Spatial Transcriptomics Technologies Research Articles

HyperGCN: an effective deep representation learning framework for the integrative analysis of spatial transcriptomics data

BackgroundAdvances of spatial transcriptomics technologies enabled simultaneously profiling gene expression and spatial locations of cells from the same tissue. Computational tools and approaches for integration of transcriptomics data and spatial context information are urgently needed to comprehensively explore the underlying structure patterns. In this manuscript, we propose HyperGCN for the integrative analysis of gene expression and spatial information profiled from the same tissue. HyperGCN enables data visualization and clustering, and facilitates downstream analysis, including domain segmentation, the characterization of marker genes for the specific domain structure and GO enrichment analysis.ResultsExtensive experiments are implemented on four real datasets from different tissues (including human dorsolateral prefrontal cortex, human positive breast tumors, mouse brain, mouse olfactory bulb tissue and Zabrafish melanoma) and technologies (including 10X visium, osmFISH, seqFISH+, 10X Xenium and Stereo-seq) with different spatial resolutions. The results show that HyperGCN achieves superior clustering performance and produces good domain segmentation effects while identifies biologically meaningful spatial expression patterns. This study provides a flexible framework to analyze spatial transcriptomics data with high geometric complexity.ConclusionsHyperGCN is an unsupervised method based on hypergraph induced graph convolutional network, where it assumes that there existed disjoint tissues with high geometric complexity, and models the semantic relationship of cells through hypergraph, which better tackles the high-order interactions of cells and levels of noise in spatial transcriptomics data.

Emerging high-resolution spatial transcriptomic technologies in kidney research.

Emerging high-resolution spatial transcriptomic technologies in kidney research.

Impeller: a path-based heterogeneous graph learning method for spatial transcriptomic data imputation.

Recent advances in spatial transcriptomics allow spatially resolved gene expression measurements with cellular or even sub-cellular resolution, directly characterizing the complex spatiotemporal gene expression landscape and cell-to-cell interactions in their native microenvironments. Due to technology limitations, most spatial transcriptomic technologies still yield incomplete expression measurements with excessive missing values. Therefore, gene imputation is critical to filling in missing data, enhancing resolution, and improving overall interpretability. However, existing methods either require additional matched single-cell RNA-seq data, which is rarely available, or ignore spatial proximity or expression similarity information. To address these issues, we introduce Impeller, a path-based heterogeneous graph learning method for spatial transcriptomic data imputation. Impeller has two unique characteristics distinct from existing approaches. First, it builds a heterogeneous graph with two types of edges representing spatial proximity and expression similarity. Therefore, Impeller can simultaneously model smooth gene expression changes across spatial dimensions and capture similar gene expression signatures of faraway cells from the same type. Moreover, Impeller incorporates both short- and long-range cell-to-cell interactions (e.g. via paracrine and endocrine) by stacking multiple GNN layers. We use a learnable path operator in Impeller to avoid the over-smoothing issue of the traditional Laplacian matrices. Extensive experiments on diverse datasets from three popular platforms and two species demonstrate the superiority of Impeller over various state-of-the-art imputation methods. The code and preprocessed data used in this study are available at https://github.com/aicb-ZhangLabs/Impeller and https://zenodo.org/records/11212604.

A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex.

The molecular organization of the human neocortex historically has been studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally defined spatial domains that move beyond classic cytoarchitecture. We used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex. Integration with paired single-nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we mapped the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains.

Spatial transcriptomics in health and disease.

The ability to localize hundreds of macromolecules to discrete locations, structures and cell types in a tissue is a powerful approach to understand the cellular and spatial organization of an organ. Spatially resolved transcriptomic technologies enable mapping of transcripts at single-cell or near single-cell resolution in a multiplex manner. The rapid development of spatial transcriptomic technologies has accelerated the pace of discovery in several fields, including nephrology. Its application to preclinical models and human samples has provided spatial information about new cell types discovered by single-cell sequencing and new insights into the cell-cell interactions within neighbourhoods, and has improved our understanding of the changes that occur in response to injury. Integration of spatial transcriptomic technologies with other omics methods, such as proteomics and spatial epigenetics, will further facilitate the generation of comprehensive molecular atlases, and provide insights into the dynamic relationships of molecular components in homeostasis and disease. This Review provides an overview of current and emerging spatial transcriptomic methods, their applications and remaining challenges for the field.

Spatial transcriptomics reveals the interplay between cancer and immune cells directed by MXene quantum dots

Nanomedicine plays an essential role in the development of tumor treatment modalities. However, tumors have a complex structure that includes a variety of immune cell types, which, along with tumor cells, comprise the heterogeneous tumor microenvironment (TME). Although nanoparticles that overcome the limitations of other classical therapeutics and navigate heterogeneous biological barriers have been developed, many unknown players within the TME still exist. The role of immune cell populations and signaling pathways in the TME, which can affect the nanoparticle distribution in the tumor or the overall outcome of nanotherapeutic treatments, are poorly described. In this study, we used spatial transcriptomics to determine in situ gene expression and identified immune cells, other than tumor-associated macrophages, that play vital roles following nanoparticle exposure. In this proof-of-concept study, a recently explored type of nanoparticle, Ti3C2Tx MXene quantum dots (MQDs), with a single particle diameter of ≤10 nm, were administered to orthotopic breast cancer model mice. Thanks to their red-emitting fluorescence properties, we could track the distribution of MQDs in the tumor. Whole-transcriptome analysis and immunofluorescence staining suggested that the heterogeneous distribution of MQDs results in different tumor and immune cell responses in situ. We observed a more tumor-suppressive phenotype at tumor regions with high MQD accumulation compared to regions with decreased MQD accumulation or naïve tumors. Based on pathway analysis and cell deconvolution, we also identified other immune cell types altered by MQDs, including B cells and neutrophils. Specifically, the application of MQDs recruited and activated B cells and resulted in neutrophil degranulation and NETosis in vivo. Using spatial transcriptomics technology, we defined the fundamental molecular and cellular changes that occur in situ in the TME following MQD administration. Future spatial omics studies involving different nanoparticles with other material characteristics will help design more effective nanotherapeutics.

CPPLS-MLP: a method for constructing cell-cell communication networks and identifying related highly variable genes based on single-cell sequencing and spatial transcriptomics data.

In the growth and development of multicellular organisms, the immune processes of the immune system and the maintenance of the organism's internal environment, cell communication plays a crucial role. It exerts a significant influence on regulating internal cellular states such as gene expression and cell functionality. Currently, the mainstream methods for studying intercellular communication are focused on exploring the ligand-receptor-transcription factor and ligand-receptor-subunit scales. However, there is relatively limited research on the association between intercellular communication and highly variable genes (HVGs). As some HVGs are closely related to cell communication, accurately identifying these HVGs can enhance the accuracy of constructing cell communication networks. The rapid development of single-cell sequencing (scRNA-seq) and spatial transcriptomics technologies provides a data foundation for exploring the relationship between intercellular communication and HVGs. Therefore, we propose CPPLS-MLP, which can identify HVGs closely related to intercellular communication and further analyze the impact of Multiple Input Multiple Output cellular communication on the differential expression of these HVGs. By comparing with the commonly used method CCPLS for constructing intercellular communication networks, we validated the superior performance of our method in identifying cell-type-specific HVGs and effectively analyzing the influence of neighboring cell types on HVG expression regulation. Source codes for the CPPLS_MLP R, python packages and the related scripts are available at 'CPPLS_MLP Github [https://github.com/wuzhenao/CPPLS-MLP]'.

ScBOL: a universal cell type identification framework for single-cell and spatial transcriptomics data.

Over the past decade, single-cell transcriptomic technologies have experienced remarkable advancements, enabling the simultaneous profiling of gene expressions across thousands of individual cells. Cell type identification plays an essential role in exploring tissue heterogeneity and characterizing cell state differences. With more and more well-annotated reference data becoming available, massive automatic identification methods have sprung up to simplify the annotation process on unlabeled target data by transferring the cell type knowledge. However, in practice, the target data often include some novel cell types that are not in the reference data. Most existing works usually classify these private cells as one generic 'unassigned' group and learn the features of known and novel cell types in a coupled way. They are susceptible to the potential batch effects and fail to explore the fine-grained semantic knowledge of novel cell types, thus hurting the model's discrimination ability. Additionally, emerging spatial transcriptomic technologies, such as in situ hybridization, sequencing and multiplexed imaging, present a novel challenge to current cell type identification strategies that predominantly neglect spatial organization. Consequently, it is imperative to develop a versatile method that can proficiently annotate single-cell transcriptomics data, encompassing both spatial and non-spatial dimensions. To address these issues, we propose a new, challenging yet realistic task called universal cell type identification for single-cell and spatial transcriptomics data. In this task, we aim to give semantic labels to target cells from known cell types and cluster labels to those from novel ones. To tackle this problem, instead of designing a suboptimal two-stage approach, we propose an end-to-end algorithm called scBOL from the perspective of Bipartite prototype alignment. Firstly, we identify the mutual nearest clusters in reference and target data as their potential common cell types. On this basis, we mine the cycle-consistent semantic anchor cells to build the intrinsic structure association between two data. Secondly, we design a neighbor-aware prototypical learning paradigm to strengthen the inter-cluster separability and intra-cluster compactness within each data, thereby inspiring the discriminative feature representations. Thirdly, driven by the semantic-aware prototypical learning framework, we can align the known cell types and separate the private cell types from them among reference and target data. Such an algorithm can be seamlessly applied to various data types modeled by different foundation models that can generate the embedding features for cells. Specifically, for non-spatial single-cell transcriptomics data, we use the autoencoder neural network to learn latent low-dimensional cell representations, and for spatial single-cell transcriptomics data, we apply the graph convolution network to capture molecular and spatial similarities of cells jointly. Extensive results on our carefully designed evaluation benchmarks demonstrate the superiority of scBOL over various state-of-the-art cell type identification methods. To our knowledge, we are the pioneers in presenting this pragmatic annotation task, as well as in devising a comprehensive algorithmic framework aimed at resolving this challenge across varied types of single-cell data. Finally, scBOL is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/aimeeyaoyao/scBOL.

Abstract 5482: Spatial and multi-omic interrogation of the primary and recurrent glioblastoma microenvironment

Abstract Glioblastoma remains as one of the deadliest malignancies with most cases likely to recur. Recent work has focused on characterizing not only glioma cells, but the tumor microenvironment via single-cell and spatial transcriptomic technologies. This has improved our understanding of the high heterogeneity of this disease and described potential tumor domains. However, there is still a need to further understand the changes that occur between the primary and the recurrent lesion. We propose that a spatial, multi-omic characterization of matched primary and recurrent glioblastoma can yield insights into recurrence mechanisms occurring throughout the tumor microenvironment. We collected 22 IDH-WT glioblastoma samples from 12 patients (11 primary, 11 recurrent, 10 matched pairs) from the Mayo Clinic Neuroscience Biobank. We performed single-nucleus RNA sequencing (15 samples, 10 patients, 5 matched pairs) and proteomics (16 samples, 6 matched pairs) from fresh-frozen tissue, as well as spatial transcriptomics with 10x Visium (14 samples, 8 patients, 6 matched pairs) and 10x Xenium in-situ molecular imaging (6 samples, 2 matched pairs). Using spatial transcriptomics data, we identified regional subsets present in different proportions and distributions between primary and recurrent glioblastoma. These clusters were characterized with gene set and gene ontology analysis, identifying regions enriched with known glioma subtypes and cellular activity. Spatial-based cluster genes were consistent with significantly differentially expressed proteins. We used single-nucleus RNAseq and in-situ molecular imaging data from matched tissue samples to further interrogate relevant cell populations and location in the tumor micro-environment. We identified regions with enriched extra cellular matrix interaction and remodeling signatures to be highly expressed in recurrent glioblastoma. Primary glioblastoma was enriched with oligodendrocyte-like regions with high cell communication and extracellular vesicle signatures. This study will further our understanding of the changes in the mechanisms at play in the complex ecosystem of glioblastoma as it recurs. Citation Format: Jean R. Clemenceau, Paola Suarez-Meade, Alfredo Quiñones-Hinojosa, Tae Hyun Hwang. Spatial and multi-omic interrogation of the primary and recurrent glioblastoma microenvironment [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 5482.

Abstract 6245: Missed opportunity at the subcellular level: Enhancing the utility of cellular imaging modality in spatial transcriptomic profiling of tumor tissues

Abstract Mini-bulk or single-spot spatial transcriptomic technologies, such as GeoMX Digital Spatial Profiler (DSP), have revolutionized our ability to probe spatial heterogeneity and examine transcripts at a subcellular level. In cancer research, this level of granularity is crucial for uncovering the distinct gene expression signatures within specific compartments or structures of interest such as tumor, immune, stroma, and tertiary lymphoid structures. However, in most GeoMX DSP studies, the spatial information obtained from multiplex immunofluorescence imaging has been primarily used for identifying the regions of interest (ROI), rather than as an integral part of the downstream transcriptomic data interpretation. To fix this missed opportunity, we developed a machine learning based analytical framework to fully leverage the rich spatial context provided by the in situ imaging modality. The framework has two main functionalities. (1) ROI cropping and cell segmentation: our framework enables the automated cropping of ROI images, followed by a cell segmentation process utilizing a multi-color-optimized deep learning model pre-trained based on the TissueNet. (2) Cell typing and integrative spatial analysis: the pipeline extracts cell-level morphological features (e.g. cell size and circularity), and AI-driven characteristics. These features are subsequently employed for cell typing and the quantification of cell mixture. The framework is equipped to perform a series of advanced spatial analyses, such as spatial metrics calculation and consensus clustering analysis with the matched gene expression data. To validate the framework, we conducted GeoMX DSP analyses on samples obtained from bladder cancer and upper tract urothelial carcinoma, encompassing 56 ROIs annotated by pathologists. Our results demonstrated high accuracy, with the calculated tumor-immune proportions aligning precisely with the original annotations. We further compared the cell deconvolution results of our framework with those obtained using SpatialDecon. Consistency was noted across both methods, with a high correlation reaching 0.90. However, we observed that SpatialDecon tends to underestimate tumor purity in tumor core regions, with discrepancies as large as 0.30, and overestimating tumor purity in stromal regions. Additionally, analysis of ROIs in tumor margins revealed that SpatialDecon consistently overestimates the overall proportions of immune cells. In conclusion, our results underscore the importance of integrating in situ imaging with subcellular-level spatial transcriptomics for a more accurate and reliable analysis of tumor tissues. Our approach provides critical insights into the tumor microenvironment and cellular interactions, with significant implications for both research and clinical applications in oncology. Citation Format: Xiaofei Song, Xiaoqing Yu, Carlos M. Moran-Segura, G Daniel Grass, Roger Li, Xuefeng Wang. Missed opportunity at the subcellular level: Enhancing the utility of cellular imaging modality in spatial transcriptomic profiling of tumor tissues [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 6245.

Abstract 5498: Multi-modal examination of spatial heterogeneity in the astrocytoma microenvironment

Abstract Astrocytoma is a diffuse tumor that infiltrates the brain. Despite post-operative therapy including radiation and chemotherapy, 52% of patients’ tumors recur within five-years of surgery. These tumors are characterized by mutations in the IDH1 or IDH2 gene that confer metabolic alterations, most notably the production of the onco-metabolite 2-hydroxyglutarate. To reveal the metabolic alterations and their upstream regulation across the tumor microenvironment, we applied spatial metabolomics and spatial transcriptomics to sequential sections from resected tumor samples of patients with low grade astrocytoma. This necessitated extensive optimization of atmospheric-pressure MALDI-TOF, including the establishment of a custom automated computational pipeline with abilities to identify metabolic niches. Additionally, using sequential sections of the same tumor, we also compared all commercially available imaging-based spatial transcriptomic technologies (10x Genomics Xenium, Nanostring CosMx, Vizgen MERSCOPE) to evaluate their suitability with regards to building a multi-modal model of metabolic activity and regulation across the tumor microenvironment. We found that 10x Genomics Xenium was most suited for this task as image registration across sequential sections was comparatively straight-forward. Combining spatial transcriptomics generated with 10x Genomics Xenium and spatial metabolomics across 8 matched sequential slices of the same tumor, we identified metabolic niches associated with distinct cell type composition and transcriptional regulation. Citation Format: Joel Moffet, Jurgen Kriel, Tianyao Lu, Lutz Freytag, James Whittle, Sarah Best, Saskia Freytag. Multi-modal examination of spatial heterogeneity in the astrocytoma microenvironment [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 5498.

Abstract 868: A novel unsupervised machine learning model applied to neuroblastoma single-cell RNA-seq data reveals a drug-induced mesenchymal-like gene expression program

Abstract High-risk neuroblastoma is one of the most difficult to treat pediatric cancers, with a survival rate of only about 50% and significant long-term consequences from current chemotherapy. Preclinical models such as cell lines and mice are the backbone of drug development and experimental-mechanistic oncology, but the development of new treatments is hampered in part by a lack of understanding of the direct clinical relevance of such data collected in preclinical models. Despite this, few formal methods have been developed to determine how these various models represent/resemble primary patient tumors. Here, we present the first comprehensive single-cell RNA-seq analysis of neuroblastoma across an extensive cohort of patient tumors and a variety of preclinical model systems (n = 126 total samples assembled - the largest cohort of its kind). By building an innovative unsupervised machine learning method, which we term “automatic consensus nonnegative matrix factorization” (acNMF), we have integrated and contrasted the transcriptional landscapes of patient tumors with those of cell lines, patient-derived xenografts (PDX), and genetic mouse models (GEMM).Using these tools, we discovered the dominant adrenergic gene expression programs found in neuroblastoma patient tumors were preserved across all preclinical models. However, the presumptive chemo-resistant mesenchymal-like programs, while identifiable in cell lines, were primarily restricted to subpopulations of cancer-associated fibroblasts and Schwann-like cells in vivo. Surprisingly, a mesenchymal-like program could be acutely chemotherapy-induced in GEMM and was evident in pre-treated patient and PDX samples, suggesting a previously uncharacterized mechanism of therapy escape. In addition to these core findings, our computational tools were able to further delineate the classical neuroblastoma adrenergic and mesenchymal gene expression programs, discovering for example, novel subpopulations of cancer associated fibroblasts. These behaviors were conserved across tumors and most preclinical models, which we validated by RNA in situ hybridization, which is a high resolution ultra sensitive spatial transcriptomics technology. Our work cautions against overreliance on traditional preclinical models without recognizing their limitations and we offer a nuanced, high-resolution view of neuroblastoma pre-clinical systems for advancing therapeutic development. We have launched an open-source web resource, featuring this integrated map to aid the scientific community in further exploration of these data and hypothesis generation (available at http://pscb.stjude.org). Citation Format: Rich Chapple, Xueying Liu, Sivaraman Natarajan, Margaret I. Alexander, Yuna Kim, Anand Patel, Christy W. LaFlamme, Min Pan, William C. Wright, Hyeong-Min Lee, Yinwen Zhang, Meifen Lu, Selene C. Koo, Courtney Long, John Harper, Chandra Savage, Melissa D. Johnson, Thomas Confer, Walter J. Akers, Michael A. Dyer, Heather Sheppard, John Easton, Paul Geeleher. A novel unsupervised machine learning model applied to neuroblastoma single-cell RNA-seq data reveals a drug-induced mesenchymal-like gene expression program [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 868.

Applications of single‑cell omics and spatial transcriptomics technologies in gastric cancer (Review).

Gastric cancer (GC) is a prominent contributor to global cancer-related mortalities, and a deeper understanding of its molecular characteristics and tumor heterogeneity is required. Single-cell omics and spatial transcriptomics (ST) technologies have revolutionized cancer research by enabling the exploration of cellular heterogeneity and molecular landscapes at the single-cell level. In the present review, an overview of the advancements in single-cell omics and ST technologies and their applications in GC research is provided. Firstly, multiple single-cell omics and ST methods are discussed, highlighting their ability to offer unique insights into gene expression, genetic alterations, epigenomic modifications, protein expression patterns and cellular location in tissues. Furthermore, a summary is provided of key findings from previous research on single-cell omics and ST methods used in GC, which have provided valuable insights into genetic alterations, tumor diagnosis and prognosis, tumor microenvironment analysis, and treatment response. In summary, the application of single-cell omics and ST technologies has revealed the levels of cellular heterogeneity and the molecular characteristics of GC, and holds promise for improving diagnostics, personalized treatments and patient outcomes in GC.

BiGATAE: a bipartite graph attention auto-encoder enhancing spatial domain identification from single-slice to multi-slices.

Recent advancements in spatial transcriptomics technology have revolutionized our ability to comprehensively characterize gene expression patterns within the tissue microenvironment, enabling us to grasp their functional significance in a spatial context. One key field of research in spatial transcriptomics is the identification of spatial domains, which refers to distinct regions within the tissue where specific gene expression patterns are observed. Diverse methodologies have been proposed, each with its unique characteristics. As the availability of spatial transcriptomics data continues to expand, there is a growing need for methods that can integrate information from multiple slices to discover spatial domains. To extend the applicability of existing single-slice analysis methods to multi-slice clustering, we introduce BiGATAE (Bipartite Graph Attention Auto Encoder) that leverages gene expression information from adjacent tissue slices to enhance spatial transcriptomics data. BiGATAE comprises two steps: aligning slices to generate an adjacency matrix for different spots in consecutive slices and constructing a bipartite graph. Subsequently, it utilizes a graph attention network to integrate information across different slices. Then it can seamlessly integrate with pre-existing techniques. To evaluate the performance of BiGATAE, we conducted benchmarking analyses on three different datasets. The experimental results demonstrate that for existing single-slice clustering methods, the integration of BiGATAE significantly enhances their performance. Moreover, single-slice clustering methods integrated with BiGATAE outperform methods specifically designed for multi-slice integration. These results underscore the proficiency of BiGATAE in facilitating information transfer across multiple slices and its capacity to broaden the applicability and sustainability of pre-existing methods.

Innovative super-resolution in spatial transcriptomics: a transformer model exploiting histology images and spatial gene expression.

Spatial transcriptomics technologies have shed light on the complexities of tissue structures by accurately mapping spatial microenvironments. Nonetheless, a myriad of methods, especially those utilized in platforms like Visium, often relinquish spatial details owing to intrinsic resolution limitations. In response, we introduce TransformerST, an innovative, unsupervised model anchored in the Transformer architecture, which operates independently of references, thereby ensuring cost-efficiency by circumventing the need for single-cell RNA sequencing. TransformerST not only elevates Visium data from a multicellular level to a single-cell granularity but also showcases adaptability across diverse spatial transcriptomics platforms. By employing a vision transformer-based encoder, it discerns latent image-gene expression co-representations and is further enhanced by spatial correlations, derived from an adaptive graph Transformer module. The sophisticated cross-scale graph network, utilized in super-resolution, significantly boosts the model's accuracy, unveiling complex structure-functional relationships within histology images. Empirical evaluations validate its adeptness in revealing tissue subtleties at the single-cell scale. Crucially, TransformerST adeptly navigates through image-gene co-representation, maximizing the synergistic utility of gene expression and histology images, thereby emerging as a pioneering tool in spatial transcriptomics. It not only enhances resolution to a single-cell level but also introduces a novel approach that optimally utilizes histology images alongside gene expression, providing a refined lens for investigating spatial transcriptomics.

Decoder-seq enhances mRNA capture efficiency in spatial RNA sequencing.

Spatial transcriptomics technologies with high resolution often lack high sensitivity in mRNA detection. Here we report a dendrimeric DNA coordinate barcoding design for spatial RNA sequencing (Decoder-seq), which offers both high sensitivity and high resolution. Decoder-seq combines dendrimeric nanosubstrates with microfluidic coordinate barcoding to generate spatial arrays with a DNA density approximately ten times higher than previously reported methods while maintaining flexibility in resolution. We show that the high RNA capture efficiency of Decoder-seq improved the detection of lowly expressed olfactory receptor (Olfr) genes in mouse olfactory bulbs and contributed to the discovery of a unique layer enrichment pattern for two Olfr genes. The near-cellular resolution provided by Decoder-seq has enabled the construction of a spatial single-cell atlas of the mouse hippocampus, revealing dendrite-enriched mRNAs in neurons. When applying Decoder-seq to human renal cell carcinomas, we dissected the heterogeneous tumor microenvironment across different cancer subtypes and identified spatial gradient-expressed genes related to epithelial-mesenchymal transition with the potential to predict tumor prognosis and progression.

Abstract C056: The initiation and progression of pancreatic ductal adenocarcinoma through a p53 lens

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a 5-year survival rate of only 11%. Slated to be the 2nd most deadly cancer by 2026, pancreatic cancer survival rates have not improved substantially over the years. With a lack of early detection methods and a highly immunosuppressive tumor microenvironment (TME), PDAC tumors are highly resistant to treatment. Oncogenic mutations in KRAS are present in >90% of cases and mutations in the tumor suppressor gene TP53 are present in ~72% of cases. However, why p53 mutations are so prevalent and how they enhance PDAC development is not well understood. To elucidate how exactly p53 loss promotes PDAC, we are using genetically engineered mouse models combined with single-cell and spatial transcriptomic technologies. In this mouse model, PDAC is driven from adult acinar cells with a tamoxifen-inducible Ptf1aCreER allele by oncogenic Kras (KrasG12D) along with either p53 wild type (p53wt) or p53 deficiency (p53flox). In this model, acinar cells can give rise to PDAC likely through a transdifferentiation process called acinar-to-ductal metaplasia (ADM), which leads to pancreatic intraepithelial neoplasias (PanINs) and then PDAC. The initial cell fate transition, ADM, can be activated by growth factors, inflammation, and injury. We have conducted single-cell and spatial transcriptomics to compare early stages of tumor development in the presence and absence of p53, and we will present our latest analyses. Providing insight into the early events of PDAC will lead to new approaches for improving clinical intervention in human PDAC. Citation Format: Kathryn J. Hanson, Allegra C. Minor, Brittany M. Flowers, Alyssa M. Kaiser, Nicholas Hughes, Hannes Vogel, Le Cong, Laura D. Attardi. The initiation and progression of pancreatic ductal adenocarcinoma through a p53 lens [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr C056.

Crosstalk between tumor and microenvironment: Insights from spatial transcriptomics.

Cancer is a dynamic disease, and clonal heterogeneity plays a fundamental role in tumor development, progression, and resistance to therapies. Single-cell and spatial multimodal technologies can provide a high-resolution molecular map of underlying genomic, epigenomic, and transcriptomic alterations involved in inter- and intra-tumor heterogeneity and interactions with the microenvironment. In this review, we provide a perspective on factors driving cancer heterogeneity, tumor evolution, and clonal states. We briefly describe spatial transcriptomic technologies and summarize recent literature that sheds light on the dynamical interactions between tumor states, cell-to-cell communication, and remodeling local microenvironment.

Current computational methods for spatial transcriptomics in cancer biology.

Cells in multicellular organisms constitute a self-organizing society by interacting with their neighbors. Cancer originates from malfunction of cellular behavior in the context of such a self-organizing system. The identities or characteristics of individual tumor cells can be represented by the hallmark of gene expression or transcriptome, which can be addressed using single-cell dissociation followed by RNA sequencing. However, the dissociation process of single cells results in losing the cellular address in tissue or neighbor information of each tumor cell, which is critical to understanding the malfunctioning cellular behavior in the microenvironment. Spatial transcriptomics technology enables measuring the transcriptome which is tagged by the address within a tissue. However, to understand cellular behavior in a self-organizing society, we need to apply mathematical or statistical methods. Here, we provide a review on current computational methods for spatial transcriptomics in cancer biology.

Single-cell RNA Sequencing and Spatial Transcriptomic Technologies and Applications in Exploring Gastric Cancer: A Review

Single-cell RNA Sequencing and Spatial Transcriptomic Technologies and Applications in Exploring Gastric Cancer: A Review