
==== Front
eBioMedicine
EBioMedicine
eBioMedicine
2352-3964
Elsevier

S2352-3964(24)00360-8
10.1016/j.ebiom.2024.105324
105324
Comment
Bridging gaps: a neural network approach for cross-species scRNA-seq analysis in COVID-19
Luo Peng ac
Ye Zi-Wei bc
Yuan Shuofeng yuansf@hku.hk
a∗
a State Key Laboratory of Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
b School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
∗ Corresponding author. yuansf@hku.hk
c Contributed equally.

04 9 2024
10 2024
04 9 2024
108 10532421 8 2024
21 8 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
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pmcRapid advances in single-cell RNA sequencing (scRNA-seq) techniques have opened new doors for understanding intricate biological systems, while also introducing various obstacles. When conducting comparative assessments across individuals and conditions, the issue of batch effects arises, and data integration can cause overcorrection, potentially eliminating crucial biological variability.1,2 In addition, correctly aligning high-dimensional gene expression matrices with phenotypic characteristics in single-cell data has posed a significant challenge for researchers, especially in the field of virology when trying to comprehend the biological distinctions between animal models and clinical patients.

In recent issue of eBioMedicine, Vincent D Friedrich and his team presented potential solutions to the aforementioned challenges.3 They developed a neural network analysis framework using scGen to effectively match disease conditions across species. The authors initially employed a structured scRNA-seq integration method, utilizing an anchor-based reversed principal component analysis (PCA) technique to merge blood leukocyte transcriptome data from humans and hamsters.4 This approach successfully eliminated batch effects while maintaining biological variability. Next, they ingeniously applied a variational autoencoder (VAE) neural network model to transform high-dimensional gene expression data into a low-dimensional latent space and “humanized” the hamster data by computing the species offset vector. To measure the similarity between disease states in different species, the authors also devised a diffusion pseudotime distance-based metric, considering the continuity of cellular states for a more accurate similarity evaluation.

Utilizing this inventive set of techniques, the researchers effectively matched time-series data from a hamster model to patients with varying severity of the coronavirus disease 2019 (COVID-19). They also conducted a transcriptome-wide differential expression analysis to pinpoint genes displaying the most significant transcriptional changes across disease stages and cell types. The results indicate that the Syrian hamster model closely resembles the immune response of moderately affected COVID-19 patients, while the Rottweiler hamster better replicates the condition of critically ill patients, particularly regarding the neutrophil response. These findings not only confirm the usefulness of existing animal models but also offer guidance for choosing more appropriate models to examine different COVID-19 stages. Moreover, the study uncovers crucial biological insights, such as the aberrant activation of neutrophils in both critically ill patients with COVID-19 and the Rottweiler hamster model, highlighting the vital role neutrophils play in disease progression.5 Simultaneously, the research identifies several species-specific responses, emphasizing the importance of caution when interpreting results from animal experiments. Overall, these findings contribute valuable knowledge to understanding COVID-19 pathomechanisms and provide an objective standard for assessing the translational relevance of animal models.

Moving forward, this research paves the way for advancements in cross-species single-cell data analysis. The VAE model can be further refined by incorporating more intricate network structures or merging additional histological data types to enhance the model's predictive capabilities. Applying this method to other disease models, like cancer or autoimmune diseases, could lead to wider-ranging implications. Another promising research direction involves exploring how this approach can be integrated with spatial transcriptomics techniques to acquire spatial information about cells within tissues, allowing for a more thorough understanding of cellular interactions and microenvironmental shifts during disease progression.

In conclusion, this research not only tackles critical obstacles in single-cell RNA-seq data analysis but also supplies a robust computational framework for cross-species disease modeling. It showcases the potential of inventive computational methods to connect basic research with clinical applications, offering fresh ideas for precision medicine development in antiviral drugs. As these methods continue to be refined and applied, substantial breakthroughs are anticipated in understanding complex diseases and devising therapeutic strategies, ultimately better preparing us for future disease events and ensuring public safety.

Contributors

Literature search, writing-original draft (P.L.); writing (Z.W.Y.); writing, review & editing (S.Y.). All authors read and approved the final manuscript.

Declaration of interests

The authors declare no competing interests.

Acknowledgements

The study was financially supported by NSFC Excellent Young Scientists Fund of China (32322087 ).
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References

1 Andreatta M. Hérault L. Gueguen P. Semi-supervised integration of single-cell transcriptomics data Nat Commun 15 1 2024 872 38287014
2 Zhang Z. Zhao X. Bindra M. scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data Nat Commun 15 1 2024 912 38291052
3 Vincent F. Peter P. Emanuel W. Neural network-assisted humanisation of COVID-19 hamster transcriptomic data reveals matching severity states in human disease EBioMedicine 2024 10.1016/j.ebiom.2024.105312
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