Nature Biotechnology
Displaying 1 - 12 of 12
Pezet, M. G., Torres, J. A., Thimraj, T. A., Matkovic, I., Schrode, N., Murray, J. W., Saqi, A., Beaumont, K. G., & Snoeck, H.-W. (2025). Human respiratory airway progenitors derived from pluripotent cells generate alveolar epithelial cells and model pulmonary fibrosis. Nature Biotechnology. https://doi.org/10.1038/s41587-025-02569-0
Publication Date
Columbia Affiliation
View
Gu, J., Iyer, A., Wesley, B., Taglialatela, A., Leuzzi, G., Hangai, S., Decker, A., Gu, R., Klickstein, N., Shuai, Y., Jankovic, K., Parker-Burns, L., Jin, Y., Zhang, J. Y., Hong, J., Niu, X., Costa, J. A., Pezet, M. G., Chou, J., … Gaublomme, J. T. (2024). Mapping multimodal phenotypes to perturbations in cells and tissue with CRISPRmap. Nature Biotechnology. https://doi.org/10.1038/s41587-024-02386-x
Publication Date
Columbia Affiliation
Vagelos College of Physicians and Surgeons; Department of Genetics and Development; Herbert Irving Comprehensive Cancer Center; Department of Pathology and Cell Biology; Department of Medicine; Division of Pulmonary, Allergy, and Critical Care Medicine; Division of Hematology/Oncology; Institute for Cancer Genetics; Department of Microbiology and Immunology; Irving Institute for Cancer Dynamics; Center for Stem Cell Therapies
Walsh, Z. H., Shah, P., Kothapalli, N., Shah, S. B., Nikolenyi, G., Brodtman, D. Z., Leuzzi, G., Rogava, M., Mu, M., Ho, P., Abuzaid, S., Vasan, N., AlQuraishi, M., Milner, J. D., Ciccia, A., Melms, J. C., & Izar, B. (2024). Mapping variant effects on anti-tumor hallmarks of primary human T cells with base-editing screens. Nature Biotechnology. https://doi.org/10.1038/s41587-024-02235-x
Publication Date
Columbia Affiliation
He, S., Jin, Y., Nazaret, A., Shi, L., Chen, X., Rampersaud, S., Dhillon, B. S., Valdez, I., Friend, L. E., Fan, J. L., Park, C. Y., Mintz, R. L., Lao, Y.-H., Carrera, D., Fang, K. W., Mehdi, K., Rohde, M., McFaline-Figueroa, J. L., Blei, D., … Azizi, E. (2024). Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor–immune hubs. Nature Biotechnology. https://doi.org/10.1038/s41587-024-02173-8
Publication Date
Columbia Affiliation
Lampe, G. D., King, R. T., Halpin-Healy, T. S., Klompe, S. E., Hogan, M. I., Vo, P. L. H., Tang, S., Chavez, A., & Sternberg, S. H. (2023). Targeted DNA integration in human cells without double-strand breaks using CRISPR-associated transposases. Nature Biotechnology, 42(1), 87–98. https://doi.org/10.1038/s41587-023-01748-1
Publication Date
Columbia Affiliation
Austin, G. I., Park, H., Meydan, Y., Seeram, D., Sezin, T., Lou, Y. C., Firek, B. A., Morowitz, M. J., Banfield, J. F., Christiano, A. M., Pe’er, I., Uhlemann, A.-C., Shenhav, L., & Korem, T. (2023). Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data. Nature Biotechnology, 41(12), 1820–1828. https://doi.org/10.1038/s41587-023-01696-w
Publication Date
Columbia Affiliation
Huang, Y., Sheth, R. U., Zhao, S., Cohen, L. A., Dabaghi, K., Moody, T., Sun, Y., Ricaurte, D., Richardson, M., Velez-Cortes, F., Blazejewski, T., Kaufman, A., Ronda, C., & Wang, H. H. (2023). High-throughput microbial culturomics using automation and machine learning. Nature Biotechnology, 41(10), 1424–1433. https://doi.org/10.1038/s41587-023-01674-2
Publication Date
Columbia Affiliation
View
Lampe, G. D., & Sternberg, S. H. (2022). Novel recombinases for large DNA insertions. Nature Biotechnology, 41(4), 471–472. https://doi.org/10.1038/s41587-022-01600-y
Publication Date
Columbia Affiliation
View
Kuwasaki, Y., Suzuki, K., Yu, G., Yamamoto, S., Otabe, T., Kakihara, Y., Nishiwaki, M., Miyake, K., Fushimi, K., Bekdash, R., Shimizu, Y., Narikawa, R., Nakajima, T., Yazawa, M., & Sato, M. (2022). A red light–responsive photoswitch for deep tissue optogenetics. Nature Biotechnology, 40(11), 1672–1679. https://doi.org/10.1038/s41587-022-01351-w
Publication Date
Columbia Affiliation
View
Chowdhury, R., Bouatta, N., Biswas, S., Floristean, C., Kharkar, A., Roy, K., Rochereau, C., Ahdritz, G., Zhang, J., Church, G. M., Sorger, P. K., & AlQuraishi, M. (2022). Single-sequence protein structure prediction using a language model and deep learning. Nature Biotechnology, 40(11), 1617–1623. https://doi.org/10.1038/s41587-022-01432-w
Publication Date
Columbia Affiliation
Rube, H. T., Rastogi, C., Feng, S., Kribelbauer, J. F., Li, A., Becerra, B., Melo, L. A. N., Do, B. V., Li, X., Adam, H. H., Shah, N. H., Mann, R. S., & Bussemaker, H. J. (2022). Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning. Nature Biotechnology, 40(10), 1520–1527. https://doi.org/10.1038/s41587-022-01307-0
Publication Date
Columbia Affiliation
Harimoto, T., Hahn, J., Chen, Y.-Y., Im, J., Zhang, J., Hou, N., Li, F., Coker, C., Gray, K., Harr, N., Chowdhury, S., Pu, K., Nimura, C., Arpaia, N., Leong, K. W., & Danino, T. (2022). A programmable encapsulation system improves delivery of therapeutic bacteria in mice. Nature Biotechnology, 40(8), 1259–1269. https://doi.org/10.1038/s41587-022-01244-y
Publication Date
Columbia Affiliation