Transfer learning aims to improve estimation in a target population with limited data by borrowing information from related external datasets, referred to as sources. However, heterogeneity in target-source relevance and predictor-outcome relationships can lead to negative transfer, where model performance is worse than using target data alone, particularly in high-dimensional settings with partially overlapping features. We propose Projection Transfer Learning (ProjectionTL), a Bayesian framework that integrates hierarchical modeling with adaptive posterior projection to enable selective knowledge transfer at both the source and feature levels. In the first stage, a source-guided hierarchical prior adaptively combines information from multiple sources using data-driven weights that capture their global alignment with the target. In the second stage, a weighted posterior-projection procedure induces sparsity by preferentially retaining features supported by stronger agreement between source and target signals. This two-stage framework distinguishes global source relevance from coordinate-specific transferability, allowing informative components to be retained while reducing the influence of heterogeneous or non-transferable signals. We establish theoretical guarantees for posterior contraction and model selection consistency under high-dimensional regularity conditions, without requiring all sources to be informative. Simulation studies and applications to Alzheimer's disease neuroimaging data demonstrate improved estimation accuracy, variable selection, and predictive performance compared with others, including settings with heterogeneous sources and block-wise missing modalities.

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