Exploring Cellular Protein Localization Through Semantic Image Synthesis
Cell-cell interactions have an integral role in tumorigenesis as they are critical in governing immune responses. As such, investigating specific cell-cell interactions has the potential to not only expand upon the understanding of tumorigenesis, but also guide clinical management of patient responses to cancer immunotherapies. A recent imaging technique for exploring cell-cell interactions, multiplexed ion beam imaging by time-of-flight (MIBI-TOF), allows for cells to be quantified in 36 different protein markers at sub-cellular resolutions in situ as high resolution multiplexed images. To explore the MIBI images, we propose a GAN for multiplexed data with protein specific attention. By conditioning image generation on cell types, sizes, and neighborhoods through semantic segmentation maps, we are able to observe how these factors affect cell-cell interactions simultaneously in different protein channels. Furthermore, we design a set of metrics and offer the first insights towards cell spatial orientations, cell protein expressions, and cell neighborhoods. Our model, cell-cell interaction GAN (CCIGAN), outperforms or matches existing image synthesis methods on all conventional measures and significantly outperforms on biologically motivated metrics. To our knowledge, we are the first to systematically model multiple cellular protein behaviors and interactions under simulated conditions through image synthesis.
The authors present a GAN for multiplexed imaging (MIBI-TOF) data called CCIGAN. They propose an interesting architecture design with protein-specific attention to find association between cell types and neighboring pattens and cell-cell interactions. They also propose new and biologically interpretable metrics including a reconstruction metric, projected EMD and regressing of expression on neighbors. They present improved reconstruction of interactions compared to other models in the context of PD-1 and PD-L1 interactions. It would be great to extend the evaluation to other interactions and tissue types. Overall the paper is well written, the application and especially the focus on cell-cell interactions is novel. The model is properly justified and evaluated, and there is a high demand for this framework in the multiplexed imaging field.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Exploring Cellular Protein Localization Through Semantic Image Synthesis
Cell-cell interactions have an integral role in tumorigenesis as they are critical in governing immune responses. As such, investigating specific cell-cell interactions has the potential to not only expand upon the understanding of tumorigenesis, but also guide clinical management of patient responses to cancer immunotherapies. A recent imaging technique for exploring cell-cell interactions, multiplexed ion beam imaging by time-of-flight (MIBI-TOF), allows for cells to be quantified in 36 different protein markers at sub-cellular resolutions in situ as high resolution multiplexed images. To explore the MIBI images, we propose a GAN for multiplexed data with protein specific attention. By conditioning image generation on cell types, sizes, and neighborhoods through semantic segmentation maps, we are able to observe how these factors affect cell-cell interactions simultaneously in different protein channels. Furthermore, we design a set of metrics and offer the first insights towards cell spatial orientations, cell protein expressions, and cell neighborhoods. Our model, cell-cell interaction GAN (CCIGAN), outperforms or matches existing image synthesis methods on all conventional measures and significantly outperforms on biologically motivated metrics. To our knowledge, we are the first to systematically model multiple cellular protein behaviors and interactions under simulated conditions through image synthesis.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The authors present a GAN for multiplexed imaging (MIBI-TOF) data called CCIGAN. They propose an interesting architecture design with protein-specific attention to find association between cell types and neighboring pattens and cell-cell interactions. They also propose new and biologically interpretable metrics including a reconstruction metric, projected EMD and regressing of expression on neighbors. They present improved reconstruction of interactions compared to other models in the context of PD-1 and PD-L1 interactions. It would be great to extend the evaluation to other interactions and tissue types. Overall the paper is well written, the application and especially the focus on cell-cell interactions is novel. The model is properly justified and evaluated, and there is a high demand for this framework in the multiplexed imaging field.
assistant
The authors present a GAN for multiplexed imaging (MIBI-TOF) data called CCIGAN. They propose an interesting architecture design with protein-specific attention to find association between cell types and neighboring pattens and cell-cell interactions. They also propose new and biologically interpretable metrics including a reconstruction metric, projected EMD and regressing of expression on neighbors. They present improved reconstruction of interactions compared to other models in the context of PD-1 and PD-L1 interactions. It would be great to extend the evaluation to other interactions and tissue types. Overall the paper is well written, the application and especially the focus on cell-cell interactions is novel. The model is properly justified and evaluated, and there is a high demand for this framework in the multiplexed imaging field.