Toward reproducible, scalable, and robust data analysis across multiplex tissue imaging platforms

Burlingame, Erik A.; Eng, Jennifer; Thibault, Guillaume; Chin, Koei; Gray, Joe W.; Chang, Young Hwan

doi:10.1016/j.crmeth.2021.100053

Cited by 28 publications

(40 citation statements)

References 32 publications

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“…We recently proposed and evaluated several normalization methods for multiplexed imaging data, which along with other recent work shows that normalization methods are important in reducing slide-to-slide variation ( Burlingame et al, 2021 ; Chang et al, 2020 ; Harris et al, 2022 ). These recently developed algorithms are the beginning of contributions to normalization literature, but lack a simple, user-friendly implementation.…”

Section: Statement Of Needmentioning

confidence: 99%

mxnorm: An R Package to Normalize Multiplexed Imaging Data

Harris¹,

Wrobel²,

Vandekar³

2022

JOSS

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Summary Multiplexed imaging is an emerging single-cell assay that can be used to understand and analyze complex processes in tissue-based cancers, autoimmune disorders, and more. These imaging technologies, which include co-detection by indexing (CODEX), multiplexed ion beam imaging (MIBI), and multiplexed immunofluorescence imaging (MxIF), provide detailed information about spatial interactions between cells ( Angelo et al., 2014 ; Gerdes et al., 2013 ; Goltsev et al., 2018 ). Multiplexed imaging experiments generate data across hundreds of slides and images, often resulting in terabytes of complex data to analyze through imaging analysis pipelines. Methods are rapidly developing to improve particular parts of the pipeline, including software packages in R and Python like , , , and ( Creed et al., 2021 ; Palla et al., 2021 ; Schapiro et al., 2021 ; Windhager et al., 2021 ). An important, but understudied component of this pipeline is the analysis of technical variation within this complex data source – intensity normalization is one way to remove this technical variability. The combination of disparate pre-processing pipelines, imaging variables, optical effects, and within-slide dependencies create batch and slide effects that can be reduced via normalization methods. Current state-of-the-art methods vary heavily across research labs and image acquisition platforms, without one singular method that is uniformly robust – optimal statistical methods seek to improve similarity across images and slides by removing this technical variability while maintaining the underlying biological signal in the data. is open-source software built with R and S3 methods that implements, evaluates, and visualizes normalization techniques for multiplexed imaging data. Extending methodology described in Harris et al. (2022) , we intend to set a foundation for the evaluation of multiplexed imaging normalization methods in R. This easily allows users to extend normalization methods into the field, and provides a robust evaluation framework to measure both technical variability and the efficacy of various normalization methods. One key component of the R package is the ability to supply user-defined normalization methods and thresholding algorithms to assess normalization in multiplexed imaging data. Core features, usage details, and extensive tutorials are available in the package documentation and vignette on CRAN and the software repository .

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Section: Statement Of Needmentioning

confidence: 99%

mxnorm: An R Package to Normalize Multiplexed Imaging Data

Harris¹,

Wrobel²,

Vandekar³

2022

JOSS

View full text Add to dashboard Cite

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“…With advancements in technology, great progress has been made in high parameter imaging of tissues in situ to allow simultaneous quantification and visualization of individual cells in tissue sections. More specifically, multiplex tissue imaging (MTI) [ 12 ] methods such as cyclic immunoflourescence (CyCIF) [ 13 ], CO-Dectection by indEXing (CODEX) [ 14 ], multiplex immunohistochemistry (mIHC) [ 15 ], imaging mass cytometry (IMC) [ 16 ], and multiplex ion beam imaging (MIBI) [ 17 ] are capable of measuring the expression of tens of markers at single-cell resolution while preserving the spatial distribution of cells. As an example, multiplex immunohistochemistry (mIHC) detects and visualizes specific antigens in cells of a tissue section by utilizing antibody-antigen reactions coupled to a flourescent dye or an enzyme [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

SPF: A spatial and functional data analytic approach to cell imaging data

Wrobel

Bitler

et al. 2022

PLoS Comput Biol

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The tumor microenvironment (TME), which characterizes the tumor and its surroundings, plays a critical role in understanding cancer development and progression. Recent advances in imaging techniques enable researchers to study spatial structure of the TME at a single-cell level. Investigating spatial patterns and interactions of cell subtypes within the TME provides useful insights into how cells with different biological purposes behave, which may consequentially impact a subject’s clinical outcomes. We utilize a class of well-known spatial summary statistics, the K-function and its variants, to explore inter-cell dependence as a function of distances between cells. Using techniques from functional data analysis, we introduce an approach to model the association between these summary spatial functions and subject-level outcomes, while controlling for other clinical scalar predictors such as age and disease stage. In particular, we leverage the additive functional Cox regression model (AFCM) to study the nonlinear impact of spatial interaction between tumor and stromal cells on overall survival in patients with non-small cell lung cancer, using multiplex immunohistochemistry (mIHC) data. The applicability of our approach is further validated using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset.

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“…The tumor micro-environment has been the focus of many studies, including therapeutic targeting [ 6 , 7 ] and spatial heterogeneity [ 8 , 9 , 10 ], as many scientists believe that the key to curing cancer lies within tumor micro-environment interactions [ 11 , 12 ]. However, to find effective therapies, many factors should be considered, and in-vivo examinations of the tumor micro-environment and its response to treatments can be expensive and straining for both patients and investigators.…”

Section: Introductionmentioning

confidence: 99%

A PDE Model of Breast Tumor Progression in MMTV-PyMT Mice

Mirzaei

Tatárová

Hao

et al. 2022

JPM

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The evolution of breast tumors greatly depends on the interaction network among different cell types, including immune cells and cancer cells in the tumor. This study takes advantage of newly collected rich spatio-temporal mouse data to develop a data-driven mathematical model of breast tumors that considers cells’ location and key interactions in the tumor. The results show that cancer cells have a minor presence in the area with the most overall immune cells, and the number of activated immune cells in the tumor is depleted over time when there is no influx of immune cells. Interestingly, in the case of the influx of immune cells, the highest concentrations of both T cells and cancer cells are in the boundary of the tumor, as we use the Robin boundary condition to model the influx of immune cells. In other words, the influx of immune cells causes a dominant outward advection for cancer cells. We also investigate the effect of cells’ diffusion and immune cells’ influx rates in the dynamics of cells in the tumor micro-environment. Sensitivity analyses indicate that cancer cells and adipocytes’ diffusion rates are the most sensitive parameters, followed by influx and diffusion rates of cytotoxic T cells, implying that targeting them is a possible treatment strategy for breast cancer.

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Toward reproducible, scalable, and robust data analysis across multiplex tissue imaging platforms

Cited by 28 publications

References 32 publications

mxnorm: An R Package to Normalize Multiplexed Imaging Data

mxnorm: An R Package to Normalize Multiplexed Imaging Data

SPF: A spatial and functional data analytic approach to cell imaging data

A PDE Model of Breast Tumor Progression in MMTV-PyMT Mice

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