95 Characterization of human breast cancer tissue with the Xenium In Situ platform reveals a novel marker for invasiveness

Henley, R; Rapicavoli, Nicole; Janesick, Amanda; Shelansky, Robert; Kim, Albert; Hensel, Janine; Meschi, Francesca; Farahani, Navid; Kumar, Vijay; Qian, Xiaoyan; Taylor, Sarah E.

doi:10.1136/jitc-2022-sitc2022.0095

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…Our approach could also extend to identify other spatially-resolved molecular features, such as protein interactions [52,53]. Validation of our findings using pathological examination of underlying cellular phenomena, through immunohistochemistry and other single-cell level spatial analysis platforms like Xenium/merScope is essential [54,55], as this study serves as a proof of concept. We anticipate applying our models to a larger cohort to identify metastasis and recurrence predictors at a population scale.…”

Section: Discussionmentioning

confidence: 89%

Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor Heterogeneity

Fatemi,

Lu,

Sharma

et al. 2023

Preprint

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BackgroundSpatial transcriptomics involves studying the spatial organization of gene expression within tissues, offering insights into the molecular diversity of tumors. While spatial gene expression is commonly amalgamated from 1-10 cells across 50-micron spots, recent methods have demonstrated the capability to disaggregate this information at subspot resolution by leveraging both expression and histological patterns. However, elucidating such information from histology alone presents a significant challenge but if solved can better permit spatial molecular analysis at cellular resolution for instances where Visium data is not available, reducing study costs. This study explores integrating single-cell histological and transcriptomic data to infer spatial mRNA expression patterns in whole slide images collected from a cohort of stage pT3 colorectal cancer patients. A cell graph neural network algorithm was developed to align histological information extracted from detected cells with single cell RNA patterns through optimal transport methods, facilitating the analysis of cellular groupings and gene relationships. This approach leveraged spot-level expression as an intermediary to co-map histological and transcriptomic information at the single-cell level.ResultsOur study demonstrated that single-cell transcriptional heterogeneity within a spot could be predicted from histological markers extracted from cells detected within a spot. Furthermore, our model exhibited proficiency in delineating overarching gene expression patterns across whole-slide images. This approach compared favorably to traditional patch-based computer vision methods as well as other methods which did not incorporate single cell expression during the model fitting procedures. Topological nuances of single-cell expression within a Visium spot were preserved using the developed methodology.ConclusionThis innovative approach augments the resolution of spatial molecular assays utilizing histology as a sole input through synergistic co-mapping of histological and transcriptomic datasets at the single-cell level, anchored by spatial transcriptomics. While initial results are promising, they warrant rigorous validation. This includes collaborating with pathologists for precise spatial identification of distinct cell types and utilizing sophisticated assays, such as Xenium, to attain deeper subcellular insights.

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Section: Discussionmentioning

confidence: 89%

Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor Heterogeneity

Fatemi,

Lu,

Sharma

et al. 2023

Preprint

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“…For instance, the enhanced workflow does not yet apply to fresh frozen sections which will be the subject of future work. To affirm the universality and adaptability of the models, varied staining methodologies, slide preparations, and tissue specimens should be considered, requiring additional forms of validation (e.g., immunostaining, alternative spatial transcriptomic assays) [44][45][46]. Such disparities can introduce unpredicted variability, with potential ramifications on model efficiency.…”

Section: Discussionmentioning

confidence: 99%

The Overlooked Role of Specimen Preparation in Bolstering Deep Learning-Enhanced Spatial Transcriptomics Workflows

Fatemi,

Lu,

Diallo

et al. 2023

Preprint

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The application of deep learning methods to spatial transcriptomics has shown promise in unraveling the complex relationships between gene expression patterns and tissue architecture as they pertain to various pathological conditions. Deep learning methods that can infer gene expression patterns directly from tissue histomorphology can expand the capability to discern spatial molecular markers within tissue slides. However, current methods utilizing these techniques are plagued by substantial variability in tissue preparation and characteristics, which can hinder the broader adoption of these tools. Furthermore, training deep learning models using spatial transcriptomics on small study cohorts remains a costly endeavor. Necessitating novel tissue preparation processes enhance assay reliability, resolution, and scalability. This study investigated the impact of an enhanced specimen processing workflow for facilitating a deep learning-based spatial transcriptomics assessment. The enhanced workflow leveraged the flexibility of the Visium CytAssist assay to permit automated H&E staining (e.g., Leica Bond) of tissue slides, whole-slide imaging at 40x-resolution, and multiplexing of tissue sections from multiple patients within individual capture areas for spatial transcriptomics profiling. Using a cohort of thirteen pT3 stage colorectal cancer (CRC) patients, we compared the efficacy of deep learning models trained on slide prepared using an enhanced workflow as compared to the traditional workflow which leverages manual tissue staining and standard imaging of tissue slides. Leveraging Inceptionv3 neural networks, we aimed to predict gene expression patterns across matched serial tissue sections, each stemming from a distinct workflow but aligned based on persistent histological structures. Findings indicate that the enhanced workflow considerably outperformed the traditional spatial transcriptomics workflow. Gene expression profiles predicted from enhanced tissue slides also yielded expression patterns more topologically consistent with the ground truth. This led to enhanced statistical precision in pinpointing biomarkers associated with distinct spatial structures. These insights can potentially elevate diagnostic and prognostic biomarker detection by broadening the range of spatial molecular markers linked to metastasis and recurrence. Future endeavors will further explore these findings to enrich our comprehension of various diseases and uncover molecular pathways with greater nuance. Combining deep learning with spatial transcriptomics provides a compelling avenue to enrich our understanding of tumor biology and improve clinical outcomes. For results of the highest fidelity, however, effective specimen processing is crucial, and fostering collaboration between histotechnicians, pathologists, and genomics specialists is essential to herald this new era in spatial transcriptomics-driven cancer research.

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“…Using Xenium, Henley et at. revealed that invasive fronts of ductal carcinoma in situ (DCIS) BC were characterized by disrupted myoepithelial layers, and low KRT14 expression which were also positive for progesterone receptor ( 37 ); Janesick et al. predicted the hormone receptor status of three BC subtypes (low-grade and high-grade DCIS, and invasive carcinoma) whose molecular signatures were also characterized using whole-transcriptomics Visium on adjacent tissue sections ( 38 ).…”

Section: St Techniquesmentioning

confidence: 99%

The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI

Lee

Rajapakse

et al. 2023

Front. Oncol.

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Growing evidence supports the critical role of tumour microenvironment (TME) in tumour progression, metastases, and treatment response. However, the in-situ interplay among various TME components, particularly between immune and tumour cells, are largely unknown, hindering our understanding of how tumour progresses and responds to treatment. While mainstream single-cell omics techniques allow deep, single-cell phenotyping, they lack crucial spatial information for in-situ cell-cell interaction analysis. On the other hand, tissue-based approaches such as hematoxylin and eosin and chromogenic immunohistochemistry staining can preserve the spatial information of TME components but are limited by their low-content staining. High-content spatial profiling technologies, termed spatial omics, have greatly advanced in the past decades to overcome these limitations. These technologies continue to emerge to include more molecular features (RNAs and/or proteins) and to enhance spatial resolution, opening new opportunities for discovering novel biological knowledge, biomarkers, and therapeutic targets. These advancements also spur the need for novel computational methods to mine useful TME insights from the increasing data complexity confounded by high molecular features and spatial resolution. In this review, we present state-of-the-art spatial omics technologies, their applications, major strengths, and limitations as well as the role of artificial intelligence (AI) in TME studies.

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95 Characterization of human breast cancer tissue with the Xenium In Situ platform reveals a novel marker for invasiveness

Cited by 5 publications

References 2 publications

Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor Heterogeneity

Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor Heterogeneity

The Overlooked Role of Specimen Preparation in Bolstering Deep Learning-Enhanced Spatial Transcriptomics Workflows

The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI

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