CellProfiler 3.0: Next-generation image processing for biology

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes

Bray, M.-A., Singh, S., Han, H., Davis, C. T., Borgeson, B., Hartland, C., Kost-Alimova, M., Gustafsdottir, S. M., Gibson, C. C., & Carpenter, A. E. (2016) · Nature Protocols · doi:10.1038/nprot.2016.105 · Cited by 1,090

Annotated high-throughput microscopy image sets for validation

Ljosa, V., Sokolnicki, K. L., & Carpenter, A. E. (2012) · Nature Methods · doi:10.1038/nmeth.2083 · Cited by 601

Identification of Host-Targeted Small Molecules That Restrict Intracellular Mycobacterium tuberculosis Growth

Stanley, S. A., Barczak, A. K., Silvis, M. R., Luo, S. S., Sogi, K., Vokes, M., Bray, M.-A., Carpenter, A. E., Moore, C. B., Siddiqi, N., et al. (2014) · PLoS Pathogens · doi:10.1371/journal.ppat.1003946 · Cited by 251

Cellpose: a generalist algorithm for cellular segmentation

Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021) · Nature Methods · doi:10.1038/s41592-020-01018-x · Cited by 4,369

A single-cell transcriptomic atlas characterizes ageing tissues in the mouse

The Tabula Muris Consortium, Almanzar, N., Antony, J., Baghel, A. S., Bakerman, I., Bansal, I., Barres, B. A., Beachy, P. A., Berdnik, D., Bilen, B., et al. (2020) · Nature · doi:10.1038/s41586-020-2496-1 · Cited by 1,247

Deep learning for cellular image analysis

Moen, E., Bannon, D., Kudo, T., Graf, W., Covert, M., & Van Valen, D. (2019) · Nature Methods · doi:10.1038/s41592-019-0403-1 · Cited by 1,225

Best practices for single-cell analysis across modalities

Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051

Organoids

Zhao, Z., Chen, X., Dowbaj, A. M., Sljukic, A., Bratlie, K., Lin, L., Fong, E. L. S., Balachander, G. M., Chen, Z., Soragni, A., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00174-y · Cited by 1,082

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Greenwald, N. F., Miller, G., Moen, E., Kong, A., Kagel, A., Dougherty, T., Fullaway, C. C., McIntosh, B. J., Leow, K. X., Schwartz, M. S., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01094-0 · Cited by 953

Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl

Caicedo, J. C., Goodman, A., Karhohs, K. W., Cimini, B. A., Ackerman, J., Haghighi, M., Heng, C., Becker, T., Doan, M., McQuin, C., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0612-7 · Cited by 809

Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment

Hwang, W. L., Jagadeesh, K. A., Guo, J. A., Hoffman, H. I., Yadollahpour, P., Reeves, J. W., Mohan, R., Drokhlyansky, E., Van Wittenberghe, N., Ashenberg, O., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01134-8 · Cited by 426

Functional, metabolic and transcriptional maturation of human pancreatic islets derived from stem cells

Balboa, D., Barsby, T., Lithovius, V., Saarimäki-Vire, J., Omar-Hmeadi, M., Dyachok, O., Montaser, H., Lund, P.-E., Yang, M., Ibrahim, H., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01219-z · Cited by 433

LABKIT: Labeling and Segmentation Toolkit for Big Image Data

Arzt, M., Deschamps, J., Schmied, C., Pietzsch, T., Schmidt, D., Tomancak, P., Haase, R., & Jug, F. (2022) · Frontiers in Computer Science · doi:10.3389/fcomp.2022.777728 · Cited by 405

Mitochondrial DNA drives abscopal responses to radiation that are inhibited by autophagy

Yamazaki, T., Kirchmair, A., Sato, A., Buqué, A., Rybstein, M., Petroni, G., Bloy, N., Finotello, F., Stafford, L., Navarro Manzano, E., et al. (2020) · Nature Immunology · doi:10.1038/s41590-020-0751-0 · Cited by 359

LIVECell—A large-scale dataset for label-free live cell segmentation

Edlund, C., Jackson, T. R., Khalid, N., Bevan, N., Dale, T., Dengel, A., Ahmed, S., Trygg, J., & Sjögren, R. (2021) · Nature Methods · doi:10.1038/s41592-021-01249-6 · Cited by 304

A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives

Mathis, A., Schneider, S., Lauer, J., & Mathis, M. W. (2020) · Neuron · doi:10.1016/j.neuron.2020.09.017 · Cited by 231

Smooth Muscle Cell Reprogramming in Aortic Aneurysms

Chen, P.-Y., Qin, L., Li, G., Malagon-Lopez, J., Wang, Z., Bergaya, S., Gujja, S., Caulk, A. W., Murtada, S.-I., Zhang, X., et al. (2020) · Cell Stem Cell · doi:10.1016/j.stem.2020.02.013 · Cited by 214

Data Integration Challenges for Machine Learning in Precision Medicine

Martínez-García, M., & Hernández-Lemus, E. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2021.784455 · Cited by 185

Depletion of the AD Risk Gene SORL1 Selectively Impairs Neuronal Endosomal Traffic Independent of Amyloidogenic APP Processing

Knupp, A., Mishra, S., Martinez, R., Braggin, J. E., Szabo, M., Kinoshita, C., Hailey, D. W., Small, S. A., Jayadev, S., & Young, J. E. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107719 · Cited by 165

The phenotypic landscape of essential human genes

Funk, L., Su, K.-C., Ly, J., Feldman, D., Singh, A., Moodie, B., Blainey, P. C., & Cheeseman, I. M. (2022) · Cell · doi:10.1016/j.cell.2022.10.017 · Cited by 160

Human TBK1 deficiency leads to autoinflammation driven by TNF-induced cell death

Taft, J., Markson, M., Legarda, D., Patel, R., Chan, M., Malle, L., Richardson, A., Gruber, C., Martín-Fernández, M., Mancini, G. M. S., et al. (2021) · Cell · doi:10.1016/j.cell.2021.07.026 · Cited by 151

Spatial Architecture of Myeloid and T Cells Orchestrates Immune Evasion and Clinical Outcome in Lung Cancer

Enfield, K. S. S., Colliver, E., Lee, C., Magness, A., Moore, D. A., Sivakumar, M., Grigoriadis, K., Pich, O., Karasaki, T., Hobson, P. S., et al. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1380 · Cited by 151

Healthspan Maintenance and Prevention of Parkinson's-like Phenotypes with Hydroxytyrosol and Oleuropein Aglycone in C. elegans

Brunetti, G., Di Rosa, G., Scuto, M., Leri, M., Stefani, M., Schmitz-Linneweber, C., Calabrese, V., & Saul, N. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21072588 · Cited by 138

Protocol for Application, Standardization and Validation of the Forskolin-Induced Swelling Assay in Cystic Fibrosis Human Colon Organoids

Vonk, A. M., van Mourik, P., Ramalho, A. S., Silva, I. A. L., Statia, M., Kruisselbrink, E., Suen, S. W. F., Dekkers, J. F., Vleggaar, F. P., Houwen, R. H. J., et al. (2020) · STAR Protocols · doi:10.1016/j.xpro.2020.100019 · Cited by 128

The Alzheimer’s gene SORL1 is a regulator of endosomal traffic and recycling in human neurons

Mishra, S., Knupp, A., Szabo, M. P., Williams, C. A., Kinoshita, C., Hailey, D. W., Wang, Y., Andersen, O. M., & Young, J. E. (2022) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-022-04182-9 · Cited by 126

Pathogenic LRRK2 control of primary cilia and Hedgehog signaling in neurons and astrocytes of mouse brain

Khan, S. S., Sobu, Y., Dhekne, H. S., Tonelli, F., Berndsen, K., Alessi, D. R., & Pfeffer, S. R. (2021) · eLife · doi:10.7554/elife.67900 · Cited by 93

Integrated gut–liver-on-a-chip platform as an in vitro human model of non-alcoholic fatty liver disease

Yang, J., Hirai, Y., Iida, K., Ito, S., Trumm, M., Terada, S., Sakai, R., Tsuchiya, T., Tabata, O., & Kamei, K.-I. (2020) · doi:10.1101/2020.06.10.141606 · Cited by 110

Common germline variants of the human APOE gene modulate melanoma progression and survival

Ostendorf, B. N., Bilanovic, J., Adaku, N., Tafreshian, K. N., Tavora, B., Vaughan, R. D., & Tavazoie, S. F. (2020) · Nature Medicine · doi:10.1038/s41591-020-0879-3 · Cited by 108

Germline genetic determination of cancer progression and survival

Ostendorf, B. N., Tafreshian, K. N., Adaku, N., Bilanovic, J., Tavora, B., & Tavazoie, S. F. (2019) · doi:10.1101/576181 · Cited by 108

A pan-cancer organoid platform for precision medicine

Larsen, B. M., Kannan, M., Langer, L. F., Leibowitz, B. D., Bentaieb, A., Cancino, A., Dolgalev, I., Drummond, B. E., Dry, J. R., Ho, C.-S., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109429 · Cited by 105

Predicting tumour mutational burden from histopathological images using multiscale deep learning

Jain, M. S., & Massoud, T. F. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0190-5 · Cited by 103

Targeted Degradation of SLC Transporters Reveals Amenability of Multi-Pass Transmembrane Proteins to Ligand-Induced Proteolysis

Bensimon, A., Pizzagalli, M. D., Kartnig, F., Dvorak, V., Essletzbichler, P., Winter, G. E., & Superti-Furga, G. (2020) · Cell Chemical Biology · doi:10.1016/j.chembiol.2020.04.003 · Cited by 102

The Allen Cell and Structure Segmenter: a new open source toolkit for segmenting 3D intracellular structures in fluorescence microscopy images

Chen, J., Ding, L., Viana, M. P., Lee, H., Sluezwski, M. F., Morris, B., Hendershott, M. C., Yang, R., Mueller, I. A., & Rafelski, S. M. (2018) · doi:10.1101/491035 · Cited by 99

Integrated-gut-liver-on-a-chip platform as an in vitro human model of non-alcoholic fatty liver disease

Yang, J., Hirai, Y., Iida, K., Ito, S., Trumm, M., Terada, S., Sakai, R., Tsuchiya, T., Tabata, O., & Kamei, K.-I. (2023) · Communications Biology · doi:10.1038/s42003-023-04710-8 · Cited by 98

Temporal dissection of an enhancer cluster reveals distinct temporal and functional contributions of individual elements

Thomas, H. F., Kotova, E., Jayaram, S., Pilz, A., Romeike, M., Lackner, A., Penz, T., Bock, C., Leeb, M., Halbritter, F., et al. (2021) · Molecular Cell · doi:10.1016/j.molcel.2020.12.047 · Cited by 98

Modeling alcohol-associated liver disease in a human Liver-Chip

Nawroth, J. C., Petropolis, D. B., Manatakis, D. V., Maulana, T. I., Burchett, G., Schlünder, K., Witt, A., Shukla, A., Kodella, K., Ronxhi, J., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109393 · Cited by 88

BIN1 protein isoforms are differentially expressed in astrocytes, neurons, and microglia: neuronal and astrocyte BIN1 are implicated in tau pathology

Taga, M., Petyuk, V. A., White, C., Marsh, G., Ma, Y., Klein, H.-U., Connor, S. M., Kroshilina, A., Yung, C. J., Khairallah, A., et al. (2020) · Molecular Neurodegeneration · doi:10.1186/s13024-020-00387-3 · Cited by 81

Evolution and impact of high content imaging.

Way, G. P., Sailem, H., Shave, S., Kasprowicz, R., & Carragher, N. O. (2023) · SLAS Discovery · doi:10.1016/j.slasd.2023.08.009 · Cited by 67

A feed-forward pathway drives LRRK2 kinase membrane recruitment and activation

Vides, E. G., Adhikari, A., Chiang, C. Y., Lis, P., Purlyte, E., Limouse, C., Shumate, J. L., Spínola-Lasso, E., Dhekne, H. S., Alessi, D. R., et al. (2022) · eLife · doi:10.7554/elife.79771 · Cited by 63

New insights into the clinical and molecular spectrum of the novel CYFIP2-related neurodevelopmental disorder and impairment of the WRC-mediated actin dynamics

Begemann, A., Sticht, H., Begtrup, A., Vitobello, A., Faivre, L., Banka, S., Alhaddad, B., Asadollahi, R., Becker, J., Bierhals, T., et al. (2021) · Genetics in Medicine · doi:10.1038/s41436-020-01011-x · Cited by 61

A comprehensive analysis of gene expression changes in a high replicate and open-source dataset of differentiating hiPSC-derived cardiomyocytes

Grancharova, T., Gerbin, K. A., Rosenberg, A. B., Roco, C. M., Arakaki, J. E., DeLizo, C. M., Dinh, S. Q., Donovan-Maiye, R. M., Hirano, M., Nelson, A. M., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-94732-1 · Cited by 61

Pooled genetic perturbation screens with image-based phenotypes

Feldman, D., Funk, L., Le, A., Carlson, R. J., Leiken, M. D., Tsai, F., Soong, B., Singh, A., & Blainey, P. C. (2022) · Nature Protocols · doi:10.1038/s41596-021-00653-8 · Cited by 59

The cholesterol transport protein GRAMD1C regulates autophagy initiation and mitochondrial bioenergetics

Ng, M. Y. W., Charsou, C., Lapao, A., Singh, S., Trachsel-Moncho, L., Schultz, S. W., Nakken, S., Munson, M. J., & Simonsen, A. (2022) · Nature Communications · doi:10.1038/s41467-022-33933-2 · Cited by 59

Cell states beyond transcriptomics: Integrating structural organization and gene expression in hiPSC-derived cardiomyocytes

Gerbin, K. A., Grancharova, T., Donovan-Maiye, R. M., Hendershott, M. C., Anderson, H. G., Brown, J. M., Chen, J., Dinh, S. Q., Gehring, J. L., Johnson, G. R., et al. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.001 · Cited by 59

Deletion of Abi3 gene locus exacerbates neuropathological features of Alzheimer’s disease in a mouse model of Aβ amyloidosis

Karahan, H., Smith, D. C., Kim, B., Dabin, L. C., Al-Amin, M. M., Wijeratne, H. R. S., Pennington, T., Viana di Prisco, G., McCord, B., Lin, P. B.-C., et al. (2021) · Science Advances · doi:10.1126/sciadv.abe3954 · Cited by 53

Single-cell mass cytometry reveals complex myeloid cell composition in active lesions of progressive multiple sclerosis

Böttcher, C., van der Poel, M., Fernández-Zapata, C., Schlickeiser, S., Leman, J. K. H., Hsiao, C.-C., Mizee, M. R., Adelia, Vincenten, M. C. J., Kunkel, D., et al. (2020) · Acta Neuropathologica Communications · doi:10.1186/s40478-020-01010-8 · Cited by 58

Cell-type-specific regulation of APOE and CLU levels in human neurons by the Alzheimer’s disease risk gene SORL1

Lee, H., Aylward, A. J., Pearse, R. V., Lish, A. M., Hsieh, Y.-C., Augur, Z. M., Benoit, C. R., Chou, V., Knupp, A., Pan, C., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112994 · Cited by 58

Hydrogel Mechanics Influence the Growth and Development of Embedded Brain Organoids

Cassel de Camps, C., Aslani, S., Stylianesis, N., Nami, H., Mohamed, N.-V., Durcan, T. M., & Moraes, C. (2022) · ACS Applied Bio Materials · doi:10.1021/acsabm.1c01047 · Cited by 55

Immunomodulatory matrix-bound nanovesicles mitigate acute and chronic pristane-induced rheumatoid arthritis

Crum, R. J., Hall, K., Molina, C. P., Hussey, G. S., Graham, E., Li, H., & Badylak, S. F. (2022) · npj Regenerative Medicine · doi:10.1038/s41536-022-00208-9 · Cited by 50

Optogenetic actuator – ERK biosensor circuits identify MAPK network nodes that shape ERK dynamics

Dessauges, C., Mikelson, J., Dobrzyński, M., Jacques, M., Frismantiene, A., Gagliardi, P. A., Khammash, M., & Pertz, O. (2022) · Molecular Systems Biology · doi:10.15252/msb.202110670 · Cited by 48

catGRANULE 2.0: accurate predictions of liquid-liquid phase separating proteins at single amino acid resolution

(2025) · doi:10.1186/s13059-025-03497-7 · Cited by 45

RIF1 acts in DNA repair through phosphopeptide recognition of 53BP1

Setiaputra, D., Escribano-Díaz, C., Reinert, J. K., Sadana, P., Zong, D., Callen, E., Sifri, C., Seebacher, J., Nussenzweig, A., Thomä, N. H., et al. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.01.025 · Cited by 43

MOSPD2 is an endoplasmic reticulum–lipid droplet tether functioning in LD homeostasis

Zouiouich, M., Di Mattia, T., Martinet, A., Eichler, J., Wendling, C., Tomishige, N., Grandgirard, E., Fuggetta, N., Fromental-Ramain, C., Mizzon, G., et al. (2022) · Journal of Cell Biology · doi:10.1083/jcb.202110044 · Cited by 40

Matrix-Bound Nanovesicles: The Effects of Isolation Method upon Yield, Purity, and Function

Quijano, L. M., Naranjo, J. D., El-Mossier, S. O., Turner, N. J., Pineda Molina, C., Bartolacci, J., Zhang, L., White, L., Li, H., & Badylak, S. F. (2020) · Tissue Engineering Part C: Methods · doi:10.1089/ten.tec.2020.0243 · Cited by 38

Presynaptic targeting of botulinum neurotoxin type A requires a tripartite PSG‐Syt1‐SV2 plasma membrane nanocluster for synaptic vesicle entry

Joensuu, M., Syed, P., Saber, S. H., Lanoue, V., Wallis, T. P., Rae, J., Blum, A., Gormal, R. S., Small, C., Sanders, S., et al. (2023) · The EMBO Journal · doi:10.15252/embj.2022112095 · Cited by 34

Integrated -omics approach reveals persistent DNA damage rewires lipid metabolism and histone hyperacetylation via MYS-1/Tip60

Hamsanathan, S., Anthonymuthu, T., Han, S., Shinglot, H., Siefken, E., Sims, A., Sen, P., Pepper, H. L., Snyder, N. W., Bayir, H., et al. (2022) · Science Advances · doi:10.1126/sciadv.abl6083 · Cited by 30

WSTF nuclear autophagy regulates chronic but not acute inflammation

Wang, Y., Eapen, V. V., Liang, Y., Kournoutis, A., Sherman, M. S., Xu, Y., Onorati, A., Li, X., Zhou, X., Corey, K. E., et al. (2025) · Nature · doi:10.1038/s41586-025-09234-1 · Cited by 30

Dissecting Drug-Induced Cytotoxicity and Metabolic Dysfunction in Conditionally Immortalized Human Proximal Tubule Cells

Hoogstraten, C. A., Smeitink, J. A. M., Russel, F. G. M., & Schirris, T. J. J. (2022) · Frontiers in Toxicology · doi:10.3389/ftox.2022.842396 · Cited by 30

Transcriptional kinetic synergy: A complex landscape revealed by integrating modeling and synthetic biology

Martinez-Corral, R., Park, M., Biette, K. M., Friedrich, D., Scholes, C., Khalil, A. S., Gunawardena, J., & DePace, A. H. (2023) · Cell Systems · doi:10.1016/j.cels.2023.02.003 · Cited by 29

Effect of distinct ECM microenvironments on the genome-wide chromatin accessibility and gene expression responses of hepatic stellate cells

Jain, I., Brougham-Cook, A., & Underhill, G. H. (2023) · Acta Biomaterialia · doi:10.1016/j.actbio.2023.06.018 · Cited by 26

Effects of SPI1-mediated transcriptome remodeling on Alzheimer's disease-related phenotypes in mouse models of Aβ amyloidosis

Kim, B., Dabin, L. C., Tate, M. D., Karahan, H., Sharify, A. D., Acri, D. J., Al-Amin, M. M., Philtjens, S., Smith, D. C., Wijeratne, H. R. S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48484-x · Cited by 28

Biodegradable magnesium materials regulate ROS-RNS balance in pro-inflammatory macrophage environment

Kwesiga, M. P., Gillette, A. A., Razaviamri, F., Plank, M. E., Canull, A. L., Alesch, Z., He, W., Lee, B. P., & Guillory, R. J. (2023) · Bioactive Materials · doi:10.1016/j.bioactmat.2022.10.017 · Cited by 25

Loss of succinyl-CoA synthetase in mouse forebrain results in hypersuccinylation with perturbed neuronal transcription and metabolism

Lancaster, M. S., Kim, B., Doud, E. H., Tate, M. D., Sharify, A. D., Gao, H., Chen, D., Simpson, E., Gillespie, P., Chu, X., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113241 · Cited by 24

TNFα-Induced LDL Cholesterol Accumulation Involve Elevated LDLR Cell Surface Levels and SR-B1 Downregulation in Human Arterial Endothelial Cells

Okoro, E. U. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22126236 · Cited by 18

Deletion of miR-33, a regulator of the ABCA1–APOE pathway, ameliorates neuropathological phenotypes in APP/PS1 mice

Tate, M., Wijeratne, H. R. S., Kim, B., Philtjens, S., You, Y., Lee, D., Gutierrez, D. A., Sharify, D., Wells, M., Perez‐Cardelo, M., et al. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.14243 · Cited by 21

A dynamic in vitro model of Down syndrome neurogenesis with trisomy 21 gene dosage correction

Bansal, P., Banda, E. C., Glatt-Deeley, H. R., Stoddard, C. E., Linsley, J. W., Arora, N., Deleschaux, C., Ahern, D. T., Kondaveeti, Y., Massey, R. E., et al. (2024) · Science Advances · doi:10.1126/sciadv.adj0385 · Cited by 13

Automated segmentation of insect anatomy from micro‐CT images using deep learning

Toulkeridou, E., Gutierrez, C. E., Baum, D., Doya, K., & Economo, E. P. (2023) · Natural Sciences · doi:10.1002/ntls.20230010 · Cited by 13

Better research software tools to elevate the rate of scientific discovery or why we need to invest in research software engineering

Deschamps, J., Dalle Nogare, D., & Jug, F. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1255159 · Cited by 11

Unleashing the potential of cell painting assays for compound activities and hazards prediction

Odje, F., Meijer, D., von Coburg, E., van der Hooft, J. J. J., Dunst, S., Medema, M. H., & Volkamer, A. (2024) · Frontiers in Toxicology · doi:10.3389/ftox.2024.1401036 · Cited by 11

White matter hyperintensity genetic risk factor TRIM47 regulates autophagy in brain endothelial cells

Yeung, S. H., Lee, R. H., Cheng, G. W., Ma, I. W., Kofler, J., Kent, C., Ma, F., Herrup, K., Fornage, M., Arai, K., et al. (2024) · The FASEB Journal · doi:10.1096/fj.202400689rr · Cited by 10

Optogenetic actuator/ERK biosensor circuits identify MAPK network nodes that shape ERK dynamics

Dessauges, C., Mikelson, J., Dobrzyński, M., Jacques, M.-A., Frismantiene, A., Gagliardi, P. A., Khammash, M., & Pertz, O. (2021) · doi:10.1101/2021.07.27.453955 · Cited by 9

Automated hiPSC culture and sample preparation for 3D live cell microscopy

Coston, M. E., Gregor, B. W., Arakaki, J., Borensztejn, A., Do, T. P., Fuqua, M. A., Haupt, A., Hendershott, M. C., Leung, W., Mueller, I. A., et al. (2020) · doi:10.1101/2020.12.18.423371 · Cited by 6

MicroRNA-eQTLs in the developing human neocortex link miR-4707-3p expression to brain size

Lafferty, M. J., Aygün, N., Patel, N. K., Krupa, O., Liang, D., Wolter, J. M., Geschwind, D. H., de la Torre-Ubieta, L., & Stein, J. L. (2023) · eLife · doi:10.7554/elife.79488 · Cited by 7

RECQ4-MUS81 interaction contributes to telomere maintenance with implications to Rothmund-Thomson syndrome

Ashraf, R., Polasek-Sedlackova, H., Marini, V., Prochazkova, J., Hasanova, Z., Zacpalova, M., Boudova, M., & Krejci, L. (2025) · Nature Communications · doi:10.1038/s41467-025-56518-1 · Cited by 6

The amino acid transporter Mct10/Tat1 is important to maintain the TSH receptor at its canonical basolateral localization and assures regular turnover of thyroid follicle cells in Male mice

Venugopalan, V., Al-Hashimi, A., Weber, J., Rehders, M., Qatato, M., Wirth, E. K., Schweizer, U., Heuer, H., Verrey, F., & Brix, K. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22115776 · Cited by 2

Cell-TIMP: Cellular Trajectory Inference Based on Morphological Parameters

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