Geyer, F. | Search bioRxiv

bioRxiv2024

Somatic gene delivery for flexible in vivo modeling of high-risk sarcoma

A particular challenge hampering therapeutic advancements for high-risk sarcoma patients is the broad spectrum of molecularly distinct sarcoma entities and the corresponding lack of suitable model systems to recapitulate and study these diseases. To overcome this predicament, we developed a novel genetically-controlled, yet versatile mouse modeling platform allowing delivery of different genetic lesions by electroporation (EPO) of the thigh muscle wildtype mice. This optimized sarcoma EPO-GEMM (EPO-based genetically engineered mouse model) platform allowed the generation of ten biologically distinct sarcoma entities, including Synovial Sarcoma (SS), fusion-positive and fusion-negative Rhabdomyosarcoma (RMS), Alveolar Soft Part Sarcoma (ASPS), Undifferentiated Pleomorphic Sarcoma (UPS) and Infantile Fibrosarcoma (IFS). Comprehensive molecular profiling and cross-species analyses confirmed faithful recapitulation of the human disease, including the expression of relevant immunotherapy targets. Syngeneic allografting enabled reliable preservation and scalability of Sarcoma-EPO-GEMMs for treatment trials, such as B7-H3-directed CAR-T cell therapy in an immunocompetent background.

cancer biology↗

bioRxiv2019

A backward encoding approach to recover subcortical auditory activity

1.Several subcortical nuclei along the auditory pathway are involved in the processing of sounds. One of the most commonly used methods of measuring the activity of these nuclei is the auditory brainstem response (ABR). Due to its low signal-to-noise ratio, ABRs have to be derived by averaging over thousands of artificial sounds such as clicks or tone bursts. This approach cannot be easily applied to natural listening situations (e.g. speech, music), which limits auditory cognitive neuroscientific studies to investigate mostly cortical processes. We propose that by training a backward encoding model to reconstruct evoked ABRs from high-density electrophysiological data, spatial filters can be tuned to auditory brainstem activity. Since these filters can be applied (i.e. generalized) to any other data set using the same spatial coverage, this could allow for the estimation of auditory brainstem activity from any continuous sensor level data. In this study, we established a proof-of-concept by using a backward encoding model generated using a click stimulation rate of 30 Hz to predict ABR activity recorded using EEG from an independent measurement using a stimulation rate of 9 Hz. We show that individually predicted and measured ABRs are highly correlated (r [~] 0.7). Importantly these predictions are stable even when applying the trained backward encoding model to a low number of trials, mimicking a situation with an unfavorable signal-to-noise ratio. Overall, this work lays the necessary foundation to use this approach in more interesting listening situations.

neuroscience↗