Lineage marker synchrony in hematopoietic genealogies refutes the PU.1/GATA1 toggle switch paradigm
Open accessAbstract
Molecular regulation of cell fate decisions underlies health and disease. To identify molecules that are active or regulated during a decision, and not before or after, the decision time point is crucial. However, cell fate markers are usually delayed and the time of decision therefore unknown. Fortunately, dividing cells induce temporal correlations in their progeny, which allow for retrospective inference of the decision time point. We present a computational method to infer decision time points from correlated marker signals in genealogies and apply it to differentiating hematopoietic stem cells. We find that myeloid lineage decisions happen generations before lineage marker onsets. Inferred decision time points are in agreement with data from colony assay experiments. The levels of the myeloid transcription factor PU.1 do not change during, but long after the predicted lineage decision event, indicating that the PU.1/GATA1 toggle switch paradigm cannot explain the initiation of early myeloid lineage choice.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
PU.1 can engage in competitive interactions with transcriptional regulators such as GATA1 to promote myeloid differentiation.
“PU.1 can also engage in competitive interactions with transcriptional regulators such as GATA1 to promote myeloid differentiation (Strasser et al., 2018; Wheat et al., 2020; Raghav and Gangenahalli, 2021).”
From PU.1 is required to restrain myelopoiesis during chronic inflammatory stress (2023) · cites this paper as Strasser et al., 2018
The Results section describes PU.1 and GATA1 as mutually binding and cross-antagonistic, and says PU.1 is important in executing the granulocyte-macrophage lineage program. But it rejects the idea that their competitive toggle initiates lineage choice: PU.1 is described as an effector that locks down an already chosen lineage, not the driver of that choice. (LLM, conf=0.87)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Pattern Recognition and Machine Learning
(2006) · doi:10.1007/978-0-387-45528-0 · Cited by 9,824 · see citing works
Dating of the human–ape splitting by a molecular clock of mitochondrial DNA.
Hasegawa, M., Kishino, H., & Yano, T.-A. (1985) · Journal of Molecular Evolution · doi:10.1007/bf02101694 · Cited by 8,687 · see citing works
McKay, M. D., Beckman, R. J., & Conover, W. J. (2000) · Technometrics · doi:10.2307/1271432 · Cited by 7,551 · see citing works
Gillespie, D. T. (1976) · Journal of Computational Physics · doi:10.1016/0021-9991(76)90041-3 · Cited by 6,029 · see citing works
Hematopoiesis: An Evolving Paradigm for Stem Cell Biology
Orkin, S. H., & Zon, L. I. (2008) · Cell · doi:10.1016/j.cell.2008.01.025 · Cited by 2,570 · see citing works
Stochasticity in gene expression: from theories to phenotypes
Kærn, M., Elston, T. C., Blake, W. J., & Collins, J. J. (2005) · Nature Reviews Genetics · doi:10.1038/nrg1615 · Cited by 2,271 · see citing works
Requirement of transcription factor PU.1 in the development of multiple hematopoietic lineages
Scott, E. W., Simon, M. C., Anastasi, J., & Singh, H. (1994) · Science · doi:10.1126/science.8079170 · Cited by 1,532 · see citing works
Stochastic State Transitions Give Rise to Phenotypic Equilibrium in Populations of Cancer Cells
Gupta, P. B., Fillmore, C. M., Jiang, G., Shapira, S. D., Tao, K., Kuperwasser, C., & Lander, E. S. (2011) · Cell · doi:10.1016/j.cell.2011.07.026 · Cited by 1,475 · see citing works
Pevny, L., Simon, M. C., Robertson, E., Klein, W. H., Tsai, S.-F., D'Agati, V., Orkin, S. H., & Costantini, F. (1991) · Nature · doi:10.1038/349257a0 · Cited by 1,308 · see citing works
Cancer stem cells: impact, heterogeneity, and uncertainty
Magee, J. A., Piskounova, E., & Morrison, S. (2012) · Cancer Cell · doi:10.1016/j.ccr.2012.03.003 · Cited by 1,137 · see citing works
Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors
Paul, F., Arkin, Y., Giladi, A., Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Winter, D., Lara-Astiaso, D., Gury, M., Weiner, A., et al. (2015) · Cell · doi:10.1016/j.cell.2015.11.013 · Cited by 1,082 · see citing works
Direct cell reprogramming is a stochastic process amenable to acceleration
Hanna, J. H., Saha, K., Pando, B., van Zon, J. S., Lengner, C. J., Creyghton, M. P., van Oudenaarden, A., & Jaenisch, R. (2009) · Nature · doi:10.1038/nature08592 · Cited by 1,006 · see citing works
Human haematopoietic stem cell lineage commitment is a continuous process
Velten, L., Haas, S. F., Raffel, S., Blaszkiewicz, S., Islam, S., Hennig, B. P., Hirche, C., Lutz, C., Buss, E. C., Nowak, D., et al. (2017) · Nature Cell Biology · doi:10.1038/ncb3493 · Cited by 925 · see citing works
Forcing cells to change lineages
Graf, T., & Enver, T. (2009) · Nature · doi:10.1038/nature08533 · Cited by 899 · see citing works
Buganim, Y., Faddah, D. A., Cheng, A. W., Itskovich, E., Markoulaki, S., Ganz, K., Klemm, S. L., van Oudenaarden, A., & Jaenisch, R. (2012) · Cell · doi:10.1016/j.cell.2012.08.023 · Cited by 800 · see citing works
Molecular phylogenetics: principles and practice
Yang, Z., & Rannala, B. H. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3186 · Cited by 762 · see citing works
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 636 · see citing works
Continuous single-cell imaging of blood generation from haemogenic endothelium
Eilken, H. M., Nishikawa, S., & Schroeder, T. (2009) · Nature · doi:10.1038/nature07760 · Cited by 564 · see citing works
Self-citationBifurcation dynamics in lineage-commitment in bipotent progenitor cells
Huang, S., Guo, Y.-P., May, G., & Enver, T. (2007) · Developmental Biology · doi:10.1016/j.ydbio.2007.02.036 · Cited by 560 · see citing works
Hematopoietic Cytokines Can Instruct Lineage Choice
Rieger, M. A., Hoppe, P. S., Smejkal, B. M., Eitelhuber, A. C., & Schroeder, T. (2009) · Science · doi:10.1126/science.1171461 · Cited by 404 · see citing works
Self-citationDynamic niches in the origination and differentiation of haematopoietic stem cells
Wang, L. D., & Wagers, A. J. (2011) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3184 · Cited by 343 · see citing works
Ditadi, A., Sturgeon, C. M., Tober, J. M., Awong, G., Kennedy, M., Yzaguirre, A. D., Azzola, L., Ng, E., Stanley, E. G., French, D. L., et al. (2015) · Nature Cell Biology · doi:10.1038/ncb3161 · Cited by 321 · see citing works
Memory Suppressor Genes: Inhibitory Constraints on the Storage of Long-Term Memory
Abel, T. G., Martin, K. C., Bartsch, D., & Kandel, E. R. (1998) · Science · doi:10.1126/science.279.5349.338 · Cited by 316 · see citing works
Single-molecule analysis of gene expression using two-color RNA labeling in live yeast
Hocine, S., Raymond, P., Zenklusen, D., Chao, J. A., & Singer, R. H. (2013) · Nature Methods · doi:10.1038/nmeth.2305 · Cited by 297 · see citing works
Positive Feedback Between PU.1 and the Cell Cycle Controls Myeloid Differentiation
Kueh, H. Y., Champhekar, A., Nutt, S. L., Elowitz, M. B., & Rothenberg, E. V. (2013) · Science · doi:10.1126/science.1240831 · Cited by 273 · see citing works
Correction: Corrigendum: Global quantification of mammalian gene expression control
Schwanhäußer, B., Busse, D., Li, N., Dittmar, G. A. G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2013) · Nature · doi:10.1038/nature11848 · Cited by 249 · see citing works
Challenges in long-term imaging and quantification of single-cell dynamics
Skylaki, S., Hilsenbeck, O., & Schroeder, T. (2016) · Nature Biotechnology · doi:10.1038/nbt.3713 · Cited by 235 · see citing works
Self-citationSURVIVAL ANALYSIS IN PUBLIC HEALTH RESEARCH
Lee, E. T., & Go, O. T. (1997) · Annual Review of Public Health · doi:10.1146/annurev.publhealth.18.1.105 · Cited by 218 · see citing works
Activation-Induced B Cell Fates Are Selected by Intracellular Stochastic Competition
Duffy, K. R., Wellard, C. J., Markham, J. F., Zhou, J. H. S., Holmberg, R., Hawkins, E. D., Hasbold, J., Dowling, M. R., & Hodgkin, P. D. (2012) · Science · doi:10.1126/science.1213230 · Cited by 207 · see citing works
Early myeloid lineage choice is not initiated by random PU.1 to GATA1 protein ratios
Hoppe, P. S., Schwarzfischer, M., Loeffler, D., Kokkaliaris, K. D., Hilsenbeck, O., Moritz, N., Endele, M., Filipczyk, A. A., Gambardella, A. R., Ahmed, N., et al. (2016) · Nature · doi:10.1038/nature18320 · Cited by 202 · see citing works
Self-citationProspective identification of hematopoietic lineage choice by deep learning
Buggenthin, F., Buettner, F., Hoppe, P. S., Endele, M., Kroiss, M., Strasser, M. K., Schwarzfischer, M., Loeffler, D., Kokkaliaris, K. D., Hilsenbeck, O., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4182 · Cited by 197 · see citing works
Self-citationHierarchical Differentiation of Myeloid Progenitors Is Encoded in the Transcription Factor Network
Krumsiek, J., Marr, C., Schroeder, T., & Theis, F. J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0022649 · Cited by 174 · see citing works
Self-citationSoftware tools for single-cell tracking and quantification of cellular and molecular properties
Hilsenbeck, O., Schwarzfischer, M., Skylaki, S., Schauberger, B., Hoppe, P. S., Loeffler, D., Kokkaliaris, K. D., Hastreiter, S., Skylaki, E., Filipczyk, A. A., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3626 · Cited by 159 · see citing works
Self-citationA single-cell pedigree analysis of alternative stochastic lymphocyte fates
Hawkins, E. D., Markham, J. F., McGuinness, L. P., & Hodgkin, P. D. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905629106 · Cited by 152 · see citing works
Roeder, I., & Glauche, I. (2006) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2006.01.021 · Cited by 143 · see citing works
Network plasticity of pluripotency transcription factors in embryonic stem cells
Filipczyk, A. A., Marr, C., Hastreiter, S., Feigelman, J. S., Schwarzfischer, M., Hoppe, P. S., Loeffler, D., Kokkaliaris, K. D., Endele, M., Schauberger, B., et al. (2015) · Nature Cell Biology · doi:10.1038/ncb3237 · Cited by 121 · see citing works
Self-citationProbing cellular processes by long-term live imaging – historic problems and current solutions
Coutu, D. L., & Schroeder, T. (2013) · Journal of Cell Science · doi:10.1242/jcs.118349 · Cited by 119 · see citing works
Self-citationQuantitative Single-Cell Approaches to Stem Cell Research
Etzrodt, M., Endele, M., & Schroeder, T. (2014) · Cell stem cell · doi:10.1016/j.stem.2014.10.015 · Cited by 113 · see citing works
Self-citationHeritable Stochastic Switching Revealed by Single-Cell Genealogy
Kaufmann-Malaga, B., Yang, Q., Mettetal, J. T., & van Oudenaarden, A. (2007) · PLoS Biology · doi:10.1371/journal.pbio.0050239 · Cited by 112 · see citing works
Pulsed Feedback Defers Cellular Differentiation
Levine, J. H., Fontes, M. E., Dworkin, J. E., & Elowitz, M. B. (2012) · PLoS Biology · doi:10.1371/journal.pbio.1001252 · Cited by 107 · see citing works
Inferring Cell-State Transition Dynamics from Lineage Trees and Endpoint Single-Cell Measurements
Hormoz, S., Singer, Z. S., Linton, J. M., Antebi, Y. E., Shraiman, B. I., & Elowitz, M. B. (2016) · Cell Systems · doi:10.1016/j.cels.2016.10.015 · Cited by 91 · see citing works
Some Doubly Exponential Sequences
Aho, A. V., & Sloawe, N. J. A. (1973) · The Fibonacci Quarterly · doi:10.1080/00150517.1973.12430815 · Cited by 83 · see citing works
Strasser, M. K., Theis, F. J., & Marr, C. (2012) · Biophysical Journal · doi:10.1016/j.bpj.2011.11.4000 · Cited by 68 · see citing works
Self-citationMcIvor, Z., Hein, S., Fiegler, H., Schroeder, T., Stocking, C., Just, U., & Cross, M. (2003) · Experimental Hematology · doi:10.1016/s0301-472x(02)01017-2 · Cited by 58 · see citing works
Self-citationMathematical approaches to modeling development and reprogramming
Morris, R., Sancho-Martinez, I., Sharpee, T. O., & Belmonte, J. C. I. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1317150111 · Cited by 38 · see citing works
Mathematical modelling of stem cell differentiation: the PU.1–GATA-1 interaction
Duff, C., Smith‐Miles, K., Lopes, L., & Tian, T. (2011) · Journal of Mathematical Biology · doi:10.1007/s00285-011-0419-3 · Cited by 33 · see citing works
A novel view on stem cell development: analysing the shape of cellular genealogies
Glauche, I., Lorenz, R., Hasenclever, D., & Roeder, I. (2009) · Cell Proliferation · doi:10.1111/j.1365-2184.2009.00586.x · Cited by 29 · see citing works
Wen, Q., O’Reilly, P. G., Dunne, P. D., Lawler, M., Schaeybroeck, S. V., Salto‐Tellez, M., Hamilton, P., & Zhang, S. (2015) · BMC Systems Biology · doi:10.1186/1752-0509-9-s5-s4 · Cited by 28 · see citing works
Dietrich, J. E., Panavaite, L., Gunther, S., Wennekamp, S., Groner, A. C., Pigge, A., Salvenmoser, S., Trono, D., Hufnagel, L., & Hiiragi, T. (2015) · EMBO Reports · doi:10.15252/embr.201540162 · Cited by 25 · see citing works
Multi‐scale modeling of GMP differentiation based on single‐cell genealogies
Marr, C., Strasser, M. K., Schwarzfischer, M., Schroeder, T., & Theis, F. J. (2012) · FEBS Journal · doi:10.1111/j.1742-4658.2012.08664.x · Cited by 22 · see citing works
Self-citationDichotomous noise models of gene switches
Potoyan, D. A., & Wolynes, P. G. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4935572 · Cited by 21 · see citing works
Continuous long-term detection of live cell surface markers by ‘in culture’ antibody staining
Schroeder, T., Eilken, H. M., Rieger, M. A., Hoppe, P. S., Hermann, A., Smejkal, B. M., Drew, E., Thum, M., Ninkovic, J., Beckervordersandforth, R., et al. (2011) · Protocol Exchange · doi:10.1038/protex.2011.205 · Cited by 21 · see citing works
Self-citationBokes, P., King, J. R., & Loose, M. (2009) · Mathematical Medicine and Biology A Journal of the IMA · doi:10.1093/imammb/dqn026 · Cited by 20 · see citing works
Modified variational Bayes EM estimation of hidden Markov tree model of cell lineages
Olariu, V., Coca, D., Billings, S. A., Tonge, P. J., Gokhale, P. J., Andrews, P. W., & Kadirkamanathan, V. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp456 · Cited by 17 · see citing works
On the statistical analysis of single cell lineage trees
Stadler, T., Skylaki, S., Kokkaliaris, K. D., & Schroeder, T. (2017) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2017.11.023 · Cited by 14 · see citing works
Self-citationFactor graph analysis of live cell–imaging data reveals mechanisms of cell fate decisions
Niederberger, T., Failmezger, H., Uskat, D., Poron, D., Glauche, I., Scherf, N., Roeder, I., Schroeder, T., & Tresch, A. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv040 · Cited by 11 · see citing works
Self-citationEtzrodt, M., & Schroeder, T. (2017) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2017.12.006 · Cited by 9 · see citing works
Self-citationInference of spatiotemporal effects on cellular state transitions from time-lapse microscopy
Strasser, M. K., Feigelman, J. S., Theis, F. J., & Marr, C. (2015) · BMC Systems Biology · doi:10.1186/s12918-015-0208-5 · Cited by 3 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Phylodynamics for cell biologists
Stadler, T., Pybus, O. G., & Stumpf, M. P. H. (2021) · Science · doi:10.1126/science.aah6266 · Cited by 97 · see citing works
Single-molecule imaging of transcription dynamics in somatic stem cells
Wheat, J. C., Sella, Y., Willcockson, M., Skoultchi, A. I., Bergman, A., Singer, R. H., & Steidl, U. (2020) · Nature · doi:10.1038/s41586-020-2432-4 · Cited by 88 · see citing works
Tyrkalska, S. D., Pérez‐Oliva, A. B., Rodríguez‐Ruiz, L., Martínez-Morcillo, F. J., Alcaraz‐Pérez, F., Martínez-Navarro, F. J., Lachaud, C., Ahmed, N., Schroeder, T., Pardo-Sánchez, I., et al. (2019) · Immunity · doi:10.1016/j.immuni.2019.05.005 · Cited by 85 · see citing works
Self-citationHidden heterogeneity and circadian-controlled cell fate inferred from single cell lineages
Chakrabarti, S., Paek, A. L., Reyes, J. S., Lasick, K. A., Lahav, G., & Michor, F. (2018) · Nature Communications · doi:10.1038/s41467-018-07788-5 · Cited by 61 · see citing works
Loughran, S. J., Haas, S. F., Wilkinson, A. C., Klein, A. M., & Brand, M. (2020) · Experimental Hematology · doi:10.1016/j.exphem.2020.07.002 · Cited by 36 · see citing works
Single-cell multi-omics and lineage tracing to dissect cell fate decision-making
Haghverdi, L., & Ludwig, L. S. (2023) · Stem Cell Reports · doi:10.1016/j.stemcr.2022.12.003 · Cited by 35 · see citing works
Understanding cell fate control by continuous single-cell quantification.
Loeffler, D., & Schroeder, T. (2019) · Blood · doi:10.1182/blood-2018-09-835397 · Cited by 26 · see citing works
Self-citationHeritable changes in division speed accompany the diversification of single T cell fate
Plambeck, M., Kazeroonian, A., Loeffler, D., Kretschmer, L., Salinno, C., Schroeder, T., Busch, D. H., Floßdorf, M., & Buchholz, V. R. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2116260119 · Cited by 26 · see citing works
Self-citationModeling the Hematopoietic Landscape
Brown, G., & Ceredig, R. (2019) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2019.00104 · Cited by 25 · see citing works
Combining single-cell tracking and omics improves blood stem cell fate regulator identification
Wehling, A., Loeffler, D., Zhang, Y., Kull, T., Donato, C., Szczerba, B. M., Ortega, G. C., Lee, M., Moor, A. E., Göttgens, B., et al. (2022) · Blood · doi:10.1182/blood.2022016880 · Cited by 26 · see citing works
Self-citationMaps of variability in cell lineage trees
Hicks, D. G., Speed, T. P., Yassin, M., & Russell, S. M. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006745 · Cited by 17 · see citing works
Chavez, J. S., Rabe, J. L., Hernandez, G., Mills, T., Niño, K. E., Davizon‐Castillo, P., & Pietras, E. M. (2022) · Cells · doi:10.3390/cells11040680 · Cited by 15 · see citing works
PU.1 is required to restrain myelopoiesis during chronic inflammatory stress
Chavez, J. S., Rabe, J. L., Niño, K. E., Wells, H. H., Gessner, R. L., Mills, T. S., Hernandez, G., & Pietras, E. M. (2023) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2023.1204160 · Cited by 14 · see citing works
“1 can also engage in competitive interactions with transcriptional regulators such as GATA1 to promote myeloid differentiation (Strasser et al., 2018; Wheat et al., 2020; Raghav and Gangenahalli, 2021).”
BackgroundOhmori, S., Ishijima, Y., Numata, S., Takahashi, M., Sekita, M., Sato, T., Chugun, K., Yamamoto, M., & Ohneda, K. (2019) · Molecular and Cellular Biology · doi:10.1128/mcb.00314-19 · Cited by 15 · see citing works
Pitfalls and requirements in quantifying asymmetric mitotic segregation
Loeffler, D., Schneiter, F., & Schroeder, T. (2019) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14284 · Cited by 12 · see citing works
Self-citationBast, L., Buck, M. C., Hecker, J. S., Oostendorp, R. A. J., Götze, K. S. S., & Marr, C. (2021) · iScience · doi:10.1016/j.isci.2021.102120 · Cited by 11 · see citing works
Self-citationBlood stem cell PU.1 upregulation is a consequence of differentiation without fast autoregulation
Ahmed, N., Etzrodt, M., Dettinger, P., Kull, T., Loeffler, D., Hoppe, P. S., Chavez, J. S., Zhang, Y., Camargo Ortega, G., Hilsenbeck, O., et al. (2022) · Journal of Experimental Medicine · doi:10.1084/jem.20202490 · Cited by 9 · see citing works
Self-citationA multi-scale approach for simulating tidal marsh evolution
Nunez, K., Zhang, Y., Herman, J., Reay, W. G., & Hershner, C. H. (2020) · Ocean Dynamics · doi:10.1007/s10236-020-01380-6 · Cited by 8 · see citing works
Mechanistic models of blood cell fate decisions in the era of single-cell data
Glauche, I., & Marr, C. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.100355 · Cited by 8 · see citing works
Self-citationAnalyzing signaling activity and function in hematopoietic cells
Kull, T., & Schroeder, T. (2021) · The Journal of Experimental Medicine · doi:10.1084/jem.20201546 · Cited by 7 · see citing works
Self-citationReprogramming Multistable Monotone Systems With Application to Cell Fate Control
Shah, R., & Vecchio, D. D. (2020) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2020.3008135 · Cited by 4 · see citing works
Probing pluripotency gene regulatory networks with quantitative live cell imaging
Plant, A. L., Halter, M. W., & Stinson, J. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.09.025 · Cited by 3 · see citing works
Identifying memory gene expression from single-sample scRNA-seq data using power-law signatures
Ghosh, S., Chakrabarti, S., & Raju, A. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101707 · Cited by 2 · see citing works
DeepKymoTracker: A tool for accurate construction of cell lineage trees for highly motile cells
Fedorchuk, K., Russell, S. M., Zibaei, K., Yassin, M., & Hicks, D. G. (2025) · PLoS ONE · doi:10.1371/journal.pone.0315947 · Cited by 1 · see citing works
Chavez, J. S., Rabe, J. L., Hernandez, G., Mills, T. S., Niño, K. E., Davizon-Castillo, P., & Pietras, E. (2021) · bioRxiv · doi:10.1101/2021.10.25.465758 · Cited by 1 · see citing works
Allegrezza, A., Beschi, R., Caudo, D., Cavagna, A., Corsi, A., Culla, A., Donsante, S., Giannicola, G., Giardina, I., Gosti, G., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.21.683667
- Authors
- Strasser, M. K., Hoppe, P. S., Loeffler, D., Kokkaliaris, K. D., Schroeder, T., Theis, F. J., & Marr, C.
- Affiliations
- Helmholtz Munich (DE)
- ETH Zurich (CH)
- Corresponding author
- Marr, Carsten
- ORCID
- Venue
- Nature Communications
- Published
- 2018-07-12
- Volume
- 9
- Issue
- 1
- Pages
- 2697-2697
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6043612
- OpenAlex
- W2871741525
- Cited by
- 26
- References
- 58
- Citations by year
- 2026: 1 · 2025: 2 · 2023: 2 · 2022: 3 · 2021: 6 · 2020: 5 · 2019: 6 · 2018: 1
- FWCI
- 1.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (MA 5282/3-1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Health Sciences › Medicine › Hematology › Hematopoietic Stem Cell Transplantation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
- Keywords
- hematopoietic stem cell differentiation, PU.1, GATA-1, genealogy, colony assay
- MeSH terms
- Proto-Oncogene Protein Spi-1, Algorithms, Animals, Cell Differentiation, Hematopoiesis, Hematopoietic Stem Cells, Models, Biological, Proto-Oncogene Proteins, Time Factors, Trans-Activators, Cell Lineage, Computational Biology, Myeloid Cells, GATA1 Transcription Factor
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review