Implantable microdevices often have static components rather than moving parts, and exhibit limited biocompatibility. This paper demonstrates a fast manufacturing method which can produce features in biocompatible materials down to tens of microns in scale, with intricate and composite patterns in each layer. By exploiting unique mechanical properties of hydrogels, we developed a “locking mechanism” for precise actuation and movement of freely moving parts, which can provide functions such as valves, manifolds, rotors, pumps, and delivery of payloads. Hydrogel components could be tuned within a wide range of mechanical and diffusive properties, and can be controlled after implantation without a sustained power supply. In a mouse model of osteosarcoma, triggering of release of doxorubicin from the device over ten days showed high treatment efficacy and low toxicity, at one-tenth of a standard systemic chemotherapy dose. Overall, this platform, called “iMEMS”, enables development of biocompatible implantable microdevices with a wide range of intricate moving components that can be wirelessly controlled on demand, in a manner that solves issues of device powering and biocompatibility.

Integrative, single-cell analyses may provide unprecedented insights into cellular and spatial diversity of the tumor microenvironment. The sparsity, noise, and high dimensionality of these data present unique challenges. Whilst approaches for integrating single-cell data are emerging and are far from being standardized, most data integration, cell clustering, cell trajectory, and analysis pipelines employ a dimension reduction step, frequently principal component analysis (PCA), a matrix factorization method that is relatively fast, and can easily scale to large datasets when used with sparse-matrix representations. In this review, we provide a guide to PCA and related methods. We describe the relationship between PCA and singular value decomposition, the difference between PCA of a correlation and covariance matrix, the impact of scaling, log-transforming, and standardization, and how to recognize a horseshoe or arch effect in a PCA. We describe canonical correlation analysis (CCA), a popular matrix factorization approach for the integration of single-cell data from different platforms or studies. We discuss alternatives to CCA and why additional preprocessing or weighting datasets within the joint decomposition should be considered.

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