Rotator cuff tears affect over 15% of Americans and impair shoulder joint biomechanics and function. Following repair of symptomatic tears, functional deficits frequently persist and re-tears are common, due to the complex anatomy and high functional demands on the rotator cuff tendons. Rotator cuff tendon tissue engineering research is focused on devices to improve immediate mechanical support to the repair and to stimulate early and rapid tendon regeneration rather than scarring and fibrosis, particularly for the supraspinatus tendon (SST), the most commonly torn tendon in the rotator cuff. Aligned electrospun scaffolds that mimic both the highly aligned medial region of the SST, and bi-axially aligned electrospun scaffolds that mimic the multi-axially aligned isotropic anterior region of the SST have been evaluated with promising results when seeded with adipose- derived stem cells (ASCs). However, progress in this area of rotator cuff tendon engineering and in other areas of tendon research is hindered by the lack of definitive markers for SST or for its regional heterogeneity, the lack of understanding to what extent ASCs are tenogenic and can assume the identity of tendon fibroblasts, the lack of specific markers for tendon fibroblast identity and tenogenic differentiation, and by a lack of markers for tendon maturation and response to mechanical loading in engineered tendon. Therefore, is it difficult to assess how successful current tendon tissue engineering approaches really are, or to predict how well tendon tissue engineered approaches will function in translation when autologous or allogeneic ASCs from diverse human populations are used to enhance rotator cuff repair via augmentation or interposition with engineered tendon devices. These studies will evaluate the epigenome (methylome), transcriptome, proteome, lipidome, metabolome and phenome (phenotype) of native human SST and donor-matched tissue engineered tendon produced from SST fibroblasts and ASCs. Bioinformatics approaches will be used to integrate the data to an integrated multiome, which will then be used with machine learning approaches to extract key causal ‘driver’ genes, or tendon specific genes or molecules responsible for: 1) SST heterogeneity between medial and anterior regions. 2) Tendon cell identity and the extent of tenogenesis by ASCs on electrospun scaffolds. 3) The heterogenetic response by ASCs on uni- vs. bi-axially aligned electrospun scaffolds that mimic the native heterogeneity of the SST. 4) The response of engineered tendon to dynamic loading. Identified driver genes or molecules will be validated though over-expression or silencing approaches, thus providing therapeutic targets for manipulation to enhance tenogenesis, and engineered tendon development and maturation. Together these innovative studies will provide a template for improved external validity of benchtop tendon tissue engineering and pre-clinical studies towards successful translation in div...