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. L. D. Wittwer et al., “A New Hyperelastic Lookup Table for RT-DC,” Soft Matter , 2023. doi:10.1039/D2SM01418A.  PDF   DATA 
  2. 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 
  3. 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 
  4. 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 
  5. 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 
  6. 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 
  7. 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 
  8. 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 
  9. 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 
  10. 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 
  11. 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 
  12. 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 
  13. 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 
  14. 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 
  15. 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 
  16. 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 
  17. 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 
  18. 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 
  19. 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 
  20. 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 
  21. 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