Integration of Computational Analysis and Spatial Transcriptomics in Single-cell Studies

This review examines recent advancements in psoriasis research through single-cell technologies, including single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics. These methods have uncovered the cellular diversity underlying psoriasis, identifying immune cell, keratinocyte, and fibroblast subtypes that play pivotal roles in disease progression. Such insights are vital for addressing the complexity and heterogeneity of psoriasis, paving the way for targeted therapies. Recent studies emphasize the roles of IL-17-producing T cells (T17), keratinocytes, and fibroblasts in driving inflammation. T-cell cytokines, including IL-17A and IL-17F, induce keratinocyte hyperproliferation and amplify inflammation through an IL-36 feed-forward loop. Fibroblast subsets, such as SFRP2+ and WNT5A+/IL24+ fibroblasts, contribute to extracellular matrix remodeling and cytokine release, worsening the inflammatory environment. These studies also reveal the intricate fibroblast-keratinocyte crosstalk via the IL-17/IL-36 and PRSS3-F2R pathways. More recently, advancement with spatial transcriptomics has uncovered metabolic dysregulation in psoriatic keratinocytes, highlighting HIF1α-driven glycolysis and lactate production as critical in sustaining chronic inflammation. Furthermore, nonlesional skin from severe psoriasis patients exhibits transcriptomic changes resembling lesional skin, suggesting systemic "prelesional" state with the upregulation of lipid metabolism genes. These discoveries have significant clinical implications. Integrating single-cell and spatial technologies into psoriasis research offers promising avenues for developing tailored treatments and improving patient outcomes. Specifically, with spatial transcriptomics revealing immune signatures and cell-cell colocalization that may serve as early indicators of disease severity and systemic involvement. Targeting metabolic pathways in keratinocytes and localized immune microenvironments may enhance precision therapies for psoriasis.

Background: Invasive lobular carcinoma (ILC) is the second most common histological breast cancer subtype; however, little is known about its tumor microenvironment (TME). Here, we aimed to identify and to characterise ILC subgroups based on TME heterogeneity by combining spatial transcriptomics (ST) and single cell RNA-sequencing (scRNA-seq). Methods: We performed ST (Visium 10x Genomics) on frozen tumor samples from 41 primary hormone receptor positive (HR+), HER2-negative (HER2-) ILCs. Information coming from the morphological annotation of ST slides and the gene expression-based clustering of ST spots was integrated and used as input for a patient level classification (using intNMF algorithm). Subgroups of patients were annotated using morphology, gene set enrichment analysis and ST deconvolution based on scRNA-seq data (CARD software). SCAN-B bulk RNA-seq dataset was used as validation set (HR+, HER2- ILC samples, n = 853). Results: Four subgroups of patients were identified: proliferative (P, n = 12, enriched in tumor cells and proliferation-related pathways), normal-stroma enriched (NSE, n = 10, enriched in fibroblasts, stroma-related pathways and showing an higher level of colocalization between invasive and in situ carcinoma), metabolic (M, n = 9, enriched in metabolic related-pathways) and metabolic-immune enriched (MIE, n = 10, enriched in adipocytes, metabolic and immune-related pathways). Deconvolution of ST spots with single cell data revealed matching results with morphology (in terms of cell types). We observed an enrichment of myofibroblasts in NSE, an enrichment of cancer epithelial in P and, interestingly, an enrichment of endothelial cells in M subgroup (fdr < 0.25). MIE was characterized by perivascular like-endothelial cells and myeloid cells (monocytes and macrophages, fdr < 0.25). Of note, at the level of the ST slide, myeloid cells in MIE subgroup were more present in the adipose tissue compartment (fdr < 0.25) compared to the rest of the slide. NSE subgroup showed a trend for association with better prognosis. Gene signature-based assignment identified the same four subgroups in SCAN-B: GSEA analysis and comparisons among the four subgroups showed matching results between our dataset and SCAN-B. Of note, in SCAN-B, we observed differences in prognosis among the subgroups, with NSE showing better, M and MIE intermediate and P worse prognosis for overall survival and relapse free interval (p < 0.01). Conclusions: Four subgroups of ILC based on TME heterogeneity and showing differences in prognosis were identified and validated in external cohort. Of note, two of these groups were related to metabolism, highlighting the value of such process in ILC biology. Importantly, myeloid cells were the predominant immune cell types in ILC, and they showed a specific compartmentalization inside the TME. Further validation is needed. Citation Format: Matteo Serra, Andreas Papagiannis, Mattia Rediti, Frederic Lifrange, Nicola Occelli, Laetitia Collet, Delphine Vincent, Ghizlane Rouas, Danai Fimereli, Ligia Craciun, Denis Larsimont, David Venet, Miikka Vikkula, Francois P. Duhoux, Françoise Rothé, Christos Sotiriou. Integrating spatial and single cell transcriptomics to identify and characterise biologically driven subgroups in invasive lobular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5066.

My talk will present three computational frameworks we developed to study cytokine signaling activities and cell-cell communications during the antitumor immune response, using tumor single-cell and spatial transcriptomics. The basic immunology tool to study cytokine signaling mostly measures cytokine release, which is transient and does not represent downstream target activities. Therefore, we first developed the CytoSig platform, providing a database of target genes modulated by cytokines and a predictive model of cytokine signaling cascades from transcriptomic profiles. We collected 20,591 transcriptome profiles for human cytokine, chemokine, and growth factor responses. This atlas of transcriptional patterns induced by cytokines enabled the reliable prediction of signaling activities in distinct cell populations in infectious diseases, chronic inflammation, and cancer using bulk and single-cell transcriptomic data. CytoSig revealed previously unidentified roles of many cytokines, such as BMP6 as an anti-inflammatory factor. Then, based on CytoSig, we developed Tres, a computational model utilizing single-cell transcriptomic data to identify signatures of T cells that are resilient to immunosuppressive signals, such as TGF-β1, TRAIL, and prostaglandin E2. Tres reliably predicts clinical responses to immunotherapy in multiple cancer types using bulk T cell transcriptomic data from pre-treatment patient tumors or infusion/pre-manufacture samples for cellular immunotherapies. Further, Tres identified FIBP as a candidate immunotherapy target to potentiate adoptive cell therapy efficacies. FIBP knockout in T cells enhanced adoptive cell therapy by down-regulating T cells' cholesterol metabolism. Last, I will briefly show our SpaCET framework for deconvolving cell fractions and identifying cell-cell interactions in tumor spatial transcriptomics data. SpaCET resolved several challenges in spatial transcriptomics analysis that previous methods did not address sufficiently. Through coupling cell fractions with ligand-receptor co-expression analysis, SpaCET reveals that intercellular interactions tend to be located at the tumor-immune boundaries. Citation Format: Peng Jiang. Inference of intercellular signaling activities in tumor spatial and single-cell transcriptomics, with applications in identifying cancer immunotherapy targets [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr IA002.

Glioblastoma (GBM), a deadly brain tumor, is still one of the few lasting challenges of contemporary oncology. Current therapies fail to significantly improve patient survival due to GBM's tremendous genetic, transcriptomic, immunological, and sex-dependent heterogeneity. Over the years, clinical differences between males and females were characterized. For instance, higher incidence of GBM in males or distinct responses to cancer chemotherapy and immunotherapy between males and females have been noted. However, despite the introduction of single-cell RNA sequencing and spatial transcriptomics, these differences were not further investigated as studies were focused only on exposing the general picture of GBM heterogeneity. Hence, in this study, we summarized the current state of knowledge on GBM heterogeneity exposed by single-cell RNA sequencing and spatial transcriptomics with regard to genetics, immunology, and sex-dependent differences. Additionally, we highlighted future research directions which would fill the gap of knowledge on the impact of patient's sex on the disease outcome.

Mammalian reproduction requires the fusion of two specialized cells: an oocyte and a sperm. In addition to producing gametes, the reproductive system also provides the environment for the appropriate development of the embryo. Deciphering the reproductive system requires understanding the functions of each cell type and cell-cell interactions. Recent single-cell omics technologies have provided insights into the gene regulatory network in discrete cellular populations of both the male and female reproductive systems. However, these approaches cannot examine how the cellular states of the gametes or embryos are regulated through their interactions with neighboring somatic cells in the native tissue environment owing to tissue disassociations. Emerging spatial omics technologies address this challenge by preserving the spatial context of the cells to be profiled. These technologies hold the potential to revolutionize our understanding of mammalian reproduction. We aim to review the state-of-the-art spatial transcriptomics (ST) technologies with a focus on highlighting the novel biological insights that they have helped to reveal about the mammalian reproductive systems in the context of gametogenesis, embryogenesis, and reproductive pathologies. We also aim to discuss the current challenges of applying ST technologies in reproductive research and provide a sneak peek at what the field of spatial omics can offer for the reproduction community in the years to come. The PubMed database was used in the search for peer-reviewed research articles and reviews using combinations of the following terms: 'spatial omics', 'fertility', 'reproduction', 'gametogenesis', 'embryogenesis', 'reproductive cancer', 'spatial transcriptomics', 'spermatogenesis', 'ovary', 'uterus', 'cervix', 'testis', and other keywords related to the subject area. All relevant publications until April 2023 were critically evaluated and discussed. First, an overview of the ST technologies that have been applied to studying the reproductive systems was provided. The basic design principles and the advantages and limitations of these technologies were discussed and tabulated to serve as a guide for researchers to choose the best-suited technologies for their own research. Second, novel biological insights into mammalian reproduction, especially human reproduction revealed by ST analyses, were comprehensively reviewed. Three major themes were discussed. The first theme focuses on genes with non-random spatial expression patterns with specialized functions in multiple reproductive systems; The second theme centers around functionally interacting cell types which are often found to be spatially clustered in the reproductive tissues; and the thrid theme discusses pathological states in reproductive systems which are often associated with unique cellular microenvironments. Finally, current experimental and computational challenges of applying ST technologies to studying mammalian reproduction were highlighted, and potential solutions to tackle these challenges were provided. Future directions in the development of spatial omics technologies and how they will benefit the field of human reproduction were discussed, including the capture of cellular and tissue dynamics, multi-modal molecular profiling, and spatial characterization of gene perturbations. Like single-cell technologies, spatial omics technologies hold tremendous potential for providing significant and novel insights into mammalian reproduction. Our review summarizes these novel biological insights that ST technologies have provided while shedding light on what is yet to come. Our review provides reproductive biologists and clinicians with a much-needed update on the state of art of ST technologies. It may also facilitate the adoption of cutting-edge spatial technologies in both basic and clinical reproductive research.

Unveiling the spatiotemporal strategies of plants in response to biotic and abiotic stresses:A comprehensive review.

Pancreatic Cancer Ductal Adenocarcinoma (PDAC) is one of the deadliest cancers with 5-year survival of 11%. Understanding the intratumor heterogeneity is a pivotal piece to unravel the complexity of PDAC. While single cell RNASeq identifies the heterogeneous cell populations within the tumor tissue, spatially characterizing the transcriptomic profile of neoplastic and pre-neoplastic populations within the tissue remains a challenge, as the spatial dimension is usually lost upon tissue dissociation. We identified an approach to integrate spatial transcriptomics data with single cell RNASeq data. To characterize the cell populations within the tissue we performed single cell RNASeq on disease pathology-free pancreas tissue and primary PDAC samples. We profiled the transcriptomic profile of Acinar, Ductal, Acinar-to-Ductal (ADM), and Pancreatic Intraepithelial Neoplasia (PanINs) regions of interest (ROIs) across the tissue using the Nanostring GeoMx platform. Differential gene expression analysis using linear mixed-effect models of the cell-type specific ROIs defined pan-marker gene sets for each cell type, which were mapped to UMAP projections of single cell RNA sequencing data using AUCell scoring. As expected, the acinar pan-markers gene set derived from the spatial transcriptomics mapped to the manually annotated acinar population in the single cell data. On the other hand, Ductal, ADM, and PanIN pan-marker gene sets were mapped to distinct clusters that previously were not well-defined by single cell sequencing. The analysis coupled with orthogonal validation using RNAScope revealed gene signatures uniquely specific to ADM lesions and PanINs, respectively. Interestingly, our list included known markers as well as novel findings, supporting the validity of the findings. Furthermore, RNA velocity analysis using scVelo revealed a trajectory of cell evolution originating from acinar cells passing through the newly-defined ADM population and ending towards the ductal population derived from tumor samples. Overall, this integration approach of spatial and single cell transcriptomics can further define the characteristics that differentiate neoplastic and pre-neoplastic populations, as well as the potential drivers for tumorigenesis that could be therapeutically targeted. Citation Format: Ahmed M. Elhossiny, Eileen Carpenter, Padma Kadiyala, Yaqing Zhang, Filip Bednar, Arvind Rao, Timothy Frankel, Marina Pasca Di Magliano. Integrating single cell and spatial transcriptomics define gene signature for pancreatic ductal adenocarcinoma pre-neoplastic lesion [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A006.

The recent advances in spatial transcriptomics have brought unprecedented opportunities to understand the cellular heterogeneity in the spatial context. However, the current limitations of spatial technologies hamper the exploration of cellular localizations and interactions at single-cell level. Here, we present spatial transcriptomics deconvolution by topic modeling (STRIDE), a computational method to decompose cell types from spatial mixtures by leveraging topic profiles trained from single-cell transcriptomics. STRIDE accurately estimated the cell-type proportions and showed balanced specificity and sensitivity compared to existing methods. We demonstrated STRIDE’s utility by applying it to different spatial platforms and biological systems. Deconvolution by STRIDE not only mapped rare cell types to spatial locations but also improved the identification of spatially localized genes and domains. Moreover, topics discovered by STRIDE were associated with cell-type-specific functions and could be further used to integrate successive sections and reconstruct the three-dimensional architecture of tissues. Taken together, STRIDE is a versatile and extensible tool for integrated analysis of spatial and single-cell transcriptomics and is publicly available at https://github.com/wanglabtongji/STRIDE.

The intestinal epithelium normally acts as a barrier and coordinates immune responses. In Ulcerative colitis (UC), this barrier is compromised, and the epithelial-immune communication becomes dysregulated, leading to persistent inflammation and impaired repair. Understanding how crosstalk changes over time or under different exposures is critical for identifying new therapeutic targets and the optimal window for intervention. While advances in single-cell and spatial transcriptomic technologies allow us to measure gene activity in individual cells and map their location in tissue, most existing cell-cell communication inference tools fail to infer how cellular crosstalk effects change dynamically as disease progresses. To address this gap, we developed DynamicCC, a cell-cell communication inference method that tracks signaling shifts over time and across conditions (Figure 1). Unlike conventional approaches, DynamicCC uses the full distributions of ligand-receptor expression (both mean & variance) to capture systematic and dynamic changes in signaling. It also integrates spatial information of the cells and enables the detection of ordered shifts in signaling as inflammation develops, peaks, and recovers. We applied DynamicCC to a high-resolution MERFISH dataset of dextran sodium sulfate (DSS)-induced colitis in mice. The dataset included ∼1.35 million cells across three stages: healthy (day 0), early inflammation (day 3), and peak inflammation (day 9). Using this approach, we uncovered dynamic signaling between epithelial, fibroblast, and immune compartments that conventional approaches overlooked. For example, signaling between Inflammation Associated Epithelial (IAE) and Fibroblast (F) via Cxcl12-Itga5 spiked sharply at day 3 before declining. This transient interaction triggered fibroblasts to secrete TGF-β, IL-6, and other cytokines, which further recruited immune cells (e.g., neutrophils, regulatory T cells) and reinforced a pro-inflammatory microenvironment. Other interactions showed distinct temporal patterns. Macrophage-T cell signaling (e.g., Col1a2-Cd44, Tgfb1-Tgfbr2) and Fibroblast-T cell crosstalk (e.g., Col1a2-Itga1, Cxcl12-Itga4) shifted over time, with some mediators strengthened progressively from day 0 to day 9 and others followed U-shaped dynamics, with early suppression followed by late reactivation. These temporal shifts converged on transcriptional programs regulated by Activator Protein 1 (AP-1) and Nuclear Factor (NF-κB), consistent with stage-specific increases in VEGFA, FOS, and ITGB5/6, which modulate inflammatory cytokine production. Overall, the results demonstrate that UC progression is not defined by static signaling events. Instead, DynamicCC captures the evolving communication patterns that orchestrate fibroblast remodeling and immune regulation, providing critical insights for therapeutic discovery. Figure 1:This figure illustrates the process of analyzing dynamic cell-cell communication over time. It begins with (i) a group of interacting cells (Epithelia, T-cell, Fibroblast, Macrophage cells) that are communicating through secreted ligands (triangles) binding to their respective receptors (Y-shaped structures). This communication is then measured at multiple time points (ii), t = 1, t = 2, and t = 3, using single-cell RNA sequencing or spatial transcriptomics to quantify the expression of ligands and receptors. Finally, the developed computational method, DynamicCC, processes this time-series data to generate a plot (iii) that shows how the communication strength between specific signaling between two cell types changes over time.

Prostate cancer is one of the most prevalent malignancies among men and remains a major clinical challenge due to the complex tumor microenvironment. Understanding gene expression dynamics at both cellular and spatial levels is essential for improving therapeutic strategies. In this study, we performed an integrated multi-omics analysis using single-cell RNA sequencing and spatial transcriptomics. scRNA-seq data from 15 prostate samples, including 8 normal and 7 tumor tissues, were analyzed to characterize distinct cellular populations. Spatial transcriptomic profiling was conducted on three FFPE prostate tissue sections, including adjacent normal tissue, acinar cell carcinoma, and invasive adenocarcinoma, using the standard 10x Genomics Visium FFPE platform (55 µm capture spots). Single-cell analysis revealed heterogeneity among epithelial, stromal, and immune cell populations, highlighting complex signaling networks in which myeloid cells may contribute to tumor progression through immune suppression and epithelial adaptability. Spatial transcriptomic analysis further identified region-specific expression patterns and spatially restricted tumor niches, including the regional establishment of TXNIP and BIRC3 as genes associated with metabolic stress and inflammatory survival pathways. The spatial colocalization of BIRC3 with tumor vasculature in invasive carcinoma tissue suggests a novel interaction. Our discoveries using an integrated single-cell and spatial transcriptomic approach reveal a high-resolution molecular map of prostate cancer with spatial features that may provide further therapeutic investigation.

Despite the recognized importance of the spinal cord in sensory processing, motor behaviors, and neural diseases, the underlying organization of neuronal clusters and their spatial location remain elusive. Recently, several studies have attempted to define the neuronal types and functional heterogeneity in the spinal cord using single-cell or single-nucleus RNA sequencing in animal models or developing humans. However, molecular evidence of cellular heterogeneity in the adult human spinal cord is limited. Here, we classified spinal cord neurons into 21 subclusters and determined their distribution from nine human donors using single-nucleus RNA sequencing and spatial transcriptomics. Moreover, we compared the human findings with previously published single-nucleus data of the mouse adult spinal cord, which revealed an overall similarity in the neuronal composition of the spinal cord between the two species while simultaneously highlighting some degree of heterogeneity. Additionally, we examined the sex differences in the spinal neuronal subclusters. Several genes, such as SCN10A and HCN1, showed sex differences in motor neurons. Finally, we classified human dorsal root ganglia (DRG) neurons using spatial transcriptomics and explored the putative interactions between DRG and spinal cord neuronal subclusters. In summary, these results illustrate the complexity and diversity of spinal neurons in humans and provide an important resource for future research to explore the molecular mechanisms underlying spinal cord physiology and diseases.

Inflammatory skin disorders are chronic diseases that present with redness, itching, and plaques and occur due to the interaction between genetic and environmental factors. The immune basis of the disease involves the action of proinflammatory cytokines and overexpression of CD4+ and CD8+ T-cells.
 
 Objective 
 To discuss the developments in chronic inflammatory disease pathogenesis and treatments using single-cell RNA sequencing and spatial transcriptomics.
 
 Approach
 
 Single-cell RNA analysis and spatial transcriptomics allow one to study a cell individually and identify the cell-to-cell differences and the mutations that can be found within individual cells in a pool. It aids in improved understanding of disease pathogenesis and helps identify the distinct mutations that arise in every disease which can be targeted for therapy. However, this technology is quite new and requires improvements on cost reduction and standardization of methods. Nevertheless, this approach has opened new avenues to studying disease pathogenesis and creating drugs targeting the active inflammatory pathway.
 
 Conclusion
 This review focuses on chronic inflammatory skin conditions like psoriasis, atopic dermatitis, acne, lichen planus, and alopecia.
 This review provides insight into their pathogenesis and targeted therapy using the latest advances in the field of single-cell RNA sequencing and transcriptomics

Global deaths attributed to gastric cancer (GC) are increasing, yet our understanding of it remains limited. Recently, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics have provided important insights into the dissection of metastasis-related biological processes in subpopulations of cells in the GC tumor microenvironment, especially intratumoral cellular heterogeneity and cell-cell interactions. In this review, we discuss the mechanisms underlying GC metastasis and potential strategies for developing upcoming immunotherapies by combining advances in scRNA-seq and spatial transcriptomics.

Chronic wounds such as diabetic foot ulcers, venous leg ulcers, and pressure ulcers are characterized by impaired healing and persistent inflammation. Cellular senescence, defined as irreversible growth arrest with a pro-inflammatory secretory phenotype (senescence-associated secretory phenotype), has emerged as a potential driver of these nonhealing states. While transient induction of senescence may aid acute repair, chronic accumulation of senescent cells is thought to disrupt tissue regeneration, promote extracellular matrix degradation, and sustain inflammation. Single-cell RNA sequencing and spatial transcriptomics have revealed diverse cell states in chronic wounds, including senescent subsets. Studies in diabetic, venous, and pressure ulcers implicate senescent fibroblasts and immune cells in impaired remodeling, often triggered by oxidative stress, hyperglycemia, or ischemia-reperfusion injury. Therapeutic strategies targeting senescent cells in delayed wound healing have demonstrated promise in preclinical models; however, interventions must be timed and targeted precisely. Despite emerging evidence, the identity, abundance, and location of senescent cells in chronic wounds remain poorly defined. Reliance on nonspecific markers such as p21 or SA-β-gal complicates interpretation. Senescence appears to play context-dependent roles, with beneficial effects during acute healing but harmful persistence in chronic wounds, presenting challenges for therapeutic targeting. More studies using single-cell RNA sequencing, spatial transcriptomics, and longitudinal profiling are needed to define senescent subpopulations, map their spatial distribution, and track dynamics during wound progression. These approaches will help distinguish transient from persistent senescence. A deeper understanding of interactions with immune, epithelial, and stromal components will guide precisely timed, cell type-specific interventions to improve outcomes.

Alternative splicing (AS) is an omnipresent regulatory mechanism of gene expression that enables the generation of diverse splice isoforms from a single gene. Recently, AS events have gained considerable momentum in the pathogenesis of inflammatory bowel disease (IBD). Our review has summarized the complex process of RNA splicing, and firstly highlighted the potential involved molecules that target aberrant splicing events in IBD. The quantitative transcriptome analyses such as microarrays, next-generation sequencing (NGS) for AS events in IBD have been also discussed. Available evidence suggests that some abnormal splicing RNAs can lead to multiple intestinal disorders during the onset of IBD as well as the progression to colitis-associated cancer (CAC), including gut microbiota perturbations, intestinal barrier dysfunctions, innate/adaptive immune dysregulations, pro-fibrosis activation and some other risk factors. Moreover, current data show that the advanced technologies, including microarrays and NGS, have been pioneeringly employed to screen the AS candidates and elucidate the potential regulatory mechanisms of IBD. Besides, other biotechnological progresses such as the applications of third-generation sequencing (TGS), single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST), will be desired with great expectations. To our knowledge, the current review is the first one to evaluate the potential regulatory mechanisms of AS events in IBD. The expanding list of aberrantly spliced genes in IBD along with the developed technologies provide us new clues to how IBD develops, and how these important AS events can be explored for future treatment.