The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome from single cells is transforming our understanding of cell biology in health and disease. In less than a decade, the field has seen tremendous technological revolutions that enable crucial new insights into the interplay between intracellular and intercellular molecular mechanisms that govern development, physiology and pathogenesis. In this Review, we highlight advances in the fast-developing field of single-cell and spatial multi-omics technologies (also known as multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers. We demonstrate their impact on fundamental cell biology and translational research, discuss current challenges and provide an outlook to the future. 

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Methods and applications for single-cell and spatial multi-omics

Advancing CAR T cell therapy through the use of multidimensional omics data

RNA splicing dysregulation and the hallmarks of cancer

Spatial omics technologies enable a deeper understanding of cellular organizations and interactions within a tissue of interest. These assays can identify specific compartments or regions in a tissue with differential transcript or protein abundance, delineate their interactions, and complement other methods in defining cellular phenotypes. A variety of spatial methodologies are being developed and commercialized; however, these techniques differ in spatial resolution, multiplexing capability, scale/throughput, and coverage. Here, we review the current and prospective landscape of single cell to subcellular resolution spatial omics technologies and analysis tools to provide a comprehensive picture for both research and clinical applications. 

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Spatial omics technologies at multimodal and single cell/subcellular level

Pancreatic cancer: Advances and challenges

High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging.

Accurate cell typing is fundamental to analysis of spatial single-cell transcriptomics, but legacy scRNA-seq algorithms can underperform in this new type of data. We have developed a cell typing algorithm, Insitutype, designed for statistical and computational efficiency in spatial transcriptomics data. Insitutype is based on a likelihood model that weighs the evidence from every expression value, extracting all the information available in each cell’s expression profile. This likelihood model underlies a Bayes classifier for supervised cell typing, and an Expectation-Maximization algorithm for unsupervised and semi-supervised clustering. Insitutype also leverages alternative data types collected in spatial studies, such as cell images and spatial context, by using them to inform prior probabilities of cell type calls. We demonstrate rapid clustering of millions of cells and accurate fine-grained cell typing of kidney and non-small cell lung cancer samples.

/pdf/insitutype-likelihood-based-cell-typing-for-single-cell-2nvy6r0c.pdf

Insitutype: likelihood-based cell typing for single cell spatial transcriptomics


 Cancer research across drug development, molecular biomarkers, and patient response depends on understanding biology that is dependent on complex interactions between malignant, immune, and stromal cells. To survive clearance mechanisms, a tumor can rely on a myriad of escape strategies, and the microenvironment is architected around the current path of escape. To enable a more comprehensive picture of tumor biology, we have developed the CosMx™ Spatial Molecular Imager (SMI) technology to capture a snapshot of thousands of RNA species resolved subcellularly from a single, standard histopathology slide. Building upon the previously released panels, this study tests a new 6,000-plex panel, the highest RNA plex measured in situ within human tissue, allowing the imputation of a spatial whole transcriptome in the tissue. We performed an ultra-high-plex RNA assay to detect 6,000 targets simultaneously in situ on an FFPE human liver cancer tissue (~1 cm2 area) using the CosMx SMI. This RNA panel covers broad biological areas with special emphasis on oncology, immunology, and signal transduction, such that all cancer researchers can benefit from the direct detection of targets of interest (sans imputation) in intact tissue. Analysis algorithms were developed to allow robust assessments of cell types, cell states, cell-cell interactions, and pathway activation. Imputation based on reference profiles from HCA, TCGA, and other public repositories allows estimation of non-measured transcripts at a ratio of approximately 1:3, compared to the approximate 1:20-1:70 imputations performed previously for spatial data.Thousands of transcripts were simultaneously detected with high sensitivity and specificity on the FFPE liver cancer tissue section at single-cell subcellular resolution. We were able to accurately map known reference profiles from scRNA-seq into this sample while identifying cancer-specific malignant, immune, and stromal cells in this tissue sample using this ultra-high plex RNA panel. In addition, we constructed sample-specific spatial neighborhoods, defined by cell types, cell states, and nearly unlimited sets of biological pathways through the imputed whole transcriptome. Finally, we measured >1,000 ligand-receptor interactions between key cell types of adjacent cells in the tissue, identifying mechanisms for tumor-mediated escape as well as reactive re-architecting of the native stroma which defines the trajectory of cancer’s evolution. Single-cell spatial measurements of gene expression at 6,000 plex from a single FFPE slide has the potential to transform our understanding of tumor biology and facilitate the next advances in cancer research by extracting the highest data density possible from rare specimens collected during patient treatment.
 Citation Format: Shanshan He, Michael Patrick, Jason W. Reeves, Patrick Danaher, Julian Preciado, Joseph Phan, Erin Piazza, Zachary Reitz, Lidan Wu, Rustem Khafizov, Haiyan Zhai, Michael Rhodes, David Ruff, Joseph Beechem. Path to the holy grail of spatial biology: Spatial single-cell whole transcriptomes using 6000-plex spatial molecular imaging on FFPE tissue. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5637.

Abstract 5637: Path to the holy grail of spatial biology: Spatial single-cell whole transcriptomes using 6000-plex spatial molecular imaging on FFPE tissue


 Patient response to immunotherapy has been revolutionary but remains limited due to the inability to convert excluded or cold tumors into ones which would be permissive to therapeutic intervention. To treat patients that evade immune therapy, comprehensive understanding of their tumor microenvironment (TME) is needed. To date, most profiling efforts have lacked the ability to capture high-plex ‘omics data while retaining the spatial architecture of the TME. We developed the CosMx™ Spatial Molecular Imager (SMI) for analyzing formalin-fixed paraffin-embedded (FFPE) or fresh-frozen (FF) tissue and capturing the expression of over 1000 RNA targets simultaneously with subcellular resolution from a single histopathology slide. We profiled a cohort of 16 slides with CosMx using the Human Universal Cell Characterization (UCC) Panel across a range of solid tumors. This cohort represents a diverse array of patients which include both infiltrated and excluded tumors. We included technical replicates for 5 of the samples to better understand reproducibility of the assay. We were able to characterize over 4.5 million cells and detected on average over 80% of the panel per patient and assigning more than 95% of the transcripts profiled to unique cells across these samples. We were able to robustly identify more than 20 cell types by integrating our data with previous single-cell sequencing projects from the human cell atlas. We demonstrate robust delineation of critical immune cell populations from across lymphoid and myeloid lineages, as well as stromal cell populations inclusive of cell types frequently missed using dissociated cell sequencing, such as vascular endothelium associated with immune cell migration into the tumor bed. We leveraged 450+ genes from our panel dedicated to cell lineage, cell-cell interaction, and ligand-receptor signaling to identify unique interactions occurring at different scales between the tumor and the TME. We find that diverse mechanisms of immune evasion can be captured, and a clear role for cell types such as SPP1+ Macrophages emerges in multiple cancer types. Utilizing the CosMx platform to profile tissues allows for robust resolution of critical immunogenic signaling cascades and cellular interactions that are necessary to truly understand the tumor architecture. By maintaining the tissue structure, we can directly measure cellular interactions and capture cells commonly missed during dissociative studies. With this new platform, we are better poised than ever to truly understand the molecular mechanisms that drive tumor response to intervention. FOR RESEARCH USE ONLY. Not for use in diagnostic procedures
 Citation Format: Claire Williams, Jason W. Reeves, Patrick Danaher, Shanshan He, Sean Kim, Michael Patrick, Julian Preciado, Mark Gregory, Zach Reitz, Justin Jenkins, Rachel Liu, Sarah Murphy, Christine Kang, Byron Hartman, Vikram Devgan, Michael Rhodes, Joseph Beechem. Spatial insights into tumor immune evasion illuminated with 1000-plex RNA profiling with CosMx Spatial Molecular Imager [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6765.

Zachary Reitz

Papers

High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging.

Insitutype: likelihood-based cell typing for single cell spatial transcriptomics

Abstract 5637: Path to the holy grail of spatial biology: Spatial single-cell whole transcriptomes using 6000-plex spatial molecular imaging on FFPE tissue

Abstract 6765: Spatial insights into tumor immune evasion illuminated with 1000-plex RNA profiling with CosMx Spatial Molecular Imager