Spatial Integration of Multi-Omics Data using the novel Multi-Omics Imaging Integration Toolset
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Abstract
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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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, …
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.
A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giaf035), where the paper and peer reviews are published openly under a CC-BY 4.0 license.
Revision 1 version
Reviewer 1: Hua Zhang
The quality of this manuscript has significantly improved in this revision. I appreciate the author's effort in thoroughly addressing all concerns and comments.
Reviewer 2: Santhoshi Krishnan
All my concerns have been adequately addressed by the authors and I have no further questions.
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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, …
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.
A version of this preprint has been published in the Open Access journal GigaScience (see paper (https://doi.org/10.1093/gigascience/giaf035)), where the paper and peer reviews are published openly under a CC-BY 4.0 license.
Original Submission Reviewer 1: Hua Zhang
Wess et al reports a Python framework, MIIT (Multi-Omics Imaging Integration Toolset), for integrating spatially resolved multi-omics data. Multi-omics imaging represents a pivotal approach for systems molecular biology and biomarker discovery. This method introduces a timely and valuable tool to advance the field. However, in my opinion, this paper still has some issues that need to be addressed before consideration for publication. Cancer tissue exhibits significant heterogeneity effects, in this study, different molecular information obtaining from different tissue sections, this means from different cells as the tissue section is 10 um thickness, almost the diameter of the cells. Please height the meaningful of co-registration information if they are obtained from different cell layers. In particular, for the datasets of spatial transcriptomics and MSI, the experiments were conducted on serial sections with an axial sectioning distance of 40 to 100 μm. This means that the mRNA and metabolites originate from different cells, raising questions about how integrating these two datasets can provide meaningful insights. The multi-omics imaging integration toolset is based on the GreedyFHist, a non-rigid registration algorithm, it suggests including more details about this algorithm and highlight the difference comparing to previously reported non-rigid image co-registration algorithm. The author should demonstrate the accuracy of background segmentation, it concerns certain low signal sample area would be removed in the denoising step. What is criterion to define the background region and sample region in the background segmentation.
In the Method section, more details need to be included in the spatial transcriptomics part, what the spatial resolution of the 10x Genomics was used. As the MALDI resolution is 30 um, how the pixel alignment of the ST and MSI data if their spatial resolution is different. In the MALDI-MSI of prostate tissue, on tissue MS/MS data is missing to confirm the identification of target analytes of citrate, ZnCl3-, and spermine.
**Reviewer 2: Santhoshi Krishnan **
Overview: In this paper, the authors present the Multi-Omics Imaging Integration Toolset, which is a python framework for integration multiple spatial omics datatypes. To facilitate this, they also development a registration method (GreedyFHist) for jointly analyzing sequential tissue layers that have undergone different types of staining/phenotyping regimens. The method validation was done on a 244 fresh-frozen prostrate tissue sections. The highly detailed methods and results section is well appreciated and helps fully contextualize the significance of the study. The definitions of study-specific terms mentioned throughout the paper at the beginning are also appreciated. Data and Code Availability: Detailed code, tutorials and associated instructions have been made available for use by the public, which is appreciated. All systems requirements have also been explicitly laid out for ease of installation and use. The workflow examples provided are quite detailed; however, a more extensive codebase with stepwise explanations within the code will be appreciated. Data has not been made available publicly, except for the raw and processed spatial transcriptomics data; however, detailed and explicit instructions have been provided on data access, keeping in mind local regulations. Revisions: Major Revisions:
- In recent years, a lot of other platforms, both free and paid, tend to support registration across multiple slides. For example, HALO has a registration feature available as well, along with a host of other open-source datatypes. In that regard, how is your platform different?
- It is mentioned that downscaling occurs during the registration process in order to reduce runtime - how are nuances in features selected as registration landmarks preserved in such a case?
- How is the fixed image determined in this case? The assumption would be that a standard H&E image is selected for this purpose- is that assumption, correct?
- The authors have stated and justified their rationale for using the mentioned evaluation metrics in the paper. However, in the general image registration space, metrics such as the dice coefficient and jaccard index are commonly used and accepted. Is there a particular reason why these were not used as well? It would offer a more complete picture for the general user if these metrics were provided as well.
- The validation of registering distance neighboring sections is quite a valuable contribution, as the authors rightly stated that in many multi-omics experiments, this might be a necessity. However, when looking at tissue sections that are 80-100 microns apart, it is quite likely that the set of cells that one may be looking at on the x-y coordinate system may not be the same at all; in fact, for a highly heterogeneous/flexible piece of tissue, they might be completely different. In such a circumstance, how much value is there in registering these two sections together instead of, say, separately analyzing them and using alternative methods to combine the results downstream?
- In the proof of concept presented in the paper, the authors mention using ST and MSI data for validating their framework. Have they also investigated ST integration with more commonly available datatypes such as IHC/mIF?
- The work that the authors have put in to validate the registration and MIIT framework using different approaches (selecting spatially distant slides, integration using augmented/artificial data) is thorough. However, different tissue types bring in their own challenges, and thus validation of this framework on an external dataset would lend more credence to this much needed framework, especially in the era of increased multiomics analyses.
Minor Revisions:
- Please ensure all typos/grammatical mistakes are corrected.
- In the 'preprocessing of stained histology images', can more details be given on the thresholding process? It is also stated that the threshold is manually adjusted for each image if necessary - how is this determination done?
- The headings/subheadings organizations within sections can be done in a more organized manner, in some parts it was challenging to determine the organization of sections/subsections.
- Can some more details be given on the landmarks that were identified per image? Could some examples be provided on what these landmarks are, and how they remain consistence across tissue layers?
- Currently, the way various samples are used for validating the GreedyFHist and MIIT frameworks are listed out in the paper is quite confusing. It would be appreciated if the authors can distinctly mention the number of samples out of the set of samples, and the associated stained slides are used for each.
- How were the annotations from the 3 annotators cross validated?
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