IST-editing: Infinite spatial transcriptomic editing in a generated gigapixel mouse pup

Authors: jiqing  wu
ingrid  berg
viktor h koelzer
Venue: MIDL_io_2024_Conference
Published: 2024-01-10 08:00:01

Abstract: Advanced spatial transcriptomics (ST) techniques provide comprehensive insights into complex organisms across multiple scales, while simultaneously posing challenges in biomedical image analysis. The spatial co-profiling of biological tissues by gigapixel whole slide images (WSI) and gene expression arrays motivates the development of innovative and efficient algorithmic approaches. Using Generative Adversarial Nets (GAN), we introduce **I**nfinite **S**patial **T**ranscriptomic **e**diting (IST-editing) and establish gene expression-guided editing in a generated gigapixel mouse pup. Trained with patch-wise high-plex gene expression (input) and matched image data (output), IST-editing enables the seamless synthesis of arbitrarily large bioimages at inference, *e.g.*, with a $106496 \times 53248$ resolution. After feeding edited gene expression values to the trained model, we simulate cell-, tissue- and animal-level morphological transitions in the generated mouse pup. Lastly, we discuss and evaluate editing effects on interpretable morphological features. The code and generated WSIs are publicly accessible via https://github.com/CTPLab/IST-editing.

Main paper: 
 introduction: 
 Recent advances in multi-omics technologies (e.g., spatial transcriptomics (ST) (Moses and Pachter, 2022) ) and generative artificial intelligence (AI) have the potential to revolutionize biomedical image analysis (Royer, 2023) . Leveraging spatial co-profiling of high-plex mRNA transcripts (acting as proxies for gene expression) and high-resolution biomedical images, researchers possess unprecedented opportunities to model the complex spatial organization of an entire organism.Concurrently, generative AI (Bermano et al., 2022; Croitoru et al., 2023) has showcased remarkable progress in creating high-quality visual content, paving the way towards novel applications in the biomedical domain. Trained with Hematoxylin and Eosin (H&E)-stained or (immuno)fluorescence images, prior studies (Carrillo-Perez et al., 2023; Lamiable et al., 2023; Wu and Koelzer, 2023) have achieved impressive results of bioimage generation and manipulation using GAN approaches.Recently, researchers (Wu and Koelzer, 2024) further demonstrated the algorithmic editability on ST data and simulated cellular morphological transitions by shifting gene expression distributions.Notably, these studies were carried out at cell-or tissue-level and the generated bioimage resolution is usually smaller than 256 ⇥ 256. Due to scalability limitations and violation of the translation equivariance property, the generative competence of such algorithmic methods cannot be extended to the entirety of a WSI without inducing visible stitching artifacts, which can be partially mitigated by employing more hardware resources. A series of StyleGAN studies (Karras et al., 2020 (Karras et al., , 2021) ) first showed the feasibility of training 1024 ⇥ 1024 images on 8 V100 GPUs. In a recent paper, a GANbased approach (Kang et al., 2023) has accomplished 4096 ⇥ 4096 image generation with remarkably fine details. Critically, this achievement was made possible by training a scaled GAN model on 96-128 A100 GPUs. Despite impressive breakthroughs in generating megapixel-resolution images, the hardware requirements for synthesizing WSIs at the gigapixel scale can be computationally intractable, making the model application prohibitively expensive in biomedical research.To extend the model applicability to arbitrarily large images, Single GAN (SinGAN) (Shaham et al., 2019) and Single Denoising Di↵usion Model (SinDDM) (Kulikov et al., 2023) In bioimage generation, the utility of coordinate priors is nonetheless undesirable. This is because the arrangement of biological structures is not dictated by a rigid coordinate system, but rather by the intricate interplay between genetic, epigenetic, and gene expression variability that leads to the phenotype of a living system (Hani↵a et al., 2021) . Here, we propose Infinite Spatial Transcriptomic editing (IST-editing) in a generated gigapixel mouse pup. To the best of our knowledge, we are the first to introduce algorithmic gene expression editing at the scale of an entire organism:• Taken gene expression data as the input, we achieve the seamless generation of 106496 ⇥ 53248 WSIs of a whole mouse pup.• By gene expression-guided editing, we simulate cell-, tissue-and animal-level morphological transitions, measured with interpretable morphological features.• Importantly, the model training and inference can be e ciently executed on a single consumergrade GPU, e.g., GeForce RTX 3090 Ti. 
the proposed ist-editing: 
 To e ciently process the paired transcript count array and biomedical image with matched gigapixel resolutions, we develop IST-editing upon the StyleGAN (Karras et al., 2020 (Karras et al., , 2021) ) framework. This is motivated by recent GAN studies (Sauer et al., 2022 (Sauer et al., , 2023;; Kang et al., 2023) Spatial gene expression as the input and representation: In the natural image domain, previous generative models (Shaham et al., 2019; Kulikov et al., 2023) typically utilize (spatial) noise input for unconditional image generation. In addition, learned textural representations (Radford et al., 2021) can be incorporated into the model to guide the image alterations (Bermano et al., 2022) . However, semantic ambiguity often occurs in interpreting a single latent code and qualitative analysis is mostly made possible for a subset of representations (Härkönen et al., 2020) . Given the well-established biological understanding of many individual genes, we utilize gene expression as both the input data and interpretable representation for bioimage generation and editing. Coordinate-and padding-free generator G: Instead of relying on strong coordinate-based priors including the vertical saturation and horizontal repetition of natural scenes, the design of our generator is driven by the intricate interaction between genes (causative factors) and phenotypes (observable characteristics). To model the directed linkage from gene expression to the biomedical image, we propose a straightforward coordinate-free generator, which is constructed using a series of paddingfree and translation equivariant StyledConv layers (Fig. 1 ). No external prior knowledge, aside from gene expression data, is incorporated into the output images. In all padding-free layers, we discard pixel values that are padded at both spatial ends of the output. Consider i = 1, 2, . . . , l, we then have the intermediate output with (2 i+2 + 5) ⇥ (2 i+2 + 5) spatial resolutions for the i-th layer. After discarding 5 boundary pixels of the last layer output, we obtain the generated patch-wise image.Leveraging the consistent 2⇥ increase of image resolution, our model can be easily adapted to output patches with 256 ⇥ 256 or 512 ⇥ 512 resolutions.Cell-subtype conditioned discriminator D: Inspired by conditional generations of well-characterized normal and cancer cellular images (Wu and Koelzer, 2024), we integrate cell subtype information into the discriminator to adversarially and conditionally train the generator. Concretely, we project cell label embeddings into D and train both models with the conditional adversarial loss L adc . Along with the R 1 regulation L R 1 and path length regulation loss L path (Karras et al., 2020) , we have the loss function min G ((max D L adc ) + ↵ R 1 L R 1 + ↵ path L path ), 
experiments: 
 We test IST-editing on the public Xenium (Janesick et al., 2022) ST dataset of a one-day mouse pup. This gigapixel-resolution dataset 1 provides a well-curated sparse 3D array of 379-plex gene transcript counts (App. We benchmark IST-editing against state-of-the-art di↵usion-and GAN-based models such as Infinity-GAN. Consistent with the IST-editing approach, we feed all the models with patch-wise spatial gene expression data (input) and DAPI images (output) for systematic and fair comparisons. Following Training data utility (100% -3%): Next, we evaluate the generation robustness of the proposed approach under conditions of increasing data scarcity. For this purpose, we utilize progressively smaller subsets of the available data for training. As depicted in Fig. 2 (d, left ), the optimal d FID and PSNR scores remain consistent as the amount of available data decreases. Only when reducing the training data to 1/36 of the original size (Fig. 2 (e )) do we start to observe a mild degradation in quantitative performance by d FID . Upon comparing the cell-, tissue-and animal-level generation quality achieved by training on the entire dataset (App. Fig. 5 (a, b )) and 3% (Fig. 2 (f, right )) of the available data, the visual discrepancy between the two gigapixel-resolution WSIs appears marginal, substantiating the adaptability of IST-editing to limited data scenarios, requiring the seamless synthesis of more than 97% of the unseen data.We investigate gene expression-guided editing of WSI data by three distinct strategies. Experiments are performed on the generated 'in-silico mouse pup' which contains co-profiled ST and WSI data of all major mammalian organ systems.(1) Direct scaling of gene expression: Organized structures of diverse tissue regions emerge when progressively scaling the expression levels of the top four genes by a factor of 0.5, 1 (baseline), and 2 (Fig. 3 (b, left and middle)), while remaining gene expression values are zeroed out. Such targeted editing is driven by the observable dominant impact on the morphological generation of the top four leading expressed genes (Fig. 2 (b) ). Interestingly, the editing e↵ects exhibited biologically explainable heterogeneity across the di↵erent regions. In the colon section, we observe the emergence of crypt epithelial structures orchestrated by the upscaling of leading genes including Epithelial Cell Adhesion Molecule, EPCAM. As muscle-specific genes are not represented in the top colon gene sets, the outer muscle layer remains absent in the reconstruction. In other examples, the clear structures and organizations of the lung region have been recovered by our approach, closely resembling the GT lung image, and image artifacts (e.g., white flu↵ on GT WSI scan) are e↵ectively eliminated in the reconstruction. Calculated on the proportional ratio between edited and GT tissue regions highlighted in the bounding boxes (Fig. 3 (d) ), the radar charts in Fig. 3 (b, right) demonstrate a consistent increase in cell-level metrics approaching the GT with the up-scaling coe cients.(2) Indirect scaling of gene expression: Similar to the cell-level manipulation study (Wu and Koelzer, 2024) , we perform algorithmic editing on the sample covariance matrix (SCM) and scale the leading eigenvalues by 0.1, 0.5, and 1 (baseline). Consider the SCM 1 n G T G = O O T , where G is the collection of n 379-plex gene expression data from a given tissue region, O i is the 379 ⇥ 379 eigenbasis and is the (sorted) diagonal eigenvalues derived from eigenvalue decomposition. Then, we control for indirectly conducting gene expression-guided editing. As illustrated in Fig. 3 (c), there exists a rather homogeneous transition of tissue structures across the various regions of interest.On the contrary to the results described above using the leading genes, the muscle layer of the colon tissue as well as global architectural features of lung and skin are already observed at the scale of 0.1 when using all genes as an input. After examining the editing e↵ects with up-scaling of the eigenvalues, we witness a further increase in DAPI pixel intensity and increased sharpening of architectural details closely resembling the GT image. This is reflected by the quantitative analysis of the interpretable morphological features Fig. 3 (c, right), where we observe an expected increase in the cellular region and DAPI signals.(3) Interpolation between unorganized and well-organized gene expression: To simulate morphological transitions at the scale of a whole 'in-silico mouse pup', we conduct linear interpolation between randomly sampled and ground truth spatial gene expression, generating WSI results at coe cients of 0 (noise), 0.5, and 1 (mouse pup). The resulting WSIs exhibit a gradual progression from chaotic cellular organization -as reflected through the appearance of 'random noise' across the entire sample -to the highly organized structure of the one-day mouse pup. We thus demonstrate the versatility of IST-editing in simulating biological processes across multiple scales.Training data utility (0.1%): Pushing the limits further, we conduct extreme stress tests on the proposed approach for reconstructing the whole mouse pup.  
discussion and conclusion: 
 This proof-of-concept study showcased the generative ability and editability in an in-silico mouse pup with DAPI nuclear staining and linked ST data. Notably, IST-editing can be readily extended to other broadly established staining techniques to visualize cellular detail, as exemplified by the first H&E generation results of the same mouse pup (App. Fig. 8 ). In-silico modeling holds great potential for the Replacement, Reduction, and Refinement of animal research and extends beyond animal modeling. In future applications, IST-editing could enable the simulated intervention on biological samples from human pathology with reduced ethical, legal, and regulatory risks and provide a novel perspective to investigate the linkage between genotype and phenotype in human diseases.  
 
 Appendix:  


Decision: All reviewers agreed that the proposed IST-editing method based on generative adversarial learning, which demonstrates superior performance over diffusion models, is intriguing. The experimental results, along with the provided code and publicly available generated WSIs for further research and validation, were noted positively. Considering the consistent positive feedback from all reviewers and the distinctive nature of the research topic on gene editing within the MIDL community, this meta-reviewer recommends accepting the paper for an oral presentation. However, the authors are strongly encouraged to thoroughly address all comments raised by the reviewers in their final revisions.

Reviews: review: paper_summary: paper_summary: The paper introduces IST-editing, a GAN-based method that achieves seamless generation of gigapixel WSI depicting a mouse pup based on high-resolution gene expression data. Training with paired spatial gene expression and image data, this method can synthesize images at a resolution of 106496×53248 pixels and simulate morphological transitions at cellular, tissue, and organism levels.
main_review: main_review: The authors have done a great job addressing my comments.

Regarding a minor point from last year's MIDL 2023, the paper by Bao et al., "Alleviating tiling effect by random walk sliding window in high-resolution histological whole slide image synthesis" (MIDL, 2023), introduces a method for alleviating stitching artifacts. The general approach of using an overlapped moving window should also be effective. While I still believe that experimenting with a pix2pix-based GAN could be worthwhile, it may be beyond the scope of this manuscript.
strength_weakness: strength_weakness: Although the method uses well-understood biological inputs like gene expressions, the internal workings of GANs can be difficult to interpret. Understanding how changes in the input data affect the output and ensuring that the output is biologically meaningful remains a concern or need to be discussed.

As the paired data synthesis, why the most simple and straightforward pixel to pixel based synthesis validation is not used to serve as baseline? 

The results in Figure 2 is bit of questionable, it might need to further elaboration.
questions: questions: Please check the weakness and detailed comments section.
paper_summary: paper_summary: This work introduces a method to generative whole slide images (WSI) conditioned on gene expression data. The method is based on a patch-based GAN, but can be applied in inference to produce full WSI with high resolution. The authors show the effect of changing gene expression data on the generated WSI on cell, tissue, and animal level.
main_review: main_review: The authors have reasonably responded to my comments, as well as, as far as I can tell, the comments of other authors. I'm sticking with my original rating, strong accept, I think this is a nicely executed piece of work.
strength_weakness: strength_weakness: I found the high density of the figures, with lots of sub-panels, made them difficult to read. I appreciate this in part is because the paper packs in a lot of results and there is a page limit, but I think moving some parts to supplementary would make it more readable.
questions: questions: It would be nice to present some evidence to support the claim that pathologists viewed the generated images as realistic.
paper_summary: paper_summary: The paper is focused on gigapixel image synthesis conditioned by spatial gene expressions. Concretely, the authors focus on using GAN-based methods, which enable faster inference times than Diffusion models. Authors focus their experiments on mouse data (patch, tissue, organ, whole animal), and show the effectiveness of the GAN-based approach. To further motivate the applicability of such methodology, authors include several experiments on style editing during inference.
main_review: main_review: The authors have satisfactorily addressed my concerns during the rebuttal regarding baselines, implementation details, and generalization experiments. The authors have included such clarifications in the manuscript.
strength_weakness: strength_weakness: - The differences w.r.t prior relevant literature (e.g. Carrillo-Perez 2023) need to be more clearly established. Why did the authors exclude their proposed RNA-GAN as a baseline?
- As stated in the manuscript, Kang et al., 2023 have recently revised GAN architectures as an efficient alternative to diffusion model-based image synthesis. Nevertheless, they showed that the latter still produces a better-conditioned generation in most cases. Nevertheless, this does not seem to be the case in this work. For instance, DDM shows largely worse performance than the GAN-based approach. Do the authors find any explanation for this? Since SinDDM implementation details are scarcely detailed, this raises concerns about the proper adjustment of the baselines.
- As far as I understand, Xenium dataset only contains one slide of one unique study. Is this the case? Please, clarify otherwise the train/eval/test partitions used. If Xenium only consists of one unique slide, how can authors prosperity evaluate the generalization capabilities of the proposed approach?
- Some experimental details are not specified. For instance, how are alpha hyperparameters in Eq. 1 fixed? To which values?
questions: questions: Please, see Weaknesses.

Comments: title: About the possibile contribution to a MELBA Special Issue
Thank you for the approval of our revision. If possible, we kindly ask your re-evaluation on the preliminary rating.
Thank you again for your careful review.
Thank you for taking the time and effort in reviewing our response.
To Reviewer HjWd
To Reviewer 2uoz
To Reviewer kxJd
General response
comment: Dear Area Chairs and Program Chairs,

we thank you for your time in reading IST-editing and your approval of its distinctive nature.   Later, we are going to upload a revised final version by carefully addressing all the reviewers' feedback.  

Having been passionate about the potential of algorithmic gene editing within the spatial context,  we are also happy to contribute to a MELBA Special Issue (if possible).  

Our commitment to the _in silico_ editing is not only encouraged by one reviewer's recommendation for the MELBA submission, but also driven by our vision of simulated intervention on gene expression that leads to the phenotype of a living system (captured by multiplexed biomedical imaging).  

We believe such a novel perspective on biomedical imaging could be of particular interest to the MIDL community.

Kind regards,

Authors of IST-editing
Kind regards, 

Authors of the IST-editing study
Thanks for the clarifications and additional generalization results. They have nicely solved my initial concerns on the paper.
To your remaining questions: 

1. We added hyperparameter details in Sec. 2.2.
2. We acknowledged the model limitation of training/testing on the same WSI and referred to our generalization experiments (Fig. 9 and 10) in Sec. 3.3 for addressing the issue. 
3. To further substantiate the generalization results and follow your suggestion,  we have included the quantitative results (PSNR and d_FID with standard deviation) in Fig. 9 and 10. 

Please check the updated manuscript for the point-by-point revision.
1. During the weekend, we plan to include the hyperparameter details and limitation discussion in the revision.

2. As it requires more time to compute the quantitative metrics, such results will be provided early next week for your follow-up examination. 

Have a nice weekend.
Thanks for the detailed response!

Most of my concerns (Q1, Q2, and Q4) have been nicely solved. I would suggest the authors include the hyperparameter details (Q4) in the final version of the manuscript.

Nevertheless, I still have some major concerns regarding Q3, which refers to the partition train/test used for evaluating the proposed approach, and the presence of one unique slide on the Xenium dataset. 

- As for the author's response, I get that the main experiments on the paper are carried out using the same slide for training/testing (please, correct me otherwise). This is, only results on the training subset are shown. I understand that the results of the proposed pipeline are still good compared to the SoTA approaches. Nevertheless, this important limitation of the work should be more explicitly stated in Sections  "3. Experiments" and "4. Discussion".

- I would like to thank the authors for the effort of providing additional experiments on ST dataset, with separate train/test splits. Although the qualitative assessment in Figures 9 and 10 shows promising performance, I would kindly ask the authors to provide quantitative metrics for such experiments separately for train and test subsets (i.e. "Generation on trained data" and "Generation on unseen test data"). This would allow us to better assess the generalization capabilities of the proposed method.
Thank you for your approval of our motivation, promising results, and computational efficiency.  

**Q1:** Why did the authors exclude RNA-GAN as a baseline?

**A: (Translation equivariance)** Thank you for this important question. The rationale behind the exclusion is that the architectural variants of RNA-GAN violate the translation equivariant property. As a result, RNA-GAN cannot generate arbitrarily large WSIs without introducing tile stitching artifacts. 

We have supplied the theoretical argument in the revision.

____

**Q2:** Do the authors find any explanation for the largely worse performance by SinDDM?

**A: (Upscaling challenge)** Following 1) the single-image training scheme at a lower resolution (e.g., 223 X 223) and 2) the coarse-to-fine upscaling trick introduced by SinDDM, the worse performance comes from the upscaling challenge from 223 X 223 to 4096 X 4096.

The original SinDDM code can be found at https://github.com/fallenshock/SinDDM. In the experiments, we follow default hyperparameters configured in the repo, and only changes in integrating gene array input have been made.
Eventually, you can access our revised [code](https://drive.google.com/file/d/1GbjDB9lnqNhwah1VUsTDMT0pxQ852SIy/view?usp=sharing) for a side-by-side implementation comparison.

____

**Q3:** If Xenium only consists of one unique slide, how can authors prosperity evaluate the generalization capabilities of the proposed approach?

**A: (Newly released data on Dec. 2023)** You are right, we indeed struggled to evaluate the model generalization due to the rare availability of ST datasets at sub-cellular resolution. 

Thanks to newly released ST data [1] of the mouse brain, we can now train and test on different brain sections from two mice (independent samples, 1 male mouse, 1 female mouse) separately, using the same configuration of IST-editing. As shown in newly added Fig. 9 and 10 in the appendix, IST-editing achieved consistent generalization results, reflected by the good reconstruction of unseen brain slices from a different mouse. For your further examination of generalization experiments, the mouse brain data, generated 73728 X 106496 WSIs, and updated code are accessible through our GitHub [repository](https://github.com/CTPLab/IST-editing).

[1] Yao, Z., van Velthoven, C.T.J., Kunst, M. et al. A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain. Nature 624, 317–332 (2023).

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**Q4:** how are alpha hyperparameters in Eq. 1 fixed? To which values?

**A: (Prior study)** Following the recommendation by the prior SST-editing [2] study, we determine alpha_R1 and alpha_path to be 10 and 2.
Please see also the SST-editing [code](https://github.com/CTPLab/SST-editing/blob/b3abbd430763ecda1a01b3dc8324da3c3109e289/train_style2.py#L410) for reference.

[2] Jiqing Wu, Viktor H Koelzer, SST-editing: in silico spatial transcriptomic editing at single-cell resolution, Bioinformatics, Volume 40, Issue 3, March 2024.
Thank you for your positive assessment of our work and your recommendation of our study for oral presentation.

**Q1:** High density of the figures and confusingly the colours used for InfinityGAN in Fig 2c.

**A:** Given the interdisciplinary nature of our study,  we aim to efficiently engage with both the DL and biomedical communities. As a result, the manuscript adopts a balanced writing style and the figure presentation adheres to conventions typically employed in biomedical publications. 

To improve the accessibility of these subplots, we revised Fig. 2 and clarified the color panel of Fig. 2(c) by highlighting the meaning of PSNR and FID scores. Note that the color panel is consistently used for both radar plots, i.e., larger PSNR and lower FID mean better performance.

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**Q2:** It would be nice to present some evidence to support the claim that pathologists viewed the generated images as realistic.

**A: (Cell morphometrics)** Thank you for the comment. Complementary to the pathologist's review, quantitative evidence of cell morphometrics has been provided in the radar plots of Fig. 3(c). Measured with the proportional **nuclei count**, **nuclear size**, etc. between ground-truth and generated organ regions, we can see that both values are close to 1, indicating (nearly) the same amount of nuclei and the same averaged nuclear size when comparing the real and generated regions. 

To ensure consistency with pathologist interpretation, this study was closely supervised by an experienced board-certified pathologist (last author of this study).
Thank you for your constructive feedback.

**Q1:** It would be beneficial for the authors to explain why this decision (higher-resolution input data with lower-resolution output targets) was made for clarity.

**A: (Boundary consistency)** To ensure boundary consistency between neighboring generated images, every boundary pixel at (x, y) of the image tile is obtained using gene expression values located at (x', y'), where |x' - x| <= 64  and |y' - y| <= 64. A larger 256 X 256 gene array, containing all these values, is thus necessary to generate a 128 X 128 image tile.

We have added the discussion in the revision.

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**Q2**: It's important to provide context cues to enhance understanding (of the gray box in Fig. 1(right)).

**A: (Reserved and removed pixel)** For clarification, we have revised annotations for gray boxes with different border colors and have updated the right plot of Fig. 1 in the revision.

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**Q3**: Could the authors please clarify the purpose of these subplots (Fig. 2) and address the issue of potential overfitting in baseline models?

**A: (Upscaling challenge)**  Following the low-resolution single-image training scheme of SinGAN/SinDDM (e.g., 250 X 250 and 223 X 223), the overfitting (or faithful generation) of the input image is an expected result. Please see also natural image examples shown in both papers. 

Utilizing the coarse-to-fine upscaling trick introduced in both methods, we thus present unsatisfactory upscaling results (4096 X 4096, right plots) next to the faithful generation of low-res input images (left subplots) for a clear side-by-side comparison.

We have clarified these subplots in the legend of Fig. 2. 

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**Q4**: Understanding how changes in the input data affect the output and ensuring that the output is biologically meaningful remains a concern or need to be discussed.

**A: (Colon region interpretation)** Thank you for raising the fundamental question relevant to most _in silico_ biomedical studies. To bridge the gap between algorithmic analysis and biological plausibility,  we have presented an exemplary discussion on morphological transitions of the colon region. 

As elaborated in lines 185-188 and the 'Colon' row in Fig. 3(b), we observe the emergence of crypt epithelial structures orchestrated by the upscaling of leading genes including Epithelial Cell Adhesion Molecule (**_EPCAM_**). As muscle-specific genes are not represented in the top colon gene sets, the outer muscle layer remains absent in the reconstruction, providing a direct and plausible link between the in silico editing results and the known biological functions of the selected genes. 

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**Q5**: Why the most simple and straightforward pixel to pixel based synthesis validation is not used to serve as baseline?

**A: (Translation equivariance)** Similar to many prior GAN models, the widely-used pix2pixel model (e.g., https://github.com/phillipi/pix2pix) violates the translation equivariant property. As a result, it cannot generate arbitrarily large WSIs without introducing tile stitching artifacts.
We thank all reviewers for your careful review and positive feedback (**Oral** recommended by R2, **Poster** by R1&3). To address your questions:

  1. Detailed responses are provided and attached below your review. 
  2. The revised manuscript has been uploaded and key changes are highlighted in red for clarity.
  3. The updated code for reproducing new experiments has been made publicly available for your examination.
     
Should you have any further questions, we are happy to answer them.