A predictive model of antibody binding in the presence of IgG-interacting bacterial surface proteins

Many bacteria can interfere with how antibodies bind to their surfaces. This bacterial antibody targeting makes it challenging to predict the immunological function of bacteria-associated antibodies. The M and M-like proteins of group A streptococci exhibit IgGFc-binding regions, which they use to reverse IgG binding orientation depending on the host environment. Unraveling the mechanism behind these binding characteristics may identify conditions under which bound IgG can drive an efficient immune response. Here, we have developed a biophysical model for describing these complex protein-antibody interactions. We show how the model can be used as a tool for studying various IgG samples’ behaviour by performing in silico simulations and correlating this data with experimental measurements. Besides its use for mechanistic understanding, this model could potentially be used as a tool to predict the effect, as well as aid in the development of antibody treatments. We illustrate this by simulating how IgG binding in serum is altered as specified amounts of monoclonal or pooled IgG is added. Phagocytosis experiments link this altered antibody binding to a physiological function and demonstrate that it is possible to predict the effect of an IgG treatment with our model. Our study gives a mechanistic understanding of bacterial antibody targeting and provides a tool for predicting the effect of antibody treatments.

Link to paper in BioRxiv (coming soon)

Analysis template download links to GitHub repository:


High-sensitivity assessment of phagocytosis by persistent association-based normalization

Phagocytosis is measured as a functional outcome in many research fields but can be challenging to quantify accurately, with no robust method available for cross-laboratory reproducibility. Here, we identified a simple, measurable parameter – persistent prey-phagocyte association – to use for normalization and dose-response analysis. We apply this in a straightforward analytical method, persistent association-based normalization (PAN), where first the multiplicity of prey (MOP) ratio needed to elicit half of the phagocytes to associate persistently (MOP50) is determined. MOP50 is then applied to normalize for experimental factors, with adhesion and internalization analyzed separately. We use THP-1 cells and different prey and opsonization conditions to compare the PAN method to standard ways of assessing phagocytosis, and find it better in all cases, with increased robustness, sensitivity, and reproducibility. The approach is easily incorporated into most existing phagocytosis assays and allows for reproducibly comparing results across experiments and laboratories with high sensitivity.

Link to paper in BioRxiv

Analysis template download links to GitHub repository:


NETQUANT – automated quantification of neutrophil extracellular traps

Neutrophils can eject extracellular traps (NETs) that are extensive webs of DNA covered with antimicrobial proteins into the extracellular environment during infection or inflammation as a part of their defense arsenal. Image acquisition of fluorescently labeled NETs and subsequent image-based quantification is frequently used to analyze NET formation (NETosis) in response to various stimuli. However, there are important limitations in the present methods for quantification. Manual methods tend to be error-prone, tedious and often quite subjective, whereas the software-rooted options are either semi-automatic or difficult to operate. Here we present an automated and uncomplicated approach for quantifying NETs from fluorescence images, built as a freely available app for MATLAB®. It is based on detection of a set of clearly defined parameters, all related to the biological manifestation of NETs and allowing for single-cell resolution quantification and analysis.

Link to paper in Frontiers in Immunology

NETQUANT – download links 


Matrix-masking to balance nonuniform illumination in microscopy

With a perfectly uniform illumination, the amount and concentration of fluorophores in any (biological) sample can be read directly from fluorescence micrographs. However, non-uniform illumination in optical micrographs is a common, yet avoidable artefact, often caused by the setup of the microscope, or by inherent properties caused by the nature of the sample. In this paper, we demonstrate simple matrix-based methods using the common computing environments MATLAB and Python to correct nonuniform illumination, using either a background image or extracting illumination information directly from the sample image, together with subsequent image processing. We compare the processes, algorithms, and results obtained from both MATLAB (commercially available) and Python (freeware). Additionally, we validate our method by evaluating commonly used alternative approaches, demonstrating that the best nonuniform illumination correction can be achieved when a separate background image is available.

Link to paper in Optics Express

Matrix-masking… – download links


sample data set

Preprints under consideration

de Neergaard, T., Sundwall, M., and Nordenfelt, P. High-sensitivity assessment of phagocytosis by persistent association-based normalization. bioRxiv 827568; doi:




Mohanty, T. & Nordenfelt, P. Automated Image-Based Quantification of Neutrophil Extracellular Traps Using NETQUANT. J. Vis. Exp. (2019). doi:10.3791/58528

Happonen, L., Hauri, S., Svensson Birkedal, G., Karlsson, C., de Neergaard, T., Khakzad, H., Nordenfelt, P., Wikström, M., Wisniewska, M., Björck, L., Malmström, L., Malmström, J., 2019. A quantitative Streptococcus pyogenes-human protein-protein interaction map reveals localization of opsonizing antibodies. Nature Communications. 10, 2727.


Nordenfelt, P., Cooper, J., and Hochstetter, A., “Matrix-masking to balance nonuniform illumination in microscopy,” Opt. Express 26, 17279-17288

Mohanty, T., Sørensen, O., and Nordenfelt, P. NETQUANT: automated quantification of neutrophil extracellular traps. Frontiers In Immunology 8, 1999.


Nordenfelt, P., Moore, T.I., Mehta, S.B., Kalappurakkal, J.M., Swaminathan, V., Koga, N., Lambert, T.J., Baker, D., Waters, J.C., Oldenbourg, R., Tani, T., Mayor, S., Waterman, C.M. and Springer, T.A. Direction of actin flow dictates integrin LFA-1 orientation during leukocyte migration. Nature Communications, 2017; 8 (1), p. 2047.
Media coverage:
TV broadcast in Vetenskapens Värld, SVT. Celler rör sig med kraft inifrån.
Interview at Lund University. “Så gör dina celler för att svänga.
Reported in myScience. “How cells are able to turn.
Reported in News Medical. “Researchers explore how cells navigate the body.

Swaminathan V, Kalappurakkal JM, Mehta SB, Nordenfelt P, Moore TI, Koga N, Baker D, Oldenbourg R, Tani, T, Mayor S, Springer TA, Waterman CM. Actin retrograde flow actively aligns and orients ligand-engaged integrins in focal adhesions. Proceedings of the National Academy of Sciences. 2017 Oct 3;114(40):10648–53.
Media coverage:
– Reported by Marine Biological Laboratory, Woods Hole. “Internal Forces Directing Cell Migration are Revealed by Live-Cell Microscopy.

Danielsen J, Nordenfelt P. Computer Vision-Based Image Analysis of Bacteria.
Methods Mol Biol. 2017;1535:161-172. PMID: 27914078

Shannon O, Nordenfelt P. Measuring Antibody Orientation at the Bacterial Surface.
Methods Mol Biol. 2017;1535:331-337. PMID: 279140902016

Nordenfelt P., Collin M. Bacterial pathogenesis: methods and protocols. Methods Mol Biol. 2017. 1535.


Nordenfelt, P., Elliott, H.L., and Springer, T.A. Coordinated integrin activation by actin-dependent force during T cell migration. Nature Communications. 2016. Oct 10;7:13119. PMID:27721490. Link to pdf

Media coverage:
Interview at Lund University. “How cells move.
Reported in Science Daily. “How cells move.


Lood, R., Wollein Woldetoft, K., and Nordenfelt P. Localization-triggered bacterial pathogenesis. Future Microbiology. 2015. Oct;10:1659-68. PMID: 26437846.
– Made the cover! 

Hurley, S., Kahn, F., Nordenfelt, P., Mörgelin M., Sørensen O.E., and Shannon, O. Platelet-dependent neutrophil function is dysregulated by M protein from Streptococcus pyogenesInfection and immunity. 2015; 83(9), pp.3515–3525. PMID: 26099589


Malmström, E., Davidova, A., Mörgelin, M., Linder, A., Larsen, M., Qvortrup, K., Nordenfelt, P., Shannon, O., Dzupova, O., Holub, M., Malmsström, J., and Herwald, H. Targeted mass spectrometry analysis of neutrophil-derived proteins released during sepsis progression. Thrombosis and Haemostasis. 2014; 112(6), 1230–1243. PMID: 2510441

Nordenfelt, P. Quantitative assessment of neutrophil phagocytosis using flow cytometry. Methods in Molecular Biology. 2014; 1124, 279–289. PMID: 24504959


Nordenfelt, P. and Björck, L. IgG-binding bacterial proteins and pathogenesis. Future Microbiology, 2013; 8, 299–301. PMID: 23464367


Nordenfelt, P., Waldemarson, S., Linder, A., Mörgelin, M., Karlsson, C., Malmström, J., and Björck, L. Antibody orientation at bacterial surfaces is related to invasive infection. Journal of Experimental Medicine, 2012; 209(13), 2367–2381. PMID: 23230002
Media coverage:
– Main cover story!
– Commentary – Nature Reviews Immunology
– Commentary – Nature Reviews Microbiology
Article of the Month – Lund Medical Faculty Monthly
Commentary – Science Daily

Malmström, L., Nordenfelt, P., and Malmström, J. 
Business intelligence strategies enables rapid analysis of quantitative proteomics data. Journal of Proteome Science & Computational Biology. 2012; 1. 2050-2273-1-5

Nordenfelt, P., Grinstein, S., Björck, L., & Tapper, H. V-ATPase-mediated phagosomal acidification is impaired by Streptococcus pyogenes through Mga-regulated surface proteins. Microbes and Infection. 2012; 14(14), 1319-1329. PMID: 22981599

Malmström, J., Karlsson, C., Nordenfelt, P., Ossola, R., Weisser, H., Quandt, A., Hansson, K., Aebersold, R., Malmström, L, and Björck L. Streptococcus pyogenes in Human Plasma: Adaptive mechanisms analyzed by mass spectrometry-based proteomics. Journal of Biological Chemistry. 2012; 287(2), 1415–1425. PMID: 22117078


Nordenfelt, P. and Tapper, H. Phagosome dynamics during phagocytosis by neutrophils. Journal of Leukocyte Biology. 2011; 90(2), 271–284. PMID: 21504950
– Main cover story! 


Källquist, L., Rosén, H., Nordenfelt, P., Calafat, J., Janssen, H., Persson, A.-M., Hansson, M, & Olsson, I. Neutrophil elastase and proteinase 3 trafficking routes in myelomonocytic cells. Experimental Cell Research. 2010; 316(19), 3182–3196. PMID: 20828556

Nordenfelt, P., and Tapper, H. The role of calcium in neutrophil granule-phagosome fusion. Communicative & Integrative Biology, 2010; 3(3), 1–3. PMID: 20714398


Nordenfelt, P., Bauer, S., Lönnbro, P., & Tapper, H. Phagocytosis of Streptococcus pyogenes by all-trans retinoic acid-differentiated HL-60 cells: roles of azurophilic granules and NADPH oxidase. PLoS ONE. 2009; 4(10), e7363. PMID: 19806211

Nordenfelt, P., Winberg, M. E., Lönnbro, P., Rasmusson, B., and Tapper, H. Different requirements for early and late phases of azurophilic granule-phagosome fusion. Traffic. 2009; 10(12), 1881–1893. PMID: 19804565


Lönnbro, P.#, Nordenfelt, P.#, and Tapper, H. Isolation of bacteria-containing phagosomes by magnetic selection. BMC Cell Biology. 2008; 9, 35. PMID: 18588680. # shared first-authorship

Linge, H.M., Collin, M., Nordenfelt, P., Mörgelin, M., Malmsten, M., Egesten, A. The human CXC chemokine granulocyte chemotactic protein 2 (GCP-2)/CXCL6 possesses membrane-disrupting properties and is antibacterial. Antimicrobial Agents and Chemotherapy. 2008 Jul;52(7):2599-607. PMID: 18443119


Lönnbro, P., Nordenfelt, P., and Tapper, H. Analysis of neutrophil membrane traffic during phagocytosis. Methods in Molecular Biology. 2007; 412, 301–318. PMID: 18453120