Assessment of spatial transcriptomics for oncology discovery

PLXDC1+ Tumor-Associated Pancreatic Stellate Cells Promote Desmoplastic and Immunosuppressive Niche in Pancreatic Ductal Adenocarcinoma

Pancreatic stellate cells (PSCs) contribute to pancreatic ductal adenocarcinoma (PDAC) progression and therapeutic resistance, yet their detailed functions remain unclear. This study combined RNA sequencing and assay for transposase-accessible chromatin using sequencing (ATAC-seq) on sorted PSCs from adjacent normal and PDAC tissues to investigate their transcriptional and epigenetic activation. PSCs heterogeneity and functions are characterized through bulk, single-cell, and spatial transcriptomes, as well as in situ sequencing. The clinical relevance of PSCs in immunotherapy is assessed using an in-house immune-checkpoint blockade (ICB) treatment cohort. Findings showed that stress and hypoxia signaling activated PSCs in PDAC. Three common PSCs (CPSCs) and four tumor-associated PSCs (TPSCs) are identified, each with distinct functions. CPSCs differentiated into CCL19+ TPSCs in immune-enriched regions, MYH11+ TPSCs in the stromal region, and PLXDC1+ TPSCs, which exhibited cancer-associated myofibroblasts (myCAFs) phenotype linked to poor prognosis. Notably, PLXDC1+ TPSCs, located near aggressive LRRC15+ myCAFs and SPP1+ macrophages, formed a desmoplastic and immunosuppressive niche around the tumor boundary, promoting CD8 T cell exhaustion. Single-cell transcriptomics of PDAC patients treated with ICB revealed that PLXDC1+ TPSCs correlated with poor immunotherapy efficacy. Overall, this study provides key insights into PSCs in PDAC and potential therapeutic targets.

Prognostic and immunotherapeutic potential of disulfidptosis-associated signature in pancreatic cancer

Disulfidptosis is a newly discovered formation of programmed cell death. However, the significance of disulfidptosis in pancreatic adenocarcinoma remains unclear. Our investigation aims to elucidate the significance of disulfidptosis in pancreatic ductal adenocarcinoma by integrating diverse datasets, including bulk RNA sequencing data, microarray profiles, single-cell transcriptome profiles, spatial transcriptome data, and biospecimens. Utilizing various bioinformatics tools, we screened disulfidptosis-related genes based on single-cell RNA sequencing profiles, subsequently validating them through enrichment analysis. An 8-gene disulfidptosis-related prognostic signature was established by constructing massive LASSO-Cox regression models and validated by multiple external PDAC cohorts. Evaluation methods, such as Kaplan-Meier curves, ROC curves, time-dependent ROC curves, and decision curve analysis, were employed to assess the prognostic signature's reliability. High disulfidptosis-related scores were associated with a poorer prognosis and diminished sensitivity to immune checkpoint blockade. Further investigation uncovered that the potential components of elevated DPS involve malignant tumor hallmarks, extensive interactions between myCAFs and tumor cells, and the exclusion of immune cells. Cell-cell communication analysis highlighted myCAFs' role in signaling, potentially influencing tumor cells towards increased malignancy through collagen, laminin, and FN1 signaling networks. Spatial transcriptome analysis confirmed the crosstalk between myCAFs and tumor cells. Biospecimens including 20 pairs of PDAC samples and adjacent normal tissues further demonstrated the robustness of DPS and its correlation with CAF markers. In conclusion, our study introduces a novel disulfidptosis-related signature with high efficacy in patient risk stratification, which has the ability to predict the sensitivity to immune checkpoint blockade.

Spatial genomics reveals cholesterol metabolism as a key factor in colorectal cancer immunotherapy resistance

Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape across multiple cancer types achieving durable responses for a significant number of patients. Despite their success, many patients still fail to respond to ICIs or develop resistance soon after treatment. We sought to identify early treatment features associated with ICI outcome. We leveraged the MC38 syngeneic tumor model because it has variable response to ICI therapy driven by tumor intrinsic heterogeneity. ICI response was assessed based on the level of immune cell infiltration into the tumor - a well-established clinical hallmark of ICI response. We generated a spatial atlas of 48,636 transcriptome-wide spots across 16 tumors using spatial transcriptomics; given the tumors were difficult to profile, we developed an enhanced transcriptome capture protocol yielding high quality spatial data. In total, we identified 8 tumor cell subsets (e.g., proliferative, inflamed, and vascularized) and 4 stroma subsets (e.g., immune and fibroblast). Each tumor had orthogonal histology and bulk-RNA sequencing data, which served to validate and benchmark observations from the spatial data. Our spatial atlas revealed that increased tumor cell cholesterol regulation, synthesis, and transport were associated with a lack of ICI response. Conversely, inflammation and T cell infiltration were associated with response. We further leveraged spatially aware gene expression analysis, to demonstrate that high cholesterol synthesis by tumor cells was associated with cytotoxic CD8 T cell exclusion. Finally, we demonstrate that bulk RNA-sequencing was able to detect immune correlates of response but lacked the sensitivity to detect cholesterol synthesis as a feature of resistance.

STMiner: Gene-centric spatial transcriptomics for deciphering tumor tissues

Analyzing spatial transcriptomics data from tumor tissues poses several challenges beyond those of healthy samples, including unclear boundaries between different regions, uneven cell densities, and relatively higher cellular heterogeneity. Collectively, these bias the background against which spatially variable genes are identified, which can result in misidentification of spatial structures and hinder potential insight into complex pathologies. To overcome this problem, STMiner leverages 2D Gaussian mixture models and optimal transport theory to directly characterize the spatial distribution of genes rather than the capture locations of the cells expressing them (spots). By effectively mitigating the impacts of both background bias and data sparsity, STMiner reveals key gene sets and spatial structures overlooked by spot-based analytic tools, facilitating novel biological discoveries. The core concept of directly analyzing overall gene expression patterns also allows for a broader application beyond spatial transcriptomics, positioning STMiner for continuous expansion as spatial omics technologies evolve.

Challenges and Opportunities in the Clinical Translation of High-Resolution Spatial Transcriptomics

Pathology has always been fueled by technological advances. Histology powered the study of tissue architecture at single-cell resolution and remains a cornerstone of clinical pathology today. In the last decade, next-generation sequencing has become informative for the targeted treatment of many diseases, demonstrating the importance of genome-scale molecular information for personalized medicine. Today, revolutionary developments in spatial transcriptomics technologies digitalize gene expression at subcellular resolution in intact tissue sections, enabling the computational analysis of cell types, cellular phenotypes, and cell-cell communication in routinely collected and archival clinical samples. Here we review how such molecular microscopes work, highlight their potential to identify disease mechanisms and guide personalized therapies, and provide guidance for clinical study design. Finally, we discuss remaining challenges to the swift translation of high-resolution spatial transcriptomics technologies and how integration of multimodal readouts and deep learning approaches is bringing us closer to a holistic understanding of tissue biology and pathology.

Programmable DNA Reactions for Advanced Fluorescence Microscopy in Bioimaging

Biological organisms are composed of billions of molecules organized across various length scales. Direct visualization of these biomolecules in situ enables the retrieval of vast molecular information, including their location, species, and quantities, which is essential for understanding biological processes. The programmability of DNA interactions has made DNA-based reactions a major driving force in extending the limits of fluorescence microscopy, allowing for the study of biological complexity at different scales. This review article provides an overview of recent technological advancements in DNA-based fluorescence microscopy, highlighting how these innovations have expanded the technique's capabilities in terms of target multiplexity, signal amplification, super-resolution, and mechanical properties. These advanced DNA-based fluorescence microscopy techniques have been widely used to uncover new biological insights at the molecular level.

Effect of cryoablation on the spatial transcriptomic landscape of the immune microenvironment in non-small cell lung cancer

BACKGROUND

Cryoablation induces antitumor immune responses. Spatial transcriptomic landscape technology has been used to determine the micron-level panoramic transcriptomics of tissue slices in situ.

METHODS

The effects of cryoablation on the immune microenvironment in non-small cell lung cancer (NSCLC) were explored by comparing the Whole Transcriptome Atlas (WTA) panel of immune cells before and after cryoablation using the spatial transcriptomic landscape.

RESULTS

The bioinformatics analysis showed that cryoablation significantly affected the WTA of immune cells, particularly genes related to cellular components, biological processes, molecular functions, proliferation and migration, and cytokine-cytokine receptor interaction signaling pathways.

CONCLUSIONS

The findings of this study suggest that cryoablation significantly impacts the biological functions of immune cells in the tumor microenvironment of NSCLC through multiple mechanisms.

Tumor-immune hybrid cells evade the immune response and potentiate colorectal cancer metastasis through CTLA4

Understanding the metastatic cascade is critical for the treatment and prevention of cancer-related death. Within a tumor, immune cells have the capacity to fuse with tumor cells to generate tumor-immune hybrid cells (THCs). THCs are hypothesized to be a subset of cancer cells with the capacity to enter circulation as circulating hybrid cells (CHC) and seed metastases. To understand the mechanism of THC metastasis, we investigated CHCs in peripheral blood from patients with stage IV colorectal cancer (CRC), as well as THCs in tissues of primary colorectal cancers and their liver metastasis sites using immunofluorescence, spatial proteomic, spatial transcriptomic, molecular classification, and molecular pathway analyses. Our findings indicated a high prevalence of CHCs and THCs in patients with stage IV CRC. THCs expressed CTLA4 in primary CRC lesions and correlated with upregulation of CD68, CD4, and HLA-DR in metastatic liver lesions, which is found in the consensus molecular subtype (CMS) 1 of primary CRC tissue. Pathway analysis of these genes suggested that THCs are associated with neutrophils due to upregulation of neutrophil extracellular trap signaling (NET) and neutrophil degranulation pathways. These data provide molecular pathways for the formation of THCs suggesting fusion with neutrophils, which may facilitate extravasation and metastatic seeding.

Targeting the peripheral neural-tumour microenvironment for cancer therapy

As the field of cancer neuroscience expands, the strategic targeting of interactions between neurons, cancer cells and other elements in the tumour microenvironment represents a potential paradigm shift in cancer treatment, comparable to the advent of our current understanding of tumour immunology. Cancer cells actively release growth factors that stimulate tumour neo-neurogenesis, and accumulating evidence indicates that tumour neo-innervation propels tumour progression, inhibits tumour-related pro-inflammatory cytokines, promotes neovascularization, facilitates metastasis and regulates immune exhaustion and evasion. In this Review, we give an up-to-date overview of the dynamics of the tumour microenvironment with an emphasis on tumour innervation by the peripheral nervous system, as well as current preclinical and clinical evidence of the benefits of targeting the nervous system in cancer, laying a scientific foundation for further clinical trials. Combining empirical data with a biomarker-driven approach to identify and hone neuronal targets implicated in cancer and its spread can pave the way for swift clinical integration.

Deep learning-based multimodal spatial transcriptomics analysis for cancer

The advent of deep learning (DL) and multimodal spatial transcriptomics (ST) has revolutionized cancer research, offering unprecedented insights into tumor biology. This book chapter explores the integration of DL with ST to advance cancer diagnostics, treatment planning, and precision medicine. DL, a subset of artificial intelligence, employs neural networks to model complex patterns in vast datasets, significantly enhancing diagnostic and treatment applications. In oncology, convolutional neural networks excel in image classification, segmentation, and tumor volume analysis, essential for identifying tumors and optimizing radiotherapy. The chapter also delves into multimodal data analysis, which integrates genomic, proteomic, imaging, and clinical data to offer a holistic understanding of cancer biology. Leveraging diverse data sources, researchers can uncover intricate details of tumor heterogeneity, microenvironment interactions, and treatment responses. Examples include integrating MRI data with genomic profiles for accurate glioma grading and combining proteomic and clinical data to uncover drug resistance mechanisms. DL's integration with multimodal data enables comprehensive and actionable insights for cancer diagnosis and treatment. The synergy between DL models and multimodal data analysis enhances diagnostic accuracy, personalized treatment planning, and prognostic modeling. Notable applications include ST, which maps gene expression patterns within tissue contexts, providing critical insights into tumor heterogeneity and potential therapeutic targets. In summary, the integration of DL and multimodal ST represents a paradigm shift towards more precise and personalized oncology. This chapter elucidates the methodologies and applications of these advanced technologies, highlighting their transformative potential in cancer research and clinical practice.

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.

STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics

Recent advances in multi-modal algorithms have driven and been driven by the increasing availability of large image-text datasets, leading to significant strides in various fields, including computational pathology. However, in most existing medical image-text datasets, the text typically provides high-level summaries that may not sufficiently describe sub-tile regions within a large pathology image. For example, an image might cover an extensive tissue area containing cancerous and healthy regions, but the accompanying text might only specify that this image is a cancer slide, lacking the nuanced details needed for in-depth analysis. In this study, we introduce STimage-1K4M, a novel dataset designed to bridge this gap by providing genomic features for sub-tile images. STimage-1K4M contains 1,149 images derived from spatial transcriptomics data, which captures gene expression information at the level of individual spatial spots within a pathology image. Specifically, each image in the dataset is broken down into smaller sub-image tiles, with each tile paired with 15,000 - 30,000 dimensional gene expressions. With 4,293,195 pairs of sub-tile images and gene expressions, STimage-1K4M offers unprecedented granularity, paving the way for a wide range of advanced research in multi-modal data analysis an innovative applications in computational pathology, and beyond.

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.

Multi-modal domain adaptation for revealing spatial functional landscape from spatially resolved transcriptomics

Spatially resolved transcriptomics (SRT) has emerged as a powerful tool for investigating gene expression in spatial contexts, providing insights into the molecular mechanisms underlying organ development and disease pathology. However, the expression sparsity poses a computational challenge to integrate other modalities (e.g. histological images and spatial locations) that are simultaneously captured in SRT datasets for spatial clustering and variation analyses. In this study, to meet such a challenge, we propose multi-modal domain adaption for spatial transcriptomics (stMDA), a novel multi-modal unsupervised domain adaptation method, which integrates gene expression and other modalities to reveal the spatial functional landscape. Specifically, stMDA first learns the modality-specific representations from spatial multi-modal data using multiple neural network architectures and then aligns the spatial distributions across modal representations to integrate these multi-modal representations, thus facilitating the integration of global and spatially local information and improving the consistency of clustering assignments. Our results demonstrate that stMDA outperforms existing methods in identifying spatial domains across diverse platforms and species. Furthermore, stMDA excels in identifying spatially variable genes with high prognostic potential in cancer tissues. In conclusion, stMDA as a new tool of multi-modal data integration provides a powerful and flexible framework for analyzing SRT datasets, thereby advancing our understanding of intricate biological systems.

Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models

Spatial transcriptomics (ST) assays represent a revolution in how the architecture of tissues is studied by allowing for the exploration of cells in their spatial context. A common element in the analysis is delineating tissue domains or "niches" followed by detecting differentially expressed genes to infer the biological identity of the tissue domains or cell types. However, many studies approach differential expression analysis by using statistical approaches often applied in the analysis of non-spatial scRNA data (e.g., two-sample t-tests, Wilcoxon's rank sum test), hence neglecting the spatial dependency observed in ST data. In this study, we show that applying linear mixed models with spatial correlation structures using spatial random effects effectively accounts for the spatial autocorrelation and reduces inflation of type-I error rate observed in non-spatial based differential expression testing. We also show that spatial linear models with an exponential correlation structure provide a better fit to the ST data as compared to non-spatial models, particularly for spatially resolved technologies that quantify expression at finer scales (i.e., single-cell resolution).

Feature-weight based measurement of cancerous transcriptome using cohort-wide and sample-specific information

Identifying cancerous samples or cells using transcriptomic data is critical for cancer related basic research, early diagnosis, and targeted therapy. However, the high transcriptional heterogeneity of cancers still hinders people from accurately recognizing cancerous transcriptome using bulk, single-cell, or spatial RNA-seq data. Here, we present a novel method named FWP (Feature Weight Pro) that helps measure cancerous transcriptome using transcriptomic data. The workflow of FWP is, first, to calculate feature weights using the training dataset, and then, for each sample in the testing dataset, calculate the feature-weight based final score by combining the cohort-wide and sample-specific information. Those two types of information are utilized through conducting weighted principal component analysis and calculating correlation perturbations. The effectiveness and superiority of FWP over other methods are shown by using bulk, single-cell, and spatial RNA-seq data of multiple cancer types. In addition, the high robustness and efficiency of FWP are also demonstrated by using different numbers of features and cells, respectively. FWP is available at https://github.com/jumphone/fwp .

scResolve: Recovering single cell expression profiles from multi-cellular spatial transcriptomics

Many popular spatial transcriptomics techniques lack single-cell resolution. Instead, these methods measure the collective gene expression for each location from a mixture of cells, potentially containing multiple cell types. Here, we developed scResolve, a method for recovering single-cell expression profiles from spatial transcriptomics measurements at multi-cellular resolution. scResolve accurately restores expression profiles of individual cells at their locations, which is unattainable from cell type deconvolution. Applications of scResolve on human breast cancer data and human lung disease data demonstrate that scResolve enables cell type-specific differential gene expression analysis between different tissue contexts and accurate identification of rare cell populations. The spatially resolved cellular-level expression profiles obtained through scResolve facilitate more flexible and precise spatial analysis that complements raw multi-cellular level analysis.

Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response

The spatial organization of the tumor microenvironment has a profound impact on biology and therapy response. Here, we perform an integrative single-cell and spatial transcriptomic analysis on HPV-negative oral squamous cell carcinoma (OSCC) to comprehensively characterize malignant cells in tumor core (TC) and leading edge (LE) transcriptional architectures. We show that the TC and LE are characterized by unique transcriptional profiles, neighboring cellular compositions, and ligand-receptor interactions. We demonstrate that the gene expression profile associated with the LE is conserved across different cancers while the TC is tissue specific, highlighting common mechanisms underlying tumor progression and invasion. Additionally, we find our LE gene signature is associated with worse clinical outcomes while TC gene signature is associated with improved prognosis across multiple cancer types. Finally, using an in silico modeling approach, we describe spatially-regulated patterns of cell development in OSCC that are predictably associated with drug response. Our work provides pan-cancer insights into TC and LE biology and interactive spatial atlases ( http://www.pboselab.ca/spatial_OSCC/ ; http://www.pboselab.ca/dynamo_OSCC/ ) that can be foundational for developing novel targeted therapies.