Disease Cell Atlas

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Single-cell and spatial trancriptomics technologies are disruptive technologies by revealing previously hidden cellular states within tissues, organs, and disease. Disease Cell Atlases (DCAs) extend these capabilities by systematically mapping the full spectrum of cell types, cell states, and molecular programs involved in a given pathology. Several recent example of disease cell atlas have been recently performed including in human lung[1], the kidney tissue atlas, the spatial myocardial infarction atlas[2] just to cite a few. These atlases integrate single-cell transcriptomics, spatial data, and clinical metadata to elucidate disease mechanisms, identify biomarkers, and guide therapeutic development. This complements efforts from the Human Cell Atlas with focus on mapping cellular states across organs in homeostasis conditions. Challenges related to the creation of disease cell atlas include high costs, sample availability and their complex computational analysis.

Computational challenges

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The challenges in the computational analysis of disease cell atlas are multifactorial. A first aspect in to align and perform batch correction of samples, which might have originatated from distinct cohorts with distinct isoluation and single cell technology measurements. Standard pipelines such as Seurat and Scanpy provides frameworks for bach correction. However, care needs to be taken on the tradeoff of batch correctio and removal of biological signals as review by Luecken and colleages[3]. After batch correction and cell annotation, additional analysis include compositional analysis, i.e. veryfing cellular changes across samples and conditions[4]. This will reveal if a particular novel or cell type specific cell population is arising.

A more recent approach is to perform sample level analysis. One approach is to pseudo-bulk sample and or cell level single cell libraries[5]. This loses however the information on the variability of the cells. Optimal transport theory, which allow to find similarities between distribution of cells, represents a powerfull alternative to pseudo-bulk. This has been explored in PheEMD[6] in the context of cellular pertubation experiments. Latter, PILOT explored OT in the context of single cell disease atlas to not only to delineate sub-cluster of patients but also to find trajectories associated with disease progression[7].

References

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  1. ^ Sikkema, Lisa; Ramírez-Suástegui, Ciro; Strobl, Daniel C.; Gillett, Tessa E.; Zappia, Luke; Madissoon, Elo; Markov, Nikolay S.; Zaragosi, Laure-Emmanuelle; Ji, Yuge; Ansari, Meshal; Arguel, Marie-Jeanne; Apperloo, Leonie; Banchero, Martin; Bécavin, Christophe; Berg, Marijn (June 2023). "An integrated cell atlas of the lung in health and disease". Nature Medicine. 29 (6): 1563–1577. doi:10.1038/s41591-023-02327-2. ISSN 1546-170X. PMID 37291214.
  2. ^ Kuppe, Christoph; Ramirez Flores, Ricardo O.; Li, Zhijian; Hayat, Sikander; Levinson, Rebecca T.; Liao, Xian; Hannani, Monica T.; Tanevski, Jovan; Wünnemann, Florian; Nagai, James S.; Halder, Maurice; Schumacher, David; Menzel, Sylvia; Schäfer, Gideon; Hoeft, Konrad (August 2022). "Spatial multi-omic map of human myocardial infarction". Nature. 608 (7924): 766–777. Bibcode:2022Natur.608..766K. doi:10.1038/s41586-022-05060-x. ISSN 1476-4687. PMC 9364862. PMID 35948637.
  3. ^ Luecken, Malte D.; Büttner, M.; Chaichoompu, K.; Danese, A.; Interlandi, M.; Mueller, M. F.; Strobl, D. C.; Zappia, L.; Dugas, M.; Colomé-Tatché, M.; Theis, Fabian J. (January 2022). "Benchmarking atlas-level data integration in single-cell genomics". Nature Methods. 19 (1): 41–50. Bibcode:2022NatCB..19...41L. doi:10.1038/s41592-021-01336-8. ISSN 1548-7105. PMID 34949812.
  4. ^ Büttner, M.; Ostner, J.; Müller, C. L.; Theis, F. J.; Schubert, B. (2021-11-25). "scCODA is a Bayesian model for compositional single-cell data analysis". Nature Communications. 12 (1): 6876. Bibcode:2021NatCo..12.6876B. doi:10.1038/s41467-021-27150-6. ISSN 2041-1723. PMC 8616929. PMID 34824236.
  5. ^ Ramirez Flores, Ricardo Omar; Lanzer, Jan David; Dimitrov, Daniel; Velten, Britta; Saez-Rodriguez, Julio (2023-11-22). "Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease". eLife. 12 e93161. doi:10.7554/eLife.93161. ISSN 2050-084X.
  6. ^ Chen, William S.; Zivanovic, Nevena; van Dijk, David; Wolf, Guy; Bodenmiller, Bernd; Krishnaswamy, Smita (March 2020). "Uncovering axes of variation among single-cell cancer specimens". Nature Methods. 17 (3): 302–310. doi:10.1038/s41592-019-0689-z. ISSN 1548-7105. PMC 7339867. PMID 31932777.
  7. ^ Joodaki, Mehdi; Shaigan, Mina; Parra, Victor; Bülow, Roman D; Kuppe, Christoph; Hölscher, David L; Cheng, Mingbo; Nagai, James S; Goedertier, Michaël; Bouteldja, Nassim; Tesar, Vladimir; Barratt, Jonathan; Roberts, Ian SD; Coppo, Rosanna; Kramann, Rafael (2024-02-02). "Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)". Molecular Systems Biology. 20 (2): 57–74. doi:10.1038/s44320-023-00003-8. ISSN 1744-4292. PMC 10883279. PMID 38177382.