Nonlinear Image Registration and Pixel Classification Pipeline for the Study of Tumor Heterogeneity Maps

Nicolás-Sáenz, Laura; Guerrero-Aspizúa, Sara; Pascau, Javier; Muñoz-Barrutia, Arrate

doi:10.3390/e22090946

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…This complicates an accurate registration between each neighboring section with increasing complexity as the distance between sections increases. Although several algorithms have been proposed to solve the task of registering serial sections [5][6][7][8][9][10][11][12][13][14][15][16][17] , they are largely based on FFPE tissue sectioned at close distance (e.g., 4-6 µm). FFPE sections are far less fragile and prone to artifacts compared to frozen tissue required for many spatial omics measurements, therefore requiring robust registration methods.…”

Section: Introductionmentioning

confidence: 99%

Spatial Integration of Multi-Omics Data using the novel Multi-Omics Imaging Integration Toolset

Wess,

Andersen,

Midtbust

et al. 2024

Preprint

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To truly understand the cancer biology of heterogenous tumors in the context of precision medicine, it is crucial to use analytical methodology capable of capturing the complexities of multiple omics levels, as well as the spatial heterogeneity of cancer tissue. Different molecular imaging techniques, such as mass spectrometry imaging (MSI) and spatial transcriptomics (ST) achieve this goal by spatially detecting metabolites and mRNA, respectively. To take full analytical advantage of such multi-omics data, the individual measurements need to be integrated into one dataset. We present MIIT (Multi-Omics Imaging Integration Toolset), a Python framework for integrating spatially resolved multi-omics data. MIIT’s integration workflow consists of performing a grid projection of spatial omics data, registration of stained serial sections, and mapping of MSI-pixels to the spot resolution of Visium 10x ST data. For the registration of serial sections, we designed GreedyFHist, a registration algorithm based on the Greedy registration tool. We validated GreedyFHist on a dataset of 245 pairs of serial sections and reported an improved registration performance compared to a similar registration algorithm. As a proof of concept, we used MIIT to integrate ST and MSI data on cancer-free tissue from 7 prostate cancer patients and assessed the spot-wise correlation of a gene signature activity for citrate-spermine secretion derived from ST with citrate, spermine, and zinc levels obtained by MSI. We confirmed a significant correlation between gene signature activity and all three metabolites. To conclude, we developed a highly accurate, customizable, computational framework for integrating spatial omics technologies and for registration of serial tissue sections.

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Section: Introductionmentioning

confidence: 99%

Spatial Integration of Multi-Omics Data using the novel Multi-Omics Imaging Integration Toolset

Wess,

Andersen,

Midtbust

et al. 2024

Preprint

View full text Add to dashboard Cite

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An overview of image registration for aligning mass spectrometry imaging with clinically relevant imaging modalities

Balluff

Heeren

Race

2022

Journal of Mass Spectrometry and Advances in the Clinical Lab

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Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration

et al. 2021

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Histopathologic assessment routinely provides rich microscopic information about tissue structure and disease process. However, the sections used are very thin, and essentially capture only 2D representations of a certain tissue sample. Accurate and robust alignment of sequentially cut 2D slices should contribute to more comprehensive assessment accounting for surrounding 3D information. Towards this end, we here propose a two-step diffeomorphic registration approach that aligns differently stained histology slides to each other, starting with an initial affine step followed by estimating a deformation field. It was quantitatively evaluated on ample (n = 481) and diverse data from the automatic non-rigid histological image registration challenge, where it was awarded the second rank. The obtained results demonstrate the ability of the proposed approach to robustly (average robustness = 0.9898) and accurately (average relative target registration error = 0.2%) align differently stained histology slices of various anatomical sites while maintaining reasonable computational efficiency (<1 min per registration). The method was developed by adapting a general-purpose registration algorithm designed for 3D radiographic scans and achieved consistently accurate results for aligning high-resolution 2D histologic images. Accurate alignment of histologic images can contribute to a better understanding of the spatial arrangement and growth patterns of cells, vessels, matrix, nerves, and immune cell interactions.

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Nonlinear Image Registration and Pixel Classification Pipeline for the Study of Tumor Heterogeneity Maps

Cited by 3 publications

References 32 publications

Spatial Integration of Multi-Omics Data using the novel Multi-Omics Imaging Integration Toolset

Spatial Integration of Multi-Omics Data using the novel Multi-Omics Imaging Integration Toolset

An overview of image registration for aligning mass spectrometry imaging with clinically relevant imaging modalities

Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration

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