A Web-based Software Resource for Interactive Analysis of Multiplex Tissue Imaging Datasets

Creason, Allison; Watson, Cameron; Gu, Qiang; Persson, Daniel; Sargent, Luke; Yuan, Chen; Lin, Jia-Ren; Sivagnanam, Shamilene; Wünnemann, Florian; Nirmal, Ajit J.; Chin, Koei; Feiler, Heidi S.; Coussens, Lisa M.; Schapiro, Denis; Grüning, Björn; Sorger, Peter K.; Sokolov, Artem; Goecks, Jeremy

doi:10.1101/2022.08.18.504436

Cited by 3 publications

(6 citation statements)

References 59 publications

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“…The TMA dearray 43 and initial segmentation steps were conducted within the Galaxy-MCMICRO Environment 5 . The cells and nuclei were segmented using common nuclear (DAPI) and membrane markers (Pan-cytokeratin, CD45) with Mesmer 44 .…”

Section: Methodsmentioning

confidence: 99%

“…All other plots were generated using the seaborn 54 and matplotlib 55 packages in Python. Visualizations of the actual multiplex tissue images were captured using Vitessce 56,57 within the Galaxy-MCMICRO Environment 5 .…”

Section: Methodsmentioning

confidence: 99%

“…Validating MTI platforms and panels is essential for reproducibility 2 , and is often achieved by comparing their performances on serial tissue sections. This comparison assumes uniformity across the sections and is conducted either at the pixel level by marker intensity or at the population level by cell phenotyping [3][4][5][6] . However, tissue sections are inherently heterogeneous, driven by factors including the sectioning process, the complexity of the tissue structure, or the presence of rare cell populations [7][8][9][10] .…”

Section: Introductionmentioning

confidence: 99%

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COEXIST: Coordinated single-cell integration of serial multiplexed tissue images

Heussner,

Watson,

Eddy

et al. 2024

Preprint

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Multiplexed tissue imaging (MTI) and other spatial profiling technologies commonly utilize serial tissue sectioning to comprehensively profile samples by imaging each section with unique biomarker panels or assays. The dependence on serial sections is attributed to technological limitations of MTI panel size or incompatible multi-assay protocols. Although image registration can align serially sectioned MTIs, integration at the single-cell level poses a challenge due to inherent biological heterogeneity. Existing computational methods overlook both cell population heterogeneity across modalities and spatial information, which are critical for effectively completing this task. To address this problem, we first use Monte-Carlo simulations to estimate the overlap between serial 5μm-thick sections. We then introduce COEXIST, a novel algorithm that synergistically combines shared molecular profiles with spatial information to seamlessly integrate serial sections at the single-cell level. We demonstrate COEXIST necessity and performance across several applications. These include combining MTI panels for improved spatial single-cell profiling, rectification of miscalled cell phenotypes using a single MTI panel, and the comparison of MTI platforms at single-cell resolution. COEXIST not only elevates MTI platform validation but also overcomes the constraints of MTI's panel size and the limitation of full nuclei on a single slide, capturing more intact nuclei in consecutive sections and thus enabling deeper profiling of cell lineages and functional states.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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COEXIST: Coordinated single-cell integration of serial multiplexed tissue images

Heussner,

Watson,

Eddy

et al. 2024

Preprint

Self Cite

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“…To accomplish this, we employed cyclic immunofluorescence (cyCIF) techniques [34], that provides an unparalleled snapshot of the proteome within the cell types in the tissue section, to generate 30-dimensional images of untreated breast tumor tissue from four patients (two samples from ER+ tumors and two samples from ER-tumors) enrolled in the NeoAva trial [35]. Utilizing Galaxy-ME for analysis [36], we identified a substantial number of individual cells in each sample. Our particular focus was on the identification of DCs, as well as ECs, to analyze their spatial distribution.…”

Section: Further Exploration Of Dcsmentioning

confidence: 99%

“…After sample preparation and imaging (see SI for details), image analysis was performed using Galaxy-ME [36]. Individual cell quantification of each marker was performed using MCQUANT [93].…”

Section: Cycifmentioning

confidence: 99%

Explainable Machine Learning Reveals the Role of the Breast Tumor Microenvironment in Neoadjuvant Chemotherapy Outcome

Azimzade,

Haugen,

Tekpli

et al. 2023

Preprint

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Recent advancements in single-cell RNA sequencing (scRNA-seq) have enabled the identification of phenotypic diversity within breast tumor tissues. However, the contribution of these cell phenotypes to tumor biology and treatment response has remained less understood. This is primarily due to the limited number of available samples and the inherent heterogeneity of breast tumors. To address this limitation, we leverage a state-of-the-art scRNA-seq atlas and employ CIBER-SORTx to estimate cell phenotype fractions by de-convolving bulk expression profiles in more than 2000 samples from patients who have undergone Neoad-juvant Chemotherapy (NAC). We introduce a pipeline based on explainable Machine Learning (XML) to robustly explore the associations between different cell phenotype fractions and the response to NAC in the general population as well as different subtypes of breast tumors. By comparing tumor subtypes, we observe that multiple cell types exhibit a distinct association with pCR within each subtype. Specifically, Dendritic cells (DCs) exhibit a negative association with pathological Complete Response (pCR) in Estrogen Receptor positive, ER+, (Luminal A/B) tumors, while showing a positive association with pCR in ER-(Basal-like/HER2-enriched) tumors. Analysis of new spatial cyclic immunoflu-orescence data and publicly available imaging mass cytometry data showed significant differences in the spatial distribution of DCs between ER subtypes. These variations underscore disparities in the engagement of DCs within the tumor microenvironment (TME), potentially driving their divergent associations with pCR across tumor subtypes. Overall, our findings on 28 different cell types provide a comprehensive understanding of the role played by cellular compo-nents of the TME in NAC outcomes. They also highlight directions for further experimental investigations at a mechanistic level.

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“Longitudinal and multimodal auditing of tumor adaptation to CDK4/6 inhibitors in HR+ metastatic breast cancers”

Creason,

Egger,

Watson

et al. 2023

Preprint

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CDK4/6 inhibitors (CDK4/6i) have transformed the treatment of hormone receptor-positive (HR+), HER2-negative (HR+) breast cancers as they are effective across all clinicopathological, age, and ethnicity subgroups for metastatic HR+ breast cancer. In metastatic ER+ breast cancer, CDK4/6i lead to strong and consistent improvement in survival across different lines of therapy. To understand how metastatic HR+ breast cancers become refractory to CDK4/6i, we have created a multimodal and longitudinal tumor atlas to investigate therapeutic adaptations in malignant cells and in the tumor immune microenvironment. This atlas is part of the NCI Cancer Moonshot Human Tumor Atlas Network and includes seven pairs of pre- and on-progression biopsies from five metastatic HR+ breast cancer patients treated with CDK4/6i. Biopsies were profiled with bulk genomics, transcriptomics, and proteomics as well as single-cell ATAC-seq and multiplex tissue imaging for spatial, single-cell resolution. These molecular datasets were then linked with detailed clinical metadata to create an atlas for understanding tumor adaptations during therapy. Analysis of our atlas datasets revealed a diverse but tractable set of tumor adaptations to CDK4/6i therapy. Malignant cells adapted to therapy via mTORC1 activation, cell cycle bypass, and increased replication stress. The tumor immune microenvironment displayed evidence of both immune activation and immune suppression, including increased PD-1 expression, features of T cell dysfunction, and CD163+macrophage infiltration. Together, our metastatic ER+ breast cancer atlas represents a rich multimodal resource to understand tumor therapeutic adaptations to CDK4/6i therapy.

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A Web-based Software Resource for Interactive Analysis of Multiplex Tissue Imaging Datasets

Cited by 3 publications

References 59 publications

COEXIST: Coordinated single-cell integration of serial multiplexed tissue images

COEXIST: Coordinated single-cell integration of serial multiplexed tissue images

Explainable Machine Learning Reveals the Role of the Breast Tumor Microenvironment in Neoadjuvant Chemotherapy Outcome

“Longitudinal and multimodal auditing of tumor adaptation to CDK4/6 inhibitors in HR+ metastatic breast cancers”

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