2021-1-28, Schleusener, Johannes, Guo, Shuxia, Darvin, Maxim E., Thiede, Gisela, Chernavskaia, Olga, Knorr, Florian, Lademann, Jürgen, Popp, Jürgen, Bocklitz, Thomas W.

Psoriasis is considered a widespread dermatological disease that can strongly affect the quality of life. Currently, the treatment is continued until the skin surface appears clinically healed. However, lesions appearing normal may contain modifications in deeper layers. To terminate the treatment too early can highly increase the risk of relapses. Therefore, techniques are needed for a better knowledge of the treatment process, especially to detect the lesion modifications in deeper layers. In this study, we developed a fiber-based SORS-SERDS system in combination with machine learning algorithms to non-invasively determine the treatment efficiency of psoriasis. The system was designed to acquire Raman spectra from three different depths into the skin, which provide rich information about the skin modifications in deeper layers. This way, it is expected to prevent the occurrence of relapses in case of a too short treatment. The method was verified with a study of 24 patients upon their two visits: the data is acquired at the beginning of a standard treatment (visit 1) and four months afterwards (visit 2). A mean sensitivity of ≥85% was achieved to distinguish psoriasis from normal skin at visit 1. At visit 2, where the patients were healed according to the clinical appearance, the mean sensitivity was ≈65%.

2021, Zhao, Renping, Zhou, Xiangda, Khan, Essak S., Alansary, Dalia, Friedmann, Kim S., Yang, Wenjuan, Schwarz, Eva C., Del Campo, Aránzazu, Hoth, Markus, Qu, Bin

Efficacy of cytotoxic T lymphocyte (CTL)-based immunotherapy is still unsatisfactory against solid tumors, which are frequently characterized by condensed extracellular matrix. Here, using a unique 3D killing assay, we identify that the killing efficiency of primary human CTLs is substantially impaired in dense collagen matrices. Although the expression of cytotoxic proteins in CTLs remained intact in dense collagen, CTL motility was largely compromised. Using light-sheet microscopy, we found that persistence and velocity of CTL migration was influenced by the stiffness and porosity of the 3D matrix. Notably, 3D CTL velocity was strongly correlated with their nuclear deformability, which was enhanced by disruption of the microtubule network especially in dense matrices. Concomitantly, CTL migration, search efficiency, and killing efficiency in dense collagen were significantly increased in microtubule-perturbed CTLs. In addition, the chemotherapeutically used microtubule inhibitor vinblastine drastically enhanced CTL killing efficiency in dense collagen. Together, our findings suggest targeting the microtubule network as a promising strategy to enhance efficacy of CTL-based immunotherapy against solid tumors, especially stiff solid tumors.