Comparative diffusivity measurements for alginate-based atomized and inkjet-bioprinted artificial cells using fluorescence microscopy

Abstract

Radial diffusivity profiles of atomized (MC, d = 1800 ± 200 µm) and inkjet-printed (MI, d = 40 ± 5 µm) alginate-based artificial cells have been generated using 2D Fluorescence Microscopy. The passive outward diffusion of FITC-Dextrans from MIs (0.5% LV alginate/15% CaCl2 coated with 0.5% Chitosan) and MCs (1.5% MV alginate/1.5% CaCl2) was measured and quantified using a Fickian model. As an expected outcome of miniaturization, the ratios of the outer layer diffusivities defined as D(MIout)/D(MCout) were 4.25 and 5.07 respectively for the 4 and 70 kDa markers, indicative of the enhanced diffusive potential of the miniaturized capsules.

Fickian-Based Empirical Approach for Diffusivity Determination in Hollow Alginate-Based Microfibers Using 2D Fluorescence Microscopy and Comparison with Theoretical Predictions

“…Across measurement techniques, reported values of pore size for alginate membranes range between 3.6 nm and 10.9 μm [12,15,16,23,25,37,38,39,40,41,42,43,44,45,46]. For the external gelation fabrication method, the molecular weight cutoff (MWCO) of the membrane has been placed between 60 and 70 kDa (≈3–6 nm) by multiple sources [7,12,20,25,37,47].…”

“…For the external gelation fabrication method, the molecular weight cutoff (MWCO) of the membrane has been placed between 60 and 70 kDa (≈3–6 nm) by multiple sources [7,12,20,25,37,47]. It is essential to distinguish pure diffusion from surface erosion, the latter being a combination of hydrodynamic drag and membrane swelling [13,48].…”

“…In conjunction, indirect diffusivity estimations using pore size characterization by various scanning probe microscopy (SPM) methods have been documented [14,15,16]. Other methods include spectrophotometry [17,18,19,20], size exclusion chromatography [8,21], mechanical release tests [22,23] and fluorescence microscopy [23,24,25]. Regardless of the diffusivity characterization method, the following are a sub-set of factors affecting cross-link homogeneity, size and density, gel isotropy and, thus, membrane diffusivity [13,19,20]: (1) the β- d -mannuronic (M) to α- l -guluronic (G) ratio, the distribution of each monomer within the chain, and alginate molecular weight (MW); (2) the kinetics of gelation, specifically external gelation (chelation) vs. internal gelation.…”

“…The advantage of fluorescence over spectrophotometry is the measurement accuracy at lower detection limits [26,27], which can be a source of error when validating numerical or analytical models derived from Fick’s laws. The diffusivity of multiple compounds across hydrogels using transient Fickian-based models has been modeled with the equilibrium concentration as a boundary condition [6,18,25]; thus, the validity of the empirical model hinges on the lower limit of detection. Data smoothing is often a recourse to eliminate the noise from the oscillatory equilibrium concentration.…”

3D bioprinting for engineering complex tissues

“…For laser printing, the effects of laser energy, substrate film thickness, and hydrogel viscosity on the viability of cells (Catros et al 2011b), as well as droplet size (Duocastella et al 2007; Mézel et al 2010; Gruene et al 2011b), cell differentiation (Gruene et al 2011a), and cell proliferation (Gruene et al 2011a) have been investigated. Some researchers also focused on post-printing modeling of cellular dynamics (McCune et al 2014), fusion (Yang et al 2012, 2013; Sun and Wang 2013; Thomas et al 2014), deformation (Sim et al 2007) and stiffness (Tirella et al 2011; Mobed-Miremadi et al 2012), as well as modeling of the typical types of printed tissues, including tumors (Zhao et al 2014) and soft tissues (Zhang et al 2013). Bio-CAM research not only provides a fast way to check design feasibility, but also gives designers a chance to better understand the physical and chemical principles governing printing.…”