Enabling automated and reproducible spatially resolved transcriptomics at scale.
Ontology highlight
ABSTRACT:
SUBMITTER: Stenbeck L
PROVIDER: S-EPMC9213715 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Spatial transcriptomics, which is capable of both measuring all gene activity in a tissue sample and mapping where this activity occurs, is vastly improving our understanding of biological processes and disease. The field has expanded rapidly in recent years, and the development of several new technologies has resulted in spatially resolved transcriptomics (SRT) becoming highly multiplexed, high-resolution, and high-throughput. Here, we summarize and compare the major methods of SRT, including imaging-based methods, sequencing-based methods, and in situ sequencing methods. We also highlight some typical applications of SRT in neuroscience, cancer biology, developmental biology, and hematology. Finally, we discuss future possibilities for improving spatially resolved transcriptomic methods and the expected applications of such methods, especially in the adult bone marrow, anticipating that new developments will unlock the full potential of spatially resolved multi-omics in both biological research and the clinic.
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Project description:Recent advances in spatially resolved transcriptomics (SRT) have brought ever-increasing opportunities to characterize expression landscape in the context of tissue spatiality. Nevertheless, there still exist multiple challenges to accurately detect spatial functional regions in tissue. Here, we present a novel contrastive learning framework, SPAtially Contrastive variational AutoEncoder (SpaCAE), which contrasts transcriptomic signals of each spot and its spatial neighbors to achieve fine-grained tissue structures detection. By employing a graph embedding variational autoencoder and incorporating a deep contrastive strategy, SpaCAE achieves a balance between spatial local information and global information of expression, enabling effective learning of representations with spatial constraints. Particularly, SpaCAE provides a graph deconvolutional decoder to address the smoothing effect of local spatial structure on expression's self-supervised learning, an aspect often overlooked by current graph neural networks. We demonstrated that SpaCAE could achieve effective performance on SRT data generated from multiple technologies for spatial domains identification and data denoising, making it a remarkable tool to obtain novel insights from SRT studies.
| S-EPMC10849194 | biostudies-literature
Project description:BackgroundThorough understanding of complex model systems requires the characterisation of processes in different cell types of an organism. This can be achieved with high-throughput spatial transcriptomics at a large scale. However, for plant model systems this is still challenging as suitable transcriptomics methods are sparsely available. Here we present GaST-seq (Grid-assisted, Spatial Transcriptome sequencing), an easy to adopt, micro-scale spatial-transcriptomics workflow that allows to study expression profiles across small areas of plant tissue at a fraction of the cost of existing sequencing-based methods.ResultsWe compare the GaST-seq method with widely used library preparation methods (Illumina TruSeq). In spatial experiments we show that the GaST-seq method is sensitive enough to identify expression differences across a plant organ. We further assess the spatial transcriptome response of Arabidopsis thaliana leaves exposed to the bacterial molecule flagellin-22, and show that with eukaryotic (Albugo laibachii) infection both host and pathogen spatial transcriptomes are obtained.ConclusionWe show that our method can be used to identify known, rapidly flagellin-22 elicited genes, plant immune response pathways to bacterial attack and spatial expression patterns of genes associated with these pathways.
| S-EPMC6785889 | biostudies-literature
Project description:Spatially resolved transcriptomics (SRT)-specific computational methods are often developed, tested, validated, and evaluated in silico using simulated data. Unfortunately, existing simulated SRT data are often poorly documented, hard to reproduce, or unrealistic. Single-cell simulators are not directly applicable for SRT simulation as they cannot incorporate spatial information. We present SRTsim, an SRT-specific simulator for scalable, reproducible, and realistic SRT simulations. SRTsim not only maintains various expression characteristics of SRT data but also preserves spatial patterns. We illustrate the benefits of SRTsim in benchmarking methods for spatial clustering, spatial expression pattern detection, and cell-cell communication identification.
| S-EPMC9983268 | biostudies-literature
Project description:Bulk and single-cell RNA sequencing do not provide full characterization of tissue spatial diversity in cancer samples, and currently available in situ techniques (multiplex immunohistochemistry and imaging mass cytometry) allow for only limited analysis of a small number of targets. The current study represents the first comprehensive approach to spatial transcriptomics of high-grade serous ovarian carcinoma using intact tumor tissue. We selected a small cohort of patients with highly annotated high-grade serous ovarian carcinoma, categorized them by response to neoadjuvant chemotherapy (poor or excellent), and analyzed pre-treatment tumor tissue specimens. Our study uncovered extensive differences in tumor composition between the poor responders and excellent responders to chemotherapy, related to cell cluster organization and localization. This in-depth characterization of high-grade serous ovarian carcinoma tumor tissue from poor and excellent responders showed that spatial interactions between cell clusters may influence chemo-responsiveness more than cluster composition alone.
| S-EPMC8891954 | biostudies-literature
Project description:Spatially resolved transcriptomics (SRT) technologies measure mRNA expression at thousands of locations in a tissue slice. However, nearly all SRT technologies measure expression in two dimensional slices extracted from a three-dimensional tissue, thus losing information that is shared across multiple slices from the same tissue. Integrating SRT data across multiple slices can help recover this information and improve downstream expression analyses, but multi-slice alignment and integration remains a challenging task. Existing methods for integrating SRT data either do not use spatial information or assume that the morphology of the tissue is largely preserved across slices, an assumption that is often violated due to biological or technical reasons. We introduce PASTE2, a method for partial alignment and 3D reconstruction of multi-slice SRT datasets, allowing only partial overlap between aligned slices and/or slice-specific cell types. PASTE2 formulates a novel partial Fused Gromov-Wasserstein Optimal Transport problem, which we solve using a conditional gradient algorithm. PASTE2 includes a model selection procedure to estimate the fraction of overlap between slices, and optionally uses information from histological images that accompany some SRT experiments. We show on both simulated and real data that PASTE2 obtains more accurate alignments than existing methods. We further use PASTE2 to reconstruct a 3D map of gene expression in a Drosophila embryo from a 16 slice Stereo-seq dataset. PASTE2 produces accurate alignments of multi-slice datasets from multiple SRT technologies, enabling detailed studies of spatial gene expression across a wide range of biological applications.Code availabilitySoftware is available at https://github.com/raphael-group/paste2.
| S-EPMC9881963 | biostudies-literature
Project description:Spatially resolved transcriptomics technologies enable the measurement of transcriptome information while retaining the spatial context at the regional, cellular or sub-cellular level. While previous computational methods have relied on gene expression information alone for clustering single-cell populations, more recent methods have begun to leverage spatial location and histology information to improve cell clustering and cell-type identification. In this study, using seven semi-synthetic datasets with real spatial locations, simulated gene expression and histology images as well as ground truth cell-type labels, we evaluate 15 clustering methods based on clustering accuracy, robustness to data variation and input parameters, computational efficiency, and software usability. Our analysis demonstrates that even though incorporating the additional spatial and histology information leads to increased accuracy in some datasets, it does not consistently improve clustering compared with using only gene expression data. Our results indicate that for the clustering of spatial transcriptomics data, there are still opportunities to enhance the overall accuracy and robustness by improving information extraction and feature selection from spatial and histology data.
| S-EPMC9851325 | biostudies-literature
Project description:SummarySpatially resolved transcriptomics technologies generate gene expression data with retained positional information from a tissue section, often accompanied by a corresponding histological image. Computational tools should make it effortless to incorporate spatial information into data analyses and present analysis results in their histological context. Here, we present semla, an R package for processing, analysis, and visualization of spatially resolved transcriptomics data generated by the Visium platform, that includes interactive web applications for data exploration and tissue annotation.Availability and implementationThe R package semla is available on GitHub (https://github.com/ludvigla/semla), under the MIT License, and deposited on Zenodo (https://doi.org/10.5281/zenodo.8321645). Documentation and tutorials with detailed descriptions of usage can be found at https://ludvigla.github.io/semla/.
| S-EPMC10597621 | biostudies-literature
Project description:ObjectiveLocal invasion is the first step of metastasis, the main cause of colorectal cancer (CRC)-related death. Recent studies have revealed extensive intertumoral and intratumoral heterogeneity. Here, we focused on revealing local invasion-related genes in CRC.MethodsWe used spatial transcriptomic techniques to study the process of local invasion in four CRC tissues. First, we compared the pre-cancerous, cancer center, and invasive margin in one section (S115) and used pseudo-time analysis to reveal the differentiation trajectories from cancer center to invasive margin. Next, we performed immunohistochemical staining for RPL5, STC1, AKR1B1, CD47, and HLA-A on CRC samples. Moreover, we knocked down AKR1B1 in CRC cell lines and performed CCK-8, wound healing, and transwell assays to assess cell proliferation, migration, and invasion.ResultsWe demonstrated that 13 genes were overexpressed in invasive clusters, among which the expression of CSTB and TM4SF1 was correlated with poor PFS in CRC patients. The ribosome pathway was increased, while the antigen processing and presentation pathway was decreased along CRC progression. RPL5 was upregulated, while HLA-A was downregulated along cancer invasion in CRC samples. Pseudo-time analysis revealed that STC1, AKR1B1, SIRPA, C4orf3, EDNRA, CES1, PRRX1, EMP1, PPIB, PLTP, SULF2, and EGFL6 were unpregulated along the trajectories. Immunohistochemic3al staining showed the expression of STC1, AKR1B1, and CD47 was increased along cancer invasion in CRC samples. Knockdown of AKR1B1 inhibited CRC cells' proliferation, migration, and invasion.ConclusionsWe revealed the spatial heterogeneity within CRC tissues and uncovered some novel genes that were associated with CRC invasion.
| S-EPMC9928961 | biostudies-literature
Project description:During tumor progression, cancer cells come into contact with various non-tumor cell types, but it is unclear how tumors adapt to these new environments. Here, we integrate spatially resolved transcriptomics, single-cell RNA-seq, and single-nucleus RNA-seq to characterize tumor-microenvironment interactions at the tumor boundary. Using a zebrafish model of melanoma, we identify a distinct "interface" cell state where the tumor contacts neighboring tissues. This interface is composed of specialized tumor and microenvironment cells that upregulate a common set of cilia genes, and cilia proteins are enriched only where the tumor contacts the microenvironment. Cilia gene expression is regulated by ETS-family transcription factors, which normally act to suppress cilia genes outside of the interface. A cilia-enriched interface is conserved in human patient samples, suggesting it is a conserved feature of human melanoma. Our results demonstrate the power of spatially resolved transcriptomics in uncovering mechanisms that allow tumors to adapt to new environments.
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