Dataset Information

Assessment of spatial transcriptomics for oncology discovery.

ABSTRACT: Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.

SUBMITTER: Lyubetskaya A

PROVIDER: S-EPMC9701619 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Assessment of spatial transcriptomics for oncology discovery.

Lyubetskaya Anna A Rabe Brian B Fisher Andrew A Lewin Anne A Neuhaus Isaac I Brett Constance C Brett Todd T Pereira Ethel E Golhar Ryan R Kebede Sami S Font-Tello Alba A Mosure Kathy K Van Wittenberghe Nicholas N Mavrakis Konstantinos J KJ MacIsaac Kenzie K Chen Benjamin J BJ Drokhlyansky Eugene E

Cell reports methods 20221115 11

Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, w ...[more]

PMID: 36452860

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Project description:Spatial transcriptomics is a rapidly advancing field, yet it lacks standardized metrics for evaluating imaging-based in situ hybridization technologies. In this study, we emphasize the importance of rigorous experimental design and standardized operating procedures (SOPs) in defining performance metrics across multiple modalities—from single-cell to spatial transcriptomics to spatial proteomics. Our Spatial Touchstone (ST) dataset, generated from six tissue types across three global sites (n=76 assays), provides a comprehensive evaluation of reproducibility, sensitivity, dynamic ranges, signal-to-noise ratio, false discovery rates, cell type annotation, and congruence with single-cell profiling. Using single-nucleus RNA sequencing (snRNA-seq) on the same formalin-fixed paraffin-embedded tissues, we created a reference for transcriptional profiles to assess cellular organization and biomarker localization. In the absence of an independent 'gold standard’ in the field, this study provides an objective assessment of technical performance through standardized protocols, Spatial Touchstone Protocols (STSOP). Our open-source software, SpatialQC, allows users to evaluate samples across all technical metrics and directly impute cell annotations from single-cell datasets. This study features the largest imaging-based spatial transcriptomics data repository available, with a total of 203 spatial profiles. It incorporates both public (n=127) and ST datasets (n=76) through the Spatial Touchstone Portal (STP), available at www.spatialtouchstone.org. This resource offers users easy access to analyze and compare their own data against an extensive collection of imaging-based datasets. We show that the ST datasets demonstrate high reproducibility (r=0.93 to 1.0) and set a new benchmark for the field. Finally, we establish metrics to evaluate and integrate imaging based multi omics data from single cell into spatial transcriptomics to spatial proteomics. The ST project provides a comprehensive, reproducible assessment across platforms and sites, establishing essential standards for imaging-based spatial multi omics (transcriptomics and proteomics) and paving the way for future research and technological advancements.

Dataset Information

Assessment of spatial transcriptomics for oncology discovery.

Publications

Assessment of spatial transcriptomics for oncology discovery.

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