Konstantin Kahnert

All Publications

• SubCell: Proteome-aware vision foundation models for microscopy capture single-cell biology. bioRxiv : the preprint server for biology Gupta, A., Wefers, Z., Kahnert, K., Hansen, J. N., Misra, M. K., Leineweber, W., Cesnik, A., Lu, D., Axelsson, U., Ballllosera, F., Altman, R. B., Karaletsos, T., Lundberg, E. 2025

  Abstract

  Cell morphology and subcellular protein organization provide important insights into cellular function and behavior. These features of cells can be studied using large-scale protein fluorescence microscopy, and machine learning has become a powerful tool to interpret the resulting images for biological insights. Here, we introduce SubCell, a suite of self-supervised deep learning models for fluorescence microscopy designed to accurately capture cellular morphology, protein localization, cellular organization, and biological function beyond what humans can readily perceive. These models were trained on the proteome-wide image collection from the Human Protein Atlas with a novel proteome-aware learning objective. SubCell outperforms state-of-the-art methods across a variety of tasks relevant to single-cell biology and generalizes to other fluorescence microscopy datasets without any fine-tuning. Additionally, we construct the first proteome-wide hierarchical map of proteome organization that is directly learned from image data. This vision-based multiscale cell map defines cellular subsystems with high resolution of protein complexes, reveals proteins with similar functions, and distinguishes dynamic and stable behaviors within cellular compartments. Finally, Subcell enables a rich multimodal protein representation when integrated with a protein sequence model, allowing for a more comprehensive capture of gene function than either vision-only or sequence-only models alone. In conclusion, SubCell creates deep, image-driven representations of cellular architecture that are applicable across diverse biological contexts and datasets.

  View details for DOI 10.1101/2024.12.06.627299

  View details for PubMedID 41278937

  View details for PubMedCentralID PMC12636579

• Intrinsic heterogeneity of primary cilia revealed through spatial proteomics. Cell Hansen, J. N., Sun, H., Kahnert, K., Westenius, E., Johannesson, A., Villegas, C., Le, T., Tzavlaki, K., Winsnes, C., Pohjanen, E., Mäkiniemi, A., Fall, J., Ballllosera Navarro, F., Bäckström, A., Lindskog, C., Johansson, F., von Feilitzen, K., Delgado-Vega, A. M., Martinez Casals, A., Mahdessian, D., Uhlén, M., Sheu, S. H., Lindstrand, A., Axelsson, U., Lundberg, E. 2025

  Abstract

  Primary cilia are critical organelles found on most human cells. Their dysfunction is linked to hereditary ciliopathies with a wide phenotypic spectrum. Despite their significance, the specific roles of cilia in different cell types remain poorly understood due to limitations in analyzing ciliary protein composition. We employed antibody-based spatial proteomics to expand the Human Protein Atlas to primary cilia. Our analysis identified the subciliary locations of 715 proteins across three cell lines, examining 128,156 individual cilia. We found that 69% of the ciliary proteome is cell-type specific, and 78% exhibited single-cilia heterogeneity. Our findings portray cilia as sensors tuning their proteome to effectively sense the environment and compute cellular responses. We reveal 91 cilia proteins and found a genetic candidate variant in CREB3 in one clinical case with features overlapping ciliopathy phenotypes. This open, spatial cilia atlas advances research on cilia and ciliopathies.

  View details for DOI 10.1016/j.cell.2025.08.039

  View details for PubMedID 41005307

• Rare and Common Genetic Variation Underlying Atrial Fibrillation Risk. JAMA cardiology Vad, O. B., Monfort, L. M., Paludan-Muller, C., Kahnert, K., Diederichsen, S. Z., Andreasen, L., Lotta, L. A., Nielsen, J. B., Lundby, A., Svendsen, J. H., Olesen, M. S., Geisinger MyCode Community Health Initiative and the Regeneron Genetics Center (RGC) Research Team, Baras, A., Abecasis, G., Ferrando, A., Cantor, M., Coppola, G., Deubler, A., Economides, A., Lotta, L. A., Overton, J. D., Reid, J. G., Shuldiner, A., Siminovitch, K., Portnoy, J., Jones, M. B., Mitnaul, L., Fenney, A., Marchini, J., Ferreira, M. A., Ghoussaini, M., Nafde, M., Salerno, W., Beechert, C., Brian, E. D., Cremona, L. M., Du, H., Forsythe, C., Gu, Z., Guevara, K., Lattari, M., Lopez, A., Manoochehri, K., Challa, P., Pradhan, M., Reynoso, R., Schiavo, R., Padilla, M. S., Wang, C., Wolf, S. E., Averitt, A., Banerjee, N., Li, D., Malhotra, S., Mower, J., Sarwar, M., Sharma, D., Staples, J. C., Yu, S., Zhang, A., Aqeel, M., Mitra, G., Gokhale, S., Bunyea, A., Punuru, K. P., Sreeram, S., Eom, G., Sultan, B., Lanche, R., Mahajan, V., Austin, E., O'Keeffe, S., Panea, R., Polanco, T., Rasool, A., Bai, X., Zhang, L., Boutkov, B., Edelstein, E., Gorovits, A., Guan, J., Habegger, L., Hawes, A., Krasheninina, O., Zarate, S., Mansfield, A. J., Maxwell, E. K., Balasubramanian, S., Bao, S., Sun, K., Zhang, C., Karuppaiya, V. R., Backman, J., Burch, K., Campos, A., Chen, L., Choi, S., Damask, A., Ganel, L., Gaynor, S., Geraghty, B., Ghosh, A., Martinez, S. R., Gillies, C., Gurski, L., Herman, J., Jorgenson, E., Joseph, T., Kessler, M., Kosmicki, J., Lin, N., Locke, A., Nakka, P., Landheer, K., Delaneau, O., Marcketta, A., Mbatchou, J., Moscati, A., Pandey, A., Pandit, A., Paulding, C., Ross, J., Sidore, C., Stahl, E., Suciu, M., Thornton, T., VandeHaar, P., Vedantam, S., Vrieze, S., Zhang, J., Wang, R., Wu, K., Ye, B., Zhang, B., Ziyatdinov, A., Zou, Y., Watanabe, K., Tang, M., Wendt, F., Hobbs, B., Silver, J., Palmer, W., Guerreiro, R., Joshi, A., Baldassari, A., Willer, C., Graham, S., Mayerhofer, E., Haas, M., Verweij, N., Hindy, G., Bovijn, J., De, T., Akbari, P., Sun, L., Sosina, O., Gilly, A., Dornbos, P., Rodriguez-Flores, J., Riaz, M., Kapoor, M., Tzoneva, G., Jallow, M. W., Alkelai, A., Ayer, A., Rajagopal, V., Gelfman, S., Kumar, V., Otto, J., Parikshak, N., Guvenek, A., Bras, J., Alvarez, S., Brown, J., He, J., Khiabanian, H., Revez, J., Skead, K., Zavala, V., Sul, J. S., Chen, E., LeBlanc, M. G., Mighty, J., Nishtala, N., Rana, N., Rico-Varela, J., Hernandez, J., Schwartz, R., Hankins, J., Hart, S., Perez-Beals, A., Solari, G., Rivera-Picart, J., Pagan, M., Siceron, S., Buchanan, A., Carey, D. J., Martin, C. L., Meyer, M., Retterer, K., Rolston, D. 2024

  Abstract

  Importance: Atrial fibrillation (AF) has a substantial genetic component. The importance of polygenic risk is well established, while the contribution of rare variants to disease risk warrants characterization in large cohorts.Objective: To identify rare predicted loss-of-function (pLOF) variants associated with AF and elucidate their role in risk of AF, cardiomyopathy (CM), and heart failure (HF) in combination with a polygenic risk score (PRS).Design, Setting, and Participants: This was a genetic association and nested case-control study. The impact of rare pLOF variants was evaluated on the risk of incident AF. HF and CM were assessed in cause-specific Cox regressions. End of follow-up was July 1, 2022. Data were analyzed from January to October 2023. The UK Biobank enrolled 502 480 individuals aged 40 to 69 years at inclusion in the United Kingdom between March 13, 2006, and October 1, 2010. UK residents of European ancestry were included. Individuals with prior diagnosis of AF were excluded from analyses of incident AF.Exposures: Rare pLOF variants and an AF PRS.Main Outcomes and Measures: Risk of AF and incident HF or CM prior to and subsequent to AF diagnosis.Results: A total of 403 990 individuals (218 489 [54.1%] female) with a median (IQR) age of 58 (51-63) years were included; 24 447 were diagnosed with incident AF over a median (IQR) follow-up period of 13.3 (12.4-14.0) years. Rare pLOF variants in 6 genes (TTN, RPL3L, PKP2, CTNNA3, KDM5B, and C10orf71) were associated with AF. Of these, TTN, RPL3L, PKP2, CTNNA3, and KDM5B replicated in an external cohort. Combined with high PRS, rare pLOF variants conferred an odds ratio of 7.08 (95% CI, 6.03-8.28) for AF. Carriers with high PRS also had a substantial 10-year risk of AF (16% in female individuals and 24% in male individuals older than 60 years). Rare pLOF variants were associated with increased risk of CM both prior to AF (hazard ratio [HR], 3.13; 95% CI, 2.24-4.36) and subsequent to AF (HR, 2.98; 95% CI, 1.89-4.69).Conclusions and Relevance: Rare and common genetic variation were associated with an increased risk of AF. The findings provide insights into the genetic underpinnings of AF and may aid in future genetic risk stratification.

  View details for DOI 10.1001/jamacardio.2024.1528

  View details for PubMedID 38922602

• Proteomics couples electrical remodelling to inflammation in a murine model of heart failure with sinus node dysfunction CARDIOVASCULAR RESEARCH Kahnert, K., Soattin, L., Mills, R., Wilson, C., Maurya, S., Sorrentino, A., Al-Othman, S., Tikhomirov, R., van de Vegte, Y., Hansen, F., Achter, J., Hu, W., Zi, M., Smith, M., van der Harst, P., Olesen, M., Olsen, K., Banner, J., Jensen, T., Zhang, H., Boyett, M., D'Souza, A., Lundby, A. 2024

  Abstract

  In patients with heart failure (HF), concomitant sinus node dysfunction (SND) is an important predictor of mortality, yet its molecular underpinnings are poorly understood. Using proteomics, this study aimed to dissect the protein and phosphorylation remodelling within the sinus node in an animal model of HF with concurrent SND.We acquired deep sinus node proteomes and phosphoproteomes in mice with heart failure and SND and report extensive remodelling. Intersecting the measured (phospho)proteome changes with human genomics pharmacovigilance data, highlighted downregulated proteins involved in electrical activity such as the pacemaker ion channel, Hcn4. We confirmed the importance of ion channel downregulation for sinus node physiology using computer modelling. Guided by the proteomics data, we hypothesized that an inflammatory response may drive the electrophysiological remodeling underlying SND in heart failure. In support of this, experimentally induced inflammation downregulated Hcn4 and slowed pacemaking in the isolated sinus node. From the proteomics data we identified proinflammatory cytokine-like protein galectin-3 as a potential target to mitigate the effect. Indeed, in vivo suppression of galectin-3 in the animal model of heart failure prevented SND.Collectively, we outline the protein and phosphorylation remodeling of SND in heart failure, we highlight a role for inflammation in electrophysiological remodelling of the sinus node, and we present galectin-3 signalling as a target to ameliorate SND in heart failure.

  View details for DOI 10.1093/cvr/cvae054

  View details for Web of Science ID 001207875900001

  View details for PubMedID 38661182

• Beta-blocker/ACE inhibitor therapy differentially impacts the steady state signaling landscape of failing and non-failing hearts SCIENTIFIC REPORTS Sorrentino, A., Bagwan, N., Linscheid, N., Poulsen, P. C., Kahnert, K., Thomsen, M. B., Delmar, M., Lundby, A. 2022; 12 (1): 4760

  Abstract

  Heart failure is a multifactorial disease that affects an estimated 38 million people worldwide. Current pharmacotherapy of heart failure with reduced ejection fraction (HFrEF) includes combination therapy with angiotensin-converting enzyme inhibitors (ACEi) and β-adrenergic receptor blockers (β-AR blockers), a therapy also used as treatment for non-cardiac conditions. Our knowledge of the molecular changes accompanying treatment with ACEi and β-AR blockers is limited. Here, we applied proteomics and phosphoproteomics approaches to profile the global changes in protein abundance and phosphorylation state in cardiac left ventricles consequent to combination therapy of β-AR blocker and ACE inhibitor in HFrEF and control hearts. The phosphorylation changes induced by treatment were profoundly different for failing than for non-failing hearts. HFrEF was characterized by profound downregulation of mitochondrial proteins coupled with derangement of β-adrenergic and pyruvate dehydrogenase signaling. Upon treatment, phosphorylation changes consequent to HFrEF were reversed. In control hearts, treatment mainly led to downregulation of canonical PKA signaling. The observation of divergent signaling outcomes depending on disease state underscores the importance of evaluating drug effects within the context of the specific conditions present in the recipient heart.

  View details for DOI 10.1038/s41598-022-08534-0

  View details for Web of Science ID 000780307600005

  View details for PubMedID 35306519

  View details for PubMedCentralID PMC8934364