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1.
Haas, D. et al.: C-COMPASS: A user-friendly neural network tool profiles cell compartments at protein and lipid levels. Nat. Methods, DOI: 10.1038/s41592-025-02880-3 (2025)
2.
Hingerl, J.C.* et al.: scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution. Nat. Methods, DOI: 10.1038/s41592-025-02854-5 (2025)
3.
Ma, R. et al.: A telescopic microscope equipped with a quanta image sensor for live-cell bioluminescence imaging. Nat. Methods, DOI: 10.1038/s41592-025-02694-3 (2025)
4.
Salas, S.M. et al.: Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows. Nat. Methods 22, 813-823 (2025)
5.
Tejada Lapuerta, A. et al.: Nicheformer: A foundation model for single-cell and spatial omics. Nat. Methods, DOI: 10.1038/s41592-025-02814-z (2025)
6.
Vonficht, D.* et al.: Ultra-high-scale cytometry-based cellular interaction mapping. Nat. Methods 22, 1887–1899 (2025)
7.
Zappia, L. et al.: Feature selection methods affect the performance of scRNA-seq data integration and querying. Nat. Methods 22, 834-844 (2025)
8.
Caporale, N.* et al.: Multiplexing cortical brain organoids for the longitudinal dissection of developmental traits at single-cell resolution. Nat. Methods, DOI: 10.1038/s41592-024-02555-5 (2024)
9.
Ertürk, A.: Deep 3D histology powered by tissue clearing, omics and AI. Nat. Methods 21, 1153-1165 (2024)
10.
Frenz-Wiessner, S.* et al.: Generation of complex bone marrow organoids from human induced pluripotent stem cells. Nat. Methods, DOI: 10.1038/s41592-024-02172-2 (2024)
11.
Gayoso, A.* et al.: Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells. Nat. Methods 21, 50-59 (2024)
12.
Hrovatin, K. et al.: Considerations for building and using integrated single-cell atlases. Nat. Methods, DOI: 10.1038/s41592-024-02532-y (2024)
13.
Johnston, K.G.* ; Grieco, S.F.* ; Nie, Q.* ; Theis, F.J. & Xu, X.*: Small data methods in omics: The power of one. Nat. Methods 21, 1597-1602 (2024)
14.
Kaltenecker, D. et al.: Virtual reality-empowered deep-learning analysis of brain cells. Nat. Methods, DOI: 10.1038/s41592-024-02245-2 (2024)
15.
Kretschmer, F.* ; Harrieder, E.-M. ; Hoffmann, M.A.* ; Böcker, S.* & Witting, M.: RepoRT: A comprehensive repository for small molecule retention times. Nat. Methods 21, 153–155 (2024)
16.
Kuemmerle, L. et al.: Probe set selection for targeted spatial transcriptomics. Nat. Methods 21, 2260–2270 (2024)
17.
Maier-Hein, L.* et al.: Metrics reloaded: Recommendations for image analysis validation. Nat. Methods 21, 195-212 (2024)
18.
Marconato, L.* et al.: SpatialData: An open and universal data framework for spatial omics. Nat. Methods, DOI: 10.1038/s41592-024-02212-x (2024)
19.
Martens, L.D. ; Fischer, D.S. ; Yépez, V.A.* ; Theis, F.J. & Gagneur, J.: Modeling fragment counts improves single-cell ATAC-seq analysis. Nat. Methods 21, 28–31 (2024)
20.
Pfeuffer, J.* et al.: OpenMS 3 enables reproducible analysis of large-scale mass spectrometry data. Nat. Methods 21, 365-367 (2024)
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