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.
This paper applies the SPADE semantic image synthesis technique (with a custom attention mechanism) to MIBI-TOF data to examine hypotheses about cell-to-cell interactions in the context of an immune infiltrated tumor sample. I think that this is potentially an interesting application of GANs -- to generalize beyond specific gathered data and instead start engaging with counterfactual scenarios like "what effect does it have to add cell type X next to cell type Y". 
Unfortunately, I think this paper is not yet finished with regard to both (1) conveying sufficient motivation for the use of image synthesis for distilling biological insights and (2) evaluating the success or failure of the technique primarily in terms of generating useful biological insights. Simultaneously, there are enough red flags with regard to knowledge of the data generation and underlying biology that it's unclear if the authors could correctly sanity check any insights they extract from their model. 
More specific criticisms & suggestions
1) It would help to provide much clearer motivation for applying image synthesis to MIBI-TOF data. Section 1.2 skips straight from describing the MIBI-TOF instrument to “we made a new kind of GAN”.  Again in the beginning of the Related Work the paper states: "We are interested in the task of generating biologically consistent expression patterns of cellular proteins given a segmentation map of cell neighborhoods”. But, why are synthetic images interesting given that we can actually look at real MIBI-TOF data. I start getting a sense of why this model might be interesting or useful only in Section 5 — more rationale is required earlier in the paper. 
2) The evaluation section is hard to follow. I think it would be helpful to more clearly describe a larger set of biological phenomena that a practitioner would expect to see in the data, choose a single metric for each case, and show that that these phenomena are recapitulated. No one is going to trust a GAN to give them scientific insights unless they're very confident that all known / simple cell-to-cell interactions have a clear signal. 
3) "MIBI-TOF bombards a tissue sample with elemental metals tethered to respective antibodies for dozens of distinct cellular proteins and detects each to obtain image channels” — this is not an accurate description of MIBI-TOF, at least not the instrument I'm familiar with. Typically the tissue is first stained with antibodies tagged with heavy metals and the instrument then bombards the tissue sample with simple ions (like O2+), causing the release of metal ions. It seems very unlikely that bombarding anything with antibody/metal conjugates could be informative. 
4) "Tumor cells could be identified by markers such as pan-keratin and beta-catenin” — I guess in the context of a tumor sample beta-catenin could be over-expressed but it's present in pretty much every cell type, including lymphocytes (https://www.proteinatlas.org/ENSG00000168036-CTNNB1)
Small nits:
* Repeated use of “antibodies” in "Engineered antibodies for PD-1/PD-L1 antibodies” — maybe better to write “Antibodies which block the interaction of PD-1/PD-L1"
* "While MIBI-TOF is capable of 36 different markers, we discarded uninformative and irrelevant markers1 resulting in M = 24.” — I’m really surprised that someone put 12/36 uninformative markers in a MIBI-TOF panel. Aren’t these tagged antibodies expensive? Can we at least get a list of what got discarded?
* The model interpretability control seems weak in that lymphocytes are more likely to express MHC-I than tumor cells (which have a potential survival advantage from not expressing it) and tumor cells may actually have more dsDNA than lymphocytes due to changes in ploidy (or original differences in ploidy from e.g. liver cells).