Semi-quantitative monitoring of confluence of adherent mesenchymal stromal cells on calcium-phosphate granules by using widefield microscopy images

  • Filippo Piccinini
  • Michela Pierini
  • Enrico Lucarelli
  • Alessandro BevilacquaEmail author


The analysis of cell confluence and proliferation is essential to design biomaterials and scaffolds to use as bone substitutes in clinical applications. Accordingly, several approaches have been proposed in the literature to estimate the area of the scaffold covered by cells. Nevertheless, most of the approaches rely on sophisticated equipment not employed for routine analyses, while the rest of them usually do not provide significant statistics about the cell distribution. This research aims at studying confluence and proliferation of mesenchymal stromal cells (MSC) adherent on OSPROLIFE®, a commercial biomaterial in the form of granules. In particular, we propose a Computer Vision approach that can routinely be employed to monitor the surface of the single granules covered by cells because only a standard widefield fluorescent microscope is required. In order to acquire significant statistics data, we analyse wide-area images built by using MicroMos v2.0, an updated version of a previously published software specific for stitching brightfield and phase-contrast images manually acquired via a widefield microscope. In particular, MicroMos v2.0 permits to build accurate “mosaics” of fluorescent images, after correcting vignetting and photo-bleaching effects, providing a consistent representation of a sample region containing numerous granules. Then, our method allows to make automatically a statistically significant estimate of the percentage of the area of the single granules covered by cells. Finally, by analysing hundreds of granules at different time intervals we also obtained reliable data regarding cell proliferation, confirming that not only MSC adhere onto the OSPROLIFE® granules, but even proliferate over time.


Ground Truth Mesenchymal Stromal Cell Cell Segmentation Cell Confluence High Confluence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors thank Eurocoating S.p.A. for providing OSPROLIFE® HA-TTCP granules; Davide Donati (Osteoarticular Regeneration Laboratory, 3rd Clinic of Orthopaedics and Traumatology, Rizzoli Orthopaedic Institute, Bologna, Italy) for performing the bone marrow harvest; Jennifer Perugini (University of Notre Dame, Notre Dame, Indiana, USA) for English revision of the manuscript; Panagiota Dimopoulou (Osteoarticular Regeneration Laboratory, Rizzoli Orthopaedic Institute) for editorial assistance.

Conflict of interest

The authors declare that they have no conflict of interest and no competing financial interests.

Supplementary material (1.1 mb)
Supplementary material 1 (ZIP 1142kb)


  1. 1.
    Bose S, Roy M, Bandyopadhyay A. Recent advances in bone tissue engineering scaffolds. Trends Biotechnol. 2012;30:546–54.CrossRefGoogle Scholar
  2. 2.
    Szpalski C, Wetterau MD, Barr J, Warren SM. Bone Tissue Engineering: current strategies and techniques-Part I: Scaffold. Tissue Eng Part B. 2012;18:246–57.CrossRefGoogle Scholar
  3. 3.
    Reyes CD, Garcia AJ. A centrifugation cell adhesion assay for high-throughput screening of biomaterial surfaces. J Biomed Mater Rest A. 2003;67A:328–33.CrossRefGoogle Scholar
  4. 4.
    Jiang H, Song C, Chen CC, Xu R, Raines KS, Fahimia BP, Lu CH, Lee TK, Nakashima A, Urano J, Ishikawa T, Tamanoi F, Miao J. Quantitative 3D imaging of whole, unstained cells by using X-ray diffraction microscopy. Proc Natl Acad Sci. 2010;107:11234–9.CrossRefGoogle Scholar
  5. 5.
    Jones JR, Atwood RC, Poologasundarampillai G, Yue S, Lee PD. Quantifying the 3D macrostructure of tissue scaffolds. J Mater Sci. 2009;20:463–71.Google Scholar
  6. 6.
    Hagenmüller H, Hofmann S, Kohler T, Merkle HP, Kaplan DL, Vunjak-Novakovic G, Müller R, Meinel L. Non-invasive time-lapsed monitoring and quantification of engineered bone-like tissue. Ann Biomed Eng. 2007;35:1657–67.CrossRefGoogle Scholar
  7. 7.
    Quinn KP, Bellas E, Fourligas N, Lee K, Kaplan DL, Georgakoudi I. Characterization of metabolic changes associated with the functional development of 3D engineered tissues by non-invasive, dynamic measurement of individual cell redox ratios. Biomaterials. 2012;33:5341–8.CrossRefGoogle Scholar
  8. 8.
    Lin JY, Lin WJ, Hong WH, Hung WC, Nowotarski SH, Gouveia SM, Cristo I, Lin KH. Morphology and organization of tissue cells in 3D microenvironment of monodisperse foam scaffolds. Soft Matter. 2011;7:10010–6.CrossRefGoogle Scholar
  9. 9.
    Kao ECY, McCanna DJ, Jones LW. Utilization of in vitro methods to determine the biocompatibility of intraocular lens materials. Toxicol In Vitro. 2011;25:1906–11.CrossRefGoogle Scholar
  10. 10.
    Friedrichs J, Helenius J, Muller DJ. Quantifying cellular adhesion to extracellular matrix components by single-cell force spectroscopy. Nat Protoc. 2010;5:1353–61.CrossRefGoogle Scholar
  11. 11.
    Myllymaa S, Kaivosoja E, Myllymaa K, Sillat T, Korhonen H, Lappalainen R, Konttinen YT. Adhesion, spreading and osteogenic differentiation of mesenchymal stem cells cultured on micropatterned amorphous diamond, titanium, tantalum and chromium coatings on silicon. J Mater Sci. 2010;21:329–41.Google Scholar
  12. 12.
    Rosset P, Deschaseaux F, Layrolle P. Cell therapy for bone repair. Orthop Traumatol. 2014;100:S107–12.Google Scholar
  13. 13.
    Rajagopalan S, Yaszemski MJ, Robb RA. Evaluation of thresholding techniques for segmenting scaffold images in tissue engineering. In: Proceedings of the SPIE the international society for optical engineering (SPIE 2004), vol. 1. March 15–18, San Diego, CA, 2004. pp. 1456–65.Google Scholar
  14. 14.
    Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybernet. 1979;9:62–6.CrossRefGoogle Scholar
  15. 15.
    Simonson LW, Ganz J, Melancon E, Eisen JS. Characterization of enteric neurons in wild-type and mutant zebrafish using semi-automated cell counting and co-expression analysis. Zebrafish. 2013;10:147–53.CrossRefGoogle Scholar
  16. 16.
    Collins TJ. ImageJ for microscopy. Biotechniques. 2007;43:S25–30.CrossRefGoogle Scholar
  17. 17.
    Lim JY, Dreiss AD, Zhou Z, Hansen JC, Siedlecki CA, Hengstebeck RW, Cheng J, Winograd N, Donahue HJ. The regulation of integrin-mediated osteoblast focal adhesion and focal adhesion kinase expression by nanoscale topography. Biomaterials. 2007;28:1787–97.CrossRefGoogle Scholar
  18. 18.
    Rodday B, Hirschhaeuser F, Walenta S, Mueller-Klieser W. Semiautomatic growth analysis of multicellular tumor spheroids. J Biomol Screen. 2011;16:1119–24.CrossRefGoogle Scholar
  19. 19.
    Guarino V, Guaccio A, Netti PA, Ambrosio L. Image processing and fractal box counting: user-assisted method for multi-scale porous scaffold characterization. J Mater Sci. 2010;21:3109–18.Google Scholar
  20. 20.
    Gudla PR, Nandy K, Collins J, Meaburn KJ, Misteli T, Lockett SJ. A high-throughput system for segmenting nuclei using multiscale techniques. Cytometry Part A. 2008;73:451–66.CrossRefGoogle Scholar
  21. 21.
    De Boodt S, Poursaberi A, Schrooten J, Berckmans D, Aerts JM. A semiautomatic cell counting tool for quantitative imaging of tissue engineering scaffolds. Tissue Eng C. 2013;19:697–707.CrossRefGoogle Scholar
  22. 22.
    Wagner J, Macher J. Automated spore measurements using microscopy, image analysis, and peak recognition of near-monodisperse aerosols. Aerosol Sci Technol. 2012;46:862–73.CrossRefGoogle Scholar
  23. 23.
    Hidalgo-Bastida LA, Barry JJA, Everitt MN, Rose FRAJ, Buttery LD, Hall IP, Claycomb WC, Shakesheff KM. Cell adhesion and mechanical properties of a flexible scaffold for cardiac tissue engineering. Acta Biomater. 2007;3:457–62.CrossRefGoogle Scholar
  24. 24.
    Piccinini F, Lucarelli E, Gherardi A, Bevilacqua A. Multi-image based method to correct vignetting effect in light microscopy images. J Microsci. 2012;248:6–22.CrossRefGoogle Scholar
  25. 25.
    Piccinini F, Bevilacqua A, Smith K, Horvath P. Vignetting and photo-bleaching correction in automated fluorescence microscopy from an array of overlapping images. In: Proceedings of the 10th IEEE International Symposium on Biomedical Imaging (ISBI 2013), vol. 1. April 7–11, San Francisco, CA, 2013. pp. 464–7.Google Scholar
  26. 26.
    Dehlinger D, Suer L, Elsheikh M, Pena J, Naraghi-Arani P. Dye free automated cell counting and analysis. Biotechnol Bioeng. 2013;110:838–47.CrossRefGoogle Scholar
  27. 27.
    Polzer H, Haasters F, Prall WC, Saller MM, Volkmer E, Drosse I, Mutschler W, Schieker M. Quantification of fluorescence intensity of labeled human mesenchymal stem cells and cell counting of unlabeled cells in phase-contrast imaging: an open-source-based algorithm. Tissue Eng C. 2010;16:1277–85.CrossRefGoogle Scholar
  28. 28.
    Carozza L, Bevilacqua A, Piccinini F. Mosaicing of optical microscope imagery based on visual information. In: Proceedings of the 33th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2011), vol. 1. Aug 30–Sept 03, Boston, MA, 2011. pp. 6162–5.Google Scholar
  29. 29.
    Piccinini F, Bevilacqua A, Lucarelli E. Automated image mosaics by non-automated light microscopes: the MicroMos software tool. J Microsc. 2013;252:226–50.CrossRefGoogle Scholar
  30. 30.
    Carozza L, Bevilacqua A, Piccinini F. An incremental method for mosaicing of optical microscope imagery. In: Proceedings of the 8th annual IEEE symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2011), vol. 1. April 11–15, Paris, 2011. pp. 55–60.Google Scholar
  31. 31.
    Bevilacqua A, Piccinini F, Gherardi A. Vignetting correction by exploiting an optical microscopy image sequence. In: Proceedings of the 33th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2011), vol. 1. Augt 30–Sept 03, Boston, MA, 2011. pp. 6166–9.Google Scholar
  32. 32.
    Pierini M, Di Bella C, Dozza B, Frisoni T, Martella E, Bellotti C, Remondini D, Lucarelli E, Giannini S, Donati D. The posterior iliac crest outperforms the anterior iliac crest when obtaining mesenchymal stem cells from bone marrow. J Bone Joint Surg. 2013;95:1101–7.CrossRefGoogle Scholar
  33. 33.
    Pierini M, Dozza B, Lucarelli E, Tazzari PL, Ricci F, Remondini D, Di Bella C, Giannini S, Donati D. Efficient isolation and enrichment of mesenchymal stem cells from bone marrow. Cytotherapy. 2012;14:686–93.CrossRefGoogle Scholar
  34. 34.
    Piccinini M, Rebaudi A, Sglavo VM, Bucciotti F, Robotti P. A new HA/TTCP material for bone augmentation: an in vivo histological pilot study in primates sinus grafting. Implant Dent. 2013;22:83–90.CrossRefGoogle Scholar
  35. 35.
    Dorozhkin SV. Medical application of calcium orthophosphate bioceramics. BIO. 2011;1:1–51.CrossRefGoogle Scholar
  36. 36.
    Ribeiro CC, Barrias CC, Barbosa MA. Preparation and characterisation of calcium-phosphate porous microspheres with a uniform size for biomedical applications. J Mater Sci. 2006;17:455–63.Google Scholar
  37. 37.
    Drey LL, Graber MC, Bieschke J. Counting unstained, confluent cells by modified bright-field microscopy. Biotechniques. 2013;55:28–33.Google Scholar
  38. 38.
    Sugiuchi H, Ando Y, Manabe M, Nakamura E, Mizuta H, Nagata S, Okabe H. Measurement of total and differential white blood cell counts in synovial fluid by means of an automated hematology analyser. J Lab Clin Med. 2005;146:36–42.CrossRefGoogle Scholar
  39. 39.
    Kuglin CD, Hines DC. The phase correlation image alignment method. In: Proceedings of the IEEE international conference on cybernetics and society, vol. 1. Sept 23–25, San Francisco, CA, 1975. pp. 163–5.Google Scholar
  40. 40.
    Schwarzfischer M, Marr C, Krumsiek J, Hoppe PS, Schroeder T, Theis FJ. Efficient fluorescence image normalization for time lapse movies. In: Proceedings of the 5th Microscopic Image Analysis with Applications in Biology (MIAAB 2011), vol. 1. Sept 2, Heidelberg, 2011. pp. 1–5.Google Scholar
  41. 41.
    Burga A, Casanueva MO, Lehner B. Predicting mutation outcome from early stochastic variation in genetic interaction partners. Nature. 2011;480:250–3.CrossRefGoogle Scholar
  42. 42.
    Moradi I, Behjati M. Six common errors cause dangerous mistakes in interpretation of electron micrographs. Microsc Res Tech. 2012;75:677–82.CrossRefGoogle Scholar
  43. 43.
    Neuman U, Korzyńska A, Lopez C, Lejeune M, Roszkowiak L, Bosch R. Equalisation of archival microscopic images from immunohistochemically stained tissue sections. Biocybernet Biomed Eng. 2013;33:63–76.CrossRefGoogle Scholar
  44. 44.
    Bulj Z, Duchi S, Bevilacqua A, Gherardi A, Dozza B, Piccinini F, Mariani GA, Lucarelli E, Giannini S, Donati D, Marmiroli S. Protein kinase B/AKT isoform 2 drives migration of human mesenchymal stem cells. Int J Oncol. 2013;42:118–26.Google Scholar
  45. 45.
    Tomazevic D, Likar B, Pernus F. Comparative evaluation of retrospective shading correction methods. J Microsc. 2002;208:212–23.CrossRefGoogle Scholar
  46. 46.
    Michálek J, Čapek M, Kubinová L. Compensation of inhomogeneous fluorescence signal distribution in 2D images acquired by confocal microscopy. Microsc Res Tech. 2011;74:831–8.Google Scholar
  47. 47.
    Model MA, Burkhardt JK. A standard for calibration and shading correction of a fluorescence microscope. Cytometry Part A. 2001;44:309–16.CrossRefGoogle Scholar
  48. 48.
    Babaloukas G, Tentolouris N, Liatis S, Sklavounou A, Perrea D. Evaluation of three methods for retrospective correction of vignetting on medical microscopy images utilizing two open source software tools. J Microsci. 2011;244:320–4.CrossRefGoogle Scholar
  49. 49.
    Jericevic Z, Wiese B, Bryan J, Smith LC. Validation of an imaging system: steps to evaluate and validate a microscope imaging system for quantitative studies. Methods In Cell Biology. : Fluorescence Microscopy Of Living Cells In. Quant Fluoresc Microsc Imaging Spectrosc. 1989;30:47–83.Google Scholar
  50. 50.
    Cavalcanti PG, Scharcanski J. Automated prescreening of pigmented skin lesions using standard cameras. Comput Med Imaging Graph. 2011;35:481–91.CrossRefGoogle Scholar
  51. 51.
    Model MA. Intensity calibration and shading correction for fluorescence microscopes. Current protocols in cytometry, vol. 10. New York: John Wiley & Sons, Inc., 2006. Pp. 14.1–14.7.Google Scholar
  52. 52.
    Model MA, Reese JL, Fraizer GC. Measurement of wheat germ agglutinin binding with a fluorescence microscope. Cytometry Part A. 2009;75:874–81.CrossRefGoogle Scholar
  53. 53.
    Yu W. Practical anti-vignetting methods for digital cameras. IEEE Trans Consum Electron. 2004;50:975–83.CrossRefGoogle Scholar
  54. 54.
    Schelshorn DW, Schneider A, Kuschinsky W, Weber D, Kruger C, Dittgen T, Burgers HF, Sabouri F, Gassler N, Bach A, Maurer MH. Expression of hemoglobin in rodent neurons. J Cereb Blood Flow Metab. 2009;29:585–95.CrossRefGoogle Scholar
  55. 55.
    Bisht S, Khan MA, Bekhit M, Bai H, Cornish T, Mizuma M, Rudek MA, Zhao M, Maitra A, Ray B, Lahiri D, Maitra A, Anders RA. A polymeric nanoparticle formulation of curcumin ameliorates CCl4-induced hepatic injury and fibrosis through reduction of pro-inflammatory cytokines and stellate cell activation. Lab Invest. 2011;91:1383–95.CrossRefGoogle Scholar
  56. 56.
    Souchier C, Brisson C, Batteux B, Robert-Nicoud M, Bryon PA. Data reproducibility in fluorescence image analysis. Methods Cell Sci. 2004;25:195–200.CrossRefGoogle Scholar
  57. 57.
    Weigum SE, Floriano PN, Christodoulides N, McDevitt JT. Cell-based sensor for analysis of EGFR biomarker expression in oral cancer. Lab Chip. 2007;7:995–1003.CrossRefGoogle Scholar
  58. 58.
    Lidke KA. Super resolution for common probes and common microscopes. Nat Methods. 2012;9:139–41.CrossRefGoogle Scholar
  59. 59.
    Lichtman JW, Conchello JA. Fluorescence microscopy. Nat Methods. 2005;2:910–9.CrossRefGoogle Scholar
  60. 60.
    Zack GW, Rogers WE, Latt SA. Automatic measurement of sister chromatid exchange frequency. J Histochem Cytochem. 1977;25:741–53.CrossRefGoogle Scholar
  61. 61.
    Beucher S. The watershed transformation applied to image segmentation. In: Proceedings of the. 30th scanning microscopy international, vol. 1. Chicago, Illinois, 1991. pp. 299–314.Google Scholar
  62. 62.
    Papadopulos F, Spinelli M, Valente S, Foroni L, Orrico C, Alviano F, Pasquinelli G. Common tasks in microscopic and ultrastructural image analysis using ImageJ. Ultrastruct Pathol. 2007;31:401–7.CrossRefGoogle Scholar
  63. 63.
    Nijhuis AWG, van den Beucken JJJP, Jansen JA, Leeuwenburgh SCG. In vitro response to alkaline phosphatase coatings immobilized onto titanium implants using electrospray deposition or polydopamine-assisted deposition. J Biomed Mater Res A. 2014;102:1102–9.CrossRefGoogle Scholar
  64. 64.
    Ali R, Gooding M, Szilágyi T, Vojnovic B, Christlieb M, Brady M. Automatic segmentation of adherent biological cell boundaries and nuclei from brightfield microscopy images. Mach Vis Appl. 2012;23:607–21.CrossRefGoogle Scholar
  65. 65.
    Impens S, Chen Y, Mullens S, Luyten F, Schrooten J. Controlled cell-seeding methodologies: a first step toward clinically relevant bone tissue engineering strategies. Tissue Eng C. 2010;16:1575–83.CrossRefGoogle Scholar
  66. 66.
    Chen CW, Betz MW, Fisher JP, Paek A, Chen Y. Macroporous hydrogel scaffolds and their characterization by optical coherence tomography. Tissue Eng C. 2011;17:101–12.CrossRefGoogle Scholar
  67. 67.
    Sobral JM, Caridade SG, Sousa RA, Mano JF, Reis RL. Three-dimensional plotted scaffolds with controlled pore size gradients: effect of scaffold geometry on mechanical performance and cell seeding efficiency. Acta Biomater. 2011;17:1009–18.CrossRefGoogle Scholar
  68. 68.
    Piccinini F, Tesei A, Zoli W, Bevilacqua A. Extended depth of focus in optical microscopy: assessment of existing methods and a new proposal. Microsc Res Tech. 2012;15:1582–92.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Filippo Piccinini
    • 1
  • Michela Pierini
    • 2
  • Enrico Lucarelli
    • 2
  • Alessandro Bevilacqua
    • 1
    • 3
    Email author
  1. 1.Advanced Research Center on Electronic Systems for Information and Communication Technologies “E. De Castro” (ARCES)University of BolognaBolognaItaly
  2. 2.Osteoarticular Regeneration LaboratoryRizzoli Orthopaedic InstituteBolognaItaly
  3. 3.Department of Computer Science and EngineeringUniversity of BolognaBolognaItaly

Personalised recommendations