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3D Cell-Scaffold Interactions

Summary: When adherent cells are cultured in tissue culture plates, they adhere to a planar surface. In native tissue in vivo, cells often exist within a three-dimensional extracellular matrix that surrounds them on all sides. 3D scaffolds are being used to provide a more realistic in vitro culture environment and as 3D templates for tissue regeneration. The chemical, structural and mechanical properties of the scaffold influence cell function, and these effects are at least partially dependent on the morphologies that cells take on during scaffold culture. We are imaging cells in different types of scaffolds in order to assess how scaffold culture influences the 3D shape of cells. We collect 3D image data from large numbers of cells by confocal microscopy and use computational methods to process, segment and analyze 3D cell shape. This work may provide an understanding of how scaffold properties can be used to direct function through modulation of cell morphology.

 

An interactive 3D-cell viewer was constructed to enable online browsing of 3D cell image data. The cell viewer can rapidly generate 3D renderings of 1000 cells on the screen to enable side-by-side morphology comparisons for verifying cell shape metric results. The viewer rotates the cells in 3D to provide dimensional perspective and the magnification can be adjusted. The cells shown are primary human bone marrow stromal cells cultured for 24 h in a collagen gel and the data were derived from staining with Alexa Fluor 546 phalloidin (actin). The four screen captures depict how 3D cell renderings can be rotated in 3D and viewed at different magnifications.
 

Link to online 3D viewer for browsing 1000 3D cell images:
https://isg.nist.gov/deepzoomweb/stemcells3d/index.html

Link for downloading 3D cell image data:
https://isg.nist.gov/deepzoomweb/zstackDownload

  

 

The image shows the 3D morphology of a primary human bone marrow stromal cell that was cultured for 24 h in a collagen gel. The data were derived from staining with Alexa Fluor 546 Phalloidin (actin) and confocal fluorescence microscopy. A computational analysis package called Zeno was used generate the surface heat mapping. The heat mapping indicates hit density when hypothetical particles are launched from an enclosing sphere via a random-walk. Red regions indicate areas of high hit density and blue regions indicate regions of low hit density. The data demonstrate that soluble factors undergoing diffusion, such as growth factors, are most likely to hit the cell at the extended regions.
 

Link to Zeno Package for Random Walks from Enclosing Sphere:
http://web.stevens.edu/zeno/

 

Primary human bone marrow stromal cell (hBMSC) cultured 1 d on a polymer fiber scaffold. Image was captured by confocal fluorescence microscopy. Nanofibers were electrospun from poly(lactic-co-glycolic acid), were spiked with fluorescein to make them fluorescent and had a fiber diameter of approximately 1 µm. The hBMSC was stained with Alexa Fluor 546 phalloidin (actin) and DAPI (nucleus). The image cube was 246 µm × 246 µm × 29 µm (length × width × depth). Green = nanofibers, red = cell actin, blue = cell nucleus.
 

Publications:

  • Pazmino Betancourt BA, Florczyk SJ, Simon M, Juba D, Douglas JF, Keyrouz W, Bajcsy P, Lee C, Simon Jr CG (2018) Effect of the scaffold microenvironment on cell polarizability and capacitance determined by probabilistic computations. Biomedical Materials 13, 025012.
  • Florczyk SJ, Simon M, Juba D, Pine PS, Sarkar S, Chen D, Baker PJ, Bodhak S, Cardone A, Brady MC, Bajcsy P, Simon Jr CG (2017) A bioinformatics 3D cellular morphotyping strategy for assessing biomaterial scaffold niches. ACS Biomaterials Science & Engineering 3, 2301-2313.
  • Tutak W, Jyotsnendu G, Bajcsy P, Simon Jr CG (2017) Measuring the 3D shapes of organelles in stem cells cultured on nanofiber scaffolds. Journal of Biomedical Materials Research: Part B - Applied Biomaterials 105, 989-1001..
  • Chen D, Sarkar S, Candia J, Florczyk SJ, Bodhak S, Driscoll MK, Simon Jr CG, Dunkers JP, Losert W (2016) Machine learning based methodology to identify cell shape phenotypes associated with microenvironmental cues. Biomaterials 104, 104-118.
  • Bajcsy P, Cardone A, Chalfoun J, Halter M, Juba D, Kociolek M, Majurski M, Peskin A, Simon Jr CG, Simon M, Vandecreme A, Brady M (2015) Survey statistics of automated segmentations applied to optical imaging of mammalian cells. BMC Bioinformatics 16, 330, 1-28.
  • Bajcsy P, Simon M, Florczyk S, Simon Jr CG, Juba D, Brady M. (2015) A method for the evaluation of thousands of automated 3D stem cell segmentations. Journal of Microscopy 260, 363-376.
  • Sarkar S, Baker BA, Chen D, Pine PS, McDaniel JH, Salit ML, Losert W, Simon Jr CG, Dunkers J (2016) Roles of nanofiber scaffold structure and chemistry in directing human bone marrow stromal cell response. Advances in Tissue Engineering & Regenerative Medicine 1, 00003.
  • Farooque TM, Camp Jr CH, Tison CK, Kumar G, Parekh SH, Simon Jr CG (2014) Measuring stem cell dimensionality in tissue scaffolds. Biomaterials 35, 2558-2567.
  • Juba D, Cardone A, Ip CY, Simon Jr CG, Tison CK, Kumar G, Brady M, Varshney A (2013) Parallel geometric classification of stem cells by their three dimensional morphology. Computational Science & Discovery 6, 015007.
  • Kumar G, Tison CK, Chatterjee K, Pine PS, McDaniel JH, Salit ML, Young MF, Simon Jr CG (2011) The determination of stem cell fate by 3D scaffold structures through the control of cell shape. Biomaterials 32, 9188-9196.
  • Dorsey SM, Lin-Gibson S, Simon Jr CG (2009) X-ray microcomputed tomography for the measurement of cell adhesion and proliferation in polymer scaffolds. Biomaterials 30, 2967–2974.

Contributors: Carl Simon, Stephen Florczyk, Peter Bajcsy, Nathan Hotaling, Nicholas Schaub, Mylene Simon, Jack Douglas, Beatriz Pazmino 

Contacts

Biomaterials Group

Created May 24, 2016, Updated November 26, 2019