publications

A list of works I contributed to, including direct links to the PDFs (either from the publisher or on arXiv), datasets, or source code.

Peer-Reviewed Publications

  1. M. Gutmann et al., “Beyond comparison: Brillouin microscopy and AFM-based indentation reveal divergent insights into the mechanical profile of the murine retina,” Journal of Physics: Photonics 6(3): 035020, 2024. doi:10.1088/2515-7647/ad5ae3.  PDF  CODE 
  2. T. Beck et al., “Optical characterization of molecular interaction strength in protein condensates,” Molecular Biology of the Cell 35(12), 2024. doi:10.1091/mbc.e24-03-0128.  PDF  CODE  CODE 
  3. J. Kolb et al., “Small leucine-rich proteoglycans inhibit CNS regeneration by modifying the structural and mechanical properties of the lesion environment,” Nature Communications 14(1), 2023. doi:10.1038/s41467-023-42339-7.  PDF  CODE  CODE  CODE 
  4. L. D. Wittwer et al., “A New Hyperelastic Lookup Table for RT-DC,” Soft Matter , 2023. doi:10.1039/D2SM01418A.  PDF   DATA   DATA   DATA 
  5. S. Abuhattum et al., “An explicit model to extract viscoelastic properties of cells from AFM force-indentation curves,” iScience 25(4): 104016, 2022. doi:https://doi.org/10.1016/j.isci.2022.104016.  PDF   DATA  CODE 
  6. N. Hauck et al., “PNIPAAm microgels with defined network architecture as temperature sensors in optical stretchers,” Mater. Adv. 3(15): 6179–6190, 2022. doi:10.1039/D2MA00296E.  PDF 
  7. R. Schlüßler et al., “Correlative all-optical quantification of mass density and mechanics of sub-cellular compartments with fluorescence specificity,” eLife 11, 2022. doi:10.7554/elife.68490. url:https://doi.org/10.7554%2Felife.68490.  PDF 
  8. C. Riquelme-Guzmán et al., “In vivo assessment of mechanical properties during axolotl development and regeneration using confocal Brillouin microscopy,” , 2022. doi:10.1101/2022.03.01.482501.  PDF 
  9. S. Abuhattum et al., “Unbiased retrieval of frequency-dependent mechanical properties from noisy time-dependent signals,” Biophysical Reports : 100054, 2022. doi:https://doi.org/10.1016/j.bpr.2022.100054.  PDF 
  10. A. A. Nawaz et al., “Intelligent image-based deformation-assisted cell sorting with molecular specificity,” Nature Methods , 2020. doi:10.1038/s41592-020-0831-y.  PDF   DATA 
  11. P. Müller et al., “DryMass: handling and analyzing quantitative phase microscopy images of spherical, cell-sized objects,” BMC Bioinformatics 21(1): 226, 2020. doi:10.1186/s12859-020-03553-y.  PDF   DATA  CODE 
  12. P. Müller et al., “nanite: using machine learning to assess the quality of atomic force microscopy-enabled nano-indentation data,” BMC Bioinformatics 20(1): 1–9, 2019. doi:10.1186/s12859-019-3010-3.  PDF   DATA  CODE 
  13. K. Wagner et al., “Colloidal crystals of compliant microgel beads to study cell migration and mechanosensitivity in 3D,” Soft Matter , 2019. doi:10.1039/C9SM01226E.  PDF 
  14. M. Herbig et al., “Statistics for real-time deformability cytometry: Clustering, dimensionality reduction, and significance testing,” Biomicrofluidics 12(4): 042214, 2018. doi:10.1063/1.5027197.  PDF 
  15. P. Müller et al., “Accurate evaluation of size and refractive index for spherical objects in quantitative phase imaging,” Optics Express 26(8): 10729–10743, 2018. doi:10.1364/OE.26.010729.  PDF   DATA  CODE 
  16. S. Girardo et al., “Standardized microgel beads as elastic cell mechanical probes,” Journal of Materials Chemistry B 6(39): 6245–6261, 2018. doi:10.1039/C8TB01421C.  PDF 
  17. N. Hauck et al., “Droplet-Assisted Microfluidic Fabrication and Characterization of Multifunctional Polysaccharide Microgels Formed by Multicomponent Reactions,” Polymers 10(10): 1055, 2018. doi:10.3390/polym10101055.  PDF 
  18. R. Schlüßler et al., “Mechanical Mapping of Spinal Cord Growth and Repair in Living Zebrafish Larvae by Brillouin Imaging,” Biophysical Journal 115(5): 911–923, 2018. doi:10.1016/j.bpj.2018.07.027.  PDF 
  19. M. Urbanska et al., “Single-cell mechanical phenotype is an intrinsic marker of reprogramming and differentiation along the mouse neural lineage,” Development 144(23): 4313–4321, 2017. doi:10.1242/dev.155218.  PDF 
  20. M. Schürmann et al., “Three-dimensional correlative single-cell imaging utilizing fluorescence and refractive index tomography,” Journal of Biophotonics 11(3): e201700145, 2017. doi:10.1002/jbio.201700145.  PDF   DATA 
  21. M. Schürmann et al., “Cell nuclei have lower refractive index and mass density than cytoplasm,” Journal of Biophotonics 9(10): 1068–1076, 2016. doi:10.1002/jbio.201500273.  PDF 
  22. M. C. Munder et al., “A pH-driven transition of the cytoplasm from a fluid- to a solid-like state promotes entry into dormancy,” eLife 5, 2016. doi:10.7554/elife.09347.  PDF 
  23. P. Müller et al., “ODTbrain: a Python library for full-view, dense diffraction tomography,” BMC Bioinformatics 16(1): 1–9, 2015. doi:10.1186/s12859-015-0764-0.  PDF   DATA  CODE 
  24. P. Müller et al., “PyCorrFit – generic data evaluation for fluorescence correlation spectroscopy,” Bioinformatics 30(17): 2532–2533, 2014. doi:10.1093/bioinformatics/btu328.  PDF  CODE 

Book Chapters

  1. M. Herbig et al., “Real-Time Deformability Cytometry: Label-Free Functional Characterization of Cells,” in Flow Cytometry Protocols, 4, eds Teresa S. Hawley and Robert G. Hawley (Springer New York, 347–369), 2017. doi:10.1007/978-1-4939-7346-0_15.
  2. M. Schürmann et al., “Refractive index measurements of single, spherical cells using digital holographic microscopy,” in Biophysical Methods in Cell Biology, 125, ed Ewa K. Paluch (Academic Press, 143–159), 2015. doi:10.1016/bs.mcb.2014.10.016.  PDF 
  3. P. Müller et al., “Scanning fluorescence correlation spectroscopy (SFCS) with a scan path perpendicular to the membrane plane,” in Methods in Molecular Biology, 1076, (635–51), 2014. doi:10.1007/978-1-62703-649-8_29.  PDF  CODE 

Other Publications

  1. P. Müller and J. Guck, “Response to Comment on ’Cell nuclei have lower refractive index and mass density than cytoplasm,’” Journal of Biophotonics, comment, e201800095, 2018. doi:10.1002/jbio.201800095.  PDF 
  2. P. Müller, “Optical Diffraction Tomography for Single Cells,” (PhD thesis, TU Dresden), 2016. url:http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-202261.  PDF 
  3. P. Müller et al., “Single-cell diffraction tomography with optofluidic rotation about a tilted axis,” Proc. of SPIE 9548: 95480U, 2015. doi:10.1117/12.2191501.  PDF  CODE 
  4. P. Müller et al., “The Theory of Diffraction Tomography,” 2015. arXiv:1507.00466 [q-bio.QM].  PDF