Spatial Gene Expression Patterns Research Articles

Whole-Genome Identification of the Flax Fatty Acid Desaturase Gene Family and Functional Analysis of the LuFAD2.1 Gene Under Cold Stress Conditions.

Fatty acid desaturase (FAD) is essential for plant growth and development and plant defence response. Although flax (Linum usitatissimum L.) is an important oil and fibre crop, but its FAD gene remains understudied. This study identified 43 LuFAD genes in the flax genome. The phylogenetic analysis divided the FAD genes into seven subfamilies. LuFAD is unevenly distributed on 15 chromosomes, and fragment duplication is the only driving force for the amplification of the LuFAD gene family. In the LuFAD gene promoter region, most elements respond to plant hormones (MeJA, ABA) and abiotic stresses (anaerobic and low temperature). The expression pattern analysis showed that the temporal and spatial expression patterns of all LuFAD genes in different tissues and the response patterns to abiotic stresses (heat and salt) were identified. Subcellular localisation showed that all LuFAD2-GFP were expressed in the endoplasmic reticulum membrane. RT-qPCR analysis revealed that LuFAD2 was significantly upregulated under cold, salt and drought stress, and its overexpression in Arabidopsis thaliana enhanced cold tolerance genes and reduced ROS accumulation. This study offers key insights into the FAD gene family's role in flax development and stress adaptation.

Machine Learning in Genomics: Applications in Whole Genome Sequencing, Whole Exome Sequencing, Single-Cell Genomics, and Spatial Transcriptomics

The application of machine learning (ML) to genomics has transformed the process of analyzing and interpreting large-scale, complex datasets, leading to important breakthroughs in our knowledge of biological systems. This review provides a comprehensive overview of ML applications in key genomic areas: Whole Genome Sequencing (WGS), Whole Exome Sequencing (WES), single-cell genomics, and spatial transcriptomics. In WGS and WES, ML techniques are employed for variant calling, genome-wide association studies, rare variant analysis, and the prediction of pathogenicity. In single-cell genomics, ML facilitates clustering, trajectory inference, and cell type identification, while in spatial transcriptomics, it aids in deciphering spatial patterns of gene expression and tissue heterogeneity. This review further explores the application of ML in related omics fields, including proteomics, transcriptomics, metagenomics, epigenomics, and microbiome research. These applications encompass protein structure prediction, functional annotation, microbial community profiling, and the analysis of epigenetic modifications. We address the challenges caused by high dimensionality, variability in the data, and the requirement for interpretable machine learning models when dealing with genomic data. Emerging technologies like explainable AI and federated learning are highlighted for their potential to address these challenges. Additionally, the review addresses ethical considerations, data privacy issues, and the necessity for standardized protocols in ML applications. This comprehensive examination underscores the transformative impact of ML in genomics and highlights its potential to drive future innovations in personalized medicine and biological research.

ANGI-08. SPATIAL TRANSCRIPTOMIC COMPARISONS OF GLIOBLASTOMA TISSUE REVEAL UPREGULATION OF NEURODEVELOPMENTAL PATHWAYS AND SYNAPTIC GENES IN INFILTRATING TUMOR CELLS

Abstract Glioblastoma remains one of the deadliest brain malignancies. First-line therapy consists of maximal surgical tumor resection, chemotherapy, and radiotherapy. Malignant cells escape surgical resection by migrating into the surrounding healthy brain tissue, where they give rise to the recurrent tumor. Gene expression profiling allows glioblastoma tumor cores to be classified into mesenchymal, proneural, and classical subtypes, each with distinct genetic alterations and cellular compositions. In contrast, the adjacent brain parenchyma where infiltrating malignant cells escape surgical resection is less characterized in patients. Using single-cell and multicellular resolution spatial transcriptomics on tissues from both the tumor core and infiltrated brain tissue (n = 11), we compared the transcriptional profiles of malignant cells in these regions. Malignant cells near regions with microvascular proliferation showed increased expression of genes related to vascular homeostasis. Within tumor cores, proneural and mesenchymal subtypes exhibited distinct spatial gene expression patterns, although these differences were reduced in the infiltrated brain tissue, apart from gene expression due to chromosomal alterations specific to the proneural subtype. We identified two transcriptional patterns of brain infiltration, with the dominant pattern showing a transition from mesenchymal to proneural states. This transition was accompanied by increased expression of genes linked to neurodevelopmental pathways and glial cell differentiation. Additionally, one gene module upregulated in infiltrating malignant cells was predictive of poor patient survival and enriched for genes associated with differentiated neuronal cells. Together, our findings provide an updated view of the spatial landscape of glioblastomas and infiltrated brain tissue, furthering our understanding of the malignant cells that invade healthy brain. This insight offers new avenues for targeted therapy after surgical resection of the primary tumor.

Spatial Analysis of Gene Expression by In Situ Hybridization.

The analysis of the spatial expression patterns of genes is important for deciphering their functions. In situ hybridization provides insight into gene expression patterns at the cellular level. Here we describe a procedure for performing in situ hybridization on sections of paraffin-embedded tissue, including synthesis of labeled RNA probes, hybridization of the probes with target mRNAs, and immunological detection of the signals. The method enables evaluation of the expression patterns of genes in a variety of tissues of rice.

Spatial transcriptomics analysis identifies therapeutic targets in diffuse high-grade gliomas.

Diffuse high-grade gliomas are the most common malignant adult neuroepithelial tumors in humans and a leading cause of cancer-related death worldwide. The advancement of high throughput transcriptome sequencing technology enables rapid and comprehensive acquisition of transcriptome data from target cells or tissues. This technology aids researchers in understanding and identifying critical therapeutic targets for the prognosis and treatment of diffuse high-grade glioma. Spatial transcriptomics was conducted on two cases of isocitrate dehydrogenase (IDH) wild-type diffuse high-grade glioma (Glio-IDH-wt) and two cases of IDH-mutant diffuse high-grade glioma (Glio-IDH-mut). Gene set enrichment analysis and clustering analysis were employed to pinpoint differentially expressed genes (DEGs) involved in the progression of diffuse high-grade gliomas. The spatial distribution of DEGs in the spatially defined regions of human glioma tissues was overlaid in the t-distributed stochastic neighbor embedding (t-SNE) plots. We identified a total of 10,693 DEGs, with 5,677 upregulated and 5,016 downregulated, in spatially defined regions of diffuse high-grade gliomas. Specifically, SPP1, IGFBP2, CALD1, and TMSB4X exhibited high expression in carcinoma regions of both Glio-IDH-wt and Glio-IDH-mut, and 3 upregulated DEGs (SMOC1, APOE, and HIPK2) and 4 upregulated DEGs (PPP1CB, UBA52, S100A6, and CTSB) were only identified in tumor regions of Glio-IDH-wt and Glio-IDH-mut, respectively. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses revealed that upregulated DEGs were closely related to PI3K/Akt signaling pathway, virus infection, and cytokine-cytokine receptor interaction. Importantly, the expression of these DEGs was validated using GEPIA databases. Furthermore, the study identified spatial expression patterns of key regulatory genes, including those involved in protein post-translational modification and RNA binding protein-encoding genes, with spatially defined regions of diffuse high-grade glioma. Spatial transcriptome analysis is one of the breakthroughs in the field of medical biotechnology as this can map the analytes such as RNA information in their physical location in tissue sections. Our findings illuminate previously unexplored spatial expression profiles of key biomarkers in diffuse high-grade glioma, offering novel insight for the development of therapeutic strategies in glioma.

Graph domain adaptation-based framework for gene expression enhancement and cell type identification in large-scale spatially resolved transcriptomics.

Spatially resolved transcriptomics (SRT) technologies facilitate gene expression profiling with spatial resolution in a naïve state. Nevertheless, current SRT technologies exhibit limitations, manifesting as either low transcript detection sensitivity or restricted gene throughput. These constraints result in diminished precision and coverage in gene measurement. In response, we introduce SpaGDA, a sophisticated deep learning-based graph domain adaptation framework for both scenarios of gene expression imputation and cell type identification in spatially resolved transcriptomics data by impartially transferring knowledge from reference scRNA-seq data. Systematic benchmarking analyses across several SRT datasets generated from different technologies have demonstrated SpaGDA's superior effectiveness compared to state-of-the-art methods in both scenarios. Further applied to three SRT datasets of different biological contexts, SpaGDA not only better recovers the well-established knowledge sourced from public atlases and existing scientific literature but also yields a more informative spatial expression pattern of genes. Together, these results demonstrate that SpaGDA can be used to overcome the challenges of current SRT data and provide more accurate insights into biological processes or disease development. The SpaGDA is available in https://github.com/shenrb/SpaGDA.

The influence of spatial distribution and transcriptional regulation of secondary metabolites on the bioactivities of Adenophora triphylla (Japanese lady bell)

The distribution of secondary metabolites in plant tissues plays a crucial role in determining their pharmacological properties. In this study, we investigated the dynamics of the bioactive compounds in Adenophora triphylla, a medicinal herb with diverse therapeutic applications.The anti-inflammatory properties of the EtOAc fraction from the aerial part extract (A_ EtF) exhibited an IC50 value of 27.2 ± 2.3 μg/mL, significantly surpassing that of the EtOAc fraction from the root extract (R_EtF) with an IC50 of 38.9 ± 2.9 μg/mL. Similarly, the anti-melanogenic activity of A_EtF (IC50 = 68.9 ± 2.3 μg/mL) outperformed that of R_EtF (IC50 = 90.0 ± 5.5 μg/mL). Analysis of the distinct chemical profiles of these tissues using UPLC-ESI-Q-TOF-MS revealed that the distribution of secondary metabolites contributes to the observed variations in pharmacological properties between the aerial parts and roots. Transcriptome analysis further elucidated spatially regulated genes associated with secondary metabolism, highlighting the role of AbtYABBYs as potential regulators of phenylpropanoid biosynthesis. To validate their function, these genes were transiently expressed in tobacco leaves via agro-infiltration, confirming their role in modulating polyphenolic compound biosynthesis. Our findings underscore the importance of understanding spatial gene expression patterns for harnessing the complete pharmacological potential of medicinal plants. This study provides valuable insights into the spatial regulation of secondary metabolism and lays the groundwork for targeted manipulation of plant bioactivity for therapeutic and industrial applications.

Spatiotemporal transcriptome atlas reveals gene regulatory patterns during the organogenesis of the rapid growing bamboo shoots.

Bamboo with its remarkable growth rate and economic significance, offers an ideal system to investigate the molecular basis of organogenesis in rapidly growing plants, particular in monocots, where gene regulatory networks governing the maintenance and differentiation of shoot apical and intercalary meristems remain a subject of controversy. We employed both spatial and single-nucleus transcriptome sequencing on 10× platform to precisely dissect the gene functions in various tissues and early developmental stages of bamboo shoots. Our comprehensive analysis reveals distinct cell trajectories during shoot development, uncovering critical genes and pathways involved in procambium differentiation, intercalary meristem formation, and vascular tissue development. Spatial and temporal expression patterns of key regulatory genes, particularly those related to hormone signaling and lipid metabolism, strongly support the hypothesis that intercalary meristem origin from surrounded parenchyma cells. Specific gene expressions in intercalary meristem exhibit regular and dispersed distribution pattern, offering clues for understanding the intricate molecular mechanisms that drive the rapid growth of bamboo shoots. The single-nucleus and spatial transcriptome analysis reveal a comprehensive landscape of gene activity, enhancing the understanding of the molecular architecture of organogenesis and providing valuable resources for future genomic and genetic studies relying on identities of specific cell types.

Spatial and temporal gene expression patterns during early human odontogenesis process.

Studies on odontogenesis are of great importance to treat dental abnormalities and tooth loss. However, the odontogenesis process was poorly studied in humans, especially at the early developmental stages. Here, we combined RNA sequencing (RNA-seq) with Laser-capture microdissection (LCM) to establish a spatiotemporal transcriptomic investigation for human deciduous tooth germs at the crucial developmental stage to offer new perspectives to understand tooth development and instruct tooth regeneration. Several hallmark events, including angiogenesis, ossification, axonogenesis, and extracellular matrix (ECM) organization, were identified during odontogenesis in human dental epithelium and mesenchyme from the cap stage to the early bell stage. ECM played an essential role in the shift of tooth-inductive capability. Species comparisons demonstrated these hallmark events both in humans and mice. This study reveals the hallmark events during odontogenesis, enriching the transcriptomic research on human tooth development at the early stage.

SpatialGE: A user-friendly web application to democratize spatial transcriptomics analysis.

Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, its potential is often underutilized due to the advanced data analysis and programming skills required. To address this, we present spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provides a user-friendly interface that guides users without programming expertise through various analysis pipelines, including quality control, normalization, domain detection, phenotyping, and multiple spatial analyses. It also enables comparative analysis among samples and supports various ST technologies. We demonstrate the utility of spatialGE through its application in studying the tumor microenvironment of melanoma brain metastasis and Merkel cell carcinoma. Our results highlight the ability of spatialGE to identify spatial gene expression patterns and enrichments, providing valuable insights into the tumor microenvironment and its utility in democratizing ST data analysis for the wider scientific community.

Simulating multiple variability in spatially resolved transcriptomics with scCube

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

Identification of a gene expression signature of vascular invasion and recurrence in stage I lung adenocarcinoma via bulk and spatial transcriptomics.

Microscopic vascular invasion (VI) is predictive of recurrence and benefit from lobectomy in stage I lung adenocarcinoma (LUAD) but is difficult to assess in resection specimens and cannot be accurately predicted prior to surgery. Thus, new biomarkers are needed to identify this aggressive subset of stage I LUAD tumors. To assess molecular and microenvironment features associated with angioinvasive LUAD we profiled 162 resected stage I tumors with and without VI by RNA-seq and explored spatial patterns of gene expression in a subset of 15 samples by high-resolution spatial transcriptomics (stRNA-seq). Despite the small size of invaded blood vessels, we identified a gene expression signature of VI from the bulk RNA-seq discovery cohort (n=103) and found that it was associated with VI foci, desmoplastic stroma, and high-grade patterns in our stRNA-seq data. We observed a stronger association with high-grade patterns from VI+ compared with VI- tumors. Using the discovery cohort, we developed a transcriptomic predictor of VI, that in an independent validation cohort (n=60) was associated with VI (AUROC=0.86; p=5.42×10-6) and predictive of recurrence-free survival (HR=1.98; p=0.024), even in VI- LUAD (HR=2.76; p=0.003). To determine our VI predictor's robustness to intra-tumor heterogeneity we used RNA-seq data from multi-region sampling of stage I LUAD cases in TRACERx, where the predictor scores showed high correlation (R=0.87, p<2.2×10-16) between two randomly sampled regions of the same tumor. Our study suggests that VI-associated gene expression changes are detectable beyond the site of intravasation and can be used to predict the presence of VI. This may enable the prediction of angioinvasive LUAD from biopsy specimens, allowing for more tailored medical and surgical management of stage I LUAD.

A multi-view graph contrastive learning framework for deciphering spatially resolved transcriptomics data.

Spatially resolved transcriptomics data are being used in a revolutionary way to decipher the spatial pattern of gene expression and the spatial architecture of cell types. Much work has been done to exploit the genomic spatial architectures of cells. Such work is based on the common assumption that gene expression profiles of spatially adjacent spots are more similar than those of more distant spots. However, related work might not consider the nonlocal spatial co-expression dependency, which can better characterize the tissue architectures. Therefore, we propose MuCoST, a Multi-view graph Contrastive learning framework for deciphering complex Spatially resolved Transcriptomic architectures with dual scale structural dependency. To achieve this, we employ spot dependency augmentation by fusing gene expression correlation and spatial location proximity, thereby enabling MuCoST to model both nonlocal spatial co-expression dependency and spatially adjacent dependency. We benchmark MuCoST on four datasets, and we compare it with other state-of-the-art spatial domain identification methods. We demonstrate that MuCoST achieves the highest accuracy on spatial domain identification from various datasets. In particular, MuCoST accurately deciphers subtle biological textures and elaborates the variation of spatially functional patterns.

Accurately deciphering spatial domains for spatially resolved transcriptomics with stCluster.

Spatial transcriptomics provides valuable insights into gene expression within the native tissue context, effectively merging molecular data with spatial information to uncover intricate cellular relationships and tissue organizations. In this context, deciphering cellular spatial domains becomes essential for revealing complex cellular dynamics and tissue structures. However, current methods encounter challenges in seamlessly integrating gene expression data with spatial information, resulting in less informative representations of spots and suboptimal accuracy in spatial domain identification. We introduce stCluster, a novel method that integrates graph contrastive learning with multi-task learning to refine informative representations for spatial transcriptomic data, consequently improving spatial domain identification. stCluster first leverages graph contrastive learning technology to obtain discriminative representations capable of recognizing spatially coherent patterns. Through jointly optimizing multiple tasks, stCluster further fine-tunes the representations to be able to capture complex relationships between gene expression and spatial organization. Benchmarked against six state-of-the-art methods, the experimental results reveal its proficiency in accurately identifying complex spatial domains across various datasets and platforms, spanning tissue, organ, and embryo levels. Moreover, stCluster can effectively denoise the spatial gene expression patterns and enhance the spatial trajectory inference. The source code of stCluster is freely available at https://github.com/hannshu/stCluster.

SpatialSPM: statistical parametric mapping for the comparison of gene expression pattern images in multiple spatial transcriptomic datasets.

Spatial transcriptomic (ST) techniques help us understand the gene expression levels in specific parts of tissues and organs, providing insights into their biological functions. Even though ST dataset provides information on the gene expression and its location for each sample, it is challenging to compare spatial gene expression patterns across tissue samples with different shapes and coordinates. Here, we propose a method, SpatialSPM, that reconstructs ST data into multi-dimensional image matrices to ensure comparability across different samples through spatial registration process. We demonstrated the applicability of this method by kidney and mouse olfactory bulb datasets as well as mouse brain ST datasets to investigate and directly compare gene expression in a specific anatomical region of interest, pixel by pixel, across various biological statuses. Beyond traditional analyses, SpatialSPM is capable of generating statistical parametric maps, including T-scores and Pearson correlation coefficients. This feature enables the identification of specific regions exhibiting differentially expressed genes across tissue samples, enhancing the depth and specificity of ST studies. Our approach provides an efficient way to analyze ST datasets and may offer detailed insights into various biological conditions.

SPADE: spatial deconvolution for domain specific cell-type estimation

Understanding gene expression in different cell types within their spatial context is a key goal in genomics research. SPADE (SPAtial DEconvolution), our proposed method, addresses this by integrating spatial patterns into the analysis of cell type composition. This approach uses a combination of single-cell RNA sequencing, spatial transcriptomics, and histological data to accurately estimate the proportions of cell types in various locations. Our analyses of synthetic data have demonstrated SPADE’s capability to discern cell type-specific spatial patterns effectively. When applied to real-life datasets, SPADE provides insights into cellular dynamics and the composition of tumor tissues. This enhances our comprehension of complex biological systems and aids in exploring cellular diversity. SPADE represents a significant advancement in deciphering spatial gene expression patterns, offering a powerful tool for the detailed investigation of cell types in spatial transcriptomics.

Pax6 isoforms shape eye development: Insights from developmental stages and organoid models

Pax6 is a critical transcription factor involved in the development of the central nervous system. However, in humans, mutations in Pax6 predominantly result in iris deficiency rather than neurological phenotypes. This may be attributed to the distinct functions of Pax6 isoforms, Pax6a and Pax6b. In this study, we investigated the spatial and temporal expression patterns of Pax6 isoforms during different stages of mouse eye development. We observed a strong correlation between Pax6a expression and the neuroretina gene Sox2, while Pax6b showed a high correlation with iris-component genes, including the mesenchymal gene Foxc1. During early patterning from E10.5, Pax6b was expressed in the hinge of the optic cup and neighboring mesenchymal cells, whereas Pax6a was absent in these regions. At E14.5, both Pax6a and Pax6b were expressed in the future iris and ciliary body, coinciding with the integration of mesenchymal cells and Mitf-positive cells in the outer region. From E18.5, Pax6 isoforms exhibited distinct expression patterns as lineage genes became more restricted. To further validate these findings, we utilized ESC-derived eye organoids, which recapitulated the temporal and spatial expression patterns of lineage genes and Pax6 isoforms. Additionally, we found that the spatial expression patterns of Foxc1 and Mitf were impaired in Pax6b-mutant ESC-derived eye organoids. This in vitro eye organoids model suggested the involvement of Pax6b-positive local mesodermal cells in iris development. These results provide valuable insights into the regulatory roles of Pax6 isoforms during iris and neuroretina development and highlight the potential of ESC-derived eye organoids as a tool for studying normal and pathological eye development.

Abstract 2329: Cancer region definition using spatial gene expression patterns by super resolution reconstruction algorithm for spatial transcriptomics data

Abstract Background: Spatially resolved transcriptomics (ST) has enabled a variety of cancer research on the heterogeneity of tumor microenvironment. However, when identifying cancer boundaries based on ST, the limited resolution of ST such as Visium hinders the accurate identification of cancerous regions. To address these issues, we have developed a novel approach for accurately classifying regions of the tumor by using deep image prior (DIP) algorithm to convert Visium data into high-resolution images. Methods: We proposed SuperST, which utilizes deep image prior to transform low-resolution ST data into high-resolution images, providing more accurate spatial marker expression patterns. This method was used to define cancer regions by combining with a pre-trained deep learning algorithm that recognizes spatial patterns. ST data of hepatocellular carcinoma (HCC) patients were used to apply SuperST. Firstly, 400 spatially variable genes were identified. SuperST leverages the deep image prior (DIP) algorithm and a U-Net neural network to transform ST data into high-resolution ones without needing ground-truth high-resolution data. The final high-resolution images of gene expression data were used to extract 512-dimensional feature vectors for each gene via a pre-trained convolutional neural network model (VGG16). Subsequently, we apply K-means clustering to these features to identify transcriptionally distinct tissue regions considering spatial information and compared with the human-labeled cancer regions. Results: The cancer region detection using spatial gene expression data and our algorithm yielded results that substantially align with the human annotations on the H&amp;E images. Furthermore, compared to the cancer definition obtained using the CopyKat and SPACET algorithms, our results were more spatially consistent for cancerous regions as well as providing high-resolution image-level definition of cancer region margin. This indicates that our algorithm better utilizes spatial information and more effectively overcomes the low resolution of ST data. Conclusion: This research demonstrates the effective application of our SuperST algorithm to the challenging problem of cancer region detection only using spatial gene expression data. It suggests that our algorithm could be utilized for a variety of problems in spatial biology in cancer research. Citation Format: Jeongbin Park, Seungho Cook, Dongjoo Lee, Jinyeong Choi, Seongjin Yoo, Hyung-Jun Im, Daeseung Lee, Hongyoon Choi. Cancer region definition using spatial gene expression patterns by super resolution reconstruction algorithm for spatial transcriptomics data [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 2329.

Abstract 4948: Automated tumor microenvironment analysis for multiple samples by image-based spatial transcriptomics on tissue microarray

Abstract Introduction. Tissue Microarrays (TMAs), widely utilized in the field of pathology, have now found a powerful ally in Image-based Spatial Transcriptomics (ST). By analyzing various gene expression data with high resolution, image-based ST data on TMA can provide the heterogeneous patterns of tumor microenvironment in multiple samples. However, an efficient method for processing multiple samples post-data acquisition is still under development. A streamlined process would expedite the discovery of spatial gene expression patterns, thereby enhancing our understanding of the tumor microenvironment and its implications for cancer diagnostics and treatment strategies. Methods. Our automated pipeline is initiated by automatic segmentation of every core in the whole TMA, derived from the processed output of the MERSCOPE or Xenium platform. Then, it subsequently removes QC failed cells unrelated to any core. Each core receives sequential naming and undergoes automated cell type mapping utilizing a reference single-cell RNAseq data. Additionally, the pipeline computes neighborhood enrichment between cell types, providing a nuanced comprehension of spatial relationships and interactions among diverse cell populations within the tissue microenvironment from multiple samples on the TMA. Results. The automated pipeline we've developed provides several key advantages. It enables the simultaneous analysis of multiple tissue cores, effectively minimizing batch effects and ensuring the reliability of results across diverse tissue samples. Furthermore, by automating core separation, labeling, and cell type mapping, our pipeline significantly streamlines the time and effort required for TMA data analysis. This cost-effective approach allows researchers to optimize resource allocation, making our automated pipeline a valuable tool in cancer research. It facilitates the exploration of gene expression patterns within specific tissue regions, ultimately contributing to the advancement of our understanding of cancer biology. Conclusion. By seamlessly integrating TMA data with image-based ST technologies such as Xenium and MERSCOPE, we can perform comprehensive spatial transcriptomics analyses and provide detailed statistical information on cell type proportions within the tumor microenvironment. This automated pipeline not only ensures robust and reliable results across diverse tissue samples but also optimizes resource allocation by minimizing batch effects and reducing analysis time. This cost-effective solution empowers researchers to delve deeper into spatial gene expression patterns, enhancing our comprehension of the tumor microenvironment. Citation Format: Dongjoo Lee, Seungho Cook, Yeonjae Jung, Myunghyun Lim, Jae Eun Lee, Hyung-Jun Im, Daeseung Lee, Hongyoon Choi. Automated tumor microenvironment analysis for multiple samples by image-based spatial transcriptomics on tissue microarray [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 4948.

Abstract 3645: Unraveling spatial complexity of the tumor microenvironment: A whole transcriptomic perspective with Visium HD

Abstract In recent years, advances in spatial transcriptomics have revolutionized our understanding of the tumor microenvironment, providing crucial insights into the complex interplay of different cell types within cancer tissues. In this study, we employed the new, high definition Visium spatial transcriptomics assay (Visium HD) to investigate the intricate molecular landscape of prostate cancer at single-cell scale resolution and across the whole transcriptome. Our research focused on deciphering the spatial heterogeneity of gene expression patterns within the tumor microenvironment, shedding light on the interactions between cancer cells, stromal cells, and vasculature. The Visium HD assay enables an unbiased exploration of the transcriptome on FFPE tissue sections mounted on standard glass slides. Combining gene expression data with H&amp;E images from the same section, it allows precise characterization of local molecular features and disease states at single-cell scale. We analyzed a set of human prostate adenocarcinoma samples with the Visium HD assay, and identified spatially regulated gene signatures associated with specific cell types and functional pathways. Our findings have recapitulated previously identified molecular markers, including VEGFA and MYC which have been implicated in tumor progression and angiogenesis. The spatial gene expression patterns had good correlation with histological annotations of cancerous and normal tissue within the H&amp;E image. However, Visium HD allowed us to further observe molecular processes and pathways specific to this adenocarcinoma sample. This integrative approach offers valuable implications for personalized cancer therapy and the development of targeted interventions tailored to the spatial context of individual tumors. Our study underscores the significance of understanding the spatial organization of the whole transcriptome in cancer tissues, and highlights the potential of the Visium HD platform as a powerful tool for unraveling the complexities of the tumor microenvironment. These insights pave the way for the development of innovative therapeutic strategies and precision medicine approaches, ultimately contributing to improved outcomes for cancer patients. Citation Format: Yifeng Yin, Jerald Sapida, David Sukovich, David Patterson, Augusto Tentori. Unraveling spatial complexity of the tumor microenvironment: A whole transcriptomic perspective with Visium HD [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 3645.