Multi-domain translation between single-cell imaging and sequencing data using autoencoders

Abstract

The development of single-cell methods for capturing different data modalities including imaging and sequencing has revolutionized our ability to identify heterogeneous cell states. Different data modalities provide different perspectives on a population of cells, and their integration is critical for studying cellular heterogeneity and its function. While various methods have been proposed to integrate different sequencing data modalities, coupling imaging and sequencing has been an open challenge. We here present an approach for integrating vastly different modalities by learning a probabilistic coupling between the different data modalities using autoencoders to map to a shared latent space. We validate this approach by integrating single-cell RNA-seq and chromatin images to identify distinct subpopulations of human naive CD4+ T-cells that are poised for activation. Collectively, our approach provides a framework to integrate and translate between data modalities that cannot yet be measured within the same cell for diverse applications in biomedical discovery.

References 40

1. 1.

   Uhler C, Shivashankar GV. Regulation of genome organization and gene expression by nuclear mechanotransduction. Nat. Rev. Mol. Cell Biol. 2017;18:717-727.

   Not in our library10.1038/nrm.2017.101

2. 2.

   Zheng H, Xie W. The role of 3D genome organization in development and cell differentiation. Nat. Rev. Mol. Cell Biol. 2019;20:535-550.

   Not in our library10.1038/s41580-019-0132-4

3. 3.

   Finn EH, et al. Extensive heterogeneity and intrinsic variation in spatial genome organization. Cell. 2019;176:1502-1515.

   Not in our library10.1016/j.cell.2019.01.020

4. 4.

   Stevens TJ, et al. 3D structures of individual mammalian genomes studied by single-cell hi-c. Nature. 2017;544:59-64.

   Not in our library10.1038/nature21429

5. 5.

   Ramani V, et al. Massively multiplex single-cell hi-c. Nat. Methods. 2017;14:263-266.

   Not in our library10.1038/nmeth.4155

6. 6.

   Klein AM, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell. 2015;161:1187-1201.

   Not in our library10.1016/j.cell.2015.04.044

7. 7.

   Macosko EZ, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161:1202-1214.

   Not in our library10.1016/j.cell.2015.05.002

8. 8.

   Buenrostro JD, et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature. 2015;523:486-490.

   Not in our library10.1038/nature14590

9. 9.

   Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 2018;36:411-420.

   Not in our library10.1038/nbt.4096

10. 10.

    Haghverdi L, Lun AT, Morgan MD, Marioni JC. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 2018;36:421-427.

    Not in our library10.1038/nbt.4091

11. 11.

    Trong TN, et al. Semisupervised generative autoencoder for single-cell data. J. Comput. Biol. 2020;27:1190-1203.

    Not in our library10.1089/cmb.2019.0337

12. 12.

    Stuart T, et al. Comprehensive integration of single-cell data. Cell. 2019;177:1888-1902.

    Not in our library10.1016/j.cell.2019.05.031

13. 13.

    Lopez, R. et al. A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements. In ICML workshop in Computational Biology (2019).

    Not in our library

14. 14.

    Stanley III, J. S., Gigante, S., Wolf, G. & Krishnaswamy, S. Harmonic alignment. In Proceedings of the 2020 SIAM International Conference on Data Mining, 316–324 (SIAM, 2020).

    Not in our library10.1137/1.9781611976236.36

15. 15.

    Liu, J., Huang, Y., Singh, R., Vert, J.-P. & Noble, W. S. Jointly embedding multiple single-cell omics measurements. In 19th International Workshop on Algorithms in Bioinformatics (WABI 2019), vol. 143, 10:1–10:13 (2019).

    Not in our library10.4230/lipics.wabi.2019.10

16. 16.

    Lopez R, Regier J, Cole MB, Jordan MI, Yosef N. Deep generative modeling for single-cell transcriptomics. Nat. Methods. 2018;15:1053-1058.

    Not in our library10.1038/s41592-018-0229-2

17. 17.

    Amodio M, et al. Exploring single-cell data with deep multitasking neural networks. Nat. Methods. 2019;16:1139-1145.

    Not in our library10.1038/s41592-019-0576-7

18. 18.

    Amodio, M. & Krishnaswamy, S. Travelgan: Image-to-image translation by transformation vector learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 8983–8992 (2019).

    Not in our library

19. 19.

    Zhu, J.-Y., Park, T., Isola, P. & Efros, A. A. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE International Conference on Computer Vision, 2223–2232 (2017).

    Not in our library

20. 20.

    Liu, R., Balsubramani, A. & Zou, J. Learning transport cost from subset correspondence. In International Conference on Learning Representations (2020).

    Not in our library

21. 21.

    Amodio, M. & Krishnaswamy, S. MAGAN: Aligning biological manifolds. In Proceedings of the 35th International Conference on Machine Learning, vol. 80, 215–223 (2018).

    Not in our library

22. 22.

    Gundersen, G., Dumitrascu, B., Ash, J. T. & Engelhardt, B. E. End-to-end training of deep probabilistic cca on paired biomedical observations. In 35th Conference on Uncertainty in Artificial Intelligence (2019).

    Not in our library

23. 23.

    Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I. & Frey, B. Adversarial autoencoders. Preprint at https://arxiv.org/abs/1511.05644 (2015).

    Not in our library

24. 24.

    Baldi, P. Autoencoders, unsupervised learning, and deep architectures. In ICML Workshop on Unsupervised and Transfer Learning, 37–49 (2012).

    Not in our library

25. 25.
    Deep learning · 2015

    No notice
26. 26.

    Ngiam, J. et al. Multimodal deep learning. In Proceedings of the 28th International Conference on Machine Learning, 689–696 (2011).

    Not in our library

27. 27.

    Yang, K. D. & Uhler, C. Multi-domain translation by learning uncoupled autoencoders. In ICML workshop in Computational Biology (2019).

    Not in our library

28. 28.

    Cao J, et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science. 2018;361:1380-1385.

    Not in our library10.1126/science.aau0730

29. 29.

    Andrew, G., Arora, R., Bilmes, J. & Livescu, K. Deep canonical correlation analysis. In Proceedings of the 30th International Conference on Machine Learning, vol. 28, 1247–1255 (2013).

    Not in our library

30. 30.

    Smith-Garvin JE, Koretzky GA, Jordan MS. T cell activation. Annu. Rev. Immunol. 2009;27:591-619.

    Not in our library10.1146/annurev.immunol.021908.132706

31. 31.
    Massively parallel digital transcriptional profiling of single cells · 2017

    No notice
32. 32.

    Uhler C, Shivashankar GV. Chromosome intermingling: Mechanical hotspots for genome regulation. Trends Cell Biol. 2017;27:810-819.

    Not in our library10.1016/j.tcb.2017.06.005

33. 33.

    Gupta S, et al. Developmental heterogeneity in DNA packaging patterns influences t-cell activation and transmigration. PloS ONE. 2012;7:e43718.

    Not in our library10.1371/journal.pone.0043718

34. 34.

    Ståhl PL, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016;353:78-82.

    Not in our library10.1126/science.aaf2403

35. 35.

    Irish JM, Kotecha N, Nolan GP. Mapping normal and cancer cell signalling networks: towards single-cell proteomics. Nat. Rev. Cancer. 2006;6:146-155.

    Not in our library10.1038/nrc1804

36. 36.

    Zenobi R. Single-cell metabolomics: analytical and biological perspectives. Science. 2013;342:1243259.

    Not in our library10.1126/science.1243259

37. 37.

    Reimand J, Kull M, Peterson H, Hansen J, Vilo J. g:Profiler-a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res. 2007;35:W193-W200.

    Not in our library10.1093/nar/gkm226

38. 38.
    g:Profiler—a web server for functional interpretation of gene lists (2016 update) · 2016

    No notice
39. 39.
    Multi-domain translation between single-cell imaging and sequencing data using autoencoders · 2021

    No notice
40. 40.

    Yang, K. D. et al. Multi-domain translation between single-cell imaging and sequencing data using autoencoders. Code available at: SaradhaVenkatachalapathy/Radial_chromatin_packing_immune_cells. 10.5281/zenodo.4267003 (2020).

    Not in our library

Cited by papers we hold 3

• A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches · 2025
  No notice
• Multi-omics single-cell data integration and regulatory inference with graph-linked embedding · 2022
  No notice
• BABEL enables cross-modality translation between multiomic profiles at single-cell resolution · 2021
  No notice

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.