Stem cells reside and interact with their native three-dimensional (3D) extracellular matrix with divergent cellular responses. Development of fluorescent labels specific for imaging particular cellular phenotypes has revolutionized microscopic visualization and understanding of stem cells behaviour in third dimension, but used often only for qualitative inspection. Non-destructive, quantitative imaging of stem cells encapsulated in 3D hydrogels both in space and time offers more appropriate understanding of cellular phenotypes, with the advancement of various image analysis and quantification algorithms. Here, we describe a confocal microscopy-based automated image analysis platform for quantifying the interaction of human adipose-derived stem cells (hASCs) different biomaterials when encapsulated in Gellan Gum (GG)-based hydrogels. The platform is based on 8-well µ-slide compatible with automated screener (Matrix M4, Leica TCS SP8). Specifically, hASCs were mixed with pre-polymer solutions of GG (1% or 1.5%) and their blends with rat-tail type I collagen (1mg/mL or 2mg/mL). Hydrogels were prepared by ionically cross-linking with α-MEM. Control collagen gels were thermally cross-linked at 37 ºC for 30 min. The MatrixScreener autoscanned phenotypic markers for viability (Calcein/PI), spreading (Phalloidin/DAPI) and proliferation (EdU/Hoescht 33342) and the imaging database generated was analyzed using the automated 3D image analysis and quantification tools (Leica LASX 3D Image analysis and Imaris 8.4.1, Bitplane). Batch processing of data generated from eight different conditions at three different time points quantitative outputs for live and dead cell counts, total spread area, mean intensity and shape of individual cells along with other geometrical attributes such as diameter and 3D centroid orientation. Heat-map data revealed that GG 1%: Col 1mg/mL and GG 1%: Col 2mg/mL hydrogels supported better viability (>90% after day 7) and cell spreading than control gellan gum counterparts, but comparable to collagen controls.  Acknowledgments: This work was supported by the European Research Council grant agreement ERC-2012-ADG 20120216-321266 for project ComplexiTE and Portuguese Foundation for Science and Technology R&D grant POCI-01-0145-FEDER-016715 (PTDC/BBB-ECT/4317/2014) for the project MicroLiver.