considerations for a first spatial experiment.

Integrating multi-modal information to detect spatial domains of spatial transcriptomics by graph attention network

Recent advances in spatially resolved transcriptomic technologies have enabled unprecedented opportunities to elucidate tissue architecture and function in situ. Spatial transcriptomics can provide multimodal and complementary information simultaneously, including gene expression profiles, spatial locations, and histology images. However, most existing methods have limitations in efficiently utilizing spatial information and matched high-resolution histology images. To fully leverage the multi-modal information, we propose a SPAtially embedded Deep Attentional graph Clustering (SpaDAC) method to identify spatial domains while reconstructing denoised gene expression profiles. This method can efficiently learn the low-dimensional embeddings for spatial transcriptomics data by constructing multi-view graph modules to capture both spatial location connectives and morphological connectives. Benchmark results demonstrate that SpaDAC outperforms other algorithms on several recent spatial transcriptomics datasets. SpaDAC is a valuable tool for spatial domain detection, facilitating the comprehension of tissue architecture and cellular microenvironment. The source code of SpaDAC is freely available at Github (https://github.com/huoyuying/SpaDAC.git).

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.

Computational approaches in rheumatic diseases - Deciphering complex spatio-temporal cell interactions

Inflammatory arthritis, including rheumatoid (RA), and psoriatic (PsA) arthritis, are clinically and immunologically heterogeneous diseases with no identified cure. Chronic inflammation of the synovial tissue ushers loss of function of the joint that severely impacts the patient's quality of life, eventually leading to disability and life-threatening comorbidities. The pathogenesis of synovial inflammation is the consequence of compounded immune and stromal cell interactions influenced by genetic and environmental factors. Deciphering the complexity of the synovial cellular landscape has accelerated primarily due to the utilisation of bulk and single cell RNA sequencing. Particularly the capacity to generate cell-cell interaction networks could reveal evidence of previously unappreciated processes leading to disease. However, there is currently a lack of universal nomenclature as a result of varied experimental and technological approaches that discombobulates the study of synovial inflammation. While spatial transcriptomic analysis that combines anatomical information with transcriptomic data of synovial tissue biopsies promises to provide more insights into disease pathogenesis, in vitro functional assays with single-cell resolution will be required to validate current bioinformatic applications. In order to provide a comprehensive approach and translate experimental data to clinical practice, a combination of clinical and molecular data with machine learning has the potential to enhance patient stratification and identify individuals at risk of arthritis that would benefit from early therapeutic intervention. This review aims to provide a comprehensive understanding of the effect of computational approaches in deciphering synovial inflammation pathogenesis and discuss the impact that further experimental and novel computational tools may have on therapeutic target identification and drug development.

Spatial tumour gene signature discriminates neoplastic from non-neoplastic compartments in colon cancer: unravelling predictive biomarkers for relapse

BACKGROUND

Opting for or against the administration of adjuvant chemotherapy in therapeutic management of stage II colon cancer remains challenging. Several studies report few survival benefits for patients treated with adjuvant therapy and additionally revealing potential side effects of overtreatment, including unnecessary exposure to chemotherapy-induced toxicities and reduced quality of life. Predictive biomarkers are urgently needed. We, therefore, hypothesise that the spatial tissue composition of relapsed and non-relapsed colon cancer stage II patients reveals relevant biomarkers.

METHODS

The spatial tissue composition of stage II colon cancer patients was examined by a novel spatial transcriptomics technology with sub-cellular resolution, namely in situ sequencing. A panel of 176 genes investigating specific cancer-associated processes such as apoptosis, proliferation, angiogenesis, stemness, oxidative stress, hypoxia, invasion and components of the tumour microenvironment was designed to examine differentially expressed genes in tissue of relapsed versus non-relapsed patients. Therefore, FFPE slides of 10 colon cancer stage II patients either classified as relapsed (5 patients) or non-relapsed (5 patients) were in situ sequenced and computationally analysed.

RESULTS

We identified a tumour gene signature that enables the subclassification of tissue into neoplastic and non-neoplastic compartments based on spatial expression patterns obtained through in situ sequencing. We developed a computational tool called Genes-To-Count (GTC), which automates the quantification of in situ signals, accurately mapping their position onto the spatial tissue map and automatically identifies neoplastic and non-neoplastic tissue compartments. The GTC tool was used to quantify gene expression of biological processes upregulated within the neoplastic tissue in comparison to non-neoplastic tissue and within relapsed versus non-relapsed stage II colon patients. Three differentially expressed genes (FGFR2, MMP11 and OTOP2) in the neoplastic tissue compartments of relapsed patients in comparison to non-relapsed patients were identified predicting recurrence in stage II colon cancer.

CONCLUSIONS

In depth spatial in situ sequencing showed potential to provide a deeper understanding of the underlying mechanisms involved in the recurrence of disease and revealed novel potential predictive biomarkers for disease relapse in colon cancer stage II patients. Our open-access GTC-tool allowed us to accurately capture the tumour compartment and quantify spatial gene expression in colon cancer tissue.

Single-cell and spatial heterogeneity landscapes of mature epicardial cells

Tbx18, Wt1, and Tcf21 have been identified as epicardial markers during the early embryonic stage. However, the gene markers of mature epicardial cells remain unclear. Single-cell transcriptomic analysis was performed with the Seurat, Monocle, and CellphoneDB packages in R software with standard procedures. Spatial transcriptomics was performed on chilled Visium Tissue Optimization Slides (10x Genomics) and Visium Spatial Gene Expression Slides (10x Genomics). Spatial transcriptomics analysis was performed with Space Ranger software and R software. Immunofluorescence, whole-mount RNA in situ hybridization and X-gal staining were performed to validate the analysis results. Spatial transcriptomics analysis revealed distinct transcriptional profiles and functions between epicardial tissue and non-epicardial tissue. Several gene markers specific to postnatal epicardial tissue were identified, including Msln, C3, Efemp1, and Upk3b. Single-cell transcriptomic analysis revealed that cardiac cells from wildtype mouse hearts (from embryonic day 9.5 to postnatal day 9) could be categorized into six major cell types, which included epicardial cells. Throughout epicardial development, Wt1, Tbx18, and Upk3b were consistently expressed, whereas genes including Msln, C3, and Efemp1 exhibited increased expression during the mature stages of development. Pseudotime analysis further revealed two epicardial cell fates during maturation. Moreover, Upk3b, Msln, Efemp1, and C3 positive epicardial cells were enriched in extracellular matrix signaling. Our results suggested Upk3b, Efemp1, Msln, C3, and other genes were mature epicardium markers. Extracellular matrix signaling was found to play a critical role in the mature epicardium, thus suggesting potential therapeutic targets for heart regeneration in future clinical practice.

Identification of cell subpopulations associated with disease phenotypes from scRNA-seq data using PACSI

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) has revolutionized the transcriptomics field by advancing analyses from tissue-level to cell-level resolution. Despite the great advances in the development of computational methods for various steps of scRNA-seq analyses, one major bottleneck of the existing technologies remains in identifying the molecular relationship between disease phenotype and cell subpopulations, where "disease phenotype" refers to the clinical characteristics of each patient sample, and subpopulation refer to groups of single cells, which often do not correspond to clusters identified by standard single-cell clustering analysis. Here, we present PACSI, a method aimed at distinguishing cell subpopulations associated with disease phenotypes at the single-cell level.

RESULTS

PACSI takes advantage of the topological properties of biological networks to introduce a proximity-based measure that quantifies the correlation between each cell and the disease phenotype of interest. Applied to simulated data and four case studies, PACSI accurately identified cells associated with disease phenotypes such as diagnosis, prognosis, and response to immunotherapy. In addition, we demonstrated that PACSI can also be applied to spatial transcriptomics data and successfully label spots that are associated with poor survival of breast carcinoma.

CONCLUSIONS

PACSI is an efficient method to identify cell subpopulations associated with disease phenotypes. Our research shows that it has a broad range of applications in revealing mechanistic and clinical insights of diseases.