Spatial Genomics Research Articles

Abstract SY19-01: Investigating cancer determinants of tumor immune composition

Abstract The cellular composition, spatial architecture, and tissue localization of a tumor have a major impact on a cancer’s progression and response to therapy. These factors are particularly relevant to tumor immunity as immune cell composition and localization within the tumor microenvironment (TME) is one of the major determinants of response to immunotherapy and patient outcome. Spatially resolved single cell genome sequencing has revealed that many genetically distinct sub-clones exist in proximity in tumors and can have different TME compositions, but how specific genes influence the TME, or how neighboring clones influence one another is not well-defined. Indeed, while some genes involved in orchestrating the TME have been identified, the potential roles of many genes in influencing the architecture and immune composition of different tumors are not established. To enable scale up studies to identify determinants of TME biology and tumor immunity, we established a first-of-its-kind in vivo spatial functional genomics platform called Perturb-map. We applied Perturb-map to knockout (KO) dozens of genes in parallel in a mouse model of lung cancer and simultaneously assessed how each KO influenced tumor growth, histopathology, and immune composition. Additionally, we paired Perturb-map and spatial transcriptomics for unbiased analysis of CRISPR-edited tumors. In contrast to in vitro CRISPR screens, we found KO of positive regulators of IFNγ signaling led to a major reduction of tumor burden, with varying impact from different pathway components. KO of Socs1, a negative regulator of IFNγ signaling, provided a growth advantage though was accompanied by increased T cell infiltration and notably, Socs1 KO tumors were more responsive to PDL1 blockade. Perturb-map also found that KO of the TGFβ Receptor 2 (Tgfbr2) resulted in a growth advantage, but unlike Socs1 KO, T cells were excluded from the lesions and the TME converted to a fibro-mucinous state. Spatial transcriptomics revealed TGFβ signaling was upregulated in Tgfbr2 KO tumors. Further analysis indicated this signal was from TGFβ-mediated activation of fibroblasts in tumors where cancer cells lost Tgfbr2, suggesting the mechanism for TME remodeling and T cell exclusion in Tgfbr2 KO tumors. These studies establish Perturb-map for functional genomics within tissue at single cell-resolution with spatial architecture preserved and provide insight into how TGFβ responsiveness of cancer cells can affect the TME (Dhainaut, Rose et al. BioRxiv 2021, doi.org/10.1101/2021.07.13.451021). Citation Format: Brian Brown. Investigating cancer determinants of tumor immune composition [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr SY19-01.

Abstract 1606: Chromosomal instability drives spatial and temporal phenotypic diversity in Schwann cancer cells

Abstract Background Malignant peripheral nerve sheath tumors (MPNSTs) are rare, highly aggressive soft tissue sarcomas derived from Schwann cells with poor prognosis. Previous studies, although limited in scale and depth, have shown that these tumors are typically genomically complex, with a moderate burden of single nucleotide variants but extensive copy number aberrations (CNAs). Methods We applied a multi-omics approach to scrutinize the evolution and heterogeneity of a primary MPNST and five recurrence regions from one patient with an NF1 phenotype. Techniques utilized included bulk whole genome sequencing (WGS), single-cell DNA sequencing (scDNA-seq), single-cell RNA sequencing (scRNA-seq), simultaneous genome and transcriptome sequencing (G&T-seq), spatial transcriptomics, and laser capture microdissection (LCM)-based spatial genomics. Results We find significant heterogeneity in copy number profiles, suggestive of ongoing chromosomal instability and evolution. We perform in-depth tumor phylogenetic reconstruction from bulk WGS data. scDNA-seq revealed further CNA heterogeneity across and within regions, allowing us to refine the tree down to single-cell resolution. De novo somatic variant calling in scDNA-seq data confirms the reconstructed tree and identifies additional subclonal mutations. We also profile different populations of tumor and non-tumor cells and confirmed this with a genotyping approach. CNA profiles inferred from scRNA-seq reflect the within-region heterogeneity seen in scDNA-seq. We find tumor cells with similar CNA profiles typically cluster together by their gene expression profiles as well, implying that the majority of gene expression heterogeneity in this tumor is underpinned by copy number changes. By using CNA profiles as a native barcoding system, we link genetic subclones to clusters of cells and their transcriptomes to characterize the effects of gene dosage. Finally, we explore the spatial relationships between tumor subclones using LCM. We detect heterogeneity within tissue sections evidencing local clonal expansions and CNA events that follow spatial distributions. Using spatial transcriptomics, we show tumor cells are homogeneously admixed with tumor microenvironment populations. Conclusions Our work demonstrates the power of combining spatial multi-omics at the single-cell and bulk levels to study cancer evolution. This enables a spatio-temporal representation of a tumor’s development with annotation of genetic and transcriptomic events. Citation Format: Haixi Yan, Jonas Demeulemeester, Annelien Verfaillie, Cristina Cotobal Martin, Tom Kaufmann, Roland Schwarz, Thierry Voet, Simone Zaccaria, Adrienne Flanagan, Maxime Tarabichi, Peter Van Loo. Chromosomal instability drives spatial and temporal phenotypic diversity in Schwann cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1606.

Abstract 74: Single-cell 3D spatial omics for tumor hypoxia

Abstract Tumor hypoxia arises from the abnormal vasculature in tumors which limit the oxygen supply to the rapidly growing cancer cells. To acclimate to this deprived microenvironment, cancer cells upregulate hypoxia-inducible factor 1-alpha (HIF-1) signaling pathway priming themselves towards more aggressive and heterogeneous phenotypes. Hence, tumor hypoxia becomes a major obstacle for effective cancer treatment. Deciphering hypoxia-driven tumor heterogeneity within the tumors is essential to understanding cancer biology and developing novel therapeutic strategies. A stronger focus should be directed towards the effect of hypoxia types and levels rather than just the absence or presence of hypoxia since hypoxia is heterogeneous in the three-dimensional (3D) tumor microenvironment (TME). There are acute and chronic types of tumor hypoxia, and the hypoxia levels are also variable, forming unpredictable and irregular 3D hypoxic areas. Although traditional techniques (i.e., single-cell RNA sequencing (scRNA-seq), immunohistochemistry (IHC), and immunofluorescence (IF)) and innovative spatial transcriptomics have been developed and applied for investigating tumor cell heterogeneity. Underlying issue of missing spatial information, limited numbers of the biomarker detection, underrepresentation of 3D tumor structure, or limited analyzed area onto the array chip size exist respectively in these methods. The objective of this project is to develop a 3D spatial genomics approach with single-cell resolution, named sc3DSG, as an advanced spatial transcriptomic method to study hypoxia-driven cancer cell heterogeneity in 3D. Given that, I developed a 3D cellular fluorescence barcoding method by microscopic photobleaching in tumor macro-sections. The barcodes would maintain a fluorescent footprint of cells in different hypoxic areas upon tissue dissociation. Cells encoded with varied barcodes could be differentiated on flow cytometry and further sorted into separate groups for scRNA-seq. With the optimization with RNA preservation reagent in the workflow, sc3DSG achieves high-quality and high-yield RNA transcript for unbiased transcriptomic analysis. In summary, sc3DSG is an advanced spatial transcriptomic method including a sequential workflow of photobleaching barcoding, tissue dissociation, fluorescence-activated cell sorting (FACS), and scRNA-seq. Sc3DSG enables the study of tissue molecular features without losing spatial information and is a better fit for the study of complex 3D structure like vessels or tumor hypoxia. This approach will unravel hypoxia-driven bimolecular and cellular features by considering the degrees and types of tumor hypoxia. Citation Format: Yi-Chien Wu, Steve Seung-Young Lee. Single-cell 3D spatial omics for tumor hypoxia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 74.

In preprints: the fast-paced field of single-cell lineage tracing

In preprints: the fast-paced field of single-cell lineage tracing

Highlights from Recent Cancer Literature

Highlights from Recent Cancer Literature

A subcellular cookie cutter for spatial genomics in human tissue

Intracellular heterogeneity contributes significantly to cellular physiology and, in a number of debilitating diseases, cellular pathophysiology. This is greatly influenced by distinct organelle populations and to understand the aetiology of disease, it is important to have tools able to isolate and differentially analyse organelles from precise location within tissues. Here, we report the development of a subcellular biopsy technology that facilitates the isolation of organelles, such as mitochondria, from human tissue. We compared the subcellular biopsy technology to laser capture microdissection (LCM) that is the state-of-the-art technique for the isolation of cells from their surrounding tissues. We demonstrate an operational limit of >20 µm for LCM and then, for the first time in human tissue, show that subcellular biopsy can be used to isolate mitochondria beyond this limit. Graphical abstract

Spatial CRISPR genomics identifies regulators of the tumor microenvironment

While CRISPR screens are helping uncover genes regulating many cell-intrinsic processes, existing approaches are suboptimal for identifying extracellular gene functions, particularly in the tissue context. Here, we developed an approach for spatial functional genomics called Perturb-map. We applied Perturb-map to knock out dozens of genes in parallel in a mouse model of lung cancer and simultaneously assessed how each knockout influenced tumor growth, histopathology, and immune composition. Moreover, we paired Perturb-map and spatial transcriptomics for unbiased analysis of CRISPR-edited tumors. We found that in Tgfbr2 knockout tumors, the tumor microenvironment (TME) was converted to a fibro-mucinous state, and Tcells excluded, concomitant with upregulated TGFβ and TGFβ-mediated fibroblast activation, indicating that TGFβ-receptor loss on cancer cells increased TGFβ bioavailability and its immunosuppressive effects on the TME. These studies establish Perturb-map for functional genomics within the tissue at single-cell resolution with spatial architecture preserved and provide insight into how TGFβ responsiveness of cancer cells can affect the TME.

Analysis and Visualization of Spatial Transcriptomic Data.

Human and animal tissues consist of heterogeneous cell types that organize and interact in highly structured manners. Bulk and single-cell sequencing technologies remove cells from their original microenvironments, resulting in a loss of spatial information. Spatial transcriptomics is a recent technological innovation that measures transcriptomic information while preserving spatial information. Spatial transcriptomic data can be generated in several ways. RNA molecules are measured by in situ sequencing, in situ hybridization, or spatial barcoding to recover original spatial coordinates. The inclusion of spatial information expands the range of possibilities for analysis and visualization, and spurred the development of numerous novel methods. In this review, we summarize the core concepts of spatial genomics technology and provide a comprehensive review of current analysis and visualization methods for spatial transcriptomics.

Complexity against current cancer research: Are we on the wrong track?

Cancer genetics has led to major discoveries, including protooncogene and tumor-suppressor concepts, and cancer genomics generated concepts like driver and passenger genes, revealed tumor heterogeneity and clonal evolution. Reconstructing trajectories of tumorigenesis using spatial and single-cell genomics is possible. Patient stratification and prognostic parameters have been improved. Yet, despite these advances, successful translation into targeted therapies has been scarce and mostly limited to kinase inhibitors. Here, we argue that current cancer research may be on the wrong track, by considering cancer more as a "monogenic" disease, trying to extract common information from thousands of patients, while not properly considering complexity and individual diversity. We suggest to empower a systems cancer approach which reconstructs the information network that has been altered by the tumorigenic events, to analyze hierarchies and predict (druggable) key nodes that could interfere with/block the aberrant information transfer. We also argue that the interindividual variability between patients of similar cohorts is too high to extract common polygenic network information from large numbers of patients and argue in favor of an individualized approach. The analysis we propose would require a structured multinational and multidisciplinary effort, in which clinicians, and cancer, developmental, cell and computational biologists together with mathematicians and informaticians develop dynamic regulatory networks which integrate the entire information transfer in and between cells and organs in (patho)physiological conditions, revealing hierarchies and available drugs to interfere with key regulators. Based on this blueprint, the altered information transfer in individual cancers could be modeled and possible targeted (combo)therapies proposed.

Integrating Multimorbidity into a Whole-Body Understanding of Disease Using Spatial Genomics.

Multimorbidity is characterized by multidimensional complexity emerging from interactions between multiple diseases across levels of biological (including genetic) and environmental determinants and the complex array of interactions between and within cells, tissues and organ systems. Advances in spatial genomic research have led to an unprecedented expansion in our ability to link alterations in genome folding with changes that are associated with human disease. Studying disease-associated genetic variants in the context of the spatial genome has enabled the discovery of transcriptional regulatory programmes that potentially link dysregulated genes to disease development. However, the approaches that have been used have typically been applied to uncover pathological molecular mechanisms occurring in a specific disease-relevant tissue. These forms of reductionist, targeted investigations are not appropriate for the molecular dissection of multimorbidity that typically involves contributions from multiple tissues. In this perspective, we emphasize the importance of a whole-body understanding of multimorbidity and discuss how spatial genomics, when integrated with additional omic datasets, could provide novel insights into the molecular underpinnings of multimorbidity.

Extracting physical characteristics of higher-order chromatin structures from 3D image data

Higher-order chromatin structures have functional impacts on gene regulation and cell identity determination. Using high-throughput sequencing (HTS)-based methods like Hi-C, active or inactive compartments and open or closed topologically associating domain (TAD) structures can be identified on a cell population level. Recently developed high-resolution three-dimensional (3D) molecular imaging techniques such as 3D electron microscopy with in situ hybridization (3D-EMSIH) and 3D structured illumination microscopy (3D-SIM) enable direct detection of physical representations of chromatin structures in a single cell. However, computational analysis of 3D image data with explainability and interpretability on functional characteristics of chromatin structures is still challenging. We developed Extracting Physical-Characteristics from Images of Chromatin Structures (EPICS), a machine-learning based computational method for processing high-resolution chromatin 3D image data. Using EPICS on images produced by 3D-EMISH or 3D-SIM techniques, we generated more direct 3D representations of higher-order chromatin structures, identified major chromatin domains, and determined the open or closed status of each domain. We identified several high-contributing features from the model as the major physical characteristics that define the open or closed chromatin domains, demonstrating the explainability and interpretability of EPICS. EPICS can be applied to the analysis of other high-resolution 3D molecular imaging data for spatial genomics studies. The R and Python codes of EPICS are available athttps://github.com/zang-lab/epics.

The Power of 3D Spatial Genomics

The Power of 3D Spatial Genomics

Spatial genomics enables multi-modal study of clonal heterogeneity in tissues.

The state and behaviour of a cell can be influenced by both genetic and environmental factors. In particular, tumour progression is determined by underlying genetic aberrations1-4 as well as the makeup of the tumour microenvironment5,6. Quantifying the contributions of these factors requires new technologies that can accurately measure the spatial location of genomic sequence together with phenotypic readouts. Here we developed slide-DNA-seq, a method for capturing spatially resolved DNA sequences from intact tissue sections. We demonstrate that this method accurately preserves local tumour architecture and enables thede novo discovery of distinct tumour clones and their copy number alterations. We then apply slide-DNA-seq to a mouse model ofmetastasis and a primary human cancer, revealing that clonal populations are confined to distinct spatial regions. Moreover, through integration with spatial transcriptomics, we uncover distinct sets of genes that are associated with clone-specific genetic aberrations, the local tumour microenvironment, or both. Together, this multi-modal spatial genomics approach provides a versatile platform for quantifying how cell-intrinsic and cell-extrinsic factors contribute to gene expression, protein abundance and other cellular phenotypes.

A Shift in Paradigms: Spatial Genomics Approaches to Reveal Single-Cell Principles of Genome Organization.

The genome tridimensional (3D) organization and its role towards the regulation of key cell processes such as transcription is currently a main question in biology. Interphase chromosomes are spatially segregated into “territories,” epigenetically-defined large domains of chromatin that interact to form “compartments” with common transcriptional status, and insulator-flanked domains called “topologically associating domains” (TADs). Moreover, chromatin organizes around nuclear structures such as lamina, speckles, or the nucleolus to acquire a higher-order genome organization. Due to recent technological advances, the different hierarchies are being solved. Particularly, advances in microscopy technologies are shedding light on the genome structure at multiple levels. Intriguingly, more and more reports point to high variability and stochasticity at the single-cell level. However, the functional consequences of such variability in genome conformation are still unsolved. Here, I will discuss the implication of the cell-to-cell heterogeneity at the different scales in the context of newly developed imaging approaches, particularly multiplexed Fluorescence in situ hybridization methods that enabled “chromatin tracing.” Extensions of these methods are now combining spatial information of dozens to thousands of genomic loci with the localization of nuclear features such as the nucleolus, nuclear speckles, or even histone modifications, creating the fast-moving field of “spatial genomics.” As our view of genome organization shifts the focus from ensemble to single-cell, new insights to fundamental questions begin to emerge.

TAD-like single-cell domain structures exist on both active and inactive X chromosomes and persist under epigenetic perturbations

BackgroundTopologically associating domains (TADs) are important building blocks of three-dimensional genome architectures. The formation of TADs has been shown to depend on cohesin in a loop-extrusion mechanism. Recently, advances in an image-based spatial genomics technique known as chromatin tracing lead to the discovery of cohesin-independent TAD-like structures, also known as single-cell domains, which are highly variant self-interacting chromatin domains with boundaries that occasionally overlap with TAD boundaries but tend to differ among single cells and among single chromosome copies. Recent computational modeling studies suggest that epigenetic interactions may underlie the formation of the single-cell domains.ResultsHere we use chromatin tracing to visualize in female human cells the fine-scale chromatin folding of inactive and active X chromosomes, which are known to have distinct global epigenetic landscapes and distinct population-averaged TAD profiles, with inactive X chromosomes largely devoid of TADs and cohesin. We show that both inactive and active X chromosomes possess highly variant single-cell domains across the same genomic region despite the fact that only active X chromosomes show clear TAD structures at the population level. These X chromosome single-cell domains exist in distinct cell lines. Perturbations of major epigenetic components and transcription mostly do not affect the frequency or strength of the single-cell domains. Increased chromatin compaction of inactive X chromosomes occurs at a length scale above that of the single-cell domains.ConclusionsIn sum, this study suggests that single-cell domains are genome architecture building blocks independent of the tested major epigenetic components.

Dark Side of Cancer Therapy: Cancer Treatment-Induced Cardiopulmonary Inflammation, Fibrosis, and Immune Modulation.

Advancements in cancer therapy increased the cancer free survival rates and reduced the malignant related deaths. Therapeutic options for patients with thoracic cancers include surgical intervention and the application of chemotherapy with ionizing radiation. Despite these advances, cancer therapy-related cardiopulmonary dysfunction (CTRCPD) is one of the most undesirable side effects of cancer therapy and leads to limitations to cancer treatment. Chemoradiation therapy or immunotherapy promote acute and chronic cardiopulmonary damage by inducing reactive oxygen species, DNA damage, inflammation, fibrosis, deregulation of cellular immunity, cardiopulmonary failure, and non-malignant related deaths among cancer-free patients who received cancer therapy. CTRCPD is a complex entity with multiple factors involved in this pathogenesis. Although the mechanisms of cancer therapy-induced toxicities are multifactorial, damage to the cardiac and pulmonary tissue as well as subsequent fibrosis and organ failure seem to be the underlying events. The available biomarkers and treatment options are not sufficient and efficient to detect cancer therapy-induced early asymptomatic cell fate cardiopulmonary toxicity. Therefore, application of cutting-edge multi-omics technology, such us whole-exome sequencing, DNA methylation, whole-genome sequencing, metabolomics, protein mass spectrometry and single cell transcriptomics, and 10 X spatial genomics, are warranted to identify early and late toxicity, inflammation-induced carcinogenesis response biomarkers, and cancer relapse response biomarkers. In this review, we summarize the current state of knowledge on cancer therapy-induced cardiopulmonary complications and our current understanding of the pathological and molecular consequences of cancer therapy-induced cardiopulmonary fibrosis, inflammation, immune suppression, and tumor recurrence, and possible treatment options for cancer therapy-induced cardiopulmonary toxicity.

Getting Hooked on MERFISH A Core Lab's Journey into Spatial Genomics

Getting Hooked on MERFISH A Core Lab's Journey into Spatial Genomics

Localizing gene expression

Spatial Genetics Single-cell RNA sequencing can provide information about cellular relationships based on shared transcriptomes, but most methods lose spatial information. Those methods that do retain spatial information can be limited to a specific set of genes and/or a small area. Srivatsan et al. introduce sci-Space, a spatial transcriptomic method that uses a grid of barcoded oligos on a slide that can be transferred to nuclei of an overlaid frozen tissue section to obtain both the spatial origin and the transcriptome of thousands of single cells per slide. The researchers used sci-Space to create a spatial atlas of mouse E14 sagittal sections, revealing spatially expressed genes across cell types. This application illustrates how sci-Space complements existing approaches in spatial genomics. Science , abb9536, this issue p. [111][1] [1]: /lookup/doi/10.1126/science.abb9536

Embryo-scale, single-cell spatial transcriptomics.

Spatial patterns of gene expression manifest at scales ranging from local (e.g., cell-cell interactions) to global (e.g., body axis patterning). However, current spatial transcriptomics methods either average local contexts or are restricted to limited fields of view. Here, we introduce sci-Space, which retains single-cell resolution while resolving spatial heterogeneity at larger scales. Applying sci-Space to developing mouse embryos, we captured approximate spatial coordinates and whole transcriptomes of about 120,000 nuclei. We identify thousands of genes exhibiting anatomically patterned expression, leverage spatial information to annotate cellular subtypes, show that cell types vary substantially in their extent of spatial patterning, and reveal correlations between pseudotime and the migratory patterns of differentiating neurons. Looking forward, we anticipate that sci-Space will facilitate the construction of spatially resolved single-cell atlases of mammalian development.

Spatial Genomics: Mapping Spatial Genetic Landscapes in Tissue Sections through Microscale Integration of Sampling Methodology into Genomic Workflows (Small 23/2021)

The GeneScape workflow, presented in article number 2007901 by Govind V. Kaigala and co-workers, uses a modular multipurpose microfluidic probe to facilitate evaluation of spatial genomic heterogeneity in tumors. Cancer cells essentially contain the same genomic blueprint but accumulate distinct corruptions at different spatial and temporal coordinates. Since these cells reside in several interacting ecosystems, it becomes very critical to study the spatial mutational landscape to evaluate and quantify tumor progression.